Sample records for climate variability observed
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Terrestrial essential climate variables (ECVs) at a glance
Stitt, Susan; Dwyer, John; Dye, Dennis; Josberger, Edward
2011-01-01
The Global Terrestrial Observing System, Global Climate Observing System, World Meteorological Organization, and Committee on Earth Observation Satellites all support consistent global land observations and measurements. To accomplish this goal, the Global Terrestrial Observing System defined 'essential climate variables' as measurements of atmosphere, oceans, and land that are technically and economically feasible for systematic observation and that are needed to meet the United Nations Framework Convention on Climate Change and requirements of the Intergovernmental Panel on Climate Change. The following are the climate variables defined by the Global Terrestrial Observing System that relate to terrestrial measurements. Several of them are currently measured most appropriately by in-place observations, whereas others are suitable for measurement by remote sensing technologies. The U.S. Geological Survey is the steward of the Landsat archive, satellite imagery collected from 1972 to the present, that provides a potential basis for deriving long-term, global-scale, accurate, timely and consistent measurements of many of these essential climate variables.
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Climate variation explains a third of global crop yield variability
Ray, Deepak K.; Gerber, James S.; MacDonald, Graham K.; West, Paul C.
2015-01-01
Many studies have examined the role of mean climate change in agriculture, but an understanding of the influence of inter-annual climate variations on crop yields in different regions remains elusive. We use detailed crop statistics time series for ~13,500 political units to examine how recent climate variability led to variations in maize, rice, wheat and soybean crop yields worldwide. While some areas show no significant influence of climate variability, in substantial areas of the global breadbaskets, >60% of the yield variability can be explained by climate variability. Globally, climate variability accounts for roughly a third (~32–39%) of the observed yield variability. Our study uniquely illustrates spatial patterns in the relationship between climate variability and crop yield variability, highlighting where variations in temperature, precipitation or their interaction explain yield variability. We discuss key drivers for the observed variations to target further research and policy interventions geared towards buffering future crop production from climate variability. PMID:25609225
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A plant’s perspective of extremes: Terrestrial plant responses to changing climatic variability
Reyer, C.; Leuzinger, S.; Rammig, A.; Wolf, A.; Bartholomeus, R. P.; Bonfante, A.; de Lorenzi, F.; Dury, M.; Gloning, P.; Abou Jaoudé, R.; Klein, T.; Kuster, T. M.; Martins, M.; Niedrist, G.; Riccardi, M.; Wohlfahrt, G.; de Angelis, P.; de Dato, G.; François, L.; Menzel, A.; Pereira, M.
2013-01-01
We review observational, experimental and model results on how plants respond to extreme climatic conditions induced by changing climatic variability. Distinguishing between impacts of changing mean climatic conditions and changing climatic variability on terrestrial ecosystems is generally underrated in current studies. The goals of our review are thus (1) to identify plant processes that are vulnerable to changes in the variability of climatic variables rather than to changes in their mean, and (2) to depict/evaluate available study designs to quantify responses of plants to changing climatic variability. We find that phenology is largely affected by changing mean climate but also that impacts of climatic variability are much less studied but potentially damaging. We note that plant water relations seem to be very vulnerable to extremes driven by changes in temperature and precipitation and that heatwaves and flooding have stronger impacts on physiological processes than changing mean climate. Moreover, interacting phenological and physiological processes are likely to further complicate plant responses to changing climatic variability. Phenological and physiological processes and their interactions culminate in even more sophisticated responses to changing mean climate and climatic variability at the species and community level. Generally, observational studies are well suited to study plant responses to changing mean climate, but less suitable to gain a mechanistic understanding of plant responses to climatic variability. Experiments seem best suited to simulate extreme events. In models, temporal resolution and model structure are crucial to capture plant responses to changing climatic variability. We highlight that a combination of experimental, observational and /or modeling studies have the potential to overcome important caveats of the respective individual approaches. PMID:23504722
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NASA Astrophysics Data System (ADS)
Poppick, A. N.; McKinnon, K. A.; Dunn-Sigouin, E.; Deser, C.
2017-12-01
Initial condition climate model ensembles suggest that regional temperature trends can be highly variable on decadal timescales due to characteristics of internal climate variability. Accounting for trend uncertainty due to internal variability is therefore necessary to contextualize recent observed temperature changes. However, while the variability of trends in a climate model ensemble can be evaluated directly (as the spread across ensemble members), internal variability simulated by a climate model may be inconsistent with observations. Observation-based methods for assessing the role of internal variability on trend uncertainty are therefore required. Here, we use a statistical resampling approach to assess trend uncertainty due to internal variability in historical 50-year (1966-2015) winter near-surface air temperature trends over North America. We compare this estimate of trend uncertainty to simulated trend variability in the NCAR CESM1 Large Ensemble (LENS), finding that uncertainty in wintertime temperature trends over North America due to internal variability is largely overestimated by CESM1, on average by a factor of 32%. Our observation-based resampling approach is combined with the forced signal from LENS to produce an 'Observational Large Ensemble' (OLENS). The members of OLENS indicate a range of spatially coherent fields of temperature trends resulting from different sequences of internal variability consistent with observations. The smaller trend variability in OLENS suggests that uncertainty in the historical climate change signal in observations due to internal variability is less than suggested by LENS.
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Slowing down of North Pacific climate variability and its implications for abrupt ecosystem change.
Boulton, Chris A; Lenton, Timothy M
2015-09-15
Marine ecosystems are sensitive to stochastic environmental variability, with higher-amplitude, lower-frequency--i.e., "redder"--variability posing a greater threat of triggering large ecosystem changes. Here we show that fluctuations in the Pacific Decadal Oscillation (PDO) index have slowed down markedly over the observational record (1900-present), as indicated by a robust increase in autocorrelation. This "reddening" of the spectrum of climate variability is also found in regionally averaged North Pacific sea surface temperatures (SSTs), and can be at least partly explained by observed deepening of the ocean mixed layer. The progressive reddening of North Pacific climate variability has important implications for marine ecosystems. Ecosystem variables that respond linearly to climate forcing will have become prone to much larger variations over the observational record, whereas ecosystem variables that respond nonlinearly to climate forcing will have become prone to more frequent "regime shifts." Thus, slowing down of North Pacific climate variability can help explain the large magnitude and potentially the quick succession of well-known abrupt changes in North Pacific ecosystems in 1977 and 1989. When looking ahead, despite model limitations in simulating mixed layer depth (MLD) in the North Pacific, global warming is robustly expected to decrease MLD. This could potentially reverse the observed trend of slowing down of North Pacific climate variability and its effects on marine ecosystems.
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NASA Astrophysics Data System (ADS)
Deser, C.
2017-12-01
Natural climate variability occurs over a wide range of time and space scales as a result of processes intrinsic to the atmosphere, the ocean, and their coupled interactions. Such internally generated climate fluctuations pose significant challenges for the identification of externally forced climate signals such as those driven by volcanic eruptions or anthropogenic increases in greenhouse gases. This challenge is exacerbated for regional climate responses evaluated from short (< 50 years) data records. The limited duration of the observations also places strong constraints on how well the spatial and temporal characteristics of natural climate variability are known, especially on multi-decadal time scales. The observational constraints, in turn, pose challenges for evaluation of climate models, including their representation of internal variability and assessing the accuracy of their responses to natural and anthropogenic radiative forcings. A promising new approach to climate model assessment is the advent of large (10-100 member) "initial-condition" ensembles of climate change simulations with individual models. Such ensembles allow for accurate determination, and straightforward separation, of externally forced climate signals and internal climate variability on regional scales. The range of climate trajectories in a given model ensemble results from the fact that each simulation represents a particular sequence of internal variability superimposed upon a common forced response. This makes clear that nature's single realization is only one of many that could have unfolded. This perspective leads to a rethinking of approaches to climate model evaluation that incorporate observational uncertainty due to limited sampling of internal variability. Illustrative examples across a range of well-known climate phenomena including ENSO, volcanic eruptions, and anthropogenic climate change will be discussed.
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Quantitative Assessment of Antarctic Climate Variability and Change
NASA Astrophysics Data System (ADS)
Ordonez, A.; Schneider, D. P.
2013-12-01
The Antarctic climate is both extreme and highly variable, but there are indications it may be changing. As the climate in Antarctica can affect global sea level and ocean circulation, it is important to understand and monitor its behavior. Observational and model data have been used to study climate change in Antarctica and the Southern Ocean, though observational data is sparse and models have difficulty reproducing many observed climate features. For example, a leading hypothesis that ozone depletion has been responsible for sea ice trends is struggling with the inability of ozone-forced models to reproduce the observed sea ice increase. The extent to which this data-model disagreement represents inadequate observations versus model biases is unknown. This research assessed a variety of climate change indicators to present an overview of Antarctic climate that will allow scientists to easily access this data and compare indicators with other observational data and model output. Indicators were obtained from observational and reanalysis data for variables such as temperature, sea ice area, and zonal wind stress. Multiple datasets were used for key variables. Monthly and annual anomaly data from Antarctica and the Southern Ocean as well as tropical indices were plotted as time series on common axes for comparison. Trends and correlations were also computed. Zonal wind, surface temperature, and austral springtime sea ice had strong relationships and were further discussed in terms of how they may relate to climate variability and change in the Antarctic. This analysis will enable hypothesized mechanisms of Antarctic climate change to be critically evaluated.
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Linking the Observation of Essential Variables to Societal Benefits
NASA Astrophysics Data System (ADS)
Sylak-Glassman, E.
2017-12-01
Different scientific communities have established sets of commonly agreed upon essential variables to help coordinate data collection in a variety of Earth observation areas. As an example, the World Meteorological Organization Global Climate Observing System has identified 50 Essential Climate Variables (ECVs), such as sea-surface temperature and carbon dioxide, which are required to monitoring the climate and detect and attribute climate change. In addition to supporting climate science, measuring these ECVs deliver many types of societal benefits, ranging from disaster mitigation to agricultural productivity to human health. While communicating the value in maintaining and improving observational records for these variables has been a challenge, quantifying how the measurement of these ECVs results in the delivery of many different societal benefits may help support their continued measurement. The 2016 National Earth Observation Assessment (EOA 2016) quantified the impact of individual Earth observation systems, sensors, networks, and surveys (or Earth observation systems, for short) on the achievement of 217 Federal objectives in 13 societal benefit areas (SBAs). This study will demonstrate the use of the EOA 2016 dataset to show the different Federal objectives and SBAs that are impacted by the Earth observation systems used to measure ECVs. Describing how the measurements from these Earth observation systems are used not only to maintain the climate record but also to meet additional Federal objectives may help articulate the continued measurement of the ECVs. This study will act as a pilot for the use of the EOA 2016 dataset to map between the measurements required to observe additional sets of variables, such as the Essential Ocean Variables and Essential Biodiversity Variables, and the ability to achieve a variety of societal benefits.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Leng, Guoyong
The United States is responsible for 35% and 60% of global corn supply and exports. Enhanced supply stability through a reduction in the year-to-year variability of US corn yield would greatly benefit global food security. Important in this regard is to understand how corn yield variability has evolved geographically in the history and how it relates to climatic and non-climatic factors. Results showed that year-to-year variation of US corn yield has decreased significantly during 1980-2010, mainly in Midwest Corn Belt, Nebraska and western arid regions. Despite the country-scale decreasing variability, corn yield variability exhibited an increasing trend in South Dakota,more » Texas and Southeast growing regions, indicating the importance of considering spatial scales in estimating yield variability. The observed pattern is partly reproduced by process-based crop models, simulating larger areas experiencing increasing variability and underestimating the magnitude of decreasing variability. And 3 out of 11 models even produced a differing sign of change from observations. Hence, statistical model which produces closer agreement with observations is used to explore the contribution of climatic and non-climatic factors to the changes in yield variability. It is found that climate variability dominate the change trends of corn yield variability in the Midwest Corn Belt, while the ability of climate variability in controlling yield variability is low in southeastern and western arid regions. Irrigation has largely reduced the corn yield variability in regions (e.g. Nebraska) where separate estimates of irrigated and rain-fed corn yield exist, demonstrating the importance of non-climatic factors in governing the changes in corn yield variability. The results highlight the distinct spatial patterns of corn yield variability change as well as its influencing factors at the county scale. I also caution the use of process-based crop models, which have substantially underestimated the change trend of corn yield variability, in projecting its future changes.« less
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NASA Astrophysics Data System (ADS)
Kravtsov, Sergey
2017-06-01
Identification and dynamical attribution of multidecadal climate undulations to either variations in external forcings or to internal sources is one of the most important topics of modern climate science, especially in conjunction with the issue of human-induced global warming. Here we utilize ensembles of twentieth century climate simulations to isolate the forced signal and residual internal variability in a network of observed and modeled climate indices. The observed internal variability so estimated exhibits a pronounced multidecadal mode with a distinctive spatiotemporal signature, which is altogether absent in model simulations. This single mode explains a major fraction of model-data differences over the entire climate index network considered; it may reflect either biases in the models' forced response or models' lack of requisite internal dynamics, or a combination of both.
Plain Language SummaryGlobal and regional warming trends over the course of the twentieth century have been nonuniform, with decadal and longer periods of faster or slower warming, or even cooling. Here we show that state-of-the-art global models used to predict climate fail to adequately reproduce such multidecadal climate variations. In particular, the models underestimate the magnitude of the observed variability and misrepresent its spatial pattern. Therefore, our ability to interpret the observed climate change using these models is limited.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23557671','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23557671"><span>Updating beliefs and combining evidence in adaptive forest management under climate change: a case study of Norway spruce (Picea abies L. Karst) in the Black Forest, Germany.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Yousefpour, Rasoul; Temperli, Christian; Bugmann, Harald; Elkin, Che; Hanewinkel, Marc; Meilby, Henrik; Jacobsen, Jette Bredahl; Thorsen, Bo Jellesmark</p> <p>2013-06-15</p> <p>We study climate uncertainty and how managers' beliefs about climate change develop and influence their decisions. We develop an approach for updating knowledge and beliefs based on the observation of forest and climate variables and illustrate its application for the adaptive management of an even-aged Norway spruce (Picea abies L. Karst) forest in the Black Forest, Germany. We simulated forest development under a range of climate change scenarios and forest management alternatives. Our analysis used Bayesian updating and Dempster's rule of combination to simulate how observations of climate and forest variables may influence a decision maker's beliefs about climate development and thereby management decisions. While forest managers may be inclined to rely on observed forest variables to infer climate change and impacts, we found that observation of climate state, e.g. temperature or precipitation is superior for updating beliefs and supporting decision-making. However, with little conflict among information sources, the strongest evidence would be offered by a combination of at least two informative variables, e.g., temperature and precipitation. The success of adaptive forest management depends on when managers switch to forward-looking management schemes. Thus, robust climate adaptation policies may depend crucially on a better understanding of what factors influence managers' belief in climate change. Copyright © 2013 Elsevier Ltd. All rights reserved.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29472598','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29472598"><span>Climate-Driven Crop Yield and Yield Variability and Climate Change Impacts on the U.S. Great Plains Agricultural Production.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kukal, Meetpal S; Irmak, Suat</p> <p>2018-02-22</p> <p>Climate variability and trends affect global crop yields and are characterized as highly dependent on location, crop type, and irrigation. U.S. Great Plains, due to its significance in national food production, evident climate variability, and extensive irrigation is an ideal region of investigation for climate impacts on food production. This paper evaluates climate impacts on maize, sorghum, and soybean yields and effect of irrigation for individual counties in this region by employing extensive crop yield and climate datasets from 1968-2013. Variability in crop yields was a quarter of the regional average yields, with a quarter of this variability explained by climate variability, and temperature and precipitation explained these in singularity or combination at different locations. Observed temperature trend was beneficial for maize yields, but detrimental for sorghum and soybean yields, whereas observed precipitation trend was beneficial for all three crops. Irrigated yields demonstrated increased robustness and an effective mitigation strategy against climate impacts than their non-irrigated counterparts by a considerable fraction. The information, data, and maps provided can serve as an assessment guide for planners, managers, and policy- and decision makers to prioritize agricultural resilience efforts and resource allocation or re-allocation in the regions that exhibit risk from climate variability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ESSD....9..471J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ESSD....9..471J"><span>Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jones, Philip D.; Harpham, Colin; Troccoli, Alberto; Gschwind, Benoit; Ranchin, Thierry; Wald, Lucien; Goodess, Clare M.; Dorling, Stephen</p> <p>2017-07-01</p> <p>The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim reanalysis is presented. A number of different, variable-dependent, bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting ERA-Interim based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed, and relative humidity. These are available on either 3 or 6 h timescales over the period 1979-2016. The resulting bias-adjusted dataset is available through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S) and can be accessed at present from <a href="ftp://ecem.climate.copernicus.eu" target="_blank">ftp://ecem.climate.copernicus.eu</a>. The benefit of performing bias adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against gridded observational fields.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JHyd..560..202T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JHyd..560..202T"><span>Optimal Interpolation scheme to generate reference crop evapotranspiration</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tomas-Burguera, Miquel; Beguería, Santiago; Vicente-Serrano, Sergio; Maneta, Marco</p> <p>2018-05-01</p> <p>We used an Optimal Interpolation (OI) scheme to generate a reference crop evapotranspiration (ETo) grid, forcing meteorological variables, and their respective error variance in the Iberian Peninsula for the period 1989-2011. To perform the OI we used observational data from the Spanish Meteorological Agency (AEMET) and outputs from a physically-based climate model. To compute ETo we used five OI schemes to generate grids for the five observed climate variables necessary to compute ETo using the FAO-recommended form of the Penman-Monteith equation (FAO-PM). The granularity of the resulting grids are less sensitive to variations in the density and distribution of the observational network than those generated by other interpolation methods. This is because our implementation of the OI method uses a physically-based climate model as prior background information about the spatial distribution of the climatic variables, which is critical for under-observed regions. This provides temporal consistency in the spatial variability of the climatic fields. We also show that increases in the density and improvements in the distribution of the observational network reduces substantially the uncertainty of the climatic and ETo estimates. Finally, a sensitivity analysis of observational uncertainties and network densification suggests the existence of a trade-off between quantity and quality of observations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.U34A..03R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.U34A..03R"><span>Challenges of coordinating global climate observations - Role of satellites in climate monitoring</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Richter, C.</p> <p>2017-12-01</p> <p>Global observation of the Earth's atmosphere, ocean and land is essential for identifying climate variability and change, and for understanding their causes. Observation also provides data that are fundamental for evaluating, refining and initializing the models that predict how the climate system will vary over the months and seasons ahead, and that project how climate will change in the longer term under different assumptions concerning greenhouse gas emissions and other human influences. Long-term observational records have enabled the Intergovernmental Panel on Climate Change to deliver the message that warming of the global climate system is unequivocal. As the Earth's climate enters a new era, in which it is forced by human activities, as well as natural processes, it is critically important to sustain an observing system capable of detecting and documenting global climate variability and change over long periods of time. High-quality climate observations are required to assess the present state of the ocean, cryosphere, atmosphere and land and place them in context with the past. The global observing system for climate is not a single, centrally managed observing system. Rather, it is a composite "system of systems" comprising a set of climate-relevant observing, data-management, product-generation and data-distribution systems. Data from satellites underpin many of the Essential Climate Variables(ECVs), and their historic and contemporary archives are a key part of the global climate observing system. In general, the ECVs will be provided in the form of climate data records that are created by processing and archiving time series of satellite and in situ measurements. Early satellite data records are very valuable because they provide unique observations in many regions which were not otherwise observed during the 1970s and which can be assimilated in atmospheric reanalyses and so extend the satellite climate data records back in time.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017PhDT........71P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017PhDT........71P"><span>Sources and Impacts of Modeled and Observed Low-Frequency Climate Variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Parsons, Luke Alexander</p> <p></p> <p>Here we analyze climate variability using instrumental, paleoclimate (proxy), and the latest climate model data to understand more about the sources and impacts of low-frequency climate variability. Understanding the drivers of climate variability at interannual to century timescales is important for studies of climate change, including analyses of detection and attribution of climate change impacts. Additionally, correctly modeling the sources and impacts of variability is key to the simulation of abrupt change (Alley et al., 2003) and extended drought (Seager et al., 2005; Pelletier and Turcotte, 1997; Ault et al., 2014). In Appendix A, we employ an Earth system model (GFDL-ESM2M) simulation to study the impacts of a weakening of the Atlantic meridional overturning circulation (AMOC) on the climate of the American Tropics. The AMOC drives some degree of local and global internal low-frequency climate variability (Manabe and Stouffer, 1995; Thornalley et al., 2009) and helps control the position of the tropical rainfall belt (Zhang and Delworth, 2005). We find that a major weakening of the AMOC can cause large-scale temperature, precipitation, and carbon storage changes in Central and South America. Our results suggest that possible future changes in AMOC strength alone will not be sufficient to drive a large-scale dieback of the Amazonian forest, but this key natural ecosystem is sensitive to dry-season length and timing of rainfall (Parsons et al., 2014). In Appendix B, we compare a paleoclimate record of precipitation variability in the Peruvian Amazon to climate model precipitation variability. The paleoclimate (Lake Limon) record indicates that precipitation variability in western Amazonia is 'red' (i.e., increasing variability with timescale). By contrast, most state-of-the-art climate models indicate precipitation variability in this region is nearly 'white' (i.e., equally variability across timescales). This paleo-model disagreement in the overall structure of the variance spectrum has important consequences for the probability of multi-year drought. Our lake record suggests there is a significant background threat of multi-year, and even decade-length, drought in western Amazonia, whereas climate model simulations indicate most droughts likely last no longer than one to three years. These findings suggest climate models may underestimate the future risk of extended drought in this important region. In Appendix C, we expand our analysis of climate variability beyond South America. We use observations, well-constrained tropical paleoclimate, and Earth system model data to examine the overall shape of the climate spectrum across interannual to century frequencies. We find a general agreement among observations and models that temperature variability increases with timescale across most of the globe outside the tropics. However, as compared to paleoclimate records, climate models generate too little low-frequency variability in the tropics (e.g., Laepple and Huybers, 2014). When we compare the shape of the simulated climate spectrum to the spectrum of a simple autoregressive process, we find much of the modeled surface temperature variability in the tropics could be explained by ocean smoothing of weather noise. Importantly, modeled precipitation tends to be similar to white noise across much of the globe. By contrast, paleoclimate records of various types from around the globe indicate that both temperature and precipitation variability should experience much more low-frequency variability than a simple autoregressive or white-noise process. In summary, state-of-the-art climate models generate some degree of dynamically driven low-frequency climate variability, especially at high latitudes. However, the latest climate models, observations, and paleoclimate data provide us with drastically different pictures of the background climate system and its associated risks. This research has important consequences for improving how we simulate climate extremes as we enter a warmer (and often drier) world in the coming centuries; if climate models underestimate low-frequency variability, we will underestimate the risk of future abrupt change and extreme events, such as megadroughts.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1030607','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1030607"><span>Towards the Prediction of Decadal to Centennial Climate Processes in the Coupled Earth System Model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Liu, Zhengyu; Kutzbach, J.; Jacob, R.</p> <p>2011-12-05</p> <p>In this proposal, we have made major advances in the understanding of decadal and long term climate variability. (a) We performed a systematic study of multidecadal climate variability in FOAM-LPJ and CCSM-T31, and are starting exploring decadal variability in the IPCC AR4 models. (b) We develop several novel methods for the assessment of climate feedbacks in the observation. (c) We also developed a new initialization scheme DAI (Dynamical Analogue Initialization) for ensemble decadal prediction. (d) We also studied climate-vegetation feedback in the observation and models. (e) Finally, we started a pilot program using Ensemble Kalman Filter in CGCM for decadalmore » climate prediction.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1813211K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1813211K"><span>Evaluation of climatic changes in South-Asia</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kjellstrom, Erik; Rana, Arun; Grigory, Nikulin; Renate, Wilcke; Hansson, Ulf; Kolax, Michael</p> <p>2016-04-01</p> <p>Literature has sufficient evidences of climate change impact all over the world and its impact on various sectors. In light of new advancements made in climate modeling, availability of several climate downscaling approaches, the more robust bias correction methods with varying complexities and strengths, in the present study we performed a systematic evaluation of climate change impact over South-Asia region. We have used different Regional Climate Models (RCMs) (from CORDEX domain), (Global Climate Models GCMs) and gridded observations for the study area to evaluate the models in historical/control period (1980-2010) and changes in future period (2010-2099). Firstly, GCMs and RCMs are evaluated against the Gridded observational datasets in the area using precipitation and temperature as indicative variables. Observational dataset are also evaluated against the reliable set of observational dataset, as pointed in literature. Bias, Correlation, and changes (among other statistical measures) are calculated for the entire region and both the variables. Eventually, the region was sub-divided into various smaller domains based on homogenous precipitation zones to evaluate the average changes over time period. Spatial and temporal changes for the region are then finally calculated to evaluate the future changes in the region. Future changes are calculated for 2 Representative Concentration Pathways (RCPs), the middle emission (RCP4.5) and high emission (RCP8.5) and for both climatic variables, precipitation and temperature. Lastly, Evaluation of Extremes is performed based on precipitation and temperature based indices for whole region in future dataset. Results have indicated that the whole study region is under extreme stress in future climate scenarios for both climatic variables i.e. precipitation and temperature. Precipitation variability is dependent on the location in the area leading to droughts and floods in various regions in future. Temperature is hinting towards a constant increase throughout the region regardless of location.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.B11C0474T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.B11C0474T"><span>Investigating the Contribution of Climate Variables to Estimates of Net Primary Productivity in a Tropical Ecosystem in India</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tripathi, P.; Behera, M. D.; Behera, S. K.; Sahu, N.</p> <p>2016-12-01</p> <p>Investigating the impact of climate variables on net primary productivity is crucial to evaluate the ecosystem health and the status of forest type response to climate change. The objective of this paper is (1) to analyze the spatio-temporal pattern of net primary productivity (NPP) in a tropical forest ecosystem situated along the Himalayan foothills in India and (2) to investigate the continuous and delayed effects of climatic variables. Weapplied simple Monteith equation based Light use efficiency model for two dominant plant functional types; sal (Shorea robusta) forest and teak (Tectona grandis) plantation to estimate the NPP for a decadal period from 2001 to 2010. The impact of climate variables on NPP for these 10 years was seen by applying two correlation analyses; generalized linear modelling (GLM) and time lag correlation approach.The impact of different climate variables was observed to vary throughout the study period.A decline in mean NPP during 2002-2003, 2005 and 2008 to 2010 could be attributed to drought, increased vapour pressure deficit, and decreased humidity and solar radiation. In time lag correlation analysis, precipitation and humidity were observed to be the major variables affecting NPP; whereas combination of temperature, humidity and VPD showed dominant effect on NPP in GLM. Shorea robusta forest showed slightly higher NPP than that of Tectona grandis plantation throughout the study period. Highest decrease in NPP was observed during 2010,pertaining to lower solar radiation, humidity and precipitation along with increased VPD.Higher gains in NPP by sal during all years indicates their better adaptability to climate compared to teak. Contribution of different climatic variables through some link process is revealed in statistical analysis clearly indicates the co-dominance of all the variables in explaining NPP. Lacking of site specific meteorological observations and microclimate put constraint on broad level analyses.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19910003143','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19910003143"><span>Climate Impact of Solar Variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Schatten, Kenneth H. (Editor); Arking, Albert (Editor)</p> <p>1990-01-01</p> <p>The conference on The Climate Impact of Solar Variability, was held at Goddard Space Flight Center from April 24 to 27, 1990. In recent years they developed a renewed interest in the potential effects of increasing greenhouse gases on climate. Carbon dioxide, methane, nitrous oxide, and the chlorofluorocarbons have been increasing at rates that could significantly change climate. There is considerable uncertainty over the magnitude of this anthropogenic change. The climate system is very complex, with feedback processes that are not fully understood. Moreover, there are two sources of natural climate variability (volcanic aerosols and solar variability) added to the anthropogenic changes which may confuse our interpretation of the observed temperature record. Thus, if we could understand the climatic impact of the natural variability, it would aid our interpretation and understanding of man-made climate changes.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li class="active"><span>1</span></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_1 --> <div id="page_2" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li class="active"><span>2</span></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="21"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.7093T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.7093T"><span>Resilience, rapid transitions and regime shifts: fingerprinting the responses of Lake Żabińskie (NE Poland) to climate variability and human disturbance since 1000 AD</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tylmann, Wojciech; Hernández-Almeida, Iván; Grosjean, Martin; José Gómez Navarro, Juan; Larocque-Tobler, Isabelle; Bonk, Alicja; Enters, Dirk; Ustrzycka, Alicja; Piotrowska, Natalia; Przybylak, Rajmund; Wacnik, Agnieszka; Witak, Małgorzata</p> <p>2016-04-01</p> <p>Rapid ecosystem transitions and adverse effects on ecosystem services as responses to combined climate and human impacts are of major concern. Yet few quantitative observational data exist, particularly for ecosystems that have a long history of human intervention. Here, we combine quantitative summer and winter climate reconstructions, climate model simulations and proxies for three major environmental pressures (land use, nutrients and erosion) to explore the system dynamics, resilience, and the role of disturbance regimes in varved eutrophic Lake Żabińskie since AD 1000. Comparison between regional and global climate simulations and quantitative climate reconstructions indicate that proxy data capture noticeably natural forced climate variability, while internal variability appears as the dominant source of climate variability in the climate model simulations during most parts of the last millennium. Using different multivariate analyses and change point detection techniques, we identify ecosystem changes through time and shifts between rather stable states and highly variable ones, as expressed by the proxies for land-use, erosion and productivity in the lake. Prior to AD 1600, the lake ecosystem was characterized by a high stability and resilience against considerable observed natural climate variability. In contrast, lake-ecosystem conditions started to fluctuate at high frequency across a broad range of states after AD 1600. The period AD 1748-1868 represents the phase with the strongest human disturbance of the ecosystem. Analyses of the frequency of change points in the multi-proxy dataset suggests that the last 400 years were highly variable and flickering with increasing vulnerability of the ecosystem to the combined effects of climate variability and anthropogenic disturbances. This led to significant rapid ecosystem transformations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19830052666&hterms=iav&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Diav','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19830052666&hterms=iav&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Diav"><span>Interannual variability and climatic noise in satellite-observed outgoing longwave radiation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Short, D. A.; Cahalan, R. F.</p> <p>1983-01-01</p> <p>Upwelling-IR observations of the North Pacific by polar orbiters NOAA 3, 4, 5, and 6 and TIROS-N from 1974 to 1981 are analyzed statistically in terms of interannual variability (IAV) in monthly averages and climatic noise due to short-term weather fluctuations. It is found that although the daily variance in the observations is the same in summer and winter months, and although IAV in winter is smaller than that in summer, the climatic noise in winter is so much smaller that a greater fraction of winter anomalies are statistically significant. The smaller winter climatic noise level is shown to be due to shorter autocorrelation times. It is demonstrated that increasing averaging area does not reduce the climatic noise level, suggesting that continuing collection of high-resolution satellite IR data on a global basis is necessary if better models of short-term variability are to be constructed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H41A1277G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H41A1277G"><span>Evaluating historical climate and hydrologic trends in the Central Appalachian region of the United States</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gaertner, B. A.; Zegre, N.</p> <p>2015-12-01</p> <p>Climate change is surfacing as one of the most important environmental and social issues of the 21st century. Over the last 100 years, observations show increasing trends in global temperatures and intensity and frequency of precipitation events such as flooding, drought, and extreme storms. Global circulation models (GCM) show similar trends for historic and future climate indicators, albeit with geographic and topographic variability at regional and local scale. In order to assess the utility of GCM projections for hydrologic modeling, it is important to quantify how robust GCM outputs are compared to robust historical observations at finer spatial scales. Previous research in the United States has primarily focused on the Western and Northeastern regions due to dominance of snow melt for runoff and aquifer recharge but the impact of climate warming in the mountainous central Appalachian Region is poorly understood. In this research, we assess the performance of GCM-generated historical climate compared to historical observations primarily in the context of forcing data for macro-scale hydrologic modeling. Our results show significant spatial heterogeneity of modeled climate indices when compared to observational trends at the watershed scale. Observational data is showing considerable variability within maximum temperature and precipitation trends, with consistent increases in minimum temperature. The geographic, temperature, and complex topographic gradient throughout the central Appalachian region is likely the contributing factor in temperature and precipitation variability. Variable climate changes are leading to more severe and frequent climate events such as temperature extremes and storm events, which can have significant impacts on our drinking water supply, infrastructure, and health of all downstream communities.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20130000599','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130000599"><span>Temporal Variability of Observed and Simulated Hyperspectral Earth Reflectance</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Roberts, Yolanda; Pilewskie, Peter; Kindel, Bruce; Feldman, Daniel; Collins, William D.</p> <p>2012-01-01</p> <p>The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is a climate observation system designed to study Earth's climate variability with unprecedented absolute radiometric accuracy and SI traceability. Observation System Simulation Experiments (OSSEs) were developed using GCM output and MODTRAN to simulate CLARREO reflectance measurements during the 21st century as a design tool for the CLARREO hyperspectral shortwave imager. With OSSE simulations of hyperspectral reflectance, Feldman et al. [2011a,b] found that shortwave reflectance is able to detect changes in climate variables during the 21st century and improve time-to-detection compared to broadband measurements. The OSSE has been a powerful tool in the design of the CLARREO imager and for understanding the effect of climate change on the spectral variability of reflectance, but it is important to evaluate how well the OSSE simulates the Earth's present-day spectral variability. For this evaluation we have used hyperspectral reflectance measurements from the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY), a shortwave spectrometer that was operational between March 2002 and April 2012. To study the spectral variability of SCIAMACHY-measured and OSSE-simulated reflectance, we used principal component analysis (PCA), a spectral decomposition technique that identifies dominant modes of variability in a multivariate data set. Using quantitative comparisons of the OSSE and SCIAMACHY PCs, we have quantified how well the OSSE captures the spectral variability of Earth?s climate system at the beginning of the 21st century relative to SCIAMACHY measurements. These results showed that the OSSE and SCIAMACHY data sets share over 99% of their total variance in 2004. Using the PCs and the temporally distributed reflectance spectra projected onto the PCs (PC scores), we can study the temporal variability of the observed and simulated reflectance spectra. Multivariate time series analysis of the PC scores using techniques such as Singular Spectrum Analysis (SSA) and Multichannel SSA will provide information about the temporal variability of the dominant variables. Quantitative comparison techniques can evaluate how well the OSSE reproduces the temporal variability observed by SCIAMACHY spectral reflectance measurements during the first decade of the 21st century. PCA of OSSE-simulated reflectance can also be used to study how the dominant spectral variables change on centennial scales for forced and unforced climate change scenarios. To have confidence in OSSE predictions of the spectral variability of hyperspectral reflectance, it is first necessary for us to evaluate the degree to which the OSSE simulations are able to reproduce the Earth?s present-day spectral variability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007EOSTr..88..111G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007EOSTr..88..111G"><span>Reconstruction of Past Mediterranean Climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>García-Herrera, Ricardo; Luterbacher, Jürg; Lionello, Piero; Gonzáles-Rouco, Fidel; Ribera, Pedro; Rodó, Xavier; Kull, Christoph; Zerefos, Christos</p> <p>2007-02-01</p> <p>First MEDCLIVAR Workshop on Reconstruction of Past Mediterranean Climate; Pablo de Olavide University, Carmona, Spain, 8-11 November 2006; Mediterranean Climate Variability and Predictability (MEDCLIVAR; http://www.medclivar.eu) is a program that coordinates and promotes research on different aspects of Mediterranean climate. The main MEDCLIVAR goals include the reconstruction of past climate, describing patterns and mechanisms characterizing climate space-time variability, extremes at different time and space scales, coupled climate model/empirical reconstruction comparisons, seasonal forecasting, and the identification of the forcings responsible for the observed changes. The program has been endorsed by CLIVAR (Climate Variability and Predictability project) and is funded by the European Science Foundation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018GeoRL..45.4438Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018GeoRL..45.4438Y"><span>Observations of Local Positive Low Cloud Feedback Patterns and Their Role in Internal Variability and Climate Sensitivity</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yuan, Tianle; Oreopoulos, Lazaros; Platnick, Steven E.; Meyer, Kerry</p> <p>2018-05-01</p> <p>Modeling studies have shown that cloud feedbacks are sensitive to the spatial pattern of sea surface temperature (SST) anomalies, while cloud feedbacks themselves strongly influence the magnitude of SST anomalies. Observational counterparts to such patterned interactions are still needed. Here we show that distinct large-scale patterns of SST and low-cloud cover (LCC) emerge naturally from objective analyses of observations and demonstrate their close coupling in a positive local SST-LCC feedback loop that may be important for both internal variability and climate change. The two patterns that explain the maximum amount of covariance between SST and LCC correspond to the Interdecadal Pacific Oscillation and the Atlantic Multidecadal Oscillation, leading modes of multidecadal internal variability. Spatial patterns and time series of SST and LCC anomalies associated with both modes point to a strong positive local SST-LCC feedback. In many current climate models, our analyses suggest that SST-LCC feedback strength is too weak compared to observations. Modeled local SST-LCC feedback strength affects simulated internal variability so that stronger feedback produces more intense and more realistic patterns of internal variability. To the extent that the physics of the local positive SST-LCC feedback inferred from observed climate variability applies to future greenhouse warming, we anticipate significant amount of delayed warming because of SST-LCC feedback when anthropogenic SST warming eventually overwhelm the effects of internal variability that may mute anthropogenic warming over parts of the ocean. We postulate that many climate models may be underestimating both future warming and the magnitude of modeled internal variability because of their weak SST-LCC feedback.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMOS13A1793K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMOS13A1793K"><span>Uncovering the Anthropogenic Sea Level Change using an Improved Sea Level Reconstruction for the Indian Ocean</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kumar, P.; Hamlington, B.; Thompson, P. R.; Han, W.</p> <p>2016-12-01</p> <p>Despite having some of the world's most densely populated and vulnerable coastal regions, sea level (SL) variability in the Indian Ocean (IO) has received considerably less attention than the Pacific Ocean. Differentiating the internal variability from the long-term trend in global mean sea level (GMSL) at decadal time-scales is vital for planning and mitigation efforts in the IO region. Understanding the dynamics of internal and anthropogenic SL change is essential for understanding the dynamic pathways that link the IO basin to terrestrial climates world-wide. With a sparse pre-satellite observational record of the IO, the Indo-Pacific internal climate variability is difficult to represent accurately. However, an improved representation of pre-satellite SL variability can be achieved by using a multivariate reconstruction technique. By using cyclostationary empirical orthogonal functions (CSEOFs) that can capture time-varying spatial patterns, gaps in the historical record when observations are sparse are filled using spatial relationships from time periods when the observational network is dense. This reconstruction method combines SL data and sea surface temperature (SST) to create a SL reconstruction that spans a period from 1900 to present, long enough to study climate signals over interannual to decadal time scales. This study aims at estimating the component of SL rise that relates to anthropogenic forcing by identifying and removing the fraction related to internal variability. An improved understanding of how the internal climate variability can affect the IO SL trend and variability, will provide an insight into the future SL changes. It is also important to study links between SL and climate variability in the past to understand how SL will respond to similar climatic events in the future and if this response will be influenced by the changing climate.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003HyPr...17.3703B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003HyPr...17.3703B"><span>An assessment of global climate model-simulated climate for the western cordillera of Canada (1961-90)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bonsal, Barrie R.; Prowse, Terry D.; Pietroniro, Alain</p> <p>2003-12-01</p> <p>Climate change is projected to significantly affect future hydrologic processes over many regions of the world. This is of particular importance for alpine systems that provide critical water supplies to lower-elevation regions. The western cordillera of Canada is a prime example where changes to temperature and precipitation could have profound hydro-climatic impacts not only for the cordillera itself, but also for downstream river systems and the drought-prone Canadian Prairies. At present, impact researchers primarily rely on global climate models (GCMs) for future climate projections. The main objective of this study is to assess several GCMs in their ability to simulate the magnitude and spatial variability of current (1961-90) temperature and precipitation over the western cordillera of Canada. In addition, several gridded data sets of observed climate for the study region are evaluated.Results reveal a close correspondence among the four gridded data sets of observed climate, particularly for temperature. There is, however, considerable variability regarding the various GCM simulations of this observed climate. The British, Canadian, German, Australian, and US GFDL models are superior at simulating the magnitude and spatial variability of mean temperature. The Japanese GCM is of intermediate ability, and the US NCAR model is least representative of temperature in this region. Nearly all the models substantially overestimate the magnitude of total precipitation, both annually and on a seasonal basis. An exception involves the British (Hadley) model, which best represents the observed magnitude and spatial variability of precipitation. This study improves our understanding regarding the accuracy of GCM climate simulations over the western cordillera of Canada. The findings may assist in producing more reliable future scenarios of hydro-climatic conditions over various regions of the country. Copyright</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMPP23D..07R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMPP23D..07R"><span>Central Tropical Pacific Variability And ENSO Response To Changing Climate Boundary Conditions: Evidence From Individual Line Island Foraminifera</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rustic, G. T.; Polissar, P. J.; Ravelo, A. C.; White, S. M.</p> <p>2017-12-01</p> <p>The El Niño Southern Oscillation (ENSO) plays a dominant role in Earth's climate variability. Paleoceanographic evidence suggests that ENSO has changed in the past, and these changes have been linked to large-scale climatic shifts. While a close relationship between ENSO evolution and climate boundary conditions has been predicted, testing these predictions remains challenging. These climate boundary conditions, including insolation, the mean surface temperature gradient of the tropical Pacific, global ice volume, and tropical thermocline depth, often co-vary and may work together to suppress or enhance the ocean-atmosphere feedbacks that drive ENSO variability. Furthermore, suitable paleo-archives spanning multiple climate states are sparse. We have aimed to test ENSO response to changing climate boundary conditions by generating new reconstructions of mixed-layer variability from sedimentary archives spanning the last three glacial-interglacial cycles from the Central Tropical Pacific Line Islands, where El Niño is strongly expressed. We analyzed Mg/Ca ratios from individual foraminifera to reconstruct mixed-layer variability at discrete time intervals representing combinations of climatic boundary conditions from the middle Holocene to Marine Isotope Stage (MIS) 8. We observe changes in the mixed-layer temperature variability during MIS 5 and during the previous interglacial (MIS 7) showing significant reductions in ENSO amplitude. Differences in variability during glacial and interglacial intervals are also observed. Additionally, we reconstructed mixed-layer and thermocline conditions using multi-species Mg/Ca and stable isotope measurements to more fully characterize the state of the Central Tropical Pacific during these intervals. These reconstructions provide us with a unique view of Central Tropical Pacific variability and water-column structure at discrete intervals under varying boundary climate conditions with which to assess factors that shape ENSO variability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29867150','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29867150"><span>Role of subsurface ocean in decadal climate predictability over the South Atlantic.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Morioka, Yushi; Doi, Takeshi; Storto, Andrea; Masina, Simona; Behera, Swadhin K</p> <p>2018-06-04</p> <p>Decadal climate predictability in the South Atlantic is explored by performing reforecast experiments using a coupled general circulation model with two initialization schemes; one is assimilated with observed sea surface temperature (SST) only, and the other is additionally assimilated with observed subsurface ocean temperature and salinity. The South Atlantic is known to undergo decadal variability exhibiting a meridional dipole of SST anomalies through variations in the subtropical high and ocean heat transport. Decadal reforecast experiments in which only the model SST is initialized with the observation do not predict well the observed decadal SST variability in the South Atlantic, while the other experiments in which the model SST and subsurface ocean are initialized with the observation skillfully predict the observed decadal SST variability, particularly in the Southeast Atlantic. In-depth analysis of upper-ocean heat content reveals that a significant improvement of zonal heat transport in the Southeast Atlantic leads to skillful prediction of decadal SST variability there. These results demonstrate potential roles of subsurface ocean assimilation in the skillful prediction of decadal climate variability over the South Atlantic.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1228364','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1228364"><span>Detection of Historical and Future Precipitation Variations and Extremes Over the Continental United States</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Anderson, Bruce T.</p> <p>2015-12-11</p> <p>Problem: The overall goal of this proposal is to detect observed seasonal-mean precipitation variations and extreme event occurrences over the United States. Detection, e.g. the process of demonstrating that an observed change in climate is unusual, first requires some means of estimating the range of internal variability absent any external drivers. Ideally, the internal variability would be derived from the observations themselves, however generally the observed variability is a confluence of both internal variability and variability in response to external drivers. Further, numerical climate models—the standard tool for detection studies—have their own estimates of intrinsic variability, which may differ substantiallymore » from that found in the observed system as well as other model systems. These problems are further compounded for weather and climate extremes, which as singular events are particularly ill-suited for detection studies because of their infrequent occurrence, limited spatial range, and underestimation within global and even regional numerical models. Rationale: As a basis for this research we will show how stochastic daily-precipitation models—models in which the simulated interannual-to-multidecadal precipitation variance is purely the result of the random evolution of daily precipitation events within a given time period—can be used to address many of these issues simultaneously. Through the novel application of these well-established models, we can first estimate the changes/trends in various means and extremes that can occur even with fixed daily-precipitation characteristics, e.g. that can occur simply as a result of the stochastic evolution of daily weather events within a given climate. Detection of a change in the observed climate—either naturally or anthropogenically forced—can then be defined as any change relative to this stochastic variability, e.g. as changes/trends in the means and extremes that could only have occurred through a change in the underlying climate. As such, this method is capable of detecting “hot spot” regions—as well as “flare ups” within the hot spot regions—that have experienced interannual to multi-decadal scale variations and trends in seasonal-mean precipitation and extreme events. Further by applying the same methods to numerical climate models we can discern the fidelity of the current-generation climate models in representing detectability within the observed climate system. In this way, we can objectively determine the utility of these model systems for performing detection studies of historical and future climate change.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4412608','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4412608"><span>Climate Variability and Human Migration in the Netherlands, 1865–1937</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Jennings, Julia A.; Gray, Clark L.</p> <p>2014-01-01</p> <p>Human migration is frequently cited as a potential social outcome of climate change and variability, and these effects are often assumed to be stronger in the past when economies were less developed and markets more localized. Yet, few studies have used historical data to test the relationship between climate and migration directly. In addition, the results of recent studies that link demographic and climate data are not consistent with conventional narratives of displacement responses. Using longitudinal individual-level demographic data from the Historical Sample of the Netherlands (HSN) and climate data that cover the same period, we examine the effects of climate variability on migration using event history models. Only internal moves in the later period and for certain social groups are associated with negative climate conditions, and the strength and direction of the observed effects change over time. International moves decrease with extreme rainfall, suggesting that the complex relationships between climate and migration that have been observed for contemporary populations extend into the nineteenth century. PMID:25937689</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A23F2435L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A23F2435L"><span>The Climate Variability & Predictability (CVP) Program at NOAA - Observing and Understanding Processes Affecting the Propagation of Intraseasonal Oscillations in the Maritime Continent Region</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lucas, S. E.</p> <p>2017-12-01</p> <p>The Climate Variability & Predictability (CVP) Program supports research aimed at providing process-level understanding of the climate system through observation, modeling, analysis, and field studies. This vital knowledge is needed to improve climate models and predictions so that scientists can better anticipate the impacts of future climate variability and change. To achieve its mission, the CVP Program supports research carried out at NOAA and other federal laboratories, NOAA Cooperative Institutes, and academic institutions. The Program also coordinates its sponsored projects with major national and international scientific bodies including the World Climate Research Programme (WCRP), the International and U.S. Climate Variability and Predictability (CLIVAR/US CLIVAR) Program, and the U.S. Global Change Research Program (USGCRP). The CVP program sits within NOAA's Climate Program Office (http://cpo.noaa.gov/CVP). In 2017, the CVP Program had a call for proposals focused on observing and understanding processes affecting the propagation of intraseasonal oscillations in the Maritime Continent region. This poster will present the recently funded CVP projects, the expected scientific outcomes, the geographic areas of their work in the Maritime Continent region, and the collaborations with the Office of Naval Research, Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG), Japan Agency for Marine-Earth Science and Technology (JAMSTEC) and other partners.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.3458S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.3458S"><span>Two centuries of observed atmospheric variability and change over the North Sea region</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stendel, Martin; van den Besselaar, Else; Hannachi, Abdel; Kent, Elizabeth; Lefebvre, Christiana; van Oldenborgh, Geert Jan; Rosenhagen, Gudrun; Schenk, Frederik; van der Schrier, Gerard</p> <p>2015-04-01</p> <p>Situated in northwestern Europe, the North Sea region is under influence of air masses from subtropical to arctic origin, and thus exhibits significant natural climate variability. As the land areas surrounding the North Sea are densely populated, climate change is an important issue in terms of e.g. coastal protection, fishery and trade. This study is part of the NOSCCA initiative (North Sea Region Climate Change Assessment) and presents observed variability and changes in atmospheric parameters during the last roughly 200 years. Circulation patterns show considerable decadal variability. In recent decades, a northward shift of storm tracks and increased cyclonic activity has been observed. There is also an indication of increased persistence of weather types. The wind climate is dominated by large multidecadal variability, and no robust long-term trends can be identified in the available datasets. There is a clear positive trend in near-surface temperatures, in particular during spring and winter. Over the region as a whole, no clear long-term precipitation trends are visible, although regional indications exist for an increased risk of extreme precipitation events.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1813442D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1813442D"><span>Impacts of climate change and internal climate variability on french rivers streamflows</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dayon, Gildas; Boé, Julien; Martin, Eric</p> <p>2016-04-01</p> <p>The assessment of the impacts of climate change often requires to set up long chains of modeling, from the model to estimate the future concentration of greenhouse gases to the impact model. Throughout the modeling chain, sources of uncertainty accumulate making the exploitation of results for the development of adaptation strategies difficult. It is proposed here to assess the impacts of climate change on the hydrological cycle over France and the associated uncertainties. The contribution of the uncertainties from greenhouse gases emission scenario, climate models and internal variability are addressed in this work. To have a large ensemble of climate simulations, the study is based on Global Climate Models (GCM) simulations from the Coupled Model Intercomparison Phase 5 (CMIP5), including several simulations from the same GCM to properly assess uncertainties from internal climate variability. Simulations from the four Radiative Concentration Pathway (RCP) are downscaled with a statistical method developed in a previous study (Dayon et al. 2015). The hydrological system Isba-Modcou is then driven by the downscaling results on a 8 km grid over France. Isba is a land surface model that calculates the energy and water balance and Modcou a hydrogeological model that routes the surface runoff given by Isba. Based on that framework, uncertainties uncertainties from greenhouse gases emission scenario, climate models and climate internal variability are evaluated. Their relative importance is described for the next decades and the end of this century. In a last part, uncertainties due to internal climate variability on streamflows simulated with downscaled GCM and Isba-Modcou are evaluated against observations and hydrological reconstructions on the whole 20th century. Hydrological reconstructions are based on the downscaling of recent atmospheric reanalyses of the 20th century and observations of temperature and precipitation. We show that the multi-decadal variability of streamflows observed in the 20th century is generally weaker in the hydrological simulations done with the historical simulations from climate models. References: Dayon et al. (2015), Transferability in the future climate of a statistical downscaling mehtod for precipitation in France, J. Geophys. Res. Atmos., 120, 1023-1043, doi:10.1002/2014JD022236</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170002646','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170002646"><span>Regionalizing Africa: Patterns of Precipitation Variability in Observations and Global Climate Models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Badr, Hamada S.; Dezfuli, Amin K.; Zaitchik, Benjamin F.; Peters-Lidard, Christa D.</p> <p>2016-01-01</p> <p>Many studies have documented dramatic climatic and environmental changes that have affected Africa over different time scales. These studies often raise questions regarding the spatial extent and regional connectivity of changes inferred from observations and proxies and/or derived from climate models. Objective regionalization offers a tool for addressing these questions. To demonstrate this potential, applications of hierarchical climate regionalizations of Africa using observations and GCM historical simulations and future projections are presented. First, Africa is regionalized based on interannual precipitation variability using Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data for the period 19812014. A number of data processing techniques and clustering algorithms are tested to ensure a robust definition of climate regions. These regionalization results highlight the seasonal and even month-to-month specificity of regional climate associations across the continent, emphasizing the need to consider time of year as well as research question when defining a coherent region for climate analysis. CHIRPS regions are then compared to those of five GCMs for the historic period, with a focus on boreal summer. Results show that some GCMs capture the climatic coherence of the Sahel and associated teleconnections in a manner that is similar to observations, while other models break the Sahel into uncorrelated subregions or produce a Sahel-like region of variability that is spatially displaced from observations. Finally, shifts in climate regions under projected twenty-first-century climate change for different GCMs and emissions pathways are examined. A projected change is found in the coherence of the Sahel, in which the western and eastern Sahel become distinct regions with different teleconnections. This pattern is most pronounced in high-emissions scenarios.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ThApC.tmp...54S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ThApC.tmp...54S"><span>Variability of precipitation in Poland under climate change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Szwed, Małgorzata</p> <p>2018-02-01</p> <p>The surface warming has been widespread over the entire globe. Central Europe, including Poland, is not an exception. Global temperature increases are accompanied by changes in other climatic variables. Climate change in Poland manifests itself also as change in annual sums of precipitation. They have been slightly growing but, what is more important, seasonal and monthly distributions of precipitation have been also changing. The most visible increases have been observed during colder half-year, especially in March. A decreasing contribution of summer precipitation total (June-August) to the annual total is observed. Climate projections for Poland predict further warming and continuation of already observed changes in the quantity of precipitation as well as its spatial and seasonal distribution.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4776956','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4776956"><span>Association between Climatic Variables and Malaria Incidence: A Study in Kokrajhar District of Assam, India</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Nath, Dilip C.; Mwchahary, Dimacha Dwibrang</p> <p>2013-01-01</p> <p>A favorable climatic condition for transmission of malaria prevails in Kokrajhar district throughout the year. A sizeable part of the district is covered by forest due to which dissimilar dynamics of malaria transmission emerge in forest and non-forest areas. Observed malaria incidence rates of forest area, non-forest area and the whole district over the period 2001-2010 were considered for analyzing temporal correlation between malaria incidence and climatic variables. Associations between the two were examined by Pearson correlation analysis. Cross-correlation tests were performed between pre-whitened series of climatic variable and malaria series. Linear regressions were used to obtain linear relationships between climatic factors and malaria incidence, while weighted least squares regression was used to construct models for explaining and estimating malaria incidence rates. Annual concentration of malaria incidence was analyzed by Markham technique by obtaining seasonal index. Forest area and non-forest area have distinguishable malaria seasons. Relative humidity was positively correlated with z malaria incidence, while temperature series were negatively correlated with non-forest malaria incidence. There was higher seasonality of concentration of malaria in the forest area than non-forest area. Significant correlation between annual changes in malaria cases in forest area and temperature was observed (coeff=0.689, p=0.040). Separate reliable models constructed for forecasting malaria incidence rates based on the combined influence of climatic variables on malaria incidence in different areas of the district were able to explain substantial percentage of observed variability in the incidence rates (R2adj=45.4%, 50.6%, 47.2%; p< .001 for all). There is an intricate association between climatic variables and malaria incidence of the district. Climatic variables influence malaria incidence in forest area and non-forest area in different ways. Rainfall plays a primary role in characterizing malaria incidences in the district. Malaria parasites in the district had adapted to a relative humidity condition higher than the normal range for transmission in India. Instead of individual influence of the climatic variables, their combined influence was utilizable for construction of models. PMID:23283041</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23283041','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23283041"><span>Association between climatic variables and malaria incidence: a study in Kokrajhar district of Assam, India.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Nath, Dilip C; Mwchahary, Dimacha Dwibrang</p> <p>2012-11-11</p> <p>A favorable climatic condition for transmission of malaria prevails in Kokrajhar district throughout the year. A sizeable part of the district is covered by forest due to which dissimilar dynamics of malaria transmission emerge in forest and non-forest areas. Observed malaria incidence rates of forest area, non-forest area and the whole district over the period 2001-2010 were considered for analyzing temporal correlation between malaria incidence and climatic variables. Associations between the two were examined by Pearson correlation analysis. Cross-correlation tests were performed between pre-whitened series of climatic variable and malaria series. Linear regressions were used to obtain linear relationships between climatic factors and malaria incidence, while weighted least squares regression was used to construct models for explaining and estimating malaria incidence rates. Annual concentration of malaria incidence was analyzed by Markham technique by obtaining seasonal index. Forest area and non-forest area have distinguishable malaria seasons. Relative humidity was positively correlated with forest malaria incidence, while temperature series were negatively correlated with non-forest malaria incidence. There was higher seasonality of concentration of malaria in the forest area than non-forest area. Significant correlation between annual changes in malaria cases in forest area and temperature was observed (coeff=0.689, p=0.040). Separate reliable models constructed for forecasting malaria incidence rates based on the combined influence of climatic variables on malaria incidence in different areas of the district were able to explain substantial percentage of observed variability in the incidence rates (R2adj=45.4%, 50.6%, 47.2%; p< .001 for all). There is an intricate association between climatic variables and malaria incidence of the district. Climatic variables influence malaria incidence in forest area and non-forest area in different ways. Rainfall plays a primary role in characterizing malaria incidences in the district. Malaria parasites in the district had adapted to a relative humidity condition higher than the normal range for transmission in India. Instead of individual influence of the climatic variables, their combined influence was utilizable for construction of models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..1413848C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1413848C"><span>Climatic variability effects on summer cropping systems of the Iberian Peninsula</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Capa-Morocho, M.; Rodríguez-Fonseca, B.; Ruiz-Ramos, M.</p> <p>2012-04-01</p> <p>Climate variability and changes in the frequency of extremes events have a direct impact on crop yield and damages. Climate anomalies projections at monthly and yearly timescale allows us for adapting a cropping system (crops, varieties and management) to take advantage of favorable conditions or reduce the effect of adverse conditions. The objective of this work is to develop indices to evaluate the effect of climatic variability in summer cropping systems of Iberian Peninsula, in an attempt of relating yield variability to climate variability, extending the work of Rodríguez-Puebla (2004). This paper analyses the evolution of the yield anomalies of irrigated maize in several representative agricultural locations in Spain with contrasting temperature and precipitation regimes and compare it to the evolution of different patterns of climate variability, extending the methodology of Porter and Semenov (2005). To simulate maize yields observed daily data of radiation, maximum and minimum temperature and precipitation were used. These data were obtained from the State Meteorological Agency of Spain (AEMET). Time series of simulated maize yields were computed with CERES-maize model for periods ranging from 22 to 49 years, depending on the observed climate data available for each location. The computed standardized anomalies yields were projected on different oceanic and atmospheric anomalous fields and the resulting patterns were compared with a set of documented patterns from the National Oceanic and Atmospheric Administration (NOAA). The results can be useful also for climate change impact assessment, providing a scientific basis for selection of climate change scenarios where combined natural and forced variability represent a hazard for agricultural production. Interpretation of impact projections would also be enhanced.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li class="active"><span>2</span></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_2 --> <div id="page_3" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li class="active"><span>3</span></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="41"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22498628','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22498628"><span>Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Booth, Ben B B; Dunstone, Nick J; Halloran, Paul R; Andrews, Timothy; Bellouin, Nicolas</p> <p>2012-04-04</p> <p>Systematic climate shifts have been linked to multidecadal variability in observed sea surface temperatures in the North Atlantic Ocean. These links are extensive, influencing a range of climate processes such as hurricane activity and African Sahel and Amazonian droughts. The variability is distinct from historical global-mean temperature changes and is commonly attributed to natural ocean oscillations. A number of studies have provided evidence that aerosols can influence long-term changes in sea surface temperatures, but climate models have so far failed to reproduce these interactions and the role of aerosols in decadal variability remains unclear. Here we use a state-of-the-art Earth system climate model to show that aerosol emissions and periods of volcanic activity explain 76 per cent of the simulated multidecadal variance in detrended 1860-2005 North Atlantic sea surface temperatures. After 1950, simulated variability is within observational estimates; our estimates for 1910-1940 capture twice the warming of previous generation models but do not explain the entire observed trend. Other processes, such as ocean circulation, may also have contributed to variability in the early twentieth century. Mechanistically, we find that inclusion of aerosol-cloud microphysical effects, which were included in few previous multimodel ensembles, dominates the magnitude (80 per cent) and the spatial pattern of the total surface aerosol forcing in the North Atlantic. Our findings suggest that anthropogenic aerosol emissions influenced a range of societally important historical climate events such as peaks in hurricane activity and Sahel drought. Decadal-scale model predictions of regional Atlantic climate will probably be improved by incorporating aerosol-cloud microphysical interactions and estimates of future concentrations of aerosols, emissions of which are directly addressable by policy actions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1511832M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1511832M"><span>Enhanced future variability during India's rainy season</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Menon, Arathy; Levermann, Anders; Schewe, Jacob</p> <p>2013-04-01</p> <p>The Indian summer monsoon shapes the livelihood of a large share of the world's population. About 80% of annual precipitation over India occurs during the monsoon season from June through September. Next to its seasonal mean rainfall the day-to-day variability is crucial for the risk of flooding, national water supply and agricultural productivity. Here we show that the latest ensemble of climate model simulations, prepared for the IPCC's AR-5, consistently projects significant increases in day-to-day rainfall variability under unmitigated climate change. While all models show an increase in day-to-day variability, some models are more realistic in capturing the observed seasonal mean rainfall over India than others. While no model's monsoon rainfall exceeds the observed value by more than two standard deviations, half of the models simulate a significantly weaker monsoon than observed. The relative increase in day-to-day variability by the year 2100 ranges from 15% to 48% under the strongest scenario (RCP-8.5), in the ten models which capture seasonal mean rainfall closest to observations. The variability increase per degree of global warming is independent of the scenario in most models, and is 8% +/- 4% per K on average. This consistent projection across 20 comprehensive climate models provides confidence in the results and suggests the necessity of profound adaptation measures in the case of unmitigated climate change.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20160009138&hterms=India+climate+change&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DIndia%2Bclimate%2Bchange','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20160009138&hterms=India+climate+change&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DIndia%2Bclimate%2Bchange"><span>Multi-Wheat-Model Ensemble Responses to Interannual Climate Variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ruane, Alex C.; Hudson, Nicholas I.; Asseng, Senthold; Camarrano, Davide; Ewert, Frank; Martre, Pierre; Boote, Kenneth J.; Thorburn, Peter J.; Aggarwal, Pramod K.; Angulo, Carlos</p> <p>2016-01-01</p> <p>We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981e2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-termwarming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/56219','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/56219"><span>Climate-based seed zones for Mexico: guiding reforestation under observed and projected climate change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Dante Castellanos-Acuña; Kenneth W. Vance-Borland; J. Bradley St. Clair; Andreas Hamann; Javier López-Upton; Erika Gómez-Pineda; Juan Manuel Ortega-Rodríguez; Cuauhtémoc Sáenz-Romero</p> <p>2018-01-01</p> <p>Seed zones for forest tree species are a widely used tool in reforestation programs to ensure that seedlings are well adapted to their planting environments. Here, we propose a climate-based seed zone system for Mexico to address observed and projected climate change. The proposed seed zone classification is based on bands of climate variables often related to genetic...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/55710','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/55710"><span>Trends in seasonal warm anomalies across the contiguous United States: Contributions from natural climate variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Lejiang Yu; Shiyuan Zhong; Warren E. Heilman; Xindi Bian</p> <p>2018-01-01</p> <p>Many studies have shown the importance of anthropogenic greenhouse gas emissions in contributing to observed upward trends in the occurrences of temperature extremes over the U.S. However, few studies have investigated the contributions of internal variability in the climate system to these observed trends. Here we use daily maximum temperature time series from the...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4999131','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4999131"><span>Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Delerce, Sylvain; Dorado, Hugo; Grillon, Alexandre; Rebolledo, Maria Camila; Prager, Steven D.; Patiño, Victor Hugo; Garcés Varón, Gabriel; Jiménez, Daniel</p> <p>2016-01-01</p> <p>Seasonal and inter-annual climate variability have become important issues for farmers, and climate change has been shown to increase them. Simultaneously farmers and agricultural organizations are increasingly collecting observational data about in situ crop performance. Agriculture thus needs new tools to cope with changing environmental conditions and to take advantage of these data. Data mining techniques make it possible to extract embedded knowledge associated with farmer experiences from these large observational datasets in order to identify best practices for adapting to climate variability. We introduce new approaches through a case study on irrigated and rainfed rice in Colombia. Preexisting observational datasets of commercial harvest records were combined with in situ daily weather series. Using Conditional Inference Forest and clustering techniques, we assessed the relationships between climatic factors and crop yield variability at the local scale for specific cultivars and growth stages. The analysis showed clear relationships in the various location-cultivar combinations, with climatic factors explaining 6 to 46% of spatiotemporal variability in yield, and with crop responses to weather being non-linear and cultivar-specific. Climatic factors affected cultivars differently during each stage of development. For instance, one cultivar was affected by high nighttime temperatures in the reproductive stage but responded positively to accumulated solar radiation during the ripening stage. Another was affected by high nighttime temperatures during both the vegetative and reproductive stages. Clustering of the weather patterns corresponding to individual cropping events revealed different groups of weather patterns for irrigated and rainfed systems with contrasting yield levels. Best-suited cultivars were identified for some weather patterns, making weather-site-specific recommendations possible. This study illustrates the potential of data mining for adding value to existing observational data in agriculture by allowing embedded knowledge to be quickly leveraged. It generates site-specific information on cultivar response to climatic factors and supports on-farm management decisions for adaptation to climate variability. PMID:27560980</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27560980','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27560980"><span>Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Delerce, Sylvain; Dorado, Hugo; Grillon, Alexandre; Rebolledo, Maria Camila; Prager, Steven D; Patiño, Victor Hugo; Garcés Varón, Gabriel; Jiménez, Daniel</p> <p>2016-01-01</p> <p>Seasonal and inter-annual climate variability have become important issues for farmers, and climate change has been shown to increase them. Simultaneously farmers and agricultural organizations are increasingly collecting observational data about in situ crop performance. Agriculture thus needs new tools to cope with changing environmental conditions and to take advantage of these data. Data mining techniques make it possible to extract embedded knowledge associated with farmer experiences from these large observational datasets in order to identify best practices for adapting to climate variability. We introduce new approaches through a case study on irrigated and rainfed rice in Colombia. Preexisting observational datasets of commercial harvest records were combined with in situ daily weather series. Using Conditional Inference Forest and clustering techniques, we assessed the relationships between climatic factors and crop yield variability at the local scale for specific cultivars and growth stages. The analysis showed clear relationships in the various location-cultivar combinations, with climatic factors explaining 6 to 46% of spatiotemporal variability in yield, and with crop responses to weather being non-linear and cultivar-specific. Climatic factors affected cultivars differently during each stage of development. For instance, one cultivar was affected by high nighttime temperatures in the reproductive stage but responded positively to accumulated solar radiation during the ripening stage. Another was affected by high nighttime temperatures during both the vegetative and reproductive stages. Clustering of the weather patterns corresponding to individual cropping events revealed different groups of weather patterns for irrigated and rainfed systems with contrasting yield levels. Best-suited cultivars were identified for some weather patterns, making weather-site-specific recommendations possible. This study illustrates the potential of data mining for adding value to existing observational data in agriculture by allowing embedded knowledge to be quickly leveraged. It generates site-specific information on cultivar response to climatic factors and supports on-farm management decisions for adaptation to climate variability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70041536','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70041536"><span>Do bioclimate variables improve performance of climate envelope models?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Watling, James I.; Romañach, Stephanie S.; Bucklin, David N.; Speroterra, Carolina; Brandt, Laura A.; Pearlstine, Leonard G.; Mazzotti, Frank J.</p> <p>2012-01-01</p> <p>Climate envelope models are widely used to forecast potential effects of climate change on species distributions. A key issue in climate envelope modeling is the selection of predictor variables that most directly influence species. To determine whether model performance and spatial predictions were related to the selection of predictor variables, we compared models using bioclimate variables with models constructed from monthly climate data for twelve terrestrial vertebrate species in the southeastern USA using two different algorithms (random forests or generalized linear models), and two model selection techniques (using uncorrelated predictors or a subset of user-defined biologically relevant predictor variables). There were no differences in performance between models created with bioclimate or monthly variables, but one metric of model performance was significantly greater using the random forest algorithm compared with generalized linear models. Spatial predictions between maps using bioclimate and monthly variables were very consistent using the random forest algorithm with uncorrelated predictors, whereas we observed greater variability in predictions using generalized linear models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1918904Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1918904Z"><span>Statistical structure of intrinsic climate variability under global warming</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhu, Xiuhua; Bye, John; Fraedrich, Klaus</p> <p>2017-04-01</p> <p>Climate variability is often studied in terms of fluctuations with respect to the mean state, whereas the dependence between the mean and variability is rarely discussed. We propose a new climate metric to measure the relationship between means and standard deviations of annual surface temperature computed over non-overlapping 100-year segments. This metric is analyzed based on equilibrium simulations of the Max Planck Institute-Earth System Model (MPI-ESM): the last millennium climate (800-1799), the future climate projection following the A1B scenario (2100-2199), and the 3100-year unforced control simulation. A linear relationship is globally observed in the control simulation and thus termed intrinsic climate variability, which is most pronounced in the tropical region with negative regression slopes over the Pacific warm pool and positive slopes in the eastern tropical Pacific. It relates to asymmetric changes in temperature extremes and associates fluctuating climate means with increase or decrease in intensity and occurrence of both El Niño and La Niña events. In the future scenario period, the linear regression slopes largely retain their spatial structure with appreciable changes in intensity and geographical locations. Since intrinsic climate variability describes the internal rhythm of the climate system, it may serve as guidance for interpreting climate variability and climate change signals in the past and the future.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.3744Q','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.3744Q"><span>Can climate variability information constrain a hydrological model for an ungauged Costa Rican catchment?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Quesada-Montano, Beatriz; Westerberg, Ida K.; Fuentes-Andino, Diana; Hidalgo-Leon, Hugo; Halldin, Sven</p> <p>2017-04-01</p> <p>Long-term hydrological data are key to understanding catchment behaviour and for decision making within water management and planning. Given the lack of observed data in many regions worldwide, hydrological models are an alternative for reproducing historical streamflow series. Additional types of information - to locally observed discharge - can be used to constrain model parameter uncertainty for ungauged catchments. Climate variability exerts a strong influence on streamflow variability on long and short time scales, in particular in the Central-American region. We therefore explored the use of climate variability knowledge to constrain the simulated discharge uncertainty of a conceptual hydrological model applied to a Costa Rican catchment, assumed to be ungauged. To reduce model uncertainty we first rejected parameter relationships that disagreed with our understanding of the system. We then assessed how well climate-based constraints applied at long-term, inter-annual and intra-annual time scales could constrain model uncertainty. Finally, we compared the climate-based constraints to a constraint on low-flow statistics based on information obtained from global maps. We evaluated our method in terms of the ability of the model to reproduce the observed hydrograph and the active catchment processes in terms of two efficiency measures, a statistical consistency measure, a spread measure and 17 hydrological signatures. We found that climate variability knowledge was useful for reducing model uncertainty, in particular, unrealistic representation of deep groundwater processes. The constraints based on global maps of low-flow statistics provided more constraining information than those based on climate variability, but the latter rejected slow rainfall-runoff representations that the low flow statistics did not reject. The use of such knowledge, together with information on low-flow statistics and constraints on parameter relationships showed to be useful to constrain model uncertainty for an - assumed to be - ungauged basin. This shows that our method is promising for reconstructing long-term flow data for ungauged catchments on the Pacific side of Central America, and that similar methods can be developed for ungauged basins in other regions where climate variability exerts a strong control on streamflow variability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC32B..01P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC32B..01P"><span>Inability of CMIP5 Climate Models to Simulate Recent Multi-decadal Climate Change in the Tropical Pacific.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Power, S.; Delage, F.; Kociuba, G.; Wang, G.; Smith, I.</p> <p>2017-12-01</p> <p>Observed 15-year surface temperature trends beginning 1998 or later have attracted a great deal of interest because of an apparent slowdown in the rate of global warming, and contrasts between climate model simulations and observations of such trends. Many studies have addressed the statistical significance of these relatively short trends, whether they indicate a possible bias in models and the implications for global warming generally. Here we analyse historical and projected changes in 38 CMIP5 climate models. All of the models simulate multi-decadal warming in the Pacific over the past half-century that exceeds observed values. This stark difference cannot be fully explained by observed, internal multi-decadal climate variability, even if allowance is made for an apparent tendency for models to underestimate internal multi-decadal variability in the Pacific. We also show that CMIP5 models are not able to simulate the magnitude of the strengthening of the Walker Circulation over the past thirty years. Some of the reasons for these major shortcomings in the ability of models to simulate multi-decadal variability in the Pacific, and the impact these findings have on our confidence in global 21st century projections, will be discussed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26648483','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26648483"><span>Multiple stressors, nonlinear effects and the implications of climate change impacts on marine coastal ecosystems.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hewitt, Judi E; Ellis, Joanne I; Thrush, Simon F</p> <p>2016-08-01</p> <p>Global climate change will undoubtedly be a pressure on coastal marine ecosystems, affecting not only species distributions and physiology but also ecosystem functioning. In the coastal zone, the environmental variables that may drive ecological responses to climate change include temperature, wave energy, upwelling events and freshwater inputs, and all act and interact at a variety of spatial and temporal scales. To date, we have a poor understanding of how climate-related environmental changes may affect coastal marine ecosystems or which environmental variables are likely to produce priority effects. Here we use time series data (17 years) of coastal benthic macrofauna to investigate responses to a range of climate-influenced variables including sea-surface temperature, southern oscillation indices (SOI, Z4), wind-wave exposure, freshwater inputs and rainfall. We investigate responses from the abundances of individual species to abundances of functional traits and test whether species that are near the edge of their tolerance to another stressor (in this case sedimentation) may exhibit stronger responses. The responses we observed were all nonlinear and some exhibited thresholds. While temperature was most frequently an important predictor, wave exposure and ENSO-related variables were also frequently important and most ecological variables responded to interactions between environmental variables. There were also indications that species sensitive to another stressor responded more strongly to weaker climate-related environmental change at the stressed site than the unstressed site. The observed interactions between climate variables, effects on key species or functional traits, and synergistic effects of additional anthropogenic stressors have important implications for understanding and predicting the ecological consequences of climate change to coastal ecosystems. © 2015 John Wiley & Sons Ltd.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018BGeo...15..919A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018BGeo...15..919A"><span>Influence of climate variability, fire and phosphorus limitation on vegetation structure and dynamics of the Amazon-Cerrado border</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ane Dionizio, Emily; Heil Costa, Marcos; de Almeida Castanho, Andrea D.; Ferreira Pires, Gabrielle; Schwantes Marimon, Beatriz; Hur Marimon-Junior, Ben; Lenza, Eddie; Martins Pimenta, Fernando; Yang, Xiaojuan; Jain, Atul K.</p> <p>2018-02-01</p> <p>Climate, fire and soil nutrient limitation are important elements that affect vegetation dynamics in areas of the forest-savanna transition. In this paper, we use the dynamic vegetation model INLAND to evaluate the influence of interannual climate variability, fire and phosphorus (P) limitation on Amazon-Cerrado transitional vegetation structure and dynamics. We assess how each environmental factor affects net primary production, leaf area index and aboveground biomass (AGB), and compare the AGB simulations to an observed AGB map. We used two climate data sets (monthly average climate for 1961-1990 and interannual climate variability for 1948-2008), two data sets of total soil P content (one based on regional field measurements and one based on global data), and the INLAND fire module. Our results show that the inclusion of interannual climate variability, P limitation and fire occurrence each contribute to simulating vegetation types that more closely match observations. These effects are spatially heterogeneous and synergistic. In terms of magnitude, the effect of fire is strongest and is the main driver of vegetation changes along the transition. Phosphorus limitation, in turn, has a stronger effect on transitional ecosystem dynamics than interannual climate variability does. Overall, INLAND typically simulates more than 80 % of the AGB variability in the transition zone. However, the AGB in many places is clearly not well simulated, indicating that important soil and physiological factors in the Amazon-Cerrado border region, such as lithology, water table depth, carbon allocation strategies and mortality rates, still need to be included in the model.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013BGD....1017511W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013BGD....1017511W"><span>Climate-mediated spatiotemporal variability in the terrestrial productivity across Europe</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wu, X.; Mahecha, M. D.; Reichstein, M.; Ciais, P.; Wattenbach, M.; Babst, F.; Frank, D.; Zang, C.</p> <p>2013-11-01</p> <p>Quantifying the interannual variability (IAV) of the terrestrial productivity and its sensitivity to climate is crucial for improving carbon budget predictions. However, the influence of climate and other mechanisms underlying the spatiotemporal patterns of IAV of productivity are not well understood. In this study we investigated the spatiotemporal patterns of IAV of historical observations of crop yields, tree ring width, remote sensing retrievals of FAPAR and NDVI, and other variables relevant to the terrestrial productivity in Europe in tandem with a set of climate variables. Our results reveal distinct spatial patterns in the IAV of most variables linked to terrestrial productivity. In particular, we find higher IAV in water-limited regions of Europe (Mediterranean and temperate continental Europe) compared to other regions. Our results further indicate that variations in the water balance during active growing season exert a more pronounced and direct effect than variations of temperature on explaining the spatial patterns in IAV of productivity related variables in temperate Europe. We also observe a~temporally increasing trend in the IAV of terrestrial productivity and an increasing sensitivity of productivity to water availability in dry regions of Europe, which is likely attributable to the recently increased IAV of water availability in these regions. These findings suggest nonlinear responses of carbon fluxes to climate variability in Europe and that the IAV of terrestrial productivity has become more sensitive and more vulnerable to changes in water availability in the dry regions in Europe. The changing climate sensitivity of terrestrial productivity accompanied by the changing IAV of climate could impact carbon stocks and the net carbon balance of European ecosystems.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.A23G0296L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.A23G0296L"><span>The Climate Variability & Predictability (CVP) Program at NOAA - Recent Program Advancements in Understanding AMOC</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lucas, S. E.</p> <p>2016-12-01</p> <p>The Climate Variability & Predictability (CVP) Program supports research aimed at providing process-level understanding of the climate system through observation, modeling, analysis, and field studies. This vital knowledge is needed to improve climate models and predictions so that scientists can better anticipate the impacts of future climate variability and change. To achieve its mission, the CVP Program supports research carried out at NOAA and other federal laboratories, NOAA Cooperative Institutes, and academic institutions. The Program also coordinates its sponsored projects with major national and international scientific bodies including the World Climate Research Programme (WCRP), the International and U.S. Climate Variability and Predictability (CLIVAR/US CLIVAR) Program, and the U.S. Global Change Research Program (USGCRP). The CVP program sits within NOAA's Climate Program Office (http://cpo.noaa.gov/CVP). This poster will present the recently funded CVP projects on improving the understanding Atlantic Meridional Overturning Circulation (AMOC), its impact on decadal predictability, and its relationship with the overall climate system.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC32B..04B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC32B..04B"><span>An integrated, indicator framework for assessing large-scale variations and change in seasonal timing and phenology (Invited)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Betancourt, J. L.; Weltzin, J. F.</p> <p>2013-12-01</p> <p>As part of an effort to develop an Indicator System for the National Climate Assessment (NCA), the Seasonality and Phenology Indicators Technical Team (SPITT) proposed an integrated, continental-scale framework for understanding and tracking seasonal timing in physical and biological systems. The framework shares several metrics with the EPA's National Climate Change Indicators. The SPITT framework includes a comprehensive suite of national indicators to track conditions, anticipate vulnerabilities, and facilitate intervention or adaptation to the extent possible. Observed, modeled, and forecasted seasonal timing metrics can inform a wide spectrum of decisions on federal, state, and private lands in the U.S., and will be pivotal for international efforts to mitigation and adaptation. Humans use calendars both to understand the natural world and to plan their lives. Although the seasons are familiar concepts, we lack a comprehensive understanding of how variability arises in the timing of seasonal transitions in the atmosphere, and how variability and change translate and propagate through hydrological, ecological and human systems. For example, the contributions of greenhouse warming and natural variability to secular trends in seasonal timing are difficult to disentangle, including earlier spring transitions from winter (strong westerlies) to summer (weak easterlies) patterns of atmospheric circulation; shifts in annual phasing of daily temperature means and extremes; advanced timing of snow and ice melt and soil thaw at higher latitudes and elevations; and earlier start and longer duration of the growing and fire seasons. The SPITT framework aims to relate spatiotemporal variability in surface climate to (1) large-scale modes of natural climate variability and greenhouse gas-driven climatic change, and (2) spatiotemporal variability in hydrological, ecological and human responses and impacts. The hierarchical framework relies on ground and satellite observations, and includes metrics of surface climate seasonality, seasonality of snow and ice, land surface phenology, ecosystem disturbance seasonality, and organismal phenology. Recommended metrics met the following requirements: (a) easily measured by day-of-year, number of days, or in the case of species migrations, by the latitude of the observation on a given date; (b) are observed or can be calculated across a high density of locations in many different regions of the U.S.; and (c) unambiguously describe both spatial and temporal variability and trends in seasonal timing that are climatically driven. The SPITT framework is meant to provide climatic and strategic guidance for the growth and application of seasonal timing and phenological monitoring efforts. The hope is that additional national indicators based on observed phenology, or evidence-based algorithms calibrated with observational data, will evolve with sustained and broad-scale monitoring of climatically sensitive species and ecological processes.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC51E0855S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC51E0855S"><span>Measuring Skin Temperatures with the IASI Hyperspectral Mission</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Safieddine, S.; George, M.; Clarisse, L.; Clerbaux, C.</p> <p>2017-12-01</p> <p>Although the role of satellites in observing the variability of the Earth system has increased in recent decades, remote-sensing observations are still underexploited to accurately assess climate change fingerprints, in particular temperature variations. The IASI - Flux and Temperature (IASI-FT) project aims at providing new benchmarks for temperature observations using the calibrated radiances measured twice a day at any location by the IASI thermal infrared instrument on the suite of MetOp satellites (2006-2025). The main challenge is to achieve the accuracy and stability needed for climate studies, particularly that required for climate trends. Time series for land and sea skin surface temperatures are derived and compared with in situ measurements and atmospheric reanalysis. The observed trends are analyzed at seasonal and regional scales in order to disentangle natural (weather/dynamical) variability and human-induced climate forcings.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017E%26PSL.476...34D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017E%26PSL.476...34D"><span>Improved spectral comparisons of paleoclimate models and observations via proxy system modeling: Implications for multi-decadal variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dee, S. G.; Parsons, L. A.; Loope, G. R.; Overpeck, J. T.; Ault, T. R.; Emile-Geay, J.</p> <p>2017-10-01</p> <p>The spectral characteristics of paleoclimate observations spanning the last millennium suggest the presence of significant low-frequency (multi-decadal to centennial scale) variability in the climate system. Since this low-frequency climate variability is critical for climate predictions on societally-relevant scales, it is essential to establish whether General Circulation models (GCMs) are able to simulate it faithfully. Recent studies find large discrepancies between models and paleoclimate data at low frequencies, prompting concerns surrounding the ability of GCMs to predict long-term, high-magnitude variability under greenhouse forcing (Laepple and Huybers, 2014a, 2014b). However, efforts to ground climate model simulations directly in paleoclimate observations are impeded by fundamental differences between models and the proxy data: proxy systems often record a multivariate and/or nonlinear response to climate, precluding a direct comparison to GCM output. In this paper we bridge this gap via a forward proxy modeling approach, coupled to an isotope-enabled GCM. This allows us to disentangle the various contributions to signals embedded in ice cores, speleothem calcite, coral aragonite, tree-ring width, and tree-ring cellulose. The paper addresses the following questions: (1) do forward-modeled ;pseudoproxies; exhibit variability comparable to proxy data? (2) if not, which processes alter the shape of the spectrum of simulated climate variability, and are these processes broadly distinguishable from climate? We apply our method to representative case studies, and broaden these insights with an analysis of the PAGES2k database (PAGES2K Consortium, 2013). We find that current proxy system models (PSMs) can help resolve model-data discrepancies on interannual to decadal timescales, but cannot account for the mismatch in variance on multi-decadal to centennial timescales. We conclude that, specific to this set of PSMs and isotope-enabled model, the paleoclimate record may exhibit larger low-frequency variability than GCMs currently simulate, indicative of incomplete physics and/or forcings.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016CliPa..12..483F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016CliPa..12..483F"><span>Climate variability and human impact in South America during the last 2000 years: synthesis and perspectives from pollen records</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Flantua, S. G. A.; Hooghiemstra, H.; Vuille, M.; Behling, H.; Carson, J. F.; Gosling, W. D.; Hoyos, I.; Ledru, M. P.; Montoya, E.; Mayle, F.; Maldonado, A.; Rull, V.; Tonello, M. S.; Whitney, B. S.; González-Arango, C.</p> <p>2016-02-01</p> <p>An improved understanding of present-day climate variability and change relies on high-quality data sets from the past 2 millennia. Global efforts to model regional climate modes are in the process of being validated against, and integrated with, records of past vegetation change. For South America, however, the full potential of vegetation records for evaluating and improving climate models has hitherto not been sufficiently acknowledged due to an absence of information on the spatial and temporal coverage of study sites. This paper therefore serves as a guide to high-quality pollen records that capture environmental variability during the last 2 millennia. We identify 60 vegetation (pollen) records from across South America which satisfy geochronological requirements set out for climate modelling, and we discuss their sensitivity to the spatial signature of climate modes throughout the continent. Diverse patterns of vegetation response to climate change are observed, with more similar patterns of change in the lowlands and varying intensity and direction of responses in the highlands. Pollen records display local-scale responses to climate modes; thus, it is necessary to understand how vegetation-climate interactions might diverge under variable settings. We provide a qualitative translation from pollen metrics to climate variables. Additionally, pollen is an excellent indicator of human impact through time. We discuss evidence for human land use in pollen records and provide an overview considered useful for archaeological hypothesis testing and important in distinguishing natural from anthropogenically driven vegetation change. We stress the need for the palynological community to be more familiar with climate variability patterns to correctly attribute the potential causes of observed vegetation dynamics. This manuscript forms part of the wider LOng-Term multi-proxy climate REconstructions and Dynamics in South America - 2k initiative that provides the ideal framework for the integration of the various palaeoclimatic subdisciplines and palaeo-science, thereby jump-starting and fostering multidisciplinary research into environmental change on centennial and millennial timescales.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20120012899&hterms=books&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dbooks','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20120012899&hterms=books&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dbooks"><span>Intraseasonal Variability in the Atmosphere-Ocean Climate System. Second Edition</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lau, William K. M.; Waliser, Duane E.</p> <p>2011-01-01</p> <p>Understanding and predicting the intraseasonal variability (ISV) of the ocean and atmosphere is crucial to improving long-range environmental forecasts and the reliability of climate change projections through climate models. This updated, comprehensive and authoritative second edition has a balance of observation, theory and modeling and provides a single source of reference for all those interested in this important multi-faceted natural phenomenon and its relation to major short-term climatic variations.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li class="active"><span>3</span></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_3 --> <div id="page_4" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="61"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20020048546','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20020048546"><span>Climate Variability Program</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Halpern, David (Editor)</p> <p>2002-01-01</p> <p>The Annual Report of the Climate Variability Program briefly describes research activities of Principal Investigators who are funded by NASA's Earth Science Enterprise Research Division. The report is focused on the year 2001. Utilization of satellite observations is a singularity of research on climate science and technology at JPL (Jet Propulsion Laboratory). Research at JPL has two foci: generate new knowledge and develop new technology.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1550767','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1550767"><span>Climate variability has a stabilizing effect on the coexistence of prairie grasses</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Adler, Peter B.; HilleRisLambers, Janneke; Kyriakidis, Phaedon C.; Guan, Qingfeng; Levine, Jonathan M.</p> <p>2006-01-01</p> <p>How expected increases in climate variability will affect species diversity depends on the role of such variability in regulating the coexistence of competing species. Despite theory linking temporal environmental fluctuations with the maintenance of diversity, the importance of climate variability for stabilizing coexistence remains unknown because of a lack of appropriate long-term observations. Here, we analyze three decades of demographic data from a Kansas prairie to demonstrate that interannual climate variability promotes the coexistence of three common grass species. Specifically, we show that (i) the dynamics of the three species satisfy all requirements of “storage effect” theory based on recruitment variability with overlapping generations, (ii) climate variables are correlated with interannual variation in species performance, and (iii) temporal variability increases low-density growth rates, buffering these species against competitive exclusion. Given that environmental fluctuations are ubiquitous in natural systems, our results suggest that coexistence based on the storage effect may be underappreciated and could provide an important alternative to recent neutral theories of diversity. Field evidence for positive effects of variability on coexistence also emphasizes the need to consider changes in both climate means and variances when forecasting the effects of global change on species diversity. PMID:16908862</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ClDy...50.2369R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ClDy...50.2369R"><span>Towards multi-resolution global climate modeling with ECHAM6-FESOM. Part II: climate variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rackow, T.; Goessling, H. F.; Jung, T.; Sidorenko, D.; Semmler, T.; Barbi, D.; Handorf, D.</p> <p>2018-04-01</p> <p>This study forms part II of two papers describing ECHAM6-FESOM, a newly established global climate model with a unique multi-resolution sea ice-ocean component. While part I deals with the model description and the mean climate state, here we examine the internal climate variability of the model under constant present-day (1990) conditions. We (1) assess the internal variations in the model in terms of objective variability performance indices, (2) analyze variations in global mean surface temperature and put them in context to variations in the observed record, with particular emphasis on the recent warming slowdown, (3) analyze and validate the most common atmospheric and oceanic variability patterns, (4) diagnose the potential predictability of various climate indices, and (5) put the multi-resolution approach to the test by comparing two setups that differ only in oceanic resolution in the equatorial belt, where one ocean mesh keeps the coarse 1° resolution applied in the adjacent open-ocean regions and the other mesh is gradually refined to 0.25°. Objective variability performance indices show that, in the considered setups, ECHAM6-FESOM performs overall favourably compared to five well-established climate models. Internal variations of the global mean surface temperature in the model are consistent with observed fluctuations and suggest that the recent warming slowdown can be explained as a once-in-one-hundred-years event caused by internal climate variability; periods of strong cooling in the model (`hiatus' analogs) are mainly associated with ENSO-related variability and to a lesser degree also to PDO shifts, with the AMO playing a minor role. Common atmospheric and oceanic variability patterns are simulated largely consistent with their real counterparts. Typical deficits also found in other models at similar resolutions remain, in particular too weak non-seasonal variability of SSTs over large parts of the ocean and episodic periods of almost absent deep-water formation in the Labrador Sea, resulting in overestimated North Atlantic SST variability. Concerning the influence of locally (isotropically) increased resolution, the ENSO pattern and index statistics improve significantly with higher resolution around the equator, illustrating the potential of the novel unstructured-mesh method for global climate modeling.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC51E1208W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC51E1208W"><span>The impact of anthropogenic climate change on wildfire across western US forests</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Williams, P.; Abatzoglou, J. T.</p> <p>2016-12-01</p> <p>Increased forest fire activity across the western United States (US) in recent decades has contributed to widespread forest mortality, carbon emissions, periods of degraded air quality, and substantial fire suppression expenditures. The increase in forest fire activity has likely been enabled by a number of factors including the legacy of fire suppression and human settlement, changes in suppression policies, natural climate variability, and human-caused climate change. We use modeled climate projections to estimate the contribution of anthropogenic climate change to observed increases in eight fuel aridity metrics and forest fire area across the western US. Anthropogenic increases in temperature and vapor pressure deficit have significantly enhanced fuel aridity across western US forests over the past several decades. Comparing observational climate records to records recalculated after removal of modeled anthropogenic trends, we find that anthropogenic climate change accounted for approximately 55% of observed increases in the eight-metric mean fuel aridity during 1979-2015 across western US forests. This implicates anthropogenic climate change as an important driver of observed increases in fuel aridity, and also highlights the importance of natural multi-decadal climate variability in influencing trends in forest fire potential on the timescales of human lives. Based on a very strong (R2 = 0.76) and mechanistically reasonable relationship between interannual variability in the eight-metric mean fuel aridity and forest-fire area in the western US, we estimate that anthropogenic increases in fuel aridity contributed to an additional 4.2 million ha (95% confidence range: 2.7-6.5 million ha) of forest fire area during 1984-2015, nearly doubling the total forest fire area expected in the absence of anthropogenic climate change. The relationship between annual forest fire area and fuel aridity is exponential and the proportion of total forest area burned in a given year has grown rapidly over the past 32 years. Natural climate variability will continue to alternate between modulating and compounding anthropogenic increases in fuel aridity, but anthropogenic climate change has emerged as a chronic driver of increased forest fire activity and should continue to do so where fuels are not limiting.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19990116067&hterms=regional+impacts+climate+change&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dregional%2Bimpacts%2Bclimate%2Bchange','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19990116067&hterms=regional+impacts+climate+change&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dregional%2Bimpacts%2Bclimate%2Bchange"><span>Advances in Understanding Decadal Climate Variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Busalaacchi, Antonio J.</p> <p>1998-01-01</p> <p>Recently, a joint Brazil-France-U.S. program, known as PIRATA (Pilot Research moored Array in the Tropical Atlantic), was proposed to begin the deployment of moored measurement platforms in the tropical Atlantic in order to enhance the existing observational data base and subsequent understanding of the processes by which the ocean and atmosphere couple in key regions of the tropical Atlantic Ocean. Empirical studies have suggested that there are strong relationships between tropical Atlantic upper ocean variability, SST, ocean-atmosphere coupling and regional climate variability. During the early 1980's a coordinated set of surface wind, subsurface thermal structure, and subsurface current observations were obtained as part of the U.S.-France SEQUAL- FOCAL process experiment designed to observe the seasonal response of the tropical Atlantic Ocean to surface forcing. Since that time, however, the observational data base for the tropical Atlantic Ocean has disintegrated to a few shiptracks measuring ocean temperatures and a small collection of tide gauge stations measuring sea level. A more comprehensive set of observations, modeling and empirical studies is now in order to make progress on understanding the regional climate variability. The proposed PIRATA program will use mooring platforms similar to the tropical Pacific Ocean TAO array to measure surface fluxes of momentum and heat and the corresponding changes in the upper ocean thermal structure. It is anticipated that the oceanic data from this monitoring array will also be used in a predictive mode for initialization studies of regional coupled climate models. Of particular interest are zonal and meridional modes of ocean-atmosphere variability within the tropical Atlantic basin that have significant impacts on the regional climate of the bordering continents.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19990064457&hterms=climate+change+temperature&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dclimate%2Bchange%2Btemperature','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19990064457&hterms=climate+change+temperature&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dclimate%2Bchange%2Btemperature"><span>Advances in Understanding Decadal Climate Variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Busalacchi, Antonio J.</p> <p>1999-01-01</p> <p>Recently, a joint Brazil-France-U.S. program, known as PIRATA (Pilot Research moored Array in the Tropical Atlantic), was proposed to begin the deployment of moored measurement platforms in the tropical Atlantic in order to enhance the existing observational data base and subsequent understanding of the processes by which the ocean and atmosphere couple in key regions of the tropical Atlantic Ocean. Empirical studies have suggested that there are strong relationships between tropical Atlantic upper ocean variability, SST, ocean-atmosphere coupling and regional climate variability. During the early 1980's a coordinated set of surface wind, subsurface thermal structure, and subsurface current observations were obtained as part of the U.S.-France SEQUAL-FOCAL process experiment designed to observe the seasonal response of the tropical Atlantic Ocean to surface forcing. Since that time, however, the observational data base for the tropical Atlantic Ocean has disintegrated to a few ship-tracks measuring ocean temperatures and a small collection of tide gauge stations measuring sea level. A more comprehensive set of observations, modeling and empirical studies is now in order to make progress on understanding the regional climate variability. The proposed PIRATA program will use mooring platforms similar to the tropical Pacific Ocean TAO array to measure surface fluxes of momentum and heat and the corresponding changes in the upper ocean thermal structure. It is anticipated that the oceanic data from this monitoring array will also be used in a predictive mode for initialization studies of regional coupled climate models. Of particular interest are zonal and meridional modes of ocean-atmosphere variability within the tropical Atlantic basin that have significant impacts on the regional climate of the bordering continents.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC33C1079S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC33C1079S"><span>Atmospheric Teleconnection and Climate Variability: Affecting Rice Productivity of Bihar, India</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Saini, A.</p> <p>2017-12-01</p> <p>Climate variability brought various negative results to the environment around us and area under rice crop in Bihar has also faced a lot of negative impacts due to variability in temperature and rainfall. Location of Bihar in Northern Plain of India automatically makes it prime location for agriculture and therefore variability in climatic variables brings highly sensitive results to the agricultural production (especially rice). In this study, rainfall and temperature variables are taken into consideration to investigate the impact on rice cultivated area. Change in climate variable with the passage of time is prevailing since the start of geological time scale, how the variability in climate variables has affected the major crops. Climate index of Pacific Ocean and Indian Ocean influences the seasonal weather in Bihar and therefore role of ENSO and IOD is an interesting point of inquiry. Does there exists direct relation between climate variability and area under agricultural crops? How many important variables directly signals towards the change in area under agriculture production? These entire questions are answered with respect to change in area under rice cultivation of Bihar State of India. Temperature, rainfall and ENSO are a good indicator with respect to rice cultivation in Indian subcontinent. Impact on the area under rice has been signaled through ONI, Niño3 and DMI. Increasing range of temperature in the rice productivity declining years is observed since 1990.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.B44C..02B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.B44C..02B"><span>Satellite-derived SIF and CO2 Observations Show Coherent Responses to Interannual Climate Variations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Butterfield, Z.; Hogikyan, A.; Kulawik, S. S.; Keppel-Aleks, G.</p> <p>2017-12-01</p> <p>Gross primary production (GPP) is the single largest carbon flux in the Earth system, but its sensitivity to changes in climate is subject to significant uncertainty. Satellite measurements of solar-induced chlorophyll fluorescence (SIF) offer insight into spatial and temporal patterns in GPP at a global scale and, combined with other satellite-derived datasets, provide unprecedented opportunity to explore interactions between atmospheric CO2, GPP, and climate variability. To explore potential drivers of GPP in the Northern Hemisphere (NH), we compare monthly-averaged SIF data from the Global Ozone Monitoring Experiment 2 (GOME-2) with observed anomalies in temperature (T; CRU-TS), liquid water equivalent (LWE) from the Gravity Recovery and Climate Experiment (GRACE), and photosynthetically active radiation (PAR; CERES SYN1deg). Using observations from 2007 through 2015 for several NH regions, we calculate month-specific sensitivities of SIF to variability in T, LWE, and PAR. These sensitivities provide insight into the seasonal progression of how productivity is affected by climate variability and can be used to effectively model the observed SIF signal. In general, we find that high temperatures are beneficial to productivity in the spring, but detrimental in the summer. The influences of PAR and LWE are more heterogeneous between regions; for example, higher LWE in North American temperate forest leads to decreased springtime productivity, while exhibiting a contrasting effect in water-limited regions. Lastly, we assess the influence of variations in terrestrial productivity on atmospheric carbon using a new lower tropospheric CO2 product derived from the Greenhouse Gases Observing Satellite (GOSAT). Together, these data shed light on the drivers of interannual variability in the annual cycle of NH atmospheric CO2, and may provide improved constraints on projections of long-term carbon cycle responses to climate change.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUOSPO34D3105A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUOSPO34D3105A"><span>Variability of Equatorward Transport in the Tropical Southwestern Pacific</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Alberty, M. S.; Sprintall, J.; MacKinnon, J. A.; Cravatte, S. E.; Ganachaud, A. S.; Germineaud, C.</p> <p>2016-02-01</p> <p>Situated in the Pacific warm pool, the Solomon Sea is a semi-enclosed sea containing a system of low latitude Western boundary currents that serve as the primary source water for the Equatorial Undercurrent. The variability of equatorward heat and volume transport through the Solomon Sea has the capability to modulate regional and basin-scale climate processes, yet there are few and synoptic observations of these fluxes. Here we present the mean and variability of heat and volume transport out of the Solomon Sea observed during the MoorSPICE experiment. MoorSPICE is the Solomon Sea mooring-based observational component of the Southwest Pacific Ocean Circulation and Climate Experiment (SPICE), an international research project working to observe and improve our understanding of the southwest Pacific Ocean circulation and climate. Arrays of moorings were deployed in the outflow channels of the Solomon Sea for July 2012 until March 2014 to resolve the temperature and velocity fields in each strait. In particular we will discuss the phasing of the observed transport variability for each channel compared to that of the satellite-observed monsoonal wind forcing and annual cycle of the mesoscale eddy field.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19950046568&hterms=Jun+Make&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DJun%2BMake','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19950046568&hterms=Jun+Make&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DJun%2BMake"><span>The Sun as a variable star: Solar and stellar irradiance variations; Colloquium of the International Astronomical Union, 143rd, Boulder, CO, Jun. 20-25, 1993</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Pap, Judit M. (Editor); Froehlich, Claus (Editor); Hudson, Hugh S. (Editor); Tobiska, W. Kent (Editor)</p> <p>1994-01-01</p> <p>Variations in solar and stellar irradiances have long been of interest. An International Astronomical Union (IAU) colloquium reviewed such relevant subjects as observations, theoretical interpretations, and empirical and physical models, with a special emphasis on climatic impact of solar irradiance variability. Specific topics discussed included: (1) General Reviews on Observations of Solar and Stellar Irradiance Variability; (2) Observational Programs for Solar and Stellar Irradiance Variability; (3) Variability of Solar and Stellar Irradiance Related to the Network, Active Regions (Sunspots and Plages), and Large-Scale Magnetic Structures; (4) Empirical Models of Solar Total and Spectral Irradiance Variability; (5) Solar and Stellar Oscillations, Irradiance Variations and their Interpretations; and (6) The Response of the Earth's Atmosphere to Solar Irradiance Variations and Sun-Climate Connections.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1356241','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1356241"><span>DOE Contribution to the 2015 US CLIVAR Project Office Budget</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>DeWeaver, Eric; Patterson, Michael</p> <p></p> <p>The primary goal of the US Climate Variability and Predictability (CLIVAR) Project Office is to enable science community planning and implementation of research to understand and predict climate variability and change on intraseasonal-to-centennial timescales, through observations and modeling with emphasis on the role of the ocean and its interaction with other elements of the Earth system, and to serve the climate community and society through the coordination and facilitation of research on outstanding climate questions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015CliPD..11.3475F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015CliPD..11.3475F"><span>Climate variability and human impact on the environment in South America during the last 2000 years: synthesis and perspectives</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Flantua, S. G. A.; Hooghiemstra, H.; Vuille, M.; Behling, H.; Carson, J. F.; Gosling, W. D.; Hoyos, I.; Ledru, M. P.; Montoya, E.; Mayle, F.; Maldonado, A.; Rull, V.; Tonello, M. S.; Whitney, B. S.; González-Arango, C.</p> <p>2015-07-01</p> <p>An improved understanding of present-day climate variability and change relies on high-quality data sets from the past two millennia. Global efforts to reconstruct regional climate modes are in the process of validating and integrating paleo-proxies. For South America, however, the full potential of vegetation records for evaluating and improving climate models has hitherto not been sufficiently acknowledged due to its unknown spatial and temporal coverage. This paper therefore serves as a guide to high-quality pollen records that capture environmental variability during the last two millennia. We identify the pollen records with the required temporal characteristics for PAGES-2 ka climate modelling and we discuss their sensitivity to the spatial signature of climate modes throughout the continent. Diverse patterns of vegetation response to climate change are observed, with more similar patterns of change in the lowlands and varying intensity and direction of responses in the highlands. Pollen records display local scale responses to climate modes, thus it is necessary to understand how vegetation-climate interactions might diverge under variable settings. Additionally, pollen is an excellent indicator of human impact through time. Evidence for human land use in pollen records is useful for archaeological hypothesis testing and important in distinguishing natural from anthropogenically driven vegetation change. We stress the need for the palynological community to be more familiar with climate variability patterns to correctly attribute the potential causes of observed vegetation dynamics. The LOTRED-SA-2 k initiative provides the ideal framework for the integration of the various paleoclimatic sub-disciplines and paleo-science, thereby jumpstarting and fostering multi-disciplinary research into environmental change on centennial and millennial time scales.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.3632C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.3632C"><span>Palaeoclimate dynamics : a voyage through scales</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Crucifix, Michel; Mitsui, Takahito</p> <p>2015-04-01</p> <p>Our knowledge of climate dynamics depends on indirect observations of past climate evolution, as well as on what can be inferred from theoretical arguments. At the scale of the Cenozoic, it is common to define a framework of nested time scales, the longest time scale of interest being related to the slow tectonic evolution, then variability associated with or controlled by the astronomical forcing, and finally the fastest dynamics associated with the natural modes of variability of the ocean and the atmosphere. For example, in a model, the astronomical modes of variability may be simulated with deterministic equations under fixed boundary conditions representing the tectonic state, and associated with stochastic parameterisations of the ocean-atmosphere (chaotic) modes of motion. Bifurcations or, more generally, qualitative changes in climate dynamics may be scanned by changing slowly the tectonic state, in order to provide explanations to observed changes in regimes such as the appearance of ice ages and their changes in length or amplitude. The above framework, largely theorized by B. Saltzman, may still be partly justified but is in need of a review. We address here specifically three questions: To what extent astronomical variability interacts with natural modes of ocean - atmosphere variability ? Specifically, how does millennial variability (e.g.: Dansgaard-Oeschger events) fit the Saltzman scheme ? The astronomical forcing is quasi-periodic, and we recently showed that it may produce somewhat counter-intuitive dynamics associated with the emergence of strange non-chaotic attractors. What are the consequences on the spectrum of climate variability ? What are the effects of centennial climate variability on the slow variability of climate ? These three questions are addressed by reference to recently published material, with the objective of emphasising research questions to be explored in the near future.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JGRA..121.3621P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JGRA..121.3621P"><span>Short-term nonmigrating tide variability in the mesosphere, thermosphere, and ionosphere</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pedatella, N. M.; Oberheide, J.; Sutton, E. K.; Liu, H.-L.; Anderson, J. L.; Raeder, K.</p> <p>2016-04-01</p> <p>The intraseasonal variability of the eastward propagating nonmigrating diurnal tide with zonal wave number 3 (DE3) during 2007 in the mesosphere, ionosphere, and thermosphere is investigated using a whole atmosphere model reanalysis and satellite observations. The atmospheric reanalysis is based on implementation of data assimilation in the Whole Atmosphere Community Climate Model (WACCM) using the Data Assimilation Research Testbed (DART) ensemble Kalman filter. The tidal variability in the WACCM+DART reanalysis is compared to the observed variability in the mesosphere and lower thermosphere (MLT) based on the Thermosphere Ionosphere Mesosphere Energetics Dynamics satellite Sounding of the Atmosphere using Broadband Emission Radiometry (TIMED/SABER) observations, in the ionosphere based on Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) observations, and in the upper thermosphere (˜475 km) based on Gravity Recovery and Climate Experiment (GRACE) neutral density observations. To obtain the short-term DE3 variability in the MLT and upper thermosphere, we apply the method of tidal deconvolution to the TIMED/SABER observations and consider the difference in the ascending and descending longitudinal wave number 4 structure in the GRACE observations. The results reveal that tidal amplitude changes of 5-10 K regularly occur on short timescales (˜10-20 days) in the MLT. Similar variability occurs in the WACCM+DART reanalysis and TIMED/SABER observations, demonstrating that the short-term variability can be captured in whole atmosphere models that employ data assimilation and in observations by the technique of tidal deconvolution. The impact of the short-term DE3 variability in the MLT on the ionosphere and thermosphere is also clearly evident in the COSMIC and GRACE observations. Analysis of the troposphere forcing in WACCM+DART and simulations of the Global Scale Wave Model (GSWM) show that the short-term DE3 variability in the MLT is not related to a single source; rather, it is due to a combination of changes in troposphere forcing, zonal mean atmosphere, and wave-wave interactions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ERL....12e4012K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ERL....12e4012K"><span>Using climate model simulations to assess the current climate risk to maize production</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kent, Chris; Pope, Edward; Thompson, Vikki; Lewis, Kirsty; Scaife, Adam A.; Dunstone, Nick</p> <p>2017-05-01</p> <p>The relationship between the climate and agricultural production is of considerable importance to global food security. However, there has been relatively little exploration of climate-variability related yield shocks. The short observational yield record does not adequately sample natural inter-annual variability thereby limiting the accuracy of probability assessments. Focusing on the United States and China, we present an innovative use of initialised ensemble climate simulations and a new agro-climatic indicator, to calculate the risk of severe water stress. Combined, these regions provide 60% of the world’s maize, and therefore, are crucial to global food security. To probe a greater range of inter-annual variability, the indicator is applied to 1400 simulations of the present day climate. The probability of severe water stress in the major maize producing regions is quantified, and in many regions an increased risk is found compared to calculations from observed historical data. Analysis suggests that the present day climate is also capable of producing unprecedented severe water stress conditions. Therefore, adaptation plans and policies based solely on observed events from the recent past may considerably under-estimate the true risk of climate-related maize shocks. The probability of a major impact event occurring simultaneously across both regions—a multi-breadbasket failure—is estimated to be up to 6% per decade and arises from a physically plausible climate state. This novel approach highlights the significance of climate impacts on crop production shocks and provides a platform for considerably improving food security assessments, in the present day or under a changing climate, as well as development of new risk based climate services.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ThApC.119..689Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ThApC.119..689Y"><span>Validation of China-wide interpolated daily climate variables from 1960 to 2011</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yuan, Wenping; Xu, Bing; Chen, Zhuoqi; Xia, Jiangzhou; Xu, Wenfang; Chen, Yang; Wu, Xiaoxu; Fu, Yang</p> <p>2015-02-01</p> <p>Temporally and spatially continuous meteorological variables are increasingly in demand to support many different types of applications related to climate studies. Using measurements from 600 climate stations, a thin-plate spline method was applied to generate daily gridded climate datasets for mean air temperature, maximum temperature, minimum temperature, relative humidity, sunshine duration, wind speed, atmospheric pressure, and precipitation over China for the period 1961-2011. A comprehensive evaluation of interpolated climate was conducted at 150 independent validation sites. The results showed superior performance for most of the estimated variables. Except for wind speed, determination coefficients ( R 2) varied from 0.65 to 0.90, and interpolations showed high consistency with observations. Most of the estimated climate variables showed relatively consistent accuracy among all seasons according to the root mean square error, R 2, and relative predictive error. The interpolated data correctly predicted the occurrence of daily precipitation at validation sites with an accuracy of 83 %. Moreover, the interpolation data successfully explained the interannual variability trend for the eight meteorological variables at most validation sites. Consistent interannual variability trends were observed at 66-95 % of the sites for the eight meteorological variables. Accuracy in distinguishing extreme weather events differed substantially among the meteorological variables. The interpolated data identified extreme events for the three temperature variables, relative humidity, and sunshine duration with an accuracy ranging from 63 to 77 %. However, for wind speed, air pressure, and precipitation, the interpolation model correctly identified only 41, 48, and 58 % of extreme events, respectively. The validation indicates that the interpolations can be applied with high confidence for the three temperatures variables, as well as relative humidity and sunshine duration based on the performance of these variables in estimating daily variations, interannual variability, and extreme events. Although longitude, latitude, and elevation data are included in the model, additional information, such as topography and cloud cover, should be integrated into the interpolation algorithm to improve performance in estimating wind speed, atmospheric pressure, and precipitation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006PhDT........63F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006PhDT........63F"><span>Vegetation coupling to global climate: Trajectories of vegetation change and phenology modeling from satellite observations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fisher, Jeremy Isaac</p> <p></p> <p>Important systematic shifts in ecosystem function are often masked by natural variability. The rich legacy of over two decades of continuous satellite observations provides an important database for distinguishing climatological and anthropogenic ecosystem changes. Examples from semi-arid Sudanian West Africa and New England (USA) illustrate the response of vegetation to climate and land-use. In Burkina Faso, West Africa, pastoral and agricultural practices compete for land area, while degradation may follow intensification. The Nouhao Valley is a natural experiment in which pastoral and agricultural land uses were allocated separate, coherent reserves. Trajectories of annual net primary productivity were derived from 18 years of coarse-grain (AVHRR) satellite data. Trends suggested that pastoral lands had responded rigorously to increasing rainfall after the 1980's droughts. A detailed analysis at Landsat resolution (30m) indicated that the increased vegetative cover was concentrated in the river basins of the pastoral region, implying a riparian wood expansion. In comparison, riparian cover was reduced in agricultural regions. We suggest that broad-scale patterns of increasing semi-arid West African greenness may be indicative of climate variability, whereas local losses may be anthropogenic in nature. The contiguous deciduous forests, ocean proximity, topography, and dense urban developments of New England provide an ideal landscape to examine influences of climate variability and the impact of urban development vegetation response. Spatial and temporal patterns of interannual climate variability were examined via green leaf phenology. Phenology, or seasonal growth and senescence, is driven by deficits of light, temperature, and water. In temperate environments, phenology variability is driven by interannual temperature and precipitation shifts. Average and interannual phenology analyses across southern New England were conducted at resolutions of 30m (Landsat) and 500m Moderate Resolution Imaging Spectrometer (MODIS). A robust logistic-growth model of canopy cover was employed to determine phenological characteristics at each forest stand. The duel analyses revealed important findings: (a) local phenological gradients from microclimatic structures are highly influential in broad-scale phenological observations; (b) satellite observed phenology reflects observations of canopy growth from field studies; (c) phenological anomalies in urban areas which were previously attributed to urban heat may be a function of urban-specific land cover (i.e. green lawns); and (d) patterns of interannual variability in phenology at the regional scale have high spatial coherency and appear to be driven by broad-scale climatic change. Satellite-observed phenology may reflect temperatures during spring and provides a proxy of climate variability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMPP41E..08T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMPP41E..08T"><span>140-year subantarctic tree-ring temperature reconstruction reveals tropical forcing of increased Southern Ocean climate variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Turney, C. S.; Fogwill, C. J.; Palmer, J. G.; VanSebille, E.; Thomas, Z.; McGlone, M.; Richardson, S.; Wilmshurst, J.; Fenwick, P.; Zunz, V.; Goosse, H.; Wilson, K. J.; Carter, L.; Lipson, M.; Jones, R. T.; Harsch, M.; Clark, G.; Marzinelli, E.; Rogers, T.; Rainsley, E.; Ciasto, L.; Waterman, S.; Thomas, E. R.; Visbeck, M.</p> <p>2017-12-01</p> <p>Occupying about 14 % of the world's surface, the Southern Ocean plays a fundamental role in ocean and atmosphere circulation, carbon cycling and Antarctic ice-sheet dynamics. Unfortunately, high interannual variability and a dearth of instrumental observations before the 1950s limits our understanding of how marine-atmosphere-ice domains interact on multi-decadal timescales and the impact of anthropogenic forcing. Here we integrate climate-sensitive tree growth with ocean and atmospheric observations on south-west Pacific subantarctic islands that lie at the boundary of polar and subtropical climates (52-54˚S). Our annually resolved temperature reconstruction captures regional change since the 1870s and demonstrates a significant increase in variability from the 1940s, a phenomenon predating the observational record, and coincident with major changes in mammalian and bird populations. Climate reanalysis and modelling show a parallel change in tropical Pacific sea surface temperatures that generate an atmospheric Rossby wave train which propagates across a large part of the Southern Hemisphere during the austral spring and summer. Our results suggest that modern observed high interannual variability was established across the mid-twentieth century, and that the influence of contemporary equatorial Pacific temperatures may now be a permanent feature across the mid- to high latitudes.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/10583952','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/10583952"><span>Global Warming and Northern Hemisphere Sea Ice Extent.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Vinnikov; Robock; Stouffer; Walsh; Parkinson; Cavalieri; Mitchell; Garrett; Zakharov</p> <p>1999-12-03</p> <p>Surface and satellite-based observations show a decrease in Northern Hemisphere sea ice extent during the past 46 years. A comparison of these trends to control and transient integrations (forced by observed greenhouse gases and tropospheric sulfate aerosols) from the Geophysical Fluid Dynamics Laboratory and Hadley Centre climate models reveals that the observed decrease in Northern Hemisphere sea ice extent agrees with the transient simulations, and both trends are much larger than would be expected from natural climate variations. From long-term control runs of climate models, it was found that the probability of the observed trends resulting from natural climate variability, assuming that the models' natural variability is similar to that found in nature, is less than 2 percent for the 1978-98 sea ice trends and less than 0.1 percent for the 1953-98 sea ice trends. Both models used here project continued decreases in sea ice thickness and extent throughout the next century.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4410635','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4410635"><span>Skilful multi-year predictions of tropical trans-basin climate variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Chikamoto, Yoshimitsu; Timmermann, Axel; Luo, Jing-Jia; Mochizuki, Takashi; Kimoto, Masahide; Watanabe, Masahiro; Ishii, Masayoshi; Xie, Shang-Ping; Jin, Fei-Fei</p> <p>2015-01-01</p> <p>Tropical Pacific sea surface temperature anomalies influence the atmospheric circulation, impacting climate far beyond the tropics. The predictability of the corresponding atmospheric signals is typically limited to less than 1 year lead time. Here we present observational and modelling evidence for multi-year predictability of coherent trans-basin climate variations that are characterized by a zonal seesaw in tropical sea surface temperature and sea-level pressure between the Pacific and the other two ocean basins. State-of-the-art climate model forecasts initialized from a realistic ocean state show that the low-frequency trans-basin climate variability, which explains part of the El Niño Southern Oscillation flavours, can be predicted up to 3 years ahead, thus exceeding the predictive skill of current tropical climate forecasts for natural variability. This low-frequency variability emerges from the synchronization of ocean anomalies in all basins via global reorganizations of the atmospheric Walker Circulation. PMID:25897996</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_4 --> <div id="page_5" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="81"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25897996','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25897996"><span>Skilful multi-year predictions of tropical trans-basin climate variability.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Chikamoto, Yoshimitsu; Timmermann, Axel; Luo, Jing-Jia; Mochizuki, Takashi; Kimoto, Masahide; Watanabe, Masahiro; Ishii, Masayoshi; Xie, Shang-Ping; Jin, Fei-Fei</p> <p>2015-04-21</p> <p>Tropical Pacific sea surface temperature anomalies influence the atmospheric circulation, impacting climate far beyond the tropics. The predictability of the corresponding atmospheric signals is typically limited to less than 1 year lead time. Here we present observational and modelling evidence for multi-year predictability of coherent trans-basin climate variations that are characterized by a zonal seesaw in tropical sea surface temperature and sea-level pressure between the Pacific and the other two ocean basins. State-of-the-art climate model forecasts initialized from a realistic ocean state show that the low-frequency trans-basin climate variability, which explains part of the El Niño Southern Oscillation flavours, can be predicted up to 3 years ahead, thus exceeding the predictive skill of current tropical climate forecasts for natural variability. This low-frequency variability emerges from the synchronization of ocean anomalies in all basins via global reorganizations of the atmospheric Walker Circulation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC52D..08I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC52D..08I"><span>Assessing the Dynamic Effects of Climate on Individual Tree Growth Across Time and Space</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Itter, M.; Finley, A. O.; D'Amato, A. W.; Foster, J. R.; Bradford, J. B.</p> <p>2015-12-01</p> <p>The relationship between climate variability and an ecosystem process, such as forest growth, is frequently not fixed over time, but changes due to complex interactions between unobserved ecological factors and the process of interest. Climate data and forecasts are frequently spatially and temporally misaligned with ecological observations making inference regarding the effects of climate on ecosystem processes particularly challenging. Here we develop a Bayesian dynamic hierarchical model for annual tree growth increment that allows the effects of climate to evolve over time, applies climate data at a spatial-temporal scale consistent with observations, and controls for individual-level variability commonly encountered in ecological datasets. The model is applied to individual tree data from northern Minnesota using a modified Thornthwaite-type water balance model to transform PRISM temperature and precipitation estimates to physiologically relevant values of actual and potential evapotranspiration (AET, PET), and climatic water deficit. Model results indicate that mean tree growth is most sensitive to AET during the growing season and PET and minimum temperature in the spring prior to growth. The effects of these variables on tree growth, however, are not stationary with significant effects observed in only a subset of years during the 111-year study period. Importantly, significant effects of climate do not result from anomalous climate observations, but follow from large growth deviations unexplained by tree age and size, and time since forest disturbance. Results differ markedly from alternative models that assume the effects of climate are stationary over time or apply climate estimates at the individual scale. Forecasts of future tree growth as a function of climate follow directly from the dynamic hierarchical model allowing for assessment of forest change. Current work is focused on extending the model framework to include regional climate and ecosystem effects for application to a larger tree growth dataset spanning a latitudinal gradient within the US from Maine to Florida.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24652258','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24652258"><span>[Modelling the effect of local climatic variability on dengue transmission in Medellin (Colombia) by means of time series analysis].</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Rúa-Uribe, Guillermo L; Suárez-Acosta, Carolina; Chauca, José; Ventosilla, Palmira; Almanza, Rita</p> <p>2013-09-01</p> <p>Dengue fever is a major impact on public health vector-borne disease, and its transmission is influenced by entomological, sociocultural and economic factors. Additionally, climate variability plays an important role in the transmission dynamics. A large scientific consensus has indicated that the strong association between climatic variables and disease could be used to develop models to explain the incidence of the disease. To develop a model that provides a better understanding of dengue transmission dynamics in Medellin and predicts increases in the incidence of the disease. The incidence of dengue fever was used as dependent variable, and weekly climatic factors (maximum, mean and minimum temperature, relative humidity and precipitation) as independent variables. Expert Modeler was used to develop a model to better explain the behavior of the disease. Climatic variables with significant association to the dependent variable were selected through ARIMA models. The model explains 34% of observed variability. Precipitation was the climatic variable showing statistically significant association with the incidence of dengue fever, but with a 20 weeks delay. In Medellin, the transmission of dengue fever was influenced by climate variability, especially precipitation. The strong association dengue fever/precipitation allowed the construction of a model to help understand dengue transmission dynamics. This information will be useful to develop appropriate and timely strategies for dengue control.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27833133','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27833133"><span>Climate variations of Central Asia on orbital to millennial timescales.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Cheng, Hai; Spötl, Christoph; Breitenbach, Sebastian F M; Sinha, Ashish; Wassenburg, Jasper A; Jochum, Klaus Peter; Scholz, Denis; Li, Xianglei; Yi, Liang; Peng, Youbing; Lv, Yanbin; Zhang, Pingzhong; Votintseva, Antonina; Loginov, Vadim; Ning, Youfeng; Kathayat, Gayatri; Edwards, R Lawrence</p> <p>2016-11-11</p> <p>The extent to which climate variability in Central Asia is causally linked to large-scale changes in the Asian monsoon on varying timescales remains a longstanding question. Here we present precisely dated high-resolution speleothem oxygen-carbon isotope and trace element records of Central Asia's hydroclimate variability from Tonnel'naya cave, Uzbekistan, and Kesang cave, western China. On orbital timescales, the supra-regional climate variance, inferred from our oxygen isotope records, exhibits a precessional rhythm, punctuated by millennial-scale abrupt climate events, suggesting a close coupling with the Asian monsoon. However, the local hydroclimatic variability at both cave sites, inferred from carbon isotope and trace element records, shows climate variations that are distinctly different from their supra-regional modes. Particularly, hydroclimatic changes in both Tonnel'naya and Kesang areas during the Holocene lag behind the supra-regional climate variability by several thousand years. These observations may reconcile the apparent out-of-phase hydroclimatic variability, inferred from the Holocene lake proxy records, between Westerly Central Asia and Monsoon Asia.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5105073','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5105073"><span>Climate variations of Central Asia on orbital to millennial timescales</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Cheng, Hai; Spötl, Christoph; Breitenbach, Sebastian F. M.; Sinha, Ashish; Wassenburg, Jasper A.; Jochum, Klaus Peter; Scholz, Denis; Li, Xianglei; Yi, Liang; Peng, Youbing; Lv, Yanbin; Zhang, Pingzhong; Votintseva, Antonina; Loginov, Vadim; Ning, Youfeng; Kathayat, Gayatri; Edwards, R. Lawrence</p> <p>2016-01-01</p> <p>The extent to which climate variability in Central Asia is causally linked to large-scale changes in the Asian monsoon on varying timescales remains a longstanding question. Here we present precisely dated high-resolution speleothem oxygen-carbon isotope and trace element records of Central Asia’s hydroclimate variability from Tonnel’naya cave, Uzbekistan, and Kesang cave, western China. On orbital timescales, the supra-regional climate variance, inferred from our oxygen isotope records, exhibits a precessional rhythm, punctuated by millennial-scale abrupt climate events, suggesting a close coupling with the Asian monsoon. However, the local hydroclimatic variability at both cave sites, inferred from carbon isotope and trace element records, shows climate variations that are distinctly different from their supra-regional modes. Particularly, hydroclimatic changes in both Tonnel’naya and Kesang areas during the Holocene lag behind the supra-regional climate variability by several thousand years. These observations may reconcile the apparent out-of-phase hydroclimatic variability, inferred from the Holocene lake proxy records, between Westerly Central Asia and Monsoon Asia. PMID:27833133</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ClDy...50...31C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ClDy...50...31C"><span>Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cannon, Alex J.</p> <p>2018-01-01</p> <p>Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another—the N-dimensional probability density function transform—is adapted for use as a multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin, particularly for annual maxima of the FWI distribution and spatiotemporal autocorrelation of precipitation fields.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26331850','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26331850"><span>Projecting the Hydrologic Impacts of Climate Change on Montane Wetlands.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Lee, Se-Yeun; Ryan, Maureen E; Hamlet, Alan F; Palen, Wendy J; Lawler, Joshua J; Halabisky, Meghan</p> <p>2015-01-01</p> <p>Wetlands are globally important ecosystems that provide critical services for natural communities and human society. Montane wetland ecosystems are expected to be among the most sensitive to changing climate, as their persistence depends on factors directly influenced by climate (e.g. precipitation, snowpack, evaporation). Despite their importance and climate sensitivity, wetlands tend to be understudied due to a lack of tools and data relative to what is available for other ecosystem types. Here, we develop and demonstrate a new method for projecting climate-induced hydrologic changes in montane wetlands. Using observed wetland water levels and soil moisture simulated by the physically based Variable Infiltration Capacity (VIC) hydrologic model, we developed site-specific regression models relating soil moisture to observed wetland water levels to simulate the hydrologic behavior of four types of montane wetlands (ephemeral, intermediate, perennial, permanent wetlands) in the U. S. Pacific Northwest. The hybrid models captured observed wetland dynamics in many cases, though were less robust in others. We then used these models to a) hindcast historical wetland behavior in response to observed climate variability (1916-2010 or later) and classify wetland types, and b) project the impacts of climate change on montane wetlands using global climate model scenarios for the 2040s and 2080s (A1B emissions scenario). These future projections show that climate-induced changes to key driving variables (reduced snowpack, higher evapotranspiration, extended summer drought) will result in earlier and faster drawdown in Pacific Northwest montane wetlands, leading to systematic reductions in water levels, shortened wetland hydroperiods, and increased probability of drying. Intermediate hydroperiod wetlands are projected to experience the greatest changes. For the 2080s scenario, widespread conversion of intermediate wetlands to fast-drying ephemeral wetlands will likely reduce wetland habitat availability for many species.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4557981','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4557981"><span>Projecting the Hydrologic Impacts of Climate Change on Montane Wetlands</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Hamlet, Alan F.; Palen, Wendy J.; Lawler, Joshua J.; Halabisky, Meghan</p> <p>2015-01-01</p> <p>Wetlands are globally important ecosystems that provide critical services for natural communities and human society. Montane wetland ecosystems are expected to be among the most sensitive to changing climate, as their persistence depends on factors directly influenced by climate (e.g. precipitation, snowpack, evaporation). Despite their importance and climate sensitivity, wetlands tend to be understudied due to a lack of tools and data relative to what is available for other ecosystem types. Here, we develop and demonstrate a new method for projecting climate-induced hydrologic changes in montane wetlands. Using observed wetland water levels and soil moisture simulated by the physically based Variable Infiltration Capacity (VIC) hydrologic model, we developed site-specific regression models relating soil moisture to observed wetland water levels to simulate the hydrologic behavior of four types of montane wetlands (ephemeral, intermediate, perennial, permanent wetlands) in the U. S. Pacific Northwest. The hybrid models captured observed wetland dynamics in many cases, though were less robust in others. We then used these models to a) hindcast historical wetland behavior in response to observed climate variability (1916–2010 or later) and classify wetland types, and b) project the impacts of climate change on montane wetlands using global climate model scenarios for the 2040s and 2080s (A1B emissions scenario). These future projections show that climate-induced changes to key driving variables (reduced snowpack, higher evapotranspiration, extended summer drought) will result in earlier and faster drawdown in Pacific Northwest montane wetlands, leading to systematic reductions in water levels, shortened wetland hydroperiods, and increased probability of drying. Intermediate hydroperiod wetlands are projected to experience the greatest changes. For the 2080s scenario, widespread conversion of intermediate wetlands to fast-drying ephemeral wetlands will likely reduce wetland habitat availability for many species. PMID:26331850</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27791053','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27791053"><span>Impact of anthropogenic climate change on wildfire across western US forests.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Abatzoglou, John T; Williams, A Park</p> <p>2016-10-18</p> <p>Increased forest fire activity across the western continental United States (US) in recent decades has likely been enabled by a number of factors, including the legacy of fire suppression and human settlement, natural climate variability, and human-caused climate change. We use modeled climate projections to estimate the contribution of anthropogenic climate change to observed increases in eight fuel aridity metrics and forest fire area across the western United States. Anthropogenic increases in temperature and vapor pressure deficit significantly enhanced fuel aridity across western US forests over the past several decades and, during 2000-2015, contributed to 75% more forested area experiencing high (>1 σ) fire-season fuel aridity and an average of nine additional days per year of high fire potential. Anthropogenic climate change accounted for ∼55% of observed increases in fuel aridity from 1979 to 2015 across western US forests, highlighting both anthropogenic climate change and natural climate variability as important contributors to increased wildfire potential in recent decades. We estimate that human-caused climate change contributed to an additional 4.2 million ha of forest fire area during 1984-2015, nearly doubling the forest fire area expected in its absence. Natural climate variability will continue to alternate between modulating and compounding anthropogenic increases in fuel aridity, but anthropogenic climate change has emerged as a driver of increased forest fire activity and should continue to do so while fuels are not limiting.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016PNAS..11311770A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016PNAS..11311770A"><span>Impact of anthropogenic climate change on wildfire across western US forests</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Abatzoglou, John T.; Park Williams, A.</p> <p>2016-10-01</p> <p>Increased forest fire activity across the western continental United States (US) in recent decades has likely been enabled by a number of factors, including the legacy of fire suppression and human settlement, natural climate variability, and human-caused climate change. We use modeled climate projections to estimate the contribution of anthropogenic climate change to observed increases in eight fuel aridity metrics and forest fire area across the western United States. Anthropogenic increases in temperature and vapor pressure deficit significantly enhanced fuel aridity across western US forests over the past several decades and, during 2000-2015, contributed to 75% more forested area experiencing high (>1 σ) fire-season fuel aridity and an average of nine additional days per year of high fire potential. Anthropogenic climate change accounted for ˜55% of observed increases in fuel aridity from 1979 to 2015 across western US forests, highlighting both anthropogenic climate change and natural climate variability as important contributors to increased wildfire potential in recent decades. We estimate that human-caused climate change contributed to an additional 4.2 million ha of forest fire area during 1984-2015, nearly doubling the forest fire area expected in its absence. Natural climate variability will continue to alternate between modulating and compounding anthropogenic increases in fuel aridity, but anthropogenic climate change has emerged as a driver of increased forest fire activity and should continue to do so while fuels are not limiting.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25818017','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25818017"><span>Bidecadal North Atlantic ocean circulation variability controlled by timing of volcanic eruptions.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Swingedouw, Didier; Ortega, Pablo; Mignot, Juliette; Guilyardi, Eric; Masson-Delmotte, Valérie; Butler, Paul G; Khodri, Myriam; Séférian, Roland</p> <p>2015-03-30</p> <p>While bidecadal climate variability has been evidenced in several North Atlantic paleoclimate records, its drivers remain poorly understood. Here we show that the subset of CMIP5 historical climate simulations that produce such bidecadal variability exhibits a robust synchronization, with a maximum in Atlantic Meridional Overturning Circulation (AMOC) 15 years after the 1963 Agung eruption. The mechanisms at play involve salinity advection from the Arctic and explain the timing of Great Salinity Anomalies observed in the 1970s and the 1990s. Simulations, as well as Greenland and Iceland paleoclimate records, indicate that coherent bidecadal cycles were excited following five Agung-like volcanic eruptions of the last millennium. Climate simulations and a conceptual model reveal that destructive interference caused by the Pinatubo 1991 eruption may have damped the observed decreasing trend of the AMOC in the 2000s. Our results imply a long-lasting climatic impact and predictability following the next Agung-like eruption.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.U13D..04B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.U13D..04B"><span>CIRUN: Climate Information Responding to User Needs</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Busalacchi, A. J.</p> <p>2009-12-01</p> <p>The Earth System will experience real climate change over the next 50 years, exceeding the scope of natural climate variability. A paramount question facing society is how to adapt to this certainty of climate variability and change. In response, OSTP and NOAA are considering how comprehensive climate services would best inform decisions about adaptation. Similarly, NASA is considering the optimal configuration of the next generation of Earth, environmental, and climate observations to be deployed over the coming 10-20 years. Moreover, much of the added-value information for specific climate-related decisions will be provided by private, academic and non-governmental organizations. In this context, over the past several years the University of Maryland has established the CIRUN (Climate Information: Responding to User Needs) initiative to identify the nature of national needs for climate information and services from a decision support perspective. To date, CIRUN has brought together decisionmakers in a number of sectors to help understand their perspectives on climate with the goal of improving the usefulness of climate information, observations and prediction products to specific user communities. CIRUN began with a major workshop in October 2007 that convened 430 participants in agriculture, parks and recreation, terrestrial ecosystems, insurance/investment, energy, national security, state/local/municipal, water, human health, commerce and manufacturing, transportation, and coastal/marine sectors. Plenary speakers such as Norman Augustine, R. James Woolsey, James Mahoney, and former Senator Joseph Tydings, breakout panel sessions, and participants provided input based on the following: - How would you characterize the exposure or vulnerability to climate variability or change impacting your organization? - Does climate variability and/or change currently factor into your organization's objectives or operations? - Are any of your existing plans being affected by climate or projections of climate change? - Is your organization developing a plan for adapting to climate change? - What are your needs for climate observations, predictions, and services? Please cite one or more specific examples when possible. - Do you currently have access to the climate information your organization needs? - What next steps are needed to assure effective use of climate services in your decision making? As a result, a dialogue with various user communities and a subsequent series of more sector specific workshops has been established regarding how significantly enhanced climate observations, data management, modeling, and predictions can provide valuable decision support for business and policy decisions. In particular, CIRUN has helped - To identify how users, stakeholders, and decision makers are influenced by climate on time scales from seasons to decades - To identify the needs and requirements of users, stakeholders, and decision makers for climate information, observations, predictions, and services from global to local scales - To identify what adaptation measures are being considered in the private and public sectors, and how this might result in new classes of information for decision support - To recommend principal elements of the path forward toward more effective use of climate services in decision making.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140013041','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140013041"><span>Detection and Attribution of Anthropogenic Climate Change Impacts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Rosenzweig, Cynthia; Neofotis, Peter</p> <p>2013-01-01</p> <p>Human-influenced climate change is an observed phenomenon affecting physical and biological systems across the globe. The majority of observed impacts are related to temperature changes and are located in the northern high- and midlatitudes. However, new evidence is emerging that demonstrates that impacts are related to precipitation changes as well as temperature, and that climate change is impacting systems and sectors beyond the Northern Hemisphere. In this paper, we highlight some of this new evidence-focusing on regions and sectors that the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) noted as under-represented-in the context of observed climate change impacts, direct and indirect drivers of change (including carbon dioxide itself), and methods of detection. We also present methods and studies attributing observed impacts to anthropogenic forcing. We argue that the expansion of methods of detection (in terms of a broader array of climate variables and data sources, inclusion of the major modes of climate variability, and incorporation of other drivers of change) is key to discerning the climate sensitivities of sectors and systems in regions where the impacts of climate change currently remain elusive. Attributing such changes to human forcing of the climate system, where possible, is important for development of effective mitigation and adaptation. Current challenges in documenting adaptation and the role of indigenous knowledge in detection and attribution are described.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25898351','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25898351"><span>Comparing the model-simulated global warming signal to observations using empirical estimates of unforced noise.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Brown, Patrick T; Li, Wenhong; Cordero, Eugene C; Mauget, Steven A</p> <p>2015-04-21</p> <p>The comparison of observed global mean surface air temperature (GMT) change to the mean change simulated by climate models has received much public and scientific attention. For a given global warming signal produced by a climate model ensemble, there exists an envelope of GMT values representing the range of possible unforced states of the climate system (the Envelope of Unforced Noise; EUN). Typically, the EUN is derived from climate models themselves, but climate models might not accurately simulate the correct characteristics of unforced GMT variability. Here, we simulate a new, empirical, EUN that is based on instrumental and reconstructed surface temperature records. We compare the forced GMT signal produced by climate models to observations while noting the range of GMT values provided by the empirical EUN. We find that the empirical EUN is wide enough so that the interdecadal variability in the rate of global warming over the 20(th) century does not necessarily require corresponding variability in the rate-of-increase of the forced signal. The empirical EUN also indicates that the reduced GMT warming over the past decade or so is still consistent with a middle emission scenario's forced signal, but is likely inconsistent with the steepest emission scenario's forced signal.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4404682','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4404682"><span>Comparing the model-simulated global warming signal to observations using empirical estimates of unforced noise</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Brown, Patrick T.; Li, Wenhong; Cordero, Eugene C.; Mauget, Steven A.</p> <p>2015-01-01</p> <p>The comparison of observed global mean surface air temperature (GMT) change to the mean change simulated by climate models has received much public and scientific attention. For a given global warming signal produced by a climate model ensemble, there exists an envelope of GMT values representing the range of possible unforced states of the climate system (the Envelope of Unforced Noise; EUN). Typically, the EUN is derived from climate models themselves, but climate models might not accurately simulate the correct characteristics of unforced GMT variability. Here, we simulate a new, empirical, EUN that is based on instrumental and reconstructed surface temperature records. We compare the forced GMT signal produced by climate models to observations while noting the range of GMT values provided by the empirical EUN. We find that the empirical EUN is wide enough so that the interdecadal variability in the rate of global warming over the 20th century does not necessarily require corresponding variability in the rate-of-increase of the forced signal. The empirical EUN also indicates that the reduced GMT warming over the past decade or so is still consistent with a middle emission scenario's forced signal, but is likely inconsistent with the steepest emission scenario's forced signal. PMID:25898351</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005GGG.....6.6001B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005GGG.....6.6001B"><span>Coral oxygen isotope records of interdecadal climate variations in the South Pacific Convergence Zone region</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bagnato, Stefan; Linsley, Braddock K.; Howe, Stephen S.; Wellington, Gerard M.; Salinger, Jim</p> <p>2005-06-01</p> <p>The South Pacific Convergence Zone (SPCZ), a region of high rainfall, is a major feature of subtropical Southern Hemisphere climate and contributes to and interacts with circulation features across the Pacific, yet its past temporal variability and forcing remain only partially understood. Here we compare coral oxygen isotopic (δ18O) series (spanning A.D. 1997-1780 and A.D. 2001-1776) from two genera of hermatypic corals in Fiji, located within the SPCZ, to examine the fidelity of these corals in recording climate change and SPCZ interdecadal dynamics. One of these coral records is a new 225-year subannually resolved δ18O series from the massive coral Diploastreaheliopora. Diploastrea's use in climate reconstructions is still relatively new, but this coral has shown encouragingly similar interannual variability to Porites, the coral genus most commonly used in Pacific paleoclimate studies. In Fiji we observe that interdecadal δ18O variance is also similar in these two coral genera, and Diploastrea contains a larger-amplitude interdecadal signal that more closely tracks instrumental-based indices of Pacific interdecadal climate change and the SPCZ than Porites. Both coral δ18O series record greater interdecadal variability from ˜1880 to 1950, which is consistent with the observations of Folland et al. (2002), who reported higher variability in SPCZ position before 1945. These observations indicate that Diploastrea will likely provide a significant new source of long-term climate information from the SPCZ region.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ThApC.126..575K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ThApC.126..575K"><span>Local air temperature tolerance: a sensible basis for estimating climate variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kärner, Olavi; Post, Piia</p> <p>2016-11-01</p> <p>The customary representation of climate using sample moments is generally biased due to the noticeably nonstationary behaviour of many climate series. In this study, we introduce a moment-free climate representation based on a statistical model fitted to a long-term daily air temperature anomaly series. This model allows us to separate the climate and weather scale variability in the series. As a result, the climate scale can be characterized using the mean annual cycle of series and local air temperature tolerance, where the latter is computed using the fitted model. The representation of weather scale variability is specified using the frequency and the range of outliers based on the tolerance. The scheme is illustrated using five long-term air temperature records observed by different European meteorological stations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70171430','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70171430"><span>Sensitivity of ground - water recharge estimates to climate variability and change, Columbia Plateau, Washington</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Vaccaro, John J.</p> <p>1992-01-01</p> <p>The sensitivity of groundwater recharge estimates was investigated for the semiarid Ellensburg basin, located on the Columbia Plateau, Washington, to historic and projected climatic regimes. Recharge was estimated for predevelopment and current (1980s) land use conditions using a daily energy-soil-water balance model. A synthetic daily weather generator was used to simulate lengthy sequences with parameters estimated from subsets of the historical record that were unusually wet and unusually dry. Comparison of recharge estimates corresponding to relatively wet and dry periods showed that recharge for predevelopment land use varies considerably within the range of climatic conditions observed in the 87-year historical observation period. Recharge variations for present land use conditions were less sensitive to the same range of historical climatic conditions because of irrigation. The estimated recharge based on the 87-year historical climatology was compared with adjustments to the historical precipitation and temperature records for the same record to reflect CO2-doubling climates as projected by general circulation models (GCMs). Two GCM scenarios were considered: an average of conditions for three different GCMs with CO2 doubling, and a most severe “maximum” case. For the average GCM scenario, predevelopment recharge increased, and current recharge decreased. Also considered was the sensitivity of recharge to the variability of climate within the historical and adjusted historical records. Predevelopment and current recharge were less and more sensitive, respectively, to the climate variability for the average GCM scenario as compared to the variability within the historical record. For the maximum GCM scenario, recharge for both predevelopment and current land use decreased, and the sensitivity to the CO2-related climate change was larger than sensitivity to the variability in the historical and adjusted historical climate records.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC51C0822B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC51C0822B"><span>Climate Observing Systems: Where are we and where do we need to be in the future</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Baker, B.; Diamond, H. J.</p> <p>2017-12-01</p> <p>Climate research and monitoring requires an observational strategy that blends long-term, carefully calibrated measurements as well as short-term, focused process studies. The operation and implementation of operational climate observing networks and the provision of related climate services, both have a significant role to play in assisting the development of national climate adaptation policies and in facilitating national economic development. Climate observing systems will require a strong research element for a long time to come. This requires improved observations of the state variables and the ability to set them in a coherent physical (as well as a chemical and biological) framework with models. Climate research and monitoring requires an integrated strategy of land/ocean/atmosphere observations, including both in situ and remote sensing platforms, and modeling and analysis. It is clear that we still need more research and analysis on climate processes, sampling strategies, and processing algorithms.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70027640','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70027640"><span>Climate science and famine early warning</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Verdin, James P.; Funk, Chris; Senay, Gabriel B.; Choularton, R.</p> <p>2005-01-01</p> <p>Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_5 --> <div id="page_6" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="101"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/16433101','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/16433101"><span>Climate science and famine early warning.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Verdin, James; Funk, Chris; Senay, Gabriel; Choularton, Richard</p> <p>2005-11-29</p> <p>Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1569579','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1569579"><span>Climate science and famine early warning</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Verdin, James; Funk, Chris; Senay, Gabriel; Choularton, Richard</p> <p>2005-01-01</p> <p>Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised. PMID:16433101</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013GeoRL..40.2296P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013GeoRL..40.2296P"><span>Natural climate variability and teleconnections to precipitation over the Pacific-North American region in CMIP3 and CMIP5 models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Polade, Suraj D.; Gershunov, Alexander; Cayan, Daniel R.; Dettinger, Michael D.; Pierce, David W.</p> <p>2013-05-01</p> <p>climate variability will continue to be an important aspect of future regional climate even in the midst of long-term secular changes. Consequently, the ability of climate models to simulate major natural modes of variability and their teleconnections provides important context for the interpretation and use of climate change projections. Comparisons reported here indicate that the CMIP5 generation of global climate models shows significant improvements in simulations of key Pacific climate mode and their teleconnections to North America compared to earlier CMIP3 simulations. The performance of 14 models with simulations in both the CMIP3 and CMIP5 archives are assessed using singular value decomposition analysis of simulated and observed winter Pacific sea surface temperatures (SSTs) and concurrent precipitation over the contiguous United States and northwestern Mexico. Most of the models reproduce basic features of the key natural mode and their teleconnections, albeit with notable regional deviations from observations in both SST and precipitation. Increasing horizontal resolution in the CMIP5 simulations is an important, but not a necessary, factor in the improvement from CMIP3 to CMIP5.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70045529','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70045529"><span>Natural climate variability and teleconnections to precipitation over the Pacific-North American region in CMIP3 and CMIP5 models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Polade, Suraj D.; Gershunov, Alexander; Cayan, Daniel R.; Dettinger, Michael D.; Pierce, David W.</p> <p>2013-01-01</p> <p>Natural climate variability will continue to be an important aspect of future regional climate even in the midst of long-term secular changes. Consequently, the ability of climate models to simulate major natural modes of variability and their teleconnections provides important context for the interpretation and use of climate change projections. Comparisons reported here indicate that the CMIP5 generation of global climate models shows significant improvements in simulations of key Pacific climate mode and their teleconnections to North America compared to earlier CMIP3 simulations. The performance of 14 models with simulations in both the CMIP3 and CMIP5 archives are assessed using singular value decomposition analysis of simulated and observed winter Pacific sea surface temperatures (SSTs) and concurrent precipitation over the contiguous United States and northwestern Mexico. Most of the models reproduce basic features of the key natural mode and their teleconnections, albeit with notable regional deviations from observations in both SST and precipitation. Increasing horizontal resolution in the CMIP5 simulations is an important, but not a necessary, factor in the improvement from CMIP3 to CMIP5.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1259767-keeping-lights-global-ocean-salinity-observation','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1259767-keeping-lights-global-ocean-salinity-observation"><span>Keeping the lights on for global ocean salinity observation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Durack, Paul J.; Lee, Tong; Vinogradova, Nadya T.; ...</p> <p>2016-02-24</p> <p>Here, insights about climate are being uncovered thanks to improved capacities to observe ocean salinity, an essential climate variable. However, cracks are beginning to appear in the ocean observing system that require prompt attention if we are to maintain the existing, hard-won capacity into the near future.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1259767','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1259767"><span>Keeping the lights on for global ocean salinity observation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Durack, Paul J.; Lee, Tong; Vinogradova, Nadya T.</p> <p></p> <p>Here, insights about climate are being uncovered thanks to improved capacities to observe ocean salinity, an essential climate variable. However, cracks are beginning to appear in the ocean observing system that require prompt attention if we are to maintain the existing, hard-won capacity into the near future.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.1732D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.1732D"><span>Linking the climatic and geochemical controls on global soil carbon cycling</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Doetterl, Sebastian; Stevens, Antoine; Six, Johan; Merckx, Roel; Van Oost, Kristof; Casanova Pinto, Manuel; Casanova-Katny, Angélica; Muñoz, Cristina; Boudin, Mathieu; Zagal Venegas, Erick; Boeckx, Pascal</p> <p>2015-04-01</p> <p>Climatic and geochemical parameters are regarded as the primary controls for soil organic carbon (SOC) storage and turnover. However, due to the difference in scale between climate and geochemical-related soil research, the interaction of these key factors for SOC dynamics have rarely been assessed. Across a large geochemical and climatic transect in similar biomes in Chile and the Antarctic Peninsula we show how abiotic geochemical soil features describing soil mineralogy and weathering pose a direct control on SOC stocks, concentration and turnover and are central to explaining soil C dynamics at larger scales. Precipitation and temperature had an only indirect control by regulating geochemistry. Soils with high SOC content have low specific potential CO2 respiration rates, but a large fraction of SOC that is stabilized via organo-mineral interactions. The opposite was observed for soils with low SOC content. The observed differences for topsoil SOC stocks along this transect of similar biomes but differing geo-climatic site conditions are of the same magnitude as differences observed for topsoil SOC stocks across all major global biomes. Using precipitation and a set of abiotic geochemical parameters describing soil mineralogy and weathering status led to predictions of high accuracy (R2 0.53-0.94) for different C response variables. Partial correlation analyses revealed that the strength of the correlation between climatic predictors and SOC response variables decreased by 51 - 83% when controlling for geochemical predictors. In contrast, controlling for climatic variables did not result in a strong decrease in the strength of the correlations of between most geochemical variables and SOC response variables. In summary, geochemical parameters describing soil mineralogy and weathering were found to be essential for accurate predictions of SOC stocks and potential CO2 respiration, while climatic factors were of minor importance as a direct control, but are important through governing soil weathering and geochemistry. In conclusion, we pledge for a stronger implementation of geochemical soil properties to predict SOC stocks on a global scale. Understanding the effects of climate (temperature and precipitation) change on SOC dynamics also requires good understanding of the relationship between climate and soil geochemistry.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMOS43C..05M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMOS43C..05M"><span>Complexity of Tropical Pacific Ecosystem and Biogeochemistry: Diurnal to Decadal, Plankters to Penguins</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Murtugudde, R. G.; Wang, X.; Valsala, V.; Karnauskas, K. B.</p> <p>2016-12-01</p> <p>Tropical Pacific spans nearly 50% of the global tropics allowing to have its own mind in terms of climate variability and physical-biogeochemical interactions. While the El Niño-Southern Oscillation (ENSO) and its flavors get much attention, it is fairly clear by now that any further improvements in ENSO prediction skills and reliability of global warming projections must begin to observe and represent bio-physical interactions in the climate and Earth System models. Coupled climate variability over the tropical Pacific has a global reach with its diurnal to decadal timescales being manifest in ecosystem and biogechemistry. Zonal and meridional contrasts in biogeochemistry across the tropical Pacific is closely related to seasonal variability, ENSO diversity and the PDO. Apparent dominance of ocean dynamic controls on biogeochemistry belies the potential biogeochemical feedbacks on ocean dynamics which may well explain some of the chronic biases in the state-of-the-art climate models. The east Pacific cold-tongue is the most productive open ocean region in the world and home to a unique physical-biogeochmical laboratory, viz., the Galapagos. The Galapagos islands not only control the coupled climate variability via their ability to terminate the equatorial undercurrent but also offer a clear example of a biological loophole in terms of their impact on local upwelling and an expanding penguin habitat in the face of global warming. The complex bio-physical interactions in the cold-tongue and their influence on climate predictions and projections require a holisti thinking on future observing systems. Tropical Pacific offers a natural laboratory for designing a robust and sustained physical-biogeochemical observation system that can effectively bridge climate predictions and projections into a unified framework for subseasonal to multidecadal timescales. Such a system will be a foundation for establishing similar systems over the rest of the World ocean to seemlessly merge climate predictions and projections with the need to constantly monitor climate impacts on marine resources. This talk will focus on the zonal contrasts of the ocean dynamics and biogechemistry across the tropical Pacific to make a case for integrated physical-biogeochemical observations for climate predictions and projections.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20080044879&hterms=Elsevier&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DElsevier','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20080044879&hterms=Elsevier&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DElsevier"><span>Contrasts Between Precipitation over Mediterranean Sea and Adjacent Continental Areas Based on Decadal Scale Satellite Estimates</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Smith, Eric A.</p> <p>2007-01-01</p> <p>Most knowledge concerning the last century's climatology and climate dynamics of precipitation over the Mediterranean Sea basin is based on observations taken from rain gauges surrounding the sea itself. In turn, most of the observations come from Southern Europe, with many fewer measurements taken from widely scattered sites situated over North Africa, the Middle East, and the Balkans. This aspect of research on the Mediterranean Sea basin is apparent in a recent compilation of studies presented in book form concerning climate variability of the Mediterranean region [Lionello, P., P. Malanotte-Rizzoli, and R. Boscolo (eds.), 2006: Mediterranean Climate Variability. Elsevier, Amsterdam, 9 chapters.] In light of this missing link to over-water observations, this study (in conjunction with four companion studies by Z. Haddad, A. Mugnai, T. Nakazawa, and G. Stephens) will contrast the nature of precipitation variability directly over the Mediterranean Sea to precipitation variability over the surrounding land areas based on three decades of satellite-based precipitation estimates which have stood up well to validation scrutiny. The satellite observations are drawn from the Global Precipitation Climatology Project (GPCP) dataset extending back to 1979 and the TRMM Merged Algorithm 3b42 dataset extending back to 1998. Both datasets are mostly produced from microwave measurements, excepting the period from 1979 to mid-1987 when only infrared satellite measurements were available for the GPCP estimates. The purpose of this study is to emphasize how the salient properties of precipitation variability over land and sea across a hierarchy of space and time scales, and the salient differences in these properties, might be used in guiding short-term climate models to better predictions of future climate states under different regional temperature-change scenarios.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.U24B..01L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.U24B..01L"><span>Assessing Extratropical Influence on Tropical Climatology and Variability with Regional Coupled Data Assimilation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lu, F.; Liu, Z.; Liu, Y.; Zhang, S.; Jacob, R. L.</p> <p>2017-12-01</p> <p>The Regional Coupled Data Assimilation (RCDA) method is introduced as a tool to study coupled climate dynamics and teleconnections. The RCDA method is built on an ensemble-based coupled data assimilation (CDA) system in a coupled general circulation model (CGCM). The RCDA method limits the data assimilation to the desired model components (e.g. atmosphere) and regions (e.g. the extratropics), and studies the ensemble-mean model response (e.g. tropical response to "observed" extratropical atmospheric variability). When applied to the extratropical influence on tropical climate, the RCDA method has shown some unique advantages, namely the combination of a fully coupled model, real-world observations and an ensemble approach. Tropical variability (e.g. El Niño-Southern Oscillation or ENSO) and climatology (e.g. asymmetric Inter-Tropical Convergence Zone or ITCZ) were initially thought to be determined mostly by local forcing and ocean-atmosphere interaction in the tropics. Since late 20th century, numerous studies have showed that extratropical forcing could affect, or even largely determine some aspects of the tropical climate. Due to the coupled nature of the climate system, however, the challenge of determining and further quantifying the causality of extratropical forcing on the tropical climate remains. Using the RCDA method, we have demonstrated significant control of extratropical atmospheric forcing on ENSO variability in a CGCM, both with model-generated and real-world observation datasets. The RCDA method has also shown robust extratropical impact on the tropical double-ITCZ bias in a CGCM. The RCDA method has provided the first systematic and quantitative assessment of extratropical influence on tropical climatology and variability by incorporating real world observations in a CGCM.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.7831M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.7831M"><span>How are interannual modes of variability IOD, ENSO, SAM, AMO excited by natural and anthropogenic forcing?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Maher, Nicola; Marotzke, Jochem</p> <p>2017-04-01</p> <p>Natural climate variability is found in observations, paleo-proxies, and climate models. Such climate variability can be intrinsic internal variability or externally forced, for example by changes in greenhouse gases or large volcanic eruptions. There are still questions concerning how external forcing, both natural (e.g., volcanic eruptions and solar variability) and anthropogenic (e.g., greenhouse gases and ozone) may excite both interannual modes of variability in the climate system. This project aims to address some of these problems, utilising the large ensemble of the MPI-ESM-LR climate model. In this study we investigate the statistics of four modes of interannual variability, namely the North Atlantic Oscillation (NAO), the Indian Ocean Dipole (IOD), the Southern Annular Mode (SAM) and the El Niño Southern Oscillation (ENSO). Using the 100-member ensemble of MPI-ESM-LR the statistical properties of these modes (amplitude and standard deviation) can be assessed over time. Here we compare the properties in the pre-industrial control run, historical run and future scenarios (RCP4.5, RCP2.6) and present preliminary results.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.usgs.gov/circ/1380/downloads/Chapter10.pdf','USGSPUBS'); return false;" href="https://pubs.usgs.gov/circ/1380/downloads/Chapter10.pdf"><span>The Borderlands and climate change: Chapter 10 in United States-Mexican Borderlands: Facing tomorrow's challenges through USGS science</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Fitzpatrick, Joan; Gray, Floyd; Dubiel, Russell; Langman, Jeff; Moring, J. Bruce; Norman, Laura M.; Page, William R.; Parcher, Jean W.</p> <p>2013-01-01</p> <p>The prediction of global climate change in response to both natural forces and human activity is one of the defining issues of our times. The unprecedented observational capacity of modern earth-orbiting satellites coupled with the development of robust computational representations (models) of the Earth’s weather and climate systems afford us the opportunity to observe and investigate how these systems work now, how they have worked in the past, and how they will work in the future when forced in specific ways. In the most recent report on global climate change by the Intergovernmental Panel on Climate Change (IPCC; Solomon and others, 2007), analyses using multiple climate models support recent observations that the Earth’s climate is changing in response to a combination of natural and human-induced causes. These changes will be significant in the United States–Mexican border region, where the process of climate change affects all of the Borderlands challenge themes discussed in the preceding chapters. The dual possibilities of both significantly-changed climate and increasing variability in climate make it challenging to take full measure of the potential effects because the Borderlands already experience a high degree of interannual variability and climatological extremes.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014BGeo...11.3057W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014BGeo...11.3057W"><span>Climate-mediated spatiotemporal variability in terrestrial productivity across Europe</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wu, X.; Babst, F.; Ciais, P.; Frank, D.; Reichstein, M.; Wattenbach, M.; Zang, C.; Mahecha, M. D.</p> <p>2014-06-01</p> <p>Quantifying the interannual variability (IAV) of the terrestrial ecosystem productivity and its sensitivity to climate is crucial for improving carbon budget predictions. In this context it is necessary to disentangle the influence of climate from impacts of other mechanisms underlying the spatiotemporal patterns of IAV of the ecosystem productivity. In this study we investigated the spatiotemporal patterns of IAV of historical observations of European crop yields in tandem with a set of climate variables. We further evaluated if relevant remote-sensing retrievals of NDVI (normalized difference vegetation index) and FAPAR (fraction of absorbed photosynthetically active radiation) depict a similar behaviour. Our results reveal distinct spatial patterns in the IAV of the analysed proxies linked to terrestrial productivity. In particular, we find higher IAV in water-limited regions of Europe (Mediterranean and temperate continental Europe) compared to other regions in both crop yield and remote-sensing observations. Our results further indicate that variations in the water balance during the active growing season exert a more pronounced and direct effect than variations of temperature on explaining the spatial patterns in IAV of productivity-related variables in temperate Europe. Overall, we observe a temporally increasing trend in the IAV of terrestrial productivity and an increasing sensitivity of productivity to water availability in dry regions of Europe during the 1975-2009 period. In the same regions, a simultaneous increase in the IAV of water availability was detected. These findings suggest intricate responses of carbon fluxes to climate variability in Europe and that the IAV of terrestrial productivity has become potentially more sensitive to changes in water availability in the dry regions in Europe. The changing sensitivity of terrestrial productivity accompanied by the changing IAV of climate is expected to impact carbon stocks and the net carbon balance of European ecosystems.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.A33P..01W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.A33P..01W"><span>Detection and Attribution of Temperature Trends in the Presence of Natural Variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wallace, J. M.</p> <p>2014-12-01</p> <p>The fingerprint of human-induced global warming stands out clearly above the noise In the time series of global-mean temperature, but not local temperature. At extratropical latitudes over land the standard error of 50-year linear temperature trends at a fixed point is as large as the cumulative rise in global-mean temperature over the past century. Much of the samping variability in local temperature trends is "dynamically-induced", i.e., attributable to the fact that the seasonally-varying mean circulation varies substantially from one year to the next and anomalous circulation patterns are generally accompanied by anomalous temperature patterns. In the presence of such large sampling variability it is virtually impossible to identify the spatial signature of greenhouse warming based on observational data or to partition observed local temperature trends into natural and human-induced components. It follows that previous IPCC assessments, which have focused on the deterministic signature of human-induced climate change, are inherently limited as to what they can tell us about the attribution of the past record of local temperature change or about how much the temperature at a particular place is likely to rise in the next few decades in response to global warming. To obtain more informative assessments of regional and local climate variability and change it will be necessary to take a probabilistic approach. Just as the use of the ensembles has contributed to more informative extended range weather predictions, large ensembles of climate model simulations can provide a statistical context for interpreting observed climate change and for framing projections of future climate. For some purposes, statistics relating to the interannual variability in the historical record can serve as a surrogate for statistics relating to the diversity of climate change scenarios in large ensembles.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JHyd..517.1019F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JHyd..517.1019F"><span>Application of a stochastic weather generator to assess climate change impacts in a semi-arid climate: The Upper Indus Basin</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Forsythe, N.; Fowler, H. J.; Blenkinsop, S.; Burton, A.; Kilsby, C. G.; Archer, D. R.; Harpham, C.; Hashmi, M. Z.</p> <p>2014-09-01</p> <p>Assessing local climate change impacts requires downscaling from Global Climate Model simulations. Here, a stochastic rainfall model (RainSim) combined with a rainfall conditioned weather generator (CRU WG) have been successfully applied in a semi-arid mountain climate, for part of the Upper Indus Basin (UIB), for point stations at a daily time-step to explore climate change impacts. Validation of the simulated time-series against observations (1961-1990) demonstrated the models' skill in reproducing climatological means of core variables with monthly RMSE of <2.0 mm for precipitation and ⩽0.4 °C for mean temperature and daily temperature range. This level of performance is impressive given complexity of climate processes operating in this mountainous context at the boundary between monsoonal and mid-latitude (westerly) weather systems. Of equal importance the model captures well the observed interannual variability as quantified by the first and last decile of 30-year climatic periods. Differences between a control (1961-1990) and future (2071-2100) regional climate model (RCM) time-slice experiment were then used to provide change factors which could be applied within the rainfall and weather models to produce perturbed ‘future' weather time-series. These project year-round increases in precipitation (maximum seasonal mean change:+27%, annual mean change: +18%) with increased intensity in the wettest months (February, March, April) and year-round increases in mean temperature (annual mean +4.8 °C). Climatic constraints on the productivity of natural resource-dependent systems were also assessed using relevant indices from the European Climate Assessment (ECA) and indicate potential future risk to water resources and local agriculture. However, the uniformity of projected temperature increases is in stark contrast to recent seasonally asymmetrical trends in observations, so an alternative scenario of extrapolated trends was also explored. We conclude that interannual variability in climate will continue to have the dominant impact on water resources management whichever trajectory is followed. This demonstrates the need for sophisticated downscaling methods which can evaluate changes in variability and sequencing of events to explore climate change impacts in this region.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A51L..02D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A51L..02D"><span>Role of the North Atlantic Ocean in Low Frequency Climate Variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Danabasoglu, G.; Yeager, S. G.; Kim, W. M.; Castruccio, F. S.</p> <p>2017-12-01</p> <p>The Atlantic Ocean is a unique basin with its extensive, North - South overturning circulation, referred to as the Atlantic meridional overturning circulation (AMOC). AMOC is thought to represent the dynamical memory of the climate system, playing an important role in decadal and longer time scale climate variability as well as prediction of the earth's future climate on these time scales via its large heat and salt transports. This oceanic memory is communicated to the atmosphere primarily through the influence of persistent sea surface temperature (SST) variations. Indeed, many modeling studies suggest that ocean circulation, i.e., AMOC, is largely responsible for the creation of coherent SST variability in the North Atlantic, referred to as Atlantic Multidecadal Variability (AMV). AMV has been linked to many (multi)decadal climate variations in, e.g., Sahel and Brazilian rainfall, Atlantic hurricane activity, and Arctic sea-ice extent. In the absence of long, continuous observations, much of the evidence for the ocean's role in (multi)decadal variability comes from model simulations. Although models tend to agree on the role of the North Atlantic Oscillation in creating the density anomalies that proceed the changes in ocean circulation, model fidelity in representing variability characteristics, mechanisms, and air-sea interactions remains a serious concern. In particular, there is increasing evidence that models significantly underestimate low frequency variability in the North Atlantic compared to available observations. Such model deficiencies can amplify the relative influence of external or stochastic atmospheric forcing in generating (multi)decadal variability, i.e., AMV, at the expense of ocean dynamics. Here, a succinct overview of the current understanding of the (North) Atlantic Ocean's role on the regional and global climate, including some outstanding questions, will be presented. In addition, a few examples of the climate impacts of the AMV via atmospheric teleconnections from a set of coupled simulations, also considering the relative roles of its tropical and extratropical components, will be highlighted.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23916199','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23916199"><span>Climate and health: observation and modeling of malaria in the Ferlo (Senegal).</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Diouf, Ibrahima; Deme, Abdoulaye; Ndione, Jacques-André; Gaye, Amadou Thierno; Rodríguez-Fonseca, Belén; Cissé, Moustapha</p> <p>2013-01-01</p> <p>The aim of this work, undertaken in the framework of QWeCI (Quantifying Weather and Climate Impacts on health in the developing countries) project, is to study how climate variability could influence malaria seasonal incidence. It will also assess the evolution of vector-borne diseases such as malaria by simulation analysis of climate models according to various climate scenarios for the next years. Climate variability seems to be determinant for the risk of malaria development (Freeman and Bradley, 1996 [1], Lindsay and Birley, 1996 [2], Kuhn et al., 2005 [3]). Climate can impact on the epidemiology of malaria by several mechanisms, directly, via the development rates and survival of both pathogens and vectors, and indirectly, through changes in vegetation and land surface characteristics such as the variability of breeding sites like ponds. Copyright © 2013 Académie des sciences. Published by Elsevier SAS. All rights reserved.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120011702','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120011702"><span>Solar Spectral Irradiance and Climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Pilewskie, P.; Woods, T.; Cahalan, R.</p> <p>2012-01-01</p> <p>Spectrally resolved solar irradiance is recognized as being increasingly important to improving our understanding of the manner in which the Sun influences climate. There is strong empirical evidence linking total solar irradiance to surface temperature trends - even though the Sun has likely made only a small contribution to the last half-century's global temperature anomaly - but the amplitudes cannot be explained by direct solar heating alone. The wavelength and height dependence of solar radiation deposition, for example, ozone absorption in the stratosphere, absorption in the ocean mixed layer, and water vapor absorption in the lower troposphere, contribute to the "top-down" and "bottom-up" mechanisms that have been proposed as possible amplifiers of the solar signal. New observations and models of solar spectral irradiance are needed to study these processes and to quantify their impacts on climate. Some of the most recent observations of solar spectral variability from the mid-ultraviolet to the near-infrared have revealed some unexpected behavior that was not anticipated prior to their measurement, based on an understanding from model reconstructions. The atmospheric response to the observed spectral variability, as quantified in climate model simulations, have revealed similarly surprising and in some cases, conflicting results. This talk will provide an overview on the state of our understanding of the spectrally resolved solar irradiance, its variability over many time scales, potential climate impacts, and finally, a discussion on what is required for improving our understanding of Sun-climate connections, including a look forward to future observations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20100027343','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20100027343"><span>Climate Benchmark Missions: CLARREO</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wielicki, Bruce A.; Young, David F.</p> <p>2010-01-01</p> <p>CLARREO (Climate Absolute Radiance and Refractivity Observatory) is one of the four Tier 1 missions recommended by the recent NRC decadal survey report on Earth Science and Applications from Space (NRC, 2007). The CLARREO mission addresses the need to rigorously observe climate change on decade time scales and to use decadal change observations as the most critical method to determine the accuracy of climate change projections such as those used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). A rigorously known accuracy of both decadal change observations as well as climate projections is critical in order to enable sound policy decisions. The CLARREO mission accomplishes this critical objective through highly accurate and SI traceable decadal change observations sensitive to many of the key uncertainties in climate radiative forcings, responses, and feedbacks that in turn drive uncertainty in current climate model projections. The same uncertainties also lead to uncertainty in attribution of climate change to anthropogenic forcing. The CLARREO breakthrough in decadal climate change observations is to achieve the required levels of accuracy and traceability to SI standards for a set of observations sensitive to a wide range of key decadal change variables. These accuracy levels are determined both by the projected decadal changes as well as by the background natural variability that such signals must be detected against. The accuracy for decadal change traceability to SI standards includes uncertainties of calibration, sampling, and analysis methods. Unlike most other missions, all of the CLARREO requirements are judged not by instantaneous accuracy, but instead by accuracy in large time/space scale average decadal changes. Given the focus on decadal climate change, the NRC Decadal Survey concluded that the single most critical issue for decadal change observations was their lack of accuracy and low confidence in observing the small but critical climate change signals. CLARREO is the recommended attack on this challenge, and builds on the last decade of climate observation advances in the Earth Observing System as well as metrological advances at NIST (National Institute of Standards and Technology) and other standards laboratories.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20170000986&hterms=Change+climate&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3DChange%2Bclimate','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20170000986&hterms=Change+climate&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3DChange%2Bclimate"><span>Cross - Scale Intercomparison of Climate Change Impacts Simulated by Regional and Global Hydrological Models in Eleven Large River Basins</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hattermann, F. F.; Krysanova, V.; Gosling, S. N.; Dankers, R.; Daggupati, P.; Donnelly, C.; Florke, M.; Huang, S.; Motovilov, Y.; Buda, S.; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20170000986'); toggleEditAbsImage('author_20170000986_show'); toggleEditAbsImage('author_20170000986_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20170000986_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20170000986_hide"></p> <p>2017-01-01</p> <p>Ideally, the results from models operating at different scales should agree in trend direction and magnitude of impacts under climate change. However, this implies that the sensitivity to climate variability and climate change is comparable for impact models designed for either scale. In this study, we compare hydrological changes simulated by 9 global and 9 regional hydrological models (HM) for 11 large river basins in all continents under reference and scenario conditions. The foci are on model validation runs, sensitivity of annual discharge to climate variability in the reference period, and sensitivity of the long-term average monthly seasonal dynamics to climate change. One major result is that the global models, mostly not calibrated against observations, often show a considerable bias in mean monthly discharge, whereas regional models show a better reproduction of reference conditions. However, the sensitivity of the two HM ensembles to climate variability is in general similar. The simulated climate change impacts in terms of long-term average monthly dynamics evaluated for HM ensemble medians and spreads show that the medians are to a certain extent comparable in some cases, but have distinct differences in other cases, and the spreads related to global models are mostly notably larger. Summarizing, this implies that global HMs are useful tools when looking at large-scale impacts of climate change and variability. Whenever impacts for a specific river basin or region are of interest, e.g. for complex water management applications, the regional-scale models calibrated and validated against observed discharge should be used.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_6 --> <div id="page_7" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="121"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014PIAHS.364..526L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014PIAHS.364..526L"><span>Reservoirs performances under climate variability: a case study</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Longobardi, A.; Mautone, M.; de Luca, C.</p> <p>2014-09-01</p> <p>A case study, the Piano della Rocca dam (southern Italy) is discussed here in order to quantify the system performances under climate variability conditions. Different climate scenarios have been stochastically generated according to the tendencies in precipitation and air temperature observed during recent decades for the studied area. Climate variables have then been filtered through an ARMA model to generate, at the monthly scale, time series of reservoir inflow volumes. Controlled release has been computed considering the reservoir is operated following the standard linear operating policy (SLOP) and reservoir performances have been assessed through the calculation of reliability, resilience and vulnerability indices (Hashimoto et al. 1982), comparing current and future scenarios of climate variability. The proposed approach can be suggested as a valuable tool to mitigate the effects of moderate to severe and persistent droughts periods, through the allocation of new water resources or the planning of appropriate operational rules.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25594780','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25594780"><span>A method for screening climate change-sensitive infectious diseases.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wang, Yunjing; Rao, Yuhan; Wu, Xiaoxu; Zhao, Hainan; Chen, Jin</p> <p>2015-01-14</p> <p>Climate change is a significant and emerging threat to human health, especially where infectious diseases are involved. Because of the complex interactions between climate variables and infectious disease components (i.e., pathogen, host and transmission environment), systematically and quantitatively screening for infectious diseases that are sensitive to climate change is still a challenge. To address this challenge, we propose a new statistical indicator, Relative Sensitivity, to identify the difference between the sensitivity of the infectious disease to climate variables for two different climate statuses (i.e., historical climate and present climate) in non-exposure and exposure groups. The case study in Anhui Province, China has demonstrated the effectiveness of this Relative Sensitivity indicator. The application results indicate significant sensitivity of many epidemic infectious diseases to climate change in the form of changing climatic variables, such as temperature, precipitation and absolute humidity. As novel evidence, this research shows that absolute humidity has a critical influence on many observed infectious diseases in Anhui Province, including dysentery, hand, foot and mouth disease, hepatitis A, hemorrhagic fever, typhoid fever, malaria, meningitis, influenza and schistosomiasis. Moreover, some infectious diseases are more sensitive to climate change in rural areas than in urban areas. This insight provides guidance for future health inputs that consider spatial variability in response to climate change.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4306891','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4306891"><span>A Method for Screening Climate Change-Sensitive Infectious Diseases</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Wang, Yunjing; Rao, Yuhan; Wu, Xiaoxu; Zhao, Hainan; Chen, Jin</p> <p>2015-01-01</p> <p>Climate change is a significant and emerging threat to human health, especially where infectious diseases are involved. Because of the complex interactions between climate variables and infectious disease components (i.e., pathogen, host and transmission environment), systematically and quantitatively screening for infectious diseases that are sensitive to climate change is still a challenge. To address this challenge, we propose a new statistical indicator, Relative Sensitivity, to identify the difference between the sensitivity of the infectious disease to climate variables for two different climate statuses (i.e., historical climate and present climate) in non-exposure and exposure groups. The case study in Anhui Province, China has demonstrated the effectiveness of this Relative Sensitivity indicator. The application results indicate significant sensitivity of many epidemic infectious diseases to climate change in the form of changing climatic variables, such as temperature, precipitation and absolute humidity. As novel evidence, this research shows that absolute humidity has a critical influence on many observed infectious diseases in Anhui Province, including dysentery, hand, foot and mouth disease, hepatitis A, hemorrhagic fever, typhoid fever, malaria, meningitis, influenza and schistosomiasis. Moreover, some infectious diseases are more sensitive to climate change in rural areas than in urban areas. This insight provides guidance for future health inputs that consider spatial variability in response to climate change. PMID:25594780</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140011181','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140011181"><span>On the Reprocessing and Reanalysis of Observations for Climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bosilovich, Michael G.; Kennedy, John; Dee, Dick; Allan, R.; O'Neill, Alan</p> <p>2013-01-01</p> <p>The long observational record is critical to our understanding of the Earths climate, but most observing systems were not developed with a climate objective in mind. As a result, tremendous efforts have gone into assessing and reprocessing the data records to improve their usefulness in climate studies. The purpose of this paper is to both review recent progress in reprocessing and reanalyzing observations, and to summarize the challenges that must be overcome in order to improve our understanding of climate and variability. Reprocessing improves data quality through more scrutiny and improved retrieval techniques for individual observing systems, while reanalysis merges many disparate observations with models through data assimilation, yet both aim to provide an climatology of Earth processes. Many challenges remain, such as tracking the improvement of processing algorithms and limited spatial coverage. Reanalyses have fostered significant research, yet reliable global trends in many physical fields are not yet attainable, despite significant advances in data assimilation and numerical modeling. Oceanic reanalyses have made significant advances in recent years, but will only be discussed here in terms of progress toward integrated Earth system analyses. Climate data sets are generally adequate for process studies and large-scale climate variability. Communication of the strengths, limitations and uncertainties of reprocessed observations and reanalysis data, not only among the community of developers, but also with the extended research community, including the new generations of researchers and the decision makers is crucial for further advancement of the observational data records. It must be emphasized that careful investigation of the data and processing methods are required to use the observations appropriately.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29888389','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29888389"><span>First-graders' allocation of attentional resources in an emotional Stroop task: The role of heart period variability and classroom climate.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Scrimin, Sara; Moscardino, Ughetta; Mason, Lucia</p> <p>2018-06-11</p> <p>Children's ability to remain focused on a task despite the presence of emotionally salient distractors in the environment is crucial for successful learning and academic performance. This study investigated first-graders' allocation of attentional resources in the presence of distracting emotional, school-related social interaction stimuli. Moreover, we examined whether such attentional processes were influenced by students' self-regulation, as indexed by heart period variability, observed classroom climate, or their interaction. Seventy-two-first graders took part in the study. To assess allocation of attentional resources, students' reaction times on an emotional Stroop task were registered by recording response times to colour frames placed around pictures of distracting emotional, school-related social interaction stimuli (i.e., emotional interference index). Moreover, heart period variability was measured by recording children's electrocardiogram at rest during an individual session, whereas classroom climate was observed during class activities by a trained researcher. Images representing negative social interactions required greater attentional resources than images depicting positive ones. Heart period variability and classroom climate were each significantly and independently associated with the emotional interference index. A significant interaction also emerged, indicating that among children experiencing a negative classroom climate, those who had a higher basal heart period variability (higher self-regulation) were less distracted by negative emotional material and remained more focused on a task compared to those with lower heart period variability (lower self-regulation). Negative interactions require greater attentional resources than positive scenes. Moreover, with a negative classroom climate, higher basal heart period variability is a protective factor. Implications for theory and practice are discussed. © 2018 The British Psychological Society.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19990064061','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19990064061"><span>NASA Scientific Forum on Climate Variability and Global Change: UNISPACE 3</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Schiffer, Robert A.; Unninayar, Sushel</p> <p>1999-01-01</p> <p>The Forum on Climate Variability and Global Change is intended to provide a glimpse into some of the advances made in our understanding of key scientific and environmental issues resulting primarily from improved observations and modeling on a global basis. This publication contains the papers presented at the forum.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1245323-grace-era-terrestrial-water-trends-driven-anthropogenic-climate-change','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1245323-grace-era-terrestrial-water-trends-driven-anthropogenic-climate-change"><span>Are GRACE-era terrestrial water trends driven by anthropogenic climate change?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Fasullo, J. T.; Lawrence, D. M.; Swenson, S. C.</p> <p>2016-01-01</p> <p>To provide context for observed trends in terrestrial water storage (TWS) during GRACE (2003–2014), trends and variability in the CESM1-CAM5 Large Ensemble (LE) are examined. Motivated in part by the anomalous nature of climate variability during GRACE, the characteristics of both forced change and internal modes are quantified and their influences on observations are estimated. Trends during the GRACE era in the LE are dominated by internal variability rather than by the forced response, with TWS anomalies in much of the Americas, eastern Australia, Africa, and southwestern Eurasia largely attributable to the negative phases of the Pacific Decadal Oscillation (PDO)more » and Atlantic Multidecadal Oscillation (AMO). While similarities between observed trends and the model-inferred forced response also exist, it is inappropriate to attribute such trends mainly to anthropogenic forcing. For several key river basins, trends in the mean state and interannual variability and the time at which the forced response exceeds background variability are also estimated while aspects of global mean TWS, including changes in its annual amplitude and decadal trends, are quantified. Lastly, the findings highlight the challenge of detecting anthropogenic climate change in temporally finite satellite datasets and underscore the benefit of utilizing models in the interpretation of the observed record.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1245323-grace-era-terrestrial-water-trends-driven-anthropogenic-climate-change','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1245323-grace-era-terrestrial-water-trends-driven-anthropogenic-climate-change"><span>Are GRACE-era terrestrial water trends driven by anthropogenic climate change?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Fasullo, J. T.; Lawrence, D. M.; Swenson, S. C.</p> <p></p> <p>To provide context for observed trends in terrestrial water storage (TWS) during GRACE (2003–2014), trends and variability in the CESM1-CAM5 Large Ensemble (LE) are examined. Motivated in part by the anomalous nature of climate variability during GRACE, the characteristics of both forced change and internal modes are quantified and their influences on observations are estimated. Trends during the GRACE era in the LE are dominated by internal variability rather than by the forced response, with TWS anomalies in much of the Americas, eastern Australia, Africa, and southwestern Eurasia largely attributable to the negative phases of the Pacific Decadal Oscillation (PDO)more » and Atlantic Multidecadal Oscillation (AMO). While similarities between observed trends and the model-inferred forced response also exist, it is inappropriate to attribute such trends mainly to anthropogenic forcing. For several key river basins, trends in the mean state and interannual variability and the time at which the forced response exceeds background variability are also estimated while aspects of global mean TWS, including changes in its annual amplitude and decadal trends, are quantified. Lastly, the findings highlight the challenge of detecting anthropogenic climate change in temporally finite satellite datasets and underscore the benefit of utilizing models in the interpretation of the observed record.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC31F1164O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC31F1164O"><span>Reducing the Uncertainty in Atlantic Meridional Overturning Circulation Projections Using Bayesian Model Averaging</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Olson, R.; An, S. I.</p> <p>2016-12-01</p> <p>Atlantic Meridional Overturning Circulation (AMOC) in the ocean might slow down in the future, which can lead to a host of climatic effects in North Atlantic and throughout the world. Despite improvements in climate models and availability of new observations, AMOC projections remain uncertain. Here we constrain CMIP5 multi-model ensemble output with observations of a recently developed AMOC index to provide improved Bayesian predictions of future AMOC. Specifically, we first calculate yearly AMOC index loosely based on Rahmstorf et al. (2015) for years 1880—2004 for both observations, and the CMIP5 models for which relevant output is available. We then assign a weight to each model based on a Bayesian Model Averaging method that accounts for differential model skill in terms of both mean state and variability. We include the temporal autocorrelation in climate model errors, and account for the uncertainty in the parameters of our statistical model. We use the weights to provide future weighted projections of AMOC, and compare them to un-weighted ones. Our projections use bootstrapping to account for uncertainty in internal AMOC variability. We also perform spectral and other statistical analyses to show that AMOC index variability, both in models and in observations, is consistent with red noise. Our results improve on and complement previous work by using a new ensemble of climate models, a different observational metric, and an improved Bayesian weighting method that accounts for differential model skill at reproducing internal variability. Reference: Rahmstorf, S., Box, J. E., Feulner, G., Mann, M. E., Robinson, A., Rutherford, S., & Schaffernicht, E. J. (2015). Exceptional twentieth-century slowdown in atlantic ocean overturning circulation. Nature Climate Change, 5(5), 475-480. doi:10.1038/nclimate2554</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23800223','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23800223"><span>Rates of projected climate change dramatically exceed past rates of climatic niche evolution among vertebrate species.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Quintero, Ignacio; Wiens, John J</p> <p>2013-08-01</p> <p>A key question in predicting responses to anthropogenic climate change is: how quickly can species adapt to different climatic conditions? Here, we take a phylogenetic approach to this question. We use 17 time-calibrated phylogenies representing the major tetrapod clades (amphibians, birds, crocodilians, mammals, squamates, turtles) and climatic data from distributions of > 500 extant species. We estimate rates of change based on differences in climatic variables between sister species and estimated times of their splitting. We compare these rates to predicted rates of climate change from 2000 to 2100. Our results are striking: matching projected changes for 2100 would require rates of niche evolution that are > 10,000 times faster than rates typically observed among species, for most variables and clades. Despite many caveats, our results suggest that adaptation to projected changes in the next 100 years would require rates that are largely unprecedented based on observed rates among vertebrate species. © 2013 John Wiley & Sons Ltd/CNRS.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMIN31F..01K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMIN31F..01K"><span>The CESM Large Ensemble Project: Inspiring New Ideas and Understanding</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kay, J. E.; Deser, C.</p> <p>2016-12-01</p> <p>While internal climate variability is known to affect climate projections, its influence is often underappreciated and confused with model error. Why? In general, modeling centers contribute a small number of realizations to international climate model assessments [e.g., phase 5 of the Coupled Model Intercomparison Project (CMIP5)]. As a result, model error and internal climate variability are difficult, and at times impossible, to disentangle. In response, the Community Earth System Model (CESM) community designed the CESM Large Ensemble (CESM-LE) with the explicit goal of enabling assessment of climate change in the presence of internal climate variability. All CESM-LE simulations use a single CMIP5 model (CESM with the Community Atmosphere Model, version 5). The core simulations replay the twenty to twenty-first century (1920-2100) 40+ times under historical and representative concentration pathway 8.5 external forcing with small initial condition differences. Two companion 2000+-yr-long preindustrial control simulations (fully coupled, prognostic atmosphere and land only) allow assessment of internal climate variability in the absence of climate change. Comprehensive outputs, including many daily fields, are available as single-variable time series on the Earth System Grid for anyone to use. Examples of scientists and stakeholders that are using the CESM-LE outputs to help interpret the observational record, to understand projection spread and to plan for a range of possible futures influenced by both internal climate variability and forced climate change will be highlighted the presentation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5081637','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5081637"><span>Impact of anthropogenic climate change on wildfire across western US forests</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Williams, A. Park</p> <p>2016-01-01</p> <p>Increased forest fire activity across the western continental United States (US) in recent decades has likely been enabled by a number of factors, including the legacy of fire suppression and human settlement, natural climate variability, and human-caused climate change. We use modeled climate projections to estimate the contribution of anthropogenic climate change to observed increases in eight fuel aridity metrics and forest fire area across the western United States. Anthropogenic increases in temperature and vapor pressure deficit significantly enhanced fuel aridity across western US forests over the past several decades and, during 2000–2015, contributed to 75% more forested area experiencing high (>1 σ) fire-season fuel aridity and an average of nine additional days per year of high fire potential. Anthropogenic climate change accounted for ∼55% of observed increases in fuel aridity from 1979 to 2015 across western US forests, highlighting both anthropogenic climate change and natural climate variability as important contributors to increased wildfire potential in recent decades. We estimate that human-caused climate change contributed to an additional 4.2 million ha of forest fire area during 1984–2015, nearly doubling the forest fire area expected in its absence. Natural climate variability will continue to alternate between modulating and compounding anthropogenic increases in fuel aridity, but anthropogenic climate change has emerged as a driver of increased forest fire activity and should continue to do so while fuels are not limiting. PMID:27791053</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20130012689','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130012689"><span>Quantitative Comparison of the Variability in Observed and Simulated Shortwave Reflectance</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Roberts, Yolanda, L.; Pilewskie, P.; Kindel, B. C.; Feldman, D. R.; Collins, W. D.</p> <p>2013-01-01</p> <p>The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is a climate observation system that has been designed to monitor the Earth's climate with unprecedented absolute radiometric accuracy and SI traceability. Climate Observation System Simulation Experiments (OSSEs) have been generated to simulate CLARREO hyperspectral shortwave imager measurements to help define the measurement characteristics needed for CLARREO to achieve its objectives. To evaluate how well the OSSE-simulated reflectance spectra reproduce the Earth s climate variability at the beginning of the 21st century, we compared the variability of the OSSE reflectance spectra to that of the reflectance spectra measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY). Principal component analysis (PCA) is a multivariate decomposition technique used to represent and study the variability of hyperspectral radiation measurements. Using PCA, between 99.7%and 99.9%of the total variance the OSSE and SCIAMACHY data sets can be explained by subspaces defined by six principal components (PCs). To quantify how much information is shared between the simulated and observed data sets, we spectrally decomposed the intersection of the two data set subspaces. The results from four cases in 2004 showed that the two data sets share eight (January and October) and seven (April and July) dimensions, which correspond to about 99.9% of the total SCIAMACHY variance for each month. The spectral nature of these shared spaces, understood by examining the transformed eigenvectors calculated from the subspace intersections, exhibit similar physical characteristics to the original PCs calculated from each data set, such as water vapor absorption, vegetation reflectance, and cloud reflectance.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70195830','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70195830"><span>Understanding the past to interpret the future: Comparison of simulated groundwater recharge in the upper Colorado River basin (USA) using observed and general-circulation-model historical climate data</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Tillman, Fred D.; Gangopadhyay, Subhrendu; Pruitt, Tom</p> <p>2017-01-01</p> <p>In evaluating potential impacts of climate change on water resources, water managers seek to understand how future conditions may differ from the recent past. Studies of climate impacts on groundwater recharge often compare simulated recharge from future and historical time periods on an average monthly or overall average annual basis, or compare average recharge from future decades to that from a single recent decade. Baseline historical recharge estimates, which are compared with future conditions, are often from simulations using observed historical climate data. Comparison of average monthly results, average annual results, or even averaging over selected historical decades, may mask the true variability in historical results and lead to misinterpretation of future conditions. Comparison of future recharge results simulated using general circulation model (GCM) climate data to recharge results simulated using actual historical climate data may also result in an incomplete understanding of the likelihood of future changes. In this study, groundwater recharge is estimated in the upper Colorado River basin, USA, using a distributed-parameter soil-water balance groundwater recharge model for the period 1951–2010. Recharge simulations are performed using precipitation, maximum temperature, and minimum temperature data from observed climate data and from 97 CMIP5 (Coupled Model Intercomparison Project, phase 5) projections. Results indicate that average monthly and average annual simulated recharge are similar using observed and GCM climate data. However, 10-year moving-average recharge results show substantial differences between observed and simulated climate data, particularly during period 1970–2000, with much greater variability seen for results using observed climate data.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940017168','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940017168"><span>Climate forcings and feedbacks</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hansen, James</p> <p>1993-01-01</p> <p>Global temperature has increased significantly during the past century. Understanding the causes of observed global temperature change is impossible in the absence of adequate monitoring of changes in global climate forcings and radiative feedbacks. Climate forcings are changes imposed on the planet's energy balance, such as change of incoming sunlight or a human-induced change of surface properties due to deforestation. Radiative feedbacks are radiative changes induced by climate change, such as alteration of cloud properties or the extent of sea ice. Monitoring of global climate forcings and feedbacks, if sufficiently precise and long-term, can provide a very strong constraint on interpretation of observed temperature change. Such monitoring is essential to eliminate uncertainties about the relative importance of various climate change mechanisms including tropospheric sulfate aerosols from burning of coal and oil smoke from slash and burn agriculture, changes of solar irradiance changes of several greenhouse gases, and many other mechanisms. The considerable variability of observed temperature, together with evidence that a substantial portion of this variability is unforced indicates that observations of climate forcings and feedbacks must be continued for decades. Since the climate system responds to the time integral of the forcing, a further requirement is that the observations be carried out continuously. However, precise observations of forcings and feedbacks will also be able to provide valuable conclusions on shorter time scales. For example, knowledge of the climate forcing by increasing CFC's relative to the forcing by changing ozone is important to policymakers, as is information on the forcing by CO2 relative to the forcing by sulfate aerosols. It will also be possible to obtain valuable tests of climate models on short time scales, if there is precise monitoring of all forcings and feedbacks during and after events such as a large volcanic eruption or an El Nino.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140008676','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140008676"><span>Reply to Comment by Laprise on 'the Added Value to Global Model Projections of Climate Change by Dynamical Downscaling: a Case Study over the Continental U.S. Using the GISS-ModelE2 and WRF Models'</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Shindell, Drew Todd; Racherla, Pavan; Milly, George Peter</p> <p>2014-01-01</p> <p>In his comment, Laprise raises several points that we agree merit consideration. His primary critique is that our study [Racherla et al., 2012] tested the ability of the WRF regional climate model to reproduce historical temperature and precipitation change relative to the driving global climate model (GCM) using only a single simulation rather than an ensemble. He asserts that the observed changes are smaller than the internal variability in the climate system (i.e., not statistically significant) and that thus a single simulation should not necessarily be able to capture the observations. Laprise points out that the statistical signal is reduced for a multi-decadal trend such as the one we analyzed in comparison with mean climatology and cites two studies showing that for particular climate parameters it can take any years for a signal to be discerned over internal variability. He states that The results of theexperiment as designed were strongly influenced by the presence of internal variability and sampling errors,which masked the rather small climate changes that may have occurred as a consequence of changes inforcing during the period considered. While Laprise discusses statistics in general terms at some length, for the actual climate trends examined in our study, he offers no evidence that the forced signal was smallcompared with internal variability. The two studies he cites [de Ela et al., 2013; Maraun, 2013] do not provide convincing evidence as they concern climate variables averaged over different times and areas. One in fact examines extreme precipitation events, which by definition are rare and thus have a lower significance level. We accept the general point that it is important to consider internal variability, and as noted in our paper we agree that an ensemble of simulations is in principle an optimal, though computationally expensive, approach. While we did not present the statistical significance of the observations in our original paper, we have now evaluated those for the regional temperature trends used in our study to evaluate the added value of WRF and thus can analyze data as to the magnitude of the trends with respect to internal variability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010NatGe...3..688O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010NatGe...3..688O"><span>External forcing as a metronome for Atlantic multidecadal variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Otterå, Odd Helge; Bentsen, Mats; Drange, Helge; Suo, Lingling</p> <p>2010-10-01</p> <p>Instrumental records, proxy data and climate modelling show that multidecadal variability is a dominant feature of North Atlantic sea-surface temperature variations, with potential impacts on regional climate. To understand the observed variability and to gauge any potential for climate predictions it is essential to identify the physical mechanisms that lead to this variability, and to explore the spatial and temporal characteristics of multidecadal variability modes. Here we use a coupled ocean-atmosphere general circulation model to show that the phasing of the multidecadal fluctuations in the North Atlantic during the past 600 years is, to a large degree, governed by changes in the external solar and volcanic forcings. We find that volcanoes play a particularly important part in the phasing of the multidecadal variability through their direct influence on tropical sea-surface temperatures, on the leading mode of northern-hemisphere atmosphere circulation and on the Atlantic thermohaline circulation. We suggest that the implications of our findings for decadal climate prediction are twofold: because volcanic eruptions cannot be predicted a decade in advance, longer-term climate predictability may prove challenging, whereas the systematic post-eruption changes in ocean and atmosphere may hold promise for shorter-term climate prediction.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28417562','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28417562"><span>Climate variability drives recent tree mortality in Europe.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Neumann, Mathias; Mues, Volker; Moreno, Adam; Hasenauer, Hubert; Seidl, Rupert</p> <p>2017-11-01</p> <p>Tree mortality is an important process in forest ecosystems, frequently hypothesized to be highly climate sensitive. Yet, tree death remains one of the least understood processes of forest dynamics. Recently, changes in tree mortality have been observed in forests around the globe, which could profoundly affect ecosystem functioning and services provisioning to society. We describe continental-scale patterns of recent tree mortality from the only consistent pan-European forest monitoring network, identifying recent mortality hotspots in southern and northern Europe. Analyzing 925,462 annual observations of 235,895 trees between 2000 and 2012, we determine the influence of climate variability and tree age on interannual variation in tree mortality using Cox proportional hazard models. Warm summers as well as high seasonal variability in precipitation increased the likelihood of tree death. However, our data also suggest that reduced cold-induced mortality could compensate increased mortality related to peak temperatures in a warming climate. Besides climate variability, age was an important driver of tree mortality, with individual mortality probability decreasing with age over the first century of a trees life. A considerable portion of the observed variation in tree mortality could be explained by satellite-derived net primary productivity, suggesting that widely available remote sensing products can be used as an early warning indicator of widespread tree mortality. Our findings advance the understanding of patterns of large-scale tree mortality by demonstrating the influence of seasonal and diurnal climate variation, and highlight the potential of state-of-the-art remote sensing to anticipate an increased likelihood of tree mortality in space and time. © 2017 John Wiley & Sons Ltd.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110012422','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110012422"><span>CLARREO Cornerstone of the Earth Observing System: Measuring Decadal Change Through Accurate Emitted Infrared and Reflected Solar Spectra and Radio Occultation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Sandford, Stephen P.</p> <p>2010-01-01</p> <p>The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is one of four Tier 1 missions recommended by the recent NRC Decadal Survey report on Earth Science and Applications from Space (NRC, 2007). The CLARREO mission addresses the need to provide accurate, broadly acknowledged climate records that are used to enable validated long-term climate projections that become the foundation for informed decisions on mitigation and adaptation policies that address the effects of climate change on society. The CLARREO mission accomplishes this critical objective through rigorous SI traceable decadal change observations that are sensitive to many of the key uncertainties in climate radiative forcings, responses, and feedbacks that in turn drive uncertainty in current climate model projections. These same uncertainties also lead to uncertainty in attribution of climate change to anthropogenic forcing. For the first time CLARREO will make highly accurate, global, SI-traceable decadal change observations sensitive to the most critical, but least understood, climate forcings, responses, and feedbacks. The CLARREO breakthrough is to achieve the required levels of accuracy and traceability to SI standards for a set of observations sensitive to a wide range of key decadal change variables. The required accuracy levels are determined so that climate trend signals can be detected against a background of naturally occurring variability. Climate system natural variability therefore determines what level of accuracy is overkill, and what level is critical to obtain. In this sense, the CLARREO mission requirements are considered optimal from a science value perspective. The accuracy for decadal change traceability to SI standards includes uncertainties associated with instrument calibration, satellite orbit sampling, and analysis methods. Unlike most space missions, the CLARREO requirements are driven not by the instantaneous accuracy of the measurements, but by accuracy in the large time/space scale averages that are key to understanding decadal changes.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3597248','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3597248"><span>Quantitative approaches in climate change ecology</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Brown, Christopher J; Schoeman, David S; Sydeman, William J; Brander, Keith; Buckley, Lauren B; Burrows, Michael; Duarte, Carlos M; Moore, Pippa J; Pandolfi, John M; Poloczanska, Elvira; Venables, William; Richardson, Anthony J</p> <p>2011-01-01</p> <p>Contemporary impacts of anthropogenic climate change on ecosystems are increasingly being recognized. Documenting the extent of these impacts requires quantitative tools for analyses of ecological observations to distinguish climate impacts in noisy data and to understand interactions between climate variability and other drivers of change. To assist the development of reliable statistical approaches, we review the marine climate change literature and provide suggestions for quantitative approaches in climate change ecology. We compiled 267 peer-reviewed articles that examined relationships between climate change and marine ecological variables. Of the articles with time series data (n = 186), 75% used statistics to test for a dependency of ecological variables on climate variables. We identified several common weaknesses in statistical approaches, including marginalizing other important non-climate drivers of change, ignoring temporal and spatial autocorrelation, averaging across spatial patterns and not reporting key metrics. We provide a list of issues that need to be addressed to make inferences more defensible, including the consideration of (i) data limitations and the comparability of data sets; (ii) alternative mechanisms for change; (iii) appropriate response variables; (iv) a suitable model for the process under study; (v) temporal autocorrelation; (vi) spatial autocorrelation and patterns; and (vii) the reporting of rates of change. While the focus of our review was marine studies, these suggestions are equally applicable to terrestrial studies. Consideration of these suggestions will help advance global knowledge of climate impacts and understanding of the processes driving ecological change.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_7 --> <div id="page_8" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="141"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H42C..05M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H42C..05M"><span>Historical floods reconstruction using NOAA 20CR global climate reanalysis over the last 150 years</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mathevet, T.; Brigode, P.; Jégonday, S.; Hingray, B.; Gailhard, J.; Wilhelm, B.</p> <p>2017-12-01</p> <p>Since several years, climatologists are producing long reanalysis for studying the variability of global climate over the last 150 years. For hydrologists, these datasets offer interesting opportunities for reconstructing historical flood events, and thus increasing the sample size used for flood frequency analysis. In this study, a streamflow reconstruction method based on the analogy of atmospheric situations (using NOAA 20CR reanalysis) for the reconstruction of climatic series and on a rainfall-runoff model for the streamflow reconstruction has been applied over different French catchments at the daily timestep. The studied catchments have been selected because of the availability of long observed streamflow series (used for quantifying the performances of the flood reconstructions) and for their different hydro-climatological regimes. Different methodologies have been tested for the reconstruction of daily climatic series over the 1851-2014 period, using geopotential heights and additional variables available within the 20CR reanalysis (relative humidity, precipitable water, etc.). Long observed climatic series have also been used when available as a reference for the climatic reconstructions. The different reconstruction methods have been finally ranked in terms of their historical flood reconstruction performances, quantified by flood types (autumn or winter floods) and atmospheric genesis (using a weather pattern classification). The obtained results indicate that using additional 20CR variables to the geopotential heights only slightly improve the flood reconstructions, while using observed climatic series improves significantly the flood reconstruction over the different catchments.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1812824M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1812824M"><span>Results from the VALUE perfect predictor experiment: process-based evaluation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Maraun, Douglas; Soares, Pedro; Hertig, Elke; Brands, Swen; Huth, Radan; Cardoso, Rita; Kotlarski, Sven; Casado, Maria; Pongracz, Rita; Bartholy, Judit</p> <p>2016-04-01</p> <p>Until recently, the evaluation of downscaled climate model simulations has typically been limited to surface climatologies, including long term means, spatial variability and extremes. But these aspects are often, at least partly, tuned in regional climate models to match observed climate. The tuning issue is of course particularly relevant for bias corrected regional climate models. In general, a good performance of a model for these aspects in present climate does therefore not imply a good performance in simulating climate change. It is now widely accepted that, to increase our condidence in climate change simulations, it is necessary to evaluate how climate models simulate relevant underlying processes. In other words, it is important to assess whether downscaling does the right for the right reason. Therefore, VALUE has carried out a broad process-based evaluation study based on its perfect predictor experiment simulations: the downscaling methods are driven by ERA-Interim data over the period 1979-2008, reference observations are given by a network of 85 meteorological stations covering all European climates. More than 30 methods participated in the evaluation. In order to compare statistical and dynamical methods, only variables provided by both types of approaches could be considered. This limited the analysis to conditioning local surface variables on variables from driving processes that are simulated by ERA-Interim. We considered the following types of processes: at the continental scale, we evaluated the performance of downscaling methods for positive and negative North Atlantic Oscillation, Atlantic ridge and blocking situations. At synoptic scales, we considered Lamb weather types for selected European regions such as Scandinavia, the United Kingdom, the Iberian Pensinsula or the Alps. At regional scales we considered phenomena such as the Mistral, the Bora or the Iberian coastal jet. Such process-based evaluation helps to attribute biases in surface variables to underlying processes and ultimately to improve climate models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23943096','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23943096"><span>Adapting to climate variability and change: experiences from cereal-based farming in the central rift and Kobo Valleys, Ethiopia.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kassie, Belay Tseganeh; Hengsdijk, Huib; Rötter, Reimund; Kahiluoto, Helena; Asseng, Senthold; Van Ittersum, Martin</p> <p>2013-11-01</p> <p>Small-holder farmers in Ethiopia are facing several climate related hazards, in particular highly variable rainfall with severe droughts which can have devastating effects on their livelihoods. Projected changes in climate are expected to aggravate the existing challenges. This study examines farmer perceptions on current climate variability and long-term changes, current adaptive strategies, and potential barriers for successful further adaptation in two case study regions-the Central Rift Valley (CRV) and Kobo Valley. The study was based on a household questionnaire, interviews with key stakeholders, and focus group discussions. The result revealed that about 99 % of the respondents at the CRV and 96 % at the Kobo Valley perceived an increase in temperature and 94 % at CRV and 91 % at the Kobo Valley perceived a decrease in rainfall over the last 20-30 years. Inter-annual and intraseasonal rainfall variability also has increased according to the farmers. The observed climate data (1977-2009) also showed an increasing trend in temperature and high inter-annual and intra-seasonal rainfall variability. In contrast to farmers' perceptions of a decrease in rainfall totals, observed rainfall data showed no statistically significant decline. The interaction among various bio-physical and socio-economic factors, changes in rainfall intensity and reduced water available to crops due to increased hot spells, may have influenced the perception of farmers with respect to rainfall trends. In recent decades, farmers in both the CRV and Kobo have changed farming practices to adapt to perceived climate change and variability, for example, through crop and variety choice, adjustment of cropping calendar, and in situ moisture conservation. These relatively low-cost changes in farm practices were within the limited adaptation capacity of farmers, which may be insufficient to deal with the impacts of future climate change. Anticipated climate change is expected to impose new risks outside the range of current experiences. To enable farmers to adapt to these impacts critical technological, institutional, and market-access constraints need to be removed. Inconsistencies between farmers' perceptions and observed climate trends (e.g., decrease in annual rainfall) could lead to sub-optimal or counterproductive adaptations, and therefore must be removed by better communication and capacity building, for example through Climate Field Schools. Enabling strategies, which are among others targeted at agricultural inputs, credit supply, market access, and strengthening of local knowledge and information services need to become integral part of government policies to assist farmers to adapt to the impacts of current and future climate change.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EnMan..52.1115K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EnMan..52.1115K"><span>Adapting to Climate Variability and Change: Experiences from Cereal-Based Farming in the Central Rift and Kobo Valleys, Ethiopia</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kassie, Belay Tseganeh; Hengsdijk, Huib; Rötter, Reimund; Kahiluoto, Helena; Asseng, Senthold; Van Ittersum, Martin</p> <p>2013-11-01</p> <p>Small-holder farmers in Ethiopia are facing several climate related hazards, in particular highly variable rainfall with severe droughts which can have devastating effects on their livelihoods. Projected changes in climate are expected to aggravate the existing challenges. This study examines farmer perceptions on current climate variability and long-term changes, current adaptive strategies, and potential barriers for successful further adaptation in two case study regions—the Central Rift Valley (CRV) and Kobo Valley. The study was based on a household questionnaire, interviews with key stakeholders, and focus group discussions. The result revealed that about 99 % of the respondents at the CRV and 96 % at the Kobo Valley perceived an increase in temperature and 94 % at CRV and 91 % at the Kobo Valley perceived a decrease in rainfall over the last 20-30 years. Inter-annual and intraseasonal rainfall variability also has increased according to the farmers. The observed climate data (1977-2009) also showed an increasing trend in temperature and high inter-annual and intra-seasonal rainfall variability. In contrast to farmers’ perceptions of a decrease in rainfall totals, observed rainfall data showed no statistically significant decline. The interaction among various bio-physical and socio-economic factors, changes in rainfall intensity and reduced water available to crops due to increased hot spells, may have influenced the perception of farmers with respect to rainfall trends. In recent decades, farmers in both the CRV and Kobo have changed farming practices to adapt to perceived climate change and variability, for example, through crop and variety choice, adjustment of cropping calendar, and in situ moisture conservation. These relatively low-cost changes in farm practices were within the limited adaptation capacity of farmers, which may be insufficient to deal with the impacts of future climate change. Anticipated climate change is expected to impose new risks outside the range of current experiences. To enable farmers to adapt to these impacts critical technological, institutional, and market-access constraints need to be removed. Inconsistencies between farmers’ perceptions and observed climate trends (e.g., decrease in annual rainfall) could lead to sub-optimal or counterproductive adaptations, and therefore must be removed by better communication and capacity building, for example through Climate Field Schools. Enabling strategies, which are among others targeted at agricultural inputs, credit supply, market access, and strengthening of local knowledge and information services need to become integral part of government policies to assist farmers to adapt to the impacts of current and future climate change.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26027583','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26027583"><span>Spatial Models for Prediction and Early Warning of Aedes aegypti Proliferation from Data on Climate Change and Variability in Cuba.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Ortiz, Paulo L; Rivero, Alina; Linares, Yzenia; Pérez, Alina; Vázquez, Juan R</p> <p>2015-04-01</p> <p>Climate variability, the primary expression of climate change, is one of the most important environmental problems affecting human health, particularly vector-borne diseases. Despite research efforts worldwide, there are few studies addressing the use of information on climate variability for prevention and early warning of vector-borne infectious diseases. Show the utility of climate information for vector surveillance by developing spatial models using an entomological indicator and information on predicted climate variability in Cuba to provide early warning of danger of increased risk of dengue transmission. An ecological study was carried out using retrospective and prospective analyses of time series combined with spatial statistics. Several entomological and climatic indicators were considered using complex Bultó indices -1 and -2. Moran's I spatial autocorrelation coefficient specified for a matrix of neighbors with a radius of 20 km, was used to identify the spatial structure. Spatial structure simulation was based on simultaneous autoregressive and conditional autoregressive models; agreement between predicted and observed values for number of Aedes aegypti foci was determined by the concordance index Di and skill factor Bi. Spatial and temporal distributions of populations of Aedes aegypti were obtained. Models for describing, simulating and predicting spatial patterns of Aedes aegypti populations associated with climate variability patterns were put forward. The ranges of climate variability affecting Aedes aegypti populations were identified. Forecast maps were generated for the municipal level. Using the Bultó indices of climate variability, it is possible to construct spatial models for predicting increased Aedes aegypti populations in Cuba. At 20 x 20 km resolution, the models are able to provide warning of potential changes in vector populations in rainy and dry seasons and by month, thus demonstrating the usefulness of climate information for epidemiological surveillance.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMOS23D..01H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMOS23D..01H"><span>Multi-decadal trend and space-time variability of sea level over the Indian Ocean since the 1950s: impact of decadal climate modes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Han, W.; Stammer, D.; Meehl, G. A.; Hu, A.; Sienz, F.</p> <p>2016-12-01</p> <p>Sea level varies on decadal and multi-decadal timescales over the Indian Ocean. The variations are not spatially uniform, and can deviate considerably from the global mean sea level rise (SLR) due to various geophysical processes. One of these processes is the change of ocean circulation, which can be partly attributed to natural internal modes of climate variability. Over the Indian Ocean, the most influential climate modes on decadal and multi-decadal timescales are the Interdecadal Pacific Oscillation (IPO) and decadal variability of the Indian Ocean dipole (IOD). Here, we first analyze observational datasets to investigate the impacts of IPO and IOD on spatial patterns of decadal and interdecadal (hereafter decal) sea level variability & multi-decadal trend over the Indian Ocean since the 1950s, using a new statistical approach of Bayesian Dynamical Linear regression Model (DLM). The Bayesian DLM overcomes the limitation of "time-constant (static)" regression coefficients in conventional multiple linear regression model, by allowing the coefficients to vary with time and therefore measuring "time-evolving (dynamical)" relationship between climate modes and sea level. For the multi-decadal sea level trend since the 1950s, our results show that climate modes and non-climate modes (the part that cannot be explained by climate modes) have comparable contributions in magnitudes but with different spatial patterns, with each dominating different regions of the Indian Ocean. For decadal variability, climate modes are the major contributors for sea level variations over most region of the tropical Indian Ocean. The relative importance of IPO and decadal variability of IOD, however, varies spatially. For example, while IOD decadal variability dominates IPO in the eastern equatorial basin (85E-100E, 5S-5N), IPO dominates IOD in causing sea level variations in the tropical southwest Indian Ocean (45E-65E, 12S-2S). To help decipher the possible contribution of external forcing to the multi-decadal sea level trend and decadal variability, we also analyze the model outputs from NCAR's Community Earth System Model (CESM) Large Ensemble Experiments, and compare the results with our observational analyses.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27676361','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27676361"><span>Influence of long-range atmospheric transport pathways and climate teleconnection patterns on the variability of surface 210Pb and 7Be concentrations in southwestern Europe.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Grossi, C; Ballester, J; Serrano, I; Galmarini, S; Camacho, A; Curcoll, R; Morguí, J A; Rodò, X; Duch, M A</p> <p>2016-12-01</p> <p>The variability of the atmospheric concentration of the 7 Be and 210 Pb radionuclides is strongly linked to the origin of air masses, the strength of their sources and the processes of wet and dry deposition. It has been shown how these processes and their variability are strongly affected by climate change. Thus, a deeper knowledge of the relationship between the atmospheric radionuclides variability measured close to the ground and these atmospheric processes could help in the analysis of climate scenarios. In the present study, we analyze the atmospheric variability of a 14-year time series of 7 Be and 210 Pb in a Mediterranean coastal city using a synergy of different indicators and tools such as: the local meteorological conditions, global and regional climate indexes and a lagrangian atmospheric transport model. We particularly focus on the relationships between the main pathways of air masses and sun spots occurrence, the variability of the local relative humidity and temperature conditions, and the main modes of regional climate variability, such as the North Atlantic Oscillation (NAO) and the Western Mediterranean Oscillation (WeMO). The variability of the observed atmospheric concentrations of both 7 Be and 210 Pb radionuclides was found to be mainly positively associated to the local climate conditions of temperature and to the pathways of air masses arriving at the station. Measured radionuclide concentrations significantly increase when air masses travel at low tropospheric levels from central Europe and the western part of the Iberian Peninsula, while low concentrations are associated with westerly air masses. We found a significant negative correlation between the WeMO index and the atmospheric variability of both radionuclides and no significant association was observed for the NAO index. Copyright © 2016 Elsevier Ltd. All rights reserved.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H43B1344L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H43B1344L"><span>Qualitatively Assessing Randomness in SVD Results</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lamb, K. W.; Miller, W. P.; Kalra, A.; Anderson, S.; Rodriguez, A.</p> <p>2012-12-01</p> <p>Singular Value Decomposition (SVD) is a powerful tool for identifying regions of significant co-variability between two spatially distributed datasets. SVD has been widely used in atmospheric research to define relationships between sea surface temperatures, geopotential height, wind, precipitation and streamflow data for myriad regions across the globe. A typical application for SVD is to identify leading climate drivers (as observed in the wind or pressure data) for a particular hydrologic response variable such as precipitation, streamflow, or soil moisture. One can also investigate the lagged relationship between a climate variable and the hydrologic response variable using SVD. When performing these studies it is important to limit the spatial bounds of the climate variable to reduce the chance of random co-variance relationships being identified. On the other hand, a climate region that is too small may ignore climate signals which have more than a statistical relationship to a hydrologic response variable. The proposed research seeks to identify a qualitative method of identifying random co-variability relationships between two data sets. The research identifies the heterogeneous correlation maps from several past results and compares these results with correlation maps produced using purely random and quasi-random climate data. The comparison identifies a methodology to determine if a particular region on a correlation map may be explained by a physical mechanism or is simply statistical chance.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20100033333&hterms=water+vapor&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dwater%2Bvapor','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20100033333&hterms=water+vapor&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dwater%2Bvapor"><span>Usefulness of AIRS-Derived OLR, Temperature, Water Vapor and Cloudiness Anomaly Trends for GCM Validation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Molnar, Gyula I.; Susskind, Joel; Iredell, Lena F.</p> <p>2010-01-01</p> <p>Mainly due to their global nature, satellite observations can provide a very useful basis for GCM validations. In particular, satellite sounders such as AIRS provide 3-D spatial information (most useful for GCMs), so the question arises: can we use AIRS datasets for climate variability assessments? We show that the recent (September 2002 February 2010) CERES-observed negative trend in OLR of approx.-0.1 W/sq m/yr averaged over the globe is found in the AIRS OLR data as well. Most importantly, even minute details (down to 1 x 1 degree GCM-scale resolution) of spatial and temporal anomalies and trends of OLR as observed by CERES and computed based on AIRS-retrieved surface and atmospheric geophysical parameters over this time period are essentially the same. The correspondence can be seen even in the very large spatial variations of these trends with local values ranging from -2.6 W/sq m/yr to +3.0 W/sq m/yr in the tropics, for example. This essentially perfect agreement of OLR anomalies and trends derived from observations by two different instruments, in totally independent and different manners, implies that both sets of results must be highly accurate, and indirectly validates the anomalies and trends of other AIRS derived products as well. These products show that global and regional anomalies and trends of OLR, water vapor and cloud cover over the last 7+ years are strongly influenced by EI-Nino-La Nina cycles . We have created climate parameter anomaly datasets using AIRS retrievals which can be compared directly with coupled GCM climate variability assessments. Moreover, interrelationships of these anomalies and trends should also be similar between the observed and GCM-generated datasets, and, in cases of discrepancies, GCM parameterizations could be improved based on the relationships observed in the data. First, we assess spatial "trends" of variability of climatic parameter anomalies [since anomalies relative to the seasonal cycle are good proxies of climate variability] at the common 1x1 degree GCM grid-scale by creating spatial anomaly "trends" based on the first 7+ years of AIRS Version 5 Leve13 data. We suggest that modelers should compare these with their (coupled) GCM's performance covering the same period. We evaluate temporal variability and interrelations of climatic anomalies on global to regional e.g., deep Tropical Hovmoller diagrams, El-Nino-related variability scales, and show the effects of El-Nino-La Nina activity on tropical anomalies and trends of water vapor cloud cover and OLR. For GCMs to be trusted highly for long-term climate change predictions, they should be able to reproduce findings similar to these. In summary, the AIRS-based climate variability analyses provide high quality, informative and physically plausible interrelationships among OLR, temperature, humidity and cloud cover both on the spatial and temporal scales. GCM validations can use these results even directly, e. g., by creating 1x1 degree trendmaps for the same period in coupled climate simulations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1911566B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1911566B"><span>Linking the variability of atmospheric carbon monoxide to climate modes in the Southern Hemisphere</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Buchholz, Rebecca; Monks, Sarah; Hammerling, Dorit; Worden, Helen; Deeter, Merritt; Emmons, Louisa; Edwards, David</p> <p>2017-04-01</p> <p>Biomass burning is a major driver of atmospheric carbon monoxide (CO) variability in the Southern Hemisphere. The magnitude of emissions, such as CO, from biomass burning is connected to climate through both the availability and dryness of fuel. We investigate the link between CO and climate using satellite measured CO and climate indices. Observations of total column CO from the satellite instrument MOPITT are used to build a record of interannual variability in CO since 2001. Four biomass burning regions in the Southern Hemisphere are explored. Data driven relationships are determined between CO and climate indices for the climate modes: El Niño Southern Oscillation (ENSO); the Indian Ocean Dipole (IOD); the Tropical Southern Atlantic (TSA); and the Southern Annular Mode (SAM). Stepwise forward and backward regression is used to select the best statistical model from combinations of lagged indices. We find evidence for the importance of first-order interaction terms of the climate modes when explaining CO variability. Implications of the model results are discussed for the Maritime Southeast Asia and Australasia regions. We also draw on the chemistry-climate model CAM-chem to explain the source contribution as well as the relative contributions of emissions and meteorology to CO variability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2645348','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2645348"><span>Normal forms for reduced stochastic climate models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Majda, Andrew J.; Franzke, Christian; Crommelin, Daan</p> <p>2009-01-01</p> <p>The systematic development of reduced low-dimensional stochastic climate models from observations or comprehensive high-dimensional climate models is an important topic for atmospheric low-frequency variability, climate sensitivity, and improved extended range forecasting. Here techniques from applied mathematics are utilized to systematically derive normal forms for reduced stochastic climate models for low-frequency variables. The use of a few Empirical Orthogonal Functions (EOFs) (also known as Principal Component Analysis, Karhunen–Loéve and Proper Orthogonal Decomposition) depending on observational data to span the low-frequency subspace requires the assessment of dyad interactions besides the more familiar triads in the interaction between the low- and high-frequency subspaces of the dynamics. It is shown below that the dyad and multiplicative triad interactions combine with the climatological linear operator interactions to simultaneously produce both strong nonlinear dissipation and Correlated Additive and Multiplicative (CAM) stochastic noise. For a single low-frequency variable the dyad interactions and climatological linear operator alone produce a normal form with CAM noise from advection of the large scales by the small scales and simultaneously strong cubic damping. These normal forms should prove useful for developing systematic strategies for the estimation of stochastic models from climate data. As an illustrative example the one-dimensional normal form is applied below to low-frequency patterns such as the North Atlantic Oscillation (NAO) in a climate model. The results here also illustrate the short comings of a recent linear scalar CAM noise model proposed elsewhere for low-frequency variability. PMID:19228943</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.B51J..05C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.B51J..05C"><span>Towards a More Biologically-meaningful Climate Characterization: Variability in Space and Time at Multiple Scales</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Christianson, D. S.; Kaufman, C. G.; Kueppers, L. M.; Harte, J.</p> <p>2013-12-01</p> <p>Sampling limitations and current modeling capacity justify the common use of mean temperature values in summaries of historical climate and future projections. However, a monthly mean temperature representing a 1-km2 area on the landscape is often unable to capture the climate complexity driving organismal and ecological processes. Estimates of variability in addition to mean values are more biologically meaningful and have been shown to improve projections of range shifts for certain species. Historical analyses of variance and extreme events at coarse spatial scales, as well as coarse-scale projections, show increasing temporal variability in temperature with warmer means. Few studies have considered how spatial variance changes with warming, and analysis for both temporal and spatial variability across scales is lacking. It is unclear how the spatial variability of fine-scale conditions relevant to plant and animal individuals may change given warmer coarse-scale mean values. A change in spatial variability will affect the availability of suitable habitat on the landscape and thus, will influence future species ranges. By characterizing variability across both temporal and spatial scales, we can account for potential bias in species range projections that use coarse climate data and enable improvements to current models. In this study, we use temperature data at multiple spatial and temporal scales to characterize spatial and temporal variability under a warmer climate, i.e., increased mean temperatures. Observational data from the Sierra Nevada (California, USA), experimental climate manipulation data from the eastern and western slopes of the Rocky Mountains (Colorado, USA), projected CMIP5 data for California (USA) and observed PRISM data (USA) allow us to compare characteristics of a mean-variance relationship across spatial scales ranging from sub-meter2 to 10,000 km2 and across temporal scales ranging from hours to decades. Preliminary spatial analysis at fine-spatial scales (sub-meter to 10-meter) shows greater temperature variability with warmer mean temperatures. This is inconsistent with the inherent assumption made in current species distribution models that fine-scale variability is static, implying that current projections of future species ranges may be biased -- the direction and magnitude requiring further study. While we focus our findings on the cross-scaling characteristics of temporal and spatial variability, we also compare the mean-variance relationship between 1) experimental climate manipulations and observed conditions and 2) temporal versus spatial variance, i.e., variability in a time-series at one location vs. variability across a landscape at a single time. The former informs the rich debate concerning the ability to experimentally mimic a warmer future. The latter informs space-for-time study design and analyses, as well as species persistence via a combined spatiotemporal probability of suitable future habitat.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JGRC..122.9695L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JGRC..122.9695L"><span>Evaluation of Oceanic Surface Observation for Reproducing the Upper Ocean Structure in ECHAM5/MPI-OM</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Luo, Hao; Zheng, Fei; Zhu, Jiang</p> <p>2017-12-01</p> <p>Better constraints of initial conditions from data assimilation are necessary for climate simulations and predictions, and they are particularly important for the ocean due to its long climate memory; as such, ocean data assimilation (ODA) is regarded as an effective tool for seasonal to decadal predictions. In this work, an ODA system is established for a coupled climate model (ECHAM5/MPI-OM), which can assimilate all available oceanic observations using an ensemble optimal interpolation approach. To validate and isolate the performance of different surface observations in reproducing air-sea climate variations in the model, a set of observing system simulation experiments (OSSEs) was performed over 150 model years. Generally, assimilating sea surface temperature, sea surface salinity, and sea surface height (SSH) can reasonably reproduce the climate variability and vertical structure of the upper ocean, and assimilating SSH achieves the best results compared to the true states. For the El Niño-Southern Oscillation (ENSO), assimilating different surface observations captures true aspects of ENSO well, but assimilating SSH can further enhance the accuracy of ENSO-related feedback processes in the coupled model, leading to a more reasonable ENSO evolution and air-sea interaction over the tropical Pacific. For ocean heat content, there are still limitations in reproducing the long time-scale variability in the North Atlantic, even if SSH has been taken into consideration. These results demonstrate the effectiveness of assimilating surface observations in capturing the interannual signal and, to some extent, the decadal signal but still highlight the necessity of assimilating profile data to reproduce specific decadal variability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26310856','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26310856"><span>Climate suitability for European ticks: assessing species distribution models against null models and projection under AR5 climate.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Williams, Hefin Wyn; Cross, Dónall Eoin; Crump, Heather Louise; Drost, Cornelis Jan; Thomas, Christopher James</p> <p>2015-08-28</p> <p>There is increasing evidence that the geographic distribution of tick species is changing. Whilst correlative Species Distribution Models (SDMs) have been used to predict areas that are potentially suitable for ticks, models have often been assessed without due consideration for spatial patterns in the data that may inflate the influence of predictor variables on species distributions. This study used null models to rigorously evaluate the role of climate and the potential for climate change to affect future climate suitability for eight European tick species, including several important disease vectors. We undertook a comparative assessment of the performance of Maxent and Mahalanobis Distance SDMs based on observed data against those of null models based on null species distributions or null climate data. This enabled the identification of species whose distributions demonstrate a significant association with climate variables. Latest generation (AR5) climate projections were subsequently used to project future climate suitability under four Representative Concentration Pathways (RCPs). Seven out of eight tick species exhibited strong climatic signals within their observed distributions. Future projections intimate varying degrees of northward shift in climate suitability for these tick species, with the greatest shifts forecasted under the most extreme RCPs. Despite the high performance measure obtained for the observed model of Hyalomma lusitanicum, it did not perform significantly better than null models; this may result from the effects of non-climatic factors on its distribution. By comparing observed SDMs with null models, our results allow confidence that we have identified climate signals in tick distributions that are not simply a consequence of spatial patterns in the data. Observed climate-driven SDMs for seven out of eight species performed significantly better than null models, demonstrating the vulnerability of these tick species to the effects of climate change in the future.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1612338M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1612338M"><span>Assessing Portuguese Guadiana Basin water management impacts under climate change and paleoclimate variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Maia, Rodrigo; Oliveira, Bruno; Ramos, Vanessa; Brekke, Levi</p> <p>2014-05-01</p> <p>The water balance in each reservoir and the subsequent, related, water resource management decisions are, presently, highly information dependent and are therefore often limited to a reactive response (even if aimed towards preventing future issues regarding the water system). Taking advantage of the availability of scenarios for climate projections, it is now possible to estimate the likely future evolution of climate which represents an important stepping stone towards proactive, adaptative, water resource management. The purpose of the present study was to assess the potential effects of climate change in terms of temperature, precipitation, runoff and water availability/scarcity for application in water resource management decisions. The analysis here presented was applied to the Portuguese portion of the Guadiana River Basin, using a combination of observed climate and runoff data and the results of the Global Climate Models. The Guadiana River Basin was represented by its reservoirs on the Portuguese portion of the basin and, for the future period, an estimated value of the inflows originating in the Spanish part of the Basin. The change in climate was determined in terms of relative and absolute variations of climate (precipitation and temperature) and hydrology (runoff and water balance related information). Apart from the previously referred data, an hydrological model and a water management model were applied so as to obtain an extended range of data regarding runoff generation (calibrated to observed data) and water balance in the reservoirs (considering the climate change impacts in the inflows, outflows and water consumption). The water management model was defined in order to represent the reservoirs interaction including upstream to downstream discharges and water transfers. Under the present climate change context, decision-makers and stakeholders are ever more vulnerable to the uncertainties of climate. Projected climate in the Guadiana basin indicates an increase in temperatures and a reduction of the precipitation values which go well beyond the observed values and, therefore, must be forcefully included in any realistic proactive water resource management decision. Using the results of this study it is possible to estimate future water availability and consumption satisfaction allowing for the elaboration of informed management decisions. In this study, the CMIP 3 Global Climate Models were considered for the definition of the effects of climate change, using the median and extreme tendencies based on the range of variation of the multiple climate projection scenarios. The observed climate variability, along with these model-derived tendencies, were used to inform the hydrology and water management models for the historical and future periods, respectively. Additionally, for a more comprehensive analysis on climate variability, a stochastic model was implemented based on the paleoclimate variability obtained from tree-ring records.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014ClDy...43.2297H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014ClDy...43.2297H"><span>On the generation of climate model ensembles</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Haughton, Ned; Abramowitz, Gab; Pitman, Andy; Phipps, Steven J.</p> <p>2014-10-01</p> <p>Climate model ensembles are used to estimate uncertainty in future projections, typically by interpreting the ensemble distribution for a particular variable probabilistically. There are, however, different ways to produce climate model ensembles that yield different results, and therefore different probabilities for a future change in a variable. Perhaps equally importantly, there are different approaches to interpreting the ensemble distribution that lead to different conclusions. Here we use a reduced-resolution climate system model to compare three common ways to generate ensembles: initial conditions perturbation, physical parameter perturbation, and structural changes. Despite these three approaches conceptually representing very different categories of uncertainty within a modelling system, when comparing simulations to observations of surface air temperature they can be very difficult to separate. Using the twentieth century CMIP5 ensemble for comparison, we show that initial conditions ensembles, in theory representing internal variability, significantly underestimate observed variance. Structural ensembles, perhaps less surprisingly, exhibit over-dispersion in simulated variance. We argue that future climate model ensembles may need to include parameter or structural perturbation members in addition to perturbed initial conditions members to ensure that they sample uncertainty due to internal variability more completely. We note that where ensembles are over- or under-dispersive, such as for the CMIP5 ensemble, estimates of uncertainty need to be treated with care.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140006609','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140006609"><span>Ground Water and Climate Change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Taylor, Richard G.; Scanlon, Bridget; Doell, Petra; Rodell, Matt; van Beek, Rens; Wada, Yoshihide; Longuevergne, Laurent; Leblanc, Marc; Famiglietti, James S.; Edmunds, Mike; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20140006609'); toggleEditAbsImage('author_20140006609_show'); toggleEditAbsImage('author_20140006609_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20140006609_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20140006609_hide"></p> <p>2013-01-01</p> <p>As the world's largest distributed store of fresh water, ground water plays a central part in sustaining ecosystems and enabling human adaptation to climate variability and change. The strategic importance of ground water for global water and food security will probably intensify under climate change as more frequent and intense climate extremes (droughts and floods) increase variability in precipitation, soil moisture and surface water. Here we critically review recent research assessing the impacts of climate on ground water through natural and human-induced processes as well as through groundwater-driven feedbacks on the climate system. Furthermore, we examine the possible opportunities and challenges of using and sustaining groundwater resources in climate adaptation strategies, and highlight the lack of groundwater observations, which, at present, limits our understanding of the dynamic relationship between ground water and climate.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70057583','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70057583"><span>Ground water and climate change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Taylor, Richard G.; Scanlon, Bridget R.; Döll, Petra; Rodell, Matt; van Beek, Rens; Wada, Yoshihide; Longuevergne, Laurent; Leblanc, Marc; Famiglietti, James S.; Edmunds, Mike; Konikow, Leonard F.; Green, Timothy R.; Chen, Jianyao; Taniguchi, Makoto; Bierkens, Marc F.P.; MacDonald, Alan; Fan, Ying; Maxwell, Reed M.; Yechieli, Yossi; Gurdak, Jason J.; Allen, Diana M.; Shamsudduha, Mohammad; Hiscock, Kevin; Yeh, Pat J.-F.; Holman, Ian; Treidel, Holger</p> <p>2012-01-01</p> <p>As the world's largest distributed store of fresh water, ground water plays a central part in sustaining ecosystems and enabling human adaptation to climate variability and change. The strategic importance of ground water for global water and food security will probably intensify under climate change as more frequent and intense climate extremes (droughts and floods) increase variability in precipitation, soil moisture and surface water. Here we critically review recent research assessing the impacts of climate on ground water through natural and human-induced processes as well as through groundwater-driven feedbacks on the climate system. Furthermore, we examine the possible opportunities and challenges of using and sustaining groundwater resources in climate adaptation strategies, and highlight the lack of groundwater observations, which, at present, limits our understanding of the dynamic relationship between ground water and climate.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1056147-multi-year-lags-between-forest-browning-soil-respiration-high-northern-latitudes','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1056147-multi-year-lags-between-forest-browning-soil-respiration-high-northern-latitudes"><span></span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Bond-Lamberty, Benjamin; Bunn, Andrew G.; Thomson, Allison M.</p> <p></p> <p>High-latitude northern ecosystems are experiencing rapid climate changes, and represent a large potential climate feedback because of their high soil carbon densities and shifting disturbance regimes. A significant carbon flow from these ecosystems is soil respiration (RS, the flow of carbon dioxide, generated by plant roots and soil fauna, from the soil surface to atmosphere), and any change in the high-latitude carbon cycle might thus be reflected in RS observed in the field. This study used two variants of a machine-learning algorithm and least squares regression to examine how remotely-sensed canopy greenness (NDVI), climate, and other variables are coupled tomore » annual RS based on 105 observations from 64 circumpolar sites in a global database. The addition of NDVI roughly doubled model performance, with the best-performing models explaining ~62% of observed RS variability« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20160011326&hterms=energy&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Denergy','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20160011326&hterms=energy&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Denergy"><span>Clouds and the Earth's Radiant Energy System (CERES) Data Products for Climate Research</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kato, Seiji; Loeb, Norman G.; Rutan, David A.; Rose, Fred G.</p> <p>2015-01-01</p> <p>NASA's Clouds and the Earth's Radiant Energy System (CERES) project integrates CERES, Moderate Resolution Imaging Spectroradiometer (MODIS), and geostationary satellite observations to provide top-of-atmosphere (TOA) irradiances derived from broadband radiance observations by CERES instruments. It also uses snow cover and sea ice extent retrieved from microwave instruments as well as thermodynamic variables from reanalysis. In addition, these variables are used for surface and atmospheric irradiance computations. The CERES project provides TOA, surface, and atmospheric irradiances in various spatial and temporal resolutions. These data sets are for climate research and evaluation of climate models. Long-term observations are required to understand how the Earth system responds to radiative forcing. A simple model is used to estimate the time to detect trends in TOA reflected shortwave and emitted longwave irradiances.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_8 --> <div id="page_9" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="161"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018PCE...105..104A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018PCE...105..104A"><span>Signature of present and projected climate change at an urban scale: The case of Addis Ababa</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Arsiso, Bisrat Kifle; Mengistu Tsidu, Gizaw; Stoffberg, Gerrit Hendrik</p> <p>2018-06-01</p> <p>Understanding climate change and variability at an urban scale is essential for water resource management, land use planning, development of adaption plans, mitigation of air and water pollution. However, there are serious challenges to meet these goals due to unavailability of observed and/or simulated high resolution spatial and temporal climate data. The statistical downscaling of general circulation climate model, for instance, is usually driven by sparse observational data hindering the use of downscaled data to investigate urban scale climate variability and change in the past. Recently, these challenges are partly resolved by concerted international effort to produce global and high spatial resolution climate data. In this study, the 1 km2 high resolution NIMR-HadGEM2-AO simulations for future projections under Representative Concentration Pathways (RCP4.5 and RCP8.5) scenarios and gridded observations provided by Worldclim data center are used to assess changes in rainfall, minimum and maximum temperature expected under the two scenarios over Addis Ababa city. The gridded 1 km2 observational data set for the base period (1950-2000) is compared to observation from a meteorological station in the city in order to assess its quality for use as a reference (baseline) data. The comparison revealed that the data set has a very good quality. The rainfall anomalies under RCPs scenarios are wet in the 2030s (2020-2039), 2050s (2040-2069) and 2080s (2070-2099). Both minimum and maximum temperature anomalies under RCPs are successively getting warmer during these periods. Thus, the projected changes under RCPs scenarios show a general increase in rainfall and temperatures with strong variabilities in rainfall during rainy season implying level of difficulty in water resource use and management as well as land use planning and management.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.B53C1971M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.B53C1971M"><span>Compound extremes of summer temperature and precipitation leading to intensified departures from natural variability.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mahony, C. R.; Cannon, A. J.</p> <p>2017-12-01</p> <p>Climate change can drive local climates outside the range of their historical year-to-year variability, straining the adaptive capacity of ecological and human communities. We demonstrate that interactions between climate variables can produce larger and earlier departures from natural variability than is detectable in individual variables. For example, summer temperature (Tx) and precipitation (Pr) are negatively correlated in most terrestrial regions, such that interannual variability lies along an axis from warm-and-dry to cool-and-wet conditions. A climate change trend perpendicular to this axis, towards warmer-wetter conditions, can depart more quickly from the range of natural variability than a warmer-drier trend. This multivariate "departure intensification" effect is evident in all six CMIP5 models that we examined: 23% (9-34%) of the land area of each model exhibits a pronounced increase in 2σ extremesin the Tx-Pr regime relative to Tx or Pr alone. Observational data suggest that Tx-Pr correlations are sufficient to produce departure intensification in distinct regions on all continents. Departures from the historical Tx-Pr regime may produce ecological disruptions, such as in plant-pathogen interactions and human diseases, that could offset the drought mitigation benefits of increased precipitation. Our study alerts researchers and adaptation practitioners to the presence of multivariate climate change signals and compound extremes that are not detectable in individual climate variables.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1911342R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1911342R"><span>Hydroclimatic variability in the Lake Mondsee region and its relationships with large-scale climate anomaly patterns</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rimbu, Norel; Ionita, Monica; Swierczynski, Tina; Brauer, Achim; Kämpf, Lucas; Czymzik, Markus</p> <p>2017-04-01</p> <p>Flood triggered detrital layers in varved sediments of Lake Mondsee, located at the northern fringe of the European Alps (47°48'N,13°23'E), provide an important archive of regional hydroclimatic variability during the mid- to late Holocene. To improve the interpretation of the flood layer record in terms of large-scale climate variability, we investigate the relationships between observational hydrological records from the region, like the Mondsee lake level, the runoff of the lake's main inflow Griesler Ache, with observed precipitation and global climate patterns. The lake level shows a strong positive linear trend during the observational period in all seasons. Additionally, lake level presents important interannual to multidecadal variations. These variations are associated with distinct seasonal atmospheric circulation patterns. A pronounced anomalous anticyclonic center over the Iberian Peninsula is associated with high lake levels values during winter. This center moves southwestward during spring, summer and autumn. In the same time, a cyclonic anomaly center is recorded over central and western Europe. This anomalous circulation extends southwestward from winter to autumn. Similar atmospheric circulation patterns are associated with river runoff and precipitation variability from the region. High lake levels are associated with positive local precipitation anomalies in all seasons as well as with negative local temperature anomalies during spring, summer and autumn. A correlation analysis reveals that lake level, runoff and precipitation variability is related to large-scale sea surface temperature anomaly patterns in all seasons suggesting a possible impact of large-scale climatic modes, like the North Atlantic Oscillation and Atlantic Multidecadal Oscillation on hydroclimatic variability in the Lake Mondsee region. The results presented in this study can be used for a more robust interpretation of the long flood layer record from Lake Mondsee sediments in terms of regional and large-scale climate variability during the past.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1616776D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1616776D"><span>Oceanic influence on seasonal malaria outbreaks over Senegal and Sahel. Predictability using S4CAST model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Diouf, Ibrahima; Deme, Abdoulaye; Rodriguez-Fonseca, Belen; Suárez-Moreno, Roberto; Cisse, Moustapha; Ndione, Jacques-André; Thierno Gaye, Amadou</p> <p>2014-05-01</p> <p>Senegal and, in general, West African regions are affected by important outbreaks of diseases with destructive consequences for human population, livestock and country's economy. The vector-borne diseases such as mainly malaria, Rift Valley Fever and dengue are affected by the interanual to decadal variability of climate. Analysis of the spatial and temporal variability of climate parameters and associated oceanic patterns is important in order to assess the climate impact on malaria transmission. In this study, the approach developed to study the malaria-climate link is predefined by the QWeCI project (Quantifying Weather and Climate Impacts on Health in Developing Countries). Preliminary observations and simulations results over Senegal Ferlo region, confirm that the risk of malaria transmission is mainly linked to climate parameters such as rainfall, temperature and relative humidity; and a lag of one to two months between the maximum of malaria and the maximum of climate parameters as rainfall is observed. As climate variables are able to be predicted from oceanic SST variability in remote regions, this study explores seasonal predictability of malaria incidence outbreaks from previous sea surface temperatures conditions in different ocean basins. We have found causal or coincident relationship between El Niño and malaria parameters by coupling LMM UNILIV malaria model and S4CAST statistiscal model with the aim of predicting the malaria parameters with more than 6 months in advance. In particular, El Niño is linked to an important decrease of the number of mosquitoes and the malaria incidence. Results from this research, after assessing the seasonal malaria parameters, are expected to be useful for decision makers to better access to climate forecasts and application on health in the framework of rolling back malaria transmission.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC51B0420S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC51B0420S"><span>Using MERRA, AMIP II, CMIP5 Outputs to Assess Actual and Potential Building Climate Zone Change and Variability From the Last 30 Years Through 2100</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stackhouse, P. W.; Westberg, D. J.; Hoell, J. M., Jr.; Chandler, W.; Zhang, T.</p> <p>2014-12-01</p> <p>In the US, residential and commercial building infrastructure combined consumes about 40% of total energy usage and emits about 39% of total CO2emission (DOE/EIA "Annual Energy Outlook 2013"). Thus, increasing the energy efficiency of buildings is paramount to reducing energy costs and emissions. Building codes, as used by local and state enforcement entities are typically tied to the dominant climate within an enforcement jurisdiction classified according to various climate zones. These climates zones are based upon a 30-year average of local surface observations and are developed by DOE and ASHRAE (formerly known as the American Society of Hearting, Refrigeration and Air-Conditioning Engineers). A significant shortcoming of the methodology used in constructing such maps is the use of surface observations (located mainly near airports) that are unequally distributed and frequently have periods of missing data that need to be filled by various approximation schemes. This paper demonstrates the usefulness of using NASA's Modern Era Retrospective-analysis for Research and Applications (MERRA) atmospheric data assimilation to derive the ASHRAE climate zone maps and then using MERRA to define the last 30 years of variability in climate zones. These results show that there is a statistically significant increase in the area covered by warmer climate zones and some tendency for a reduction of area in colder climate zones that require longer time series to confirm. Using the uncertainties of the basic surface temperature and precipitation parameters from MERRA as determined by comparison to surface measurements, we first compare patterns and variability of ASHRAE climate zones from MERRA relative to present day climate model runs from AMIP simulations to establish baseline sensitivity. Based upon these results, we assess the variability of the ASHRAE climate zones according to CMIP runs through 2100 using an ensemble analysis that classifies model output changes by percentiles. Estimates of statistical significance are then compared to original model variability during the AMIP period. This work quantifies and tests for significance the changes seen in the various US regions that represent a potential contribution by NASA to the ongoing National Climate Assessment.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19..296G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19..296G"><span>The role of climate variability in extreme floods in Europe</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Guimarães Nobre, Gabriela; Aerts, Jeroen C. J. H.; Jongman, Brenden; Ward, Philip J.</p> <p>2017-04-01</p> <p>Between 1980 and 2015, Europe experienced 18% of worldwide weather-related loss events, which accounted for over US500 billion in damage. Consequently, it is urgent to further develop adaptation strategies to mitigate the consequences of weather-related disasters, such as floods. Europe's capability to prepare for such disasters is challenged by a large range of uncertainties and a limited understanding of the driving forces of hydrometeorological hazards. One of the major sources of uncertainty is the relationship between climate variability and weather-related losses. Previous studies show that climate variability drives temporal changes in hydrometereological variables in Europe. However, their influence on flood risk has received little attention. We investigated the influence of the positive and negative phases of El Niño Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Arctic Oscillation (AO), on the seasonal frequency and intensity of extreme rainfall, and anomalies in flood occurrence and damage compared to the neutral phases of the indices of climate variability. Using statistical methods to analyze relationships between the indices of climate variability and four indicators of flooding, we found that positive and negative phases of NAO and AO are associated with more (or less) frequent and intense seasonal extreme rainfall over large areas of Europe. The relationship between ENSO and both the occurrence of extreme rainfall and intensity of extreme rainfall in Europe is much smaller than the relationship with NAO or AO, but still significant in some regions. We observe that flood damage and flood occurrence have strong links with climate variability, especially in southern and eastern Europe. Therefore, when investigating flooding across Europe, all three indices of climate variability should be considered. Seasonal forecasting of flooding could be enhanced by the inclusion of climate variability indicators .</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.A51I0173L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.A51I0173L"><span>Multi-objective optimization for evaluation of simulation fidelity for precipitation, cloudiness and insolation in regional climate models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lee, H.</p> <p>2016-12-01</p> <p>Precipitation is one of the most important climate variables that are taken into account in studying regional climate. Nevertheless, how precipitation will respond to a changing climate and even its mean state in the current climate are not well represented in regional climate models (RCMs). Hence, comprehensive and mathematically rigorous methodologies to evaluate precipitation and related variables in multiple RCMs are required. The main objective of the current study is to evaluate the joint variability of climate variables related to model performance in simulating precipitation and condense multiple evaluation metrics into a single summary score. We use multi-objective optimization, a mathematical process that provides a set of optimal tradeoff solutions based on a range of evaluation metrics, to characterize the joint representation of precipitation, cloudiness and insolation in RCMs participating in the North American Regional Climate Change Assessment Program (NARCCAP) and Coordinated Regional Climate Downscaling Experiment-North America (CORDEX-NA). We also leverage ground observations, NASA satellite data and the Regional Climate Model Evaluation System (RCMES). Overall, the quantitative comparison of joint probability density functions between the three variables indicates that performance of each model differs markedly between sub-regions and also shows strong seasonal dependence. Because of the large variability across the models, it is important to evaluate models systematically and make future projections using only models showing relatively good performance. Our results indicate that the optimized multi-model ensemble always shows better performance than the arithmetic ensemble mean and may guide reliable future projections.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20160000443&hterms=Change+climate&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3DChange%2Bclimate','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20160000443&hterms=Change+climate&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3DChange%2Bclimate"><span>Relative Contributions of Mean-State Shifts and ENSO-Driven Variability to Precipitation Changes in a Warming Climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bonfils, Celine J. W.; Santer, Benjamin D.; Phillips, Thomas J.; Marvel, Kate; Leung, L. Ruby; Doutriaux, Charles; Capotondi, Antonietta</p> <p>2015-01-01</p> <p>El Niño-Southern Oscillation (ENSO) is an important driver of regional hydroclimate variability through far-reaching teleconnections. This study uses simulations performed with coupled general circulation models (CGCMs) to investigate how regional precipitation in the twenty-first century may be affected by changes in both ENSO-driven precipitation variability and slowly evolving mean rainfall. First, a dominant, time-invariant pattern of canonical ENSO variability (cENSO) is identified in observed SST data. Next, the fidelity with which 33 state-of-the-art CGCMs represent the spatial structure and temporal variability of this pattern (as well as its associated precipitation responses) is evaluated in simulations of twentieth-century climate change. Possible changes in both the temporal variability of this pattern and its associated precipitation teleconnections are investigated in twenty-first-century climate projections. Models with better representation of the observed structure of the cENSO pattern produce winter rainfall teleconnection patterns that are in better accord with twentieth-century observations and more stationary during the twenty-first century. Finally, the model-predicted twenty-first-century rainfall response to cENSO is decomposed into the sum of three terms: 1) the twenty-first-century change in the mean state of precipitation, 2) the historical precipitation response to the cENSO pattern, and 3) a future enhancement in the rainfall response to cENSO, which amplifies rainfall extremes. By examining the three terms jointly, this conceptual framework allows the identification of regions likely to experience future rainfall anomalies that are without precedent in the current climate.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20150022866&hterms=climate+change&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dclimate%2Bchange','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20150022866&hterms=climate+change&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dclimate%2Bchange"><span>Relative Contributions of Mean-State Shifts and ENSO-Driven Variability to Precipitation Changes in a Warming Climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bonfils, Celine J. W.; Santer, Benjamin D.; Phillips, Thomas J.; Marvel, Kate; Leung, L. Ruby; Doutriaux, Charles; Capotondi, Antonietta</p> <p>2015-01-01</p> <p>The El Nino-Southern Oscillation (ENSO) is an important driver of regional hydroclimate variability through far-reaching teleconnections. This study uses simulations performed with Coupled General Circulation Models (CGCMs) to investigate how regional precipitation in the 21st century may be affected by changes in both ENSO-driven precipitation variability and slowly-evolving mean rainfall. First, a dominant, time-invariant pattern of canonical ENSO variability (cENSO) is identified in observed SST data. Next, the fidelity with which 33 state-of-the-art CGCMs represent the spatial structure and temporal variability of this pattern (as well as its associated precipitation responses) is evaluated in simulations of 20th century climate change. Possible changes in both the temporal variability of this pattern and its associated precipitation teleconnections are investigated in 21st century climate projections. Models with better representation of the observed structure of the cENSO pattern produce winter rainfall teleconnection patterns that are in better accord with 20th century observations and more stationary during the 21st century. Finally, the model-predicted 21st century rainfall response to cENSO is decomposed into the sum of three terms: 1) the 21st century change in the mean state of precipitation; 2) the historical precipitation response to the cENSO pattern; and 3) a future enhancement in the rainfall response to cENSO, which amplifies rainfall extremes. By examining the three terms jointly, this conceptual framework allows the identification of regions likely to experience future rainfall anomalies that are without precedent in the current climate.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.9768A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.9768A"><span>The variability of runoff and soil erosion in the Brazilian Cerrado biome due to the potential land use and climate changes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Alexandre Ayach Anache, Jamil; Wendland, Edson; Malacarne Pinheiro Rosalem, Lívia; Srivastava, Anurag; Flanagan, Dennis</p> <p>2017-04-01</p> <p>Changes in land use and climate can influence runoff and soil loss, threatening soil and water conservation in the Cerrado biome in Brazil. Due to the lack of long term observed data for runoff and soil erosion in Brazil, the adoption of a process-based model was necessary, representing the variability of both variables in a continuous simulation approach. Thus, we aimed to calibrate WEPP (Water Erosion Prediction Project) model for different land uses (undisturbed Cerrado, fallow, pasture, and sugarcane) under subtropical conditions inside the Cerrado biome; predict runoff and soil erosion for these different land uses; and simulate runoff and soil erosion considering climate change scenarios. We performed the model calibration using a 4-year dataset of observed runoff and soil loss in four different land uses (undisturbed Cerrado, fallow, pasture, and sugarcane). The WEPP model components (climate, topography, soil, and management) were calibrated according to field data. However, soil and management were optimized according to each land use using a parameter estimation tool. The observations were conducted between 2012 and 2015 in experimental plots (5 m width, 20 m length, 9% slope gradient, 3 replicates per treatment). The simulations were done using the calibrated WEPP model components, but changing the 4-year observed climate file by a 100-year dataset created with CLIGEN (weather generator) based on regional climate statistics. Afterwards, using MarkSim DSSAT Weather File Generator, runoff and soil loss were simulated using future climate scenarios for 2030, 2060, and 2090. To analyze the data, we used non-parametric statistics as data do not follow normal distribution. The results show that WEPP model had an acceptable performance for the considered conditions. In addition, both land use and climate can influence on runoff and soil loss rates. Potential climate changes which consider the increase of rainfall intensities and depths in the studied region may increase the variability and rates for runoff and soil erosion. However, the climate did not change the differences and similarities between the rates of the four analyzed land uses. The runoff behavior is distinct for all land uses, but for soil loss we found similarities between pasture and undisturbed Cerrado, suggesting that soil sustainability could be reached when the management follows conservation principles.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.pnas.org/content/106/suppl.2/19685.abstract','USGSPUBS'); return false;" href="http://www.pnas.org/content/106/suppl.2/19685.abstract"><span>Ecology and the ratchet of events: climate variability, niche dimensions, and species distributions</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Jackson, Stephen T.; Betancourt, Julio L.; Booth, Robert K.; Gray, Stephen T.</p> <p>2009-01-01</p> <p>Climate change in the coming centuries will be characterized by interannual, decadal, and multidecadal fluctuations superimposed on anthropogenic trends. Predicting ecological and biogeographic responses to these changes constitutes an immense challenge for ecologists. Perspectives from climatic and ecological history indicate that responses will be laden with contingencies, resulting from episodic climatic events interacting with demographic and colonization events. This effect is compounded by the dependency of environmental sensitivity upon life-stage for many species. Climate variables often used in empirical niche models may become decoupled from the proximal variables that directly influence individuals and populations. Greater predictive capacity, and more-fundamental ecological and biogeographic understanding, will come from integration of correlational niche modeling with mechanistic niche modeling, dynamic ecological modeling, targeted experiments, and systematic observations of past and present patterns and dynamics.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70034289','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70034289"><span>Ecology and the ratchet of events: Climate variability, niche dimensions, and species distributions</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Jackson, S.T.; Betancourt, J.L.; Booth, R.K.; Gray, S.T.</p> <p>2009-01-01</p> <p>Climate change in the coming centuries will be characterized by interannual, decadal, and multidecadal fluctuations superimposed on anthropogenic trends. Predicting ecological and biogeographic responses to these changes constitutes an immense challenge for ecologists. Perspectives from climatic and ecological history indicate that responses will be laden with contingencies, resulting from episodic climatic events interacting with demographic and colonization events. This effect is compounded by the dependency of environmental sensitivity upon life-stage for many species. Climate variables often used in empirical niche models may become decoupled from the proximal variables that directly influence individuals and populations. Greater predictive capacity, and morefundamental ecological and biogeographic understanding, will come from integration of correlational niche modeling with mechanistic niche modeling, dynamic ecological modeling, targeted experiments, and systematic observations of past and present patterns and dynamics.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70037655','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70037655"><span>Millennial-scale variability during the last glacial in vegetation records from North America</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Jiménez-Moreno, Gonzalo; Anderson, R. Scott; Desprat, S.; Grigg, L.D.; Grimm, E.C.; Heusser, L.E.; Jacobs, Brian F.; Lopez-Martinez, C.; Whitlock, C.L.; Willard, D.A.</p> <p>2010-01-01</p> <p>High-resolution pollen records from North America show that terrestrial environments were affected by Dansgaard-Oeschger (D-O) and Heinrich climate variability during the last glacial. In the western, more mountainous regions, these climate changes are generally observed in the pollen records as altitudinal movements of climate-sensitive plant species, whereas in the southeast, they are recorded as latitudinal shifts in vegetation. Heinrich (HS) and Greenland (GS) stadials are generally correlated with cold and dry climate and Greenland interstadials (GI) with warm-wet phases. The pollen records from North America confirm that vegetation responds rapidly to millennial-scale climate variability, although the difficulties in establishing independent age models for the pollen records make determination of the absolute phasing of the records to surface temperatures in Greenland somewhat uncertain. ?? 2009 Elsevier Ltd.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2780932','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2780932"><span>Ecology and the ratchet of events: Climate variability, niche dimensions, and species distributions</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Jackson, Stephen T.; Betancourt, Julio L.; Booth, Robert K.; Gray, Stephen T.</p> <p>2009-01-01</p> <p>Climate change in the coming centuries will be characterized by interannual, decadal, and multidecadal fluctuations superimposed on anthropogenic trends. Predicting ecological and biogeographic responses to these changes constitutes an immense challenge for ecologists. Perspectives from climatic and ecological history indicate that responses will be laden with contingencies, resulting from episodic climatic events interacting with demographic and colonization events. This effect is compounded by the dependency of environmental sensitivity upon life-stage for many species. Climate variables often used in empirical niche models may become decoupled from the proximal variables that directly influence individuals and populations. Greater predictive capacity, and more-fundamental ecological and biogeographic understanding, will come from integration of correlational niche modeling with mechanistic niche modeling, dynamic ecological modeling, targeted experiments, and systematic observations of past and present patterns and dynamics. PMID:19805104</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1311771','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1311771"><span>Final Technical Report: The effects of climate, forest age, and disturbance history on carbon and water processes at AmeriFlux sites across gradients in Pacific Northwest forests</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Law, Beverly E.</p> <p></p> <p>Investigate the effects of disturbance and climate variables on processes controlling carbon and water processes at AmeriFlux cluster sites in semi-arid and mesic forests in Oregon. The observations were made at three existing and productive AmeriFlux research sites that represent climate and disturbance gradients as a natural experiment of the influence of climatic and hydrologic variability on carbon sequestration and resulting atmospheric CO 2 feedback that includes anomalies during the warm/ dry phase of the Pacific Decadal Oscillation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20050192562','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20050192562"><span>Improving the Representation of Land in Climate Models by Application of EOS Observations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>2004-01-01</p> <p>The PI's IDS current and previous investigation has focused on the applications of the land data toward the improvement of climate models. The previous IDS research identified the key factors limiting the accuracy of climate models to be the representation of albedos, land cover, fraction of landscape covered by vegetation, roughness lengths, surface skin temperature and canopy properties such as leaf area index (LAI) and average stomatal conductance. Therefore, we assembled a team uniquely situated to focus on these key variables and incorporate the remotely sensed measures of these variables into the next generation of climate models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012GeoRL..3921705A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012GeoRL..3921705A"><span>The amplitude of decadal to multidecadal variability in precipitation simulated by state-of-the-art climate models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ault, T. R.; Cole, J. E.; St. George, S.</p> <p>2012-11-01</p> <p>We assess the magnitude of decadal to multidecadal (D2M) variability in Climate Model Intercomparison Project 5 (CMIP5) simulations that will be used to understand, and plan for, climate change as part of the Intergovernmental Panel on Climate Change's 5th Assessment Report. Model performance on D2M timescales is evaluated using metrics designed to characterize the relative and absolute magnitude of variability at these frequencies. In observational data, we find that between 10% and 35% of the total variance occurs on D2M timescales. Regions characterized by the high end of this range include Africa, Australia, western North America, and the Amazon region of South America. In these areas D2M fluctuations are especially prominent and linked to prolonged drought. D2M fluctuations account for considerably less of the total variance (between 5% and 15%) in the CMIP5 archive of historical (1850-2005) simulations. The discrepancy between observation and model based estimates of D2M prominence reflects two features of the CMIP5 archive. First, interannual components of variability are generally too energetic. Second, decadal components are too weak in several key regions. Our findings imply that projections of the future lack sufficient decadal variability, presenting a limited view of prolonged drought and pluvial risk.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC13G0755C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC13G0755C"><span>Regional Climate and Streamflow Projections in North America Under IPCC CMIP5 Scenarios</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chang, H. I.; Castro, C. L.; Troch, P. A. A.; Mukherjee, R.</p> <p>2014-12-01</p> <p>The Colorado River system is the predominant source of water supply for the Southwest U.S. and is already fully allocated, making the region's environmental and economic health particularly sensitive to annual and multi-year streamflow variability. Observed streamflow declines in the Colorado Basin in recent years are likely due to synergistic combination of anthropogenic global warming and natural climate variability, which are creating an overall warmer and more extreme climate. IPCC assessment reports have projected warmer and drier conditions in arid to semi-arid regions (e.g. Solomon et al. 2007). The NAM-related precipitation contributes to substantial Colorado streamflows. Recent climate change studies for the Southwest U.S. region project a dire future, with chronic drought, and substantially reduced Colorado River flows. These regional effects reflect the general observation that climate is being more extreme globally, with areas climatologically favored to be wet getting wetter and areas favored to be dry getting drier (Wang et al. 2012). Multi-scale downscaling modeling experiments are designed using recent IPCC AR5 global climate projections, which incorporate regional climate and hydrologic modeling components. The Weather Research and Forecasting model (WRF) has been selected as the main regional modeling tool; the Variable Infiltration Capacity model (VIC) will be used to generate streamflow projections for the Colorado River Basin. The WRF domain is set up to follow the CORDEX-North America guideline with 25km grid spacing, and VIC model is individually calibrated for upper and lower Colorado River basins in 1/8° resolution. The multi-scale climate and hydrology study aims to characterize how the combination of climate change and natural climate variability is changing cool and warm season precipitation. Further, to preserve the downscaled RCM sensitivity and maintain a reasonable climatology mean based on observed record, a new bias correction technique is applied when using the RCM climatology to the streamflow model. Of specific interest is how major droughts associated with La Niña-like conditions may worsen in the future, as these are the times when the Colorado River system is most critically stressed and would define the "worst case" scenario for water resource planning.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC21A1057R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC21A1057R"><span>Cloudy Windows: What GCM Ensembles, Reanalyses and Observations Tell Us About Uncertainty in Greenland's Future Climate and Surface Melting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Reusch, D. B.</p> <p>2016-12-01</p> <p>Any analysis that wants to use a GCM-based scenario of future climate benefits from knowing how much uncertainty the GCM's inherent variability adds to the development of climate change predictions. This is extra relevant in the polar regions due to the potential of global impacts (e.g., sea level rise) from local (ice sheet) climate changes such as more frequent/intense surface melting. High-resolution, regional-scale models using GCMs for boundary/initial conditions in future scenarios inherit a measure of GCM-derived externally-driven uncertainty. We investigate these uncertainties for the Greenland ice sheet using the 30-member CESM1.0-CAM5-BGC Large Ensemble (CESMLE) for recent (1981-2000) and future (2081-2100, RCP 8.5) decades. Recent simulations are skill-tested against the ERA-Interim reanalysis and AWS observations with results informing future scenarios. We focus on key variables influencing surface melting through decadal climatologies, nonlinear analysis of variability with self-organizing maps (SOMs), regional-scale modeling (Polar WRF), and simple melt models. Relative to the ensemble average, spatially averaged climatological July temperature anomalies over a Greenland ice-sheet/ocean domain are mostly between +/- 0.2 °C. The spatial average hides larger local anomalies of up to +/- 2 °C. The ensemble average itself is 2 °C cooler than ERA-Interim. SOMs extend our diagnostics by providing a concise, objective summary of model variability as a set of generalized patterns. For CESMLE, the SOM patterns summarize the variability of multiple realizations of climate. Changes in pattern frequency by ensemble member show the influence of initial conditions. For example, basic statistical analysis of pattern frequency yields interquartile ranges of 2-4% for individual patterns across the ensemble. In climate terms, this tells us about climate state variability through the range of the ensemble, a potentially significant source of melt-prediction uncertainty. SOMs can also capture the different trajectories of climate due to intramodel variability over time. Polar WRF provides higher resolution regional modeling with improved, polar-centric model physics. Simple melt models allow us to characterize impacts of the upstream uncertainties on estimates of surface melting.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28002419','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28002419"><span>Seasonality of Influenza and Respiratory Syncytial Viruses and the Effect of Climate Factors in Subtropical-Tropical Asia Using Influenza-Like Illness Surveillance Data, 2010 -2012.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kamigaki, Taro; Chaw, Liling; Tan, Alvin G; Tamaki, Raita; Alday, Portia P; Javier, Jenaline B; Olveda, Remigio M; Oshitani, Hitoshi; Tallo, Veronica L</p> <p>2016-01-01</p> <p>The seasonality of influenza and respiratory syncytial virus (RSV) is well known, and many analyses have been conducted in temperate countries; however, this is still not well understood in tropical countries. Previous studies suggest that climate factors are involved in the seasonality of these viruses. However, the extent of the effect of each climate variable is yet to be defined. We investigated the pattern of seasonality and the effect of climate variables on influenza and RSV at three sites of different latitudes: the Eastern Visayas region and Baguio City in the Philippines, and Okinawa Prefecture in Japan. Wavelet analysis and the dynamic linear regression model were applied. Climate variables used in the analysis included mean temperature, relative and specific humidity, precipitation, and number of rainy days. The Akaike Information Criterion estimated in each model was used to test the improvement of fit in comparison with the baseline model. At all three study sites, annual seasonal peaks were observed in influenza A and RSV; peaks were unclear for influenza B. Ranges of climate variables at the two Philippine sites were narrower and mean variables were significantly different among the three sites. Whereas all climate variables except the number of rainy days improved model fit to the local trend model, their contributions were modest. Mean temperature and specific humidity were positively associated with influenza and RSV at the Philippine sites and negatively associated with influenza A in Okinawa. Precipitation also improved model fit for influenza and RSV at both Philippine sites, except for the influenza A model in the Eastern Visayas. Annual seasonal peaks were observed for influenza A and RSV but were less clear for influenza B at all three study sites. Including additional data from subsequent more years would help to ascertain these findings. Annual amplitude and variation in climate variables are more important than their absolute values for determining their effect on the seasonality of influenza and RSV.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_9 --> <div id="page_10" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="181"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29374176','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29374176"><span>Decadal climate predictability in the southern Indian Ocean captured by SINTEX-F using a simple SST-nudging scheme.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Morioka, Yushi; Doi, Takeshi; Behera, Swadhin K</p> <p>2018-01-26</p> <p>Decadal climate variability in the southern Indian Ocean has great influences on southern African climate through modulation of atmospheric circulation. Although many efforts have been made to understanding physical mechanisms, predictability of the decadal climate variability, in particular, the internally generated variability independent from external atmospheric forcing, remains poorly understood. This study investigates predictability of the decadal climate variability in the southern Indian Ocean using a coupled general circulation model, called SINTEX-F. The ensemble members of the decadal reforecast experiments were initialized with a simple sea surface temperature (SST) nudging scheme. The observed positive and negative peaks during late 1990s and late 2000s are well reproduced in the reforecast experiments initiated from 1994 and 1999, respectively. The experiments initiated from 1994 successfully capture warm SST and high sea level pressure anomalies propagating from the South Atlantic to the southern Indian Ocean. Also, the other experiments initiated from 1999 skillfully predict phase change from a positive to negative peak. These results suggest that the SST-nudging initialization has the essence to capture the predictability of the internally generated decadal climate variability in the southern Indian Ocean.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014APS..MARG40005C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014APS..MARG40005C"><span>Causes and implications of the growing divergence between climate model simulations and observations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Curry, Judith</p> <p>2014-03-01</p> <p>For the past 15+ years, there has been no increase in global average surface temperature, which has been referred to as a 'hiatus' in global warming. By contrast, estimates of expected warming in the first several decades of 21st century made by the IPCC AR4 were 0.2C/decade. This talk summarizes the recent CMIP5 climate model simulation results and comparisons with observational data. The most recent climate model simulations used in the AR5 indicate that the warming stagnation since 1998 is no longer consistent with model projections even at the 2% confidence level. Potential causes for the model-observation discrepancies are discussed. A particular focus of the talk is the role of multi-decadal natural internal variability on the climate variability of the 20th and early 21st centuries. The ``stadium wave'' climate signal is described, which propagates across the Northern Hemisphere through a network of ocean, ice, and atmospheric circulation regimes that self-organize into a collective tempo. The stadium wave hypothesis provides a plausible explanation for the hiatus in warming and helps explain why climate models did not predict this hiatus. Further, the new hypothesis suggests how long the hiatus might last. Implications of the hiatus are discussed in context of climate model sensitivity to CO2 forcing and attribution of the warming that was observed in the last quarter of the 20th century.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ems..confE.529W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ems..confE.529W"><span>Time series of Essential Climate Variables from Satellite Data</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Werscheck, M.</p> <p>2010-09-01</p> <p>Climate change is a fact. We need to know how the climate system will develop in future and how this will affect workaday life. To do this we need climate models for prediction of the future on all time scales, and models to assess the impact of the prediction results to the various sectors of social and economic life. With this knowledge we can take measures to mitigate the causes and adapt to changes. Prerequisite for this is a careful and thorough monitoring of the climate systems. Satellite data are an increasing & valuable source of information to observe the climate system. For many decades now satellite data are available to derive information about our planet earth. EUMETSAT is the European Organisation in charge of the exploitation of satellite data for meteorology and (since the year 2000) climatology. Within the EUMETSAT Satellite Application Facility (SAF) Network, comprising 8 initiatives to derive geophysical parameters from satellite, the Satellite Application Facility on Climate Monitoring (CM SAF) is especially dedicated to provide climate relevant information from satellite data. Many products as e.g. water vapour, radiation at surface and top of atmosphere, cloud properties are available, some of these for more then 2 decades. Just recently the European Space Agency (ESA) launched the Climate Change Initiative (CCI) to derive Essential Climate Variables (ECVs) from satellite data, including e.g. cloud properties, aerosol, ozone, sea surface temperature etc.. The presentation will give an overview on some relevant European activities to derive Essential Climate Variables from satellite data and the links to Global Climate Observing System (GCOS), the Global Satellite Intercalibration System (GSICS) as well as the Sustained Co-ordinated Processing of Environmental Satellite Data for Climate Monitoring (SCOPE CM).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A12C..03P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A12C..03P"><span>Evaluation of a Mesoscale Convective System in Variable-Resolution CESM</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Payne, A. E.; Jablonowski, C.</p> <p>2017-12-01</p> <p>Warm season precipitation over the Southern Great Plains (SGP) follows a well observed diurnal pattern of variability, peaking at night-time, due to the eastward propagation of mesoscale convection systems that develop over the eastern slopes of the Rockies in the late afternoon. While most climate models are unable to adequately capture the organization of convection and characteristic pattern of precipitation over this region, models with high enough resolution to explicitly resolve convection show improvement. However, high resolution simulations are computationally expensive and, in the case of regional climate models, are subject to boundary conditions. Newly developed variable resolution global climate models strike a balance between the benefits of high-resolution regional climate models and the large-scale dynamics of global climate models and low computational cost. Recently developed parameterizations that are insensitive to the model grid scale provide a way to improve model performance. Here, we present an evaluation of the newly available Cloud Layers Unified by Binormals (CLUBB) parameterization scheme in a suite of variable-resolution CESM simulations with resolutions ranging from 110 km to 7 km within a regionally refined region centered over the SGP Atmospheric Radiation Measurement (ARM) site. Simulations utilize the hindcast approach developed by the Department of Energy's Cloud-Associated Parameterizations Testbed (CAPT) for the assessment of climate models. We limit our evaluation to a single mesoscale convective system that passed over the region on May 24, 2008. The effects of grid-resolution on the timing and intensity of precipitation, as well as, on the transition from shallow to deep convection are assessed against ground-based observations from the SGP ARM site, satellite observations and ERA-Interim reanalysis.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1990JCli....3.1053K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1990JCli....3.1053K"><span>A Method of Relating General Circulation Model Simulated Climate to the Observed Local Climate. Part I: Seasonal Statistics.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Karl, Thomas R.; Wang, Wei-Chyung; Schlesinger, Michael E.; Knight, Richard W.; Portman, David</p> <p>1990-10-01</p> <p>Important surface observations such as the daily maximum and minimum temperature, daily precipitation, and cloud ceilings often have localized characteristics that are difficult to reproduce with the current resolution and the physical parameterizations in state-of-the-art General Circulation climate Models (GCMs). Many of the difficulties can be partially attributed to mismatches in scale, local topography. regional geography and boundary conditions between models and surface-based observations. Here, we present a method, called climatological projection by model statistics (CPMS), to relate GCM grid-point flee-atmosphere statistics, the predictors, to these important local surface observations. The method can be viewed as a generalization of the model output statistics (MOS) and perfect prog (PP) procedures used in numerical weather prediction (NWP) models. It consists of the application of three statistical methods: 1) principle component analysis (FICA), 2) canonical correlation, and 3) inflated regression analysis. The PCA reduces the redundancy of the predictors The canonical correlation is used to develop simultaneous relationships between linear combinations of the predictors, the canonical variables, and the surface-based observations. Finally, inflated regression is used to relate the important canonical variables to each of the surface-based observed variables.We demonstrate that even an early version of the Oregon State University two-level atmospheric GCM (with prescribed sea surface temperature) produces free-atmosphere statistics than can, when standardized using the model's internal means and variances (the MOS-like version of CPMS), closely approximate the observed local climate. When the model data are standardized by the observed free-atmosphere means and variances (the PP version of CPMS), however, the model does not reproduce the observed surface climate as well. Our results indicate that in the MOS-like version of CPMS the differences between the output of a ten-year GCM control run and the surface-based observations are often smaller than the differences between the observations of two ten-year periods. Such positive results suggest that GCMs may already contain important climatological information that can be used to infer the local climate.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28465575','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28465575"><span>Ocean currents modify the coupling between climate change and biogeographical shifts.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>García Molinos, J; Burrows, M T; Poloczanska, E S</p> <p>2017-05-02</p> <p>Biogeographical shifts are a ubiquitous global response to climate change. However, observed shifts across taxa and geographical locations are highly variable and only partially attributable to climatic conditions. Such variable outcomes result from the interaction between local climatic changes and other abiotic and biotic factors operating across species ranges. Among them, external directional forces such as ocean and air currents influence the dispersal of nearly all marine and many terrestrial organisms. Here, using a global meta-dataset of observed range shifts of marine species, we show that incorporating directional agreement between flow and climate significantly increases the proportion of explained variance. We propose a simple metric that measures the degrees of directional agreement of ocean (or air) currents with thermal gradients and considers the effects of directional forces in predictions of climate-driven range shifts. Ocean flows are found to both facilitate and hinder shifts depending on their directional agreement with spatial gradients of temperature. Further, effects are shaped by the locations of shifts in the range (trailing, leading or centroid) and taxonomic identity of species. These results support the global effects of climatic changes on distribution shifts and stress the importance of framing climate expectations in reference to other non-climatic interacting factors.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27713662','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27713662"><span>Decadal variability of extreme wave height representing storm severity in the northeast Atlantic and North Sea since the foundation of the Royal Society.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Santo, H; Taylor, P H; Gibson, R</p> <p>2016-09-01</p> <p>Long-term estimation of extreme wave height remains a key challenge because of the short duration of available wave data, and also because of the possible impact of climate variability on ocean waves. Here, we analyse storm-based statistics to obtain estimates of extreme wave height at locations in the northeast Atlantic and North Sea using the NORA10 wave hindcast (1958-2011), and use a 5 year sliding window to examine temporal variability. The decadal variability is correlated to the North Atlantic oscillation and other atmospheric modes, using a six-term predictor model incorporating the climate indices and their Hilbert transforms. This allows reconstruction of the historic extreme climate back to 1661, using a combination of known and proxy climate indices. Significant decadal variability primarily driven by the North Atlantic oscillation is observed, and this should be considered for the long-term survivability of offshore structures and marine renewable energy devices. The analysis on wave climate reconstruction reveals that the variation of the mean, 99th percentile and extreme wave climates over decadal time scales for locations close to the dominant storm tracks in the open North Atlantic are comparable, whereas the wave climates for the rest of the locations including the North Sea are rather different.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016RSPSA.47260376S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016RSPSA.47260376S"><span>Decadal variability of extreme wave height representing storm severity in the northeast Atlantic and North Sea since the foundation of the Royal Society</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Santo, H.; Taylor, P. H.; Gibson, R.</p> <p>2016-09-01</p> <p>Long-term estimation of extreme wave height remains a key challenge because of the short duration of available wave data, and also because of the possible impact of climate variability on ocean waves. Here, we analyse storm-based statistics to obtain estimates of extreme wave height at locations in the northeast Atlantic and North Sea using the NORA10 wave hindcast (1958-2011), and use a 5 year sliding window to examine temporal variability. The decadal variability is correlated to the North Atlantic oscillation and other atmospheric modes, using a six-term predictor model incorporating the climate indices and their Hilbert transforms. This allows reconstruction of the historic extreme climate back to 1661, using a combination of known and proxy climate indices. Significant decadal variability primarily driven by the North Atlantic oscillation is observed, and this should be considered for the long-term survivability of offshore structures and marine renewable energy devices. The analysis on wave climate reconstruction reveals that the variation of the mean, 99th percentile and extreme wave climates over decadal time scales for locations close to the dominant storm tracks in the open North Atlantic are comparable, whereas the wave climates for the rest of the locations including the North Sea are rather different.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1612004M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1612004M"><span>Phenology at the crossroads?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Menzel, Annette</p> <p>2014-05-01</p> <p>Phenology is the study of the timing of natural events such as plant growth or animal migration. Currently nearly 500 papers are published annually that include 'phenolog*' in their title; many are related to anthropogenic change. Since seasonal events are triggered predominantly by climate, phenology has emerged as a key asset in identifying fingerprints of climate change in natural systems, especially since recent warming has been mirrored by significantly advancing spring events. Phenological changes have been reported across continents, habitats and taxa, predominantly as mean temporal changes ('trends') or as relationships to temperature and other drivers ('responses'), and have been summarised in various meta-analyses. However, a considerable variability in observed trends and responses is reported along with mixed messages of the footprint of climate change in nature. Phenology has made considerable advances but is a crossroads of understanding this variability. At the same time a change of emphasis in explanation, prediction and adaptation is emerging, which needs a full acknowledgement of this variability; likely yielding to more plasticity and resilience. In this review, I summarize current knowledge and recent insights into the role of • different observation methods, their accuracy and their target phenophases • observed events, species, traits, ontogenetic effects • species-specific safeguarding strategies, e.g. chilling, photoperiod • additional drivers other than climate, e.g. nutrients, GHG, biotic effects, anthropogenic / agricultural management • seasonal as well as spatio-temporal variation, effects of regional climate changes and analogous climates. This review clearly demonstrated that, comparable to weather and climate ensembles, only a full consideration of variation in responses allows a complete understanding of ecological, cultural and socioeconomic consequences of these phenological changes.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27508001','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27508001"><span>Relationship of suicide rates with climate and economic variables in Europe during 2000-2012.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Fountoulakis, Konstantinos N; Chatzikosta, Isaia; Pastiadis, Konstantinos; Zanis, Prodromos; Kawohl, Wolfram; Kerkhof, Ad J F M; Navickas, Alvydas; Höschl, Cyril; Lecic-Tosevski, Dusica; Sorel, Eliot; Rancans, Elmars; Palova, Eva; Juckel, Georg; Isacsson, Goran; Jagodic, Helena Korosec; Botezat-Antonescu, Ileana; Rybakowski, Janusz; Azorin, Jean Michel; Cookson, John; Waddington, John; Pregelj, Peter; Demyttenaere, Koen; Hranov, Luchezar G; Stevovic, Lidija Injac; Pezawas, Lucas; Adida, Marc; Figuera, Maria Luisa; Jakovljević, Miro; Vichi, Monica; Perugi, Giulio; Andreassen, Ole A; Vukovic, Olivera; Mavrogiorgou, Paraskevi; Varnik, Peeter; Dome, Peter; Winkler, Petr; Salokangas, Raimo K R; From, Tiina; Danileviciute, Vita; Gonda, Xenia; Rihmer, Zoltan; Forsman, Jonas; Grady, Anne; Hyphantis, Thomas; Dieset, Ingrid; Soendergaard, Susan; Pompili, Maurizio; Bech, Per</p> <p>2016-01-01</p> <p>It is well known that suicidal rates vary considerably among European countries and the reasons for this are unknown, although several theories have been proposed. The effect of economic variables has been extensively studied but not that of climate. Data from 29 European countries covering the years 2000-2012 and concerning male and female standardized suicidal rates (according to WHO), economic variables (according World Bank) and climate variables were gathered. The statistical analysis included cluster and principal component analysis and categorical regression. The derived models explained 62.4 % of the variability of male suicidal rates. Economic variables alone explained 26.9 % and climate variables 37.6 %. For females, the respective figures were 41.7, 11.5 and 28.1 %. Male suicides correlated with high unemployment rate in the frame of high growth rate and high inflation and low GDP per capita, while female suicides correlated negatively with inflation. Both male and female suicides correlated with low temperature. The current study reports that the climatic effect (cold climate) is stronger than the economic one, but both are present. It seems that in Europe suicidality follows the climate/temperature cline which interestingly is not from south to north but from south to north-east. This raises concerns that climate change could lead to an increase in suicide rates. The current study is essentially the first successful attempt to explain the differences across countries in Europe; however, it is an observational analysis based on aggregate data and thus there is a lack of control for confounders.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010111482','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010111482"><span>Constraints on Variability of Brightness and Surface Magnetism on Time Scales of Decades to Centuries in the Sun and Sun-Like Stars: A Source of Potential Terrestrial Climate Variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Baliunas, Sallie L.; Sharber, James (Technical Monitor)</p> <p>2001-01-01</p> <p>These four points summarize our work to date. (1) Conciliation of solar and stellar photometric variability. Previous research by us and colleagues suggested that the Sun might at present be showing unusually low photometric variability compared to other sun-like stars. Those early results would question the suitability of the technique of using sun-like stars as proxies for solar irradiance change on time scales of decades to centuries. However, our results indicate the contrary: the Sun's observed short-term (seasonal) and longterm (year-to-year) brightness variations closely agree with observed brightness variations in stars of similar mass and age. (2) We have demonstrated an inverse correlation between the global temperature of the terrestrial lower troposphere, inferred from the NASA Microwave Sounding Unit (MSU) radiometers, and the total area of the Sun covered by coronal holes from January 1979 to present (up to May 2000). Variable fluxes of either solar charged particles or cosmic rays, or both, may influence the terrestrial tropospheric temperature. The geographical pattern of the correlation is consistent with our interpretation of an extra-terrestrial charged particle forcing. (3) Possible climate mechanism amplifying the impact of solar ultraviolet irradiance variations. The key points of our proposed climate hypersensitivity mechanism are: (a) The Sun is more variable in the UV (ultraviolet) than in the visible. However, the increased UV irradiance is mainly absorbed in the lower stratosphere/upper troposphere rather than at the surface. (b) Absorption in the stratosphere raises the temperature moderately around the vicinity of the tropopause, and tends to stabilize the atmosphere against vertical convective/diffusive transport, thus decreasing the flux of heat and moisture carried upward from surface. (c) The decrease in the upward convection of heat and moisture tends to raise the surface temperature because a drier upper atmosphere becomes less cloudy, which in turn allows more solar radiation to reach the Earth's surface. (4) Natural variability in an ocean-atmosphere climate model. We use a 14-region, 6-layer, global thermo-hydrodynamic ocean-atmosphere model to study natural climate variability. All the numerical experiments were performed with no change in the prescribed external boundary conditions (except for the seasonal cycle of the Sun's tilt angle). Therefore, the observed inter-annual variability is of an internal kind. The model results are helpful toward the understanding of the role of nonlinearity in climate change. We have demonstrated a range of possible climate behaviors using our newly developed ocean-atmosphere model. These include climate configurations with no interannual variability, with multi-year periodicities, with continuous chaos, or with chaotically occuring transitions between two discrete substrates. These possible modes of climate behavior are all possible for the real climate, as well as the model. We have shown that small temporary climate influences can trigger shifts both in the mean climate, and among these different types of behavior. Such shifts are not only theoretically plausible, as shown here and elsewhere; they are omnipresent in the climate record on time scales from several years to the age of the Earth. This has two apparently opposite implications for the possibility of anthropogenic global warming. First, any warming which might occur as a result of human influence would be only a fraction of the small-to-large unpredictable natural changes and changes which result from other external causes. On the other hand, small temporary influences such as human influence do have the potential of causing large permanent shifts in mean climate and interannual variability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011ERL.....6c1002H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011ERL.....6c1002H"><span>Global warming: it's not only size that matters</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hegerl, Gabriele C.</p> <p>2011-09-01</p> <p>Observed and model simulated warming is particularly large in high latitudes, and hence the Arctic is often seen as the posterchild of vulnerability to global warming. However, Mahlstein et al (2011) point out that the signal of climate change is emerging locally from that of climate variability earliest in regions of low climate variability, based on climate model data, and in agreement with observations. This is because high latitude regions are not only regions of strong feedbacks that enhance the global warming signal, but also regions of substantial climate variability, driven by strong dynamics and enhanced by feedbacks (Hall 2004). Hence the spatial pattern of both observed warming and simulated warming for the 20th century shows strong warming in high latitudes, but this warming occurs against a backdrop of strong variability. Thus, the ratio of the warming to internal variability is not necessarily highest in the regions that warm fastest—and Mahlstein et al illustrate that it is actually the low-variability regions where the signal of local warming emerges first from that of climate variability. Thus, regions with strongest warming are neither the most important to diagnose that forcing changes climate, nor are they the regions which will necessarily experience the strongest impact. The importance of the signal-to-noise ratio has been known to the detection and attribution community, but has been buried in technical 'optimal fingerprinting' literature (e.g., Hasselmann 1979, Allen and Tett 1999), where it was used for an earlier detection of climate change by emphasizing aspects of the fingerprint of global warming associated with low variability in estimates of the observed warming. What, however, was not discussed was that the local signal-to-noise ratio is of interest also for local climate change: where temperatures emerge from the range visited by internal climate variability, it is reasonable to assume that changes in climate will also cause more impacts than temperatures that have occurred frequently due to internal climate variability. Determining when exactly temperatures enter unusual ranges may be done in many different ways (and the paper shows several, and more could be imagined), but the main result of first local emergence in low latitudes remains robust. A worrying factor is that the regions where the signal is expected to emerge first, or is already emerging are largely regions in Africa, parts of South and Central America, and the Maritime Continent; regions that are vulnerable to climate change for a variety of regions (see IPCC 2007), and regions which contribute generally little to global greenhouse gas emissions. In contrast, strong emissions of greenhouse gases occur in regions of low warming-to-variability ratio. To get even closer to the relevance of this finding for impacts, it would be interesting to place the emergence of highly unusual summer temperatures in the context not of internal variability, but in the context of variability experienced by the climate system prior to the 20th century, as, e.g. documented in palaeoclimatic reconstructions and simulated in simulations of the last millennium (see Jansen et al 2007). External forcing has moved the temperature range around more strongly for some regions and in some seasons than others. For example, while reconstructions of summer temperatures in Europe appear to show small long-term variations, winter shows deep drops in temperature in the little Ice Age and a long-term increase since then (Luterbacher et al 2004), which was at least partly caused by external forcing (Hegerl et al 2011a) and therefore 'natural variability' may be different from internal variability. A further interesting question in attempts to provide a climate-based proxy for impacts of climate change is: to what extent does the rapidity of change matter, and how does it compare to trends due to natural variability? It is reasonable to assume that fast changes impact ecosystems and society more than slow, gradual ones. Also, is it really the mean seasonal temperature that counts, or should the focus change to extremes (see Hegerl et al 2011b)? Is seasonal mean exceedance of the prior temperature envelope a good and robust measure that also reflects these other, more complex diagnostics? Lots of food for thought and research! References Allen M R and Tett S F B 1999 Checking for model consistency in optimal finger printing Clim. Dyn. 15 419-34 Hall A 2004 The role of surface albedo feedback in climate J. Clim. 17 1550-68 Hasselmann K 1979 On the signal-to-noise problem in atmospheric response studies Meteorology of Tropical Oceans ed D B Shaw (Bracknell: Royal Meteorological Society) pp 251-9 Hegerl G C, Luterbacher J, Gonzalez-Ruoco F, Tett S F B and Xoplaki E 2011a Influence of human and natural forcing on European seasonal temperatures Nature Geoscience 4 99-103 Hegerl G, Hanlon H and Beierkuhnlein C 2011b Climate science: elusive extremes Nature Geoscience 4 142-3 IPCC 2007 Climate Change 2007: Impacts, Adaption and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change ed M L Parry, O F Canziani, J P Palutikof, P J van der Linden and C E Hanson (Cambridge: Cambridge University Press) Jansen E et al 2007 Palaeoclimate Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change ed S Solomon et al (Cambridge: Cambridge University Press) Luterbacher J et al 2004 European seasonal and annual temperature variability, trends, and extremes since 1500 Science 303 1499-503 Mahlstein I, Knutti R, Solomon S and Portmann R W 2011 Early onset of significant local warming in low latitude countries Environ. Res. Lett. 6 034009</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017GeoRL..44.4246B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017GeoRL..44.4246B"><span>Role of internal variability in recent decadal to multidecadal tropical Pacific climate changes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bordbar, Mohammad Hadi; Martin, Thomas; Latif, Mojib; Park, Wonsun</p> <p>2017-05-01</p> <p>While the Earth's surface has considerably warmed over the past two decades, the tropical Pacific has featured a cooling of sea surface temperatures in its eastern and central parts, which went along with an unprecedented strengthening of the equatorial trade winds, the surface component of the Pacific Walker Circulation (PWC). Previous studies show that this decadal trend in the trade winds is generally beyond the range of decadal trends simulated by climate models when forced by historical radiative forcing. There is still a debate on the origin of and the potential role that internal variability may have played in the recent decadal surface wind trend. Using a number of long control (unforced) integrations of global climate models and several observational data sets, we address the question as to whether the recent decadal to multidecadal trends are robustly classified as an unusual event or the persistent response to external forcing. The observed trends in the tropical Pacific surface climate are still within the range of the long-term internal variability spanned by the models but represent an extreme realization of this variability. Thus, the recent observed decadal trends in the tropical Pacific, though highly unusual, could be of natural origin. We note that the long-term trends in the selected PWC indices exhibit a large observational uncertainty, even hindering definitive statements about the sign of the trends.<abstract type="synopsis"><title type="main">Plain Language SummaryWhile the Earth's surface has considerably warmed over the past two decades, the tropical Pacific has featured a cooling of sea surface temperatures in its eastern and central parts, which went along with an unprecedented strengthening of the equatorial trade winds. Here we show that climate models simulate a high level of internal variability, so that the recent changes in the tropical Pacific could still be due to natural processes.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70032531','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70032531"><span>Attribution of declining Western U.S. Snowpack to human effects</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Pierce, D.W.; Barnett, T.P.; Hidalgo, H.G.; Das, T.; Bonfils, Celine; Santer, B.D.; Bala, G.; Dettinger, M.D.; Cayan, D.R.; Mirin, A.; Wood, A.W.; Nozawa, T.</p> <p>2008-01-01</p> <p>Observations show snowpack has declined across much of the western United States over the period 1950-99. This reduction has important social and economic implications, as water retained in the snowpack from winter storms forms an important part of the hydrological cycle and water supply in the region. A formal model-based detection and attribution (D-A) study of these reductions is performed. The detection variable is the ratio of 1 April snow water equivalent (SWE) to water-year-to-date precipitation (P), chosen to reduce the effect of P variability on the results. Estimates of natural internal climate variability are obtained from 1600 years of two control simulations performed with fully coupled ocean-atmosphere climate models. Estimates of the SWE/P response to anthropogenic greenhouse gases, ozone, and some aerosols are taken from multiple-member ensembles of perturbation experiments run with two models. The D-A shows the observations and anthropogenically forced models have greater SWE/P reductions than can be explained by natural internal climate variability alone. Model-estimated effects of changes in solar and volcanic forcing likewise do not explain the SWE/P reductions. The mean model estimate is that about half of the SWE/P reductions observed in the west from 1950 to 1999 are the result of climate changes forced by anthropogenic greenhouse gases, ozone, and aerosols. ?? 2008 American Meteorological Society.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ThApC.128...71A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ThApC.128...71A"><span>Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ahmadalipour, Ali; Rana, Arun; Moradkhani, Hamid; Sharma, Ashish</p> <p>2017-04-01</p> <p>Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120013713','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120013713"><span>Comparative Analysis of Upper Ocean Heat Content Variability from Ensemble Operational Ocean Analyses</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Xue, Yan; Balmaseda, Magdalena A.; Boyer, Tim; Ferry, Nicolas; Good, Simon; Ishikawa, Ichiro; Rienecker, Michele; Rosati, Tony; Yin, Yonghong; Kumar, Arun</p> <p>2012-01-01</p> <p>Upper ocean heat content (HC) is one of the key indicators of climate variability on many time-scales extending from seasonal to interannual to long-term climate trends. For example, HC in the tropical Pacific provides information on thermocline anomalies that is critical for the longlead forecast skill of ENSO. Since HC variability is also associated with SST variability, a better understanding and monitoring of HC variability can help us understand and forecast SST variability associated with ENSO and other modes such as Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO), Tropical Atlantic Variability (TAV) and Atlantic Multidecadal Oscillation (AMO). An accurate ocean initialization of HC anomalies in coupled climate models could also contribute to skill in decadal climate prediction. Errors, and/or uncertainties, in the estimation of HC variability can be affected by many factors including uncertainties in surface forcings, ocean model biases, and deficiencies in data assimilation schemes. Changes in observing systems can also leave an imprint on the estimated variability. The availability of multiple operational ocean analyses (ORA) that are routinely produced by operational and research centers around the world provides an opportunity to assess uncertainties in HC analyses, to help identify gaps in observing systems as they impact the quality of ORAs and therefore climate model forecasts. A comparison of ORAs also gives an opportunity to identify deficiencies in data assimilation schemes, and can be used as a basis for development of real-time multi-model ensemble HC monitoring products. The OceanObs09 Conference called for an intercomparison of ORAs and use of ORAs for global ocean monitoring. As a follow up, we intercompared HC variations from ten ORAs -- two objective analyses based on in-situ data only and eight model analyses based on ocean data assimilation systems. The mean, annual cycle, interannual variability and longterm trend of HC have been analyzed</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/17851202','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/17851202"><span>New climate change scenarios for the Netherlands.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>van den Hurk, B; Tank, A K; Lenderink, G; Ulden, A van; Oldenborgh, G J van; Katsman, C; Brink, H van den; Keller, F; Bessembinder, J; Burgers, G; Komen, G; Hazeleger, W; Drijfhout, S</p> <p>2007-01-01</p> <p>A new set of climate change scenarios for 2050 for the Netherlands was produced recently. The scenarios span a wide range of possible future climate conditions, and include climate variables that are of interest to a broad user community. The scenario values are constructed by combining output from an ensemble of recent General Climate Model (GCM) simulations, Regional Climate Model (RCM) output, meteorological observations and a touch of expert judgment. For temperature, precipitation, potential evaporation and wind four scenarios are constructed, encompassing ranges of both global mean temperature rise in 2050 and the strength of the response of the dominant atmospheric circulation in the area of interest to global warming. For this particular area, wintertime precipitation is seen to increase between 3.5 and 7% per degree global warming, but mean summertime precipitation shows opposite signs depending on the assumed response of the circulation regime. Annual maximum daily mean wind speed shows small changes compared to the observed (natural) variability of this variable. Sea level rise in the North Sea in 2100 ranges between 35 and 85 cm. Preliminary assessment of the impact of the new scenarios on water management and coastal defence policies indicate that particularly dry summer scenarios and increased intensity of extreme daily precipitation deserves additional attention in the near future.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC51E1230W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC51E1230W"><span>Climate Variability and Wildfires: Insights from Global Earth System Models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ward, D. S.; Shevliakova, E.; Malyshev, S.; Lamarque, J. F.; Wittenberg, A. T.</p> <p>2016-12-01</p> <p>Better understanding of the relationship between variability in global climate and emissions from wildfires is needed for predictions of fire activity on interannual to multi-decadal timescales. Here we investigate this relationship using the long, preindustrial control simulations and historical ensembles of two Earth System models; CESM1 and the NOAA/GFDL ESM2Mb. There is smaller interannual variability of global fires in both models than in present day inventories, especially in boreal regions where observed fires vary substantially from year to year. Patterns of fire response to climate oscillation indices, including the El Niño / Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Atlantic Meridional Oscillation (AMO) are explored with the model results and compared to the response derived from satellite measurements and proxy observations. Increases in fire emissions in southeast Asia and boreal North America are associated with positive ENSO and PDO, while United States fires and Sahel fires decrease for the same climate conditions. Boreal fire emissions decrease in CESM1 for the warm phase of the AMO, while ESM2Mb did not produce a reliable AMO. CESM1 produces a weak negative trend in global fire emissions for the period 1920 to 2005, while ESM2Mb produces a positive trend over the same period. Both trends are statistically significant at a confidence level of 95% or greater given the variability derived from the respective preindustrial controls. In addition to climate variability impacts on fires, we also explore the impacts of fire emissions on climate variability and atmospheric chemistry. We analyze three long, free-evolving ESM2Mb simulations; one without fire emissions, one with constant year-over-year fire emissions based on a present day inventory, and one with interannually varying fire emissions coupled between the terrestrial and atmospheric components of the model, to gain a better understanding of the role of fire emissions in climate over long timescales.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1347844-cross-scale-intercomparison-climate-change-impacts-simulated-regional-global-hydrological-models-eleven-large-river-basins','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1347844-cross-scale-intercomparison-climate-change-impacts-simulated-regional-global-hydrological-models-eleven-large-river-basins"><span>Cross-scale intercomparison of climate change impacts simulated by regional and global hydrological models in eleven large river basins</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Hattermann, F. F.; Krysanova, V.; Gosling, S. N.</p> <p></p> <p>Ideally, the results from models operating at different scales should agree in trend direction and magnitude of impacts under climate change. However, this implies that the sensitivity of impact models designed for either scale to climate variability and change is comparable. In this study, we compare hydrological changes simulated by 9 global and 9 regional hydrological models (HM) for 11 large river basins in all continents under reference and scenario conditions. The foci are on model validation runs, sensitivity of annual discharge to climate variability in the reference period, and sensitivity of the long-term average monthly seasonal dynamics to climatemore » change. One major result is that the global models, mostly not calibrated against observations, often show a considerable bias in mean monthly discharge, whereas regional models show a much better reproduction of reference conditions. However, the sensitivity of two HM ensembles to climate variability is in general similar. The simulated climate change impacts in terms of long-term average monthly dynamics evaluated for HM ensemble medians and spreads show that the medians are to a certain extent comparable in some cases with distinct differences in others, and the spreads related to global models are mostly notably larger. Summarizing, this implies that global HMs are useful tools when looking at large-scale impacts of climate change and variability, but whenever impacts for a specific river basin or region are of interest, e.g. for complex water management applications, the regional-scale models validated against observed discharge should be used.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFMED51A0507D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFMED51A0507D"><span>Does the weather influence public opinion about climate change?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Donner, S. D.; McDaniel, J.</p> <p>2010-12-01</p> <p>Public opinion in North America about the science of anthropogenic climate change and the motivation for policy action has been variable over the past twenty years. The trends in public opinion over time have been attributed the general lack of pressing public concern about climate change to a range of political, economic and psychological factors. One driving force behind the variability in polling data from year to year may be the weather itself. The difference between what we “expect” - the climate - and what we “get” - the weather - can be a major source of confusion and obfuscation in the public discourse about climate change. For example, reaction to moderate global temperatures in 2007 and 2008 may have helped prompt the spread of a “global cooling” meme in the public and the news media. At the same time, a decrease in the belief in the science of climate change and the need for action has been noted in opinion polls. This study analyzes the relationship between public opinion about climate change and the weather in the U.S. since the mid-1980s using historical polling data from several major organizations (e.g. Gallup, Pew, Harris Interactive, ABC News), historical monthly air temperature (NCDC) and a survey of opinion articles from major U.S. newspapers (Washington Post, New York Times, Wall Street Journal, Houston Chronicle, USA Today). Seasonal and annual monthly temperature anomalies for the northeastern U.S and the continental U.S are compared with available national opinion data for three general categories of questions: i) Is the climate warming?, ii) Is the observed warming due to human activity?, and iii) Are you concerned about climate change? The variability in temperature and public opinion over time is also compared with the variability in the fraction of opinion articles in the newspapers (n ~ 7000) which express general agreement or disagreement with IPCC Summary for Policymakers consensus statements on climate change (“most of the observed increase in global average temperature is very likely to be due to the observed increase in anthropogenic greenhouse gas concentrations”). The results reveal a possible link between opinion leaders, particularly in the northeastern U.S, public confusion about the difference between weather and climate, and the evolution of U.S. public opinion about climate change.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_10 --> <div id="page_11" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="201"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC51C0816K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC51C0816K"><span>Modelling Spatial Dependence Structures Between Climate Variables by Combining Mixture Models with Copula Models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Khan, F.; Pilz, J.; Spöck, G.</p> <p>2017-12-01</p> <p>Spatio-temporal dependence structures play a pivotal role in understanding the meteorological characteristics of a basin or sub-basin. This further affects the hydrological conditions and consequently will provide misleading results if these structures are not taken into account properly. In this study we modeled the spatial dependence structure between climate variables including maximum, minimum temperature and precipitation in the Monsoon dominated region of Pakistan. For temperature, six, and for precipitation four meteorological stations have been considered. For modelling the dependence structure between temperature and precipitation at multiple sites, we utilized C-Vine, D-Vine and Student t-copula models. For temperature, multivariate mixture normal distributions and for precipitation gamma distributions have been used as marginals under the copula models. A comparison was made between C-Vine, D-Vine and Student t-copula by observational and simulated spatial dependence structure to choose an appropriate model for the climate data. The results show that all copula models performed well, however, there are subtle differences in their performances. The copula models captured the patterns of spatial dependence structures between climate variables at multiple meteorological sites, however, the t-copula showed poor performance in reproducing the dependence structure with respect to magnitude. It was observed that important statistics of observed data have been closely approximated except of maximum values for temperature and minimum values for minimum temperature. Probability density functions of simulated data closely follow the probability density functions of observational data for all variables. C and D-Vines are better tools when it comes to modelling the dependence between variables, however, Student t-copulas compete closely for precipitation. Keywords: Copula model, C-Vine, D-Vine, Spatial dependence structure, Monsoon dominated region of Pakistan, Mixture models, EM algorithm.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.A21B0034B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.A21B0034B"><span>Interactive influence of the Atlantic and Pacific climates and their contribution to the multidecadal variations of global temperature and precipitation.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Barcikowska, M. J.; Knutson, T. R.; Zhang, R.</p> <p>2016-12-01</p> <p>This study investigates mechanisms and global-scale climate impacts of multidecadal climate variability. Here we show, using observations and CSIRO-Mk3.6.0 model control run, that multidecadal variability of the Atlantic Meridional Overturning Circulation (AMOC) may have a profound impact on the thermal- and hydro-climatic changes over the Pacific region. In our model-based analysis we propose a mechanism, which comprises a coupled ocean-atmosphere teleconnection, established through the atmospheric overturning circulation cell between the tropical North Atlantic and tropical Pacific. For example, warming SSTs over the tropical North Atlantic intensify local convection and reinforce subsidence, low-level divergence in the eastern tropical Pacific. This is also accompanied with an intensification of trade winds, cooling and drying anomalies in the tropical central-east Pacific. The derived multidecadal changes, associated with the AMOC, contribute remarkably to the global temperature and precipitation variations. This highlights its potential predictive value. Shown here results suggest a possibility that: 1) recently observed slowdown in global warming may partly originate from internal variability, 2) climate system may be undergoing a transition to a cold AMO phase which could prolong the global slowdown.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=%22global+warming%22+OR+%22climate+change%22+AND+%22evidence%22&pg=6&id=EJ773926','ERIC'); return false;" href="https://eric.ed.gov/?q=%22global+warming%22+OR+%22climate+change%22+AND+%22evidence%22&pg=6&id=EJ773926"><span>A Cooperative Classroom Investigation of Climate Change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Constible, Juanita; Sandro, Luke; Lee, Richard E., Jr.</p> <p>2007-01-01</p> <p>Scientists have a particularly difficult time explaining warming trends in Antarctica--a region with a relatively short history of scientific observation and a highly variable climate (Clarke et al. 2007). Regardless of the mechanism of warming, however, climate change is having a dramatic impact on Antarctic ecosystems. In this article, the…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19910003164','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19910003164"><span>Comparison of Solar and Other Influences on Long-term Climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hansen, James E.; Lacis, Andrew A.; Ruedy, Reto A.</p> <p>1990-01-01</p> <p>Examples are shown of climate variability, and unforced climate fluctuations are discussed, as evidenced in both model simulations and observations. Then the author compares different global climate forcings, a comparison which by itself has significant implications. Finally, the author discusses a new climate simulation for the 1980s and 1990s which incorporates the principal known global climate forcings. The results indicate a likelihood of rapid global warming in the early 1990s.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26369503','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26369503"><span>Solar forcing synchronizes decadal North Atlantic climate variability.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Thiéblemont, Rémi; Matthes, Katja; Omrani, Nour-Eddine; Kodera, Kunihiko; Hansen, Felicitas</p> <p>2015-09-15</p> <p>Quasi-decadal variability in solar irradiance has been suggested to exert a substantial effect on Earth's regional climate. In the North Atlantic sector, the 11-year solar signal has been proposed to project onto a pattern resembling the North Atlantic Oscillation (NAO), with a lag of a few years due to ocean-atmosphere interactions. The solar/NAO relationship is, however, highly misrepresented in climate model simulations with realistic observed forcings. In addition, its detection is particularly complicated since NAO quasi-decadal fluctuations can be intrinsically generated by the coupled ocean-atmosphere system. Here we compare two multi-decadal ocean-atmosphere chemistry-climate simulations with and without solar forcing variability. While the experiment including solar variability simulates a 1-2-year lagged solar/NAO relationship, comparison of both experiments suggests that the 11-year solar cycle synchronizes quasi-decadal NAO variability intrinsic to the model. The synchronization is consistent with the downward propagation of the solar signal from the stratosphere to the surface.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017GMD....10.4563L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017GMD....10.4563L"><span>A method to encapsulate model structural uncertainty in ensemble projections of future climate: EPIC v1.0</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lewis, Jared; Bodeker, Greg E.; Kremser, Stefanie; Tait, Andrew</p> <p>2017-12-01</p> <p>A method, based on climate pattern scaling, has been developed to expand a small number of projections of fields of a selected climate variable (X) into an ensemble that encapsulates a wide range of indicative model structural uncertainties. The method described in this paper is referred to as the Ensemble Projections Incorporating Climate model uncertainty (EPIC) method. Each ensemble member is constructed by adding contributions from (1) a climatology derived from observations that represents the time-invariant part of the signal; (2) a contribution from forced changes in X, where those changes can be statistically related to changes in global mean surface temperature (Tglobal); and (3) a contribution from unforced variability that is generated by a stochastic weather generator. The patterns of unforced variability are also allowed to respond to changes in Tglobal. The statistical relationships between changes in X (and its patterns of variability) and Tglobal are obtained in a <q>training</q> phase. Then, in an <q>implementation</q> phase, 190 simulations of Tglobal are generated using a simple climate model tuned to emulate 19 different global climate models (GCMs) and 10 different carbon cycle models. Using the generated Tglobal time series and the correlation between the forced changes in X and Tglobal, obtained in the <q>training</q> phase, the forced change in the X field can be generated many times using Monte Carlo analysis. A stochastic weather generator is used to generate realistic representations of weather which include spatial coherence. Because GCMs and regional climate models (RCMs) are less likely to correctly represent unforced variability compared to observations, the stochastic weather generator takes as input measures of variability derived from observations, but also responds to forced changes in climate in a way that is consistent with the RCM projections. This approach to generating a large ensemble of projections is many orders of magnitude more computationally efficient than running multiple GCM or RCM simulations. Such a large ensemble of projections permits a description of a probability density function (PDF) of future climate states rather than a small number of individual story lines within that PDF, which may not be representative of the PDF as a whole; the EPIC method largely corrects for such potential sampling biases. The method is useful for providing projections of changes in climate to users wishing to investigate the impacts and implications of climate change in a probabilistic way. A web-based tool, using the EPIC method to provide probabilistic projections of changes in daily maximum and minimum temperatures for New Zealand, has been developed and is described in this paper.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JGRD..122.7922D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JGRD..122.7922D"><span>Sensitivity of the regional climate in the Middle East and North Africa to volcanic perturbations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dogar, Muhammad Mubashar; Stenchikov, Georgiy; Osipov, Sergey; Wyman, Bruce; Zhao, Ming</p> <p>2017-08-01</p> <p>The Middle East and North Africa (MENA) regional climate appears to be extremely sensitive to volcanic eruptions. Winter cooling after the 1991 Pinatubo eruption far exceeded the mean hemispheric temperature anomaly, even causing snowfall in Israel. To better understand MENA climate variability, the climate responses to the El Chichón and Pinatubo volcanic eruptions are analyzed using observations, NOAA/National Centers for Environmental Prediction Climate Forecast System Reanalysis, and output from the Geophysical Fluid Dynamics Laboratory's High-Resolution Atmospheric Model. A multiple regression analysis both for the observations and the model output is performed on seasonal summer and winter composites to separate out the contributions from climate trends, El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian summer monsoon, and volcanic aerosols. Strong regional temperature and precipitation responses over the MENA region are found in both winter and summer. The model and the observations both show that a positive NAO amplifies the MENA volcanic winter cooling. In boreal summer, the patterns of changing temperature and precipitation suggest a weakening and southward shift of the Intertropical Convergence Zone, caused by volcanic surface cooling and weakening of the Indian and West African monsoons. The model captures the main features of the climate response; however, it underestimates the total cooling, especially in winter, and exhibits a different spatial pattern of the NAO climate response in MENA compared to the observations. The conducted analysis sheds light on the internal mechanisms of MENA climate variability and helps to selectively diagnose the model deficiencies.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20140005478&hterms=climate+change&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dclimate%2Bchange','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20140005478&hterms=climate+change&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dclimate%2Bchange"><span>Achieving Climate Change Absolute Accuracy in Orbit</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wielicki, Bruce A.; Young, D. F.; Mlynczak, M. G.; Thome, K. J; Leroy, S.; Corliss, J.; Anderson, J. G.; Ao, C. O.; Bantges, R.; Best, F.; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20140005478'); toggleEditAbsImage('author_20140005478_show'); toggleEditAbsImage('author_20140005478_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20140005478_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20140005478_hide"></p> <p>2013-01-01</p> <p>The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission will provide a calibration laboratory in orbit for the purpose of accurately measuring and attributing climate change. CLARREO measurements establish new climate change benchmarks with high absolute radiometric accuracy and high statistical confidence across a wide range of essential climate variables. CLARREO's inherently high absolute accuracy will be verified and traceable on orbit to Système Internationale (SI) units. The benchmarks established by CLARREO will be critical for assessing changes in the Earth system and climate model predictive capabilities for decades into the future as society works to meet the challenge of optimizing strategies for mitigating and adapting to climate change. The CLARREO benchmarks are derived from measurements of the Earth's thermal infrared spectrum (5-50 micron), the spectrum of solar radiation reflected by the Earth and its atmosphere (320-2300 nm), and radio occultation refractivity from which accurate temperature profiles are derived. The mission has the ability to provide new spectral fingerprints of climate change, as well as to provide the first orbiting radiometer with accuracy sufficient to serve as the reference transfer standard for other space sensors, in essence serving as a "NIST [National Institute of Standards and Technology] in orbit." CLARREO will greatly improve the accuracy and relevance of a wide range of space-borne instruments for decadal climate change. Finally, CLARREO has developed new metrics and methods for determining the accuracy requirements of climate observations for a wide range of climate variables and uncertainty sources. These methods should be useful for improving our understanding of observing requirements for most climate change observations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26315724','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26315724"><span>Alternating high and low climate variability: The context of natural selection and speciation in Plio-Pleistocene hominin evolution.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Potts, Richard; Faith, J Tyler</p> <p>2015-10-01</p> <p>Interaction of orbital insolation cycles defines a predictive model of alternating phases of high- and low-climate variability for tropical East Africa over the past 5 million years. This model, which is described in terms of climate variability stages, implies repeated increases in landscape/resource instability and intervening periods of stability in East Africa. It predicts eight prolonged (>192 kyr) eras of intensified habitat instability (high variability stages) in which hominin evolutionary innovations are likely to have occurred, potentially by variability selection. The prediction that repeated shifts toward high climate variability affected paleoenvironments and evolution is tested in three ways. In the first test, deep-sea records of northeast African terrigenous dust flux (Sites 721/722) and eastern Mediterranean sapropels (Site 967A) show increased and decreased variability in concert with predicted shifts in climate variability. These regional measurements of climate dynamics are complemented by stratigraphic observations in five basins with lengthy stratigraphic and paleoenvironmental records: the mid-Pleistocene Olorgesailie Basin, the Plio-Pleistocene Turkana and Olduvai Basins, and the Pliocene Tugen Hills sequence and Hadar Basin--all of which show that highly variable landscapes inhabited by hominin populations were indeed concentrated in predicted stages of prolonged high climate variability. Second, stringent null-model tests demonstrate a significant association of currently known first and last appearance datums (FADs and LADs) of the major hominin lineages, suites of technological behaviors, and dispersal events with the predicted intervals of prolonged high climate variability. Palynological study in the Nihewan Basin, China, provides a third test, which shows the occupation of highly diverse habitats in eastern Asia, consistent with the predicted increase in adaptability in dispersing Oldowan hominins. Integration of fossil, archeological, sedimentary, and paleolandscape evidence illustrates the potential influence of prolonged high variability on the origin and spread of critical adaptations and lineages in the evolution of Homo. The growing body of data concerning environmental dynamics supports the idea that the evolution of adaptability in response to climate and overall ecological instability represents a unifying theme in hominin evolutionary history. Published by Elsevier Ltd.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4932881','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4932881"><span>Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Lorenz, David J.; Nieto-Lugilde, Diego; Blois, Jessica L.; Fitzpatrick, Matthew C.; Williams, John W.</p> <p>2016-01-01</p> <p>Increasingly, ecological modellers are integrating paleodata with future projections to understand climate-driven biodiversity dynamics from the past through the current century. Climate simulations from earth system models are necessary to this effort, but must be debiased and downscaled before they can be used by ecological models. Downscaling methods and observational baselines vary among researchers, which produces confounding biases among downscaled climate simulations. We present unified datasets of debiased and downscaled climate simulations for North America from 21 ka BP to 2100AD, at 0.5° spatial resolution. Temporal resolution is decadal averages of monthly data until 1950AD, average climates for 1950–2005 AD, and monthly data from 2010 to 2100AD, with decadal averages also provided. This downscaling includes two transient paleoclimatic simulations and 12 climate models for the IPCC AR5 (CMIP5) historical (1850–2005), RCP4.5, and RCP8.5 21st-century scenarios. Climate variables include primary variables and derived bioclimatic variables. These datasets provide a common set of climate simulations suitable for seamlessly modelling the effects of past and future climate change on species distributions and diversity. PMID:27377537</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27377537','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27377537"><span>Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Lorenz, David J; Nieto-Lugilde, Diego; Blois, Jessica L; Fitzpatrick, Matthew C; Williams, John W</p> <p>2016-07-05</p> <p>Increasingly, ecological modellers are integrating paleodata with future projections to understand climate-driven biodiversity dynamics from the past through the current century. Climate simulations from earth system models are necessary to this effort, but must be debiased and downscaled before they can be used by ecological models. Downscaling methods and observational baselines vary among researchers, which produces confounding biases among downscaled climate simulations. We present unified datasets of debiased and downscaled climate simulations for North America from 21 ka BP to 2100AD, at 0.5° spatial resolution. Temporal resolution is decadal averages of monthly data until 1950AD, average climates for 1950-2005 AD, and monthly data from 2010 to 2100AD, with decadal averages also provided. This downscaling includes two transient paleoclimatic simulations and 12 climate models for the IPCC AR5 (CMIP5) historical (1850-2005), RCP4.5, and RCP8.5 21st-century scenarios. Climate variables include primary variables and derived bioclimatic variables. These datasets provide a common set of climate simulations suitable for seamlessly modelling the effects of past and future climate change on species distributions and diversity.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.A23G0302K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.A23G0302K"><span>Data-driven Climate Modeling and Prediction</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kondrashov, D. A.; Chekroun, M.</p> <p>2016-12-01</p> <p>Global climate models aim to simulate a broad range of spatio-temporal scales of climate variability with state vector having many millions of degrees of freedom. On the other hand, while detailed weather prediction out to a few days requires high numerical resolution, it is fairly clear that a major fraction of large-scale climate variability can be predicted in a much lower-dimensional phase space. Low-dimensional models can simulate and predict this fraction of climate variability, provided they are able to account for linear and nonlinear interactions between the modes representing large scales of climate dynamics, as well as their interactions with a much larger number of modes representing fast and small scales. This presentation will highlight several new applications by Multilayered Stochastic Modeling (MSM) [Kondrashov, Chekroun and Ghil, 2015] framework that has abundantly proven its efficiency in the modeling and real-time forecasting of various climate phenomena. MSM is a data-driven inverse modeling technique that aims to obtain a low-order nonlinear system of prognostic equations driven by stochastic forcing, and estimates both the dynamical operator and the properties of the driving noise from multivariate time series of observations or a high-end model's simulation. MSM leads to a system of stochastic differential equations (SDEs) involving hidden (auxiliary) variables of fast-small scales ranked by layers, which interact with the macroscopic (observed) variables of large-slow scales to model the dynamics of the latter, and thus convey memory effects. New MSM climate applications focus on development of computationally efficient low-order models by using data-adaptive decomposition methods that convey memory effects by time-embedding techniques, such as Multichannel Singular Spectrum Analysis (M-SSA) [Ghil et al. 2002] and recently developed Data-Adaptive Harmonic (DAH) decomposition method [Chekroun and Kondrashov, 2016]. In particular, new results by DAH-MSM modeling and prediction of Arctic Sea Ice, as well as decadal predictions of near-surface Earth temperatures will be presented.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23281447','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23281447"><span>Climate change and occurrence of diarrheal diseases: evolving facts from Nepal.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Bhandari, G P; Gurung, S; Dhimal, M; Bhusal, C L</p> <p>2012-09-01</p> <p>Climate change is becoming huge threat to health especially for those from developing countries. Diarrhea as one of the major diseases linked with changing climate. This study has been carried out to assess the relationship between climatic variables, and malaria and to find out the range of non-climatic factors that can confound the relationship of climate change and human health. It is a Retrospective study where data of past ten years relating to climate and disease (diarrhea) variable were analyzed. The study conducted trend analysis based on correlation. The climate related data were obtained from Department of Hydrology and Meteorology. Time Series analysis was also being conducted. The trend of number of yearly cases of diarrhea has been increasing from 1998 to 2001 after which the cases remain constant till 2006.The climate types in Jhapa vary from humid to per-humid based on the moisture index and Mega-thermal based on thermal efficiency. The mean annual temperature is increasing at an average of 0.04 °C/year with maximum temperature increasing faster than the minimum temperature. The annual total rainfall of Jhapa is decreasing at an average rate of -7.1 mm/year. Statistically significant correlation between diarrheal cases occurrence and temperature and rainfall has been observed. However, climate variables were not the significant predictors of diarrheal occurrence. The association among climate variables and diarrheal disease occurrence cannot be neglected which has been showed by this study. Further prospective longitudinal study adjusting influence of non-climatic factors is recommended.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JESS..127...25B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JESS..127...25B"><span>Analysis of rainfall and temperature time series to detect long-term climatic trends and variability over semi-arid Botswana</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Byakatonda, Jimmy; Parida, B. P.; Kenabatho, Piet K.; Moalafhi, D. B.</p> <p>2018-03-01</p> <p>Arid and semi-arid environments have been identified with locations prone to impacts of climate variability and change. Investigating long-term trends is one way of tracing climate change impacts. This study investigates variability through annual and seasonal meteorological time series. Possible inhomogeneities and years of intervention are analysed using four absolute homogeneity tests. Trends in the climatic variables were determined using Mann-Kendall and Sen's Slope estimator statistics. Association of El Niño Southern Oscillation (ENSO) with local climate is also investigated through multivariate analysis. Results from the study show that rainfall time series are fully homogeneous with 78.6 and 50% of the stations for maximum and minimum temperature, respectively, showing homogeneity. Trends also indicate a general decrease of 5.8, 7.4 and 18.1% in annual, summer and winter rainfall, respectively. Warming trends are observed in annual and winter temperature at 0.3 and 1.5% for maximum temperature and 1.7 and 6.5% for minimum temperature, respectively. Rainfall reported a positive correlation with Southern Oscillation Index (SOI) and at the same time negative association with Sea Surface Temperatures (SSTs). Strong relationships between SSTs and maximum temperature are observed during the El Niño and La Niña years. These study findings could facilitate planning and management of agricultural and water resources in Botswana.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.4597C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.4597C"><span>Mapping the changing pattern of local climate as an observed distribution</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chapman, Sandra; Stainforth, David; Watkins, Nicholas</p> <p>2013-04-01</p> <p>It is at local scales that the impacts of climate change will be felt directly and at which adaptation planning decisions must be made. This requires quantifying the geographical patterns in trends at specific quantiles in distributions of variables such as daily temperature or precipitation. Here we focus on these local changes and on the way observational data can be analysed to inform us about the pattern of local climate change. We present a method[1] for analysing local climatic timeseries data to assess which quantiles of the local climatic distribution show the greatest and most robust trends. We demonstrate this approach using E-OBS gridded data[2] timeseries of local daily temperature from specific locations across Europe over the last 60 years. Our method extracts the changing cumulative distribution function over time and uses a simple mathematical deconstruction of how the difference between two observations from two different time periods can be assigned to the combination of natural statistical variability and/or the consequences of secular climate change. This deconstruction facilitates an assessment of the sensitivity of different quantiles of the distributions to changing climate. Geographical location and temperature are treated as independent variables, we thus obtain as outputs the pattern of variation in sensitivity with temperature (or occurrence likelihood), and with geographical location. We find as an output many regionally consistent patterns of response of potential value in adaptation planning. We discuss methods to quantify and map the robustness of these observed sensitivities and their statistical likelihood. This also quantifies the level of detail needed from climate models if they are to be used as tools to assess climate change impact. [1] S C Chapman, D A Stainforth, N W Watkins, 2013, On Estimating Local Long Term Climate Trends, Phil. Trans. R. Soc. A, in press [2] Haylock, M.R., N. Hofstra, A.M.G. Klein Tank, E.J. Klok, P.D. Jones and M. New. 2008: A European daily high-resolution gridded dataset of surface temperature and precipitation. J. Geophys. Res (Atmospheres), 113, D20119, doi:10.1029/2008JD10201</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29133863','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29133863"><span>A real-time Global Warming Index.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Haustein, K; Allen, M R; Forster, P M; Otto, F E L; Mitchell, D M; Matthews, H D; Frame, D J</p> <p>2017-11-13</p> <p>We propose a simple real-time index of global human-induced warming and assess its robustness to uncertainties in climate forcing and short-term climate fluctuations. This index provides improved scientific context for temperature stabilisation targets and has the potential to decrease the volatility of climate policy. We quantify uncertainties arising from temperature observations, climate radiative forcings, internal variability and the model response. Our index and the associated rate of human-induced warming is compatible with a range of other more sophisticated methods to estimate the human contribution to observed global temperature change.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ClDy..tmp..379M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ClDy..tmp..379M"><span>Statistical link between external climate forcings and modes of ocean variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Malik, Abdul; Brönnimann, Stefan; Perona, Paolo</p> <p>2017-07-01</p> <p>In this study we investigate statistical link between external climate forcings and modes of ocean variability on inter-annual (3-year) to centennial (100-year) timescales using de-trended semi-partial-cross-correlation analysis technique. To investigate this link we employ observations (AD 1854-1999), climate proxies (AD 1600-1999), and coupled Atmosphere-Ocean-Chemistry Climate Model simulations with SOCOL-MPIOM (AD 1600-1999). We find robust statistical evidence that Atlantic multi-decadal oscillation (AMO) has intrinsic positive correlation with solar activity in all datasets employed. The strength of the relationship between AMO and solar activity is modulated by volcanic eruptions and complex interaction among modes of ocean variability. The observational dataset reveals that El Niño southern oscillation (ENSO) has statistically significant negative intrinsic correlation with solar activity on decadal to multi-decadal timescales (16-27-year) whereas there is no evidence of a link on a typical ENSO timescale (2-7-year). In the observational dataset, the volcanic eruptions do not have a link with AMO on a typical AMO timescale (55-80-year) however the long-term datasets (proxies and SOCOL-MPIOM output) show that volcanic eruptions have intrinsic negative correlation with AMO on inter-annual to multi-decadal timescales. The Pacific decadal oscillation has no link with solar activity, however, it has positive intrinsic correlation with volcanic eruptions on multi-decadal timescales (47-54-year) in reconstruction and decadal to multi-decadal timescales (16-32-year) in climate model simulations. We also find evidence of a link between volcanic eruptions and ENSO, however, the sign of relationship is not consistent between observations/proxies and climate model simulations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19820014259','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19820014259"><span>Solar diameter measurements for study of Sun climate coupling</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hill, H. A.</p> <p>1981-01-01</p> <p>Variability in solar shape and diameters was examined as a possible probe of an important climatic driving function, solar luminosity variability. The techniques and facilities developed for measuring the solar diameter were used. The observing program and the requisite data reduction were conducted simultaneously. The development of a technique to calibrate the scale in the telescope field progressed to the design and construction phase.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..1212851B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1212851B"><span>Establishing a baseline precipitation and temperature regime for the Guianas from observations and reanalysis data</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bovolo, C. Isabella; Pereira, Ryan; Parkin, Geoff; Wagner, Thomas</p> <p>2010-05-01</p> <p>The tropical rainforests of the Guianas, north of the Amazon, are home to several Amerindian communities, hold high levels of biodiversity and, importantly, remain some of the world's most pristine and intact rainforests. Not only do they have important functions in the global carbon cycle, but they regulate the local and regional climate and help generate rain over vast distances. Despite their significance however, the climate and hydrology of this region is poorly understood. It is important to establish the current climate regime of the area as a baseline against which any impacts of future climate change or deforestation can be measured but observed historical climate datasets are generally sparse and of low quality. Here we examine the available precipitation and temperature datasets for the region and derive tentative precipitation and temperature maps focussed on Guyana. To overcome the limitations in the inadequate observational data coverage we also make use of a reanalysis dataset from the European Centre for Medium-range Weather Forecasts (ECMWF). The ECMWF ERA40 dataset comprises a spatially consistent global historical climate for the period 1957-2002 at a ~125 km2 (1.125 degree) resolution at the equator and is particularly valuable for establishing the climate of data-poor areas. Once validated for the area of interest, ERA40 is used to determine the precipitation and temperature regime of the Guianas. Grid-cell by grid-cell analysis provides a complete picture of spatial patterns of averaged monthly precipitation variability across the area, vital for establishing a basis from which to compare any future effects of climate change. This is the first comprehensive study of the recent historical climate and its variability in this area, placing a new hydroclimate monitoring and research program at the Iwokrama International Centre for Rainforest Conservation and Development, Guyana, into the broader climate context. Mean differences (biases) and annual average spatial correlations are examined between modelled ERA40 and observed time series comparing the seasonal cycles and the yearly, monthly and monthly anomaly time series. This is to evaluate if the reanalysis data correctly reproduces the areally averaged observed mean annual precipitation, interannual variability and seasonal precipitation cycle over the region. Results show that reanalysis precipitation for the region compares favourably with areally averaged observations where available, although the model underestimates precipitation in some zones of higher elevation. Also ERA40 data is slightly positively biased along the coast and negatively biased inland. Comparisons between observed and modelled data show that although correlations of annual time series are low (<0.6), correlations of monthly time series reach 0.8 demonstrating that the model captures much of the seasonal variation in precipitation. However correlations between monthly precipitation anomalies, where the averaged seasonal cycle has been removed from the comparison, are lower (< 0.6). As precipitation observations are not assimilated into the reanalysis these results provide a good validation of model performance. The seasonal cycle of precipitation is found to be highly variable across the region. Two wet-seasons (June and December) occur in northern Guyana which relate to the twice yearly passage of the inter-tropical convergence zone whereas a single wet season (April-August) occurs in the savannah zone, which stretches from Venezuela through the southern third of Guyana. The climate transition zone lies slightly north of the distinctive forest-savannah boundary which suggests that the boundary may be highly sensitive to future alterations in climate, such as those due to climate change or deforestation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1252621-relative-contributions-mean-state-shifts-enso-driven-variability-precipitation-changes-warming-climate','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1252621-relative-contributions-mean-state-shifts-enso-driven-variability-precipitation-changes-warming-climate"><span>Relative contributions of mean-state shifts and ENSO-driven variability to precipitation changes in a warming climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Bonfils, Celine J. W.; Santer, Benjamin D.; Phillips, Thomas J.; ...</p> <p>2015-12-18</p> <p>The El Niño–Southern Oscillation (ENSO) is an important driver of regional hydroclimate variability through far-reaching teleconnections. This study uses simulations performed with coupled general circulation models (CGCMs) to investigate how regional precipitation in the twenty-first century may be affected by changes in both ENSO-driven precipitation variability and slowly evolving mean rainfall. First, a dominant, time-invariant pattern of canonical ENSO variability (cENSO) is identified in observed SST data. Next, the fidelity with which 33 state-of-the-art CGCMs represent the spatial structure and temporal variability of this pattern (as well as its associated precipitation responses) is evaluated in simulations of twentieth-century climate change.more » Possible changes in both the temporal variability of this pattern and its associated precipitation teleconnections are investigated in twenty-first-century climate projections. Models with better representation of the observed structure of the cENSO pattern produce winter rainfall teleconnection patterns that are in better accord with twentieth-century observations and more stationary during the twenty-first century. Finally, the model-predicted twenty-first-century rainfall response to cENSO is decomposed into the sum of three terms: 1) the twenty-first-century change in the mean state of precipitation, 2) the historical precipitation response to the cENSO pattern, and 3) a future enhancement in the rainfall response to cENSO, which amplifies rainfall extremes. Lastly, by examining the three terms jointly, this conceptual framework allows the identification of regions likely to experience future rainfall anomalies that are without precedent in the current climate.« less</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_11 --> <div id="page_12" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="221"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70170590','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70170590"><span>Direct observations of ice seasonality reveal changes in climate over the past 320–570 years</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Sharma, Sapna; Magnuson, John J.; Batt, Ryan D.; Winslow, Luke; Korhonen, Johanna; Yasuyuki Aono,</p> <p>2016-01-01</p> <p>Lake and river ice seasonality (dates of ice freeze and breakup) responds sensitively to climatic change and variability. We analyzed climate-related changes using direct human observations of ice freeze dates (1443–2014) for Lake Suwa, Japan, and of ice breakup dates (1693–2013) for Torne River, Finland. We found a rich array of changes in ice seasonality of two inland waters from geographically distant regions: namely a shift towards later ice formation for Suwa and earlier spring melt for Torne, increasing frequencies of years with warm extremes, changing inter-annual variability, waning of dominant inter-decadal quasi-periodic dynamics, and stronger correlations of ice seasonality with atmospheric CO2 concentration and air temperature after the start of the Industrial Revolution. Although local factors, including human population growth, land use change, and water management influence Suwa and Torne, the general patterns of ice seasonality are similar for both systems, suggesting that global processes including climate change and variability are driving the long-term changes in ice seasonality.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4844970','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4844970"><span>Direct observations of ice seasonality reveal changes in climate over the past 320–570 years</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Sharma, Sapna; Magnuson, John J.; Batt, Ryan D.; Winslow, Luke A.; Korhonen, Johanna; Aono, Yasuyuki</p> <p>2016-01-01</p> <p>Lake and river ice seasonality (dates of ice freeze and breakup) responds sensitively to climatic change and variability. We analyzed climate-related changes using direct human observations of ice freeze dates (1443–2014) for Lake Suwa, Japan, and of ice breakup dates (1693–2013) for Torne River, Finland. We found a rich array of changes in ice seasonality of two inland waters from geographically distant regions: namely a shift towards later ice formation for Suwa and earlier spring melt for Torne, increasing frequencies of years with warm extremes, changing inter-annual variability, waning of dominant inter-decadal quasi-periodic dynamics, and stronger correlations of ice seasonality with atmospheric CO2 concentration and air temperature after the start of the Industrial Revolution. Although local factors, including human population growth, land use change, and water management influence Suwa and Torne, the general patterns of ice seasonality are similar for both systems, suggesting that global processes including climate change and variability are driving the long-term changes in ice seasonality. PMID:27113125</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1352763','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1352763"><span>Testing a land model in ecosystem functional space via a comparison of observed and modeled ecosystem flux responses to precipitation regimes and associated stresses in a Central U.S. forest: Test Model in Ecosystem Functional Space</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Gu, Lianhong; Pallardy, Stephen G.; Yang, Bai</p> <p></p> <p>Testing complex land surface models has often proceeded by asking the question: does the model prediction agree with the observation? This approach has yet led to high-performance terrestrial models that meet the challenges of climate and ecological studies. Here we test the Community Land Model (CLM) by asking the question: does the model behave like an ecosystem? We pursue its answer by testing CLM in the ecosystem functional space (EFS) at the Missouri Ozark AmeriFlux (MOFLUX) forest site in the Central U.S., focusing on carbon and water flux responses to precipitation regimes and associated stresses. In the observed EFS, precipitationmore » regimes and associated water and heat stresses controlled seasonal and interannual variations of net ecosystem exchange (NEE) of CO 2 and evapotranspiration in this deciduous forest ecosystem. Such controls were exerted more strongly by precipitation variability than by the total precipitation amount per se. A few simply constructed climate variability indices captured these controls, suggesting a high degree of potential predictability. While the interannual fluctuation in NEE was large, a net carbon sink was maintained even during an extreme drought year. Although CLM predicted seasonal and interanual variations in evapotranspiration reasonably well, its predictions of net carbon uptake were too small across the observed range of climate variability. Also, the model systematically underestimated the sensitivities of NEE and evapotranspiration to climate variability and overestimated the coupling strength between carbon and water fluxes. Its suspected that the modeled and observed trajectories of ecosystem fluxes did not overlap in the EFS and the model did not behave like the ecosystem it attempted to simulate. A definitive conclusion will require comprehensive parameter and structural sensitivity tests in a rigorous mathematical framework. We also suggest that future model improvements should focus on better representation and parameterization of process responses to environmental stresses and on more complete and robust representations of carbon-specific processes so that adequate responses to climate variability and a proper degree of coupling between carbon and water exchanges are captured.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1352763-testing-land-model-ecosystem-functional-space-via-comparison-observed-modeled-ecosystem-flux-responses-precipitation-regimes-associated-stresses-central-forest-test-model-ecosystem-functional-space','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1352763-testing-land-model-ecosystem-functional-space-via-comparison-observed-modeled-ecosystem-flux-responses-precipitation-regimes-associated-stresses-central-forest-test-model-ecosystem-functional-space"><span>Testing a land model in ecosystem functional space via a comparison of observed and modeled ecosystem flux responses to precipitation regimes and associated stresses in a Central U.S. forest: Test Model in Ecosystem Functional Space</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Gu, Lianhong; Pallardy, Stephen G.; Yang, Bai; ...</p> <p>2016-07-14</p> <p>Testing complex land surface models has often proceeded by asking the question: does the model prediction agree with the observation? This approach has yet led to high-performance terrestrial models that meet the challenges of climate and ecological studies. Here we test the Community Land Model (CLM) by asking the question: does the model behave like an ecosystem? We pursue its answer by testing CLM in the ecosystem functional space (EFS) at the Missouri Ozark AmeriFlux (MOFLUX) forest site in the Central U.S., focusing on carbon and water flux responses to precipitation regimes and associated stresses. In the observed EFS, precipitationmore » regimes and associated water and heat stresses controlled seasonal and interannual variations of net ecosystem exchange (NEE) of CO 2 and evapotranspiration in this deciduous forest ecosystem. Such controls were exerted more strongly by precipitation variability than by the total precipitation amount per se. A few simply constructed climate variability indices captured these controls, suggesting a high degree of potential predictability. While the interannual fluctuation in NEE was large, a net carbon sink was maintained even during an extreme drought year. Although CLM predicted seasonal and interanual variations in evapotranspiration reasonably well, its predictions of net carbon uptake were too small across the observed range of climate variability. Also, the model systematically underestimated the sensitivities of NEE and evapotranspiration to climate variability and overestimated the coupling strength between carbon and water fluxes. Its suspected that the modeled and observed trajectories of ecosystem fluxes did not overlap in the EFS and the model did not behave like the ecosystem it attempted to simulate. A definitive conclusion will require comprehensive parameter and structural sensitivity tests in a rigorous mathematical framework. We also suggest that future model improvements should focus on better representation and parameterization of process responses to environmental stresses and on more complete and robust representations of carbon-specific processes so that adequate responses to climate variability and a proper degree of coupling between carbon and water exchanges are captured.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..12.5847K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..12.5847K"><span>Pollen-based reconstruction of Holocene climate variability in the Eifel region evaluated with stable isotopes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kühl, Norbert; Moschen, Robert; Wagner, Stefanie</p> <p>2010-05-01</p> <p>Pollen as well as stable isotopes have great potential as climate proxy data. While variability in these proxy data is frequently assumed to reflect climate variability, other factors than climate, including human impact and statistical noise, can often not be excluded as primary cause for the observed variability. Multiproxy studies offer the opportunity to test different drivers by providing different lines of evidence for environmental change such as climate variability and human impact. In this multiproxy study we use pollen and peat humification to evaluate to which extent stable oxygen and carbon isotope series from the peat bog "Dürres Maar" reflect human impact rather than climate variability. For times before strong anthropogenic vegetation change, isotope series from Dürres Maar were used to validate quantitative reconstructions based on pollen. Our study site is the kettle hole peat bog "Dürres Maar" in the Eifel low mountain range, Germany (450m asl), which grew 12m during the last 10,000 years. Pollen was analysed with a sum of at least 1000 terrestrial pollen grains throughout the profile to minimize statistical effects on the reconstructions. A recently developed probabilistic indicator taxa method ("pdf-method") was used for the quantitative climate estimates (January and July temperature) based on pollen. For isotope analysis, attention was given to use monospecific Sphagnum leaves whenever possible, reducing the potential of a species effect and any potential artefact that can originate from selective degradation of different morphological parts of Sphagnum plants (Moschen et al., 2009). Pollen at "Dürres Maar" reflect the variable and partly strong human impact on vegetation during the last 4000 years. Stable isotope time series were apparently not influenced by human impact at this site. This highlights the potential of stable isotope investigations from peat for climatic interpretation, because stable isotope series from lacustrine sediments might strongly react to anthropogenic deforestation, as carbon isotope time series from the adjacent Lake Holzmaar suggest. Reconstructions based on pollen with the pdf-method are robust to the human impact during the last 4000 years, but do not reproduce the fine scale climate variability that can be derived from the stable isotope series (Kühl et al., in press). In contrast, reconstructions on the basis of pollen data show relatively pronounced climate variability (here: January temperature) during the Mid-Holocene, which is known from many other European records. The oxygen isotope time series as available now indicate that at least some of the observed variability indeed reflects climate variability. However, stable carbon isotopes show little concordance. At this stage our results point in the direction that 1) the isotopic composition might reflect a shift in influencing factors during the Holocene, 2) climate trends can robustly be reconstructed with the pdf method and 3) fine scale climate variability can potentially be reconstructed using the pdf-method, given that climate sensitive taxa at their distribution limit are present. The latter two conclusions are of particular importance for the reconstruction of climatic trends and variability of interglacials older than the Holocene, when sites are rare and pollen is often the only suitable proxy in terrestrial records. Kühl, N., Moschen, R., Wagner, S., Brewer, S., Peyron, O., in press. A multiproxy record of Late Holocene natural and anthropogenic environmental change from the Sphagnum peat bog Dürres Maar, Germany: implications for quantitative climate reconstructions based on pollen. J. Quat. Sci., DOI: 10.1002/jqs.1342. Available online. Moschen, R., Kühl, N., Rehberger, I., Lücke, A., 2009. Stable carbon and oxygen isotopes in sub-fossil Sphagnum: Assessment of their applicability for palaeoclimatology. Chemical Geology 259, 262-272.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC41F..01B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC41F..01B"><span>The Grand Challenges of WCRP and the Climate Observing System of the Future</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Brasseur, G. P.</p> <p>2017-12-01</p> <p>The successful implementation the Paris agreement on climate change (COP21) calls for a well-designed global monitoring system of essential climate variables, climate processes and Earth system budgets. The Grand Challenges implemented by the World Climate Research Programme (WCRP) provide an opportunity to investigate issues of high societal relevance, directly related to sea level rise, droughts, floods, extreme heat events, food security, and fresh water availability. These challenges would directly benefit from a well-designed suite of systematic climate observations. Quantification of the evolution of the global energy, water and carbon budgets as well as the development and the production of near-term and regional climate predictions require that a comprehensive, focused, multi-platform observing system (satellites, ground-based and in situ observations) be established in an international context. This system must be accompanied by the development of climate services that should translate and disseminate scientific outcomes as actionable information for users and stakeholders.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70048449','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70048449"><span>Forecasting conditional climate-change using a hybrid approach</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Esfahani, Akbar Akbari; Friedel, Michael J.</p> <p>2014-01-01</p> <p>A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on self-similarity in quantile trends using the fractionally differenced auto-regressive integrated moving average technique. The proposed modeling approach is applied to states (Arizona, California, Colorado, Nevada, New Mexico, and Utah) in the southwestern U.S., where conditional forecasts of climate-change variables are evaluated against recent (2012) observations, evaluated at a future time period (2030), and evaluated as future trends (2009–2059). These results have broad economic, political, and social implications because they quantify uncertainty in climate-change forecasts affecting various sectors of society. Another benefit of the proposed hybrid approach is that it can be extended to any spatiotemporal scale providing self-similarity exists.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1916891M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1916891M"><span>Studying Climate Response to Forcing by the Nonlinear Dynamical Mode Decomposition</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mukhin, Dmitry; Gavrilov, Andrey; Loskutov, Evgeny; Feigin, Alexander</p> <p>2017-04-01</p> <p>An analysis of global climate response to external forcing, both anthropogenic (mainly, CO2 and aerosol) and natural (solar and volcanic), is needed for adequate predictions of global climate change. Being complex dynamical system, the climate reacts to external perturbations exciting feedbacks (both positive and negative) making the response non-trivial and poorly predictable. Thus an extraction of internal modes of climate system, investigation of their interaction with external forcings and further modeling and forecast of their dynamics, are all the problems providing the success of climate modeling. In the report the new method for principal mode extraction from climate data is presented. The method is based on the Nonlinear Dynamical Mode (NDM) expansion [1,2], but takes into account a number of external forcings applied to the system. Each NDM is represented by hidden time series governing the observed variability, which, together with external forcing time series, are mapped onto data space. While forcing time series are considered to be known, the hidden unknown signals underlying the internal climate dynamics are extracted from observed data by the suggested method. In particular, it gives us an opportunity to study the evolution of principal system's mode structure in changing external conditions and separate the internal climate variability from trends forced by external perturbations. Furthermore, the modes so obtained can be extrapolated beyond the observational time series, and long-term prognosis of modes' structure including characteristics of interconnections and responses to external perturbations, can be carried out. In this work the method is used for reconstructing and studying the principal modes of climate variability on inter-annual and decadal time scales accounting the external forcings such as anthropogenic emissions, variations of the solar activity and volcanic activity. The structure of the obtained modes as well as their response to external factors, e.g. forecast their change in 21 century under different CO2 emission scenarios, are discussed. [1] Mukhin, D., Gavrilov, A., Feigin, A., Loskutov, E., & Kurths, J. (2015). Principal nonlinear dynamical modes of climate variability. Scientific Reports, 5, 15510. http://doi.org/10.1038/srep15510 [2] Gavrilov, A., Mukhin, D., Loskutov, E., Volodin, E., Feigin, A., & Kurths, J. (2016). Method for reconstructing nonlinear modes with adaptive structure from multidimensional data. Chaos: An Interdisciplinary Journal of Nonlinear Science, 26(12), 123101. http://doi.org/10.1063/1.4968852</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A11N0269L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A11N0269L"><span>The Climate Variability & Predictability (CVP) Program at NOAA - Recent Program Advancements</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lucas, S. E.; Todd, J. F.</p> <p>2015-12-01</p> <p>The Climate Variability & Predictability (CVP) Program supports research aimed at providing process-level understanding of the climate system through observation, modeling, analysis, and field studies. This vital knowledge is needed to improve climate models and predictions so that scientists can better anticipate the impacts of future climate variability and change. To achieve its mission, the CVP Program supports research carried out at NOAA and other federal laboratories, NOAA Cooperative Institutes, and academic institutions. The Program also coordinates its sponsored projects with major national and international scientific bodies including the World Climate Research Programme (WCRP), the International and U.S. Climate Variability and Predictability (CLIVAR/US CLIVAR) Program, and the U.S. Global Change Research Program (USGCRP). The CVP program sits within NOAA's Climate Program Office (http://cpo.noaa.gov/CVP). The CVP Program currently supports multiple projects in areas that are aimed at improved representation of physical processes in global models. Some of the topics that are currently funded include: i) Improved Understanding of Intraseasonal Tropical Variability - DYNAMO field campaign and post -field projects, and the new climate model improvement teams focused on MJO processes; ii) Climate Process Teams (CPTs, co-funded with NSF) with projects focused on Cloud macrophysical parameterization and its application to aerosol indirect effects, and Internal-Wave Driven Mixing in Global Ocean Models; iii) Improved Understanding of Tropical Pacific Processes, Biases, and Climatology; iv) Understanding Arctic Sea Ice Mechanism and Predictability;v) AMOC Mechanisms and Decadal Predictability Recent results from CVP-funded projects will be summarized. Additional information can be found at http://cpo.noaa.gov/CVP.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUOSPO44A3119H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUOSPO44A3119H"><span>Forced Atlantic Multidecadal Variability Over the Past Millennium</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Halloran, P. R.; Reynolds, D.; Scourse, J. D.; Hall, I. R.</p> <p>2016-02-01</p> <p>Paul R. Halloran, David J. Reynolds, Ian R. Hall and James D. Scourse Multidecadal variability in Atlantic sea surface temperatures (SSTs) plays a first order role in determining regional atmospheric circulation and moisture transport, with major climatic consequences. These regional climate impacts range from drought in the Sahel and South America, though increased hurricane activity and temperature extremes, to modified monsoonal rainfall. Multidecadal Atlantic SST variability could arise through internal variability in the Atlantic Meridional Overturning Circulation (AMOC) (e.g., Knight et al., 2006), or through externally forced change (e.g. Booth et al., 2012). It is critical that we know whether internal or external forcing dominates if we are to provide useful near-term climate projections in the Atlantic region. A persuasive argument that internal variability plays an important role in Atlantic Multidecadal Variability is that periodic SST variability has been observed throughout much of the last millennium (Mann et al., 2009), and the hypothesized external forcing of historical Atlantic Multidecadal Variability (Booth et al., 2012) is largely anthropogenic in origin. Here we combine the first annually-resolved millennial marine reconstruction with multi-model analysis, to show that the Atlantic SST variability of the last millennium can be explained by a combination of direct volcanic forcing, and indirect, forced, AMOC variability. Our results indicate that whilst climate models capture the timing of both the directly forced SST and forced AMOC-mediated SST variability, the models fail to capture the magnitude of the forced AMOC change. Does this mean that models underestimate the 21st century reduction in AMOC strength? J. Knight, C. Folland and A. Scaife., Climate impacts of the Atlantic Multidecadal Oscillation, GRL, 2006 B.B.B Booth, N. Dunstone, P.R. Halloran et al., Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability, Nature, 2012 M.E. Mann, Z. Zhang, S. Rutherford et al., Global Signatures and Dynamical Origins of the Little Ice Age and Medieval Climate Anomaly, Science, 2009</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19886481','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19886481"><span>Interannual variation of carbon fluxes from three contrasting evergreen forests: the role of forest dynamics and climate.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Sierra, Carlos A; Loescher, Henry W; Harmon, Mark E; Richardson, Andrew D; Hollinger, David Y; Perakis, Steven S</p> <p>2009-10-01</p> <p>Interannual variation of carbon fluxes can be attributed to a number of biotic and abiotic controls that operate at different spatial and temporal scales. Type and frequency of disturbance, forest dynamics, and climate regimes are important sources of variability. Assessing the variability of carbon fluxes from these specific sources can enhance the interpretation of past and current observations. Being able to separate the variability caused by forest dynamics from that induced by climate will also give us the ability to determine if the current observed carbon fluxes are within an expected range or whether the ecosystem is undergoing unexpected change. Sources of interannual variation in ecosystem carbon fluxes from three evergreen ecosystems, a tropical, a temperate coniferous, and a boreal forest, were explored using the simulation model STANDCARB. We identified key processes that introduced variation in annual fluxes, but their relative importance differed among the ecosystems studied. In the tropical site, intrinsic forest dynamics contributed approximately 30% of the total variation in annual carbon fluxes. In the temperate and boreal sites, where many forest processes occur over longer temporal scales than those at the tropical site, climate controlled more of the variation among annual fluxes. These results suggest that climate-related variability affects the rates of carbon exchange differently among sites. Simulations in which temperature, precipitation, and radiation varied from year to year (based on historical records of climate variation) had less net carbon stores than simulations in which these variables were held constant (based on historical records of monthly average climate), a result caused by the functional relationship between temperature and respiration. This suggests that, under a more variable temperature regime, large respiratory pulses may become more frequent and high enough to cause a reduction in ecosystem carbon stores. Our results also show that the variation of annual carbon fluxes poses an important challenge in our ability to determine whether an ecosystem is a source, a sink, or is neutral in regard to CO2 at longer timescales. In simulations where climate change negatively affected ecosystem carbon stores, there was a 20% chance of committing Type II error, even with 20 years of sequential data.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060030240&hterms=ocean+climate+changes&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Docean%2Bclimate%2Bchanges','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060030240&hterms=ocean+climate+changes&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Docean%2Bclimate%2Bchanges"><span>Ocean state estimation for climate studies</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lee, T.</p> <p>2002-01-01</p> <p>Climate variabilities, which are of interest to CLIVAR, involve a broad range of spatial and temporal scales. Ocean state estimation (often referred to as ocean data assimilation), by optimally combining observations and models, becomes an important element of CLIVAR.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29375800','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29375800"><span>Shifts in frog size and phenology: Testing predictions of climate change on a widespread anuran using data from prior to rapid climate warming.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Sheridan, Jennifer A; Caruso, Nicholas M; Apodaca, Joseph J; Rissler, Leslie J</p> <p>2018-01-01</p> <p>Changes in body size and breeding phenology have been identified as two major ecological consequences of climate change, yet it remains unclear whether climate acts directly or indirectly on these variables. To better understand the relationship between climate and ecological changes, it is necessary to determine environmental predictors of both size and phenology using data from prior to the onset of rapid climate warming, and then to examine spatially explicit changes in climate, size, and phenology, not just general spatial and temporal trends. We used 100 years of natural history collection data for the wood frog, Lithobates sylvaticus with a range >9 million km 2 , and spatially explicit environmental data to determine the best predictors of size and phenology prior to rapid climate warming (1901-1960). We then tested how closely size and phenology changes predicted by those environmental variables reflected actual changes from 1961 to 2000. Size, phenology, and climate all changed as expected (smaller, earlier, and warmer, respectively) at broad spatial scales across the entire study range. However, while spatially explicit changes in climate variables accurately predicted changes in phenology, they did not accurately predict size changes during recent climate change (1961-2000), contrary to expectations from numerous recent studies. Our results suggest that changes in climate are directly linked to observed phenological shifts. However, the mechanisms driving observed body size changes are yet to be determined, given the less straightforward relationship between size and climate factors examined in this study. We recommend that caution be used in "space-for-time" studies where measures of a species' traits at lower latitudes or elevations are considered representative of those under future projected climate conditions. Future studies should aim to determine mechanisms driving trends in phenology and body size, as well as the impact of climate on population density, which may influence body size.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25157192','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25157192"><span>Slow science: the value of long ocean biogeochemistry records.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Henson, Stephanie A</p> <p>2014-09-28</p> <p>Sustained observations (SOs) have provided invaluable information on the ocean's biology and biogeochemistry for over 50 years. They continue to play a vital role in elucidating the functioning of the marine ecosystem, particularly in the light of ongoing climate change. Repeated, consistent observations have provided the opportunity to resolve temporal and/or spatial variability in ocean biogeochemistry, which has driven exploration of the factors controlling biological parameters and processes. Here, I highlight some of the key breakthroughs in biological oceanography that have been enabled by SOs, which include areas such as trophic dynamics, understanding variability, improved biogeochemical models and the role of ocean biology in the global carbon cycle. In the near future, SOs are poised to make progress on several fronts, including detecting climate change effects on ocean biogeochemistry, high-resolution observations of physical-biological interactions and greater observational capability in both the mesopelagic zone and harsh environments, such as the Arctic. We are now entering a new era for biological SOs, one in which our motivations have evolved from the need to acquire basic understanding of the ocean's state and variability, to a need to understand ocean biogeochemistry in the context of increasing pressure in the form of climate change, overfishing and eutrophication.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003E%26PSL.210..437R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003E%26PSL.210..437R"><span>A mid-twentieth century reduction in tropical upwelling inferred from coralline trace element proxies</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Reuer, Matthew K.; Boyle, Edward A.; Cole, Julia E.</p> <p>2003-05-01</p> <p>The Cariaco Basin is an important archive of past climate variability given its response to inter- and extratropical climate forcing and the accumulation of annually laminated sediments within an anoxic water column. This study presents high-resolution surface coral trace element records ( Montastrea annularis and Siderastrea siderea) from Isla Tortuga, Venezuela, located within the upwelling center of this region. A two-fold reduction in Cd/Ca ratios (3.5-1.7 nmol/mol) is observed from 1946 to 1952 with no concurrent shift in Ba/Ca ratios. This reduction agrees with the hydrographic distribution of dissolved cadmium and barium and their expected response to upwelling. Significant anthropogenic variability is also observed from Pb/Ca analysis, observing three lead maxima since 1920. Kinetic control of trace element ratios is inferred from an interspecies comparison of Cd/Ca and Ba/Ca ratios (consistent with the Sr/Ca kinetic artifact), but these artifacts are smaller than the environmental signal and do not explain the Cd/Ca transition. The trace element records agree with historical climate data and differ from sedimentary faunal abundance records, suggesting a linear response to North Atlantic extratropical forcing cannot account for the observed historical variability in this region.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014IJBm...58.1021L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014IJBm...58.1021L"><span>Temporal changes in climatic variables and their impact on crop yields in southwestern China</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, Hong-Bin; Gou, Yu; Wang, Hong-Ye; Li, Hong-Mei; Wu, Wei</p> <p>2014-08-01</p> <p>Knowledge of variability in climatic variables changes and its impact on crop yields is important for farmers and policy makers, especially in southwestern China where rainfed agriculture is dominant. In the current study, six climatic parameters (mean temperature, rainfall, relative humidity, sunshine hours, temperature difference, and rainy days) and aggregated yields of three main crops (rice: Oryza sativa L., oilseed rape: Brassica napus L., and tobacco: Nicotiana tabacum L.) during 1985-2010 were collected and analyzed for Chongqing—a large agricultural municipality of China. Climatic variables changes were detected by Mann-Kendall test. Increased mean temperature and temperature difference and decreased relative humidity were found in annual and oilseed rape growth time series ( P < 0.05). Increased sunshine hours were observed during the oilseed rape growth period ( P < 0.05). Rainy days decreased slightly in annual and oilseed rape growth time series ( P < 0.10). Correlation analysis showed that yields of all three crops could benefit from changes in climatic variables in this region. Yield of rice increased with rainfall ( P < 0.10). Yield of oilseed rape increased with mean temperature and temperature difference but decreased with relative humidity ( P < 0.01). Tobacco yield increased with mean temperature ( P < 0.05). Path analysis provided additional information about the importance and contribution paths of climatic variables to crop yields. Temperature difference and sunshine hours had higher direct and indirect effects via other climatic variables on yields of rice and tobacco. Mean temperature, relative humidity, rainy days, and temperature difference had higher direct and indirect effects via others on yield of oilseed rape.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23736776','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23736776"><span>Temporal changes in climatic variables and their impact on crop yields in southwestern China.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Liu, Hong-Bin; Gou, Yu; Wang, Hong-Ye; Li, Hong-Mei; Wu, Wei</p> <p>2014-08-01</p> <p>Knowledge of variability in climatic variables changes and its impact on crop yields is important for farmers and policy makers, especially in southwestern China where rainfed agriculture is dominant. In the current study, six climatic parameters (mean temperature, rainfall, relative humidity, sunshine hours, temperature difference, and rainy days) and aggregated yields of three main crops (rice: Oryza sativa L., oilseed rape: Brassica napus L., and tobacco: Nicotiana tabacum L.) during 1985-2010 were collected and analyzed for Chongqing-a large agricultural municipality of China. Climatic variables changes were detected by Mann-Kendall test. Increased mean temperature and temperature difference and decreased relative humidity were found in annual and oilseed rape growth time series (P<0.05). Increased sunshine hours were observed during the oilseed rape growth period (P<0.05). Rainy days decreased slightly in annual and oilseed rape growth time series (P<0.10). Correlation analysis showed that yields of all three crops could benefit from changes in climatic variables in this region. Yield of rice increased with rainfall (P<0.10). Yield of oilseed rape increased with mean temperature and temperature difference but decreased with relative humidity (P<0.01). Tobacco yield increased with mean temperature (P<0.05). Path analysis provided additional information about the importance and contribution paths of climatic variables to crop yields. Temperature difference and sunshine hours had higher direct and indirect effects via other climatic variables on yields of rice and tobacco. Mean temperature, relative humidity, rainy days, and temperature difference had higher direct and indirect effects via others on yield of oilseed rape.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5460593','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5460593"><span>A monthly global paleo-reanalysis of the atmosphere from 1600 to 2005 for studying past climatic variations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Franke, Jörg; Brönnimann, Stefan; Bhend, Jonas; Brugnara, Yuri</p> <p>2017-01-01</p> <p>Climatic variations at decadal scales such as phases of accelerated warming or weak monsoons have profound effects on society and economy. Studying these variations requires insights from the past. However, most current reconstructions provide either time series or fields of regional surface climate, which limit our understanding of the underlying dynamics. Here, we present the first monthly paleo-reanalysis covering the period 1600 to 2005. Over land, instrumental temperature and surface pressure observations, temperature indices derived from historical documents and climate sensitive tree-ring measurements were assimilated into an atmospheric general circulation model ensemble using a Kalman filtering technique. This data set combines the advantage of traditional reconstruction methods of being as close as possible to observations with the advantage of climate models of being physically consistent and having 3-dimensional information about the state of the atmosphere for various variables and at all points in time. In contrast to most statistical reconstructions, centennial variability stems from the climate model and its forcings, no stationarity assumptions are made and error estimates are provided. PMID:28585926</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ARMS....9..125M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ARMS....9..125M"><span>Natural Variability and Anthropogenic Trends in the Ocean Carbon Sink</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>McKinley, Galen A.; Fay, Amanda R.; Lovenduski, Nicole S.; Pilcher, Darren J.</p> <p>2017-01-01</p> <p>Since preindustrial times, the ocean has removed from the atmosphere 41% of the carbon emitted by human industrial activities. Despite significant uncertainties, the balance of evidence indicates that the globally integrated rate of ocean carbon uptake is increasing in response to increasing atmospheric CO2 concentrations. The El Niño-Southern Oscillation in the equatorial Pacific dominates interannual variability of the globally integrated sink. Modes of climate variability in high latitudes are correlated with variability in regional carbon sinks, but mechanistic understanding is incomplete. Regional sink variability, combined with sparse sampling, means that the growing oceanic sink cannot yet be directly detected from available surface data. Accurate and precise shipboard observations need to be continued and increasingly complemented with autonomous observations. These data, together with a variety of mechanistic and diagnostic models, are needed for better understanding, long-term monitoring, and future projections of this critical climate regulation service.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5046986','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5046986"><span>Decadal variability of extreme wave height representing storm severity in the northeast Atlantic and North Sea since the foundation of the Royal Society</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Taylor, P. H.; Gibson, R.</p> <p>2016-01-01</p> <p>Long-term estimation of extreme wave height remains a key challenge because of the short duration of available wave data, and also because of the possible impact of climate variability on ocean waves. Here, we analyse storm-based statistics to obtain estimates of extreme wave height at locations in the northeast Atlantic and North Sea using the NORA10 wave hindcast (1958–2011), and use a 5 year sliding window to examine temporal variability. The decadal variability is correlated to the North Atlantic oscillation and other atmospheric modes, using a six-term predictor model incorporating the climate indices and their Hilbert transforms. This allows reconstruction of the historic extreme climate back to 1661, using a combination of known and proxy climate indices. Significant decadal variability primarily driven by the North Atlantic oscillation is observed, and this should be considered for the long-term survivability of offshore structures and marine renewable energy devices. The analysis on wave climate reconstruction reveals that the variation of the mean, 99th percentile and extreme wave climates over decadal time scales for locations close to the dominant storm tracks in the open North Atlantic are comparable, whereas the wave climates for the rest of the locations including the North Sea are rather different. PMID:27713662</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_12 --> <div id="page_13" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="241"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70168668','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70168668"><span>Climate variables explain neutral and adaptive variation within salmonid metapopulations: The importance of replication in landscape genetics</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Hand, Brian K.; Muhlfeld, Clint C.; Wade, Alisa A.; Kovach, Ryan; Whited, Diane C.; Narum, Shawn R.; Matala, Andrew P.; Ackerman, Michael W.; Garner, B. A.; Kimball, John S; Stanford, Jack A.; Luikart, Gordon</p> <p>2016-01-01</p> <p>Understanding how environmental variation influences population genetic structure is important for conservation management because it can reveal how human stressors influence population connectivity, genetic diversity and persistence. We used riverscape genetics modelling to assess whether climatic and habitat variables were related to neutral and adaptive patterns of genetic differentiation (population-specific and pairwise FST) within five metapopulations (79 populations, 4583 individuals) of steelhead trout (Oncorhynchus mykiss) in the Columbia River Basin, USA. Using 151 putatively neutral and 29 candidate adaptive SNP loci, we found that climate-related variables (winter precipitation, summer maximum temperature, winter highest 5% flow events and summer mean flow) best explained neutral and adaptive patterns of genetic differentiation within metapopulations, suggesting that climatic variation likely influences both demography (neutral variation) and local adaptation (adaptive variation). However, we did not observe consistent relationships between climate variables and FST across all metapopulations, underscoring the need for replication when extrapolating results from one scale to another (e.g. basin-wide to the metapopulation scale). Sensitivity analysis (leave-one-population-out) revealed consistent relationships between climate variables and FST within three metapopulations; however, these patterns were not consistent in two metapopulations likely due to small sample sizes (N = 10). These results provide correlative evidence that climatic variation has shaped the genetic structure of steelhead populations and highlight the need for replication and sensitivity analyses in land and riverscape genetics.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19990063741&hterms=Global+warming&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DGlobal%2Bwarming','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19990063741&hterms=Global+warming&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DGlobal%2Bwarming"><span>Global Warming: Discussion for EOS Science Writers Workshop</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hansen, James E</p> <p>1999-01-01</p> <p>The existence of global warming this century is no longer an issue of scientific debate. But there are many important questions about the nature and causes of long-term climate change, th roles of nature and human-made climate forcings and unforced (chaotic) climate variability, the practical impacts of climate change, and what, if anything, should be done to reduce global warming, Global warming is not a uniform increase of temperature, but rather involves at complex geographically varying climate change. Understanding of global warming will require improved observations of climate change itself and the forcing factors that can lead to climate change. The NASA Terra mission and other NASA Earth Science missions will provide key measurement of climate change and climate forcings. The strategy to develop an understanding of the causes and predictability of long-term climate change must be based on combination of observations with models and analysis. The upcoming NASA missions will make important contributions to the required observations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMPP52A..08C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMPP52A..08C"><span>Two Centuries of Climate Variability From a Gulf of Papua Coral Confirms a Coherent, Widespread Multidecadal Signal</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cole, J. E.; Lough, J.; Reed, E. V.; Schrag, D. P.</p> <p>2016-12-01</p> <p>The Indo-Pacific warm pool is intimately involved with large-scale climate variability on seasonal to secular time scales. The lack of long instrumental observations in this region has motivated paleoclimatic analyses using diverse proxy data sources. We present here new multicentury paleoclimate records from a Gulf of Papua coral that capture past variability with a Pacific-wide signature. We have developed stable isotope, Sr/Ca, skeletal density, and luminescence data from a coral core recovered at Bramble Cay, Australia (9°S, 144°E). The geochemical records span CE 1775-1993 and are dominated by low-frequency (decade-century scale) variability that is consistent with records from other proxies in the same region, and with other coral records from far-flung sites across the southwest Pacific. Unlike in many Pacific coral records, we observe no strong trend towards warmer conditions. Although skeletal density bands are clearly visible, they show inconsistent seasonal phasing with the geochemical tracers of sea surface temperature (SST; Sr/Ca and oxygen isotope content), and skeletal density does not correlate with these tracers on longer time scales. In this coral, density banding must be controlled by a more complex mix of internal and/or external factors. Luminescent banding and reconstructed salinity provide similar histories, suggesting a common hydroclimatic signal with significant variability at periods of decades and longer. The strong low-frequency behavior in these new climate records of SST and hydroclimate, from a remote region of the Indo-Pacific, confirms an important source of internal climate variability, on a poorly documented time scale, from a region with far-reaching climatic importance.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ThApC.131..899K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ThApC.131..899K"><span>High-resolution grids of hourly meteorological variables for Germany</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Krähenmann, S.; Walter, A.; Brienen, S.; Imbery, F.; Matzarakis, A.</p> <p>2018-02-01</p> <p>We present a 1-km2 gridded German dataset of hourly surface climate variables covering the period 1995 to 2012. The dataset comprises 12 variables including temperature, dew point, cloud cover, wind speed and direction, global and direct shortwave radiation, down- and up-welling longwave radiation, sea level pressure, relative humidity and vapour pressure. This dataset was constructed statistically from station data, satellite observations and model data. It is outstanding in terms of spatial and temporal resolution and in the number of climate variables. For each variable, we employed the most suitable gridding method and combined the best of several information sources, including station records, satellite-derived data and data from a regional climate model. A module to estimate urban heat island intensity was integrated for air and dew point temperature. Owing to the low density of available synop stations, the gridded dataset does not capture all variations that may occur at a resolution of 1 km2. This applies to areas of complex terrain (all the variables), and in particular to wind speed and the radiation parameters. To achieve maximum precision, we used all observational information when it was available. This, however, leads to inhomogeneities in station network density and affects the long-term consistency of the dataset. A first climate analysis for Germany was conducted. The Rhine River Valley, for example, exhibited more than 100 summer days in 2003, whereas in 1996, the number was low everywhere in Germany. The dataset is useful for applications in various climate-related studies, hazard management and for solar or wind energy applications and it is available via doi: 10.5676/DWD_CDC/TRY_Basis_v001.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4079655','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4079655"><span>Climate Exposure of US National Parks in a New Era of Change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Monahan, William B.; Fisichelli, Nicholas A.</p> <p>2014-01-01</p> <p>US national parks are challenged by climate and other forms of broad-scale environmental change that operate beyond administrative boundaries and in some instances are occurring at especially rapid rates. Here, we evaluate the climate change exposure of 289 natural resource parks administered by the US National Park Service (NPS), and ask which are presently (past 10 to 30 years) experiencing extreme (<5th percentile or >95th percentile) climates relative to their 1901–2012 historical range of variability (HRV). We consider parks in a landscape context (including surrounding 30 km) and evaluate both mean and inter-annual variation in 25 biologically relevant climate variables related to temperature, precipitation, frost and wet day frequencies, vapor pressure, cloud cover, and seasonality. We also consider sensitivity of findings to the moving time window of analysis (10, 20, and 30 year windows). Results show that parks are overwhelmingly at the extreme warm end of historical temperature distributions and this is true for several variables (e.g., annual mean temperature, minimum temperature of the coldest month, mean temperature of the warmest quarter). Precipitation and other moisture patterns are geographically more heterogeneous across parks and show greater variation among variables. Across climate variables, recent inter-annual variation is generally well within the range of variability observed since 1901. Moving window size has a measureable effect on these estimates, but parks with extreme climates also tend to exhibit low sensitivity to the time window of analysis. We highlight particular parks that illustrate different extremes and may facilitate understanding responses of park resources to ongoing climate change. We conclude with discussion of how results relate to anticipated future changes in climate, as well as how they can inform NPS and neighboring land management and planning in a new era of change. PMID:24988483</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24988483','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24988483"><span>Climate exposure of US national parks in a new era of change.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Monahan, William B; Fisichelli, Nicholas A</p> <p>2014-01-01</p> <p>US national parks are challenged by climate and other forms of broad-scale environmental change that operate beyond administrative boundaries and in some instances are occurring at especially rapid rates. Here, we evaluate the climate change exposure of 289 natural resource parks administered by the US National Park Service (NPS), and ask which are presently (past 10 to 30 years) experiencing extreme (<5th percentile or >95th percentile) climates relative to their 1901-2012 historical range of variability (HRV). We consider parks in a landscape context (including surrounding 30 km) and evaluate both mean and inter-annual variation in 25 biologically relevant climate variables related to temperature, precipitation, frost and wet day frequencies, vapor pressure, cloud cover, and seasonality. We also consider sensitivity of findings to the moving time window of analysis (10, 20, and 30 year windows). Results show that parks are overwhelmingly at the extreme warm end of historical temperature distributions and this is true for several variables (e.g., annual mean temperature, minimum temperature of the coldest month, mean temperature of the warmest quarter). Precipitation and other moisture patterns are geographically more heterogeneous across parks and show greater variation among variables. Across climate variables, recent inter-annual variation is generally well within the range of variability observed since 1901. Moving window size has a measureable effect on these estimates, but parks with extreme climates also tend to exhibit low sensitivity to the time window of analysis. We highlight particular parks that illustrate different extremes and may facilitate understanding responses of park resources to ongoing climate change. We conclude with discussion of how results relate to anticipated future changes in climate, as well as how they can inform NPS and neighboring land management and planning in a new era of change.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JSR....99...74S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JSR....99...74S"><span>Using non-systematic surveys to investigate effects of regional climate variability on Australasian gannets in the Hauraki Gulf, New Zealand</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Srinivasan, Mridula; Dassis, Mariela; Benn, Emily; Stockin, Karen A.; Martinez, Emmanuelle; Machovsky-Capuska, Gabriel E.</p> <p>2015-05-01</p> <p>Few studies have investigated regional and natural climate variability on seabird populations using ocean reanalysis datasets (e.g. Simple Ocean Data Assimilation (SODA)) that integrate atmospheric information to supplement ocean observations and provide improved estimates of ocean conditions. Herein we use a non-systematic dataset on Australasian gannets (Morus serrator) from 2001 to 2009 to identify potential connections between Gannet Sightings Per Unit Effort (GSPUE) and climate and oceanographic variability in a region of known importance for breeding seabirds, the Hauraki Gulf (HG), New Zealand. While no statistically significant relationships between GSPUE and global climate indices were determined, there was a significant correlation between GSPUE and regional SST anomaly for HG. Also, there appears to be a strong link between global climate indices and regional climate in the HG. Further, based on cross-correlation function coefficients and lagged multiple regression models, we identified potential leading and lagging climate variables, and climate variables but with limited predictive capacity in forecasting future GSPUE. Despite significant inter-annual variability and marginally cooler SSTs since 2001, gannet sightings appear to be increasing. We hypothesize that at present underlying physical changes in the marine ecosystem may be insufficient to affect supply of preferred gannet main prey (pilchard Sardinops spp.), which tolerate a wide thermal range. Our study showcases the potential scientific value of lengthy non-systematic data streams and when designed properly (i.e., contain abundance, flock size, and spatial data), can yield useful information in climate impact studies on seabirds and other marine fauna. Such information can be invaluable for enhancing conservation measures for protected species in fiscally constrained research environments.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017PrOce.152...15T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017PrOce.152...15T"><span>Managing living marine resources in a dynamic environment: The role of seasonal to decadal climate forecasts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tommasi, Desiree; Stock, Charles A.; Hobday, Alistair J.; Methot, Rick; Kaplan, Isaac C.; Eveson, J. Paige; Holsman, Kirstin; Miller, Timothy J.; Gaichas, Sarah; Gehlen, Marion; Pershing, Andrew; Vecchi, Gabriel A.; Msadek, Rym; Delworth, Tom; Eakin, C. Mark; Haltuch, Melissa A.; Séférian, Roland; Spillman, Claire M.; Hartog, Jason R.; Siedlecki, Samantha; Samhouri, Jameal F.; Muhling, Barbara; Asch, Rebecca G.; Pinsky, Malin L.; Saba, Vincent S.; Kapnick, Sarah B.; Gaitan, Carlos F.; Rykaczewski, Ryan R.; Alexander, Michael A.; Xue, Yan; Pegion, Kathleen V.; Lynch, Patrick; Payne, Mark R.; Kristiansen, Trond; Lehodey, Patrick; Werner, Francisco E.</p> <p>2017-03-01</p> <p>Recent developments in global dynamical climate prediction systems have allowed for skillful predictions of climate variables relevant to living marine resources (LMRs) at a scale useful to understanding and managing LMRs. Such predictions present opportunities for improved LMR management and industry operations, as well as new research avenues in fisheries science. LMRs respond to climate variability via changes in physiology and behavior. For species and systems where climate-fisheries links are well established, forecasted LMR responses can lead to anticipatory and more effective decisions, benefitting both managers and stakeholders. Here, we provide an overview of climate prediction systems and advances in seasonal to decadal prediction of marine-resource relevant environmental variables. We then describe a range of climate-sensitive LMR decisions that can be taken at lead-times of months to decades, before highlighting a range of pioneering case studies using climate predictions to inform LMR decisions. The success of these case studies suggests that many additional applications are possible. Progress, however, is limited by observational and modeling challenges. Priority developments include strengthening of the mechanistic linkages between climate and marine resource responses, development of LMR models able to explicitly represent such responses, integration of climate driven LMR dynamics in the multi-driver context within which marine resources exist, and improved prediction of ecosystem-relevant variables at the fine regional scales at which most marine resource decisions are made. While there are fundamental limits to predictability, continued advances in these areas have considerable potential to make LMR managers and industry decision more resilient to climate variability and help sustain valuable resources. Concerted dialog between scientists, LMR managers and industry is essential to realizing this potential.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19860021711','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19860021711"><span>Application of solar max ACRIM data to analyze solar-driven climatic variability on Earth</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hoffert, M. I.</p> <p>1986-01-01</p> <p>Terrestrial climatic effects associated with solar variability have been proposed for at least a century, but could not be assessed quantitatively owing to observational uncertainities in solar flux variations. Measurements from 1980 to 1984 by the Active Cavity Radiometer Irradiance Monitor (ACRIM), capable of resolving fluctuations above the sensible atmosphere less than 0.1% of the solar constant, permit direct albeit preliminary assessments of solar forcing effects on global temperatures during this period. The global temperature response to ACRIM-measured fluctuations was computed from 1980 to 1985 using the NYU transient climate model including thermal inertia effects of the world ocean; and compared the results with observations of recent temperature trends. Monthly mean ACRIM-driven global surface temperature fluctuations computed with the climate model are an order of magnitude smaller, of order 0.01 C. In constrast, global mean surface temperature observations indicate an approx. 0.1 C increase during this period. Solar variability is therefore likely to have been a minor factor in global climate change during this period compared with variations in atmospheric albedo, greenhouse gases and internal self-inducedoscillations. It was not possible to extend the applicability of the measured flux variations to longer periods since a possible correlation of luminosity with solar annual activity is not supported by statistical analysis. The continuous monitoring of solar flux by satellite-based instruments over timescales of 20 years or more comparable to timescales for thermal relaxation of the oceans and of the solar cycle itself is needed to resolve the question of long-term solar variation effects on climate.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ClDy...48.1841V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ClDy...48.1841V"><span>Decadal climate prediction with a refined anomaly initialisation approach</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Volpi, Danila; Guemas, Virginie; Doblas-Reyes, Francisco J.; Hawkins, Ed; Nichols, Nancy K.</p> <p>2017-03-01</p> <p>In decadal prediction, the objective is to exploit both the sources of predictability from the external radiative forcings and from the internal variability to provide the best possible climate information for the next decade. Predicting the climate system internal variability relies on initialising the climate model from observational estimates. We present a refined method of anomaly initialisation (AI) applied to the ocean and sea ice components of the global climate forecast model EC-Earth, with the following key innovations: (1) the use of a weight applied to the observed anomalies, in order to avoid the risk of introducing anomalies recorded in the observed climate, whose amplitude does not fit in the range of the internal variability generated by the model; (2) the AI of the ocean density, instead of calculating it from the anomaly initialised state of temperature and salinity. An experiment initialised with this refined AI method has been compared with a full field and standard AI experiment. Results show that the use of such refinements enhances the surface temperature skill over part of the North and South Atlantic, part of the South Pacific and the Mediterranean Sea for the first forecast year. However, part of such improvement is lost in the following forecast years. For the tropical Pacific surface temperature, the full field initialised experiment performs the best. The prediction of the Arctic sea-ice volume is improved by the refined AI method for the first three forecast years and the skill of the Atlantic multidecadal oscillation is significantly increased compared to a non-initialised forecast, along the whole forecast time.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018E%26ES..148a2023P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018E%26ES..148a2023P"><span>Rainfall pattern variability as climate change impact in The Wallacea Region</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pujiastuti, I.; Nurjani, E.</p> <p>2018-04-01</p> <p>The objective of the study is to observe the characteristic variability of rainfall pattern in the city located in every rainfall type, local (Kendari), monsoon (Manado), and equatorial (Palu). The result will be compared to determine which has the most significantly precipitation changing due to climate change impact. Rainfall variability in Indonesia illustrates precipitation variation thus the important variability is the variability of monthly rainfall. Monthly precipitation data for the period of 1961-2010 are collected from Indonesian Agency for Meteorological, Climatological, and Geophysical Agency. This data is calculated with the normal test statistical method to analyze rainfall variability. The result showed the pattern of trend and variability of rainfall in every city with the own characteristic which determines the rainfall type. Moreover, there is comparison of rainfall pattern changing between every rainfall type. This information is useful for climate change mitigation and adaptation strategies especially in water resource management form precipitation as well as the occurrence of meteorological disasters.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70037880','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70037880"><span>GFDL's ESM2 global coupled climate-carbon Earth System Models. Part I: physical formulation and baseline simulation characteristics</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Dunne, John P.; John, Jasmin G.; Adcroft, Alistair J.; Griffies, Stephen M.; Hallberg, Robert W.; Shevalikova, Elena; Stouffer, Ronald J.; Cooke, William; Dunne, Krista A.; Harrison, Matthew J.; Krasting, John P.; Malyshev, Sergey L.; Milly, P.C.D.; Phillipps, Peter J.; Sentman, Lori A.; Samuels, Bonita L.; Spelman, Michael J.; Winton, Michael; Wittenberg, Andrew T.; Zadeh, Niki</p> <p>2012-01-01</p> <p>We describe the physical climate formulation and simulation characteristics of two new global coupled carbon-climate Earth System Models, ESM2M and ESM2G. These models demonstrate similar climate fidelity as the Geophysical Fluid Dynamics Laboratory's previous CM2.1 climate model while incorporating explicit and consistent carbon dynamics. The two models differ exclusively in the physical ocean component; ESM2M uses Modular Ocean Model version 4.1 with vertical pressure layers while ESM2G uses Generalized Ocean Layer Dynamics with a bulk mixed layer and interior isopycnal layers. Differences in the ocean mean state include the thermocline depth being relatively deep in ESM2M and relatively shallow in ESM2G compared to observations. The crucial role of ocean dynamics on climate variability is highlighted in the El Niño-Southern Oscillation being overly strong in ESM2M and overly weak ESM2G relative to observations. Thus, while ESM2G might better represent climate changes relating to: total heat content variability given its lack of long term drift, gyre circulation and ventilation in the North Pacific, tropical Atlantic and Indian Oceans, and depth structure in the overturning and abyssal flows, ESM2M might better represent climate changes relating to: surface circulation given its superior surface temperature, salinity and height patterns, tropical Pacific circulation and variability, and Southern Ocean dynamics. Our overall assessment is that neither model is fundamentally superior to the other, and that both models achieve sufficient fidelity to allow meaningful climate and earth system modeling applications. This affords us the ability to assess the role of ocean configuration on earth system interactions in the context of two state-of-the-art coupled carbon-climate models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26086887','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26086887"><span>Climate Change and Spatiotemporal Distributions of Vector-Borne Diseases in Nepal--A Systematic Synthesis of Literature.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Dhimal, Meghnath; Ahrens, Bodo; Kuch, Ulrich</p> <p>2015-01-01</p> <p>Despite its largely mountainous terrain for which this Himalayan country is a popular tourist destination, Nepal is now endemic for five major vector-borne diseases (VBDs), namely malaria, lymphatic filariasis, Japanese encephalitis, visceral leishmaniasis and dengue fever. There is increasing evidence about the impacts of climate change on VBDs especially in tropical highlands and temperate regions. Our aim is to explore whether the observed spatiotemporal distributions of VBDs in Nepal can be related to climate change. A systematic literature search was performed and summarized information on climate change and the spatiotemporal distribution of VBDs in Nepal from the published literature until December 2014 following providing items for systematic review and meta-analysis (PRISMA) guidelines. We found 12 studies that analysed the trend of climatic data and are relevant for the study of VBDs, 38 studies that dealt with the spatial and temporal distribution of disease vectors and disease transmission. Among 38 studies, only eight studies assessed the association of VBDs with climatic variables. Our review highlights a pronounced warming in the mountains and an expansion of autochthonous cases of VBDs to non-endemic areas including mountain regions (i.e., at least 2,000 m above sea level). Furthermore, significant relationships between climatic variables and VBDs and their vectors are found in short-term studies. Taking into account the weak health care systems and difficult geographic terrain of Nepal, increasing trade and movements of people, a lack of vector control interventions, observed relationships between climatic variables and VBDs and their vectors and the establishment of relevant disease vectors already at least 2,000 m above sea level, we conclude that climate change can intensify the risk of VBD epidemics in the mountain regions of Nepal if other non-climatic drivers of VBDs remain constant.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70170085','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70170085"><span>Semi-arid vegetation response to antecedent climate and water balance windows</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Thoma, David P.; Munson, Seth M.; Irvine, Kathryn M.; Witwicki, Dana L.; Bunting, Erin</p> <p>2016-01-01</p> <p>Questions Can we improve understanding of vegetation response to water availability on monthly time scales in semi-arid environments using remote sensing methods? What climatic or water balance variables and antecedent windows of time associated with these variables best relate to the condition of vegetation? Can we develop credible near-term forecasts from climate data that can be used to prepare for future climate change effects on vegetation? Location Semi-arid grasslands in Capitol Reef National Park, Utah, USA. Methods We built vegetation response models by relating the normalized difference vegetation index (NDVI) from MODIS imagery in Mar–Nov 2000–2013 to antecedent climate and water balance variables preceding the monthly NDVI observations. We compared how climate and water balance variables explained vegetation greenness and then used a multi-model ensemble of climate and water balance models to forecast monthly NDVI for three holdout years. Results Water balance variables explained vegetation greenness to a greater degree than climate variables for most growing season months. Seasonally important variables included measures of antecedent water input and storage in spring, switching to indicators of drought, input or use in summer, followed by antecedent moisture availability in autumn. In spite of similar climates, there was evidence the grazed grassland showed a response to drying conditions 1 mo sooner than the ungrazed grassland. Lead times were generally short early in the growing season and antecedent window durations increased from 3 mo early in the growing season to 1 yr or more as the growing season progressed. Forecast accuracy for three holdout years using a multi-model ensemble of climate and water balance variables outperformed forecasts made with a naïve NDVI climatology. Conclusions We determined the influence of climate and water balance on vegetation at a fine temporal scale, which presents an opportunity to forecast vegetation response with short lead times. This understanding was obtained through high-frequency vegetation monitoring using remote sensing, which reduces the costs and time necessary for field measurements and can lead to more rapid detection of vegetation changes that could help managers take appropriate actions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017NatCC...7..350S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017NatCC...7..350S"><span>Climate change enhances interannual variability of the Nile river flow</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Siam, Mohamed S.; Eltahir, Elfatih A. B.</p> <p>2017-04-01</p> <p>The human population living in the Nile basin countries is projected to double by 2050, approaching one billion. The increase in water demand associated with this burgeoning population will put significant stress on the available water resources. Potential changes in the flow of the Nile River as a result of climate change may further strain this critical situation. Here, we present empirical evidence from observations and consistent projections from climate model simulations suggesting that the standard deviation describing interannual variability of total Nile flow could increase by 50% (+/-35%) (multi-model ensemble mean +/- 1 standard deviation) in the twenty-first century compared to the twentieth century. We attribute the relatively large change in interannual variability of the Nile flow to projected increases in future occurrences of El Niño and La Niña events and to observed teleconnection between the El Niño-Southern Oscillation and Nile River flow. Adequacy of current water storage capacity and plans for additional storage capacity in the basin will need to be re-evaluated given the projected enhancement of interannual variability in the future flow of the Nile river.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.9370G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.9370G"><span>West African Monsoon dynamics in idealized simulations: the competitive roles of SST warming and CO2</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gaetani, Marco; Flamant, Cyrille; Hourdin, Frederic; Bastin, Sophie; Braconnot, Pascale; Bony, Sandrine</p> <p>2015-04-01</p> <p>The West African Monsoon (WAM) is affected by large climate variability at different timescales, from interannual to multidecadal, with strong environmental and socio-economic impacts associated to climate-related rainfall variability, especially in the Sahelian belt. State-of-the-art coupled climate models still show poor ability in correctly simulating the WAM past variability and also a large spread is observed in future climate projections. In this work, the July-to-September (JAS) WAM variability in the period 1979-2008 is studied in AMIP-like simulations (SST-forced) from CMIP5. The individual roles of global SST warming and CO2 concentration increasing are investigated through idealized experiments simulating a 4K warmer SST and a 4x CO2 concentration, respectively. Results show a dry response in Sahel to SST warming, with dryer conditions over western Sahel. On the contrary, wet conditions are observed when CO2 is increased, with the strongest response over central-eastern Sahel. The precipitation changes are associated to modifications in the regional atmospheric circulation: dry (wet) conditions are associated with reduced (increased) convergence in the lower troposphere, a southward (northward) shift of the African Easterly Jet, and a weaker (stronger) Tropical Easterly Jet. The co-variability between global SST and WAM precipitation is also investigated, highlighting a reorganization of the main co-variability modes. Namely, in the 4xCO2 simulation the influence of Tropical Pacific is dominant, while it is reduced in the 4K simulation, which also shows an increased coupling with the eastern Pacific and the Indian Ocean. The above results suggest a competitive action of SST warming and CO2 increasing on the WAM climate variability, with opposite effects on precipitation. The combination of the observed positive and negative response in precipitation, with wet conditions in central-eastern Sahel and dry conditions in western Sahel, is consistent with the future precipitation trends over West Africa resulting from CMIP5 coupled simulations. It is argued that the large spread in CMIP5 future projections may be related to the weight given to SST warming and direct CO2 effect by individual models. The capability of climate models in reproducing the SST-precipitation relationship appears to be crucial in this respect.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24605456','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24605456"><span>Worthy of their name: how floods drive outbreaks of two major floodwater mosquitoes (Diptera: Culicidae).</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Berec, Ludĕk; Gelbic, Ivan; Sebesta, Oldrich</p> <p>2014-01-01</p> <p>An understanding of how climate variables drive seasonal dynamics of mosquito populations is critical to mitigating negative impacts of potential outbreaks, including both nuisance effects and risk of mosquito-borne infectious disease. Here, we identify climate variables most affecting seasonal dynamics of two major floodwater mosquitoes, Aedes vexans (Meigen, 1830) and Aedes sticticus (Meigen, 1838) (Diptera: Culicidae), along the lower courses of the Dyje River, at the border between the Czech Republic and Austria. Monthly trap counts of both floodwater mosquitoes varied both across sites and years. Despite this variability, both models used to fit the observed data at all sites (and especially that for Ae. sticticus) and site-specific models fitted the observed data quite well. The most important climate variables we identified-temperature and especially flooding-were driving seasonal dynamics of both Aedes species. We suggest that flooding determines seasonal peaks in the monthly mosquito trap counts while temperature modulates seasonality in these counts. Hence, floodwater mosquitoes indeed appear worthy of their name. Moreover, the climate variables we considered for modeling were able reasonably to predict mosquito trap counts in the month ahead. Our study can help in planning flood management; timely notification of people, given that these mosquitoes are a real nuisance in this region; public health policy management to mitigate risk from such mosquito-borne diseases as that caused in humans by the Tahyna virus; and anticipating negative consequences of climate change, which are expected only to worsen unless floods, or the mosquitoes themselves, are satisfactorily managed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26796233','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26796233"><span>Climate change but not unemployment explains the changing suicidality in Thessaloniki Greece (2000-2012).</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Fountoulakis, Konstantinos N; Savopoulos, Christos; Zannis, Prodromos; Apostolopoulou, Martha; Fountoukidis, Ilias; Kakaletsis, Nikolaos; Kanellos, Ilias; Dimellis, Dimos; Hyphantis, Thomas; Tsikerdekis, Athanasios; Pompili, Maurizio; Hatzitolios, Apostolos I</p> <p>2016-03-15</p> <p>Recently there was a debate concerning the etiology behind attempts and completed suicides. The aim of the current study was to search for possible correlations between the rates of attempted and completed suicide and climate variables and regional unemployment per year in the county of Thessaloniki, Macedonia, northern Greece, for the years 2000-12. The regional rates of suicide and attempted suicide as well as regional unemployment were available from previous publications of the authors. The climate variables were calculated from the daily E-OBS gridded dataset which is based on observational data Only the male suicide rates correlate significantly with high mean annual temperature but not with unemployment. The multiple linear regression analysis results suggest that temperature is the only variable that determines male suicides and explains 51% of their variance. Unemployment fails to contribute significantly to the model. There seems to be a seasonal distribution for attempts with mean rates being higher for the period from May to October and the rates clearly correlate with temperature. The highest mean rates were observed during May and August and the lowest during December and February. Multiple linear regression analysis suggests that temperature also determines the female attempts rate although the explained variable is significant but very low (3-5%) Climate variables and specifically high temperature correlate both with suicide and attempted suicide rates but with a different way between males and females. The climate effect was stronger than the effect of unemployment. Copyright © 2016 Elsevier B.V. All rights reserved.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=238311&Lab=NERL&keyword=health+AND+physics&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=238311&Lab=NERL&keyword=health+AND+physics&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>Meteorological Modes of Variability for Fine Particulate Matter (PM2.5) Air Quality in the United States: Implications for PM2.5 Sensitivity to Climate Change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>We applied a multiple linear regression model to understand the relationships of PM<SUB>2.5</SUB> with meteorological variables in the contiguous US and from there to infer the sensitivity of PM<SUB>2.5</SUB> to climate change. We used 2004-2008 PM<SUB>2.5</SUB> observations fro...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18..651G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18..651G"><span>Nonlinear dynamical modes of climate variability: from curves to manifolds</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gavrilov, Andrey; Mukhin, Dmitry; Loskutov, Evgeny; Feigin, Alexander</p> <p>2016-04-01</p> <p>The necessity of efficient dimensionality reduction methods capturing dynamical properties of the system from observed data is evident. Recent study shows that nonlinear dynamical mode (NDM) expansion is able to solve this problem and provide adequate phase variables in climate data analysis [1]. A single NDM is logical extension of linear spatio-temporal structure (like empirical orthogonal function pattern): it is constructed as nonlinear transformation of hidden scalar time series to the space of observed variables, i. e. projection of observed dataset onto a nonlinear curve. Both the hidden time series and the parameters of the curve are learned simultaneously using Bayesian approach. The only prior information about the hidden signal is the assumption of its smoothness. The optimal nonlinearity degree and smoothness are found using Bayesian evidence technique. In this work we do further extension and look for vector hidden signals instead of scalar with the same smoothness restriction. As a result we resolve multidimensional manifolds instead of sum of curves. The dimension of the hidden manifold is optimized using also Bayesian evidence. The efficiency of the extension is demonstrated on model examples. Results of application to climate data are demonstrated and discussed. The study is supported by Government of Russian Federation (agreement #14.Z50.31.0033 with the Institute of Applied Physics of RAS). 1. Mukhin, D., Gavrilov, A., Feigin, A., Loskutov, E., & Kurths, J. (2015). Principal nonlinear dynamical modes of climate variability. Scientific Reports, 5, 15510. http://doi.org/10.1038/srep15510</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_13 --> <div id="page_14" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="261"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016RvGeo..54....5B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016RvGeo..54....5B"><span>Observations, inferences, and mechanisms of the Atlantic Meridional Overturning Circulation: A review</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Buckley, Martha W.; Marshall, John</p> <p>2016-03-01</p> <p>This is a review about the Atlantic Meridional Overturning Circulation (AMOC), its mean structure, temporal variability, controlling mechanisms, and role in the coupled climate system. The AMOC plays a central role in climate through its heat and freshwater transports. Northward ocean heat transport achieved by the AMOC is responsible for the relative warmth of the Northern Hemisphere compared to the Southern Hemisphere and is thought to play a role in setting the mean position of the Intertropical Convergence Zone north of the equator. The AMOC is a key means by which heat anomalies are sequestered into the ocean's interior and thus modulates the trajectory of climate change. Fluctuations in the AMOC have been linked to low-frequency variability of Atlantic sea surface temperatures with a host of implications for climate variability over surrounding landmasses. On intra-annual timescales, variability in AMOC is large and primarily reflects the response to local wind forcing; meridional coherence of anomalies is limited to that of the wind field. On interannual to decadal timescales, AMOC changes are primarily geostrophic and related to buoyancy anomalies on the western boundary. A pacemaker region for decadal AMOC changes is located in a western "transition zone" along the boundary between the subtropical and subpolar gyres. Decadal AMOC anomalies are communicated meridionally from this region. AMOC observations, as well as the expanded ocean observational network provided by the Argo array and satellite altimetry, are inspiring efforts to develop decadal predictability systems using coupled atmosphere-ocean models initialized by ocean data.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016NatGe...9..518J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016NatGe...9..518J"><span>Recent slowing of Atlantic overturning circulation as a recovery from earlier strengthening</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jackson, Laura C.; Peterson, K. Andrew; Roberts, Chris D.; Wood, Richard A.</p> <p>2016-07-01</p> <p>The Atlantic meridional overturning circulation (AMOC) has weakened substantially over the past decade. Some weakening may already have occurred over the past century, and global climate models project further weakening in response to anthropogenic climate change. Such a weakening could have significant impacts on the surface climate. However, ocean model simulations based on historical conditions have often found an increase in overturning up to the mid-1990s, followed by a decrease. It is therefore not clear whether the observed weakening over the past decade is part of decadal variability or a persistent weakening. Here we examine a state-of-the-art global-ocean reanalysis product, GloSea5, which covers the years 1989 to 2015 and closely matches observations of the AMOC at 26.5° N, capturing the interannual variability and decadal trend with unprecedented accuracy. The reanalysis data place the ten years of observations--April 2004 to February 2014--into a longer-term context and suggest that the observed decrease in the overturning circulation is consistent with a recovery following a previous increase. We find that density anomalies that propagate southwards from the Labrador Sea are the most likely cause of these variations. We conclude that decadal variability probably played a key role in the decline of the AMOC observed over the past decade.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMIN53C1894H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMIN53C1894H"><span>Leveraging federal science data and tools to help communities & business build climate resilience</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Herring, D.</p> <p>2016-12-01</p> <p>Decision-makers in every sector and region of the United States are seeking actionable science-based information to help them understand and manage their climate-related risks. Translating data, tools and information from the domain of climate science to the domains of municipal, social, and economic decision-making raises complex questions—e.g., how to communicate causes and impacts of climate variability and change; how to show projections of plausible future climate scenarios; how to characterize and quantify vulnerabilities, risks, and opportunities facing communities and businesses; and how to make and implement "win-win" adaptation plans. These are the types of challenges being addressed by a public-private partnership of federal agencies, academic institutions, non-governmental organizations, and private businesses that are contributing to the development of the U.S. Climate Resilience Toolkit (toolkit.climate.gov), a new website designed to help people build resilience to extreme events caused by both natural climate variability and long-term climate change. The site's Climate Explorer is designed to help people understand potential climate conditions over the course of this century. It offers easy access to downloadable maps, graphs, and data tables of observed and projected temperature, precipitation and other decision-relevant climate variables dating back to 1950 and out to 2100. Of course, climate change is only one of many variables affecting decisions about the future so the Toolkit also ties climate information to a wide range of other relevant tools and information to help users to explore their vulnerabilities and risks. In this session, we will describe recent enhancements to the Toolkit, lessons learned from user engagements, and evidence that our approach of coupling scientific information with actionable decision-making processes is helping Americans build resilience to climate-related impacts.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140009601','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140009601"><span>Tree Density and Species Decline in the African Sahel Attributable to Climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Gonzalez, Patrick; Tucker, Compton J.; Sy, H.</p> <p>2012-01-01</p> <p>Increased aridity and human population have reduced tree cover in parts of the African Sahel and degraded resources for local people. Yet, tree cover trends and the relative importance of climate and population remain unresolved. From field measurements, aerial photos, and Ikonos satellite images, we detected significant 1954-2002 tree density declines in the western Sahel of 18 +/- 14% (P = 0.014, n = 204) and 17 +/- 13% (P = 0.0009, n = 187). From field observations, we detected a significant 1960-2000 species richness decline of 21 +/- 11% (P = 0.0028, n = 14) across the Sahel and a southward shift of the Sahel, Sudan, and Guinea zones. Multivariate analyses of climate, soil, and population showed that temperature most significantly (P < 0.001) explained tree cover changes. Multivariate and bivariate tests and field observations indicated the dominance of temperature and precipitation, supporting attribution of tree cover changes to climate variability. Climate change forcing of Sahel climate variability, particularly the significant (P < 0.05) 1901-2002 temperature increases and precipitation decreases in the research areas, connects Sahel tree cover changes to global climate change. This suggests roles for global action and local adaptation to address ecological change in the Sahel.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018NatGe..11...44S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018NatGe..11...44S"><span>Substantial large-scale feedbacks between natural aerosols and climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Scott, C. E.; Arnold, S. R.; Monks, S. A.; Asmi, A.; Paasonen, P.; Spracklen, D. V.</p> <p>2018-01-01</p> <p>The terrestrial biosphere is an important source of natural aerosol. Natural aerosol sources alter climate, but are also strongly controlled by climate, leading to the potential for natural aerosol-climate feedbacks. Here we use a global aerosol model to make an assessment of terrestrial natural aerosol-climate feedbacks, constrained by observations of aerosol number. We find that warmer-than-average temperatures are associated with higher-than-average number concentrations of large (>100 nm diameter) particles, particularly during the summer. This relationship is well reproduced by the model and is driven by both meteorological variability and variability in natural aerosol from biogenic and landscape fire sources. We find that the calculated extratropical annual mean aerosol radiative effect (both direct and indirect) is negatively related to the observed global temperature anomaly, and is driven by a positive relationship between temperature and the emission of natural aerosol. The extratropical aerosol-climate feedback is estimated to be -0.14 W m-2 K-1 for landscape fire aerosol, greater than the -0.03 W m-2 K-1 estimated for biogenic secondary organic aerosol. These feedbacks are comparable in magnitude to other biogeochemical feedbacks, highlighting the need for natural aerosol feedbacks to be included in climate simulations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940026129','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940026129"><span>The challenge of identifying greenhouse gas-induced climatic change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Maccracken, Michael C.</p> <p>1992-01-01</p> <p>Meeting the challenge of identifying greenhouse gas-induced climatic change involves three steps. First, observations of critical variables must be assembled, evaluated, and analyzed to determine that there has been a statistically significant change. Second, reliable theoretical (model) calculations must be conducted to provide a definitive set of changes for which to search. Third, a quantitative and statistically significant association must be made between the projected and observed changes to exclude the possibility that the changes are due to natural variability or other factors. This paper provides a qualitative overview of scientific progress in successfully fulfilling these three steps.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29195206','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29195206"><span>Effects of changing climate on European stream invertebrate communities: A long-term data analysis.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Jourdan, Jonas; O'Hara, Robert B; Bottarin, Roberta; Huttunen, Kaisa-Leena; Kuemmerlen, Mathias; Monteith, Don; Muotka, Timo; Ozoliņš, Dāvis; Paavola, Riku; Pilotto, Francesca; Springe, Gunta; Skuja, Agnija; Sundermann, Andrea; Tonkin, Jonathan D; Haase, Peter</p> <p>2018-04-15</p> <p>Long-term observations on riverine benthic invertebrate communities enable assessments of the potential impacts of global change on stream ecosystems. Besides increasing average temperatures, many studies predict greater temperature extremes and intense precipitation events as a consequence of climate change. In this study we examined long-term observation data (10-32years) of 26 streams and rivers from four ecoregions in the European Long-Term Ecological Research (LTER) network, to investigate invertebrate community responses to changing climatic conditions. We used functional trait and multi-taxonomic analyses and combined examinations of general long-term changes in communities with detailed analyses of the impact of different climatic drivers (i.e., various temperature and precipitation variables) by focusing on the response of communities to climatic conditions of the previous year. Taxa and ecoregions differed substantially in their response to climate change conditions. We did not observe any trend of changes in total taxonomic richness or overall abundance over time or with increasing temperatures, which reflects a compensatory turnover in the composition of communities; sensitive Plecoptera decreased in response to warmer years and Ephemeroptera increased in northern regions. Invasive species increased with an increasing number of extreme days which also caused an apparent upstream community movement. The observed changes in functional feeding group diversity indicate that climate change may be associated with changes in trophic interactions within aquatic food webs. These findings highlight the vulnerability of riverine ecosystems to climate change and emphasize the need to further explore the interactive effects of climate change variables with other local stressors to develop appropriate conservation measures. Copyright © 2017 Elsevier B.V. All rights reserved.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140007330','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140007330"><span>Use of Machine Learning Techniques for Iidentification of Robust Teleconnections to East African Rainfall Variability in Observations and Models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Roberts, J. Brent; Robertson, Franklin R.; Funk, Chris</p> <p>2014-01-01</p> <p>Providing advance warning of East African rainfall variations is a particular focus of several groups including those participating in the Famine Early Warming Systems Network. Both seasonal and long-term model projections of climate variability are being used to examine the societal impacts of hydrometeorological variability on seasonal to interannual and longer time scales. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of both seasonal and climate model projections to develop downscaled scenarios for using in impact modeling. The utility of these projections is reliant on the ability of current models to capture the embedded relationships between East African rainfall and evolving forcing within the coupled ocean-atmosphere-land climate system. Previous studies have posited relationships between variations in El Niño, the Walker circulation, Pacific decadal variability (PDV), and anthropogenic forcing. This study applies machine learning methods (e.g. clustering, probabilistic graphical model, nonlinear PCA) to observational datasets in an attempt to expose the importance of local and remote forcing mechanisms of East African rainfall variability. The ability of the NASA Goddard Earth Observing System (GEOS5) coupled model to capture the associated relationships will be evaluated using Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1198147-climate-simulations-projections-super-parameterized-climate-model','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1198147-climate-simulations-projections-super-parameterized-climate-model"><span>Climate simulations and projections with a super-parameterized climate model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Stan, Cristiana; Xu, Li</p> <p>2014-07-01</p> <p>The mean climate and its variability are analyzed in a suite of numerical experiments with a fully coupled general circulation model in which subgrid-scale moist convection is explicitly represented through embedded 2D cloud-system resolving models. Control simulations forced by the present day, fixed atmospheric carbon dioxide concentration are conducted using two horizontal resolutions and validated against observations and reanalyses. The mean state simulated by the higher resolution configuration has smaller biases. Climate variability also shows some sensitivity to resolution but not as uniform as in the case of mean state. The interannual and seasonal variability are better represented in themore » simulation at lower resolution whereas the subseasonal variability is more accurate in the higher resolution simulation. The equilibrium climate sensitivity of the model is estimated from a simulation forced by an abrupt quadrupling of the atmospheric carbon dioxide concentration. The equilibrium climate sensitivity temperature of the model is 2.77 °C, and this value is slightly smaller than the mean value (3.37 °C) of contemporary models using conventional representation of cloud processes. As a result, the climate change simulation forced by the representative concentration pathway 8.5 scenario projects an increase in the frequency of severe droughts over most of the North America.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMGC31D..02K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMGC31D..02K"><span>Century long observation constrained global dynamic downscaling and hydrologic implication</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kim, H.; Yoshimura, K.; Chang, E.; Famiglietti, J. S.; Oki, T.</p> <p>2012-12-01</p> <p>It has been suggested that greenhouse gas induced warming climate causes the acceleration of large scale hydrologic cycles, and, indeed, many regions on the Earth have been suffered by hydrologic extremes getting more frequent. However, historical observations are not able to provide enough information in comprehensive manner to understand their long-term variability and/or global distributions. In this study, a century long high resolution global climate data is developed in order to break through existing limitations. 20th Century Reanalysis (20CR) which has relatively low spatial resolution (~2.0°) and longer term availability (140 years) is dynamically downscaled into global T248 (~0.5°) resolution using Experimental Climate Prediction Center (ECPC) Global Spectral Model (GSM) by spectral nudging data assimilation technique. Also, Global Precipitation Climatology Centre (GPCC) and Climate Research Unit (CRU) observational data are adopted to reduce model dependent uncertainty. Downscaled product successfully represents realistic geographical detail keeping low frequency signal in mean state and spatiotemporal variability, while previous bias correction method fails to reproduce high frequency variability. Newly developed data is used to investigate how long-term large scale terrestrial hydrologic cycles have been changed globally and how they have been interacted with various climate modes, such as El-Niño Southern Oscillation (ENSO) and Atlantic Multidecadal Oscillation (AMO). As a further application, it will be used to provide atmospheric boundary condition of multiple land surface models in the Global Soil Wetness Project Phase 3 (GSWP3).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC21E0984M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC21E0984M"><span>The contribution of natural variability to GCM bias: Can we effectively bias-correct climate projections?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>McAfee, S. A.; DeLaFrance, A.</p> <p>2017-12-01</p> <p>Investigating the impacts of climate change often entails using projections from inherently imperfect general circulation models (GCMs) to drive models that simulate biophysical or societal systems in great detail. Error or bias in the GCM output is often assessed in relation to observations, and the projections are adjusted so that the output from impacts models can be compared to historical or observed conditions. Uncertainty in the projections is typically accommodated by running more than one future climate trajectory to account for differing emissions scenarios, model simulations, and natural variability. The current methods for dealing with error and uncertainty treat them as separate problems. In places where observed and/or simulated natural variability is large, however, it may not be possible to identify a consistent degree of bias in mean climate, blurring the lines between model error and projection uncertainty. Here we demonstrate substantial instability in mean monthly temperature bias across a suite of GCMs used in CMIP5. This instability is greatest in the highest latitudes during the cool season, where shifts from average temperatures below to above freezing could have profound impacts. In models with the greatest degree of bias instability, the timing of regional shifts from below to above average normal temperatures in a single climate projection can vary by about three decades, depending solely on the degree of bias assessed. This suggests that current bias correction methods based on comparison to 20- or 30-year normals may be inappropriate, particularly in the polar regions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.4103L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.4103L"><span>A Possible Cause for Recent Decadal Atlantic Meridional Overturning Circulation Decline</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Latif, Mojib; Park, Taewook; Park, Wonsun</p> <p>2017-04-01</p> <p>The Atlantic Meridional Overturning Circulation (AMOC) is a major oceanic current system with widespread climate impacts. AMOC influences have been discussed among others with regard to Atlantic hurricane activity, regional sea level variability, and surface air temperature and precipitation changes on land areas adjacent to the North Atlantic Ocean. Most climate models project significant AMOC slowing during the 21st century, if atmospheric greenhouse gas concentrations continue to rise unabatedly. Recently, a marked decadal decline in AMOC strength has been observed, which was followed by strongly reduced oceanic poleward heat transport and record low sea surface temperature in parts of the North Atlantic. Here, we provide evidence from observations, re-analyses and climate models that the AMOC decline was due to the combined action of the North Atlantic Oscillation and East Atlantic Pattern, the two leading modes of North Atlantic atmospheric surface pressure variability, which prior to the decline both transitioned into their negative phases. This change in atmospheric circulation diminished oceanic heat loss over the Labrador Sea and forced ocean circulation changes lowering upper ocean salinity transport into that region. As a consequence, Labrador Sea deep convection weakened, which eventually slowed the AMOC. This study suggests a new mechanism for decadal AMOC variability, which is important to multiyear climate predictability and climate change detection in the North Atlantic sector.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A51J0198H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A51J0198H"><span>High-resolution regional climate model evaluation using variable-resolution CESM over California</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Huang, X.; Rhoades, A.; Ullrich, P. A.; Zarzycki, C. M.</p> <p>2015-12-01</p> <p>Understanding the effect of climate change at regional scales remains a topic of intensive research. Though computational constraints remain a problem, high horizontal resolution is needed to represent topographic forcing, which is a significant driver of local climate variability. Although regional climate models (RCMs) have traditionally been used at these scales, variable-resolution global climate models (VRGCMs) have recently arisen as an alternative for studying regional weather and climate allowing two-way interaction between these domains without the need for nudging. In this study, the recently developed variable-resolution option within the Community Earth System Model (CESM) is assessed for long-term regional climate modeling over California. Our variable-resolution simulations will focus on relatively high resolutions for climate assessment, namely 28km and 14km regional resolution, which are much more typical for dynamically downscaled studies. For comparison with the more widely used RCM method, the Weather Research and Forecasting (WRF) model will be used for simulations at 27km and 9km. All simulations use the AMIP (Atmospheric Model Intercomparison Project) protocols. The time period is from 1979-01-01 to 2005-12-31 (UTC), and year 1979 was discarded as spin up time. The mean climatology across California's diverse climate zones, including temperature and precipitation, is analyzed and contrasted with the Weather Research and Forcasting (WRF) model (as a traditional RCM), regional reanalysis, gridded observational datasets and uniform high-resolution CESM at 0.25 degree with the finite volume (FV) dynamical core. The results show that variable-resolution CESM is competitive in representing regional climatology on both annual and seasonal time scales. This assessment adds value to the use of VRGCMs for projecting climate change over the coming century and improve our understanding of both past and future regional climate related to fine-scale processes. This assessment is also relevant for addressing the scale limitation of current RCMs or VRGCMs when next-generation model resolution increases to ~10km and beyond.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..1112317W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..1112317W"><span>Rainfall variability and extremes over southern Africa: assessment of a climate model to reproduce daily extremes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Williams, C.; Kniveton, D.; Layberry, R.</p> <p>2009-04-01</p> <p>It is increasingly accepted that that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA). This dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. The ability of a climate model to simulate current climate provides some indication of how much confidence can be applied to its future predictions. In this paper, simulations of current climate from the UK Meteorological Office Hadley Centre's climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. This concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of rainfall variability over southern Africa. Secondly, the ability of the model to reproduce daily rainfall extremes will be assessed, again by a comparison with extremes from the MIRA dataset.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/21241505','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/21241505"><span>Raised temperatures over the Kericho tea estates: revisiting the climate in the East African highlands malaria debate.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Omumbo, Judith A; Lyon, Bradfield; Waweru, Samuel M; Connor, Stephen J; Thomson, Madeleine C</p> <p>2011-01-17</p> <p>Whether or not observed increases in malaria incidence in the Kenyan Highlands during the last thirty years are associated with co-varying changes in local temperature, possibly connected to global changes in climate, has been debated for over a decade. Studies, using differing data sets and methodologies, produced conflicting results regarding the occurrence of temperature trends and their likelihood of being responsible, at least in part, for the increases in malaria incidence in the highlands of western Kenya. A time series of quality controlled daily temperature and rainfall data from Kericho, in the Kenyan Highlands, may help resolve the controversy. If significant temperature trends over the last three decades have occurred then climate should be included (along with other factors such as land use change and drug resistance) as a potential driver of the observed increases in malaria in the region. Over 30 years (1 January 1979 to 31 December 2009) of quality controlled daily observations ( > 97% complete) of maximum, minimum and mean temperature were used in the analysis of trends at Kericho meteorological station, sited in a tea growing area of Kenya's western highlands. Inhomogeneities in all the time series were identified and corrected. Linear trends were identified via a least-squares regression analysis with statistical significance assessed using a two-tailed t-test. These 'gold standard' meteorological observations were compared with spatially interpolated temperature datasets that have been developed for regional or global applications. The relationship of local climate processes with larger climate variations, including tropical sea surface temperatures (SST), and El Niño-Southern Oscillation (ENSO) was also assessed. An upward trend of ≈0.2°C/decade was observed in all three temperature variables (P < 0.01). Mean temperature variations in Kericho were associated with large-scale climate variations including tropical SST (r = 0.50; p < 0.01). Local rainfall was found to have inverse effects on minimum and maximum temperature. Three versions of a spatially interpolated temperature data set showed markedly different trends when compared with each other and with the Kericho station observations. This study presents evidence of a warming trend in observed maximum, minimum and mean temperatures at Kericho during the period 1979 to 2009 using gold standard meteorological observations. Although local factors may be contributing to these trends, the findings are consistent with variability and trends that have occurred in correlated global climate processes. Climate should therefore not be dismissed as a potential driver of observed increases in malaria seen in the region during recent decades, however its relative importance compared to other factors needs further elaboration. Climate services, pertinent to the achievement of development targets such as the Millennium Development Goals and the analysis of infectious disease in the context of climate variability and change are being developed and should increase the availability of relevant quality controlled climate data for improving development decisions. The malaria community should seize this opportunity to make their needs heard.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5389124','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5389124"><span>Climate Variability and Inter-Provincial Migration in South America, 1970-2011</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Thiede, Brian; Gray, Clark; Mueller, Valerie</p> <p>2016-01-01</p> <p>We examine the effect of climate variability on human migration in South America. Our analyses draw on over 21 million observations of adults aged 15-40 from 25 censuses conducted in eight South American countries. Addressing limitations associated with methodological diversity among prior studies, we apply a common analytic approach and uniform definitions of migration and climate across all countries. We estimate the effects of climate variability on migration overall and also investigate heterogeneity across sex, age, and socioeconomic groups, across countries, and across historical climate conditions. We also disaggregate migration by the rural/urban status of destination. We find that exposure to monthly temperature shocks has the most consistent effects on migration relative to monthly rainfall shocks and gradual changes in climate over multi-year periods. We also find evidence of heterogeneity across demographic groups and countries. Analyses that disaggregate migration by the rural/urban status of destination suggest that much of the climate-related inter-province migration is directed toward urban areas. Overall, our results underscore the complexity of environment-migration linkages and challenge simplistic narratives that envision a linear and monolithic migratory response to changing climates. PMID:28413264</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28413264','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28413264"><span>Climate Variability and Inter-Provincial Migration in South America, 1970-2011.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Thiede, Brian; Gray, Clark; Mueller, Valerie</p> <p>2016-11-01</p> <p>We examine the effect of climate variability on human migration in South America. Our analyses draw on over 21 million observations of adults aged 15-40 from 25 censuses conducted in eight South American countries. Addressing limitations associated with methodological diversity among prior studies, we apply a common analytic approach and uniform definitions of migration and climate across all countries. We estimate the effects of climate variability on migration overall and also investigate heterogeneity across sex, age, and socioeconomic groups, across countries, and across historical climate conditions. We also disaggregate migration by the rural/urban status of destination. We find that exposure to monthly temperature shocks has the most consistent effects on migration relative to monthly rainfall shocks and gradual changes in climate over multi-year periods. We also find evidence of heterogeneity across demographic groups and countries. Analyses that disaggregate migration by the rural/urban status of destination suggest that much of the climate-related inter-province migration is directed toward urban areas. Overall, our results underscore the complexity of environment-migration linkages and challenge simplistic narratives that envision a linear and monolithic migratory response to changing climates.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29769728','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29769728"><span>Emerging trends in global freshwater availability.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Rodell, M; Famiglietti, J S; Wiese, D N; Reager, J T; Beaudoing, H K; Landerer, F W; Lo, M-H</p> <p>2018-05-01</p> <p>Freshwater availability is changing worldwide. Here we quantify 34 trends in terrestrial water storage observed by the Gravity Recovery and Climate Experiment (GRACE) satellites during 2002-2016 and categorize their drivers as natural interannual variability, unsustainable groundwater consumption, climate change or combinations thereof. Several of these trends had been lacking thorough investigation and attribution, including massive changes in northwestern China and the Okavango Delta. Others are consistent with climate model predictions. This observation-based assessment of how the world's water landscape is responding to human impacts and climate variations provides a blueprint for evaluating and predicting emerging threats to water and food security.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.B41L..05C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.B41L..05C"><span>Biomass and the Climatic Space from historical to future scenarios of a Seasonally Dry Tropical Forest - Caatinga</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Castanho, A. D. D. A.; Coe, M. T.; Maia Andrade, E.; Walker, W.; Baccini, A.; Brando, P. M.; Farina, M.</p> <p>2017-12-01</p> <p>The Caatinga found in the semiarid region in northeastern Brazil is the largest continuous seasonally dry tropical forest in South America. The region has for centuries been subject to anthropogenic activities of land conversion, abandonment, and regrowth. The region also has a large spatial variability of edaphic-climatic properties. These effects together contribute to a wide variability of plant physiognomies and biomass concentration. In addition to land use change due to anthropogenic activities the region is exposed in the near and long term to dryer conditions. The main goal of this work was to validate a high spatial resolution (30 m) map of above ground biomass, understand the climatic role in the biomass spatial variability in the present, and the potential threat to vegetation for future climatic shifts. Satellite-derived biomass products are advanced tools that can address spatial changes in forest structure for an extended region. Here we combine a compilation of published field phytosociological observations across the region with a new 30-meter spatial resolution satellite biomass product. Climate data used for this analyses were based on the CRU (Climate Research Unit, UEA) for the historical time period and for the future a mean and 25-75% quantiles of the CMIP Global Climate model estimates for the RCP scenarios of 4.5 and 8.5 W/m2. The high heterogeneity in the biomass and physiognomy distribution across the Caatinga region is mostly explained by the climatic space defined by the precipitation and dryness index. The Caatinga region has historically already been exposed to shift in its climatic properties, driving all the physiognomies, to a dryer climatic space within the last decade. Future climate intensify the observed trends. This study provides a clearer understanding of the spatial distribution of Caatinga vegetation, its biomass, and relationships to climate, which are essential for strategic development planning, preservation of the biome functions, human services, and biodiversity, face future climate scenarios.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70178805','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70178805"><span>Climate variability and extremes, interacting with nitrogen storage, amplify eutrophication risk</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Lee, Minjin; Shevliakova, Elena; Malyshev, Sergey; Milly, P.C.D.; Jaffe, Peter R.</p> <p>2016-01-01</p> <p>Despite 30 years of basin-wide nutrient-reduction efforts, severe hypoxia continues to be observed in the Chesapeake Bay. Here we demonstrate the critical influence of climate variability, interacting with accumulated nitrogen (N) over multidecades, on Susquehanna River dissolved nitrogen (DN) loads, known precursors of the hypoxia in the Bay. We used the process model LM3-TAN (Terrestrial and Aquatic Nitrogen), which is capable of capturing both seasonal and decadal-to-century changes in vegetation-soil-river N storage, and produced nine scenarios of DN-load distributions under different short-term scenarios of climate variability and extremes. We illustrate that after 1 to 3 yearlong dry spells, the likelihood of exceeding a threshold DN load (56 kt yr−1) increases by 40 to 65% due to flushing of N accumulated throughout the dry spells and altered microbial processes. Our analyses suggest that possible future increases in climate variability/extremes—specifically, high precipitation occurring after multiyear dry spells—could likely lead to high DN-load anomalies and hypoxia.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_14 --> <div id="page_15" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="281"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20040082190&hterms=Asian&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DAsian','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20040082190&hterms=Asian&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DAsian"><span>The North Pacific as a Regulator of Summertime Climate Over North America and the Asian Monsoon</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lau, William K. M.; Wang, H.</p> <p>2004-01-01</p> <p>The interannual variability of summertime rainfall over the U.S. may be linked to climate anomalies over Pacific and East Asia through teleconnection patterns that may be components of recurring global climate modes in boreal summer (Lau and Weng 2002). In this study, maintenance of the boreal summer teleconnection patterns is investigated. The particular focus is on the potential effects of North Pacific air-sea interaction on climate anomalies over the U.S. Observational data, reanalysis and outputs of a series of NASA NSIPP AGCM and AGCM coupled to NASA GSFC MLO model experiments are used. Statistical analysis of observations and NSIPP AMIP type simulations indicates that, the interannual variability of observed warm season precipitation over the U.S. is related to SST variation in both tropical and North Pacific, whereas the NSIPP AMIP simulated summertime US. precipitation variation mainly reflects impact of ENS0 in tropical Pacific. This implies the potential importance of air-sea interaction in North Pacific in contributing to the interannual variability of observed summer climate over the U.S. The anomalous atmospheric circulation associated with the dominant summertime teleconnection modes in both observations and NSIPP AMIP simulations are further diagnosed, using stationary wave modeling approach. In observations, for the two dominant modes, both anomalous diabatic heating and anomalous transients significantly contribute to the anomalous circulation. The distributions of the anomalous diabatic heating and transient forcing are quadrature configured over North Pacific and North America, so that both forcings act constructively to maintain the teleconnection patterns. The contrast between observations and NSIPP AMIP simulations from stationary wave modeling diagnosis confirms the previous conclusion based on statistical analysis. To better appreciate the role of extra-tropical air-sea interaction in maintaining the summertime teleconnection pattern, various dynamical and physical fields and their inter- linkage in the series of NSIPP AGCM and AGCM coupled to MLO model experiments are examined in-depth. Based on comparison between different model experiments, we will discuss the physical and dynamical mechanisms through which the air-sea interaction in extratropics, and transient mean flow interactions over the North Pacific, affects interannual variation of U.S. climate during boreal summer.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMNG41A0121M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMNG41A0121M"><span>Extracting Leading Nonlinear Modes of Changing Climate From Global SST Time Series</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mukhin, D.; Gavrilov, A.; Loskutov, E. M.; Feigin, A. M.; Kurths, J.</p> <p>2017-12-01</p> <p>Data-driven modeling of climate requires adequate principal variables extracted from observed high-dimensional data. For constructing such variables it is needed to find spatial-temporal patterns explaining a substantial part of the variability and comprising all dynamically related time series from the data. The difficulties of this task rise from the nonlinearity and non-stationarity of the climate dynamical system. The nonlinearity leads to insufficiency of linear methods of data decomposition for separating different processes entangled in the observed time series. On the other hand, various forcings, both anthropogenic and natural, make the dynamics non-stationary, and we should be able to describe the response of the system to such forcings in order to separate the modes explaining the internal variability. The method we present is aimed to overcome both these problems. The method is based on the Nonlinear Dynamical Mode (NDM) decomposition [1,2], but takes into account external forcing signals. An each mode depends on hidden, unknown a priori, time series which, together with external forcing time series, are mapped onto data space. Finding both the hidden signals and the mapping allows us to study the evolution of the modes' structure in changing external conditions and to compare the roles of the internal variability and forcing in the observed behavior. The method is used for extracting of the principal modes of SST variability on inter-annual and multidecadal time scales accounting the external forcings such as CO2, variations of the solar activity and volcanic activity. The structure of the revealed teleconnection patterns as well as their forecast under different CO2 emission scenarios are discussed.[1] Mukhin, D., Gavrilov, A., Feigin, A., Loskutov, E., & Kurths, J. (2015). Principal nonlinear dynamical modes of climate variability. Scientific Reports, 5, 15510. [2] Gavrilov, A., Mukhin, D., Loskutov, E., Volodin, E., Feigin, A., & Kurths, J. (2016). Method for reconstructing nonlinear modes with adaptive structure from multidimensional data. Chaos: An Interdisciplinary Journal of Nonlinear Science, 26(12), 123101.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFM.H53D1107Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFM.H53D1107Z"><span>An Integrated Hydro-Economic Model for Economy-Wide Climate Change Impact Assessment for Zambia</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhu, T.; Thurlow, J.; Diao, X.</p> <p>2008-12-01</p> <p>Zambia is a landlocked country in Southern Africa, with a total population of about 11 million and a total area of about 752 thousand square kilometers. Agriculture in the country depends heavily on rainfall as the majority of cultivated land is rain-fed. Significant rainfall variability has been a huge challenge for the country to keep a sustainable agricultural growth, which is an important condition for the country to meet the United Nations Millennium Development Goals. The situation is expected to become even more complex as climate change would impose additional impacts on rainwater availability and crop water requirements, among other changes. To understand the impacts of climate variability and change on agricultural production and national economy, a soil hydrology model and a crop water production model are developed to simulate actual crop water uses and yield losses under water stress which provide annual shocks for a recursive dynamic computational general equilibrium (CGE) model developed for Zambia. Observed meteorological data of the past three decades are used in the integrated hydro-economic model for climate variability impact analysis, and as baseline climatology for climate change impact assessment together with several GCM-based climate change scenarios that cover a broad range of climate projections. We found that climate variability can explain a significant portion of the annual variations of agricultural production and GDP of Zambia in the past. Hidden beneath climate variability, climate change is found to have modest impacts on agriculture and national economy of Zambia around 2025 but the impacts would be pronounced in the far future if appropriate adaptations are not implemented. Policy recommendations are provided based on scenario analysis.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC32A..01A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC32A..01A"><span>Assessing the Impact of Climatic Variability and Change on Maize Production in the Midwestern USA</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Andresen, J.; Jain, A. K.; Niyogi, D. S.; Alagarswamy, G.; Biehl, L.; Delamater, P.; Doering, O.; Elias, A.; Elmore, R.; Gramig, B.; Hart, C.; Kellner, O.; Liu, X.; Mohankumar, E.; Prokopy, L. S.; Song, C.; Todey, D.; Widhalm, M.</p> <p>2013-12-01</p> <p>Weather and climate remain among the most important uncontrollable factors in agricultural production systems. In this study, three process-based crop simulation models were used to identify the impacts of climate on the production of maize in the Midwestern U.S.A. during the past century. The 12-state region is a key global production area, responsible for more than 80% of U.S. domestic and 25% of total global production. The study is a part of the Useful to Useable (U2U) Project, a USDA NIFA-sponsored project seeking to improve the resilience and profitability of farming operations in the region amid climate variability and change. Three process-based crop simulation models were used in the study: CERES-Maize (DSSAT, Hoogenboom et al., 2012), the Hybrid-Maize model (Yang et al., 2004), and the Integrated Science Assessment Model (ISAM, Song et al., 2013). Model validation was carried out with individual plot and county observations. The models were run with 4 to 50 km spatial resolution gridded weather data for representative soils and cultivars, 1981-2012, to examine spatial and temporal yield variability within the region. We also examined the influence of different crop models and spatial scales on regional scale yield estimation, as well as a yield gap analysis between observed and attainable yields. An additional study was carried out with the CERES-Maize model at 18 individual site locations 1901-2012 to examine longer term historical trends. For all simulations, all input variables were held constant in order to isolate the impacts of climate. In general, the model estimates were in good agreement with observed yields, especially in central sections of the region. Regionally, low precipitation and soil moisture stress were chief limitations to simulated crop yields. The study suggests that at least part of the observed yield increases in the region during recent decades have occurred as the result of wetter, less stressful growing season weather conditions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1918650F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1918650F"><span>Analysis of the historical precipitation in the South East Iberian Peninsula at different spatio-temporal scale. Study of the meteorological drought</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fernández-Chacón, Francisca; Pulido-Velazquez, David; Jiménez-Sánchez, Jorge; Luque-Espinar, Juan Antonio</p> <p>2017-04-01</p> <p>Precipitation is a fundamental climate variable that has a pronounced spatial and temporal variability on a global scale, as well as at regional and sub-regional scales. Due to its orographic complexity and its latitude the Iberian Peninsula (IP), located to the west of the Mediterranean Basin between the Atlantic Ocean and the Mediterranean Sea, has a complex climate. Over the peninsula there are strong north-south and east-west gradients, as a consequence of the different low-frequency atmospheric patterns, and he overlap of these over the year will be determinants in the variability of climatic variables. In the southeast of the Iberian Peninsula dominates a dry Mediterranean climate, the precipitation is characterized as being an intermittent and discontinuous variable. In this research information coming from the Spain02 v4 database was used to study the South East (SE) IP for the 1971-2010 period with a spatial resolution of 0.11 x 0.11. We analysed precipitation at different time scale (daily, monthly, seasonal, annual,…) to study the spatial distribution and temporal tendencies. The high spatial, intra-annual and inter-annual climatic variability observed makes it necessary to propose a climatic regionalization. In addition, for the identified areas and subareas of homogeneous climate we have analysed the evolution of the meteorological drought for the same period at different time scales. The standardized precipitation index has been used at 12, 24 and 48 month temporal scale. The climatic complexity of the area determines a high variability in the drought characteristics, duration, intensity and frequency in the different climatic areas. This research has been supported by the GESINHIMPADAPT project (CGL2013-48424-C2-2-R) with Spanish MINECO funds. We would also like to thank Spain02 project for the data provided for this study.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H11O..08S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H11O..08S"><span>a New Framework for Characterising Simulated Droughts for Future Climates</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sharma, A.; Rashid, M.; Johnson, F.</p> <p>2017-12-01</p> <p>Significant attention has been focussed on metrics for quantifying drought. Lesser attention has been given to the unsuitability of current metrics in quantifying drought in a changing climate due to the clear non-stationarity in potential and actual evapotranspiration well into the future (Asadi-Zarch et al, 2015). This talk presents a new basis for simulating drought designed specifically for use with climate model simulations. Given the known uncertainty of climate model rainfall simulations, along with their inability to represent low-frequency variability attributes, the approach here adopts a predictive model for drought using selected atmospheric indicators. This model is based on a wavelet decomposition of relevant atmospheric predictors to filter out less relevant frequencies and formulate a better characterisation of the drought metric chosen as response. Once ascertained using observed precipication and associated atmospheric variables, these can be formulated from GCM simulations using a multivariate bias correction tool (Mehrotra and Sharma, 2016) that accounts for low-frequency variability, and a regression tool that accounts for nonlinear dependence (Sharma and Mehrotra, 2014). Use of only the relevant frequencies, as well as the corrected representation of cross-variable dependence, allows greater accuracy in characterising observed drought, from GCM simulations. Using simulations from a range of GCMs across Australia, we show here that this new method offers considerable advantages in representing drought compared to traditionally followed alternatives that rely on modelled rainfall instead. Reference:Asadi Zarch, M. A., B. Sivakumar, and A. Sharma (2015), Droughts in a warming climate: A global assessment of Standardized precipitation index (SPI) and Reconnaissance drought index (RDI), Journal of Hydrology, 526, 183-195. Mehrotra, R., and A. Sharma (2016), A Multivariate Quantile-Matching Bias Correction Approach with Auto- and Cross-Dependence across Multiple Time Scales: Implications for Downscaling, Journal of Climate, 29(10), 3519-3539. Sharma, A., and R. Mehrotra (2014), An information theoretic alternative to model a natural system using observational information alone, Water Resources Research, 50, 650-660, doi:10.1002/2013WR013845.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20160013888&hterms=eastern+western&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Deastern%2Bwestern%26Nf%3DPublication-Date%257CBTWN%2B20070101%2B20180604','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20160013888&hterms=eastern+western&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Deastern%2Bwestern%26Nf%3DPublication-Date%257CBTWN%2B20070101%2B20180604"><span>An Assessment of Multimodel Simulations for the Variability of Western North Pacific Tropical Cyclones and Its Association with ENSO</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Han, Rongqing; Wang, Hui; Hu, Zeng-Zhen; Kumar, Arun; Li, Weijing; Long, Lindsey N.; Schemm, Jae-Kyung E.; Peng, Peitao; Wang, Wanqiu; Si, Dong; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20160013888'); toggleEditAbsImage('author_20160013888_show'); toggleEditAbsImage('author_20160013888_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20160013888_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20160013888_hide"></p> <p>2016-01-01</p> <p>An assessment of simulations of the interannual variability of tropical cyclones (TCs) over the western North Pacific (WNP) and its association with El Niño-Southern Oscillation (ENSO), as well as a subsequent diagnosis for possible causes of model biases generated from simulated large-scale climate conditions, are documented in the paper. The model experiments are carried out by the Hurricane Work Group under the U.S. Climate Variability and Predictability Research Program (CLIVAR) using five global climate models (GCMs) with a total of 16 ensemble members forced by the observed sea surface temperature and spanning the 28-yr period from 1982 to 2009. The results show GISS and GFDL model ensemble means best simulate the interannual variability of TCs, and the multimodel ensemble mean (MME) follows. Also, the MME has the closest climate mean annual number of WNP TCs and the smallest root-mean-square error to the observation. Most GCMs can simulate the interannual variability of WNP TCs well, with stronger TC activities during two types of El Niño-namely, eastern Pacific (EP) and central Pacific (CP) El Niño-and weaker activity during La Niña. However, none of the models capture the differences in TC activity between EP and CP El Niño as are shown in observations. The inability of models to distinguish the differences in TC activities between the two types of El Niño events may be due to the bias of the models in response to the shift of tropical heating associated with CP El Niño.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUOSPC14C2085A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUOSPC14C2085A"><span>Observed Temporal and Spatial Variability in the Marine Environment at the Sub-Antarctic Prince Edward Islands - Evidence of a Changing Climate?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Asdar, S.; Deshayes, J.; Ansorge, I. J.</p> <p>2016-02-01</p> <p>The sub-Antarctic Prince Edward Islands (PEI) (47°S,38°E) are classified as isolated, hostile, impoverished regions, in which the terrestrial and marine ecosystems are relatively simple and extremely sensitive to perturbations. Their location between the Sub-Antarctic Front (SAF) and the Antarctic Polar Front (APF), bordering the Antarctic Circumpolar Current (ACC) provides an ideal natural laboratory for studying how organisms, ecological processes and ecosystems respond to a changing ocean climate in the Southern Ocean. Recent studies have proposed that climate changes reported at the PEI may correspond in time to a southward shift of the ACC and in particular of the SAF. This southward migration in the geographic position is likely to coincide with dramatic changes in the distribution of species and total productivity of this region. This study focuses on the inter-comparison of observations available at these islands. Using spectral analysis which is a study of the frequency domain characteristics of a process, we first determine the dominant characteristics of both the temporal and spatial variability of physical and biogeochemical properties. In doing so the authors are able to determine whether and how these indices of variability interact with one another in order to understand better the mechanisms underpinning this variability, i.e. the seasonal zonal migrations associated with the SAF. Additionally, we include in our analysis recent data from 2 ADCP moorings deployed between the islands from 2014 to 2015. These in-situ observations of circulation and hydrography in the vicinity of the islands provide a unique opportunity to establish a better understanding of how large scale climatic variability may impact local conditions, and more importantly its influence on the fragile ecosystem surrounding the PEI.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMGC21H..06G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMGC21H..06G"><span>Creating Near-Term Climate Scenarios for AgMIP</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Goddard, L.; Greene, A. M.; Baethgen, W.</p> <p>2012-12-01</p> <p>For the next assessment report of the IPCC (AR5), attention is being given to development of climate information that is appropriate for adaptation, such as decadal-scale and near-term predictions intended to capture the combined effects of natural climate variability and the emerging climate change signal. While the science and practice evolve for the production and use of dynamic decadal prediction, information relevant to agricultural decision-makers can be gained from analysis of past decadal-scale trends and variability. Statistical approaches that mimic the characteristics of observed year-to-year variability can indicate the range of possibilities and their likelihood. In this talk we present work towards development of near-term climate scenarios, which are needed to engage decision-makers and stakeholders in the regions in current decision-making. The work includes analyses of decadal-scale variability and trends in the AgMIP regions, and statistical approaches that capture year-to-year variability and the associated persistence of wet and dry years. We will outline the general methodology and some of the specific considerations in the regional application of the methodology for different AgMIP regions, such those for Western Africa versus southern Africa. We will also show some examples of quality checks and informational summaries of the generated data, including (1) metrics of information quality such as probabilistic reliability for a suite of relevant climate variables and indices important for agriculture; (2) quality checks relative to the use of this climate data in crop models; and, (3) summary statistics (e.g., for 5-10-year periods or across given spatial scales).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMOS23B1398B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMOS23B1398B"><span>Uncertainties in Future Regional Sea Level Trends: How to Deal with the Internal Climate Variability?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Becker, M.; Karpytchev, M.; Hu, A.; Deser, C.; Lennartz-Sassinek, S.</p> <p>2017-12-01</p> <p>Today, the Climate models (CM) are the main tools for forecasting sea level rise (SLR) at global and regional scales. The CM forecasts are accompanied by inherent uncertainties. Understanding and reducing these uncertainties is becoming a matter of increasing urgency in order to provide robust estimates of SLR impact on coastal societies, which need sustainable choices of climate adaptation strategy. These CM uncertainties are linked to structural model formulation, initial conditions, emission scenario and internal variability. The internal variability is due to complex non-linear interactions within the Earth Climate System and can induce diverse quasi-periodic oscillatory modes and long-term persistences. To quantify the effects of internal variability, most studies used multi-model ensembles or sea level projections from a single model ran with perturbed initial conditions. However, large ensembles are not generally available, or too small, and computationally expensive. In this study, we use a power-law scaling of sea level fluctuations, as observed in many other geophysical signals and natural systems, which can be used to characterize the internal climate variability. From this specific statistical framework, we (1) use the pre-industrial control run of the National Center for Atmospheric Research Community Climate System Model (NCAR-CCSM) to test the robustness of the power-law scaling hypothesis; (2) employ the power-law statistics as a tool for assessing the spread of regional sea level projections due to the internal climate variability for the 21st century NCAR-CCSM; (3) compare the uncertainties in predicted sea level changes obtained from a NCAR-CCSM multi-member ensemble simulations with estimates derived for power-law processes, and (4) explore the sensitivity of spatial patterns of the internal variability and its effects on regional sea level projections.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018GeoRL..45.2487W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018GeoRL..45.2487W"><span>Disentangling Global Warming, Multidecadal Variability, and El Niño in Pacific Temperatures</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wills, Robert C.; Schneider, Tapio; Wallace, John M.; Battisti, David S.; Hartmann, Dennis L.</p> <p>2018-03-01</p> <p>A key challenge in climate science is to separate observed temperature changes into components due to internal variability and responses to external forcing. Extended integrations of forced and unforced climate models are often used for this purpose. Here we demonstrate a novel method to separate modes of internal variability from global warming based on differences in time scale and spatial pattern, without relying on climate models. We identify uncorrelated components of Pacific sea surface temperature variability due to global warming, the Pacific Decadal Oscillation (PDO), and the El Niño-Southern Oscillation (ENSO). Our results give statistical representations of PDO and ENSO that are consistent with their being separate processes, operating on different time scales, but are otherwise consistent with canonical definitions. We isolate the multidecadal variability of the PDO and find that it is confined to midlatitudes; tropical sea surface temperatures and their teleconnections mix in higher-frequency variability. This implies that midlatitude PDO anomalies are more persistent than previously thought.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C53B1033S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C53B1033S"><span>Spatio-temporal Variability of Stratified Snowpack Cold Content Observed in the Rocky Mountains</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schmidt, J. S.; Sexstone, G. A.; Serreze, M. C.</p> <p>2017-12-01</p> <p>Snowpack cold content (CCsnow) is the energy required to bring a snowpack to an isothermal temperature of 0.0°C. The spatio-temporal variability of CCsnow is complex as it is a measure that integrates the response of a snowpack to each component of the snow-cover energy balance. Snow and ice at high elevation is climate sensitive water storage for the Western U.S. Therefore, an improved understanding of the spatio-temporal variability of CCsnow may provide insight into snowpack dynamics and sensitivity to climate change. In this study, stratified snowpit observations of snow water equivalent (SWE) and snow temperature (Tsnow) from the USGS Rocky Mountain Snowpack network (USGS RMS) were used to evaluate vertical CCsnow profiles over a 16-year period in Montana, Idaho, Wyoming, Colorado and New Mexico. Since 1993, USGS RMS has collected snow chemistry, snow temperature, and SWE data throughout the Rocky Mountain region, making it well positioned for Anthropocene cryosphere benchmarking and climate change interpretation. Spatial grouping of locations based on similar CCsnow characteristics was evaluated and trend analyses were performed. Additionally, we evaluated the regional relation of CCsnow to snowmelt timing. CCsnow was more precisely calculated and more representative using vertically stratified field observed values than bulk values, which highlights the utility of the snowpack dataset presented here. Location specific annual and 16 year mean stratified snowpit profiles of SWE, Tsnow, and CCsnow well represent the physical geography and past weather patterns acting on the snowpack. Observed trends and spatial variability of CCsnow profiles explored by this study provides an improved understanding of changing snowpack behavior in the western U.S., and will be useful for assessing the regional sensitivity of snowpacks to future climate change.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..11.1941V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..11.1941V"><span>The GCOS Reference Upper-Air Network (GRUAN)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vömel, H.; Berger, F. H.; Immler, F. J.; Seidel, D.; Thorne, P.</p> <p>2009-04-01</p> <p>While the global upper-air observing network has provided useful observations for operational weather forecasting for decades, its measurements lack the accuracy and long-term continuity needed for understanding climate change. Consequently, the scientific community faces uncertainty on such key issues as the trends of temperature in the upper troposphere and stratosphere or the variability and trends of stratospheric water vapour. To address these shortcomings, and to ensure that future climate records will be more useful than the records to date, the Global Climate Observing System (GCOS) program initiated the GCOS Reference Upper Air Network (GRUAN). GRUAN will be a network of about 30-40 observatories with a representative sampling of geographic regions and surface types. These stations will provide upper-air reference observations of the essential climate variables, i.e. temperature, geopotential, humidity, wind, radiation and cloud properties using specialized radiosondes and complementary remote sensing profiling instrumentation. Long-term stability, quality assurance / quality control, and a detailed assessment of measurement uncertainties will be the key aspects of GRUAN observations. The network will not be globally complete but will serve to constrain and adjust data from more spatially comprehensive global observing systems including satellites and the current radiosonde networks. This paper outlines the scientific rationale for GRUAN, its role in the Global Earth Observation System of Systems, network requirements and likely instrumentation, management structure, current status and future plans.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014cosp...40E2425P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014cosp...40E2425P"><span>EO based Agro-ecosystem approach for climate change adaptation in enhancing the crop production efficiency in the Indo-gangetic plains of India</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pandey, Suraj</p> <p></p> <p>This study develops a spatial mapping of agro-ecological zones based on earth observation model using MODIS regional dataset as a tool to guide key areas of cropping system and targeting to climate change strategies. This tool applies to the Indo-gangetic Plains of north India to target the domains of bio-physical characteristics and socio-economics with respect to changing climate in the region. It derive on secondary data for spatially-explicit variables at the state/district level, which serve as indicators of climate variability based on sustainable livelihood approach, natural, social and human. The study details the methodology used and generates the spatial climate risk maps for composite indicators of livelihood and vulnerability index in the region.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMOS43B2030N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMOS43B2030N"><span>How Well Has Global Ocean Heat Content Variability Been Measured?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nelson, A.; Weiss, J.; Fox-Kemper, B.; Fabienne, G.</p> <p>2016-12-01</p> <p>We introduce a new strategy that uses synthetic observations of an ensemble of model simulations to test the fidelity of an observational strategy, quantifying how well it captures the statistics of variability. We apply this test to the 0-700m global ocean heat content anomaly (OHCA) as observed with in-situ measurements by the Coriolis Dataset for Reanalysis (CORA), using the Community Climate System Model (CCSM) version 3.5. One-year running mean OHCAs for the years 2005 onward are found to faithfully capture the variability. During these years, synthetic observations of the model are strongly correlated at 0.94±0.06 with the actual state of the model. Overall, sub-annual variability and data before 2005 are significantly affected by the variability of the observing system. In contrast, the sometimes-used weighted integral of observations is not a good indicator of OHCA as variability in the observing system contaminates dynamical variability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GBioC..30...18A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GBioC..30...18A"><span>Changing Amazon biomass and the role of atmospheric CO2 concentration, climate, and land use</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Almeida Castanho, Andrea D.; Galbraith, David; Zhang, Ke; Coe, Michael T.; Costa, Marcos H.; Moorcroft, Paul</p> <p>2016-01-01</p> <p>The Amazon tropical evergreen forest is an important component of the global carbon budget. Its forest floristic composition, structure, and function are sensitive to changes in climate, atmospheric composition, and land use. In this study biomass and productivity simulated by three dynamic global vegetation models (Integrated Biosphere Simulator, Ecosystem Demography Biosphere Model, and Joint UK Land Environment Simulator) for the period 1970-2008 are compared with observations from forest plots (Rede Amazónica de Inventarios Forestales). The spatial variability in biomass and productivity simulated by the DGVMs is low in comparison to the field observations in part because of poor representation of the heterogeneity of vegetation traits within the models. We find that over the last four decades the CO2 fertilization effect dominates a long-term increase in simulated biomass in undisturbed Amazonian forests, while land use change in the south and southeastern Amazonia dominates a reduction in Amazon aboveground biomass, of similar magnitude to the CO2 biomass gain. Climate extremes exert a strong effect on the observed biomass on short time scales, but the models are incapable of reproducing the observed impacts of extreme drought on forest biomass. We find that future improvements in the accuracy of DGVM predictions will require improved representation of four key elements: (1) spatially variable plant traits, (2) soil and nutrients mediated processes, (3) extreme event mortality, and (4) sensitivity to climatic variability. Finally, continued long-term observations and ecosystem-scale experiments (e.g. Free-Air CO2 Enrichment experiments) are essential for a better understanding of the changing dynamics of tropical forests.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.2969Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.2969Z"><span>Smoothing of millennial scale climate variability in European Loess (and other records)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zeeden, Christian; Obreht, Igor; Hambach, Ulrich; Veres, Daniel; Marković, Slobodan B.; Lehmkuhl, Frank</p> <p>2017-04-01</p> <p>Millennial scale climate variability is seen in various records of the northern hemisphere in the last glacial cycle, and their expression represents a correlation tool beyond the resolution of e.g. luminescence dating. Highest (correlative) dating accuracy is a prerequisite of comparing different geoarchives, especially when related to archaeological findings. Here we attempt to constrain the timing of loess geoarchives representing the environmental context of early humans in south-eastern Europe, and discuss the challenge of dealing with smoothed records. In this contribution, we present rock magnetic and grain size data from the Rasova loess record in the Lower Danube basin (Romania), showing millennial scale climate variability. Additionally, we summarize similar data from the Lower and Middle Danube Basins. A comparison of these loess data and reference records from Greenland ice cores and the Mediterranean-Black Sea region indicates a rather unusual expression of millennial scale climate variability recorded in loess. To explain the observed patterns, we experiment with low-pass filters of reference records to simulate a signal smoothing by natural processes such as e.g. bioturbation and pervasive diagenesis. Low-pass filters avoid high frequency oscillations and focus on the longer period (lower frequency) variability, here using cut-off periods from 1-15 kyr. In our opinion low-pass filters represent simple models for the expression of millennial scale climate variability in low sedimentation environments, and in sediments where signals are smoothed by e.g. bioturbation and/or diagenesis. Using different low-pass filter thresholds allows us to (a) explain observed patterns and their relation to millennial scale climate variability, (b) propose these filtered/smoothed signals as correlation targets for records lacking millennial scale recording, but showing smoothed climate variability on supra-millennial scales, and (c) determine which time resolution specific (loess) records can reproduce. Comparing smoothed records to reference data may be a step forward especially for last glacial stratigraphies, where millennial scale patterns are certainly present but not directly recorded in some geoarchives. Interestingly, smoothed datasets from Greenland and the Black Sea-Mediterranean region are most similar in the last ca. 15 ka and again from ca. 30-50 ka. During the cold phase from ca. 30-15 ka records show dissimilarities, challenging robust correlative time scales in this age range. A potential explanation may be related to the expansion of Northern European and Alpine ice sheets influencing atmospheric systems in the North Atlantic and Eurasian regions and thus leading to regionally and temporally differentiated climatic responses.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFMPP31A1473M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFMPP31A1473M"><span>Medieval Warm Period Archives Preserved in Limpet Shells (Patella Vulgata) From Viking Deposits, United Kingdom</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mobilia, M.; Surge, D.</p> <p>2008-12-01</p> <p>The Medieval Warm Period (700-1100 YBP) represents a recent period of warm climate, and as such provides a powerful comparison to today's continuing warming trend. However, the spatial and temporal variability inherent in the Medieval Warm Period (MWP) makes it difficult to differentiate between global climate trends and regional variability. The continued study of this period will allow for the better understanding of temperature variability, both regional and global, during this climate interval. Our study is located in the Orkney Islands, Scotland, which is a critical area to understand climate dynamics. The North Atlantic Oscillation and Gulf Stream heavily influence climate in this region, and the study of climate intervals during the MWP will improve our understanding of the behavior of these climate mechanisms during this interval. Furthermore, the vast majority of the climate archive has been derived from either deep marine or arctic environments. Studying a coastal environment will offer valuable insight into the behavior of maritime climate during the MWP. Estimated seasonal sea surface temperature data were derived through isotopic analysis of limpet shells (Patella vulgata). Analysis of modern shells confirms that growth temperature tracks seasonal variation in ambient water temperature. Preliminary data from MWP shells record a seasonal temperature range comparable to that observed in the modern temperature data. We will extend the range of temperature data from the 10th through 14th centuries to advance our knowledge of seasonal temperature variability during the late Holocene.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H43D1667D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H43D1667D"><span>Climate Drivers of Spatiotemporal Variability of Precipitation in the Source Region of Yangtze River</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Du, Y.; Berndtsson, R.; An, D.; Yuan, F.</p> <p>2017-12-01</p> <p>Variability of precipitation regime has significant influence on the environment sustainability in the source region of Yangtze River, especially when the vegetation degradation and biodiversity reduction have already occurred. Understanding the linkage between variability of local precipitation and global teleconnection patterns is essential for water resources management. Based on physical reasoning, indices of the climate drivers can provide a practical way of predicting precipitation. Due to high seasonal variability of precipitation, climate drivers of the seasonal precipitation also varies. However, few reports have gone through the teleconnections between large scale patterns with seasonal precipitation in the source region of Yangtze River. The objectives of this study are therefore (1) assessment of temporal trend and spatial variability of precipitation in the source region of Yangtze River; (2) identification of climate indices with strong influence on seasonal precipitation anomalies; (3) prediction of seasonal precipitation based on revealed climate indices. Principal component analysis and Spearman rank correlation were used to detect significant relationships. A feed-forward artificial neural network(ANN) was developed to predict seasonal precipitation using significant correlated climate indices. Different influencing climate indices were revealed for precipitation in each season, with significant level and lag times. Significant influencing factors were selected to be the predictors for ANN model. With correlation coefficients between observed and simulated precipitation over 0.5, the results were eligible to predict the precipitation of spring, summer and winter using teleconnections, which can improve integrated water resources management in the source region of Yangtze River.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ThApC.102..417D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ThApC.102..417D"><span>Multivariate geostatistical application for climate characterization of Minas Gerais State, Brazil</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>de Carvalho, Luiz G.; de Carvalho Alves, Marcelo; de Oliveira, Marcelo S.; Vianello, Rubens L.; Sediyama, Gilberto C.; de Carvalho, Luis M. T.</p> <p>2010-11-01</p> <p>The objective of the present study was to assess for Minas Gerais the cokriging methodology, in order to characterize the spatial variability of Thornthwaite annual moisture index, annual rainfall, and average annual air temperature, based on geographical coordinates, altitude, latitude, and longitude. The climatic element data referred to 39 INMET climatic stations located in the state of Minas Gerais and in nearby areas and the covariables altitude, latitude, and longitude to the SRTM digital elevation model. Spatial dependence of data was observed through spherical cross semivariograms and cross covariance models. Box-Cox and log transformation were applied to the positive variables. In these situations, kriged predictions were back-transformed and returned to the same scale as the original data. Trend was removed using global polynomial interpolation. Universal simple cokriging best characterized the climate variables without tendentiousness and with high accuracy and precision when compared to simple cokriging. Considering the satisfactory implementation of universal simple cokriging for the monitoring of climatic elements, this methodology presents enormous potential for the characterization of climate change impact in Minas Gerais state.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_15 --> <div id="page_16" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="301"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3766876','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3766876"><span>Climate Variability and Oceanographic Settings Associated with Interannual Variability in the Initiation of Dinophysis acuminata Blooms</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Díaz, Patricio A.; Reguera, Beatriz; Ruiz-Villarreal, Manuel; Pazos, Yolanda; Velo-Suárez, Lourdes; Berger, Henrick; Sourisseau, Marc</p> <p>2013-01-01</p> <p>In 2012, there were exceptional blooms of D. acuminata in early spring in what appeared to be a mesoscale event affecting Western Iberia and the Bay of Biscay. The objective of this work was to identify common climatic patterns to explain the observed anomalies in two important aquaculture sites, the Galician Rías Baixas (NW Spain) and Arcachon Bay (SW France). Here, we examine climate variability through physical-biological couplings, Sea Surface Temperature (SST) anomalies and time of initiation of the upwelling season and its intensity over several decades. In 2012, the mesoscale features common to the two sites were positive anomalies in SST and unusual wind patterns. These led to an atypical predominance of upwelling in winter in the Galician Rías, and increased haline stratification associated with a southward advection of the Gironde plume in Arcachon Bay. Both scenarios promoted an early phytoplankton growth season and increased stability that enhanced D. acuminata growth. Therefore, a common climate anomaly caused exceptional blooms of D. acuminata in two distant regions through different triggering mechanisms. These results increase our capability to predict intense diarrhetic shellfish poisoning outbreaks in the early spring from observations in the preceding winter. PMID:23959151</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70031051','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70031051"><span>Climate variability controls on unsaturated water and chemical movement, High Plains aquifer, USA</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Gurdak, J.J.; Hanson, R.T.; McMahon, P.B.; Bruce, B.W.; McCray, J.E.; Thyne, G.D.; Reedy, R.C.</p> <p>2007-01-01</p> <p>Responses in the vadose zone and groundwater to interannual, interdecadal, and multidecadal climate variability have important implications for groundwater resource sustainability, yet they are poorly documented and not well understood in most aquifers of the USA. This investigation systematically examines the role of interannual to multidecadal climate variability on groundwater levels, deep infiltration (3-23 m) events, and downward displacement (>1 m) of chloride and nitrate reservoirs in thick (15-50 m) vadose zones across the regionally extensive High Plains aquifer. Such vadose zone responses are unexpected across much of the aquifer given a priori that unsaturated total-potential profiles indicate upward water movement from the water table toward the root zone, mean annual potential evapotranspiration exceeds mean annual precipitation, and millennia-scale evapoconcentration results in substantial vadose zone chloride and nitrate reservoirs. Using singular spectrum analysis (SSA) to reconstruct precipitation and groundwater level time-series components, variability was identified in all time series as partially coincident with known climate cycles, such as the Pacific Decadal Oscillation (PDO) (10-25 yr) and the El Nin??o/Southern Oscillation (ENSO) (2-6 yr). Using these lag-correlated hydrologic time series, a new method is demonstrated to estimate climate-varying unsaturated water flux. The results suggest the importance of interannual to interdecadal climate variability on water-flux estimation in thick vadose zones and provide better understanding of the climate-induced transients responsible for the observed deep infiltration and chemical-mobilization events. Based on these results, we discuss implications for climate-related sustainability of the High Plains aquifer. ?? Soil Science Society of America.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1916932D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1916932D"><span>Analysis of shifts in the spatial distribution of vegetation due to climate change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>del Jesus, Manuel; Díez-Sierra, Javier; Rinaldo, Andrea; Rodríguez-Iturbe, Ignacio</p> <p>2017-04-01</p> <p>Climate change will modify the statistical regime of most climatological variables, inducing changes on average values and in the natural variability of environmental variables. These environmental variables may be used to explain the spatial distribution of functional types of vegetation in arid and semiarid watersheds through the use of plant optimization theories. Therefore, plant optimization theories may be used to approximate the response of the spatial distribution of vegetation to a changing climate. Predicting changes in these spatial distributions is important to understand how climate change may affect vegetated ecosystems, but it is also important for hydrological engineering applications where climate change effects on water availability are assessed. In this work, Maximum Entropy Production (MEP) is used as the plant optimization theory that describes the spatial distribution of functional types of vegetation. Current climatological conditions are obtained from direct observations from meteorological stations. Climate change effects are evaluated for different temporal horizons and different climate change scenarios using numerical model outputs from the CMIP5. Rainfall estimates are downscaled by means of a stochastic point process used to model rainfall. The study is carried out for the Rio Salado watershed, located within the Sevilleta LTER site, in New Mexico (USA). Results show the expected changes in the spatial distribution of vegetation and allow to evaluate the expected variability of the changes. The updated spatial distributions allow to evaluate the vegetated ecosystem health and its updated resilience. These results can then be used to inform the hydrological modeling part of climate change assessments analyzing water availability in arid and semiarid watersheds.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017BGeo...14.4355F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017BGeo...14.4355F"><span>The influence of El Niño-Southern Oscillation regimes on eastern African vegetation and its future implications under the RCP8.5 warming scenario</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fer, Istem; Tietjen, Britta; Jeltsch, Florian; Wolff, Christian</p> <p>2017-09-01</p> <p>The El Niño-Southern Oscillation (ENSO) is the main driver of the interannual variability in eastern African rainfall, with a significant impact on vegetation and agriculture and dire consequences for food and social security. In this study, we identify and quantify the ENSO contribution to the eastern African rainfall variability to forecast future eastern African vegetation response to rainfall variability related to a predicted intensified ENSO. To differentiate the vegetation variability due to ENSO, we removed the ENSO signal from the climate data using empirical orthogonal teleconnection (EOT) analysis. Then, we simulated the ecosystem carbon and water fluxes under the historical climate without components related to ENSO teleconnections. We found ENSO-driven patterns in vegetation response and confirmed that EOT analysis can successfully produce coupled tropical Pacific sea surface temperature-eastern African rainfall teleconnection from observed datasets. We further simulated eastern African vegetation response under future climate change as it is projected by climate models and under future climate change combined with a predicted increased ENSO intensity. Our EOT analysis highlights that climate simulations are still not good at capturing rainfall variability due to ENSO, and as we show here the future vegetation would be different from what is simulated under these climate model outputs lacking accurate ENSO contribution. We simulated considerable differences in eastern African vegetation growth under the influence of an intensified ENSO regime which will bring further environmental stress to a region with a reduced capacity to adapt effects of global climate change and food security.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70186908','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70186908"><span>Geomorphic determinants of species composition of alpine tundra, Glacier National Park, U.S.A.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>George P. Malanson,; Bengtson, Lindsey E.; Fagre, Daniel B.</p> <p>2012-01-01</p> <p>Because the distribution of alpine tundra is associated with spatially limited cold climates, global warming may threaten its local extent or existence. This notion has been challenged, however, based on observations of the diversity of alpine tundra in small areas primarily due to topographic variation. The importance of diversity in temperature or moisture conditions caused by topographic variation is an open question, and we extend this to geomorphology more generally. The extent to which geomorphic variation per se, based on relatively easily assessed indicators, can account for the variation in alpine tundra community composition is analyzed versus the inclusion of broad indicators of regional climate variation. Visual assessments of topography are quantified and reduced using principal components analysis (PCA). Observations of species cover are reduced using detrended correspondence analysis (DCA). A “best subsets” regression approach using the Akaike Information Criterion for selection of variables is compared to a simple stepwise regression with DCA scores as the dependent variable and scores on significant PCA axes plus more direct measures of topography as independent variables. Models with geographic coordinates (representing regional climate gradients) excluded explain almost as much variation in community composition as models with them included, although they are important contributors to the latter. The geomorphic variables in the model are those associated with local moisture differences such as snowbeds. The potential local variability of alpine tundra can be a buffer against climate change, but change in precipitation may be as important as change in temperature.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27778064','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27778064"><span>Climate Trends and Farmers' Perceptions of Climate Change in Zambia.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Mulenga, Brian P; Wineman, Ayala; Sitko, Nicholas J</p> <p>2017-02-01</p> <p>A number of studies use meteorological records to analyze climate trends and assess the impact of climate change on agricultural yields. While these provide quantitative evidence on climate trends and the likely effects thereof, they incorporate limited qualitative analysis of farmers' perceptions of climate change and/or variability. The present study builds on the quantitative methods used elsewhere to analyze climate trends, and in addition compares local narratives of climate change with evidence found in meteorological records in Zambia. Farmers offer remarkably consistent reports of a rainy season that is growing shorter and less predictable. For some climate parameters-notably, rising average temperature-there is a clear overlap between farmers' observations and patterns found in the meteorological records. However, the data do not support the perception that the rainy season used to begin earlier, and we generally do not detect a reported increase in the frequency of dry spells. Several explanations for these discrepancies are offered. Further, we provide policy recommendations to help farmers adapt to climate change/variability, as well as suggestions to shape future climate change policies, programs, and research in developing countries.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC13G1258K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC13G1258K"><span>Detecting potential anomalies in projections of rainfall trends and patterns using human observations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kohfeld, K. E.; Savo, V.; Sillmann, J.; Morton, C.; Lepofsky, D.</p> <p>2016-12-01</p> <p>Shifting precipitation patterns are a well-documented consequence of climate change, but their spatial variability is particularly difficult to assess. While the accuracy of global models has increased, specific regional changes in precipitation regimes are not well captured by these models. Typically, researchers who wish to detect trends and patterns in climatic variables, such as precipitation, use instrumental observations. In our study, we combined observations of rainfall by subsistence-oriented communities with several metrics of rainfall estimated from global instrumental records for comparable time periods (1955 - 2005). This comparison was aimed at identifying: 1) which rainfall metrics best match human observations of changes in precipitation; 2) areas where local communities observe changes not detected by global models. The collated observations ( 3800) made by subsistence-oriented communities covered 129 countries ( 1830 localities). For comparable time periods, we saw a substantial correspondence between instrumental records and human observations (66-77%) at the same locations, regardless of whether we considered trends in general rainfall, drought, or extreme rainfall. We observed a clustering of mismatches in two specific regions, possibly indicating some climatic phenomena not completely captured by the currently available global models. Many human observations also indicated an increased unpredictability in the start, end, duration, and continuity of the rainy seasons, all of which may hamper the performance of subsistence activities. We suggest that future instrumental metrics should capture this unpredictability of rainfall. This information would be important for thousands of subsistence-oriented communities in planning, coping, and adapting to climate change.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.9347A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.9347A"><span>Inter-model variability in hydrological extremes projections for Amazonian sub-basins</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Andres Rodriguez, Daniel; Garofolo, Lucas; Lázaro de Siqueira Júnior, José; Samprogna Mohor, Guilherme; Tomasella, Javier</p> <p>2014-05-01</p> <p>Irreducible uncertainties due to knowledge's limitations, chaotic nature of climate system and human decision-making process drive uncertainties in Climate Change projections. Such uncertainties affect the impact studies, mainly when associated to extreme events, and difficult the decision-making process aimed at mitigation and adaptation. However, these uncertainties allow the possibility to develop exploratory analyses on system's vulnerability to different sceneries. The use of different climate model's projections allows to aboard uncertainties issues allowing the use of multiple runs to explore a wide range of potential impacts and its implications for potential vulnerabilities. Statistical approaches for analyses of extreme values are usually based on stationarity assumptions. However, nonstationarity is relevant at the time scales considered for extreme value analyses and could have great implications in dynamic complex systems, mainly under climate change transformations. Because this, it is required to consider the nonstationarity in the statistical distribution parameters. We carried out a study of the dispersion in hydrological extremes projections using climate change projections from several climate models to feed the Distributed Hydrological Model of the National Institute for Spatial Research, MHD-INPE, applied in Amazonian sub-basins. This model is a large-scale hydrological model that uses a TopModel approach to solve runoff generation processes at the grid-cell scale. MHD-INPE model was calibrated for 1970-1990 using observed meteorological data and comparing observed and simulated discharges by using several performance coeficients. Hydrological Model integrations were performed for present historical time (1970-1990) and for future period (2010-2100). Because climate models simulate the variability of the climate system in statistical terms rather than reproduce the historical behavior of climate variables, the performances of the model's runs during the historical period, when feed with climate model data, were tested using descriptors of the Flow Duration Curves. The analyses of projected extreme values were carried out considering the nonstationarity of the GEV distribution parameters and compared with extremes events in present time. Results show inter-model variability in a broad dispersion on projected extreme's values. Such dispersion implies different degrees of socio-economic impacts associated to extreme hydrological events. Despite the no existence of one optimum result, this variability allows the analyses of adaptation strategies and its potential vulnerabilities.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005JCli...18..551S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005JCli...18..551S"><span>Climate Downscaling over Nordeste, Brazil, Using the NCEP RSM97.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sun, Liqiang; Ferran Moncunill, David; Li, Huilan; Divino Moura, Antonio; de Assis de Souza Filho, Francisco</p> <p>2005-02-01</p> <p>The NCEP Regional Spectral Model (RSM), with horizontal resolution of 60 km, was used to downscale the ECHAM4.5 AGCM (T42) simulations forced with observed SSTs over northeast Brazil. An ensemble of 10 runs for the period January-June 1971-2000 was used in this study. The RSM can resolve the spatial patterns of observed seasonal precipitation and capture the interannual variability of observed seasonal precipitation as well. The AGCM bias in displacement of the Atlantic ITCZ is partially corrected in the RSM. The RSM probability distribution function of seasonal precipitation anomalies is in better agreement with observations than that of the driving AGCM. Good potential prediction skills are demonstrated by the RSM in predicting the interannual variability of regional seasonal precipitation. The RSM can also capture the interannual variability of observed precipitation at intraseasonal time scales, such as precipitation intensity distribution and dry spells. A drought index and a flooding index were adopted to indicate the severity of drought and flooding conditions, and their interannual variability was reproduced by the RSM. The overall RSM performance in the downscaled climate of the ECHAM4.5 AGCM is satisfactory over Nordeste. The primary deficiency is a systematic dry bias for precipitation simulation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ERL....10b4017W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ERL....10b4017W"><span>How model and input uncertainty impact maize yield simulations in West Africa</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Waha, Katharina; Huth, Neil; Carberry, Peter; Wang, Enli</p> <p>2015-02-01</p> <p>Crop models are common tools for simulating crop yields and crop production in studies on food security and global change. Various uncertainties however exist, not only in the model design and model parameters, but also and maybe even more important in soil, climate and management input data. We analyze the performance of the point-scale crop model APSIM and the global scale crop model LPJmL with different climate and soil conditions under different agricultural management in the low-input maize-growing areas of Burkina Faso, West Africa. We test the models’ response to different levels of input information from little to detailed information on soil, climate (1961-2000) and agricultural management and compare the models’ ability to represent the observed spatial (between locations) and temporal variability (between years) in crop yields. We found that the resolution of different soil, climate and management information influences the simulated crop yields in both models. However, the difference between models is larger than between input data and larger between simulations with different climate and management information than between simulations with different soil information. The observed spatial variability can be represented well from both models even with little information on soils and management but APSIM simulates a higher variation between single locations than LPJmL. The agreement of simulated and observed temporal variability is lower due to non-climatic factors e.g. investment in agricultural research and development between 1987 and 1991 in Burkina Faso which resulted in a doubling of maize yields. The findings of our study highlight the importance of scale and model choice and show that the most detailed input data does not necessarily improve model performance.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017GMD....10.4443F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017GMD....10.4443F"><span>A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Forkel, Matthias; Dorigo, Wouter; Lasslop, Gitta; Teubner, Irene; Chuvieco, Emilio; Thonicke, Kirsten</p> <p>2017-12-01</p> <p>Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. However, the climatic, environmental, and socioeconomic factors that control global fire activity in vegetation are only poorly understood, and in various complexities and formulations are represented in global process-oriented vegetation-fire models. Data-driven model approaches such as machine learning algorithms have successfully been used to identify and better understand controlling factors for fire activity. However, such machine learning models cannot be easily adapted or even implemented within process-oriented global vegetation-fire models. To overcome this gap between machine learning-based approaches and process-oriented global fire models, we introduce a new flexible data-driven fire modelling approach here (Satellite Observations to predict FIre Activity, SOFIA approach version 1). SOFIA models can use several predictor variables and functional relationships to estimate burned area that can be easily adapted with more complex process-oriented vegetation-fire models. We created an ensemble of SOFIA models to test the importance of several predictor variables. SOFIA models result in the highest performance in predicting burned area if they account for a direct restriction of fire activity under wet conditions and if they include a land cover-dependent restriction or allowance of fire activity by vegetation density and biomass. The use of vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. We further analyse spatial patterns of the sensitivity between anthropogenic, climate, and vegetation predictor variables and burned area. We finally discuss how multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with data-driven modelling and model-data integration approaches can guide the future development of global process-oriented vegetation-fire models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017NatCC...7..839L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017NatCC...7..839L"><span>Coral bleaching pathways under the control of regional temperature variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Langlais, C. E.; Lenton, A.; Heron, S. F.; Evenhuis, C.; Sen Gupta, A.; Brown, J. N.; Kuchinke, M.</p> <p>2017-11-01</p> <p>Increasing sea surface temperatures (SSTs) are predicted to adversely impact coral populations worldwide through increasing thermal bleaching events. Future bleaching is unlikely to be spatially uniform. Therefore, understanding what determines regional differences will be critical for adaptation management. Here, using a cumulative heat stress metric, we show that characteristics of regional SST determine the future bleaching risk patterns. Incorporating observed information on SST variability, in assessing future bleaching risk, provides novel options for management strategies. As a consequence, the known biases in climate model variability and the uncertainties in regional warming rate across climate models are less detrimental than previously thought. We also show that the thresholds used to indicate reef viability can strongly influence a decision on what constitutes a potential refugia. Observing and understanding the drivers of regional variability, and the viability limits of coral reefs, is therefore critical for making meaningful projections of coral bleaching risk.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/20657765','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/20657765"><span>Climatic variability leads to later seasonal flowering of Floridian plants.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Von Holle, Betsy; Wei, Yun; Nickerson, David</p> <p>2010-07-21</p> <p>Understanding species responses to global change will help predict shifts in species distributions as well as aid in conservation. Changes in the timing of seasonal activities of organisms over time may be the most responsive and easily observable indicator of environmental changes associated with global climate change. It is unknown how global climate change will affect species distributions and developmental events in subtropical ecosystems or if climate change will differentially favor nonnative species. Contrary to previously observed trends for earlier flowering onset of plant species with increasing spring temperatures from mid and higher latitudes, we document a trend for delayed seasonal flowering among plants in Florida. Additionally, there were few differences in reproductive responses by native and nonnative species to climatic changes. We argue that plants in Florida have different reproductive cues than those from more northern climates. With global change, minimum temperatures have become more variable within the temperate-subtropical zone that occurs across the peninsula and this variation is strongly associated with delayed flowering among Florida plants. Our data suggest that climate change varies by region and season and is not a simple case of species responding to consistently increasing temperatures across the region. Research on climate change impacts need to be extended outside of the heavily studied higher latitudes to include subtropical and tropical systems in order to properly understand the complexity of regional and seasonal differences of climate change on species responses.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC31J..05Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC31J..05Z"><span>Identifying the interferences of irrigation on evapotranspiration variability over the Northern High Plains</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zeng, R.; Cai, X.</p> <p>2016-12-01</p> <p>Irrigation has considerably interfered with hydrological processes in arid and semi-arid areas with heavy irrigated agriculture. With the increasing demand for food production and evaporative demand due to climate change, irrigation water consumption is expected to increase, which would aggravate the interferences to hydrologic processes. Current studies focus on the impact of irrigation on the mean value of evapotranspiration (ET) at either local or regional scale, however, how irrigation changes the variability of ET has not been well understood. This study analyzes the impact of extensive irrigation on ET variability in the Northern High Plains. We apply an ET variance decomposition framework developed from our previous work to quantify the effects of both climate and irrigation on ET variance in the Northern High Plains watersheds. Based on climate and water table observations, we assess the monthly ET variance and its components for two periods: 1930s-1960s with less irrigation development 970s-2010s with more development. It is found that irrigation not only caused the well-recognized groundwater drawdown and stream depletion problems in the region, but also buffered ET variance from climatic fluctuations. In addition to increasing food productivity, irrigation also stabilizes crop yield by mitigating the impact of hydroclimatic variability. With complementary water supply from irrigation, ET often approaches to the potential ET, and thus the observed ET variance is more attributed to climatic variables especially temperature; meanwhile irrigation causes significant seasonal fluctuations to groundwater storage. For sustainable water resources management in the Northern High Plains, we argue that both the mean value and the variance of ET should be considered together for the regulation of irrigation in this region.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4150291','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4150291"><span>Slow science: the value of long ocean biogeochemistry records</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Henson, Stephanie A.</p> <p>2014-01-01</p> <p>Sustained observations (SOs) have provided invaluable information on the ocean's biology and biogeochemistry for over 50 years. They continue to play a vital role in elucidating the functioning of the marine ecosystem, particularly in the light of ongoing climate change. Repeated, consistent observations have provided the opportunity to resolve temporal and/or spatial variability in ocean biogeochemistry, which has driven exploration of the factors controlling biological parameters and processes. Here, I highlight some of the key breakthroughs in biological oceanography that have been enabled by SOs, which include areas such as trophic dynamics, understanding variability, improved biogeochemical models and the role of ocean biology in the global carbon cycle. In the near future, SOs are poised to make progress on several fronts, including detecting climate change effects on ocean biogeochemistry, high-resolution observations of physical–biological interactions and greater observational capability in both the mesopelagic zone and harsh environments, such as the Arctic. We are now entering a new era for biological SOs, one in which our motivations have evolved from the need to acquire basic understanding of the ocean's state and variability, to a need to understand ocean biogeochemistry in the context of increasing pressure in the form of climate change, overfishing and eutrophication. PMID:25157192</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18..266A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18..266A"><span>Understanding extreme rainfall events in Australia through historical data</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ashcroft, Linden; Karoly, David John</p> <p>2016-04-01</p> <p>Historical climate data recovery is still an emerging field in the Australian region. The majority of Australia's instrumental climate analyses begin in 1900 for rainfall and 1910 for temperature, particularly those focussed on extreme event analysis. This data sparsity for the past in turn limits our understanding of long-term climate variability, constraining efforts to predict the impact of future climate change. To address this need for improved historical data in Australia, a new network of recovered climate observations has recently been developed, centred on the highly populated southeastern Australian region (Ashcroft et al., 2014a, 2014b). The dataset includes observations from more than 39 published and unpublished sources and extends from British settlement in 1788 to the formation of the Australian Bureau of Meteorology in 1908. Many of these historical sources provide daily temperature and rainfall information, providing an opportunity to improve understanding of the multidecadal variability of Australia's extreme events. In this study we combine the historical data for three major Australian cities - Melbourne, Sydney and Adelaide - with modern observations to examine extreme rainfall variability over the past 174 years (1839-2013). We first explore two case studies, combining instrumental and documentary evidence to support the occurrence of severe storms in Sydney in 1841 and 1844. These events appear to be at least as extreme as Sydney's modern 24-hour rainfall record. Next we use a suite of rainfall indices to assess the long-term variability of rainfall in southeastern Australia. In particular, we focus on the stationarity of the teleconnection between the El Niño-Southern Oscillation (ENSO) phenomenon and extreme rainfall events. Using ENSO reconstructions derived from both palaeoclimatic and documentary sources, we determine the historical relationship between extreme rainfall in southeastern Australia and ENSO, and examine whether or not this relationship has remained stable since the early to mid-19th century. Ashcroft, L., Gergis, J., Karoly, D.J., 2014a. A historical climate dataset for southeastern Australia, 1788-1859. Geosci. Data J. 1, 158-178. doi:10.1002/gdj3.19 Ashcroft, L., Karoly, D.J., Gergis, J., 2014b. Southeastern Australian climate variability 1860-2009: A multivariate analysis. Int. J. Climatol. 34, 1928-1944. doi:10.1002/joc.3812</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/39185','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/39185"><span>Climate Variability and Its Impact on Forest Hydrology on South Carolina Coastal Plain, USA</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Zhaohua Dai; Devendra Amatya; Ge Sun; Carl Trettin; Changsheng Li; Harbin Li</p> <p>2011-01-01</p> <p>Understanding the changes in hydrology of coastal forested wetlands induced by climate change is fundamental for developing strategies to sustain their functions and services. This study examined 60 years of climatic observations and 30 years of hydrological data, collected at the Santee Experimental Forest (SEF) in coastal South Carolina. We also applied a physically-...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/55301','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/55301"><span>Detection of the Coupling between Vegetation Leaf Area and Climate in a Multifunctional Watershed, Northwestern China</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Lu Hao; Cen Pan; Peilong Liu; Decheng Zhou; Liangxia Zhang; Zhe Xiong; Yongqiang Liu; Ge Sun</p> <p>2016-01-01</p> <p>Accurate detection and quantification of vegetation dynamics and drivers of observed climatic and anthropogenic change in space and time is fundamental for our understanding of the atmosphereâbiosphere interactions at local and global scales. This case study examined the coupled spatial patterns of vegetation dynamics and climatic variabilities during the past...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014PalOc..29..143K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014PalOc..29..143K"><span>Assessing millennial-scale variability during the Holocene: A perspective from the western tropical Pacific</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Khider, D.; Jackson, C. S.; Stott, L. D.</p> <p>2014-03-01</p> <p>We investigate the relationship between tropical Pacific and Southern Ocean variability during the Holocene using the stable oxygen isotope and magnesium/calcium records of cooccurring planktonic and benthic foraminifera from a marine sediment core collected in the western equatorial Pacific. The planktonic record exhibits millennial-scale sea surface temperature (SST) oscillations over the Holocene of 0.5°C while the benthic δ18Oc document 0.10‰ millennial-scale changes of Upper Circumpolar Deep Water (UCDW), a water mass which outcrops in the Southern Ocean. Solar forcing as an explanation for millennial-scale SST variability requires (1) a large climate sensitivity and (2) a long 400 year delayed response, suggesting that if solar forcing is the cause of the variability, it would need to be considerably amplified by processes within the climate system at least at the core location. We also explore the possibility that SST variability arose from volcanic forcing using a simple red noise model. Our best estimates of volcanic forcing falls short of reproducing the amplitude of observed SST variations although it produces power at low-frequency similar to that observed in the MD81 record. Although we cannot totally discount the volcanic and solar forcing hypotheses, we are left to consider that the most plausible source for Holocene millennial-scale variability lies within the climate system itself. In particular, UCDW variability coincided with deep North Atlantic changes, indicating a role for the deep ocean in Holocene millennial-scale variability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/55697','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/55697"><span>The influence of ENSO, PDO and PNA on secular rainfall variations in Hawai‘i</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Abby G. Frazier; Oliver Elison Timm; Thomas W. Giambelluca; Henry F. Diaz</p> <p>2017-01-01</p> <p>Over the last century, significant declines in rainfall across the state of Hawaiâi have been observed, and it is unknown whether these declines are due to natural variations in climate, or manifestations of human-induced climate change. Here, a statistical analysis of the observed rainfall variability was applied as first step towards better understanding causes for...</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_16 --> <div id="page_17" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="321"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26989581','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26989581"><span>Workshop on Bridging Satellite Climate Data Gaps.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Cooksey, Catherine; Datla, Raju</p> <p>2011-01-01</p> <p>Detecting the small signals of climate change for the most essential climate variables requires that satellite sensors make highly accurate and consistent measurements. Data gaps in the time series (such as gaps resulting from launch delay or failure) and inconsistencies in radiometric scales between satellites undermine the credibility of fundamental climate data records, and can lead to erroneous analysis in climate change detection. To address these issues, leading experts in Earth observations from National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Adminstration (NOAA), United States Geological Survey (USGS), and academia assembled at the National Institute of Standards and Technology on December 10, 2009 for a workshop to prioritize strategies for bridging and mitigating data gaps in the climate record. This paper summarizes the priorities for ensuring data continuity of variables relevant to climate change in the areas of atmosphere, land, and ocean measurements and the recommendations made at the workshop for overcoming planned and unplanned gaps in the climate record.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1422299-arm-cloud-radar-simulator-global-climate-models-bridging-field-data-climate-models','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1422299-arm-cloud-radar-simulator-global-climate-models-bridging-field-data-climate-models"><span>The ARM Cloud Radar Simulator for Global Climate Models: Bridging Field Data and Climate Models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Zhang, Yuying; Xie, Shaocheng; Klein, Stephen A.</p> <p></p> <p>Clouds play an important role in Earth’s radiation budget and hydrological cycle. However, current global climate models (GCMs) have had difficulties in accurately simulating clouds and precipitation. To improve the representation of clouds in climate models, it is crucial to identify where simulated clouds differ from real world observations of them. This can be difficult, since significant differences exist between how a climate model represents clouds and what instruments observe, both in terms of spatial scale and the properties of the hydrometeors which are either modeled or observed. To address these issues and minimize impacts of instrument limitations, the conceptmore » of instrument “simulators”, which convert model variables into pseudo-instrument observations, has evolved with the goal to improve and to facilitate the comparison of modeled clouds with observations. Many simulators have (and continue to be developed) for a variety of instruments and purposes. A community satellite simulator package, the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011), contains several independent satellite simulators and is being widely used in the global climate modeling community to exploit satellite observations for model cloud evaluation (e.g., Klein et al. 2013; Zhang et al. 2010). This article introduces a ground-based cloud radar simulator developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program for comparing climate model clouds with ARM observations from its vertically pointing 35-GHz radars. As compared to CloudSat radar observations, ARM radar measurements occur with higher temporal resolution and finer vertical resolution. This enables users to investigate more fully the detailed vertical structures within clouds, resolve thin clouds, and quantify the diurnal variability of clouds. Particularly, ARM radars are sensitive to low-level clouds, which are difficult for the CloudSat radar to detect due to surface contamination (Mace et al. 2007; Marchand et al. 2008). Therefore, the ARM ground-based cloud observations can provide important observations of clouds that complement measurements from space.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA493298','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA493298"><span>Preview of Our Changing Planet. The U.S. Climate Change Science Program for Fiscal Year 2008</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2007-04-01</p> <p>reduce the uncertainty in predictions of the global and regional water cycle and surface climate. Sunlight not reflected back to space provides the...research elements include atmospheric composition, climate variability and change, the global water cycle , land-use and land-cover change, the global...entire planet, and researchers with the ability to better explain observed changes in the climate system. Global Water Cycle – Research associated with</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19990064333&hterms=Influence+clouds+climate&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3DInfluence%2Bclouds%2Bclimate','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19990064333&hterms=Influence+clouds+climate&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3DInfluence%2Bclouds%2Bclimate"><span>The Role of Global Hydrologic Processes in Interannual and Long-Term Climate Variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Robertson, Franklin R.</p> <p>1997-01-01</p> <p>The earth's climate and its variability is linked inextricably with the presence of water on our planet. El Nino / Southern Oscillation-- the major mode of interannual variability-- is characterized by strong perturbations in oceanic evaporation, tropical rainfall, and radiation. On longer time scales, the major feedback mechanism in CO2-induced global warming is actually that due to increased water vapor holding capacity of the atmosphere. The global hydrologic cycle effects on climate are manifested through influence of cloud and water vapor on energy fluxes at the top of atmosphere and at the surface. Surface moisture anomalies retain the "memory" of past precipitation anomalies and subsequently alter the partitioning of latent and sensible heat fluxes at the surface. At the top of atmosphere, water vapor and cloud perturbations alter the net amount of radiation that the earth's climate system receives. These pervasive linkages between water, radiation, and surface processes present major complexities for observing and modeling climate variations. Major uncertainties in the observations include vertical structure of clouds and water vapor, surface energy balance, and transport of water and heat by wind fields. Modeling climate variability and change on a physical basis requires accurate by simplified submodels of radiation, cloud formation, radiative exchange, surface biophysics, and oceanic energy flux. In the past, we m safely say that being "data poor' has limited our depth of understanding and impeded model validation and improvement. Beginning with pre-EOS data sets, many of these barriers are being removed. EOS platforms with the suite of measurements dedicated to specific science questions are part of our most cost effective path to improved understanding and predictive capability. This talk will highlight some of the major questions confronting global hydrology and the prospects for significant progress afforded by EOS-era measurements.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMPP21B2279L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMPP21B2279L"><span>Spatial-temporal analysis of climate variations in mid-17th through 19th centuries in East China and the possible relationships with Monsoon climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lin, K. H. E.; Wang, P. K.; Liao, Y. C.; Lee, S. Y.; Tan, P.</p> <p>2016-12-01</p> <p>IPCC AR5 has revealed more frequent extreme climate events and higher climate variability in the near future. Regardless of all the improvements, East Asia monsoon climate is still less understood and/or poorly projected due partly to insufficient records. Most areas of the Asian region lack sufficient observational records to draw conclusions about trends in annual precipitation over the past century (i.e. WGIAR5 Chapter 2). Precipitation trends, including extremes, are characterized by strong variability, with both increasing and decreasing observed in different parts and seasons of Asia. Understanding the variations of the monsoon climate in historical time may bring significant insights to reveal its spatial and temporal patterns embedded in the atmospheric dynamics at different decadal or centennial scales. This study presents some preliminary research results of high resolution climate reconstruction, in both time and space coverage, in east China, by using RCEC historical climate dataset that is developed under interdisciplinary collaboration led by Research Center for Environmental Changes at Academia Sinica, Taiwan. The present research results are derived from chronological meteorological records in the RCEC dataset in Qing dynasty labeling mid-17th to 19th centuries. In total, the dataset comprises more than 1,300 cities/counties in China that has had more than sixty thousands meteorological records in the period. The analysis comprises three parts. Firstly, the frequency of extreme temperature, precipitation, drought, and flood in every recorded cities/counties were computed to depicting climate variabilities in northeast, central-east and southeast China. Secondly, the multivariate regression model was conducted to estimate the coefficients among the climatic index (temperature, precipitation, and drought). It is found that the temperature and wet-dry characteristics have great seasonal and yearly variations; northeast China compared with central-east or southeast tends to have higher variability. Thirdly, those data was used to conduct empirical orthogonal function (EOF) analysis to decompose possible mechanisms that might have cause changes in East Asia monsoon regime during the time period. The reconstructed data were also compared against paleoclimate simulation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19..813A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19..813A"><span>Regional influence of decadal to multidecadal Atlantic Oscillations during the last two millennia in Morocco, inferred from two high resolution δ18O speleothem records</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ait Brahim, Yassine; Sifeddine, Abdelfettah; Khodri, Myriam; Bouchaou, Lhoussaine; Cruz, Francisco W.; Pérez-Zanón, Núria; Wassenburg, Jasper A.; Cheng, Hai</p> <p>2017-04-01</p> <p>Climate projections predict substantial increase of extreme heats and drought occurrences during the coming decades in Morocco. It is however not clear what can be attributed to natural climate variability and to anthropogenic forcing, as hydroclimate variations observed in areas such as Morocco are highly influenced by the Atlantic climate modes. Since observational data sets are too short to resolve properly natural modes of variability acting on decadal to multidecadal timescales, high resolution paleoclimate reconstructions are the only alternative to reconstruct climate variability in the remote past. Herein, we present two high resolution and well dated speleothems oxygen isotope (δ18O) records sampled from Chaara and Ifoulki caves (located in Northeastern and Southwestern Morocco respectively) to investigate hydroclimate variations during the last 2000 years. Our results are supported by a monitoring network of δ18O in precipitation from 17 stations in Morocco. The new paleoclimate records are discussed in the light of existing continental and marine paleoclimate proxies in Morocco to identify significant correlations at various lead times with the main reconstructed oceanic and atmospheric variability modes and possible climate teleconnections that have potentially influenced the climate during the last two millennia in Morocco. The results reveal substantial decadal to multidecadal swings between dry and humid periods, consistent with regional paleorecords. Evidence of dry conditions exist during the Medieval Climate Anomaly (MCA) period and the Climate Warm Period (CWP) and humid conditions during the Little Ice Age (LIA) period. Statistical analyses suggest that the climate of southwestern Morocco remained under the combined influence of both the Atlantic Multidecadal Oscillation (AMO) and the North Atlantic Oscillation (NAO) over the last two millennia. Interestingly, the generally warmer MCA and colder LIA at longer multidecadal timescales probably influenced the regional climate in North Africa through the influence on Sahara Low which weakened and strengthened the mean moisture inflow from the Atlantic Ocean during the MCA and LIA respectively. Keywords: Speleothems, δ18O, Morocco, Hydroclimate, AMO, NAO.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ClDy..tmp.2335P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ClDy..tmp.2335P"><span>An effective drift correction for dynamical downscaling of decadal global climate predictions</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Paeth, Heiko; Li, Jingmin; Pollinger, Felix; Müller, Wolfgang A.; Pohlmann, Holger; Feldmann, Hendrik; Panitz, Hans-Jürgen</p> <p>2018-04-01</p> <p>Initialized decadal climate predictions with coupled climate models are often marked by substantial climate drifts that emanate from a mismatch between the climatology of the coupled model system and the data set used for initialization. While such drifts may be easily removed from the prediction system when analyzing individual variables, a major problem prevails for multivariate issues and, especially, when the output of the global prediction system shall be used for dynamical downscaling. In this study, we present a statistical approach to remove climate drifts in a multivariate context and demonstrate the effect of this drift correction on regional climate model simulations over the Euro-Atlantic sector. The statistical approach is based on an empirical orthogonal function (EOF) analysis adapted to a very large data matrix. The climate drift emerges as a dramatic cooling trend in North Atlantic sea surface temperatures (SSTs) and is captured by the leading EOF of the multivariate output from the global prediction system, accounting for 7.7% of total variability. The SST cooling pattern also imposes drifts in various atmospheric variables and levels. The removal of the first EOF effectuates the drift correction while retaining other components of intra-annual, inter-annual and decadal variability. In the regional climate model, the multivariate drift correction of the input data removes the cooling trends in most western European land regions and systematically reduces the discrepancy between the output of the regional climate model and observational data. In contrast, removing the drift only in the SST field from the global model has hardly any positive effect on the regional climate model.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1357048-role-climate-covariability-crop-yields-conterminous-united-states','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1357048-role-climate-covariability-crop-yields-conterminous-united-states"><span>The Role of Climate Covariability on Crop Yields in the Conterminous United States</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Leng, Guoyong; Zhang, Xuesong; Huang, Maoyi; ...</p> <p>2016-09-12</p> <p>The covariability of temperature (T), precipitation (P) and radiation (R) is an important aspect in understanding the climate influence on crop yields. Here in this paper, we analyze county-level corn and soybean yields and observed climate for the period 1983–2012 to understand how growing-season (June, July and August) mean T, P and R influence crop yields jointly and in isolation across the CONterminous United States (CONUS). Results show that nationally averaged corn and soybean yields exhibit large interannual variability of 21% and 22%, of which 35% and 32% can be significantly explained by T and P, respectively. By including R,more » an additional of 5% in variability can be explained for both crops. Using partial regression analyses, we find that studies that ignore the covariability among T, P, and R can substantially overestimate the sensitivity of crop yields to a single climate factor at the county scale. Further analyses indicate large spatial variation in the relative contributions of different climate variables to the variability of historical corn and soybean yields. Finally, the structure of the dominant climate factors did not change substantially over 1983–2012, confirming the robustness of the findings, which have important implications for crop yield prediction and crop model validations.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70037185','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70037185"><span>Interannual variation of carbon fluxes from three contrasting evergreen forests: The role of forest dynamics and climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Sierra, C.A.; Loescher, H.W.; Harmon, M.E.; Richardson, A.D.; Hollinger, D.Y.; Perakis, S.S.</p> <p>2009-01-01</p> <p>Interannual variation of carbon fluxes can be attributed to a number of biotic and abiotic controls that operate at different spatial and temporal scales. Type and frequency of disturbance, forest dynamics, and climate regimes are important sources of variability. Assessing the variability of carbon fluxes from these specific sources can enhance the interpretation of past and current observations. Being able to separate the variability caused by forest dynamics from that induced by climate will also give us the ability to determine if the current observed carbon fluxes are within an expected range or whether the ecosystem is undergoing unexpected change. Sources of interannual variation in ecosystem carbon fluxes from three evergreen ecosystems, a tropical, a temperate coniferous, and a boreal forest, were explored using the simulation model STANDCARB. We identified key processes that introduced variation in annual fluxes, but their relative importance differed among the ecosystems studied. In the tropical site, intrinsic forest dynamics contributed ?? 30% of the total variation in annual carbon fluxes. In the temperate and boreal sites, where many forest processes occur over longer temporal scales than those at the tropical site, climate controlled more of the variation among annual fluxes. These results suggest that climate-related variability affects the rates of carbon exchange differently among sites. Simulations in which temperature, precipitation, and radiation varied from year to year (based on historical records of climate variation) had less net carbon stores than simulations in which these variables were held constant (based on historical records of monthly average climate), a result caused by the functional relationship between temperature and respiration. This suggests that, under a more variable temperature regime, large respiratory pulses may become more frequent and high enough to cause a reduction in ecosystem carbon stores. Our results also show that the variation of annual carbon fluxes poses an important challenge in our ability to determine whether an ecosystem is a source, a sink, or is neutral in regard to CO2 at longer timescales. In simulations where climate change negatively affected ecosystem carbon stores, there was a 20% chance of committing Type II error, even with 20 years of sequential data. ?? 2009 by the Ecological Society of America.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/11102172','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/11102172"><span>Low cloud properties influenced by cosmic rays</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Marsh; Svensmark</p> <p>2000-12-04</p> <p>The influence of solar variability on climate is currently uncertain. Recent observations have indicated a possible mechanism via the influence of solar modulated cosmic rays on global cloud cover. Surprisingly the influence of solar variability is strongest in low clouds (</=3 km), which points to a microphysical mechanism involving aerosol formation that is enhanced by ionization due to cosmic rays. If confirmed it suggests that the average state of the heliosphere is important for climate on Earth.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PrOce.132....1Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PrOce.132....1Y"><span>A new collective view of oceanography of the Arctic and North Atlantic basins</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yashayaev, Igor; Seidov, Dan; Demirov, Entcho</p> <p>2015-03-01</p> <p>We review some historical aspects of the major observational programs in the North Atlantic and adjacent regions that contributed to establishing and maintaining the global ocean climate monitoring network. The paper also presents the oceanic perspectives of climate change and touches the important issues of ocean climate variability on time scales from years to decades. Some elements of the improved understanding of the causes and mechanisms of variability in the subpolar North Atlantic and adjacent seas are discussed in detail. The sophistication of current oceanographic analysis, especially in connection with the most recent technological breakthroughs - notably the launch of the global array of profiling Argo floats - allows us to approach new challenges in ocean research. We demonstrate how the ocean-climate changes in the subpolar basins and polar seas correlate with variations in the major climate indices such as the North Atlantic Oscillation and Atlantic Multidecadal Oscillation, and discuss possible connections between the unprecedented changes in the Arctic and Greenland ice-melt rates observed over the past decade and variability of hydrographic conditions in the Labrador Sea. Furthermore, a synthesis of shipboard and Argo measurements in the Labrador Sea reveals the effects of the regional climate trends such as freshening of the upper layer - possible causes of which are also discussed - on the winter convection in the Labrador Sea including its strength, duration and spatial extent. These changes could have a profound impact on the regional and planetary climates. A section with the highlights of all papers comprising the Special Issue concludes the Preface.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016FrES...10..644W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016FrES...10..644W"><span>An assessment of precipitation and surface air temperature over China by regional climate models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Xueyuan; Tang, Jianping; Niu, Xiaorui; Wang, Shuyu</p> <p>2016-12-01</p> <p>An analysis of a 20-year summer time simulation of present-day climate (1989-2008) over China using four regional climate models coupled with different land surface models is carried out. The climatic means, interannual variability, linear trends, and extremes are examined, with focus on precipitation and near surface air temperature. The models are able to reproduce the basic features of the observed summer mean precipitation and temperature over China and the regional detail due to topographic forcing. Overall, the model performance is better for temperature than that of precipitation. The models reasonably grasp the major anomalies and standard deviations over China and the five subregions studied. The models generally reproduce the spatial pattern of high interannual variability over wet regions, and low variability over the dry regions. The models also capture well the variable temperature gradient increase to the north by latitude. Both the observed and simulated linear trend of precipitation shows a drying tendency over the Yangtze River Basin and wetting over South China. The models capture well the relatively small temperature trends in large areas of China. The models reasonably simulate the characteristics of extreme precipitation indices of heavy rain days and heavy precipitation fraction. Most of the models also performed well in capturing both the sign and magnitude of the daily maximum and minimum temperatures over China.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25644630','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25644630"><span>Importance of anthropogenic climate impact, sampling error and urban development in sewer system design.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Egger, C; Maurer, M</p> <p>2015-04-15</p> <p>Urban drainage design relying on observed precipitation series neglects the uncertainties associated with current and indeed future climate variability. Urban drainage design is further affected by the large stochastic variability of precipitation extremes and sampling errors arising from the short observation periods of extreme precipitation. Stochastic downscaling addresses anthropogenic climate impact by allowing relevant precipitation characteristics to be derived from local observations and an ensemble of climate models. This multi-climate model approach seeks to reflect the uncertainties in the data due to structural errors of the climate models. An ensemble of outcomes from stochastic downscaling allows for addressing the sampling uncertainty. These uncertainties are clearly reflected in the precipitation-runoff predictions of three urban drainage systems. They were mostly due to the sampling uncertainty. The contribution of climate model uncertainty was found to be of minor importance. Under the applied greenhouse gas emission scenario (A1B) and within the period 2036-2065, the potential for urban flooding in our Swiss case study is slightly reduced on average compared to the reference period 1981-2010. Scenario planning was applied to consider urban development associated with future socio-economic factors affecting urban drainage. The impact of scenario uncertainty was to a large extent found to be case-specific, thus emphasizing the need for scenario planning in every individual case. The results represent a valuable basis for discussions of new drainage design standards aiming specifically to include considerations of uncertainty. Copyright © 2015 Elsevier Ltd. All rights reserved.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24376707','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24376707"><span>Climate variability, weather and enteric disease incidence in New Zealand: time series analysis.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Lal, Aparna; Ikeda, Takayoshi; French, Nigel; Baker, Michael G; Hales, Simon</p> <p>2013-01-01</p> <p>Evaluating the influence of climate variability on enteric disease incidence may improve our ability to predict how climate change may affect these diseases. To examine the associations between regional climate variability and enteric disease incidence in New Zealand. Associations between monthly climate and enteric diseases (campylobacteriosis, salmonellosis, cryptosporidiosis, giardiasis) were investigated using Seasonal Auto Regressive Integrated Moving Average (SARIMA) models. No climatic factors were significantly associated with campylobacteriosis and giardiasis, with similar predictive power for univariate and multivariate models. Cryptosporidiosis was positively associated with average temperature of the previous month (β = 0.130, SE = 0.060, p <0.01) and inversely related to the Southern Oscillation Index (SOI) two months previously (β = -0.008, SE = 0.004, p <0.05). By contrast, salmonellosis was positively associated with temperature (β = 0.110, SE = 0.020, p<0.001) of the current month and SOI of the current (β = 0.005, SE = 0.002, p<0.050) and previous month (β = 0.005, SE = 0.002, p<0.05). Forecasting accuracy of the multivariate models for cryptosporidiosis and salmonellosis were significantly higher. Although spatial heterogeneity in the observed patterns could not be assessed, these results suggest that temporally lagged relationships between climate variables and national communicable disease incidence data can contribute to disease prediction models and early warning systems.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..1113664W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..1113664W"><span>Rainfall variability and extremes over southern Africa: Assessment of a climate model to reproduce daily extremes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Williams, C. J. R.; Kniveton, D. R.; Layberry, R.</p> <p>2009-04-01</p> <p>It is increasingly accepted that that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA). This dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. The ability of a climate model to simulate current climate provides some indication of how much confidence can be applied to its future predictions. In this paper, simulations of current climate from the UK Meteorological Office Hadley Centre's climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. This concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of rainfall variability over southern Africa. Secondly, the ability of the model to reproduce daily rainfall extremes will be assessed, again by a comparison with extremes from the MIRA dataset. The paper will conclude by discussing the user needs of satellite rainfall retrievals from a climate change modelling prospective.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70023945','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70023945"><span>Evaluation of terrestrial carbon cycle models with atmospheric CO2 measurements: Results from transient simulations considering increasing CO2, climate, and land-use effects</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Dargaville, R.J.; Heimann, Martin; McGuire, A.D.; Prentice, I.C.; Kicklighter, D.W.; Joos, F.; Clein, Joy S.; Esser, G.; Foley, J.; Kaplan, J.; Meier, R.A.; Melillo, J.M.; Moore, B.; Ramankutty, N.; Reichenau, T.; Schloss, A.; Sitch, S.; Tian, H.; Williams, L.J.; Wittenberg, U.</p> <p>2002-01-01</p> <p>An atmospheric transport model and observations of atmospheric CO2 are used to evaluate the performance of four Terrestrial Carbon Models (TCMs) in simulating the seasonal dynamics and interannual variability of atmospheric CO2 between 1980 and 1991. The TCMs were forced with time varying atmospheric CO2 concentrations, climate, and land use to simulate the net exchange of carbon between the terrestrial biosphere and the atmosphere. The monthly surface CO2 fluxes from the TCMs were used to drive the Model of Atmospheric Transport and Chemistry and the simulated seasonal cycles and concentration anomalies are compared with observations from several stations in the CMDL network. The TCMs underestimate the amplitude of the seasonal cycle and tend to simulate too early an uptake of CO2 during the spring by approximately one to two months. The model fluxes show an increase in amplitude as a result of land-use change, but that pattern is not so evident in the simulated atmospheric amplitudes, and the different models suggest different causes for the amplitude increase (i.e., CO2 fertilization, climate variability or land use change). The comparison of the modeled concentration anomalies with the observed anomalies indicates that either the TCMs underestimate interannual variability in the exchange of CO2 between the terrestrial biosphere and the atmosphere, or that either the variability in the ocean fluxes or the atmospheric transport may be key factors in the atmospheric interannual variability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013ClDy...40.1841F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013ClDy...40.1841F"><span>Assessment of a stochastic downscaling methodology in generating an ensemble of hourly future climate time series</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fatichi, S.; Ivanov, V. Y.; Caporali, E.</p> <p>2013-04-01</p> <p>This study extends a stochastic downscaling methodology to generation of an ensemble of hourly time series of meteorological variables that express possible future climate conditions at a point-scale. The stochastic downscaling uses general circulation model (GCM) realizations and an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). Marginal distributions of factors of change are computed for several climate statistics using a Bayesian methodology that can weight GCM realizations based on the model relative performance with respect to a historical climate and a degree of disagreement in projecting future conditions. A Monte Carlo technique is used to sample the factors of change from their respective marginal distributions. As a comparison with traditional approaches, factors of change are also estimated by averaging GCM realizations. With either approach, the derived factors of change are applied to the climate statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series of meteorological variables that can be considered to be representative of future climate conditions. In this study, the time series are generated in an ensemble mode to fully reflect the uncertainty of GCM projections, climate stochasticity, as well as uncertainties of the downscaling procedure. Applications of the methodology in reproducing future climate conditions for the periods of 2000-2009, 2046-2065 and 2081-2100, using the period of 1962-1992 as the historical baseline are discussed for the location of Firenze (Italy). The inferences of the methodology for the period of 2000-2009 are tested against observations to assess reliability of the stochastic downscaling procedure in reproducing statistics of meteorological variables at different time scales.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ThApC..99....9W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ThApC..99....9W"><span>Assessment of a climate model to reproduce rainfall variability and extremes over Southern Africa</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Williams, C. J. R.; Kniveton, D. R.; Layberry, R.</p> <p>2010-01-01</p> <p>It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The sub-continent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite-derived rainfall data from the Microwave Infrared Rainfall Algorithm (MIRA). This dataset covers the period from 1993 to 2002 and the whole of southern Africa at a spatial resolution of 0.1° longitude/latitude. This paper concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of present-day rainfall variability over southern Africa and is not intended to discuss possible future changes in climate as these have been documented elsewhere. Simulations of current climate from the UK Meteorological Office Hadley Centre's climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. Secondly, the ability of the model to reproduce daily rainfall extremes is assessed, again by a comparison with extremes from the MIRA dataset. The results suggest that the model reproduces the number and spatial distribution of rainfall extremes with some accuracy, but that mean rainfall and rainfall variability is under-estimated (over-estimated) over wet (dry) regions of southern Africa.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014WRR....50.9177C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014WRR....50.9177C"><span>Regional patterns of interannual variability of catchment water balances across the continental U.S.: A Budyko framework</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Carmona, Alejandra M.; Sivapalan, Murugesu; Yaeger, Mary A.; Poveda, Germán.</p> <p>2014-12-01</p> <p>Patterns of interannual variability of the annual water balance are explored using data from 190 MOPEX catchments across the continental U.S. This analysis has led to the derivation of a quantitative, dimensionless, Budyko-type framework to characterize the observed interannual variability of annual water balances. The resulting model is expressed in terms of a humidity index that measures the competition between water and energy availability at the annual time scale, and a similarity parameter (α) that captures the net effects of other short-term climate features and local landscape characteristics. This application of the model to the 190 study catchments revealed the existence of space-time symmetry between spatial (between-catchment) variability and general trends in the temporal (between-year) variability of the annual water balances. The MOPEX study catchments were classified into eight similar catchment groups on the basis of magnitudes of the similarity parameter α. Interesting regional trends of α across the continental U.S. were brought out through identification of similarities between the spatial positions of the catchment groups with the mapping of distinctive ecoregions that implicitly take into account common climatic and vegetation characteristics. In this context, this study has introduced a deep sense of similarity that is evident in observed space-time variability of water balances that also reflect the codependence and coevolution of climate and landscape properties.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.H53K..04L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H53K..04L"><span>Sustainability or Collapse: Interplay Between Decadal Climate Variability and Human Activities Matters</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lu, Y.; Hu, H.; Tian, F.</p> <p>2016-12-01</p> <p>The Aral Sea Crisis and the deterioration of Tarim River Basin are representative cases of emergent water deficit problems in arid areas. Comparing cases of water deficit problems in different regions and considering the in the perspective of socio-hydrology is helpful to obtain guidance on integrated management of arid area basins. Analyzing the interplay between decadal climate variability and human activities in both basins, the important role of human activities is observed. Decadal climate variability tempts people to adapt fast to increasing water resources and slowly to decreasing water resources, while using unsustainable technical measures to offset water shortage. Due to this asymmetry the situation deteriorates with technically enhanced capabilities of societies to exploit water resources, and more integrated long-term management capacity is in high demand.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_17 --> <div id="page_18" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="341"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19890001409','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19890001409"><span>MECA Symposium on Mars: Evolution of its Climate and Atmosphere</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Baker, Victor (Editor); Carr, Michael (Editor); Fanale, Fraser (Editor); Greeley, Ronald (Editor); Haberle, Robert (Editor); Leovy, Conway (Editor); Maxwell, Ted (Editor)</p> <p>1987-01-01</p> <p>The geological, atmospheric, and climatic history of Mars is explored in reviews and reports of recent observational and interpretive investigations. Topics addressed include evidence for a warm wet climate on early Mars, volatiles on Earth and on Mars, CO2 adsorption on palagonite and its implications for Martian regolith partitioning, and the effect of spatial resolution on interpretations of Martian subsurface volatiles. Consideration is given to high resolution observations of rampart craters, ring furrows in highland terrains, the interannual variability of the south polar cap, telescopic observations of the north polar cap and circumpolar clouds, and dynamical modeling of a planetary wave polar warming mechanism.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4136776','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4136776"><span>Region-Specific Sensitivity of Anemophilous Pollen Deposition to Temperature and Precipitation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Donders, Timme H.; Hagemans, Kimberley; Dekker, Stefan C.; de Weger, Letty A.; de Klerk, Pim; Wagner-Cremer, Friederike</p> <p>2014-01-01</p> <p>Understanding relations between climate and pollen production is important for several societal and ecological challenges, importantly pollen forecasting for pollinosis treatment, forensic studies, global change biology, and high-resolution palaeoecological studies of past vegetation and climate fluctuations. For these purposes, we investigate the role of climate variables on annual-scale variations in pollen influx, test the regional consistency of observed patterns, and evaluate the potential to reconstruct high-frequency signals from sediment archives. A 43-year pollen-trap record from the Netherlands is used to investigate relations between annual pollen influx, climate variables (monthly and seasonal temperature and precipitation values), and the North Atlantic Oscillation climate index. Spearman rank correlation analysis shows that specifically in Alnus, Betula, Corylus, Fraxinus, Quercus and Plantago both temperature in the year prior to (T-1), as well as in the growing season (T), are highly significant factors (TApril rs between 0.30 [P<0.05[ and 0.58 [P<0.0001]; TJuli-1 rs between 0.32 [P<0.05[ and 0.56 [P<0.0001]) in the annual pollen influx of wind-pollinated plants. Total annual pollen prediction models based on multiple climate variables yield R2 between 0.38 and 0.62 (P<0.0001). The effect of precipitation is minimal. A second trapping station in the SE Netherlands, shows consistent trends and annual variability, suggesting the climate factors are regionally relevant. Summer temperature is thought to influence the formation of reproductive structures, while temperature during the flowering season influences pollen release. This study provides a first predictive model for seasonal pollen forecasting, and also aides forensic studies. Furthermore, variations in pollen accumulation rates from a sub-fossil peat deposit are comparable with the pollen trap data. This suggests that high frequency variability pollen records from natural archives reflect annual past climate variability, and can be used in palaeoecological and -climatological studies to bridge between population- and species-scale responses to climate forcing. PMID:25133631</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4922588','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4922588"><span>Hydrological Impacts of Land Use Change and Climate Variability in the Headwater Region of the Heihe River Basin, Northwest China</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Zhang, Ling; Nan, Zhuotong; Xu, Yi; Li, Shuo</p> <p>2016-01-01</p> <p>Land use change and climate variability are two key factors impacting watershed hydrology, which is strongly related to the availability of water resources and the sustainability of local ecosystems. This study assessed separate and combined hydrological impacts of land use change and climate variability in the headwater region of a typical arid inland river basin, known as the Heihe River Basin, northwest China, in the recent past (1995–2014) and near future (2015–2024), by combining two land use models (i.e., Markov chain model and Dyna-CLUE) with a hydrological model (i.e., SWAT). The potential impacts in the near future were explored using projected land use patterns and hypothetical climate scenarios established on the basis of analyzing long-term climatic observations. Land use changes in the recent past are dominated by the expansion of grassland and a decrease in farmland; meanwhile the climate develops with a wetting and warming trend. Land use changes in this period induce slight reductions in surface runoff, groundwater discharge and streamflow whereas climate changes produce pronounced increases in them. The joint hydrological impacts are similar to those solely induced by climate changes. Spatially, both the effects of land use change and climate variability vary with the sub-basin. The influences of land use changes are more identifiable in some sub-basins, compared with the basin-wide impacts. In the near future, climate changes tend to affect the hydrological regimes much more prominently than land use changes, leading to significant increases in all hydrological components. Nevertheless, the role of land use change should not be overlooked, especially if the climate becomes drier in the future, as in this case it may magnify the hydrological responses. PMID:27348224</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27348224','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27348224"><span>Hydrological Impacts of Land Use Change and Climate Variability in the Headwater Region of the Heihe River Basin, Northwest China.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Zhang, Ling; Nan, Zhuotong; Xu, Yi; Li, Shuo</p> <p>2016-01-01</p> <p>Land use change and climate variability are two key factors impacting watershed hydrology, which is strongly related to the availability of water resources and the sustainability of local ecosystems. This study assessed separate and combined hydrological impacts of land use change and climate variability in the headwater region of a typical arid inland river basin, known as the Heihe River Basin, northwest China, in the recent past (1995-2014) and near future (2015-2024), by combining two land use models (i.e., Markov chain model and Dyna-CLUE) with a hydrological model (i.e., SWAT). The potential impacts in the near future were explored using projected land use patterns and hypothetical climate scenarios established on the basis of analyzing long-term climatic observations. Land use changes in the recent past are dominated by the expansion of grassland and a decrease in farmland; meanwhile the climate develops with a wetting and warming trend. Land use changes in this period induce slight reductions in surface runoff, groundwater discharge and streamflow whereas climate changes produce pronounced increases in them. The joint hydrological impacts are similar to those solely induced by climate changes. Spatially, both the effects of land use change and climate variability vary with the sub-basin. The influences of land use changes are more identifiable in some sub-basins, compared with the basin-wide impacts. In the near future, climate changes tend to affect the hydrological regimes much more prominently than land use changes, leading to significant increases in all hydrological components. Nevertheless, the role of land use change should not be overlooked, especially if the climate becomes drier in the future, as in this case it may magnify the hydrological responses.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27716414','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27716414"><span>Analyzing climate variations at multiple timescales can guide Zika virus response measures.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Muñoz, Ángel G; Thomson, Madeleine C; Goddard, Lisa; Aldighieri, Sylvain</p> <p>2016-10-06</p> <p>The emergence of Zika virus (ZIKV) in Latin America and the Caribbean in 2014-2016 occurred during a period of severe drought and unusually high temperatures, conditions that have been associated with the 2015-2016 El Niño event, and/or climate change; however, no quantitative assessment has been made to date. Analysis of related flaviviruses transmitted by the same vectors suggests that ZIKV dynamics are sensitive to climate seasonality and longer-term variability and trends. A better understanding of the climate conditions conducive to the 2014-2016 epidemic may permit the development of climate-informed short and long-term strategies for ZIKV prevention and control. Using a novel timescale-decomposition methodology, we demonstrate that the extreme climate anomalies observed in most parts of South America during the current epidemic are not caused exclusively by El Niño or climate change, but by a combination of climate signals acting at multiple timescales. In Brazil, the dry conditions present in 2013-2015 are primarily explained by year-to-year variability superimposed on decadal variability, but with little contribution of long-term trends. In contrast, the warm temperatures of 2014-2015 resulted from the compound effect of climate change, decadal and year-to-year climate variability. ZIKV response strategies made in Brazil during the drought concurrent with the 2015-2016 El Niño event, may require revision in light of the likely return of rainfall associated with the borderline La Niña event expected in 2016-2017. Temperatures are likely to remain warm given the importance of long term and decadal scale climate signals. The Author(s)</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20080015381','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20080015381"><span>CEOS SEO and GISS Meeting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Killough, Brian; Stover, Shelley</p> <p>2008-01-01</p> <p>The Committee on Earth Observation Satellites (CEOS) provides a brief to the Goddard Institute for Space Studies (GISS) regarding the CEOS Systems Engineering Office (SEO) and current work on climate requirements and analysis. A "system framework" is provided for the Global Earth Observation System of Systems (GEOSS). SEO climate-related tasks are outlined including the assessment of essential climate variable (ECV) parameters, use of the "systems framework" to determine relevant informational products and science models and the performance of assessments and gap analyses of measurements and missions for each ECV. Climate requirements, including instruments and missions, measurements, knowledge and models, and decision makers, are also outlined. These requirements would establish traceability from instruments to products and services allowing for benefit evaluation of instruments and measurements. Additionally, traceable climate requirements would provide a better understanding of global climate models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A41C2289V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A41C2289V"><span>Evaluating the ClimEx Single Model Large Ensemble in Comparison with EURO-CORDEX Results of Seasonal Means and Extreme Precipitation Indicators</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>von Trentini, F.; Schmid, F. J.; Braun, M.; Brisette, F.; Frigon, A.; Leduc, M.; Martel, J. L.; Willkofer, F.; Wood, R. R.; Ludwig, R.</p> <p>2017-12-01</p> <p>Meteorological extreme events seem to become more frequent in the present and future, and a seperation of natural climate variability and a clear climate change effect on these extreme events gains more and more interest. Since there is only one realisation of historical events, natural variability in terms of very long timeseries for a robust statistical analysis is not possible with observation data. A new single model large ensemble (SMLE), developed for the ClimEx project (Climate change and hydrological extreme events - risks and perspectives for water management in Bavaria and Québec) is supposed to overcome this lack of data by downscaling 50 members of the CanESM2 (RCP 8.5) with the Canadian CRCM5 regional model (using the EURO-CORDEX grid specifications) for timeseries of 1950-2099 each, resulting in 7500 years of simulated climate. This allows for a better probabilistic analysis of rare and extreme events than any preceding dataset. Besides seasonal sums, several extreme indicators like R95pTOT, RX5day and others are calculated for the ClimEx ensemble and several EURO-CORDEX runs. This enables us to investigate the interaction between natural variability (as it appears in the CanESM2-CRCM5 members) and a climate change signal of those members for past, present and future conditions. Adding the EURO-CORDEX results to this, we can also assess the role of internal model variability (or natural variability) in climate change simulations. A first comparison shows similar magnitudes of variability of climate change signals between the ClimEx large ensemble and the CORDEX runs for some indicators, while for most indicators the spread of the SMLE is smaller than the spread of different CORDEX models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3031277','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3031277"><span>Raised temperatures over the Kericho tea estates: revisiting the climate in the East African highlands malaria debate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p></p> <p>2011-01-01</p> <p>Background Whether or not observed increases in malaria incidence in the Kenyan Highlands during the last thirty years are associated with co-varying changes in local temperature, possibly connected to global changes in climate, has been debated for over a decade. Studies, using differing data sets and methodologies, produced conflicting results regarding the occurrence of temperature trends and their likelihood of being responsible, at least in part, for the increases in malaria incidence in the highlands of western Kenya. A time series of quality controlled daily temperature and rainfall data from Kericho, in the Kenyan Highlands, may help resolve the controversy. If significant temperature trends over the last three decades have occurred then climate should be included (along with other factors such as land use change and drug resistance) as a potential driver of the observed increases in malaria in the region. Methods Over 30 years (1 January 1979 to 31 December 2009) of quality controlled daily observations ( > 97% complete) of maximum, minimum and mean temperature were used in the analysis of trends at Kericho meteorological station, sited in a tea growing area of Kenya's western highlands. Inhomogeneities in all the time series were identified and corrected. Linear trends were identified via a least-squares regression analysis with statistical significance assessed using a two-tailed t-test. These 'gold standard' meteorological observations were compared with spatially interpolated temperature datasets that have been developed for regional or global applications. The relationship of local climate processes with larger climate variations, including tropical sea surface temperatures (SST), and El Niño-Southern Oscillation (ENSO) was also assessed. Results An upward trend of ≈0.2°C/decade was observed in all three temperature variables (P < 0.01). Mean temperature variations in Kericho were associated with large-scale climate variations including tropical SST (r = 0.50; p < 0.01). Local rainfall was found to have inverse effects on minimum and maximum temperature. Three versions of a spatially interpolated temperature data set showed markedly different trends when compared with each other and with the Kericho station observations. Conclusion This study presents evidence of a warming trend in observed maximum, minimum and mean temperatures at Kericho during the period 1979 to 2009 using gold standard meteorological observations. Although local factors may be contributing to these trends, the findings are consistent with variability and trends that have occurred in correlated global climate processes. Climate should therefore not be dismissed as a potential driver of observed increases in malaria seen in the region during recent decades, however its relative importance compared to other factors needs further elaboration. Climate services, pertinent to the achievement of development targets such as the Millennium Development Goals and the analysis of infectious disease in the context of climate variability and change are being developed and should increase the availability of relevant quality controlled climate data for improving development decisions. The malaria community should seize this opportunity to make their needs heard. PMID:21241505</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.8467R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.8467R"><span>North Atlantic sub-decadal variability in climate models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Reintges, Annika; Martin, Thomas; Latif, Mojib; Park, Wonsun</p> <p>2017-04-01</p> <p>The North Atlantic Oscillation (NAO) is the dominant variability mode for the winter climate of the North Atlantic sector. During a positive (negative) NAO phase, the sea level pressure (SLP) difference between the subtropical Azores high and the subpolar Icelandic low is anomalously strong (weak). This affects, for example, temperature, precipitation, wind, and surface heat flux over the North Atlantic, and over large parts of Europe. In observations we find enhanced sub-decadal variability of the NAO index that goes along with a dipolar sea surface temperature (SST) pattern. The corresponding SLP and SST patterns are reproduced in a control experiment of the Kiel Climate Model (KCM). Large-scale air-sea interaction is suggested to be essential for the North Atlantic sub-decadal variability in the KCM. The Atlantic Meridional Overturning Circulation (AMOC) plays a key role, setting the timescale of the variability by providing a delayed negative feedback to the NAO. The interplay of the NAO and the AMOC on the sub-decadal timescale is further investigated in the CMIP5 model ensemble. For example, the average CMIP5 model AMOC pattern associated with sub-decadal variability is characterized by a deep-reaching dipolar structure, similar to the KCM's sub-decadal AMOC variability pattern. The results suggest that dynamical air-sea interactions are crucial to generate enhanced sub-decadal variability in the North Atlantic climate.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20070031957&hterms=land+use+change&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dland%2Buse%2Bchange','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20070031957&hterms=land+use+change&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dland%2Buse%2Bchange"><span>Effects of Global Change on U.S. Urban Areas: Vulnerabilities, Impacts, and Adaptation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Quattrochi, Dale A.; Wilbanks, Thomas J.; Kirshen, Paul; Romero-Lnkao, Patricia; Rosenzweig, Cynthia; Ruth, Matthias; Solecki, William; Tarr, Joel</p> <p>2007-01-01</p> <p>Human settlements, both large and small, are where the vast majority of people on the Earth live. Expansion of cities both in population and areal extent, is a relentless process that will accelerate in the 21st century. As a consequence of urban growth both in the United States and around the globe, it is important to develop an understanding of how urbanization will affect the local and regional environment. Of equal importance, however, is the assessment of how cities will be impacted by the looming prospects of global climate change and climate variability. The potential impacts of climate change and variability has recently been annunciated by the IPCC's "Climate Change 2007" report. Moreover, the U.S. Climate Change Science Program (CCSP) is preparing a series of "Synthesis and Assessment Products" (SAPs) reports to support informed discussion and decision making regarding climate change and variability by policy matters, resource managers, stakeholders, the media, and the general public. We are authors on a SAP describing the effects of global climate change on human settlements. This paper will present the elements of our SAP report that relate to what vulnerabilities and impacts will occur, what adaptation responses may take place, and what possible effects on settlement patterns and characteristics will potentially arise, on human settlements in the U.S. as a result of climate change and climate variability. We will also present some recommendations about what should be done to further research on how climate change and variability will impact human settlements in the U.S., as well as how to engage government officials, policy and decision makers, and the general public in understanding the implications of climate change and variability on the local and regional levels. Additionally, we wish to explore how technology such as remote sensing data coupled with modeling, can be employed as synthesis tools for deriving insight across a spectrum of impacts (e.g. public health, urban planning for mitigation strategies) on how cities can cope and adapt to climate change and variability. This latter point parallels the concepts and ideas presented in the U.S. National Academy of Sciences, Decadal Survey report on "Earth Science Applications from Space: National Imperatives for the Next Decade and Beyond" wherein the analysis of the impacts of climate change and variability, human health, and land use change are listed as key areas for development of future Earth observing remote sensing systems.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMIN32B..01T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMIN32B..01T"><span>A new statistical tool for NOAA local climate studies</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Timofeyeva, M. M.; Meyers, J. C.; Hollingshead, A.</p> <p>2011-12-01</p> <p>The National Weather Services (NWS) Local Climate Analysis Tool (LCAT) is evolving out of a need to support and enhance the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) field offices' ability to efficiently access, manipulate, and interpret local climate data and characterize climate variability and change impacts. LCAT will enable NOAA's staff to conduct regional and local climate studies using state-of-the-art station and reanalysis gridded data and various statistical techniques for climate analysis. The analysis results will be used for climate services to guide local decision makers in weather and climate sensitive actions and to deliver information to the general public. LCAT will augment current climate reference materials with information pertinent to the local and regional levels as they apply to diverse variables appropriate to each locality. The LCAT main emphasis is to enable studies of extreme meteorological and hydrological events such as tornadoes, flood, drought, severe storms, etc. LCAT will close a very critical gap in NWS local climate services because it will allow addressing climate variables beyond average temperature and total precipitation. NWS external partners and government agencies will benefit from the LCAT outputs that could be easily incorporated into their own analysis and/or delivery systems. Presently we identified five existing requirements for local climate: (1) Local impacts of climate change; (2) Local impacts of climate variability; (3) Drought studies; (4) Attribution of severe meteorological and hydrological events; and (5) Climate studies for water resources. The methodologies for the first three requirements will be included in the LCAT first phase implementation. Local rate of climate change is defined as a slope of the mean trend estimated from the ensemble of three trend techniques: (1) hinge, (2) Optimal Climate Normals (running mean for optimal time periods), (3) exponentially-weighted moving average. Root mean squared error is used to determine the best fit of trend to the observations with the least error. The studies of climate variability impacts on local extremes use composite techniques applied to various definitions of local variables: from specified percentiles to critical thresholds. Drought studies combine visual capabilities of Google maps with statistical estimates of drought severity indices. The process of development will be linked to local office interactions with users to ensure the tool will meet their needs as well as provide adequate training. A rigorous internal and tiered peer-review process will be implemented to ensure the studies are scientifically-sound that will be published and submitted to the local studies catalog (database) and eventually to external sources, such as the Climate Portal.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016cosp...41E.250B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016cosp...41E.250B"><span>Climate Observations from Space</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Briggs, Stephen</p> <p>2016-07-01</p> <p>The latest Global Climate Observing System (GCOS) Status Report on global climate observations, delivered to the UNFCCC COP21 in November 2016, showed how satellite data are critical for observations relating to climate. Of the 50 Essential Climate Variables (ECVs) identified by GCOS as necessary for understanding climate change, about half are derived only from satellite data while half of the remainder have a significant input from satellites. Hence data from Earth observing satellite systems are now a fundamental requirement for understanding the climate system and for managing the consequences of climate change. Following the Paris Agreement of COP21 this need is only greater. Not only will satellites have to continue to provide data for modelling and predicting climate change but also for a much wider range of actions relating to climate. These include better information on loss and damage, resilience, improved adaptation to change, and on mitigation including information on greenhouse gas emissions. In addition there is an emerging need for indicators of the risks associated with future climate change which need to be better quantified, allowing policy makers both to understand what decisions need to be taken, and to see the consequences of their actions. The presentation will set out some of the ways in which satellite data are important in all aspects of understanding, managing and predicting climate change and how they may be used to support future decisions by those responsible for policy related to managing climate change and its consequences.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.A21I..01M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A21I..01M"><span>AIRS Observations Based Evaluation of Relative Climate Feedback Strengths on a GCM Grid-Scale</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Molnar, G. I.; Susskind, J.</p> <p>2012-12-01</p> <p>Climate feedback strengths, especially those associated with moist processes, still have a rather wide range in GCMs, the primary tools to predict future climate changes associated with man's ever increasing influences on our planet. Here, we make use of the first 10 years of AIRS observations to evaluate interrelationships/correlations of atmospheric moist parameter anomalies computed from AIRS Version 5 Level-3 products, and demonstrate their usefulness to assess relative feedback strengths. Although one may argue about the possible usability of shorter-term, observed climate parameter anomalies for estimating the strength of various (mostly moist processes related) feedbacks, recent works, in particular analyses by Dessler [2008, 2010], have demonstrated their usefulness in assessing global water vapor and cloud feedbacks. First, we create AIRS-observed monthly anomaly time-series (ATs) of outgoing longwave radiation, water vapor, clouds and temperature profile over a 10-year long (Sept. 2002 through Aug. 2012) period using 1x1 degree resolution (a common GCM grid-scale). Next, we evaluate the interrelationships of ATs of the above parameters with the corresponding 1x1 degree, as well as global surface temperature ATs. The latter provides insight comparable with more traditional climate feedback definitions (e. g., Zelinka and Hartmann, 2012) whilst the former is related to a new definition of "local (in surface temperature too) feedback strengths" on a GCM grid-scale. Comparing the correlation maps generated provides valuable new information on the spatial distribution of relative climate feedback strengths. We argue that for GCMs to be trusted for predicting longer-term climate variability, they should be able to reproduce these observed relationships/metrics as closely as possible. For this time period the main climate "forcing" was associated with the El Niño/La Niña variability (e. g., Dessler, 2010), so these assessments may not be descriptive of longer-term climate feedbacks due to global warming, for example. Nevertheless, one should take more confidence of greenhouse warming predictions of those GCMs that reproduce the (high quality observations-based) shorter-term feedback-relationships the best.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25501852','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25501852"><span>Future of endemic flora of biodiversity hotspots in India.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Chitale, Vishwas Sudhir; Behera, Mukund Dev; Roy, Partha Sarthi</p> <p>2014-01-01</p> <p>India is one of the 12 mega biodiversity countries of the world, which represents 11% of world's flora in about 2.4% of global land mass. Approximately 28% of the total Indian flora and 33% of angiosperms occurring in India are endemic. Higher human population density in biodiversity hotspots in India puts undue pressure on these sensitive eco-regions. In the present study, we predict the future distribution of 637 endemic plant species from three biodiversity hotspots in India; Himalaya, Western Ghats, Indo-Burma, based on A1B scenario for year 2050 and 2080. We develop individual variable based models as well as mixed models in MaxEnt by combining ten least co-related bioclimatic variables, two disturbance variables and one physiography variable as predictor variables. The projected changes suggest that the endemic flora will be adversely impacted, even under such a moderate climate scenario. The future distribution is predicted to shift in northern and north-eastern direction in Himalaya and Indo-Burma, while in southern and south-western direction in Western Ghats, due to cooler climatic conditions in these regions. In the future distribution of endemic plants, we observe a significant shift and reduction in the distribution range compared to the present distribution. The model predicts a 23.99% range reduction and a 7.70% range expansion in future distribution by 2050, while a 41.34% range reduction and a 24.10% range expansion by 2080. Integration of disturbance and physiography variables along with bioclimatic variables in the models improved the prediction accuracy. Mixed models provide most accurate results for most of the combinations of climatic and non-climatic variables as compared to individual variable based models. We conclude that a) regions with cooler climates and higher moisture availability could serve as refugia for endemic plants in future climatic conditions; b) mixed models provide more accurate results, compared to single variable based models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4264876','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4264876"><span>Future of Endemic Flora of Biodiversity Hotspots in India</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Chitale, Vishwas Sudhir; Behera, Mukund Dev; Roy, Partha Sarthi</p> <p>2014-01-01</p> <p>India is one of the 12 mega biodiversity countries of the world, which represents 11% of world's flora in about 2.4% of global land mass. Approximately 28% of the total Indian flora and 33% of angiosperms occurring in India are endemic. Higher human population density in biodiversity hotspots in India puts undue pressure on these sensitive eco-regions. In the present study, we predict the future distribution of 637 endemic plant species from three biodiversity hotspots in India; Himalaya, Western Ghats, Indo-Burma, based on A1B scenario for year 2050 and 2080. We develop individual variable based models as well as mixed models in MaxEnt by combining ten least co-related bioclimatic variables, two disturbance variables and one physiography variable as predictor variables. The projected changes suggest that the endemic flora will be adversely impacted, even under such a moderate climate scenario. The future distribution is predicted to shift in northern and north-eastern direction in Himalaya and Indo-Burma, while in southern and south-western direction in Western Ghats, due to cooler climatic conditions in these regions. In the future distribution of endemic plants, we observe a significant shift and reduction in the distribution range compared to the present distribution. The model predicts a 23.99% range reduction and a 7.70% range expansion in future distribution by 2050, while a 41.34% range reduction and a 24.10% range expansion by 2080. Integration of disturbance and physiography variables along with bioclimatic variables in the models improved the prediction accuracy. Mixed models provide most accurate results for most of the combinations of climatic and non-climatic variables as compared to individual variable based models. We conclude that a) regions with cooler climates and higher moisture availability could serve as refugia for endemic plants in future climatic conditions; b) mixed models provide more accurate results, compared to single variable based models. PMID:25501852</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC23E..02P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC23E..02P"><span>Utility of High Temporal Resolution Observations for Heat Health Event Characterization</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Palecki, M. A.</p> <p>2017-12-01</p> <p>Many heat health watch systems produce a binary on/off warning when conditions are predicted to exceed a given threshold during a day. Days with warnings and their mortality/morbidity statistics are analyzed relative to days not warned to determine the impacts of the event on human health, the effectiveness of warnings, and other statistics. The climate analyses of the heat waves or extreme temperature events are often performed with hourly or daily observations of air temperature, humidity, and other measured or derived variables, especially the maxima and minima of these data. However, since the beginning of the century, 5-minute observations are readily available for many weather and climate stations in the United States. NOAA National Centers for Environmental Information (NCEI) has been collecting 5-minute observations from the NOAA Automated Surface Observing System (ASOS) stations since 2000, and from the U.S. Climate Reference Network (USCRN) stations since 2005. This presentation will demonstrate the efficacy of utilizing 5-minute environmental observations to characterize heat waves by counting the length of time conditions exceed extreme thresholds based on individual and multiple variables and on derived variables such as the heat index. The length and depth of recovery periods between daytime heating periods will also be examined. The length of time under extreme conditions will influence health outcomes for those directly exposed. Longer periods of dangerous conditions also could increase the chances for poor health outcomes for those only exposed intermittently through cumulative impacts.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/49499','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/49499"><span>Seeing the climate through the trees: observing climate and forestry impacts on streamflow using a 60-year record</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>T. P. Burt; N. J. K. Howden; J. J. McDonnell; J. A. Jones; G. R. Hancock</p> <p>2014-01-01</p> <p>Paired watershed experiments involving the removal or manipulation of forest cover in one of the watersheds have been conducted for more than a century to quantify the impact of forestry operations on streamflow. Because climate variability is expected to be large, forestry treatment effects would be undetectable without the treatmentâcontrol comparison. New...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20070030112','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20070030112"><span>Utility of AIRS Retrievals for Climate Studies</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Molnar, Guyla I.; Susskind, Joel</p> <p>2007-01-01</p> <p>Satellites provide an ideal platform to study the Earth-atmosphere system on practically all spatial and temporal scales. Thus, one may expect that their rapidly growing datasets could provide crucial insights not only for short-term weather processes/predictions but into ongoing and future climate change processes as well. Though Earth-observing satellites have been around for decades, extracting climatically reliable information from their widely varying datasets faces rather formidable challenges. AIRS/AMSU is a state of the art infrared/microwave sounding system that was launched on the EOS Aqua platform on May 4, 2002, and has been providing operational quality measurements since September 2002. In addition to temperature and atmospheric constituent profiles, outgoing longwave radiation and basic cloud parameters are also derived from the AIRS/AMSU observations. However, so far the AIRS products have not been rigorously evaluated and/or validated on a large scale. Here we present preliminary assessments of monthly and 8-day mean AIRS "Version 4.0" retrieved products (available to the public through the DAAC at NASA/GSFC) to assess their utility for climate studies. First we present "consistency checks" by evaluating the time series of means, and "anomalies" (relative to the first 4 full years' worth of AIRS "climate statistics") of several climatically important retrieved parameters. Finally, we also present preliminary results regarding interrelationships of some of these geophysical variables, to assess to what extent they are consistent with the known physics of climate variability/change. In particular, we find at least one observed relationship which contradicts current general circulation climate (GCM) model results: the global water vapor climate feedback which is expected to be strongly positive is deduced to be slightly negative (shades of the "Lindzen effect"?). Though the current AIRS climatology covers only -4.5 years, it will hopefully extend much further into the future.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFM.C11C0514W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFM.C11C0514W"><span>Variability and Change in the Canadian Cryosphere: A Canadian Science Contribution to International Polar Year</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Walker, A. E.; Derksen, C.</p> <p>2008-12-01</p> <p>The cryosphere (snow, permafrost and seasonally frozen ground, ice caps and glaciers, sea-, river-, and lake ice) represents a significant feature of the Canadian landscape that impacts climate, hydrology, the economy and the daily lives of all Canadians, especially those living in northern communities. Over the past few decades significant changes have been observed in cryospheric elements (e.g. decreases in snow cover, glacier extent, sea ice cover) that have been attributed to a warming climate. This poster presentation will highlight initial scientific results from the approved Canadian International Polar Year project "Variability and Change in the Canadian Cryosphere" that is being led by Environment Canada and involves 33 co- investigators from government, academia and the private sector and links with international collaborators. This project builds on Canadian strengths in remote sensing, climate analysis and modeling with the overall objective to observe and understand the current state of the cryosphere in Canada and determine how fast it is changing and why. Research activities are focused on: (1) developing new satellite-based capabilities to provide information on the current state of the Canadian cryosphere during the IPY period; (2) placing current cryospheric conditions in the context of the historical record to document the magnitude of changes over the 50 years since the last International Polar Year (IGY 1957-1958); (3) characterizing and explaining the observed variability and changes in the context of the coupled climate cryosphere system; and (4) improving the representation of the cryosphere in Canadian land surface and climate models to provide current and future climate simulations of the cryosphere for climate impact studies. The project also includes several outreach activities to engage northern communities in cryospheric monitoring and incorporate traditional knowledge with remotely-sensed information to generate new maps on local river ice and sea ice conditions to assist residents in planning safe navigation routes.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1379116-detection-attribution-climate-extremes-observed-record','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1379116-detection-attribution-climate-extremes-observed-record"><span>Detection and attribution of climate extremes in the observed record</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Easterling, David R.; Kunkel, Kenneth E.; Wehner, Michael F.; ...</p> <p>2016-01-18</p> <p>We present an overview of practices and challenges related to the detection and attribution of observed changes in climate extremes. Detection is the identification of a statistically significant change in the extreme values of a climate variable over some period of time. Issues in detection discussed include data quality, coverage, and completeness. Attribution takes that detection of a change and uses climate model simulations to evaluate whether a cause can be assigned to that change. Additionally, we discuss a newer field of attribution, event attribution, where individual extreme events are analyzed for the express purpose of assigning some measure ofmore » whether that event was directly influenced by anthropogenic forcing of the climate system.« less</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_18 --> <div id="page_19" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="361"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ems..confE.161D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ems..confE.161D"><span>Homogenising time series: Beliefs, dogmas and facts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Domonkos, P.</p> <p>2010-09-01</p> <p>For obtaining reliable information about climate change and climate variability the use of high quality data series is essentially important, and one basic tool of quality improvements is the statistical homogenisation of observed time series. In the recent decades large number of homogenisation methods has been developed, but the real effects of their application on time series are still not known entirely. The ongoing COST HOME project (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with higher confidence than earlier. As part of the COST activity, a benchmark dataset was built whose characteristics approach well the characteristics of real networks of observed time series. This dataset offers much better opportunity than ever to test the wide variety of homogenisation methods, and analyse the real effects of selected theoretical recommendations. The author believes that several old theoretical rules have to be re-evaluated. Some examples of the hot questions, a) Statistically detected change-points can be accepted only with the confirmation of metadata information? b) Do semi-hierarchic algorithms for detecting multiple change-points in time series function effectively in practise? c) Is it good to limit the spatial comparison of candidate series with up to five other series in the neighbourhood? Empirical results - those from the COST benchmark, and other experiments too - show that real observed time series usually include several inhomogeneities of different sizes. Small inhomogeneities seem like part of the climatic variability, thus the pure application of classic theory that change-points of observed time series can be found and corrected one-by-one is impossible. However, after homogenisation the linear trends, seasonal changes and long-term fluctuations of time series are usually much closer to the reality, than in raw time series. The developers and users of homogenisation methods have to bear in mind that the eventual purpose of homogenisation is not to find change-points, but to have the observed time series with statistical properties those characterise well the climate change and climate variability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ThApC.130..635S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ThApC.130..635S"><span>Assessment of climate change impacts on climate variables using probabilistic ensemble modeling and trend analysis</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Safavi, Hamid R.; Sajjadi, Sayed Mahdi; Raghibi, Vahid</p> <p>2017-10-01</p> <p>Water resources in snow-dependent regions have undergone significant changes due to climate change. Snow measurements in these regions have revealed alarming declines in snowfall over the past few years. The Zayandeh-Rud River in central Iran chiefly depends on winter falls as snow for supplying water from wet regions in high Zagrous Mountains to the downstream, (semi-)arid, low-lying lands. In this study, the historical records (baseline: 1971-2000) of climate variables (temperature and precipitation) in the wet region were chosen to construct a probabilistic ensemble model using 15 GCMs in order to forecast future trends and changes while the Long Ashton Research Station Weather Generator (LARS-WG) was utilized to project climate variables under two A2 and B1 scenarios to a future period (2015-2044). Since future snow water equivalent (SWE) forecasts by GCMs were not available for the study area, an artificial neural network (ANN) was implemented to build a relationship between climate variables and snow water equivalent for the baseline period to estimate future snowfall amounts. As a last step, homogeneity and trend tests were performed to evaluate the robustness of the data series and changes were examined to detect past and future variations. Results indicate different characteristics of the climate variables at upstream stations. A shift is observed in the type of precipitation from snow to rain as well as in its quantities across the subregions. The key role in these shifts and the subsequent side effects such as water losses is played by temperature.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007PhDT.......106H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007PhDT.......106H"><span>An observational and modeling study of the regional impacts of climate variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Horton, Radley M.</p> <p></p> <p>Climate variability has large impacts on humans and their agricultural systems. Farmers are at the center of this agricultural network, but it is often agricultural planners---regional planners, extension agents, commodity groups and cooperatives---that translate climate information for users. Global climate models (GCMs) are a leading tool for understanding and predicting climate and climate change. Armed with climate projections and forecasts, agricultural planners adapt their decision-making to optimize outcomes. This thesis explores what GCMs can, and cannot, tell us about climate variability and change at regional scales. The question is important, since high-quality regional climate projections could assist farmers and regional planners in key management decisions, contributing to better agricultural outcomes. To answer these questions, climate variability and its regional impacts are explored in observations and models for the current and future climate. The goals are to identify impacts of observed variability, assess model simulation of variability, and explore how climate variability and its impacts may change under enhanced greenhouse warming. Chapter One explores how well Goddard Institute for Space Studies (GISS) atmospheric models, forced by historical sea surface temperatures (SST), simulate climatology and large-scale features during the exceptionally strong 1997--1999 El Nino Southern Oscillation (ENSO) cycle. Reasonable performance in this 'proof of concept' test is considered a minimum requirement for further study of variability in models. All model versions produce appropriate local changes with ENSO, indicating that with correct ocean temperatures these versions are capable of simulating the large-scale effects of ENSO around the globe. A high vertical resolution model (VHR) provides the best simulation. Evidence is also presented that SST anomalies outside the tropical Pacific may play a key role in generating remote teleconnections even during El Nino events. Based on the results from Chapter One, the analysis is expanded in several ways in Chapter Two. To gain a more complete and statistically meaningful understanding of ENSO, a 25 year time period is used instead of a single event. To gain a fuller understanding of climate variability, additional patterns are analyzed. Finally analysis is conducted at the regional scales that are of interest to farmers and agricultural planners. Key findings are that GISS ModelE can reproduce: (1) the spatial pattern associated with two additional related modes, the Arctic Oscillation (AO) and the North Atlantic Oscillation (NAO); (2) rainfall patterns in Indonesia; and (3) dynamical features such as sea level pressure (SLP) gradients and wind in the study regions. When run in coupled mode, the same model reproduces similar modes spatially but with reduced variance and weak teleconnections. Since Chapter Two identified Western Indonesia as the region where GCMs hold the most promise for agricultural applications, in Chapter Three a finer spatial and temporal scale analysis of ENSO's effects is presented. Agricultural decision-making is also linked to ENSO's climate effects. Early rainy season precipitation and circulation, and same-season planting and harvesting dates, are shown to be sensitive to ENSO. The locus of ENSO convergence and rainfall anomalies is shown to be near the axis of rainy season establishment, defined as the 6--8 mm/day isohyet, an approximate threshold for irrigated rice cultivation. As the axis tracks south and east between October and January, so do ENSO anomalies. Circulation anomalies associated with ENSO are shown to be similar to those associated with rainfall anomalies, suggesting that long lead-time ENSO forecasts may allow more adaptation than 'wait and see' methods, with little loss of forecast skill. Additional findings include: (1) rice and corn yields are lower (higher) during dry (wet) trimesters and El Nino (La Nina) years; and (2) a statistically significant negative relationship exists between malaria cases and ENSO. The final chapter adds climate change to the climate variability story. Under high CO2, the model able to capture ENSO dynamics---an atmospheric model coupled to the Cane-Zebiak ocean model ('C4' here)---generates more El Nino-like mean conditions in the tropical Pacific. These changes produce a 4x larger increase in maximum precipitation with warming in C4 than an atmospheric model with a slab ocean (Q4), dramatically enhancing the Pacific Hadley and Walker circulations, and through positive feedbacks, increasing the global temperature. Near Nordeste warming alone (Q4) produces added rainfall, which in C4 is partially cancelled out by El Nino-like changes in the Walker Cell. Both Q4 and C4 produce small changes in Indonesia, although C4 generates large circulation and precipitation anomalies over the Western Indian Ocean. C4 changes in the midlatitudes produce a very strong Pacific North American pattern (PNA) response that dominates a small positive AO change associated with Q4. These PNA changes produce increased rainfall over the Southeastern United States (SEUS) in C4. AO and NAO-like variability are also found to increase with enhanced CO2. This thesis highlights how climate variability influences regional climate variability, with an emphasis on four regions: Nordeste, Brazil, Western Indonesia, the Southeastern United States (SEUS), and the Mediterranean. It links El Nino-driven delay in the onset of rainy season drivers in Western Indonesia to decision-making about when to plant the year's largest crop. In a coupled configuration, the GISS GCM produces strong El Nino-like changes with global warming. This result suggests that the impacts---climatological and agricultural---of climate change may ultimately exceed the impacts of current variability. Somewhat paradoxically, these results indicate that one of the central manifestations of climate change is likely to be changes in patterns of climate variability and their regional impacts.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009IJBm...53..287M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009IJBm...53..287M"><span>Urbanisation induces early flowering: evidence from Platanus acerifolia and Prunus cerasus</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mimet, A.; Pellissier, V.; Quénol, H.; Aguejdad, R.; Dubreuil, V.; Rozé, F.</p> <p>2009-05-01</p> <p>The effect of towns on plant phenology, i.e. advancement of spring development compared with a rural environment, via the urban heat island (UHI) phenomenon, has been shown for many towns in many countries. This work combines experimental and observational methodology to provide a better and deeper view of climatic habitat in an urban context with a view to understanding the relationship between plant development and urban climate on the intra-urban scale (by taking into account town structure). A dense network of 17 meteorological stations was set up in Rennes, France, enabling us to identify and quantify climatic changes associated with the UHI. Meanwhile, phenological observations were made during early spring (March and April) in 2005 on Platanus acerifolia and Prunus cerasus to study the relationship between climatic and phenological data. The results show that there is both a climatic gradient and a developmental gradient corresponding to the type of urbanisation in the town of Rennes. The town influences plant phenology by reducing the diurnal temperature range and by increasing the minimum temperature as one approaches the town centre. The influence of ground cover type (plants or buildings) on development is also shown. The developmental phases of preflowering and flowering are influenced to differing extents by climatic variables. The period during which climatic variables are effective before a given developmental phase varies considerably. The preflowering phases are best correlated with the mean of the minimum air temperature for the 15-day period before the observation, whereas flowering appears to be more dependent on the mean of the daily diurnal temperature range for the 8 days preceding the observation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19219464','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19219464"><span>Urbanisation induces early flowering: evidence from Platanus acerifolia and Prunus cerasus.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Mimet, A; Pellissier, V; Quénol, H; Aguejdad, R; Dubreuil, V; Rozé, F</p> <p>2009-05-01</p> <p>The effect of towns on plant phenology, i.e. advancement of spring development compared with a rural environment, via the urban heat island (UHI) phenomenon, has been shown for many towns in many countries. This work combines experimental and observational methodology to provide a better and deeper view of climatic habitat in an urban context with a view to understanding the relationship between plant development and urban climate on the intra-urban scale (by taking into account town structure). A dense network of 17 meteorological stations was set up in Rennes, France, enabling us to identify and quantify climatic changes associated with the UHI. Meanwhile, phenological observations were made during early spring (March and April) in 2005 on Platanus acerifolia and Prunus cerasus to study the relationship between climatic and phenological data. The results show that there is both a climatic gradient and a developmental gradient corresponding to the type of urbanisation in the town of Rennes. The town influences plant phenology by reducing the diurnal temperature range and by increasing the minimum temperature as one approaches the town centre. The influence of ground cover type (plants or buildings) on development is also shown. The developmental phases of preflowering and flowering are influenced to differing extents by climatic variables. The period during which climatic variables are effective before a given developmental phase varies considerably. The preflowering phases are best correlated with the mean of the minimum air temperature for the 15-day period before the observation, whereas flowering appears to be more dependent on the mean of the daily diurnal temperature range for the 8 days preceding the observation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C31C..06S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C31C..06S"><span>High resolution climate scenarios for snowmelt modelling in small alpine catchments</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schirmer, M.; Peleg, N.; Burlando, P.; Jonas, T.</p> <p>2017-12-01</p> <p>Snow in the Alps is affected by climate change with regard to duration, timing and amount. This has implications with respect to important societal issues as drinking water supply or hydropower generation. In Switzerland, the latter received a lot of attention following the political decision to phase out of nuclear electricity production. An increasing number of authorization requests for small hydropower plants located in small alpine catchments was observed in the recent years. This situation generates ecological conflicts, while the expected climate change poses a threat to water availability thus putting at risk investments in such hydropower plants. Reliable high-resolution climate scenarios are thus required, which account for small-scale processes to achieve realistic predictions of snowmelt runoff and its variability in small alpine catchments. We therefore used a novel model chain by coupling a stochastic 2-dimensional weather generator (AWE-GEN-2d) with a state-of-the-art energy balance snow cover model (FSM). AWE-GEN-2d was applied to generate ensembles of climate variables at very fine temporal and spatial resolution, thus providing all climatic input variables required for the energy balance modelling. The land-surface model FSM was used to describe spatially variable snow cover accumulation and melt processes. The FSM was refined to allow applications at very high spatial resolution by specifically accounting for small-scale processes, such as a subgrid-parametrization of snow covered area or an improved representation of forest-snow processes. For the present study, the model chain was tested for current climate conditions using extensive observational dataset of different spatial and temporal coverage. Small-scale spatial processes such as elevation gradients or aspect differences in the snow distribution were evaluated using airborne LiDAR data. 40-year of monitoring data for snow water equivalent, snowmelt and snow-covered area for entire Switzerland was used to verify snow distribution patterns at coarser spatial and temporal scale. The ability of the model chain to reproduce current climate conditions in small alpine catchments makes this model combination an outstanding candidate to produce high resolution climate scenarios of snowmelt in small alpine catchments.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4472520','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4472520"><span>Climate Change and Spatiotemporal Distributions of Vector-Borne Diseases in Nepal – A Systematic Synthesis of Literature</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Dhimal, Meghnath; Ahrens, Bodo; Kuch, Ulrich</p> <p>2015-01-01</p> <p>Background Despite its largely mountainous terrain for which this Himalayan country is a popular tourist destination, Nepal is now endemic for five major vector-borne diseases (VBDs), namely malaria, lymphatic filariasis, Japanese encephalitis, visceral leishmaniasis and dengue fever. There is increasing evidence about the impacts of climate change on VBDs especially in tropical highlands and temperate regions. Our aim is to explore whether the observed spatiotemporal distributions of VBDs in Nepal can be related to climate change. Methodology A systematic literature search was performed and summarized information on climate change and the spatiotemporal distribution of VBDs in Nepal from the published literature until December2014 following providing items for systematic review and meta-analysis (PRISMA) guidelines. Principal Findings We found 12 studies that analysed the trend of climatic data and are relevant for the study of VBDs, 38 studies that dealt with the spatial and temporal distribution of disease vectors and disease transmission. Among 38 studies, only eight studies assessed the association of VBDs with climatic variables. Our review highlights a pronounced warming in the mountains and an expansion of autochthonous cases of VBDs to non-endemic areas including mountain regions (i.e., at least 2,000 m above sea level). Furthermore, significant relationships between climatic variables and VBDs and their vectors are found in short-term studies. Conclusion Taking into account the weak health care systems and difficult geographic terrain of Nepal, increasing trade and movements of people, a lack of vector control interventions, observed relationships between climatic variables and VBDs and their vectors and the establishment of relevant disease vectors already at least 2,000 m above sea level, we conclude that climate change can intensify the risk of VBD epidemics in the mountain regions of Nepal if other non-climatic drivers of VBDs remain constant. PMID:26086887</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110007894','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110007894"><span>New and Improved GLDAS and NLDAS Data Sets and Data Services at HDISC/NASA</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Rui, Hualan; Beaudoing, Hiroko Kato; Mocko, David M.; Rodell, Matthew; Teng, William L.; Vollmer. Bruce</p> <p>2010-01-01</p> <p>Terrestrial hydrological variables are important in global hydrology, climate, and carbon cycle studies. Generating global fields of these variables, however, is still a challenge. The goal of a land data assimilation system (LDAS)is to ingest satellite-and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes data and, thereby, facilitate hydrology and climate modeling, research, and forecast.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1054032-testing-possible-influence-unknown-climate-forcings-upon-global-temperature-increases-from','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1054032-testing-possible-influence-unknown-climate-forcings-upon-global-temperature-increases-from"><span>Testing for the Possible Influence of Unknown Climate Forcings upon Global Temperature Increases from 1950-2000</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Anderson, Bruce T.; Knight, Jeff R.; Ringer, Mark A.</p> <p>2012-10-15</p> <p>Global-scale variations in the climate system over the last half of the twentieth century, including long-term increases in global-mean near-surface temperatures, are consistent with concurrent human-induced emissions of radiatively active gases and aerosols. However, such consistency does not preclude the possible influence of other forcing agents, including internal modes of climate variability or unaccounted for aerosol effects. To test whether other unknown forcing agents may have contributed to multidecadal increases in global-mean near-surface temperatures from 1950 to 2000, data pertaining to observed changes in global-scale sea surface temperatures and observed changes in radiatively active atmospheric constituents are incorporated into numericalmore » global climate models. Results indicate that the radiative forcing needed to produce the observed long-term trends in sea surface temperatures—and global-mean near-surface temperatures—is provided predominantly by known changes in greenhouse gases and aerosols. Further, results indicate that less than 10% of the long-term historical increase in global-mean near-surface temperatures over the last half of the twentieth century could have been the result of internal climate variability. In addition, they indicate that less than 25%of the total radiative forcing needed to produce the observed long-term trend in global-mean near-surface temperatures could have been provided by changes in net radiative forcing from unknown sources (either positive or negative). These results, which are derived from simple energy balance requirements, emphasize the important role humans have played in modifying the global climate over the last half of the twentieth century.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/15010453-hydrologic-implications-dynamical-statistical-approaches-downscaling-climate-model-outputs','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/15010453-hydrologic-implications-dynamical-statistical-approaches-downscaling-climate-model-outputs"><span>Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Wood, Andrew W; Leung, Lai R; Sridhar, V</p> <p></p> <p>Six approaches for downscaling climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. Comparisons were made on the basis of a twenty-year retrospective (1975–1995) climate simulation produced by the NCAR-DOE Parallel Climate Model (PCM), and the implications of the comparison for a future (2040–2060) PCM climate scenario were also explored. The six approaches were made up of three relatively simple statistical downscaling methods – linear interpolation (LI), spatial disaggregationmore » (SD), and bias-correction and spatial disaggregation (BCSD) – each applied to both PCM output directly (at T42 spatial resolution), and after dynamical downscaling via a Regional Climate Model (RCM – at ½-degree spatial resolution), for downscaling the climate model outputs to the 1/8-degree spatial resolution of the hydrological model. For the retrospective climate simulation, results were compared to an observed gridded climatology of temperature and precipitation, and gridded hydrologic variables resulting from forcing the hydrologic model with observations. The most significant findings are that the BCSD method was successful in reproducing the main features of the observed hydrometeorology from the retrospective climate simulation, when applied to both PCM and RCM outputs. Linear interpolation produced better results using RCM output than PCM output, but both methods (PCM-LI and RCM-LI) lead to unacceptably biased hydrologic simulations. Spatial disaggregation of the PCM output produced results similar to those achieved with the RCM interpolated output; nonetheless, neither PCM nor RCM output was useful for hydrologic simulation purposes without a bias-correction step. For the future climate scenario, only the BCSD-method (using PCM or RCM) was able to produce hydrologically plausible results. With the BCSD method, the RCM-derived hydrology was more sensitive to climate change than the PCM-derived hydrology.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC11F..07S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC11F..07S"><span>Using historical and projected future climate model simulations as drivers of agricultural and biological models (Invited)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stefanova, L. B.</p> <p>2013-12-01</p> <p>Climate model evaluation is frequently performed as a first step in analyzing climate change simulations. Atmospheric scientists are accustomed to evaluating climate models through the assessment of model climatology and biases, the models' representation of large-scale modes of variability (such as ENSO, PDO, AMO, etc) and the relationship between these modes and local variability (e.g. the connection between ENSO and the wintertime precipitation in the Southeast US). While these provide valuable information about the fidelity of historical and projected climate model simulations from an atmospheric scientist's point of view, the application of climate model data to fields such as agriculture, ecology and biology may require additional analyses focused on the particular application's requirements and sensitivities. Typically, historical climate simulations are used to determine a mapping between the model and observed climate, either through a simple (additive for temperature or multiplicative for precipitation) or a more sophisticated (such as quantile matching) bias correction on a monthly or seasonal time scale. Plants, animals and humans however are not directly affected by monthly or seasonal means. To assess the impact of projected climate change on living organisms and related industries (e.g. agriculture, forestry, conservation, utilities, etc.), derivative measures such as the heating degree-days (HDD), cooling degree-days (CDD), growing degree-days (GDD), accumulated chill hours (ACH), wet season onset (WSO) and duration (WSD), among others, are frequently useful. We will present a comparison of the projected changes in such derivative measures calculated by applying: (a) the traditional temperature/precipitation bias correction described above versus (b) a bias correction based on the mapping between the historical model and observed derivative measures themselves. In addition, we will present and discuss examples of various application-based climate model evaluations, such as: (a) agricultural crop yield estimates and (b) species population viability estimates modeled using observed climate data vs. historical climate simulations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.H43K1632I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H43K1632I"><span>Hydrological Dynamics of Central America: Time-of-Emergence of the Global Warming Signal</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Imbach, P. A.; Georgiou, S.; Calderer, L.; Coto, A.; Nakaegawa, T.; Chou, S. C.; Lyra, A. A.; Hidalgo, H. G.; Ciais, P.</p> <p>2016-12-01</p> <p>Central America is among the world's most vulnerable regions to climate variability and change. Country economies are highly dependent on the agricultural sector and over 40 million people's rural livelihoods directly depend on the use of natural resources. Future climate scenarios show a drier outlook (higher temperatures and lower precipitation) over a region where rural livelihoods are already compromised by water availability and climate variability. Previous efforts to validate modelling of the regional hydrology have been based on high resolution (1 km2) equilibrium models (Imbach et al., 2010) or using dynamic models (Variable Infiltration Capacity) with coarse climate forcing (0.5°) (Hidalgo et al., 2013; Maurer et al., 2009). We present here: (i) validation of the hydrological outputs from high-resolution simulations (10 km2) of a dynamic vegetation model (Orchidee), using 7 different sets of model input forcing data, with monthly runoff observations from 182 catchments across Central America; (ii) the first assessments of the region's hydrological variability using the historical simulations (iii) an estimation of the time of emergence of the climate change signal (under the SRES emission scenarios) on the water balance. We found model performance to be comparable with that from studies in other world regions (Yang et al. 2016) when forced with high resolution precipitation data (monthly values at 5 km2, Funk et al. (2015)) and the Climate Research Unit (CRU 3.2, Harris et al. (2014)) dataset of meteorological parameters. Validation results showed a Pearson correlation coefficient ≈ 0.6, general underestimation of runoff of ≈ 60% and variability close to observed values (ratio of standard deviations of ≈ 0.7). Maps of historical runoff are presented to show areas where high runoff variability follows high mean annual runoff, with opposite trends over the Caribbean. Future scenarios show large areas where future maximum water availability will always fall below minus-one standard deviation of the historical values by mid-century. Additionally, our results highlight the time horizon left to develop adaptation strategies to cope with future reductions in water availability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22833269','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22833269"><span>Variability in solar radiation and temperature explains observed patterns and trends in tree growth rates across four tropical forests.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Dong, Shirley Xiaobi; Davies, Stuart J; Ashton, Peter S; Bunyavejchewin, Sarayudh; Supardi, M N Nur; Kassim, Abd Rahman; Tan, Sylvester; Moorcroft, Paul R</p> <p>2012-10-07</p> <p>The response of tropical forests to global climate variability and change remains poorly understood. Results from long-term studies of permanent forest plots have reported different, and in some cases opposing trends in tropical forest dynamics. In this study, we examined changes in tree growth rates at four long-term permanent tropical forest research plots in relation to variation in solar radiation, temperature and precipitation. Temporal variation in the stand-level growth rates measured at five-year intervals was found to be positively correlated with variation in incoming solar radiation and negatively related to temporal variation in night-time temperatures. Taken alone, neither solar radiation variability nor the effects of night-time temperatures can account for the observed temporal variation in tree growth rates across sites, but when considered together, these two climate variables account for most of the observed temporal variability in tree growth rates. Further analysis indicates that the stand-level response is primarily driven by the responses of smaller-sized trees (less than 20 cm in diameter). The combined temperature and radiation responses identified in this study provide a potential explanation for the conflicting patterns in tree growth rates found in previous studies.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1713004M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1713004M"><span>Oscar: a portable prototype system for the study of climate variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Madonna, Fabio; Rosoldi, Marco; Amato, Francesco</p> <p>2015-04-01</p> <p>The study of the techniques for the exploitation of solar energy implies the knowledge of nature, ecosystem, biological factors and local climate. Clouds, fog, water vapor, and the presence of large concentrations of dust can significantly affect the way to exploit the solar energy. Therefore, a quantitative characterization of the impact of climate variability at the regional scale is needed to increase the efficiency and sustainability of the energy system. OSCAR (Observation System for Climate Application at Regional scale) project, funded in the frame of the PO FESR 2007-2013, aims at the design of a portable prototype system for the study of correlations among the trends of several Essential Climate Variables (ECVs) and the change in the amount of solar irradiance at the ground level. The final goal of this project is to provide a user-friendly low cost solution for the quantification of the impact of regional climate variability on the efficiency of solar cell and concentrators to improve the exploitation of natural sources. The prototype has been designed on the basis of historical measurements performed at CNR-IMAA Atmospheric Observatory (CIAO). Measurements from satellite and data from models have been also considered as ancillary to the study, above all, to fill in the gaps of existing datasets. In this work, the results outcome from the project activities will be presented. The results include: the design and implementation of the prototype system; the development of a methodology for the estimation of the impact of climate variability, mainly due to aerosol, cloud and water vapor, on the solar irradiance using the integration of the observations potentially provided by prototype; the study of correlation between the surface radiation, precipitation and aerosols transport. In particular, a statistical study will be presented to assess the impact of the atmosphere on the solar irradiance at the ground, quantifying the contribution due to aerosol and clouds and separating their effect on the direct and the diffuse components of the solar radiation. This also aims to provide recommendations to the manufacturer of the devices used to exploit solar radiation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19880045212&hterms=GLOBAL+WARNING&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DGLOBAL%2BWARNING','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19880045212&hterms=GLOBAL+WARNING&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DGLOBAL%2BWARNING"><span>The GISS global climate-middle atmosphere model. II - Model variability due to interactions between planetary waves, the mean circulation and gravity wave drag</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Rind, D.; Suozzo, R.; Balachandran, N. K.</p> <p>1988-01-01</p> <p>The variability which arises in the GISS Global Climate-Middle Atmosphere Model on two time scales is reviewed: interannual standard deviations, derived from the five-year control run, and intraseasonal variability as exemplified by statospheric warnings. The model's extratropical variability for both mean fields and eddy statistics appears reasonable when compared with observations, while the tropical wind variability near the stratopause may be excessive possibly, due to inertial oscillations. Both wave 1 and wave 2 warmings develop, with connections to tropospheric forcing. Variability on both time scales results from a complex set of interactions among planetary waves, the mean circulation, and gravity wave drag. Specific examples of these interactions are presented, which imply that variability in gravity wave forcing and drag may be an important component of the variability of the middle atmosphere.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A51V..07S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A51V..07S"><span>Spatial patterns of Antarctic surface temperature trends in the context of natural variability: Lessons from the CMIP5 Models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Smith, K. L.; Polvani, L. M.</p> <p>2015-12-01</p> <p>The recent annually averaged warming of the Antarctic Peninsula, and of West Antarctica, stands in stark contrast to very small and weakly negative trends over East Antarctica. This asymmetry arises primarily from a highly significant warming of West Antarctica in austral spring and a strong cooling of East Antarctic in austral autumn. Here we examine whether this East-West asymmetry is a response to anthropogenic climate forcings or a manifestation of natural climate variability. We compare the observed Antarctic surface air temperature (SAT) trends from five temperature reconstructions over two distinct time periods (1979-2005 and 1960-2005), and with those simulated by 40 coupled models participating in Phase 5 of the Coupled Model Intercomparison Project. We find that the observed East-West asymmetry differs substantially over the two time periods and, furthermore, is completely absent from the CMIP5 multi-model mean (from which all natural variability is eliminated by the averaging). We compare the CMIP5 SAT trends to those of 29 historical atmosphere-only simulations with prescribed sea surface temperatures (SSTs) and sea ice and find that these simulations are in better agreement with the observations. This suggests that natural multi-decadal variability associated with SSTs and sea ice and not external forcings is the primary driver of Antarctic SAT trends. We confirm this by showing that the observed trends lie within the distribution of multi-decadal trends from the CMIP5 pre-industrial integrations. These results, therefore, offer new evidence which points to natural climate variability as the more likely cause of the recent warming of West Antarctica and of the Peninsula.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/18903','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/18903"><span>Land resources: forests and arid lands</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>M.G. Ryan; S.R. Archer; R.A. Birdsey; C.N. Dahm; L.S. Heath; J.A. Hicke; D.Y. Hollinger; T.E. Huxman; G.S. Okin; R. Oren; J.T. Randerson; W.H. Schlesinger</p> <p>2008-01-01</p> <p>This synthesis and assessment report builds on an extensive scientific literature and series of recent assessments of the historical and potential impacts of climate change and climate variability on managed and unmanaged ecosystems and their constituent biota and processes. It identifies changes in resource conditions that are now being observed and examines whether...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=303118','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=303118"><span>A multi-site stochastic weather generator of daily precipitation and temperature</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Stochastic weather generators are used to generate time series of climate variables that have statistical properties similar to those of observed data. Most stochastic weather generators work for a single site, and can only generate climate data at a single point, or independent time series at sever...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1915496Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1915496Y"><span>Estimation of water storage changes in small endorheic lakes in Burabay National Nature Park (Northern Kazakhstan, Central Asia); the effect of climate change and anthropogenic influences</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yapiyev, Vadim; Sagintayev, Zhanay; Verhoef, Anne; Samarkhanov, Kanat; Jumassultanova, Saltanat</p> <p>2017-04-01</p> <p>Both climate change and anthropogenic activities contribute to deterioration of terrestrial water resources and ecosystems worldwide. It has been observed in recent decades that water-limited steppe regions of Central Asia are among ecosystems found to exhibit enhanced responses to climate variability. In fact, the largest share of worldwide net loss of permanent water extent is geographically concentrated in the Central Asia and Middle East regions attributed to both climate variability/change and human activities impacts. We used a digital elevation model, digitized bathymetry maps and high resolution Landsat images to estimate the areal water cover extent and volumetric storage changes in small terminal lakes in Burabay National Nature Park (BNNP), located in Northern Central Asia, for the period 2000-2016. Based on the analysis of long-term climatic data from meteorological stations, hydrometeorological network observations as well as regional climate model projections we evaluate the impacts of past thirty years and future climatic conditions on the water balance of BNNP lake catchments. The anthropogenic water consumption was estimated based on data collected at a local water supply company and regulation authorities. One the one hand historical in-situ observations and future climate projections do not show a significant change in precipitation in BNNP. On the other hand both observations and the model demonstrate steadily rising air temperatures in the area. It is concluded that the long-term decline in water levels for most of these lakes can be largely attributed to climate change (but only via changes in air temperature, causing evaporation to exceed precipitation) and not to direct anthropogenic influences such as increased water withdrawals. In addition, the two largest lakes, showing the highest historical water level decline, do not have sufficient water drainage basin area to sustain water levels under increased evaporation rates.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.9548R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.9548R"><span>An overview of new insights from satellite salinity missions on oceanography</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Reul, Nicolas</p> <p>2015-04-01</p> <p>The Soil Moisture and Ocean Salinity (SMOS) mission, launched on 2 November 2009, is the European Space Agency's (ESA) second Earth Explorer Opportunity mission. The scientific objectives of the SMOS mission directly respond to the need for global observations of soil moisture and ocean salinity, two key variables describing the Earth's water cycle and having been identified as Essential Climate Variables (ECVs) by the Global Climate Observing System (GCOS). After five years of satellite Sea Surface Salinity (SSS) monitoring from SMOS data, we will present an overview of the scientific highlights these data have brougtht to the oceanographic communities. In particular, we shall review the impact of SMOS SSS and brightness tempeaerture data for the monitoring of: -Mesoscale variability of SSS (and density) in frontal structures, eddies, -Ocean propagative SSS signals (e.g. TIW, planetary waves), -Freshwater flux Monitoring (Evaportaion minus precipitation, river run off), -Large scale SSS anomalies related to climate fluctuations (e.g. ENSO, IOD), -Air-Sea interactions (equatorial upwellings, Tropical cyclone wakes) -Temperature-Salinity dependencies, -Sea Ice thickness, -Tropical Storm and high wind monitoring, -Ocean surface bio-geo chemistry.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_19 --> <div id="page_20" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="381"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC54A..08F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC54A..08F"><span>Variability of western Amazon dry-season precipitation extremes: importance of decadal fluctuations and implications for predictability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fernandes, K.; Baethgen, W.; Verchot, L. V.; Giannini, A.; Pinedo-Vasquez, M.</p> <p>2014-12-01</p> <p>A complete assessment of climate change projections requires understanding the combined effects of decadal variability and long-term trends and evaluating the ability of models to simulate them. The western Amazon severe droughts of the 2000s were the result of a modest drying trend enhanced by reduced moisture transport from the tropical Atlantic. Most of the WA dry-season precipitation decadal variability is attributable to decadal fluctuations of the north-south gradient (NSG) in Atlantic sea surface temperature (SST). The observed WA and NSG decadal co-variability is well reproduced in Global Climate Models (GCMs) pre-industrial control (PIC) and historical (HIST) experiments that were part of the Intergovernmental Panel on Climate Change fifth assessment report (IPCC-AR5). This suggests that unforced or natural climate variability, characteristic of the PIC simulations, determines the nature of this coupling, as the results from HIST simulations (forced with greenhouse gases (GHG) and natural and anthropogenic aerosols) are comparable in magnitude and spatial distribution. Decadal fluctuation in the NSG also determines shifts in the probability of repeated droughts and pluvials in WA, as there is a 65% chance of 3 or more years of droughts per decade when NSG>0 compared to 18% when NSG<0. The HIST and PIC model simulations also reproduce the observed shifts in probability distribution of droughts and pluvials as a function of the NSG decadal phase, suggesting there is great potential for decadal predictability based on GCMs. Persistence of the current NSG positive phase may lead to continuing above normal frequencies of western Amazon dry-season droughts.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018IJBm...62..861B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018IJBm...62..861B"><span>Past crops yield dynamics reconstruction from tree-ring chronologies in the forest-steppe zone based on low- and high-frequency components</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Babushkina, Elena A.; Belokopytova, Liliana V.; Shah, Santosh K.; Zhirnova, Dina F.</p> <p>2018-05-01</p> <p>Interrelations of the yield variability of the main crops (wheat, barley, and oats) with hydrothermal regime and growth of conifer trees ( Pinus sylvestris and Larix sibirica) in forest-steppes were investigated in Khakassia, South Siberia. An attempt has been made to understand the role and mechanisms of climatic impact on plants productivity. It was found that amongst variables describing moisture supply, wetness index had maximum impact. Strength of climatic response and correlations with tree growth are different for rain-fed and irrigated crops yield. Separated high-frequency variability components of yield and tree-ring width have more pronounced relationships between each other and with climatic variables than their chronologies per se. Corresponding low-frequency variability components are strongly correlated with maxima observed after 1- to 5-year time shift of tree-ring width. Results of analysis allowed us to develop original approach of crops yield dynamics reconstruction on the base of high-frequency variability component of the growth of pine and low-frequency one of larch.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060026284&hterms=climate+change+anthropogenic&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dclimate%2Bchange%2Banthropogenic','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060026284&hterms=climate+change+anthropogenic&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dclimate%2Bchange%2Banthropogenic"><span>Satellite Observations of the Effect of Natural and Anthropogenic Aerosols on Clouds</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kaufman, Yoram J.</p> <p>2006-01-01</p> <p>Our knowledge of atmospheric aerosols (smoke, pollution, dust or sea salt particles, small enough to be suspended in the air), their evolution, composition, variability in space and time and interaction with clouds and precipitation is still lacking despite decades of research. Understanding the global aerosol system is critical to quantifying anthropogenic climate change, to determine climate sensitivity from observations and to understand the hydrological cycle. While a single instrument was used to demonstrate 50 years ago that the global CO2 levels are rising, posing threat of global warming, we need an array of satellites and field measurements coupled with chemical transport models to understand the global aerosol system. This complexity of the aerosol problem results from their short lifetime (1 week) and variable chemical composition. A new generation of satellites provides exciting opportunities to measure the global distribution of aerosols, distinguishing natural from anthropogenic aerosol and measuring their interaction with clouds and climate.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1427767','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1427767"><span>Role of Perturbing Ocean Initial Condition in Simulated Regional Sea Level Change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Hu, Aixue; Meehl, Gerald; Stammer, Detlef</p> <p></p> <p>Multiple lines of observational evidence indicate that the global climate has been getting warmer since the early 20th century. This warmer climate has led to a global mean sea level rise of about 18 cm during the 20th century, and over 6 cm for the first 15 years of the 21st century. Regionally the sea level rise is not uniform due in large part to internal climate variability. To better serve the community, the uncertainties of predicting/projecting regional sea level changes associated with internal climate variability need to be quantified. Previous research on this topic has used single-model large ensemblesmore » with perturbed atmospheric initial conditions (ICs). Here we compare uncertainties associated with perturbing ICs in just the atmosphere and just the ocean using a state-of-the-art coupled climate model. We find that by perturbing the oceanic ICs, the uncertainties in regional sea level changes increase compared to those with perturbed atmospheric ICs. In order for us to better assess the full spectrum of the impacts of such internal climate variability on regional and global sea level rise, approaches that involve perturbing both atmospheric and oceanic initial conditions are thus necessary.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1427767-role-perturbing-ocean-initial-condition-simulated-regional-sea-level-change','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1427767-role-perturbing-ocean-initial-condition-simulated-regional-sea-level-change"><span>Role of Perturbing Ocean Initial Condition in Simulated Regional Sea Level Change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Hu, Aixue; Meehl, Gerald; Stammer, Detlef; ...</p> <p>2017-06-05</p> <p>Multiple lines of observational evidence indicate that the global climate has been getting warmer since the early 20th century. This warmer climate has led to a global mean sea level rise of about 18 cm during the 20th century, and over 6 cm for the first 15 years of the 21st century. Regionally the sea level rise is not uniform due in large part to internal climate variability. To better serve the community, the uncertainties of predicting/projecting regional sea level changes associated with internal climate variability need to be quantified. Previous research on this topic has used single-model large ensemblesmore » with perturbed atmospheric initial conditions (ICs). Here we compare uncertainties associated with perturbing ICs in just the atmosphere and just the ocean using a state-of-the-art coupled climate model. We find that by perturbing the oceanic ICs, the uncertainties in regional sea level changes increase compared to those with perturbed atmospheric ICs. In order for us to better assess the full spectrum of the impacts of such internal climate variability on regional and global sea level rise, approaches that involve perturbing both atmospheric and oceanic initial conditions are thus necessary.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC34C..01D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC34C..01D"><span>Probabilistic attribution of individual unprecedented extreme events</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Diffenbaugh, N. S.</p> <p>2016-12-01</p> <p>The last decade has seen a rapid increase in efforts to understand the influence of global warming on individual extreme climate events. Although trends in the distributions of climate observations have been thoroughly analyzed, rigorously quantifying the contribution of global-scale warming to individual events that are unprecedented in the observed record presents a particular challenge. This paper describes a method for leveraging observations and climate model ensembles to quantify the influence of historical global warming on the severity and probability of unprecedented events. This approach uses formal inferential techniques to quantify four metrics: (1) the contribution of the observed trend to the event magnitude, (2) the contribution of the observed trend to the event probability, (3) the probability of the observed trend in the current climate and a climate without human influence, and (4) the probability of the event magnitude in the current climate and a climate without human influence. Illustrative examples are presented, spanning a range of climate variables, timescales, and regions. These examples illustrate that global warming can influence the severity and probability of unprecedented extremes. In some cases - particularly high temperatures - this change is indicated by changes in the mean. However, changes in probability do not always arise from changes in the mean, suggesting that global warming can alter the frequency with which complex physical conditions co-occur. Because our framework is transparent and highly generalized, it can be readily applied to a range of climate events, regions, and levels of climate forcing.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2908116','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2908116"><span>Climatic Variability Leads to Later Seasonal Flowering of Floridian Plants</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Von Holle, Betsy; Wei, Yun; Nickerson, David</p> <p>2010-01-01</p> <p>Understanding species responses to global change will help predict shifts in species distributions as well as aid in conservation. Changes in the timing of seasonal activities of organisms over time may be the most responsive and easily observable indicator of environmental changes associated with global climate change. It is unknown how global climate change will affect species distributions and developmental events in subtropical ecosystems or if climate change will differentially favor nonnative species. Contrary to previously observed trends for earlier flowering onset of plant species with increasing spring temperatures from mid and higher latitudes, we document a trend for delayed seasonal flowering among plants in Florida. Additionally, there were few differences in reproductive responses by native and nonnative species to climatic changes. We argue that plants in Florida have different reproductive cues than those from more northern climates. With global change, minimum temperatures have become more variable within the temperate-subtropical zone that occurs across the peninsula and this variation is strongly associated with delayed flowering among Florida plants. Our data suggest that climate change varies by region and season and is not a simple case of species responding to consistently increasing temperatures across the region. Research on climate change impacts need to be extended outside of the heavily studied higher latitudes to include subtropical and tropical systems in order to properly understand the complexity of regional and seasonal differences of climate change on species responses. PMID:20657765</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010GeoRL..3721602C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010GeoRL..3721602C"><span>Biological communities in San Francisco Bay track large-scale climate forcing over the North Pacific</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cloern, James E.; Hieb, Kathryn A.; Jacobson, Teresa; Sansó, Bruno; Di Lorenzo, Emanuele; Stacey, Mark T.; Largier, John L.; Meiring, Wendy; Peterson, William T.; Powell, Thomas M.; Winder, Monika; Jassby, Alan D.</p> <p>2010-11-01</p> <p>Long-term observations show that fish and plankton populations in the ocean fluctuate in synchrony with large-scale climate patterns, but similar evidence is lacking for estuaries because of shorter observational records. Marine fish and invertebrates have been sampled in San Francisco Bay since 1980 and exhibit large, unexplained population changes including record-high abundances of common species after 1999. Our analysis shows that populations of demersal fish, crabs and shrimp covary with the Pacific Decadal Oscillation (PDO) and North Pacific Gyre Oscillation (NPGO), both of which reversed signs in 1999. A time series model forced by the atmospheric driver of NPGO accounts for two-thirds of the variability in the first principal component of species abundances, and generalized linear models forced by PDO and NPGO account for most of the annual variability of individual species. We infer that synchronous shifts in climate patterns and community variability in San Francisco Bay are related to changes in oceanic wind forcing that modify coastal currents, upwelling intensity, surface temperature, and their influence on recruitment of marine species that utilize estuaries as nursery habitat. Ecological forecasts of estuarine responses to climate change must therefore consider how altered patterns of atmospheric forcing across ocean basins influence coastal oceanography as well as watershed hydrology.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=%22Classroom+Observation%22&pg=3&id=EJ923172','ERIC'); return false;" href="https://eric.ed.gov/?q=%22Classroom+Observation%22&pg=3&id=EJ923172"><span>Using Informal Classroom Observations to Improve Instruction</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Ing, Marsha</p> <p>2010-01-01</p> <p>Purpose: The purpose of this study is to describe the variability of principals' classroom observations across schools and to relate classroom observations to the schools' instructional climate. This helps identify the conditions under which classroom observations effectively improve instruction in some schools and not in other schools.…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.A53R..03P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.A53R..03P"><span>Linkages Between Terrestrial Carbon Uptake and Interannual Climate Variability over the Texas-northern Mexico High Plains</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Parazoo, N.; Barnes, E. A.; Worden, J.; Harper, A. B.; Bowman, K. W.; Frankenberg, C.</p> <p>2014-12-01</p> <p>The Texas-northern Mexico high plains experienced record drought conditions in 2011 during strong negative phases of ENSO and the NAO. Given predictions of increased frequency and severity of drought under projected climate change [e.g., Reichstein et al., 2013] and recent findings of CO2 growth rate sensitivity to interannual variability of carbon uptake in semi-arid ecosystems [Poulter et al., 2014], we investigate the response of carbon uptake in the Texas high plains to interannual climate variability with the goal of improved mechanistic understanding of climate-carbon cycle links. Specifically, we examine (1) observed tendencies in regional scale carbon uptake and soil moisture from 2010 to 2011 using satellite observations of gross primary production (GPP) (from plant fluorescence) from GOSAT and soil moisture from SMOS, and (2) the interannual relationship between GPP and ENSO & NAO variability using terrestrial biosphere simulations from 1950-2012. Observations reveal widespread decline of GPP in 2011 (0.42 +/- 0.04 Pg C yr-1) correlated with negative soil moisture tendencies (r = 0.85 +/- 0.21) which leads to corresponding declines in net carbon uptake and transpiration (according to model simulations). Further examination of model results over the period 1950-2012 indicates that negative GPP anomalies are linked systematically to winter and spring precipitation deficits associated with overlapping negative phases of winter NAO and ENSO, with increasing magnitude of negative anomalies in strong La Niña years. Furthermore, the strongest decline of GPP, carbon uptake, and transpiration on record occurred during the 2011 drought and were associated with extreme negative phases of ENSO and NAO, with 2011 being the only year since 1950 that both indices exceeded 1 σ standard deviation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70160057','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70160057"><span>Human activities and climate variability drive fast-paced change across the world's estuarine-coastal ecosystems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Cloern, James E.; Abreu, Paulo C.; Carstensen, Jacob; Chauvaud, Laurent; Elmgren, Ragnar; Grall, Jacques; Greening, Holly; Johansson, John O.R.; Kahru, Mati; Sherwood, Edward T.; Xu, Jie; Yin, Kedong</p> <p>2016-01-01</p> <p>Time series of environmental measurements are essential for detecting, measuring and understanding changes in the Earth system and its biological communities. Observational series have accumulated over the past 2–5 decades from measurements across the world's estuaries, bays, lagoons, inland seas and shelf waters influenced by runoff. We synthesize information contained in these time series to develop a global view of changes occurring in marine systems influenced by connectivity to land. Our review is organized around four themes: (i) human activities as drivers of change; (ii) variability of the climate system as a driver of change; (iii) successes, disappointments and challenges of managing change at the sea-land interface; and (iv) discoveries made from observations over time. Multidecadal time series reveal that many of the world's estuarine–coastal ecosystems are in a continuing state of change, and the pace of change is faster than we could have imagined a decade ago. Some have been transformed into novel ecosystems with habitats, biogeochemistry and biological communities outside the natural range of variability. Change takes many forms including linear and nonlinear trends, abrupt state changes and oscillations. The challenge of managing change is daunting in the coastal zone where diverse human pressures are concentrated and intersect with different responses to climate variability over land and over ocean basins. The pace of change in estuarine–coastal ecosystems will likely accelerate as the human population and economies continue to grow and as global climate change accelerates. Wise stewardship of the resources upon which we depend is critically dependent upon a continuing flow of information from observations to measure, understand and anticipate future changes along the world's coastlines.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1281659-clouds-more-arm-climate-modeling-best-estimate-data-new-data-product-climate-studies','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1281659-clouds-more-arm-climate-modeling-best-estimate-data-new-data-product-climate-studies"><span>Clouds and more: ARM climate modeling best estimate data: A new data product for climate studies</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Xie, Shaocheng; McCoy, Renata B.; Klein, Stephen A.; ...</p> <p>2010-01-01</p> <p>The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program (www.arm.gov) was created in 1989 to address scientific uncertainties related to global climate change, with a focus on the crucial role of clouds and their influence on the transfer of radiation atmosphere. Here, a central activity is the acquisition of detailed observations of clouds and radiation, as well as related atmospheric variables for climate model evaluation and improvement.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A12H..05M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A12H..05M"><span>Variability of North Atlantic Hurricane Frequency in a Large Ensemble of High-Resolution Climate Simulations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mei, W.; Kamae, Y.; Xie, S. P.</p> <p>2017-12-01</p> <p>Forced and internal variability of North Atlantic hurricane frequency during 1951-2010 is studied using a large ensemble of climate simulations by a 60-km atmospheric general circulation model that is forced by observed sea surface temperatures (SSTs). The simulations well capture the interannual-to-decadal variability of hurricane frequency in best track data, and further suggest a possible underestimate of hurricane counts in the current best track data prior to 1966 when satellite measurements were unavailable. A genesis potential index (GPI) averaged over the Main Development Region (MDR) accounts for more than 80% of the forced variations in hurricane frequency, with potential intensity and vertical wind shear being the dominant factors. In line with previous studies, the difference between MDR SST and tropical mean SST is a simple but useful predictor; a one-degree increase in this SST difference produces 7.1±1.4 more hurricanes. The hurricane frequency also exhibits internal variability that is comparable in magnitude to the interannual variability. The 100-member ensemble allows us to address the following important questions: (1) Are the observations equivalent to one realization of such a large ensemble? (2) How many ensemble members are needed to reproduce the variability in observations and in the forced component of the simulations? The sources of the internal variability in hurricane frequency will be identified and discussed. The results provide an explanation for the relatively week correlation ( 0.6) between MDR GPI and hurricane frequency on interannual timescales in observations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC53B1203G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC53B1203G"><span>Applying Multimodel Ensemble from Regional Climate Models for Improving Runoff Projections on Semiarid Regions of Spain</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Garcia Galiano, S. G.; Olmos, P.; Giraldo Osorio, J. D.</p> <p>2015-12-01</p> <p>In the Mediterranean area, significant changes on temperature and precipitation are expected throughout the century. These trends could exacerbate the existing conditions in regions already vulnerable to climatic variability, reducing the water availability. Improving knowledge about plausible impacts of climate change on water cycle processes at basin scale, is an important step for building adaptive capacity to the impacts in this region, where severe water shortages are expected for the next decades. RCMs ensemble in combination with distributed hydrological models with few parameters, constitutes a valid and robust methodology to increase the reliability of climate and hydrological projections. For reaching this objective, a novel methodology for building Regional Climate Models (RCMs) ensembles of meteorological variables (rainfall an temperatures), was applied. RCMs ensembles are justified for increasing the reliability of climate and hydrological projections. The evaluation of RCMs goodness-of-fit to build the ensemble is based on empirical probability density functions (PDF) extracted from both RCMs dataset and a highly resolution gridded observational dataset, for the time period 1961-1990. The applied method is considering the seasonal and annual variability of the rainfall and temperatures. The RCMs ensembles constitute the input to a distributed hydrological model at basin scale, for assessing the runoff projections. The selected hydrological model is presenting few parameters in order to reduce the uncertainties involved. The study basin corresponds to a head basin of Segura River Basin, located in the South East of Spain. The impacts on runoff and its trend from observational dataset and climate projections, were assessed. Considering the control period 1961-1990, plausible significant decreases in runoff for the time period 2021-2050, were identified.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29358696','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29358696"><span>Land-surface initialisation improves seasonal climate prediction skill for maize yield forecast.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Ceglar, Andrej; Toreti, Andrea; Prodhomme, Chloe; Zampieri, Matteo; Turco, Marco; Doblas-Reyes, Francisco J</p> <p>2018-01-22</p> <p>Seasonal crop yield forecasting represents an important source of information to maintain market stability, minimise socio-economic impacts of crop losses and guarantee humanitarian food assistance, while it fosters the use of climate information favouring adaptation strategies. As climate variability and extremes have significant influence on agricultural production, the early prediction of severe weather events and unfavourable conditions can contribute to the mitigation of adverse effects. Seasonal climate forecasts provide additional value for agricultural applications in several regions of the world. However, they currently play a very limited role in supporting agricultural decisions in Europe, mainly due to the poor skill of relevant surface variables. Here we show how a combined stress index (CSI), considering both drought and heat stress in summer, can predict maize yield in Europe and how land-surface initialised seasonal climate forecasts can be used to predict it. The CSI explains on average nearly 53% of the inter-annual maize yield variability under observed climate conditions and shows how concurrent heat stress and drought events have influenced recent yield anomalies. Seasonal climate forecast initialised with realistic land-surface achieves better (and marginally useful) skill in predicting the CSI than with climatological land-surface initialisation in south-eastern Europe, part of central Europe, France and Italy.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H43D1670D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H43D1670D"><span>Improve projections of changes in southern African summer rainfall through comprehensive multi-timescale empirical statistical downscaling</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dieppois, B.; Pohl, B.; Eden, J.; Crétat, J.; Rouault, M.; Keenlyside, N.; New, M. G.</p> <p>2017-12-01</p> <p>The water management community has hitherto neglected or underestimated many of the uncertainties in climate impact scenarios, in particular, uncertainties associated with decadal climate variability. Uncertainty in the state-of-the-art global climate models (GCMs) is time-scale-dependant, e.g. stronger at decadal than at interannual timescales, in response to the different parameterizations and to internal climate variability. In addition, non-stationarity in statistical downscaling is widely recognized as a key problem, in which time-scale dependency of predictors plays an important role. As with global climate modelling, therefore, the selection of downscaling methods must proceed with caution to avoid unintended consequences of over-correcting the noise in GCMs (e.g. interpreting internal climate variability as a model bias). GCM outputs from the Coupled Model Intercomparison Project 5 (CMIP5) have therefore first been selected based on their ability to reproduce southern African summer rainfall variability and their teleconnections with Pacific sea-surface temperature across the dominant timescales. In observations, southern African summer rainfall has recently been shown to exhibit significant periodicities at the interannual timescale (2-8 years), quasi-decadal (8-13 years) and inter-decadal (15-28 years) timescales, which can be interpret as the signature of ENSO, the IPO, and the PDO over the region. Most of CMIP5 GCMs underestimate southern African summer rainfall variability and their teleconnections with Pacific SSTs at these three timescales. In addition, according to a more in-depth analysis of historical and pi-control runs, this bias is might result from internal climate variability in some of the CMIP5 GCMs, suggesting potential for bias-corrected prediction based empirical statistical downscaling. A multi-timescale regression based downscaling procedure, which determines the predictors across the different timescales, has thus been used to simulate southern African summer rainfall. This multi-timescale procedure shows much better skills in simulating decadal timescales of variability compared to commonly used statistical downscaling approaches.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1328543-spatial-patterns-sea-level-variability-associated-natural-internal-climate-modes','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1328543-spatial-patterns-sea-level-variability-associated-natural-internal-climate-modes"><span>Spatial Patterns of Sea Level Variability Associated with Natural Internal Climate Modes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Han, Weiqing; Meehl, Gerald A.; Stammer, Detlef</p> <p></p> <p>Sea level rise (SLR) can exert significant stress on highly populated coastal societies and low-lying island countries around the world. Because of this, there is huge societal demand for improved decadal predictions and future projections of SLR, particularly on a local scale along coastlines. Regionally, sea level variations can deviate considerably from the global mean due to various geophysical processes. These include changes of ocean circulations, which partially can be attributed to natural, internal modes of variability in the complex Earth’s climate system. Anthropogenic influence may also contribute to regional sea level variations. Separating the effects of natural climate modesmore » and anthropogenic forcing, however, remains a challenge and requires identification of the imprint of specific climate modes in observed sea level change patterns. In this article, we review our current state of knowledge about spatial patterns of sea level variability associated with natural climate modes on interannual-to-multidecadal timescales, with particular focus on decadal-to-multidecadal variability. Relevant climate modes and our current state of understanding their associated sea level patterns and driving mechanisms are elaborated separately for the Pacific, the Indian, the Atlantic, and the Arctic and Southern Oceans. We also discuss the issues, challenges and future outlooks for understanding the regional sea level patterns associated with climate modes. Effects of these internal modes have to be taken into account in order to achieve more reliable near-term predictions and future projections of regional SLR.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1328543-spatial-patterns-sea-level-variability-associated-natural-internal-climate-modes','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1328543-spatial-patterns-sea-level-variability-associated-natural-internal-climate-modes"><span>Spatial Patterns of Sea Level Variability Associated with Natural Internal Climate Modes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Han, Weiqing; Meehl, Gerald A.; Stammer, Detlef; ...</p> <p>2016-10-04</p> <p>Sea level rise (SLR) can exert significant stress on highly populated coastal societies and low-lying island countries around the world. Because of this, there is huge societal demand for improved decadal predictions and future projections of SLR, particularly on a local scale along coastlines. Regionally, sea level variations can deviate considerably from the global mean due to various geophysical processes. These include changes of ocean circulations, which partially can be attributed to natural, internal modes of variability in the complex Earth’s climate system. Anthropogenic influence may also contribute to regional sea level variations. Separating the effects of natural climate modesmore » and anthropogenic forcing, however, remains a challenge and requires identification of the imprint of specific climate modes in observed sea level change patterns. In this article, we review our current state of knowledge about spatial patterns of sea level variability associated with natural climate modes on interannual-to-multidecadal timescales, with particular focus on decadal-to-multidecadal variability. Relevant climate modes and our current state of understanding their associated sea level patterns and driving mechanisms are elaborated separately for the Pacific, the Indian, the Atlantic, and the Arctic and Southern Oceans. We also discuss the issues, challenges and future outlooks for understanding the regional sea level patterns associated with climate modes. Effects of these internal modes have to be taken into account in order to achieve more reliable near-term predictions and future projections of regional SLR.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017SGeo...38..217H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017SGeo...38..217H"><span>Spatial Patterns of Sea Level Variability Associated with Natural Internal Climate Modes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Han, Weiqing; Meehl, Gerald A.; Stammer, Detlef; Hu, Aixue; Hamlington, Benjamin; Kenigson, Jessica; Palanisamy, Hindumathi; Thompson, Philip</p> <p>2017-01-01</p> <p>Sea level rise (SLR) can exert significant stress on highly populated coastal societies and low-lying island countries around the world. Because of this, there is huge societal demand for improved decadal predictions and future projections of SLR, particularly on a local scale along coastlines. Regionally, sea level variations can deviate considerably from the global mean due to various geophysical processes. These include changes of ocean circulations, which partially can be attributed to natural, internal modes of variability in the complex Earth's climate system. Anthropogenic influence may also contribute to regional sea level variations. Separating the effects of natural climate modes and anthropogenic forcing, however, remains a challenge and requires identification of the imprint of specific climate modes in observed sea level change patterns. In this paper, we review our current state of knowledge about spatial patterns of sea level variability associated with natural climate modes on interannual-to-multidecadal timescales, with particular focus on decadal-to-multidecadal variability. Relevant climate modes and our current state of understanding their associated sea level patterns and driving mechanisms are elaborated separately for the Pacific, the Indian, the Atlantic, and the Arctic and Southern Oceans. We also discuss the issues, challenges and future outlooks for understanding the regional sea level patterns associated with climate modes. Effects of these internal modes have to be taken into account in order to achieve more reliable near-term predictions and future projections of regional SLR.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19920045127&hterms=Global+warming&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3DGlobal%2Bwarming','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19920045127&hterms=Global+warming&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3DGlobal%2Bwarming"><span>Chapman Conference on the Hydrologic Aspects of Global Climate Change, Lake Chelan, WA, June 12-14, 1990, Selected Papers</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lettenmaier, Dennis P. (Editor); Rind, D. (Editor)</p> <p>1992-01-01</p> <p>The present conference on the hydrological aspects of global climate change discusses land-surface schemes for future climate models, modeling of the land-surface boundary in climate models as a composite of independent vegetation, a land-surface hydrology parameterizaton with subgrid variability for general circulation models, and conceptual aspects of a statistical-dynamical approach to represent landscape subgrid-scale heterogeneities in atmospheric models. Attention is given to the impact of global warming on river runoff, the influence of atmospheric moisture transport on the fresh water balance of the Atlantic drainage basin, a comparison of observations and model simulations of tropospheric water vapor, and the use of weather types to disaggregate the prediction of general circulation models. Topics addressed include the potential response of an Arctic watershed during a period of global warming and the sensitivity of groundwater recharge estimates to climate variability and change.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_20 --> <div id="page_21" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="401"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26912856','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26912856"><span>A decade of sea level rise slowed by climate-driven hydrology.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Reager, J T; Gardner, A S; Famiglietti, J S; Wiese, D N; Eicker, A; Lo, M-H</p> <p>2016-02-12</p> <p>Climate-driven changes in land water storage and their contributions to sea level rise have been absent from Intergovernmental Panel on Climate Change sea level budgets owing to observational challenges. Recent advances in satellite measurement of time-variable gravity combined with reconciled global glacier loss estimates enable a disaggregation of continental land mass changes and a quantification of this term. We found that between 2002 and 2014, climate variability resulted in an additional 3200 ± 900 gigatons of water being stored on land. This gain partially offset water losses from ice sheets, glaciers, and groundwater pumping, slowing the rate of sea level rise by 0.71 ± 0.20 millimeters per year. These findings highlight the importance of climate-driven changes in hydrology when assigning attribution to decadal changes in sea level. Copyright © 2016, American Association for the Advancement of Science.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017NatCo...815875A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017NatCo...815875A"><span>Skillful prediction of northern climate provided by the ocean</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Årthun, Marius; Eldevik, Tor; Viste, Ellen; Drange, Helge; Furevik, Tore; Johnson, Helen L.; Keenlyside, Noel S.</p> <p>2017-06-01</p> <p>It is commonly understood that a potential for skillful climate prediction resides in the ocean. It nevertheless remains unresolved to what extent variable ocean heat is imprinted on the atmosphere to realize its predictive potential over land. Here we assess from observations whether anomalous heat in the Gulf Stream's northern extension provides predictability of northwestern European and Arctic climate. We show that variations in ocean temperature in the high latitude North Atlantic and Nordic Seas are reflected in the climate of northwestern Europe and in winter Arctic sea ice extent. Statistical regression models show that a significant part of northern climate variability thus can be skillfully predicted up to a decade in advance based on the state of the ocean. Particularly, we predict that Norwegian air temperature will decrease over the coming years, although staying above the long-term (1981-2010) average. Winter Arctic sea ice extent will remain low but with a general increase towards 2020.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20050214234','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20050214234"><span>Solar Effects on Global Climate Due to Cosmic Rays and Solar Energetic Particles</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Turco, R. P.; Raeder, J.; DAuria, R.</p> <p>2005-01-01</p> <p>Although the work reported here does not directly connect solar variability with global climate change, this research establishes a plausible quantitative causative link between observed solar activity and apparently correlated variations in terrestrial climate parameters. Specifically, we have demonstrated that ion-mediated nucleation of atmospheric particles is a likely, and likely widespread, phenomenon that relates solar variability to changes in the microphysical properties of clouds. To investigate this relationship, we have constructed and applied a new model describing the formation and evolution of ionic clusters under a range of atmospheric conditions throughout the lower atmosphere. The activation of large ionic clusters into cloud nuclei is predicted to be favorable in the upper troposphere and mesosphere, and possibly in the lower stratosphere. The model developed under this grant needs to be extended to include additional cluster families, and should be incorporated into microphysical models to further test the cause-and-effect linkages that may ultimately explain key aspects of the connections between solar variability and climate.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1235117-el-nino-southern-oscillation-frequency-cascade','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1235117-el-nino-southern-oscillation-frequency-cascade"><span>El Niño$-$Southern Oscillation frequency cascade</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Stuecker, Malte F.; Jin, Fei -Fei; Timmermann, Axel</p> <p>2015-10-19</p> <p>The El Niño$-$Southern Oscillation (ENSO) phenomenon, the most pronounced feature of internally generated climate variability, occurs on interannual timescales and impacts the global climate system through an interaction with the annual cycle. The tight coupling between ENSO and the annual cycle is particularly pronounced over the tropical Western Pacific. In this paper, we show that this nonlinear interaction results in a frequency cascade in the atmospheric circulation, which is characterized by deterministic high-frequency variability on near-annual and subannual timescales. Finally, through climate model experiments and observational analysis, it is documented that a substantial fraction of the anomalous Northwest Pacific anticyclonemore » variability, which is the main atmospheric link between ENSO and the East Asian Monsoon system, can be explained by these interactions and is thus deterministic and potentially predictable.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1235117-el-nino-southern-oscillation-frequency-cascade','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1235117-el-nino-southern-oscillation-frequency-cascade"><span>El Niño$-$Southern Oscillation frequency cascade</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Stuecker, Malte F.; Jin, Fei -Fei; Timmermann, Axel</p> <p></p> <p>The El Niño$-$Southern Oscillation (ENSO) phenomenon, the most pronounced feature of internally generated climate variability, occurs on interannual timescales and impacts the global climate system through an interaction with the annual cycle. The tight coupling between ENSO and the annual cycle is particularly pronounced over the tropical Western Pacific. In this paper, we show that this nonlinear interaction results in a frequency cascade in the atmospheric circulation, which is characterized by deterministic high-frequency variability on near-annual and subannual timescales. Finally, through climate model experiments and observational analysis, it is documented that a substantial fraction of the anomalous Northwest Pacific anticyclonemore » variability, which is the main atmospheric link between ENSO and the East Asian Monsoon system, can be explained by these interactions and is thus deterministic and potentially predictable.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170003148','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170003148"><span>Brief Communication: Upper Air Relaxation in RACMO2 Significantly Improves Modelled Interannual Surface Mass Balance Variability in Antarctica</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>van de Berg, W. J.; Medley, B.</p> <p>2016-01-01</p> <p>The Regional Atmospheric Climate Model (RACMO2) has been a powerful tool for improving surface mass balance (SMB) estimates from GCMs or reanalyses. However, new yearly SMB observations for West Antarctica show that the modelled interannual variability in SMB is poorly simulated by RACMO2, in contrast to ERA-Interim, which resolves this variability well. In an attempt to remedy RACMO2 performance, we included additional upper-air relaxation (UAR) in RACMO2. With UAR, the correlation to observations is similar for RACMO2 and ERA-Interim. The spatial SMB patterns and ice-sheet-integrated SMB modelled using UAR remain very similar to the estimates of RACMO2 without UAR. We only observe an upstream smoothing of precipitation in regions with very steep topography like the Antarctic Peninsula. We conclude that UAR is a useful improvement for regional climate model simulations, although results in regions with steep topography should be treated with care.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013JHyd..479...75J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013JHyd..479...75J"><span>How well do the GCMs/RCMs capture the multi-scale temporal variability of precipitation in the Southwestern United States?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jiang, Peng; Gautam, Mahesh R.; Zhu, Jianting; Yu, Zhongbo</p> <p>2013-02-01</p> <p>SummaryMulti-scale temporal variability of precipitation has an established relationship with floods and droughts. In this paper, we present the diagnostics on the ability of 16 General Circulation Models (GCMs) from Bias Corrected and Downscaled (BCSD) World Climate Research Program's (WCRP's) Coupled Model Inter-comparison Project Phase 3 (CMIP3) projections and 10 Regional Climate Models (RCMs) that participated in the North American Regional Climate Change Assessment Program (NARCCAP) to represent multi-scale temporal variability determined from the observed station data. Four regions (Los Angeles, Las Vegas, Tucson, and Cimarron) in the Southwest United States are selected as they represent four different precipitation regions classified by clustering method. We investigate how storm properties and seasonal, inter-annual, and decadal precipitation variabilities differed between GCMs/RCMs and observed records in these regions. We find that current GCMs/RCMs tend to simulate longer storm duration and lower storm intensity compared to those from observed records. Most GCMs/RCMs fail to produce the high-intensity summer storms caused by local convective heat transport associated with the summer monsoon. Both inter-annual and decadal bands are present in the GCM/RCM-simulated precipitation time series; however, these do not line up to the patterns of large-scale ocean oscillations such as El Nino/La Nina Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). Our results show that the studied GCMs/RCMs can capture long-term monthly mean as the examined data is bias-corrected and downscaled, but fail to simulate the multi-scale precipitation variability including flood generating extreme events, which suggests their inadequacy for studies on floods and droughts that are strongly associated with multi-scale temporal precipitation variability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24080415','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24080415"><span>A global assessment of climate-water quality relationships in large rivers: an elasticity perspective.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Jiang, Jiping; Sharma, Ashish; Sivakumar, Bellie; Wang, Peng</p> <p>2014-01-15</p> <p>To uncover climate-water quality relationships in large rivers on a global scale, the present study investigates the climate elasticity of river water quality (CEWQ) using long-term monthly records observed at 14 large rivers. Temperature and precipitation elasticities of 12 water quality parameters, highlighted by N- and P-nutrients, are assessed. General observations on elasticity values show the usefulness of this approach to describe the magnitude of stream water quality responses to climate change, which improves that of simple statistical correlation. Sensitivity type, intensity and variability rank of CEWQ are reported and specific characteristics and mechanism of elasticity of nutrient parameters are also revealed. Among them, the performance of ammonia, total phosphorus-air temperature models, and nitrite, orthophosphorus-precipitation models are the best. Spatial and temporal assessment shows that precipitation elasticity is more variable in space than temperature elasticity and that seasonal variation is more evident for precipitation elasticity than for temperature elasticity. Moreover, both anthropogenic activities and environmental factors are found to impact CEWQ for select variables. The major relationships that can be inferred include: (1) human population has a strong linear correlation with temperature elasticity of turbidity and total phosphorus; and (2) latitude has a strong linear correlation with precipitation elasticity of turbidity and N nutrients. As this work improves our understanding of the relation between climate factors and surface water quality, it is potentially helpful for investigating the effect of climate change on water quality in large rivers, such as on the long-term change of nutrient concentrations. © 2013.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010047842','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010047842"><span>Northern Hemisphere Winter Climate Response to Greenhouse Gas, Ozone, Solar and Volcanic Forcing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Shindell, Drew T.; Schmidt, Gavin A.; Miller, Ron L.; Rind, David; Hansen, James E. (Technical Monitor)</p> <p>2001-01-01</p> <p>The Goddard Institute for Space Studies (GISS) climate/middle atmosphere model has been used to study the impacts of increasing greenhouse gases, polar ozone depletion, volcanic eruptions, and solar cycle variability. We focus on the projection of the induced responses onto Northern Hemisphere winter surface climate. Changes in the model's surface climate take place largely through enhancement of existing variability patterns, with greenhouse gases, polar ozone depletion and volcanic eruptions primarily affecting the Arctic Oscillation (AO) pattern. Perturbations descend from the stratosphere to the surface in the model by altering the propagation of planetary waves coming up from the surface, in accord with observational evidence. Models lacking realistic stratospheric dynamics fail to capture these wave flux changes. The results support the conclusion that the stratosphere plays a crucial role in recent AO trends. We show that in our climate model, while ozone depletion has a significant effect, greenhouse gas forcing is the only one capable of causing the large, sustained increase in the AO observed over recent decades. This suggests that the AO trend, and a concurrent strengthening of the stratospheric vortex over the Arctic, are very likely anthropogenic in origin.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H13K1542E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H13K1542E"><span>Identification of reliable gridded reference data for statistical downscaling methods in Alberta</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Eum, H. I.; Gupta, A.</p> <p>2017-12-01</p> <p>Climate models provide essential information to assess impacts of climate change at regional and global scales. However, statistical downscaling methods have been applied to prepare climate model data for various applications such as hydrologic and ecologic modelling at a watershed scale. As the reliability and (spatial and temporal) resolution of statistically downscaled climate data mainly depend on a reference data, identifying the most reliable reference data is crucial for statistical downscaling. A growing number of gridded climate products are available for key climate variables which are main input data to regional modelling systems. However, inconsistencies in these climate products, for example, different combinations of climate variables, varying data domains and data lengths and data accuracy varying with physiographic characteristics of the landscape, have caused significant challenges in selecting the most suitable reference climate data for various environmental studies and modelling. Employing various observation-based daily gridded climate products available in public domain, i.e. thin plate spline regression products (ANUSPLIN and TPS), inverse distance method (Alberta Townships), and numerical climate model (North American Regional Reanalysis) and an optimum interpolation technique (Canadian Precipitation Analysis), this study evaluates the accuracy of the climate products at each grid point by comparing with the Adjusted and Homogenized Canadian Climate Data (AHCCD) observations for precipitation, minimum and maximum temperature over the province of Alberta. Based on the performance of climate products at AHCCD stations, we ranked the reliability of these publically available climate products corresponding to the elevations of stations discretized into several classes. According to the rank of climate products for each elevation class, we identified the most reliable climate products based on the elevation of target points. A web-based system was developed to allow users to easily select the most reliable reference climate data at each target point based on the elevation of grid cell. By constructing the best combination of reference data for the study domain, the accurate and reliable statistically downscaled climate projections could be significantly improved.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20180002900&hterms=ECS&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DECS','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20180002900&hterms=ECS&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DECS"><span>Internal Variability and Disequilibrium Confound Estimates of Climate Sensitivity from Observations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Marvel, Kate; Pincus, Robert; Schmidt, Gavin A.; Miller, Ron L.</p> <p>2018-01-01</p> <p>An emerging literature suggests that estimates of equilibrium climate sensitivity (ECS) derived from recent observations and energy balance models are biased low because models project more positive climate feedback in the far future. Here we use simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to show that across models, ECS inferred from the recent historical period (1979-2005) is indeed almost uniformly lower than that inferred from simulations subject to abrupt increases in CO2-radiative forcing. However, ECS inferred from simulations in which sea surface temperatures are prescribed according to observations is lower still. ECS inferred from simulations with prescribed sea surface temperatures is strongly linked to changes to tropical marine low clouds. However, feedbacks from these clouds are a weak constraint on long-term model ECS. One interpretation is that observations of recent climate changes constitute a poor direct proxy for long-term sensitivity.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018GeoRL..45.1595M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018GeoRL..45.1595M"><span>Internal Variability and Disequilibrium Confound Estimates of Climate Sensitivity From Observations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Marvel, Kate; Pincus, Robert; Schmidt, Gavin A.; Miller, Ron L.</p> <p>2018-02-01</p> <p>An emerging literature suggests that estimates of equilibrium climate sensitivity (ECS) derived from recent observations and energy balance models are biased low because models project more positive climate feedback in the far future. Here we use simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to show that across models, ECS inferred from the recent historical period (1979-2005) is indeed almost uniformly lower than that inferred from simulations subject to abrupt increases in CO2 radiative forcing. However, ECS inferred from simulations in which sea surface temperatures are prescribed according to observations is lower still. ECS inferred from simulations with prescribed sea surface temperatures is strongly linked to changes to tropical marine low clouds. However, feedbacks from these clouds are a weak constraint on long-term model ECS. One interpretation is that observations of recent climate changes constitute a poor direct proxy for long-term sensitivity.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1244792-interannual-modulation-subtropical-atlantic-boreal-summer-dust-variability-enso','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1244792-interannual-modulation-subtropical-atlantic-boreal-summer-dust-variability-enso"><span>Interannual Modulation of Subtropical Atlantic Boreal Summer Dust Variability by ENSO</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>DeFlorio, Mike; Goodwin, Ian D.; Cayan, Dan</p> <p>2016-01-01</p> <p>Dust variability in the climate system has been studied for several decades, yet there remains an incomplete understanding of the dynamical mechanisms controlling interannual and decadal variations in dust transport. The sparseness of multi-year observational datasets has limited our understanding of the relationship between climate variations and atmospheric dust. We use available observations and a century-length fully coupled Community Earth System Model (CESM) simulation to show that the El Niño- Southern Oscillation (ENSO) exerts a control on North African dust transport during boreal summer. In CESM, this relationship is stronger over the dusty tropical North Atlantic than near Barbados, onemore » of the few sites having a multi-decadal observed record. During strong La Niña summers in CESM, a statistically significant increase in lower tropospheric easterly wind is associated with an increase in North African dust transport over the Atlantic. Barbados dust and Pacific SST variability are only weakly correlated in both observations and CESM, suggesting that other processes are controlling the crossbasin variability of dust. We also use our CESM simulation to show that the relationship between downstream North African dust transport and ENSO fluctuates on multidecadal timescales and may be modulated by the North Atlantic Oscillation (NAO). Our findings indicate that existing observations of dust over the tropical North Atlantic are not extensive enough to completely describe the variability of dust and dust transport, and demonstrate the importance of global models to supplement and interpret observational records.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ems..confE.538L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ems..confE.538L"><span>Sun's influence on climate: Explored with SDO</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lundstedt, H.</p> <p>2010-09-01</p> <p>Stunning images and movies recorded of the Sun, with Solar Dynamics Observatory (SDO), makes one wonder: How would this change our view on the Sun-Earth climate coupling? SDO shows a much more variable Sun, on all spatial and temporal scales. Detailed pictures of solar storms are foreseen to improve our understanding of the direct Sun-Earth coupling. Dynamo models, described by dynamical systems using input from helioseismic observations, are foreseen to improve our knowledge of the the Sun's cyclic influence on climate. Both the direct-, and the cycle-influence will be discussed in view of the new SDO observations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3599478','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3599478"><span>Increasing influence of heat stress on French maize yields from the 1960s to the 2030s</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Hawkins, Ed; Fricker, Thomas E; Challinor, Andrew J; Ferro, Christopher A T; Kit Ho, Chun; Osborne, Tom M</p> <p>2013-01-01</p> <p>Improved crop yield forecasts could enable more effective adaptation to climate variability and change. Here, we explore how to combine historical observations of crop yields and weather with climate model simulations to produce crop yield projections for decision relevant timescales. Firstly, the effects on historical crop yields of improved technology, precipitation and daily maximum temperatures are modelled empirically, accounting for a nonlinear technology trend and interactions between temperature and precipitation, and applied specifically for a case study of maize in France. The relative importance of precipitation variability for maize yields in France has decreased significantly since the 1960s, likely due to increased irrigation. In addition, heat stress is found to be as important for yield as precipitation since around 2000. A significant reduction in maize yield is found for each day with a maximum temperature above 32 °C, in broad agreement with previous estimates. The recent increase in such hot days has likely contributed to the observed yield stagnation. Furthermore, a general method for producing near-term crop yield projections, based on climate model simulations, is developed and utilized. We use projections of future daily maximum temperatures to assess the likely change in yields due to variations in climate. Importantly, we calibrate the climate model projections using observed data to ensure both reliable temperature mean and daily variability characteristics, and demonstrate that these methods work using retrospective predictions. We conclude that, to offset the projected increased daily maximum temperatures over France, improved technology will need to increase base level yields by 12% to be confident about maintaining current levels of yield for the period 2016–2035; the current rate of yield technology increase is not sufficient to meet this target. PMID:23504849</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005AGUFMPP11A1426A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AGUFMPP11A1426A"><span>Annual Proxy Records from Tropical Cloud Forest Trees in the Monteverde Cloud Forest, Costa Rica</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Anchukaitis, K. J.; Evans, M. N.; Wheelwright, N. T.; Schrag, D. P.</p> <p>2005-12-01</p> <p>The extinction of the Golden Toad (Bufo periglenes) from Costa Rica's Monteverde Cloud Forest prompted research into the causes of ecological change in the montane forests of Costa Rica. Subsequent analysis of meteorological data has suggested that warmer global surface and tropical Pacific sea surface temperatures contribute to an observed decrease in cloud cover at Monteverde. However, while recent studies may have concluded that climate change is already having an effect on cloud forest environments in Costa Rica, without the context provided by long-term climate records, it is difficult to confidently conclude that the observed ecological changes are the result of anthropogenic climate forcing, land clearance in the lowland rainforest, or natural variability in tropical climate. To address this, we develop high-resolution proxy paleoclimate records from trees without annual rings in the Monteverde Cloud Forest in Costa Rica. Calibration of an age model in these trees is a fundamental prerequisite for proxy paleoclimate reconstructions. Our approach exploits the isotopic seasonality in the δ18O of water sources (fog versus rainfall) used by trees over the course of a single year. Ocotea tenera individuals of known age and measured annual growth increments were sampled in long-term monitored plantation sites in order to test this proposed age model. High-resolution (200μm increments) stable isotope measurements on cellulose reveal distinct, coherent δ18O cycles of 6 to 10‰. The calculated growth rates derived from the isotope timeseries match those observed from basal growth increment measurements. Spatial fidelity in the age model and climate signal is examined by using multiple cores from multiple trees and multiple sites. These data support our hypothesis that annual isotope cycles in these trees can be used to provide chronological control in the absence of rings. The ability of trees to record interannual climate variability in local hydrometeorology and remote climate forcing is evaluated using the isotope signal from multiple trees, local meteorological observations, and climate field data for the well-observed 1997-1998 warm El Niño-Southern Oscillation (ENSO) event. The successful calibration of our age model is a necessary step toward the development of long, annually-resolved paleoclimate reconstructions from old trees, even without rings, which will be used to evaluate the cause of recent observed climate change at Monteverde and as proxies for tropical climate field reconstructions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20060011212','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20060011212"><span>Revealing Relationships among Relevant Climate Variables with Information Theory</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Knuth, Kevin H.; Golera, Anthony; Curry, Charles T.; Huyser, Karen A.; Kevin R. Wheeler; Rossow, William B.</p> <p>2005-01-01</p> <p>The primary objective of the NASA Earth-Sun Exploration Technology Office is to understand the observed Earth climate variability, thus enabling the determination and prediction of the climate's response to both natural and human-induced forcing. We are currently developing a suite of computational tools that will allow researchers to calculate, from data, a variety of information-theoretic quantities such as mutual information, which can be used to identify relationships among climate variables, and transfer entropy, which indicates the possibility of causal interactions. Our tools estimate these quantities along with their associated error bars, the latter of which is critical for describing the degree of uncertainty in the estimates. This work is based upon optimal binning techniques that we have developed for piecewise-constant, histogram-style models of the underlying density functions. Two useful side benefits have already been discovered. The first allows a researcher to determine whether there exist sufficient data to estimate the underlying probability density. The second permits one to determine an acceptable degree of round-off when compressing data for efficient transfer and storage. We also demonstrate how mutual information and transfer entropy can be applied so as to allow researchers not only to identify relations among climate variables, but also to characterize and quantify their possible causal interactions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20060012295','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20060012295"><span>A Model Assessment of Satellite Observed Trends in Polar Sea Ice Extents</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Vinnikov, Konstantin Y.; Cavalieri, Donald J.; Parkinson, Claire L.</p> <p>2005-01-01</p> <p>For more than three decades now, satellite passive microwave observations have been used to monitor polar sea ice. Here we utilize sea ice extent trends determined from primarily satellite data for both the Northern and Southern Hemispheres for the period 1972(73)-2004 and compare them with results from simulations by eleven climate models. In the Northern Hemisphere, observations show a statistically significant decrease of sea ice extent and an acceleration of sea ice retreat during the past three decades. However, from the modeled natural variability of sea ice extents in control simulations, we conclude that the acceleration is not statistically significant and should not be extrapolated into the future. Observations and model simulations show that the time scale of climate variability in sea ice extent in the Southern Hemisphere is much larger than in the Northern Hemisphere and that the Southern Hemisphere sea ice extent trends are not statistically significant.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016BGeo...13.1071S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016BGeo...13.1071S"><span>Transient Earth system responses to cumulative carbon dioxide emissions: linearities, uncertainties, and probabilities in an observation-constrained model ensemble</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Steinacher, M.; Joos, F.</p> <p>2016-02-01</p> <p>Information on the relationship between cumulative fossil CO2 emissions and multiple climate targets is essential to design emission mitigation and climate adaptation strategies. In this study, the transient response of a climate or environmental variable per trillion tonnes of CO2 emissions, termed TRE, is quantified for a set of impact-relevant climate variables and from a large set of multi-forcing scenarios extended to year 2300 towards stabilization. An ˜ 1000-member ensemble of the Bern3D-LPJ carbon-climate model is applied and model outcomes are constrained by 26 physical and biogeochemical observational data sets in a Bayesian, Monte Carlo-type framework. Uncertainties in TRE estimates include both scenario uncertainty and model response uncertainty. Cumulative fossil emissions of 1000 Gt C result in a global mean surface air temperature change of 1.9 °C (68 % confidence interval (c.i.): 1.3 to 2.7 °C), a decrease in surface ocean pH of 0.19 (0.18 to 0.22), and a steric sea level rise of 20 cm (13 to 27 cm until 2300). Linearity between cumulative emissions and transient response is high for pH and reasonably high for surface air and sea surface temperatures, but less pronounced for changes in Atlantic meridional overturning, Southern Ocean and tropical surface water saturation with respect to biogenic structures of calcium carbonate, and carbon stocks in soils. The constrained model ensemble is also applied to determine the response to a pulse-like emission and in idealized CO2-only simulations. The transient climate response is constrained, primarily by long-term ocean heat observations, to 1.7 °C (68 % c.i.: 1.3 to 2.2 °C) and the equilibrium climate sensitivity to 2.9 °C (2.0 to 4.2 °C). This is consistent with results by CMIP5 models but inconsistent with recent studies that relied on short-term air temperature data affected by natural climate variability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4886642','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4886642"><span>Climatic change controls productivity variation in global grasslands</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Gao, Qingzhu; Zhu, Wenquan; Schwartz, Mark W.; Ganjurjav, Hasbagan; Wan, Yunfan; Qin, Xiaobo; Ma, Xin; Williamson, Matthew A.; Li, Yue</p> <p>2016-01-01</p> <p>Detection and identification of the impacts of climate change on ecosystems have been core issues in climate change research in recent years. In this study, we compared average annual values of the normalized difference vegetation index (NDVI) with theoretical net primary productivity (NPP) values based on temperature and precipitation to determine the effect of historic climate change on global grassland productivity from 1982 to 2011. Comparison of trends in actual productivity (NDVI) with climate-induced potential productivity showed that the trends in average productivity in nearly 40% of global grassland areas have been significantly affected by climate change. The contribution of climate change to variability in grassland productivity was 15.2–71.2% during 1982–2011. Climate change contributed significantly to long-term trends in grassland productivity mainly in North America, central Eurasia, central Africa, and Oceania; these regions will be more sensitive to future climate change impacts. The impacts of climate change on variability in grassland productivity were greater in the Western Hemisphere than the Eastern Hemisphere. Confirmation of the observed trends requires long-term controlled experiments and multi-model ensembles to reduce uncertainties and explain mechanisms. PMID:27243565</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_21 --> <div id="page_22" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="421"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ThApC.129..503H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ThApC.129..503H"><span>Effects of diurnal temperature range and drought on wheat yield in Spain</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hernandez-Barrera, S.; Rodriguez-Puebla, C.; Challinor, A. J.</p> <p>2017-07-01</p> <p>This study aims to provide new insight on the wheat yield historical response to climate processes throughout Spain by using statistical methods. Our data includes observed wheat yield, pseudo-observations E-OBS for the period 1979 to 2014, and outputs of general circulation models in phase 5 of the Coupled Models Inter-comparison Project (CMIP5) for the period 1901 to 2099. In investigating the relationship between climate and wheat variability, we have applied the approach known as the partial least-square regression, which captures the relevant climate drivers accounting for variations in wheat yield. We found that drought occurring in autumn and spring and the diurnal range of temperature experienced during the winter are major processes to characterize the wheat yield variability in Spain. These observable climate processes are used for an empirical model that is utilized in assessing the wheat yield trends in Spain under different climate conditions. To isolate the trend within the wheat time series, we implemented the adaptive approach known as Ensemble Empirical Mode Decomposition. Wheat yields in the twenty-first century are experiencing a downward trend that we claim is a consequence of widespread drought over the Iberian Peninsula and an increase in the diurnal range of temperature. These results are important to inform about the wheat vulnerability in this region to coming changes and to develop adaptation strategies.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMPP44B..01E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMPP44B..01E"><span>The Paleoclimate Uncertainty Cascade: Tracking Proxy Errors Via Proxy System Models.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Emile-Geay, J.; Dee, S. G.; Evans, M. N.; Adkins, J. F.</p> <p>2014-12-01</p> <p>Paleoclimatic observations are, by nature, imperfect recorders of climate variables. Empirical approaches to their calibration are challenged by the presence of multiple sources of uncertainty, which may confound the interpretation of signals and the identifiability of the noise. In this talk, I will demonstrate the utility of proxy system models (PSMs, Evans et al, 2013, 10.1016/j.quascirev.2013.05.024) to quantify the impact of all known sources of uncertainty. PSMs explicitly encode the mechanistic knowledge of the physical, chemical, biological and geological processes from which paleoclimatic observations arise. PSMs may be divided into sensor, archive and observation components, all of which may conspire to obscure climate signals in actual paleo-observations. As an example, we couple a PSM for the δ18O of speleothem calcite to an isotope-enabled climate model (Dee et al, submitted) to analyze the potential of this measurement as a proxy for precipitation amount. A simple soil/karst model (Partin et al, 2013, 10.1130/G34718.1) is used as sensor model, while a hiatus-permitting chronological model (Haslett & Parnell, 2008, 10.1111/j.1467-9876.2008.00623.x) is used as part of the observation model. This subdivision allows us to explicitly model the transformation from precipitation amount to speleothem calcite δ18O as a multi-stage process via a physical and chemical sensor model, and a stochastic archive model. By illustrating the PSM's behavior within the context of the climate simulations, we show how estimates of climate variability may be affected by each submodel's transformation of the signal. By specifying idealized climate signals(periodic vs. episodic, slow vs. fast) to the PSM, we investigate how frequency and amplitude patterns are modulated by sensor and archive submodels. To the extent that the PSM and the climate models are representative of real world processes, then the results may help us more accurately interpret existing paleodata, characterize their uncertainties, and design sampling strategies that exploit their strengths while mitigating their weaknesses.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ClDy..tmp..107D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ClDy..tmp..107D"><span>Climate downscaling over South America for 1971-2000: application in SMAP rainfall-runoff model for Grande River Basin</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>da Silva, Felipe das Neves Roque; Alves, José Luis Drummond; Cataldi, Marcio</p> <p>2018-03-01</p> <p>This paper aims to validate inflow simulations concerning the present-day climate at Água Vermelha Hydroelectric Plant (AVHP—located on the Grande River Basin) based on the Soil Moisture Accounting Procedure (SMAP) hydrological model. In order to provide rainfall data to the SMAP model, the RegCM regional climate model was also used working with boundary conditions from the MIROC model. Initially, present-day climate simulation performed by RegCM model was analyzed. It was found that, in terms of rainfall, the model was able to simulate the main patterns observed over South America. A bias correction technique was also used and it was essential to reduce mistakes related to rainfall simulation. Comparison between rainfall simulations from RegCM and MIROC showed improvements when the dynamical downscaling was performed. Then, SMAP, a rainfall-runoff hydrological model, was used to simulate inflows at Água Vermelha Hydroelectric Plant. After calibration with observed rainfall, SMAP simulations were evaluated in two different periods from the one used in calibration. During calibration, SMAP captures the inflow variability observed at AVHP. During validation periods, the hydrological model obtained better results and statistics with observed rainfall. However, in spite of some discrepancies, the use of simulated rainfall without bias correction captured the interannual flow variability. However, the use of bias removal in the simulated rainfall performed by RegCM brought significant improvements to the simulation of natural inflows performed by SMAP. Not only the curve of simulated inflow became more similar to the observed inflow, but also the statistics improved their values. Improvements were also noticed in the inflow simulation when the rainfall was provided by the regional climate model compared to the global model. In general, results obtained so far prove that there was an added value in rainfall when regional climate model was compared to global climate model and that data from regional models must be bias-corrected so as to improve their results.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25906047','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25906047"><span>Spatial and temporal variability of cv. Tempranillo phenology and grape quality within the Ribera del Duero DO (Spain) and relationships with climate.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Ramos, M C; Jones, G V; Yuste, J</p> <p>2015-12-01</p> <p>The aim of this work was to analyze spatial phenology and grape quality variability related to the climatic characteristics within the Ribera del Duero Designation of Origin (DO). Twenty plots planted with cv. Tempranillo and distributed within the DO were analyzed for phenology from 2004 to 2013. Grape quality parameters at ripening (berry weight, sugar content, acidity and pH, and anthocyanins) were analyzed in 26 plots for the period 2003-2013. The relationships between phenology and grape parameters with different climatic variables were confirmed with a multivariate analysis. On average, bud break was April 27(th), bloom June 17(th), and veraison August 12th. However, phenology during the time period showed high variability, with differences between years of up to 21 days for a phenology stage. The earliest dates were observed in dry years (2005, 2006, and to a lesser degree in 2009) while the later phenology dates occurred in the wettest year of the period (2008). High correlations were found between veraison date and temperature variables as well as with precipitation-evapotranspiration recorded during the bloom-veraison period. These effects tended to be higher in the central part of the DO. Grape quality parameters also showed high variability among the dry and the wet years, and the influence of extreme temperatures on color development as well as the effect of available water on acidity were observed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015IJBm...59.1849R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015IJBm...59.1849R"><span>Spatial and temporal variability of cv. Tempranillo phenology and grape quality within the Ribera del Duero DO (Spain) and relationships with climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ramos, M. C.; Jones, G. V.; Yuste, J.</p> <p>2015-12-01</p> <p>The aim of this work was to analyze spatial phenology and grape quality variability related to the climatic characteristics within the Ribera del Duero Designation of Origin (DO). Twenty plots planted with cv. Tempranillo and distributed within the DO were analyzed for phenology from 2004 to 2013. Grape quality parameters at ripening (berry weight, sugar content, acidity and pH, and anthocyanins) were analyzed in 26 plots for the period 2003-2013. The relationships between phenology and grape parameters with different climatic variables were confirmed with a multivariate analysis. On average, bud break was April 27th, bloom June 17th, and veraison August 12th. However, phenology during the time period showed high variability, with differences between years of up to 21 days for a phenology stage. The earliest dates were observed in dry years (2005, 2006, and to a lesser degree in 2009) while the later phenology dates occurred in the wettest year of the period (2008). High correlations were found between veraison date and temperature variables as well as with precipitation-evapotranspiration recorded during the bloom-veraison period. These effects tended to be higher in the central part of the DO. Grape quality parameters also showed high variability among the dry and the wet years, and the influence of extreme temperatures on color development as well as the effect of available water on acidity were observed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC51C0823R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC51C0823R"><span>Observationally-based Metrics of Ocean Carbon and Biogeochemical Variables are Essential for Evaluating Earth System Model Projections</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Russell, J. L.; Sarmiento, J. L.</p> <p>2017-12-01</p> <p>The Southern Ocean is central to the climate's response to increasing levels of atmospheric greenhouse gases as it ventilates a large fraction of the global ocean volume. Global coupled climate models and earth system models, however, vary widely in their simulations of the Southern Ocean and its role in, and response to, the ongoing anthropogenic forcing. Due to its complex water-mass structure and dynamics, Southern Ocean carbon and heat uptake depend on a combination of winds, eddies, mixing, buoyancy fluxes and topography. Understanding how the ocean carries heat and carbon into its interior and how the observed wind changes are affecting this uptake is essential to accurately projecting transient climate sensitivity. Observationally-based metrics are critical for discerning processes and mechanisms, and for validating and comparing climate models. As the community shifts toward Earth system models with explicit carbon simulations, more direct observations of important biogeochemical parameters, like those obtained from the biogeochemically-sensored floats that are part of the Southern Ocean Carbon and Climate Observations and Modeling project, are essential. One goal of future observing systems should be to create observationally-based benchmarks that will lead to reducing uncertainties in climate projections, and especially uncertainties related to oceanic heat and carbon uptake.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=308462&Lab=NERL&keyword=simulation+AND+processes&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=308462&Lab=NERL&keyword=simulation+AND+processes&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>“ How Reliable is the Couple of WRF & VIC Models”</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>The ability of the fully coupling of Weather Research & Forecasting Model (WRF) and Variable Infiltration Capacity (VIC) model to produce hydrological and climate variables was evaluated. First, the VIC model was run by using observed meteorological data and calibrated in the Upp...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1713739C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1713739C"><span>Global distribution of carbon turnover times in terrestrial ecosystems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Carvalhais, Nuno; Forkel, Matthias; Khomik, Myroslava; Bellarby, Jessica; Jung, Martin; Migliavacca, Mirco; Mu, Mingquan; Saatchi, Sassan; Santoro, Maurizio; Thurner, Martin; Weber, Ulrich; Ahrens, Bernhard; Beer, Christian; Cescatti, Alessandro; Randerson, James T.; Reichstein, Markus</p> <p>2015-04-01</p> <p>The response of the carbon cycle in terrestrial ecosystems to climate variability remains one of the largest uncertainties affecting future projections of climate change. This feedback between the terrestrial carbon cycle and climate is partly determined by the response of carbon uptake and by changes in the residence time of carbon in land ecosystems, which depend on climate, soil, and vegetation type. Thus, it is of foremost importance to quantify the turnover times of carbon in terrestrial ecosystems and its spatial co-variability with climate. Here, we develop a global, spatially explicit and observation-based assessment of whole-ecosystem carbon turnover times (τ) to investigate its co-variation with climate at global scale. Assuming a balance between uptake (gross primary production, GPP) and emission fluxes, τ can be defined as the ratio between the total stock (C_total) and the output or input fluxes (GPP). The estimation of vegetation (C_veg) stocks relies on new remote sensing-based estimates from Saatchi et al (2011) and Thurner et al (2014), while soil carbon stocks (C_soil) are estimated based on state of the art global (Harmonized World Soil Database) and regional (Northern Circumpolar Soil Carbon Database) datasets. The uptake flux estimates are based on global observation-based fields of GPP (Jung et al., 2011). Globally, we find an overall mean global carbon turnover time of 23-4+7 years (95% confidence interval). A strong spatial variability globally is also observed, from shorter residence times in equatorial regions to longer periods at latitudes north of 75°N (mean τ of 15 and 255 years, respectively). The observed latitudinal pattern reflect the clear dependencies on temperature, showing increases from the equator to the poles, which is consistent with our current understanding of temperature controls on ecosystem dynamics. However, long turnover times are also observed in semi-arid and forest-herbaceous transition regions. Furthermore, based on a local correlation analysis, our results reveal a similarly strong association between τ and precipitation. A further analysis of carbon turnover times as simulated by state-of-the-art coupled climate carbon-cycle models from the CMIP5 experiments reveals wide variations between models and a tendency to underestimate the global τ by 36%. The latitudinal patterns correlate significantly with the observation-based patterns. However, the models show stronger associations between τ and temperature than the observation-based estimates. In general, the stronger relationship between τ and precipitation is not reproduced and the modeled turnover times are significantly faster in many semi-arid regions. Ultimately, these results suggest a strong role of the hydrological cycle in the carbon cycle-climate interactions, which is not currently reproduced by Earth system models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1422909','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1422909"><span>Climate Modeling and Causal Identification for Sea Ice Predictability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Hunke, Elizabeth Clare; Urrego Blanco, Jorge Rolando; Urban, Nathan Mark</p> <p></p> <p>This project aims to better understand causes of ongoing changes in the Arctic climate system, particularly as decreasing sea ice trends have been observed in recent decades and are expected to continue in the future. As part of the Sea Ice Prediction Network, a multi-agency effort to improve sea ice prediction products on seasonal-to-interannual time scales, our team is studying sensitivity of sea ice to a collection of physical process and feedback mechanism in the coupled climate system. During 2017 we completed a set of climate model simulations using the fully coupled ACME-HiLAT model. The simulations consisted of experiments inmore » which cloud, sea ice, and air-ocean turbulent exchange parameters previously identified as important for driving output uncertainty in climate models were perturbed to account for parameter uncertainty in simulated climate variables. We conducted a sensitivity study to these parameters, which built upon a previous study we made for standalone simulations (Urrego-Blanco et al., 2016, 2017). Using the results from the ensemble of coupled simulations, we are examining robust relationships between climate variables that emerge across the experiments. We are also using causal discovery techniques to identify interaction pathways among climate variables which can help identify physical mechanisms and provide guidance in predictability studies. This work further builds on and leverages the large ensemble of standalone sea ice simulations produced in our previous w14_seaice project.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PCE....87...60C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PCE....87...60C"><span>Notable shifting in the responses of vegetation activity to climate change in China</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, Aifang; He, Bin; Wang, Honglin; Huang, Ling; Zhu, Yunhua; Lv, Aifeng</p> <p></p> <p>The weakening relationship between inter-annual temperature variability and vegetation activity in the Northern Hemisphere over the last three decades has been reported by a recent study. However, how and to what extent vegetation activity responds to climate change in China is still unclear. We applied the Pearson correlation and partial correlation methods with a moving 15-y window to the GIMMS NDVI dataset from NOAA/AVHRR and observed climate data to examine the variation in the relationships between vegetation activity and climate variables. Results showed that there was an expanding negative response of vegetation growth to climate warming and a positive role of precipitation. The change patterns between NDVI and climate variables over vegetation types during the past three decades pointed an expending negative correlation between NDVI and temperature and a positive role of precipitation over most of the vegetation types (meadow, grassland, shrub, desert, cropland, and forest). Specifically, correlation between NDVI and temperature (PNDVI-T) have shifted from positive to negative in most of the station of temperature-limited areas with evergreen broadleaf forests, whereas precipitation-limited temperate grassland and desert were characterized by a positive PNDVI-P. This study contributes to ongoing investigations of the effects of climate change on vegetation activity. It is also of great importance for designing forest management strategies to cope with climate change.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3641520','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3641520"><span>Coralline algal Barium as indicator for 20th century northwestern North Atlantic surface ocean freshwater variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Hetzinger, S.; Halfar, J.; Zack, T.; Mecking, J. V.; Kunz, B. E.; Jacob, D. E.; Adey, W. H.</p> <p>2013-01-01</p> <p>During the past decades climate and freshwater dynamics in the northwestern North Atlantic have undergone major changes. Large-scale freshening episodes, related to polar freshwater pulses, have had a strong influence on ocean variability in this climatically important region. However, little is known about variability before 1950, mainly due to the lack of long-term high-resolution marine proxy archives. Here we present the first multidecadal-length records of annually resolved Ba/Ca variations from Northwest Atlantic coralline algae. We observe positive relationships between algal Ba/Ca ratios from two Newfoundland sites and salinity observations back to 1950. Both records capture episodical multi-year freshening events during the 20th century. Variability in algal Ba/Ca is sensitive to freshwater-induced changes in upper ocean stratification, which affect the transport of cold, Ba-enriched deep waters onto the shelf (highly stratified equals less Ba/Ca). Algal Ba/Ca ratios therefore may serve as a new resource for reconstructing past surface ocean freshwater changes. PMID:23636135</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23636135','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23636135"><span>Coralline algal barium as indicator for 20th century northwestern North Atlantic surface ocean freshwater variability.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hetzinger, S; Halfar, J; Zack, T; Mecking, J V; Kunz, B E; Jacob, D E; Adey, W H</p> <p>2013-01-01</p> <p>During the past decades climate and freshwater dynamics in the northwestern North Atlantic have undergone major changes. Large-scale freshening episodes, related to polar freshwater pulses, have had a strong influence on ocean variability in this climatically important region. However, little is known about variability before 1950, mainly due to the lack of long-term high-resolution marine proxy archives. Here we present the first multidecadal-length records of annually resolved Ba/Ca variations from Northwest Atlantic coralline algae. We observe positive relationships between algal Ba/Ca ratios from two Newfoundland sites and salinity observations back to 1950. Both records capture episodical multi-year freshening events during the 20th century. Variability in algal Ba/Ca is sensitive to freshwater-induced changes in upper ocean stratification, which affect the transport of cold, Ba-enriched deep waters onto the shelf (highly stratified equals less Ba/Ca). Algal Ba/Ca ratios therefore may serve as a new resource for reconstructing past surface ocean freshwater changes.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMIN21B1389L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMIN21B1389L"><span>Climate Model Diagnostic Analyzer Web Service System</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lee, S.; Pan, L.; Zhai, C.; Tang, B.; Jiang, J. H.</p> <p>2013-12-01</p> <p>The latest Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report stressed the need for the comprehensive and innovative evaluation of climate models with newly available global observations. The traditional approach to climate model evaluation, which compares a single parameter at a time, identifies symptomatic model biases and errors but fails to diagnose the model problems. The model diagnosis process requires physics-based multi-variable comparisons that typically involve large-volume and heterogeneous datasets, making them both computationally- and data-intensive. To address these challenges, we are developing a parallel, distributed web-service system that enables the physics-based multi-variable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. We have developed a methodology to transform an existing science application code into a web service using a Python wrapper interface and Python web service frameworks (i.e., Flask, Gunicorn, and Tornado). The web-service system, called Climate Model Diagnostic Analyzer (CMDA), currently supports (1) all the datasets from Obs4MIPs and a few ocean datasets from NOAA and Argo, which can serve as observation-based reference data for model evaluation and (2) many of CMIP5 model outputs covering a broad range of atmosphere, ocean, and land variables from the CMIP5 specific historical runs and AMIP runs. Analysis capabilities currently supported by CMDA are (1) the calculation of annual and seasonal means of physical variables, (2) the calculation of time evolution of the means in any specified geographical region, (3) the calculation of correlation between two variables, and (4) the calculation of difference between two variables. A web user interface is chosen for CMDA because it not only lowers the learning curve and removes the adoption barrier of the tool but also enables instantaneous use, avoiding the hassle of local software installation and environment incompatibility. CMDA is planned to be used as an educational tool for the summer school organized by JPL's Center for Climate Science in 2014. The requirements of the educational tool are defined with the interaction with the school organizers, and CMDA is customized to meet the requirements accordingly. The tool needs to be production quality for 30+ simultaneous users. The summer school will thus serve as a valuable testbed for the tool development, preparing CMDA to serve the Earth-science modeling and model-analysis community at the end of the project. This work was funded by the NASA Earth Science Program called Computational Modeling Algorithms and Cyberinfrastructure (CMAC).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC33H..05L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC33H..05L"><span>The summer North Atlantic Oscillation (SNAO) variability on decadal to paleoclimate time scales</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Linderholm, H. W.; Folland, C. K.; Zhang, P.; Gunnarson, B. E.; Jeong, J. H.; Ren, H.</p> <p>2017-12-01</p> <p>The summer North Atlantic Oscillation (SNAO), strongly related to the latitude of the North Atlantic and European summer storm tracks, exerts a considerable influence on European summer climate variability and extremes. Here we extend the period covered by the SNAO from July and August to June, July and August (JJA). As well as marked interannual variability, the JJA SNAO has shown a large inter-decadal change since the 1970s. Decadally averaged, there has been a change from a very positive to a rather negative SNAO phase. This change in SNAO phase is opposite in sign from that expected by a number of climate models under enhanced greenhouse forcing by the late twenty first century. It has led to noticeably wetter summers in North West Europe in the last decade. On interannual to multidecadal timescales, SNAO variability is linked to variations in North Atlantic sea surface temperature (SST): observations and models indicate an association between the Atlantic Multi-decadal Oscillation (AMO) where the cold (warm) phase of the AMO corresponds a positive (negative) phase of the SNAO. Observations also indicate a link with SST in the Gulf Stream region of the North Atlantic where, particularly on decadal time scales, SST warming may favour a more positive phase of the SNAO. Influences of Arctic climate change on North Atlantic and European atmospheric circulation may also exist, particularly reduced sea ice coverage, perhaps favouring the negative phase of the SNAO. A new tree-ring data based JJA SNAO reconstruction extending over the last millennium, as well as climate model output for the same period, enables us to examine the influence of North Atlantic SST and Arctic sea-ice coverage, as well as SNAO impacts on European summer climate, in a long-term, pre-industrial context.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.4575S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.4575S"><span>An observationally centred method to quantify local climate change as a distribution</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stainforth, David; Chapman, Sandra; Watkins, Nicholas</p> <p>2013-04-01</p> <p>For planning and adaptation, guidance on trends in local climate is needed at the specific thresholds relevant to particular impact or policy endeavours. This requires quantifying trends at specific quantiles in distributions of variables such as daily temperature or precipitation. These non-normal distributions vary both geographically and in time. The trends in the relevant quantiles may not simply follow the trend in the distribution mean. We present a method[1] for analysing local climatic timeseries data to assess which quantiles of the local climatic distribution show the greatest and most robust trends. We demonstrate this approach using E-OBS gridded data[2] timeseries of local daily temperature from specific locations across Europe over the last 60 years. Our method extracts the changing cumulative distribution function over time and uses a simple mathematical deconstruction of how the difference between two observations from two different time periods can be assigned to the combination of natural statistical variability and/or the consequences of secular climate change. This deconstruction facilitates an assessment of the sensitivity of different quantiles of the distributions to changing climate. Geographical location and temperature are treated as independent variables, we thus obtain as outputs how the trend or sensitivity varies with temperature (or occurrence likelihood), and with geographical location. These sensitivities are found to be geographically varying across Europe; as one would expect given the different influences on local climate between, say, Western Scotland and central Italy. We find as an output many regionally consistent patterns of response of potential value in adaptation planning. We discuss methods to quantify the robustness of these observed sensitivities and their statistical likelihood. This also quantifies the level of detail needed from climate models if they are to be used as tools to assess climate change impact. [1] S C Chapman, D A Stainforth, N W Watkins, 2013, On Estimating Local Long Term Climate Trends, Phil. Trans. R. Soc. A, in press [2] Haylock, M.R., N. Hofstra, A.M.G. Klein Tank, E.J. Klok, P.D. Jones and M. New. 2008: A European daily high-resolution gridded dataset of surface temperature and precipitation. J. Geophys. Res (Atmospheres), 113, D20119, doi:10.1029/2008JD10201</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H42C..04M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H42C..04M"><span>The impacts of climatologically-driven megadrought, past and future, on semi-arid watersheds and the water resource system they support in central Arizona, USA.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Murphy, K. W.; Ellis, A. W.</p> <p>2017-12-01</p> <p>The sustainability of water resource systems in the western United States has previously been brought into question by drought concerns and how it will be influenced by future climate change. Although decadal droughts are observed in instrumental records, the data are typically too short and the droughts too few to render the range of hydroclimatic variability that might impact modern water resource systems in the future. Natural modes of variability are not well represented in climate models, which limits the applicability of their downscaled projections in a region of interest since drought risk would be understated. Paleoclimate data have provided evidence of megadroughts from centuries ago whose hydrologic manifestations of climate variability could readily reoccur again in the future. These can be applied to research into watershed hydrologic response and resource system resilience - past, present, and future. A 645-year tree ring reconstruction of stream flow for the Salt and Verde River watersheds in central Arizona has revealed several drought periods, some more severe than seen in the 129-year instrumental record, including a late 16th century megadrought which affected large portions of the United States. This research study translated the tree ring record into net basin water supply which drives a reservoir operations simulation model to assess how the resource system performs under such severe drought. Regional climate change scenarios were developed from the observation that watershed climate sensitivity has been twice the global warming response. These were applied to the watersheds' temperature sensitivities and precipitation elasticities (reported at AGU2014) to obtain detailed renditions of hydrologic response should megadrought reoccur in a future climate. This provided one of the first rigorous projections of surface water supply under future climate change that amplifies the impact of megadrought arising from modes of climate variability often seen in the western United States. The implications to a large reservoir system serving 40% of water demand in the metropolitan Phoenix, Arizona area is reported which enables decision making for future adaptation planning.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.2998W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.2998W"><span>Disentangling the effects of climate variability and functional change on ecosystem carbon dynamics using semi-empirical modelling</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wu, J.; van der Linden, L.; Lasslop, G.; Carvalhais, N.; Pilegaard, K.; Beier, C.; Ibrom, A.</p> <p>2012-04-01</p> <p>The ecosystem carbon balance is affected by both external climatic forcing (e.g. solar radiation, air temperature and humidity) and internal dynamics in the ecosystem functional properties (e.g. canopy structure, leaf photosynthetic capacity and carbohydrate reserve). In order to understand to what extent and at which temporal scale, climatic variability and functional changes regulated the interannual variation (IAV) in the net ecosystem exchange of CO2 (NEE), data-driven analysis and semi-empirical modelling (Lasslop et al. 2010) were performed based on a 13 year NEE record in a temperate deciduous forest (Pilegaard et al 2011, Wu et al. 2012). We found that the sensitivity of carbon fluxes to climatic variability was significantly higher at shorter than at longer time scales and changed seasonally. This implied that the changing distribution of climate anomalies during the vegetation period could have stronger impacts on future ecosystem carbon balances than changes in average climate. At the annual time scale, approximately 80% of the interannual variability in NEE was attributed to the variation in the model parameters, indicating the observed IAV in the carbon dynamics at the investigated site was dominated by changes in ecosystem functioning. In general this study showed the need for understanding the mechanisms of ecosystem functional change. The method can be applied at other sites to explore ecosystem behavior across different plant functional types and climate gradients. Incorporating ecosystem functional change into process based models will reduce the uncertainties in long-term predictions of ecosystem carbon balances in global climate change projections. Acknowledgements. This work was supported by the EU FP7 project CARBO-Extreme, the DTU Climate Centre and the Danish national project ECOCLIM (Danish Council for Strategic Research).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018GeoRL..45.5071M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018GeoRL..45.5071M"><span>Climate Change Amplifications of Climate-Fire Teleconnections in the Southern Hemisphere</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mariani, Michela; Holz, Andrés.; Veblen, Thomas T.; Williamson, Grant; Fletcher, Michael-Shawn; Bowman, David M. J. S.</p> <p>2018-05-01</p> <p>Recent changes in trend and variability of the main Southern Hemisphere climate modes are driven by a variety of factors, including increasing atmospheric greenhouse gases, changes in tropical sea surface temperature, and stratospheric ozone depletion and recovery. One of the most important implications for climatic change is its effect via climate teleconnections on natural ecosystems, water security, and fire variability in proximity to populated areas, thus threatening human lives and properties. Only sparse and fragmentary knowledge of relationships between teleconnections, lightning strikes, and fire is available during the observed record within the Southern Hemisphere. This constitutes a major knowledge gap for undertaking suitable management and conservation plans. Our analysis of documentary fire records from Mediterranean and temperate regions across the Southern Hemisphere reveals a critical increased strength of climate-fire teleconnections during the onset of the 21st century including a tight coupling between lightning-ignited fire occurrences, the upward trend in the Southern Annular Mode, and rising temperatures across the Southern Hemisphere.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMPP33A1202F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMPP33A1202F"><span>Centennial-scale winter climate variability over the last two millennia in the northern Gulf of Mexico based on paired δ18O and Mg/Ca in Globorotalia truncatulinoides</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fortiz, V.; Thirumalai, K.; Richey, J. N.; Quinn, T. M.</p> <p>2014-12-01</p> <p>We present a replicated record of paired foraminiferal δ18O and Mg/Ca variations in multi-cores collected from the Garrison Basin (26º43'N, 93º55'W) in the northern Gulf of Mexico (GOM). Using δ18O (sea surface temperature, SST; sea surface salinity, SSS proxy) and Mg/Ca (SST proxy) variations in non-encrusted planktic foraminifer Globorotalia truncatulinoides we produce time series spanning the last two millennia that is characterized by centennial-scale climate variability. We interpret geochemical variations in G. truncatulinoides to reflect winter climate variability because data from a sediment trap, located ~350 km east of the core site, reveal that annual flux of G. truncatulinoides is heavily weighted towards winter (peak production in January-February; Spear et al., 2011). Similar centennial-scale variability is also observed in the foraminiferal geochemistry of Globigerinoides ruber in the same multi-cores, which likely reflect mean annual climate variations. Our replicated results and comparisons to other SST reconstructions from the region lend confidence that the northern GOM surface ocean underwent large, centennial-scale variability, most likely dominated by changes in winter climate. This variability occurred in a time period where climate forcing is small and background conditions are similar to pre-industrial times. References: Spear, J.W.; Poore, R.Z., and Quinn, T.M., 2011, Globorotalia truncatulinoides (dextral) Mg/Ca as a proxy for Gulf of Mexico winter mixed-layer temperature: Evidence from a sediment trap in the northern Gulf of Mexico. Marine Micropaleontology, 80, 53-61.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/30489','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/30489"><span>Land resources: Forest and arid lands [Chapter 3</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>M. G. Ryan; S. R. Archer; R. A. Birdsey; C. N. Dahm; L. S. Heath; J. A. Hicke; D. Y. Hollinger; T. E. Huxman; G. S. Okin; R. Oren; J. T. Randerson; W. H. Schlesinger</p> <p>2008-01-01</p> <p>This synthesis and assessment report builds on an extensive scientific literature and series of recent assessments of the historical and potential impacts of climate change and climate variability on managed and unmanaged ecosystems and their constituent biota and processes. It identifies changes in resource conditions that are now being observed and examines whether...</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_22 --> <div id="page_23" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active"><span>23</span></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="441"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018IJBm...62..459O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018IJBm...62..459O"><span>Relationship between rice yield and climate variables in southwest Nigeria using multiple linear regression and support vector machine analysis</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Oguntunde, Philip G.; Lischeid, Gunnar; Dietrich, Ottfried</p> <p>2018-03-01</p> <p>This study examines the variations of climate variables and rice yield and quantifies the relationships among them using multiple linear regression, principal component analysis, and support vector machine (SVM) analysis in southwest Nigeria. The climate and yield data used was for a period of 36 years between 1980 and 2015. Similar to the observed decrease ( P < 0.001) in rice yield, pan evaporation, solar radiation, and wind speed declined significantly. Eight principal components exhibited an eigenvalue > 1 and explained 83.1% of the total variance of predictor variables. The SVM regression function using the scores of the first principal component explained about 75% of the variance in rice yield data and linear regression about 64%. SVM regression between annual solar radiation values and yield explained 67% of the variance. Only the first component of the principal component analysis (PCA) exhibited a clear long-term trend and sometimes short-term variance similar to that of rice yield. Short-term fluctuations of the scores of the PC1 are closely coupled to those of rice yield during the 1986-1993 and the 2006-2013 periods thereby revealing the inter-annual sensitivity of rice production to climate variability. Solar radiation stands out as the climate variable of highest influence on rice yield, and the influence was especially strong during monsoon and post-monsoon periods, which correspond to the vegetative, booting, flowering, and grain filling stages in the study area. The outcome is expected to provide more in-depth regional-specific climate-rice linkage for screening of better cultivars that can positively respond to future climate fluctuations as well as providing information that may help optimized planting dates for improved radiation use efficiency in the study area.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1225814','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1225814"><span>Characterization of the Dynamics of Climate Systems and Identification of Missing Mechanisms Impacting the Long Term Predictive Capabilities of Global Climate Models Utilizing Dynamical Systems Approaches to the Analysis of Observed and Modeled Climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Bhatt, Uma S.; Wackerbauer, Renate; Polyakov, Igor V.</p> <p></p> <p>The goal of this research was to apply fractional and non-linear analysis techniques in order to develop a more complete characterization of climate change and variability for the oceanic, sea ice and atmospheric components of the Earth System. This research applied two measures of dynamical characteristics of time series, the R/S method of calculating the Hurst exponent and Renyi entropy, to observational and modeled climate data in order to evaluate how well climate models capture the long-term dynamics evident in observations. Fractional diffusion analysis was applied to ARGO ocean buoy data to quantify ocean transport. Self organized maps were appliedmore » to North Pacific sea level pressure and analyzed in ways to improve seasonal predictability for Alaska fire weather. This body of research shows that these methods can be used to evaluate climate models and shed light on climate mechanisms (i.e., understanding why something happens). With further research, these methods show promise for improving seasonal to longer time scale forecasts of climate.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26768143','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26768143"><span>Spatiotemporal variability and predictability of Normalized Difference Vegetation Index (NDVI) in Alberta, Canada.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Jiang, Rengui; Xie, Jiancang; He, Hailong; Kuo, Chun-Chao; Zhu, Jiwei; Yang, Mingxiang</p> <p>2016-09-01</p> <p>As one of the most popular vegetation indices to monitor terrestrial vegetation productivity, Normalized Difference Vegetation Index (NDVI) has been widely used to study the plant growth and vegetation productivity around the world, especially the dynamic response of vegetation to climate change in terms of precipitation and temperature. Alberta is the most important agricultural and forestry province and with the best climatic observation systems in Canada. However, few studies pertaining to climate change and vegetation productivity are found. The objectives of this paper therefore were to better understand impacts of climate change on vegetation productivity in Alberta using the NDVI and provide reference for policy makers and stakeholders. We investigated the following: (1) the variations of Alberta's smoothed NDVI (sNDVI, eliminated noise compared to NDVI) and two climatic variables (precipitation and temperature) using non-parametric Mann-Kendall monotonic test and Thiel-Sen's slope; (2) the relationships between sNDVI and climatic variables, and the potential predictability of sNDVI using climatic variables as predictors based on two predicted models; and (3) the use of a linear regression model and an artificial neural network calibrated by the genetic algorithm (ANN-GA) to estimate Alberta's sNDVI using precipitation and temperature as predictors. The results showed that (1) the monthly sNDVI has increased during the past 30 years and a lengthened growing season was detected; (2) vegetation productivity in northern Alberta was mainly temperature driven and the vegetation in southern Alberta was predominantly precipitation driven for the period of 1982-2011; and (3) better performances of the sNDVI-climate relationships were obtained by nonlinear model (ANN-GA) than using linear (regression) model. Similar results detected in both monthly and summer sNDVI prediction using climatic variables as predictors revealed the applicability of two models for different period of year ecologists might focus on.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20080045519','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20080045519"><span>The Spatiotemporal Structure of 20th Century Climate Variations in Observations and Reanalyses. Part 2; Pacific Pan-Decadal Variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Chen, Junye; DelGenio, Anthony D.; Carlson, Barbara E.; Bosilovich, Michael G.</p> <p>2007-01-01</p> <p>The dominant interannual El Nino-Southern Oscillation phenomenon (ENSO) and the short length of climate observation records make it difficult to study long-term climate variations in the spatiotemporal domain. Based on the fact that the ENS0 signal spreads to remote regions and induces delayed climate variation through atmospheric teleconnections, we develop an ENSO-removal method through which the ENS0 signal can be approximately removed at the grid box level from the spatiotemporal field of a climate parameter. After this signal is removed, long-term climate variations, namely, the global warming trend (GW) and the Pacific pan-decadal variability (PDV), are isolated at middle and low latitudes in the climate parameter fields from observed and reanalyses datasets. In this study, we show that one of several PDV interdecadal regime shifts occurred during the 1990s. This significant change in the Pacific basin is comparable but opposite in phase to the 1976 climate regime shift, which results persisting warming in the central-eastern Pacific, and cooling in the North and South Pacific. The 1990s PDV regime shift is consistent with observed changes in ocean biosphere and ocean circulation. A comprehensive picture of PDV as manifested in the troposphere and at the surface is described. In general, the PDV spatial patterns in different parameter fields share some similarities with the patterns associated with ENSO, but important differences exist. First, the PDV atmospheric circulation pattern is shifted westward by about 20deg and its zonal extent is limited to approx.60deg compared to approx.110deg for ENS0 pattern. The westward shift of the PDV wave train produces a different, more west-east oriented, North American teleconnection pattern. The lack of a strong PDV surface temperature (ST) signal in the western equatorial Pacific and the relatively strong ST signal in the subtropical regions are consistent with an atmospheric overturning circulation response that differs from the one associated with ENSO.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28446701','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28446701"><span>Wind and rain are the primary climate factors driving changing phenology of an aerial insectivore.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Irons, Rachel D; Harding Scurr, April; Rose, Alexandra P; Hagelin, Julie C; Blake, Tricia; Doak, Daniel F</p> <p>2017-04-26</p> <p>While the ecological effects of climate change have been widely observed, most efforts to document these impacts in terrestrial systems have concentrated on the impacts of temperature. We used tree swallow ( Tachycineta bicolor ) nest observations from two widely separated sites in central Alaska to examine the aspects of climate affecting breeding phenology at the northern extent of this species' range. We found that two measures of breeding phenology, annual lay and hatch dates, are more strongly predicted by windiness and precipitation than by temperature. At our longest-monitored site, breeding phenology has advanced at nearly twice the rate seen in more southern populations, and these changes correspond to long-term declines in windiness. Overall, adverse spring climate conditions known to negatively impact foraging success of swallows (wet, windy weather) appear to influence breeding phenology more than variation in temperature. Separate analyses show that short windy periods significantly delay initiation of individual clutches within years. While past reviews have emphasized that increasing variability in climate conditions may create physiological and ecological challenges for natural populations, we find that long-term reductions in inclement weather corresponded to earlier reproduction in one of our study populations. To better predict climate change impacts, ecologists need to more carefully test effects of multiple climate variables, including some, like windiness, that may be of paramount importance to some species, but have rarely been considered as strong drivers of ecological responses to climate alteration. © 2017 The Author(s).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFMGC43C0756B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFMGC43C0756B"><span>Climate Change in Colorado: Findings and Scientific Challenges</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Barsugli, J.; Ray, A.; Averyt, K.; Wolter, K.; Hoerling, M. P.</p> <p>2008-12-01</p> <p>In response to the risks associated with anthropogenic climate change, Governor Ritter issued the Colorado Climate Action Plan (CCAP) in 2007. In support of the adaptation component of the CCAP, the Colorado Water Conservation Board commissioned the Western Water Assessment at the University of Colorado to prepare the report "Climate Change in Colorado: A Synthesis to Support Water Resources Management and Adaptation." The objective of "Climate Change in Colorado" is to communicate the state of the science regarding the physical aspects of climate change that are important for evaluating impacts on Colorado's water resources. Accordingly, the document focuses on observed trends, modeling, attribution, and projections of hydroclimatic variables that are important for Colorado's water supply. Although many published datasets include information about Colorado, there are few climate studies that focus on the state. Consequently, many important analyses for Colorado are lacking. The report summarizes Colorado-specific findings from peer-reviewed regional studies, and presents new analyses derived from existing datasets. Here we will summarize the findings of the report, discuss the extent to which conclusions from West-wide studies hold in Colorado, and highlight the many scientific challenges that were faced in the preparation of the report. These challenges include interpreting observed and projected precipitation and temperature variability and trends, dealing with attribution and uncertainty at the state level, and justifying the relevance of climate model projections in a topographically complex state. A second presentation (Ray et al.) discusses the process of developing the report.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5413930','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5413930"><span>Wind and rain are the primary climate factors driving changing phenology of an aerial insectivore</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Irons, Rachel D.; Harding Scurr, April; Rose, Alexandra P.; Hagelin, Julie C.; Blake, Tricia</p> <p>2017-01-01</p> <p>While the ecological effects of climate change have been widely observed, most efforts to document these impacts in terrestrial systems have concentrated on the impacts of temperature. We used tree swallow (Tachycineta bicolor) nest observations from two widely separated sites in central Alaska to examine the aspects of climate affecting breeding phenology at the northern extent of this species' range. We found that two measures of breeding phenology, annual lay and hatch dates, are more strongly predicted by windiness and precipitation than by temperature. At our longest-monitored site, breeding phenology has advanced at nearly twice the rate seen in more southern populations, and these changes correspond to long-term declines in windiness. Overall, adverse spring climate conditions known to negatively impact foraging success of swallows (wet, windy weather) appear to influence breeding phenology more than variation in temperature. Separate analyses show that short windy periods significantly delay initiation of individual clutches within years. While past reviews have emphasized that increasing variability in climate conditions may create physiological and ecological challenges for natural populations, we find that long-term reductions in inclement weather corresponded to earlier reproduction in one of our study populations. To better predict climate change impacts, ecologists need to more carefully test effects of multiple climate variables, including some, like windiness, that may be of paramount importance to some species, but have rarely been considered as strong drivers of ecological responses to climate alteration. PMID:28446701</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018GeoRL..45..983L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018GeoRL..45..983L"><span>Meridional Modes and Increasing Pacific Decadal Variability Under Anthropogenic Forcing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liguori, Giovanni; Di Lorenzo, Emanuele</p> <p>2018-01-01</p> <p>Pacific decadal variability has strong impacts on the statistics of weather, atmosphere extremes, droughts, hurricanes, marine heatwaves, and marine ecosystems. Sea surface temperature (SST) observations show that the variance of the El Niño-like decadal variability has increased by 30% (1920-2015) with a stronger coupling between the major Pacific climate modes. Although we cannot attribute these trends to global climate change, the examination of 30 members of the Community Earth System Model Large Ensemble (LENS) forced with the RCP8.5 radiative forcing scenario (1920-2100) suggests that significant anthropogenic trends in Pacific decadal variance will emerge by 2020 in response to a more energetic North Pacific Meridional Mode (PMM)—a well-known El Niño precursor. The PMM is a key mechanism for energizing and coupling tropical and extratropical decadal variability. In the LENS, the increase in PMM variance is consistent with an intensification of the winds-evaporation-SST thermodynamic feedback that results from a warmer mean climate.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014HESSD..1110515T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014HESSD..1110515T"><span>Effect of year-to-year variability of leaf area index on variable infiltration capacity model performance and simulation of streamflow during drought</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tesemma, Z. K.; Wei, Y.; Peel, M. C.; Western, A. W.</p> <p>2014-09-01</p> <p>This study assessed the effect of using observed monthly leaf area index (LAI) on hydrologic model performance and the simulation of streamflow during drought using the variable infiltration capacity (VIC) hydrological model in the Goulburn-Broken catchment of Australia, which has heterogeneous vegetation, soil and climate zones. VIC was calibrated with both observed monthly LAI and long-term mean monthly LAI, which were derived from the Global Land Surface Satellite (GLASS) observed monthly LAI dataset covering the period from 1982 to 2012. The model performance under wet and dry climates for the two different LAI inputs was assessed using three criteria, the classical Nash-Sutcliffe efficiency, the logarithm transformed flow Nash-Sutcliffe efficiency and the percentage bias. Finally, the percentage deviation of the simulated monthly streamflow using the observed monthly LAI from simulated streamflow using long-term mean monthly LAI was computed. The VIC model predicted monthly streamflow in the selected sub-catchments with model efficiencies ranging from 61.5 to 95.9% during calibration (1982-1997) and 59 to 92.4% during validation (1998-2012). Our results suggest systematic improvements from 4 to 25% in the Nash-Sutcliffe efficiency in pasture dominated catchments when the VIC model was calibrated with the observed monthly LAI instead of the long-term mean monthly LAI. There was limited systematic improvement in tree dominated catchments. The results also suggest that the model overestimation or underestimation of streamflow during wet and dry periods can be reduced to some extent by including the year-to-year variability of LAI in the model, thus reflecting the responses of vegetation to fluctuations in climate and other factors. Hence, the year-to-year variability in LAI should not be neglected; rather it should be included in model calibration as well as simulation of monthly water balance.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMIN53E..02L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMIN53E..02L"><span>Climate Model Diagnostic Analyzer Web Service System</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lee, S.; Pan, L.; Zhai, C.; Tang, B.; Jiang, J. H.</p> <p>2014-12-01</p> <p>We have developed a cloud-enabled web-service system that empowers physics-based, multi-variable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. We have developed a methodology to transform an existing science application code into a web service using a Python wrapper interface and Python web service frameworks. The web-service system, called Climate Model Diagnostic Analyzer (CMDA), currently supports (1) all the observational datasets from Obs4MIPs and a few ocean datasets from NOAA and Argo, which can serve as observation-based reference data for model evaluation, (2) many of CMIP5 model outputs covering a broad range of atmosphere, ocean, and land variables from the CMIP5 specific historical runs and AMIP runs, and (3) ECMWF reanalysis outputs for several environmental variables in order to supplement observational datasets. Analysis capabilities currently supported by CMDA are (1) the calculation of annual and seasonal means of physical variables, (2) the calculation of time evolution of the means in any specified geographical region, (3) the calculation of correlation between two variables, (4) the calculation of difference between two variables, and (5) the conditional sampling of one physical variable with respect to another variable. A web user interface is chosen for CMDA because it not only lowers the learning curve and removes the adoption barrier of the tool but also enables instantaneous use, avoiding the hassle of local software installation and environment incompatibility. CMDA will be used as an educational tool for the summer school organized by JPL's Center for Climate Science in 2014. In order to support 30+ simultaneous users during the school, we have deployed CMDA to the Amazon cloud environment. The cloud-enabled CMDA will provide each student with a virtual machine while the user interaction with the system will remain the same through web-browser interfaces. The summer school will serve as a valuable testbed for the tool development, preparing CMDA to serve its target community: Earth-science modeling and model-analysis community.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMIN31A1746G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMIN31A1746G"><span>iClimate: a climate data and analysis portal</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Goodman, P. J.; Russell, J. L.; Merchant, N.; Miller, S. J.; Juneja, A.</p> <p>2015-12-01</p> <p>We will describe a new climate data and analysis portal called iClimate that facilitates direct comparisons between available climate observations and climate simulations. Modeled after the successful iPlant Collaborative Discovery Environment (www.iplantcollaborative.org) that allows plant scientists to trade and share environmental, physiological and genetic data and analyses, iClimate provides an easy-to-use platform for large-scale climate research, including the storage, sharing, automated preprocessing, analysis and high-end visualization of large and often disparate observational and model datasets. iClimate will promote data exploration and scientific discovery by providing: efficient and high-speed transfer of data from nodes around the globe (e.g. PCMDI and NASA); standardized and customized data/model metrics; efficient subsampling of datasets based on temporal period, geographical region or variable; and collaboration tools for sharing data, workflows, analysis results, and data visualizations with collaborators or with the community at large. We will present iClimate's capabilities, and demonstrate how it will simplify and enhance the ability to do basic or cutting-edge climate research by professionals, laypeople and students.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110006348','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110006348"><span>Effects of Global Change on U.S. Urban Areas: Vulnerabilities, Impacts, and Adaptation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Quattrochi, Dale A.; Wilbanks, Thomas J.; Kirshen, Paul; Romero-Lankao, Patricia; Rosenzweig, Cynthia; Ruth, Mattias; Solecki, William; Tarr, Joel</p> <p>2008-01-01</p> <p>This slide presentation reviews some of the effects that global change has on urban areas in the United States and how the growth of urban areas will affect the environment. It presents the elements of our Synthesis and Assessment Report (SAP) report that relate to what vulnerabilities and impacts will occur, what adaptation responses may take place, and what possible effects on settlement patterns and characteristics will potentially arise, on human settlements in the U.S. as a result of climate change and climate variability. We will also present some recommendations about what should be done to further research on how climate change and variability will impact human settlements in the U.S., as well as how to engage government officials, policy and decision makers, and the general public in understanding the implications of climate change and variability on the local and regional levels. Additionally, we wish to explore how technology such as remote sensing data coupled with modeling, can be employed as synthesis tools for deriving insight across a spectrum of impacts (e.g. public health, urban planning for mitigation strategies) on how cities can cope and adapt to climate change and variability. This latter point parallels the concepts and ideas presented in the U.S. National Academy of Sciences, Decadal Survey report on "Earth Science Applications from Space: National Imperatives for the Next Decade and Beyond" wherein the analysis of the impacts of climate change and variability, human health, and land use change are listed as key areas for development of future Earth observing remote sensing systems.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1238786-future-warming-patterns-linked-todays-climate-variability','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1238786-future-warming-patterns-linked-todays-climate-variability"><span>Future warming patterns linked to today’s climate variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Dai, Aiguo</p> <p>2016-01-11</p> <p>The reliability of model projections of greenhouse gas (GHG)-induced future climate change is often assessed based on models’ ability to simulate the current climate, but there has been little evidence that connects the two. In fact, this practice has been questioned because the GHG-induced future climate change may involve additional physical processes that are not important for the current climate. Here I show that the spatial patterns of the GHG-induced future warming in the 21 st century is highly correlated with the patterns of the year-to-year variations of surface air temperature for today’s climate, with areas of larger variations duringmore » 1950–1979 having more GHG-induced warming in the 21 st century in all climate models. Such a relationship also exists in other climate fields such as atmospheric water vapor, and it is evident in observed temperatures from 1950–2010. The results suggest that many physical processes may work similarly in producing the year-to-year climate variations in the current climate and the GHG-induced long-term changes in the 21 st century in models and in the real world. Furthermore, they support the notion that models that simulate present-day climate variability better are likely to make more reliable predictions of future climate change.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1238786','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1238786"><span>Future warming patterns linked to today’s climate variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Dai, Aiguo</p> <p></p> <p>The reliability of model projections of greenhouse gas (GHG)-induced future climate change is often assessed based on models’ ability to simulate the current climate, but there has been little evidence that connects the two. In fact, this practice has been questioned because the GHG-induced future climate change may involve additional physical processes that are not important for the current climate. Here I show that the spatial patterns of the GHG-induced future warming in the 21 st century is highly correlated with the patterns of the year-to-year variations of surface air temperature for today’s climate, with areas of larger variations duringmore » 1950–1979 having more GHG-induced warming in the 21 st century in all climate models. Such a relationship also exists in other climate fields such as atmospheric water vapor, and it is evident in observed temperatures from 1950–2010. The results suggest that many physical processes may work similarly in producing the year-to-year climate variations in the current climate and the GHG-induced long-term changes in the 21 st century in models and in the real world. Furthermore, they support the notion that models that simulate present-day climate variability better are likely to make more reliable predictions of future climate change.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27984645','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27984645"><span>Detrending phenological time series improves climate-phenology analyses and reveals evidence of plasticity.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Iler, Amy M; Inouye, David W; Schmidt, Niels M; Høye, Toke T</p> <p>2017-03-01</p> <p>Time series have played a critical role in documenting how phenology responds to climate change. However, regressing phenological responses against climatic predictors involves the risk of finding potentially spurious climate-phenology relationships simply because both variables also change across years. Detrending by year is a way to address this issue. Additionally, detrending isolates interannual variation in phenology and climate, so that detrended climate-phenology relationships can represent statistical evidence of phenotypic plasticity. Using two flowering phenology time series from Colorado, USA and Greenland, we detrend flowering date and two climate predictors known to be important in these ecosystems: temperature and snowmelt date. In Colorado, all climate-phenology relationships persist after detrending. In Greenland, 75% of the temperature-phenology relationships disappear after detrending (three of four species). At both sites, the relationships that persist after detrending suggest that plasticity is a major component of sensitivity of flowering phenology to climate. Finally, simulations that created different strengths of correlations among year, climate, and phenology provide broader support for our two empirical case studies. This study highlights the utility of detrending to determine whether phenology is related to a climate variable in observational data sets. Applying this as a best practice will increase our understanding of phenological responses to climatic variation and change. © 2016 by the Ecological Society of America.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26750759','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26750759"><span>Future Warming Patterns Linked to Today's Climate Variability.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Dai, Aiguo</p> <p>2016-01-11</p> <p>The reliability of model projections of greenhouse gas (GHG)-induced future climate change is often assessed based on models' ability to simulate the current climate, but there has been little evidence that connects the two. In fact, this practice has been questioned because the GHG-induced future climate change may involve additional physical processes that are not important for the current climate. Here I show that the spatial patterns of the GHG-induced future warming in the 21(st) century is highly correlated with the patterns of the year-to-year variations of surface air temperature for today's climate, with areas of larger variations during 1950-1979 having more GHG-induced warming in the 21(st) century in all climate models. Such a relationship also exists in other climate fields such as atmospheric water vapor, and it is evident in observed temperatures from 1950-2010. The results suggest that many physical processes may work similarly in producing the year-to-year climate variations in the current climate and the GHG-induced long-term changes in the 21(st) century in models and in the real world. They support the notion that models that simulate present-day climate variability better are likely to make more reliable predictions of future climate change.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMPP41B1363W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMPP41B1363W"><span>1,500 Year Periodicity in Central Texas Moisture Source Variability Reconstructed from Speleothems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wong, C. I.; James, E. W.; Silver, M. M.; Banner, J. L.; Musgrove, M.</p> <p>2014-12-01</p> <p>Delineating the climate processes governing precipitation variability in drought-prone Texas is critical for predicting and mitigating climate change effects, and requires the reconstruction of past climate beyond the instrumental record. Presently, there are few high-resolution Holocene climate records for this region, which limits the assessment of precipitation variability during a relatively stable climatic interval that comprises the closest analogue to the modern climate state. To address this, we present speleothem growth rate and δ18O records from two central Texas caves that span the mid to late Holocene, and assess hypotheses about the climate processes that can account for similarity in the timing and periodicity of variability with other regional and global records. A key finding is the independent variation of speleothem growth rate and δ18O values, suggesting the decoupling of moisture amount and source. This decoupling likely occurs because i) the often direct relation between speleothem growth rate and moisture availability is complicated by changes in the overlying ecosystem that affect subsurface CO2 production, and ii) speleothem δ18O variations reflect changes in moisture source (i.e., proportion of Pacific- vs. Gulf of Mexico-derived moisture) that appear not to be linked to moisture amount. Furthermore, we document a 1,500-year periodicity in δ18O values that is consistent with variability in the percent of hematite-stained grains in North Atlantic sediments, North Pacific SSTs, and El Nino events preserved in an Ecuadorian lake. Previous modeling experiments and analysis of observational data delineate the coupled atmospheric-ocean processes that can account for the coincidence of such variability in climate archives across the northern hemisphere. Reduction of the thermohaline circulation results in North Atlantic cooling, which translates to cooler North Pacific SSTs. The resulting reduction of the meridional SST gradient in the Pacific weakens the air-sea coupling that modulates ENSO activity, resulting in faster growth of interannual anomalies and larger mature El Niño relative to La Niña events. The asymmetrically enhanced ENSO variability can account for a greater portion of Pacific-derived moisture reflected by speleothem δ18O values.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1916788H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1916788H"><span>The PIRATA Observing System in the Tropical Atlantic: Enhancements and perspectives</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hernandez, Fabrice; Araujo, Moacyr; Bourlès, Bernard; Brandt, Peter; Campos, Edmo; Giordani, Hervé; Lumpkin, Rick; McPhaden, Michael J.; Nobre, Paulo; Saravanan, Ramalingam</p> <p>2017-04-01</p> <p>PIRATA (Prediction and Research Moored Array in the Tropical Atlantic) is a multinational program established to improve our knowledge and understanding of ocean-atmosphere variability in the tropical Atlantic, a region that strongly influences the regional hydro-climates and, consequently, the economies of the regions bordering the Atlantic Ocean (e.g. West Africa, North-Eastern Brazil, the West Indies and the United States). PIRATA is motivated not only by fundamental scientific questions but also by societal needs for improved prediction of climatic variability and its impacts. PIRATA, initiated in 1997, is based around an array of moored buoys providing meteorological and oceanographic measurements transmitted in real-time, disseminated via GTS and Global Data Servers. Then, through yearly mooring maintenance, recorded high frequency data are collected and calibrated. The dedicated cruises of yearly maintenance allow complementary acquisition of a large number of measurements along repeated ship track lines and also provide platforms for deployments of other components of the observing system. Several kinds of operations are carried out in collaboration with other international programs. PIRATA provides invaluable data for numerous and varied applications, among which are analyses of climate variability on intraseasonal-to-decadal timescales, equatorial dynamics, mixed-layer temperature and salinity budgets, air-sea fluxes, data assimilation, and weather and climate forecasts. PIRATA is now 20 years old, well established and recognized as the backbone of the tropical Atlantic sustained observing system. Several enhancements have been achieved during recent years, including progressive updating of mooring systems and sensors, also in collaborations with and as a contribution to other programs (such as EU PREFACE and AtlantOS). Recent major accomplishments in terms of air-sea exchanges and climate predictability will be highlighted in this presentation. Future perspectives for the network will also be discussed in the framework of a sustainable Atlantic Ocean Observing System.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1914238D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1914238D"><span>Harmonising and semantically linking key variables from in-situ observing networks of an Integrated Atlantic Ocean Observing System, AtlantOS</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Darroch, Louise; Buck, Justin</p> <p>2017-04-01</p> <p>Atlantic Ocean observation is currently undertaken through loosely-coordinated, in-situ observing networks, satellite observations and data management arrangements at regional, national and international scales. The EU Horizon 2020 AtlantOS project aims to deliver an advanced framework for the development of an Integrated Atlantic Ocean Observing System that strengthens the Global Ocean Observing System (GOOS) and contributes to the aims of the Galway Statement on Atlantic Ocean Cooperation. One goal is to ensure that data from different and diverse in-situ observing networks are readily accessible and useable to a wider community, including the international ocean science community and other stakeholders in this field. To help achieve this goal, the British Oceanographic Data Centre (BODC) produced a parameter matrix to harmonise data exchange, data flow and data integration for the key variables acquired by multiple in-situ AtlantOS observing networks such as ARGO, Seafloor Mapping and OceanSITES. Our solution used semantic linking of controlled vocabularies and metadata for parameters that were "mappable" to existing EU and international standard vocabularies. An AtlantOS Essential Variables list of terms (aggregated level) based on Global Climate Observing System (GCOS) Essential Climate Variables (ECV), GOOS Essential Ocean Variables (EOV) and other key network variables was defined and published on the Natural Environment Research Council (NERC) Vocabulary Server (version 2.0) as collection A05 (http://vocab.nerc.ac.uk/collection/A05/current/). This new vocabulary was semantically linked to standardised metadata for observed properties and units that had been validated by the AtlantOS community: SeaDataNet parameters (P01), Climate and Forecast (CF) Standard Names (P07) and SeaDataNet units (P06). Observed properties were mapped to biological entities from the internationally assured AphiaID from the WOrld Register of Marine Species (WoRMS), http://www.marinespecies.org/aphia.php?p=webservice. The AtlantOS parameter matrix offers a way to harmonise the globally important variables (such as ECVs and EOVs) from in-situ observing networks that use different flavours of exchange formats based on SeaDataNet and CF parameter metadata. It also offers a way to standardise data in the wider Integrated Ocean Observing System. It uses sustainable and trusted standardised vocabularies that are governed by internationally renowned and long-standing organisations and is interoperable through the use of persistent resource identifiers, such as URNs and PURLs. It is the first step to integrating and serving data in a variety of international exchange formats using Application programming interfaces (API) improving both data discoverability and utility for users.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA482695','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA482695"><span>The ARGO Project: Global Ocean Observations for Understanding and Prediction of Climate Variability. Report for Calendar Year 2004</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2004-01-01</p> <p>international Argo practices. Data appropriate for research applications and for comparison with climate change models are not available for several...global ocean heat and fresh water storage and the detection and attribution of climate change . These presentations can be accessed at http...stresses on ocean ecosystems have serious consequences, and sometimes dramatic ones, such as coral reef bleaching . In the future, the impacts of a</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active"><span>23</span></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_23 --> <div id="page_24" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li class="active"><span>24</span></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="461"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.A53Q..05M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.A53Q..05M"><span>Regional Arctic System Model (RASM): A Tool to Advance Understanding and Prediction of Arctic Climate Change at Process Scales</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Maslowski, W.; Roberts, A.; Osinski, R.; Brunke, M.; Cassano, J. J.; Clement Kinney, J. L.; Craig, A.; Duvivier, A.; Fisel, B. J.; Gutowski, W. J., Jr.; Hamman, J.; Hughes, M.; Nijssen, B.; Zeng, X.</p> <p>2014-12-01</p> <p>The Arctic is undergoing rapid climatic changes, which are some of the most coordinated changes currently occurring anywhere on Earth. They are exemplified by the retreat of the perennial sea ice cover, which integrates forcing by, exchanges with and feedbacks between atmosphere, ocean and land. While historical reconstructions from Global Climate and Global Earth System Models (GC/ESMs) are in broad agreement with these changes, the rate of change in the GC/ESMs remains outpaced by observations. Reasons for that stem from a combination of coarse model resolution, inadequate parameterizations, unrepresented processes and a limited knowledge of physical and other real world interactions. We demonstrate the capability of the Regional Arctic System Model (RASM) in addressing some of the GC/ESM limitations in simulating observed seasonal to decadal variability and trends in the sea ice cover and climate. RASM is a high resolution, fully coupled, pan-Arctic climate model that uses the Community Earth System Model (CESM) framework. It uses the Los Alamos Sea Ice Model (CICE) and Parallel Ocean Program (POP) configured at an eddy-permitting resolution of 1/12° as well as the Weather Research and Forecasting (WRF) and Variable Infiltration Capacity (VIC) models at 50 km resolution. All RASM components are coupled via the CESM flux coupler (CPL7) at 20-minute intervals. RASM is an example of limited-area, process-resolving, fully coupled earth system model, which due to the additional constraints from lateral boundary conditions and nudging within a regional model domain facilitates detailed comparisons with observational statistics that are not possible with GC/ESMs. In this talk, we will emphasize the utility of RASM to understand sensitivity to variable parameter space, importance of critical processes, coupled feedbacks and ultimately to reduce uncertainty in arctic climate change projections.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..11.8181B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..11.8181B"><span>A downscaling method for the assessment of local climate change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bruno, E.; Portoghese, I.; Vurro, M.</p> <p>2009-04-01</p> <p>The use of complimentary models is necessary to study the impact of climate change scenarios on the hydrological response at different space-time scales. However, the structure of GCMs is such that their space resolution (hundreds of kilometres) is too coarse and not adequate to describe the variability of extreme events at basin scale (Burlando and Rosso, 2002). To bridge the space-time gap between the climate scenarios and the usual scale of the inputs for hydrological prediction models is a fundamental requisite for the evaluation of climate change impacts on water resources. Since models operate a simplification of a complex reality, their results cannot be expected to fit with climate observations. Identifying local climate scenarios for impact analysis implies the definition of more detailed local scenario by downscaling GCMs or RCMs results. Among the output correction methods we consider the statistical approach by Déqué (2007) reported as a ‘Variable correction method' in which the correction of model outputs is obtained by a function build with the observation dataset and operating a quantile-quantile transformation (Q-Q transform). However, in the case of daily precipitation fields the Q-Q transform is not able to correct the temporal property of the model output concerning the dry-wet lacunarity process. An alternative correction method is proposed based on a stochastic description of the arrival-duration-intensity processes in coherence with the Poissonian Rectangular Pulse scheme (PRP) (Eagleson, 1972). In this proposed approach, the Q-Q transform is applied to the PRP variables derived from the daily rainfall datasets. Consequently the corrected PRP parameters are used for the synthetic generation of statistically homogeneous rainfall time series that mimic the persistency of daily observations for the reference period. Then the PRP parameters are forced through the GCM scenarios to generate local scale rainfall records for the 21st century. The statistical parameters characterizing daily storm occurrence, storm intensity and duration needed to apply the PRP scheme are considered among STARDEX collection of extreme indices.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ESD.....9..647S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ESD.....9..647S"><span>Interannual variability in the gravity wave drag - vertical coupling and possible climate links</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Šácha, Petr; Miksovsky, Jiri; Pisoft, Petr</p> <p>2018-05-01</p> <p>Gravity wave drag (GWD) is an important driver of the middle atmospheric dynamics. However, there are almost no observational constraints on its strength and distribution (especially horizontal). In this study we analyze orographic GWD (OGWD) output from Canadian Middle Atmosphere Model simulation with specified dynamics (CMAM-sd) to illustrate the interannual variability in the OGWD distribution at particular pressure levels in the stratosphere and its relation to major climate oscillations. We have found significant changes in the OGWD distribution and strength depending on the phase of the North Atlantic Oscillation (NAO), quasi-biennial oscillation (QBO) and El Niño-Southern Oscillation. The OGWD variability is shown to be induced by lower-tropospheric wind variations to a large extent, and there is also significant variability detected in near-surface momentum fluxes. We argue that the orographic gravity waves (OGWs) and gravity waves (GWs) in general can be a quick mediator of the tropospheric variability into the stratosphere as the modifications of the OGWD distribution can result in different impacts on the stratospheric dynamics during different phases of the studied climate oscillations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28117155','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28117155"><span>Active layer and permafrost thermal regime in a patterned ground soil in Maritime Antarctica, and relationship with climate variability models.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Chaves, D A; Lyra, G B; Francelino, M R; Silva, Ldb; Thomazini, A; Schaefer, Cegr</p> <p>2017-04-15</p> <p>Permafrost and active layer studies are important to understand and predict regional climate changes. The objectives of this work were: i) to characterize the soil thermal regime (active layer thickness and permafrost formation) and its interannual variability and ii) to evaluate the influence of different climate variability modes to the observed soil thermal regime in a patterned ground soil in Maritime Antarctica. The study was carried out at Keller Peninsula, King George Island, Maritime Antarctica. Six soil temperatures probes were installed at different depths (10, 30 and 80cm) in the polygon center (Tc) and border (Tb) of a patterned ground soil. We applied cross-correlation analysis and standardized series were related to the Antarctic Oscillation Index (AAO). The estimated active layer thickness was approximately 0.75cm in the polygon border and 0.64cm in the center, indicating the presence of permafrost (within 80cm). Results indicate that summer and winter temperatures are becoming colder and warmer, respectively. Considering similar active layer thickness, the polygon border presented greater thawing days, resulting in greater vulnerability to warming, cooling faster than the center, due to its lower volumetric heat capacity (Cs). Cross-correlation analysis indicated statistically significant delay of 1day (at 10cm depth) in the polygon center, and 5days (at 80cm depth) for the thermal response between atmosphere and soil. Air temperature showed a delay of 5months with the climate variability models. The influence of southern winds from high latitudes, in the south facing slopes, favored freeze in the upper soil layers, and also contributed to keep permafrost closer to the surface. The observed cooling trend is linked to the regional climate variability modes influenced by atmospheric circulation, although longer monitoring period is required to reach a more precise scenario. Copyright © 2017 Elsevier B.V. All rights reserved.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004AGUSM.A21A..04C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004AGUSM.A21A..04C"><span>23 Years of Cloud Statistics Using HIRS Over Australia</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chedzey, H. C.; Menzel, W. P.; Lynch, M. J.; McGann, B. T.</p> <p>2004-05-01</p> <p>Clouds are an integral factor in the Earth's water and radiation budgets. Observations and improvements to the accuracy of measurements of cloud properties are crucial in supporting global climate change studies. Regional studies are also of interest and analysis of regional climate variability provides an insight into local weather systems. HIRS is the High-Resolution Infrared Radiation Sounder aboard polar orbiting satellites operated by NOAA (National Oceanographic and Atmospheric Administration). An archive of HIRS data obtained between 1979 (NOAA-5) through to 2001 (NOAA-16) was made available by CIMSS (Cooperative Institute for Meteorological Satellite Studies) at the University of Wisconsin-Madison. The data is obtained from near nadir and frequencies of observations are converted into percentages based on total number of observations for each 1 by 1 degree cell. An assessment of cloud frequency percentages for a region including areas of the Indian Ocean and Australia (0\\deg - 60\\deg S; 80\\deg E - 170\\deg E) will be presented. Climate variability and possible associations with future work to be conducted into cloud frequency and rainfall of North West Cloud Bands using MODIS data will also be covered.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018GeoRL..45.1586A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018GeoRL..45.1586A"><span>Twentieth Century Regional Climate Change During the Summer in the Central United States Attributed to Agricultural Intensification</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Alter, Ross E.; Douglas, Hunter C.; Winter, Jonathan M.; Eltahir, Elfatih A. B.</p> <p>2018-02-01</p> <p>Both land use changes and greenhouse gas (GHG) emissions have significantly modified regional climate over the last century. In the central United States, for example, observational data indicate that rainfall increased, surface air temperature decreased, and surface humidity increased during the summer over the course of the twentieth century concurrently with increases in both agricultural production and global GHG emissions. However, the relative contributions of each of these forcings to the observed regional changes remain unclear. Results of both regional climate model simulations and observational analyses suggest that much of the observed rainfall increase—as well as the decrease in temperature and increase in humidity—is attributable to agricultural intensification in the central United States, with natural variability and GHG emissions playing secondary roles. Thus, we conclude that twentieth century land use changes contributed more to forcing observed regional climate change during the summer in the central United States than increasing GHG emissions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017PhDT........10Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017PhDT........10Y"><span>Statistical and dynamical assessment of land-ocean-atmosphere interactions across North Africa</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yu, Yan</p> <p></p> <p>North Africa is highly vulnerable to hydrologic variability and extremes, including impacts of climate change. The current understanding of oceanic versus terrestrial drivers of North African droughts and pluvials is largely model-based, with vast disagreement among models in terms of the simulated oceanic impacts and vegetation feedbacks. Regarding oceanic impacts, the relative importance of the tropical Pacific, tropical Indian, and tropical Atlantic Oceans in regulating the North African rainfall variability, as well as the underlying mechanism, remains debated among different modeling studies. Classic theory of land-atmosphere interactions across the Sahel ecotone, largely based on climate modeling experiments, has promoted positive vegetation-rainfall feedbacks associated with a dominant surface albedo mechanism. However, neither the proposed positive vegetation-rainfall feedback with its underlying albedo mechanism, nor its relative importance compared with oceanic drivers, has been convincingly demonstrated up to now using observational data. Here, the multivariate Generalized Equilibrium Feedback Assessment (GEFA) is applied in order to identify the observed oceanic and terrestrial drivers of North African climate and quantify their impacts. The reliability of the statistical GEFA method is first evaluated against dynamical experiments within the Community Earth System Model (CESM). In order to reduce the sampling error caused by short data records, the traditional GEFA approach is refined through stepwise GEFA, in which unimportant forcings are dropped through stepwise selection. In order to evaluate GEFA's reliability in capturing oceanic impacts, the atmospheric response to a sea-surface temperature (SST) forcing across the tropical Pacific, tropical Indian, and tropical Atlantic Ocean is estimated independently through ensembles of dynamical experiments and compared with GEFA-based assessments. Furthermore, GEFA's performance in capturing terrestrial impacts is evaluated through ensembles of fully coupled CESM dynamical experiments, with modified leaf area index (LAI) and soil moisture across the Sahel or West African Monsoon (WAM) region. The atmospheric responses to oceanic and terrestrial forcings are generally consistent between the dynamical experiments and statistical GEFA, confirming GEFA's capability of isolating the individual impacts of oceanic and terrestrial forcings on North African climate. Furthermore, with the incorporation of stepwise selection, GEFA can now provide reliable estimates of the oceanic and terrestrial impacts on the North African climate with the typical length of observational datasets, thereby enhancing the method's applicability. After the successful validation of GEFA, the key observed oceanic and terrestrial drivers of North African climate are identified through the application of GEFA to gridded observations, remote sensing products, and reanalyses. According to GEFA, oceanic drivers dominate over terrestrial drivers in terms of their observed impacts on North African climate in most seasons. Terrestrial impacts are comparable to, or more important than, oceanic impacts on rainfall during the post-monsoon across the Sahel and WAM region, and after the short rain across the Horn of Africa (HOA). The key ocean basins that regulate North African rainfall are typically located in the tropics. While the observed impacts of SST variability across the tropical Pacific and tropical Atlantic Oceans on the Sahel rainfall are largely consistent with previous model-based findings, minimal impacts from tropical Indian Ocean variability on Sahel rainfall are identified in observations, in contrast to previous modeling studies. The current observational analysis verifies model-hypothesized positive vegetation-rainfall feedback across the Sahel and HOA, which is confined to the post-monsoon and post-short rains season, respectively. However, the observed positive vegetation feedback to rainfall in the semi-arid Sahel and HOA is largely due to moisture recycling, rather than the classic albedo mechanism. Future projections of Sahel rainfall remain highly uncertain in terms of both sign and magnitude within phases three and five of the Coupled Model Intercomparison Project (CMIP3 and CMIP5). The GEFA-based observational analyses will provide a benchmark for evaluating climate models, which will facilitate effective process-based model weighting for more reliable projections of regional climate, as well as model development.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.B51E0465X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.B51E0465X"><span>Modeling biogeochemical responses of vegetation to ENSO: comparison and analysis on subgrid PFT patches</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xu, M.; Hoffman, F. M.</p> <p>2016-12-01</p> <p>The El Niño Southern Oscillation (ENSO) is an important interannual climate variability and has significant consequences and impacts on the global biosphere. The responses of vegetation to ENSO are highly heterogeneous and generally depend on the biophysical and biochemical characteristics associated with model plant functional types (PFTs). The modeled biogeochemical variables from Earth System Models (ESMs) are generally grid averages consisting of several PFTs within a gridcell, which will lead to difficulties in directly comparing them with site observations and large uncertainties in studying their responses to large scale climate variability. In this study, we conducted a transient ENSO simulation for the previoustwo decades from 1995 to 2020 using the DOE ACME v0.3 model. It has a comprehensive terrestrial biogeochemistry model that is fully coupled with a sophisticated atmospheric model with an advanced spectral element dynamical core. The model was driven by the NOAA optimum interpolation sea surface temperature (SST) for contemporary years and CFS v2 nine-month seasonal predicted and reconstructed SST for future years till to 2020. We saved the key biogeochemical variables in the subgrid PFT patches and compared them with site observations directly. Furthermore, we studied the biogeochemical responses of terrestrial vegetation to two largest ENSO events (1997-1998 and 2015-2016) for different PFTs. Our results show that it is useful and meaningful to compare and analyze model simulations in subgrid patches. The comparison and analysis not only gave us the details of responses of terrestrial ecosystem to global climate variability under changing climate, but also the insightful view on the model performance on the PFT level.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JHyd..556.1078S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JHyd..556.1078S"><span>Application of multi-scale wavelet entropy and multi-resolution Volterra models for climatic downscaling</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sehgal, V.; Lakhanpal, A.; Maheswaran, R.; Khosa, R.; Sridhar, Venkataramana</p> <p>2018-01-01</p> <p>This study proposes a wavelet-based multi-resolution modeling approach for statistical downscaling of GCM variables to mean monthly precipitation for five locations at Krishna Basin, India. Climatic dataset from NCEP is used for training the proposed models (Jan.'69 to Dec.'94) and are applied to corresponding CanCM4 GCM variables to simulate precipitation for the validation (Jan.'95-Dec.'05) and forecast (Jan.'06-Dec.'35) periods. The observed precipitation data is obtained from the India Meteorological Department (IMD) gridded precipitation product at 0.25 degree spatial resolution. This paper proposes a novel Multi-Scale Wavelet Entropy (MWE) based approach for clustering climatic variables into suitable clusters using k-means methodology. Principal Component Analysis (PCA) is used to obtain the representative Principal Components (PC) explaining 90-95% variance for each cluster. A multi-resolution non-linear approach combining Discrete Wavelet Transform (DWT) and Second Order Volterra (SoV) is used to model the representative PCs to obtain the downscaled precipitation for each downscaling location (W-P-SoV model). The results establish that wavelet-based multi-resolution SoV models perform significantly better compared to the traditional Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) based frameworks. It is observed that the proposed MWE-based clustering and subsequent PCA, helps reduce the dimensionality of the input climatic variables, while capturing more variability compared to stand-alone k-means (no MWE). The proposed models perform better in estimating the number of precipitation events during the non-monsoon periods whereas the models with clustering without MWE over-estimate the rainfall during the dry season.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUOSPC54C2265P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUOSPC54C2265P"><span>Alexander Polonsky Global warming hiatus, ocean variability and regional climate change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Polonsky, A.</p> <p>2016-02-01</p> <p>This presentation generalizes the results concerning ocean variability, large-scale interdecadal ocean-atmosphere interaction in the Atlantic and Pacific Oceans and their impact on global and regional climate change carried out by the author and his colleagues for about 20 years. It is demonstrated once more that Atlantic Multidecadal Oscillation (AMO, which was early referred by the author as "interdecadal mode of North Atlantic Oscillation") is the crucial natural interdecadal climatic signal for the Atlantic-European and Mediterranean regions. It is characterized by amplitude which is the same order as human-induced centennial climate change and exceeds trend-like anthropogenic change at the decadal scale. Fast increasing of the global and Northern Hemisphere air temperature in the last 30 yrs of XX century (especially pronounced in the North Atlantic region and surrounded areas) is due to coincidence of human-induced positive trend and transition from the negative to the positive phase of AMO. AMO accounts for about 50% (60%) of the global (Northern Hemisphere) temperature trend in that period. Recent global warming hiatus is mostly the result of switch off the AMO phase. Typical AMO temporal scale is dictated by meridional overturning variability in the Atlantic Ocean and associated magnitude of meridional heat transport. Pacific Decadal Oscillation (PDO) is the other natural interdecadal signal which significantly impacts the global and regional climate variability. The rate of the ocean warming for different periods assessed separately for the upper mixed layer and deeper layers using data of oceanic re-analysis since 1959 confirms the principal role of the natural interdecadal oceanic modes (AMO and PDO) in observing climate change. At the same time a lack of deep-ocean long-term observing system restricts the accuracy of assessment of the heat redistribution in the World Ocean. I thanks to Pavel Sukhonos for help in the presentation preparing.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC11D1171C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC11D1171C"><span>Global patterns in the poleward expansion of mangrove forests</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cavanaugh, K. C.; Feller, I. C.</p> <p>2016-12-01</p> <p>Understanding the processes that limit the geographic ranges of species is one of the central goals of ecology and biogeography. This issue is particularly relevant for coastal wetlands given that climate change is expected to lead to a `tropicalization' of temperate coastal and marine ecosystems. In coastal wetlands around the world, there have already been observations of mangroves expanding into salt marshes near the current poleward range limits of mangroves. However, there is still uncertainty regarding regional variability in the factors that control mangrove range limits. Here we used time series of Landsat satellite imagery to characterize patterns of mangrove abundance near their poleward range limits around the world. We tested the commonly held assumption that temporal variation in abundance should increase towards the edge of the range. We also compared variability in mangrove abundance to climate factors thought to set mangrove range limits (air temperature, water temperature, and aridity). In general, variability in mangrove abundance at range edges was high relative to range centers and this variability was correlated to one or more climate factors. However, the strength of these relationships varied among poleward range limits, suggesting that some mangrove range limits are control by processes other than climate, such as dispersal limitation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A51K..03S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A51K..03S"><span>Using Empirical Orthogonal Teleconnections to Analyze Interannual Precipitation Variability in China</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stephan, C.; Klingaman, N. P.; Vidale, P. L.; Turner, A. G.; Demory, M. E.; Guo, L.</p> <p>2017-12-01</p> <p>Interannual rainfall variability in China affects agriculture, infrastructure and water resource management. A consistent and objective method, Empirical Orthogonal Teleconnection (EOT) analysis, is applied to precipitation observations over China in all seasons. Instead of maximizing the explained space-time variance, the method identifies regions in China that best explain the temporal variability in domain-averaged rainfall. It produces known teleconnections, that include high positive correlations with ENSO in eastern China in winter, along the Yangtze River in summer, and in southeast China during spring. New findings include that variability along the southeast coast in winter, in the Yangtze valley in spring, and in eastern China in autumn, are associated with extratropical Rossby wave trains. The same analysis is applied to six climate simulations of the Met Office Unified Model with and without air-sea coupling and at various horizontal resolutions of 40, 90 and 200 km. All simulations reproduce the observed patterns of interannual rainfall variability in winter, spring and autumn; the leading pattern in summer is present in all but one simulation. However, only in two simulations are all patterns associated with the observed physical mechanism. Coupled simulations capture more observed patterns of variability and associate more of them with the correct physical mechanism, compared to atmosphere-only simulations at the same resolution. Finer resolution does not improve the fidelity of these patterns or their associated mechanisms. Evaluating climate models by only geographical distribution of mean precipitation and its interannual variance is insufficient; attention must be paid to associated mechanisms.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.B51D1827J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.B51D1827J"><span>Ecosystem response to climatic variables - air temperature and precipitation: How can these variables alter plant productions in C4-grass dominant ecosystem?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jung, C. G.; Jiang, L.; Luo, Y.</p> <p>2017-12-01</p> <p>Understanding net primary production (NPP) response to the key climatic variables, temperature and precipitation, is essential since the response could be represented by one of future consequences from ecosystem responses. Under future climatic warming, fluctuating precipitation is expected. In addition, NPP solely could not explain whole ecosystem response; therefore, not only NPP, but also above- and below-ground NPP (ANPP and BNPP, respectively) need to be examined. This examination needs to include how the plant productions response along temperature and precipitation gradients. Several studies have examined the response of NPP against each of single climatic variable, but understanding the response of ANPP and BNPP to the multiple variables is notably poor. In this study, we used the plant productions data (NPP, ANPP, and BNPP) with climatic variables, i.e., air temperature and precipitation, from 1999 to 2015 under warming and clipping treatments (mimicking hay-harvesting) in C4-grass dominant ecosystem located in central Oklahoma, United States. Firstly, we examined the nonlinear relationships with the climatic variables for NPP, ANPP and BNPP; and then predicted possible responses in the temperature - precipitation space by using a linear mixed effect model. Nonlinearities of NPP, ANPP and BNPP to the climatic variables have been found to show unimodal curves, and nonlinear models have better goodness of fit as shown lower Akaike information criterion (AIC) than linear models. Optimum condition for NPP is represented at high temperature and precipitation level whereas BNPP is maximized at moderate precipitation levels while ANPP has same range of NPP's optimum condition. Clipping significantly reduced ANPP while there was no clipping effect on NPP and BNPP. Furthermore, inclining NPP and ANPP have shown in a range from moderate to high precipitation level with increasing temperature while inclining pattern for BNPP was observed in moderate precipitation level. Overall, the C4-grass dominant ecosystem has a potential for considerable increases in NPP in hotter and wetter conditions as shown a range from moderate to high temperature and precipitation levels; ANPP has peaked at the high temperature and precipitation level, but maximum BNPP needs moderate precipitation level and high temperature.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5819482','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5819482"><span>Inter-comparison of multiple statistically downscaled climate datasets for the Pacific Northwest, USA</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Jiang, Yueyang; Kim, John B.; Still, Christopher J.; Kerns, Becky K.; Kline, Jeffrey D.; Cunningham, Patrick G.</p> <p>2018-01-01</p> <p>Statistically downscaled climate data have been widely used to explore possible impacts of climate change in various fields of study. Although many studies have focused on characterizing differences in the downscaling methods, few studies have evaluated actual downscaled datasets being distributed publicly. Spatially focusing on the Pacific Northwest, we compare five statistically downscaled climate datasets distributed publicly in the US: ClimateNA, NASA NEX-DCP30, MACAv2-METDATA, MACAv2-LIVNEH and WorldClim. We compare the downscaled projections of climate change, and the associated observational data used as training data for downscaling. We map and quantify the variability among the datasets and characterize the spatio-temporal patterns of agreement and disagreement among the datasets. Pair-wise comparisons of datasets identify the coast and high-elevation areas as areas of disagreement for temperature. For precipitation, high-elevation areas, rainshadows and the dry, eastern portion of the study area have high dissimilarity among the datasets. By spatially aggregating the variability measures into watersheds, we develop guidance for selecting datasets within the Pacific Northwest climate change impact studies. PMID:29461513</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29461513','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29461513"><span>Inter-comparison of multiple statistically downscaled climate datasets for the Pacific Northwest, USA.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Jiang, Yueyang; Kim, John B; Still, Christopher J; Kerns, Becky K; Kline, Jeffrey D; Cunningham, Patrick G</p> <p>2018-02-20</p> <p>Statistically downscaled climate data have been widely used to explore possible impacts of climate change in various fields of study. Although many studies have focused on characterizing differences in the downscaling methods, few studies have evaluated actual downscaled datasets being distributed publicly. Spatially focusing on the Pacific Northwest, we compare five statistically downscaled climate datasets distributed publicly in the US: ClimateNA, NASA NEX-DCP30, MACAv2-METDATA, MACAv2-LIVNEH and WorldClim. We compare the downscaled projections of climate change, and the associated observational data used as training data for downscaling. We map and quantify the variability among the datasets and characterize the spatio-temporal patterns of agreement and disagreement among the datasets. Pair-wise comparisons of datasets identify the coast and high-elevation areas as areas of disagreement for temperature. For precipitation, high-elevation areas, rainshadows and the dry, eastern portion of the study area have high dissimilarity among the datasets. By spatially aggregating the variability measures into watersheds, we develop guidance for selecting datasets within the Pacific Northwest climate change impact studies.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23958789','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23958789"><span>Berry composition and climate: responses and empirical models.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Barnuud, Nyamdorj N; Zerihun, Ayalsew; Gibberd, Mark; Bates, Bryson</p> <p>2014-08-01</p> <p>Climate is a strong modulator of berry composition. Accordingly, the projected change in climate is expected to impact on the composition of berries and of the resultant wines. However, the direction and extent of climate change impact on fruit composition of winegrape cultivars are not fully known. This study utilised a climate gradient along a 700 km transect, covering all wine regions of Western Australia, to explore and empirically describe influences of climate on anthocyanins, pH and titratable acidity (TA) levels in two or three cultivars of Vitis vinifera (Cabernet Sauvignon, Chardonnay and Shiraz). The results showed that, at a common maturity of 22° Brix total soluble solids, berries from the warmer regions had low levels of anthocyanins and TA as well as high pH compared to berries from the cooler regions. Most of these regional variations in berry composition reflected the prevailing climatic conditions of the regions. Thus, depending on cultivar, 82-87 % of TA, 83 % of anthocyanins and about half of the pH variations across the gradient were explained by climate-variable-based empirical models. Some of the variables that were relevant in describing the variations in berry attributes included: diurnal ranges and ripening period temperature (TA), vapour pressure deficit in October and growing degree days (pH), and ripening period temperatures (anthocyanins). Further, the rates of change in these berry attributes in response to climate variables were cultivar dependent. Based on the observed patterns along the climate gradient, it is concluded that: (1) in a warming climate, all other things being equal, berry anthocyanins and TA levels will decline whereas pH levels will rise; and (2) despite variations in non-climatic factors (e.g. soil type and management) along the sampling transect, variations in TA and anthocyanins were satisfactorily described using climate-variable-based empirical models, indicating the overriding impact of climate on berry composition. The models presented here are useful tools for assessing likely changes in berry TA and anthocyanins in response to changing climate for the wine regions and cultivars covered in this study.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014IJBm...58.1207B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014IJBm...58.1207B"><span>Berry composition and climate: responses and empirical models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Barnuud, Nyamdorj N.; Zerihun, Ayalsew; Gibberd, Mark; Bates, Bryson</p> <p>2014-08-01</p> <p>Climate is a strong modulator of berry composition. Accordingly, the projected change in climate is expected to impact on the composition of berries and of the resultant wines. However, the direction and extent of climate change impact on fruit composition of winegrape cultivars are not fully known. This study utilised a climate gradient along a 700 km transect, covering all wine regions of Western Australia, to explore and empirically describe influences of climate on anthocyanins, pH and titratable acidity (TA) levels in two or three cultivars of Vitis vinifera (Cabernet Sauvignon, Chardonnay and Shiraz). The results showed that, at a common maturity of 22° Brix total soluble solids, berries from the warmer regions had low levels of anthocyanins and TA as well as high pH compared to berries from the cooler regions. Most of these regional variations in berry composition reflected the prevailing climatic conditions of the regions. Thus, depending on cultivar, 82-87 % of TA, 83 % of anthocyanins and about half of the pH variations across the gradient were explained by climate-variable-based empirical models. Some of the variables that were relevant in describing the variations in berry attributes included: diurnal ranges and ripening period temperature (TA), vapour pressure deficit in October and growing degree days (pH), and ripening period temperatures (anthocyanins). Further, the rates of change in these berry attributes in response to climate variables were cultivar dependent. Based on the observed patterns along the climate gradient, it is concluded that: (1) in a warming climate, all other things being equal, berry anthocyanins and TA levels will decline whereas pH levels will rise; and (2) despite variations in non-climatic factors (e.g. soil type and management) along the sampling transect, variations in TA and anthocyanins were satisfactorily described using climate-variable-based empirical models, indicating the overriding impact of climate on berry composition. The models presented here are useful tools for assessing likely changes in berry TA and anthocyanins in response to changing climate for the wine regions and cultivars covered in this study.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H33G1789D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H33G1789D"><span>Catchments' hedging strategy on evapotranspiration for climatic variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ding, W.; Zhang, C.; Li, Y.; Tang, Y.; Wang, D.; Xu, B.</p> <p>2017-12-01</p> <p>Hydrologic responses to climate variability and change are important for human society. Here we test the hypothesis that natural catchments utilize hedging strategies for evapotranspiration and water storage carryover with uncertain future precipitation. The hedging strategy for evapotranspiration in catchments under different levels of water availability is analytically derived from the economic perspective. It is found that there exists hedging between evapotranspiration for current and future only with a portion of water availability. Observation data sets of 160 catchments in the United States covering the period from 1983 to 2003 demonstrate the existence of hedging in catchment hydrology and validate the proposed hedging strategies. We also find that more water is allocated to carryover storage for hedging against the future evapotranspiration risk in the catchments with larger aridity indexes or with larger uncertainty in future precipitation, i.e., long-term climate and precipitation variability control the degree of hedging.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20020022509&hterms=climate+change+evidence&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dclimate%2Bchange%2Bevidence','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20020022509&hterms=climate+change+evidence&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dclimate%2Bchange%2Bevidence"><span>Evidence for Large Decadal Variability in the Tropical Mean Radiative Energy Budget</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wielicki, Bruce A.; Wong, Takmeng; Allan, Richard; Slingo, Anthony; Kiehl, Jeffrey T.; Soden, Brian J.; Gordon, C. T.; Miller, Alvin J.; Yang, Shi-Keng; Randall, David R.; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20020022509'); toggleEditAbsImage('author_20020022509_show'); toggleEditAbsImage('author_20020022509_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20020022509_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20020022509_hide"></p> <p>2001-01-01</p> <p>It is widely assumed that variations in the radiative energy budget at large time and space scales are very small. We present new evidence from a compilation of over two decades of accurate satellite data that the top-of-atmosphere (TOA) tropical radiative energy budget is much more dynamic and variable than previously thought. We demonstrate that the radiation budget changes are caused by changes In tropical mean cloudiness. The results of several current climate model simulations fall to predict this large observed variation In tropical energy budget. The missing variability in the models highlights the critical need to Improve cloud modeling in the tropics to support Improved prediction of tropical climate on Inter-annual and decadal time scales. We believe that these data are the first rigorous demonstration of decadal time scale changes In the Earth's tropical cloudiness, and that they represent a new and necessary test of climate models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24451542','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24451542"><span>Impacts of the north and tropical Atlantic Ocean on the Antarctic Peninsula and sea ice.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Li, Xichen; Holland, David M; Gerber, Edwin P; Yoo, Changhyun</p> <p>2014-01-23</p> <p>In recent decades, Antarctica has experienced pronounced climate changes. The Antarctic Peninsula exhibited the strongest warming of any region on the planet, causing rapid changes in land ice. Additionally, in contrast to the sea-ice decline over the Arctic, Antarctic sea ice has not declined, but has instead undergone a perplexing redistribution. Antarctic climate is influenced by, among other factors, changes in radiative forcing and remote Pacific climate variability, but none explains the observed Antarctic Peninsula warming or the sea-ice redistribution in austral winter. However, in the north and tropical Atlantic Ocean, the Atlantic Multidecadal Oscillation (a leading mode of sea surface temperature variability) has been overlooked in this context. Here we show that sea surface warming related to the Atlantic Multidecadal Oscillation reduces the surface pressure in the Amundsen Sea and contributes to the observed dipole-like sea-ice redistribution between the Ross and Amundsen-Bellingshausen-Weddell seas and to the Antarctic Peninsula warming. Support for these findings comes from analysis of observational and reanalysis data, and independently from both comprehensive and idealized atmospheric model simulations. We suggest that the north and tropical Atlantic is important for projections of future climate change in Antarctica, and has the potential to affect the global thermohaline circulation and sea-level change.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li class="active"><span>24</span></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_24 --> <div id="page_25" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li class="active"><span>25</span></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="481"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20160006535','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20160006535"><span>An Assessment of Actual and Potential Building Climate Zone Change and Variability From the Last 30 Years Through 2100 Using NASA's MERRA and CMIP5 Simulations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Stackhouse, Paul W., Jr.; Chandler, William S.; Hoell, James M.; Westberg, David; Zhang, Taiping</p> <p>2015-01-01</p> <p>Background: In the US, residential and commercial building infrastructure combined consumes about 40% of total energy usage and emits about 39% of total CO2 emission (DOE/EIA "Annual Energy Outlook 2013"). Building codes, as used by local and state enforcement entities are typically tied to the dominant climate within an enforcement jurisdiction classified according to various climate zones. These climate zones are based upon a 30-year average of local surface observations and are developed by DOE and ASHRAE. Establishing the current variability and potential changes to future building climate zones is very important for increasing the energy efficiency of buildings and reducing energy costs and emissions in the future. Objectives: This paper demonstrates the usefulness of using NASA's Modern Era Retrospective-analysis for Research and Applications (MERRA) atmospheric data assimilation to derive the DOE/ASHRAE building climate zone maps and then using MERRA to define the last 30 years of variability in climate zones for the Continental US. An atmospheric assimilation is a global atmospheric model optimized to satellite, atmospheric and surface in situ measurements. Using MERRA as a baseline, we then evaluate the latest Climate Model Inter-comparison Project (CMIP) climate model Version 5 runs to assess potential variability in future climate zones under various assumptions. Methods: We derive DOE/ASHRAE building climate zones using surface and temperature data products from MERRA. We assess these zones using the uncertainties derived by comparison to surface measurements. Using statistical tests, we evaluate variability of the climate zones in time and assess areas in the continental US for statistically significant trends by region. CMIP 5 produced a data base of over two dozen detailed climate model runs under various greenhouse gas forcing assumptions. We evaluate the variation in building climate zones for 3 different decades using an ensemble and quartile statistics to provide an assessment of potential building climate zone changes relative to the uncertainties demonstrated using MERRA. Findings and Conclusions: These results show that there is a statistically significant increase in the area covered by warmer climate zones and a tendency for a reduction of area in colder climate zones in some limited regions. The CMIP analysis shows that models vary from relatively little building climate zone change for the least sensitive and conservation assumptions to a warming of at most 3 zones for certain areas, particularly the north central US by the end of the 21st century.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002ClDy...19..181P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002ClDy...19..181P"><span>Ocean angular momentum signals in a climate model and implications for Earth rotation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ponte, R. M.; Rajamony, J.; Gregory, J. M.</p> <p>2002-03-01</p> <p>Estimates of ocean angular momentum (OAM) provide an integrated measure of variability in ocean circulation and mass fields and can be directly related to observed changes in Earth rotation. We use output from a climate model to calculate 240 years of 3-monthly OAM values (two equatorial terms L1 and L2, related to polar motion or wobble, and axial term L3, related to length of day variations) representing the period 1860-2100. Control and forced runs permit the study of the effects of natural and anthropogenically forced climate variability on OAM. All OAM components exhibit a clear annual cycle, with large decadal modulations in amplitude, and also longer period fluctuations, all associated with natural climate variability in the model. Anthropogenically induced signals, inferred from the differences between forced and control runs, include an upward trend in L3, related to inhomogeneous ocean warming and increases in the transport of the Antarctic Circumpolar Current, and a significantly weaker seasonal cycle in L2 in the second half of the record, related primarily to changes in seasonal bottom pressure variability in the Southern Ocean and North Pacific. Variability in mass fields is in general more important to OAM signals than changes in circulation at the seasonal and longer periods analyzed. Relation of OAM signals to changes in surface atmospheric forcing are discussed. The important role of the oceans as an excitation source for the annual, Chandler and Markowitz wobbles, is confirmed. Natural climate variability in OAM and related excitation is likely to measurably affect the Earth rotation, but anthropogenically induced effects are comparatively weak.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29569794','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29569794"><span>Nitrogen deposition outweighs climatic variability in driving annual growth rate of canopy beech trees: Evidence from long-term growth reconstruction across a geographic gradient.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Gentilesca, Tiziana; Rita, Angelo; Brunetti, Michele; Giammarchi, Francesco; Leonardi, Stefano; Magnani, Federico; van Noije, Twan; Tonon, Giustino; Borghetti, Marco</p> <p>2018-07-01</p> <p>In this study, we investigated the role of climatic variability and atmospheric nitrogen deposition in driving long-term tree growth in canopy beech trees along a geographic gradient in the montane belt of the Italian peninsula, from the Alps to the southern Apennines. We sampled dominant trees at different developmental stages (from young to mature tree cohorts, with tree ages spanning from 35 to 160 years) and used stem analysis to infer historic reconstruction of tree volume and dominant height. Annual growth volume (G V ) and height (G H ) variability were related to annual variability in model simulated atmospheric nitrogen deposition and site-specific climatic variables, (i.e. mean annual temperature, total annual precipitation, mean growing period temperature, total growing period precipitation, and standard precipitation evapotranspiration index) and atmospheric CO 2 concentration, including tree cambial age among growth predictors. Generalized additive models (GAM), linear mixed-effects models (LMM), and Bayesian regression models (BRM) were independently employed to assess explanatory variables. The main results from our study were as follows: (i) tree age was the main explanatory variable for long-term growth variability; (ii) GAM, LMM, and BRM results consistently indicated climatic variables and CO 2 effects on G V and G H were weak, therefore evidence of recent climatic variability influence on beech annual growth rates was limited in the montane belt of the Italian peninsula; (iii) instead, significant positive nitrogen deposition (N dep ) effects were repeatedly observed in G V and G H ; the positive effects of N dep on canopy height growth rates, which tended to level off at N dep values greater than approximately 1.0 g m -2 y -1 , were interpreted as positive impacts on forest stand above-ground net productivity at the selected study sites. © 2018 John Wiley & Sons Ltd.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC13D0809V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC13D0809V"><span>Evaluating the ClimEx Single Model large ensemble in comparison with EURO-CORDEX results of heatwave and drought indicators</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>von Trentini, F.; Schmid, F. J.; Braun, M.; Frigon, A.; Leduc, M.; Martel, J. L.; Willkofer, F.; Wood, R. R.; Ludwig, R.</p> <p>2017-12-01</p> <p>Meteorological extreme events seem to become more frequent in the present and future, and a seperation of natural climate variability and a clear climate change effect on these extreme events gains more and more interest. Since there is only one realisation of historical events, natural variability in terms of very long timeseries for a robust statistical analysis is not possible with observation data. A new single model large ensemble (SMLE), developed for the ClimEx project (Climate change and hydrological extreme events - risks and perspectives for water management in Bavaria and Québec) is supposed to overcome this lack of data by downscaling 50 members of the CanESM2 (RCP 8.5) with the Canadian CRCM5 regional model (using the EURO-CORDEX grid specifications) for timeseries of 1950-2099 each, resulting in 7500 years of simulated climate. This allows for a better probabilistic analysis of rare and extreme events than any preceding dataset. Besides seasonal sums, several indicators concerning heatwave frequency, duration and mean temperature a well as number and maximum length of dry periods (cons. days <1mm) are calculated for the ClimEx ensemble and several EURO-CORDEX runs. This enables us to investigate the interaction between natural variability (as it appears in the CanESM2-CRCM5 members) and a climate change signal of those members for past, present and future conditions. Adding the EURO-CORDEX results to this, we can also assess the role of internal model variability (or natural variability) in climate change simulations. A first comparison shows similar magnitudes of variability of climate change signals between the ClimEx large ensemble and the CORDEX runs for some indicators, while for most indicators the spread of the SMLE is smaller than the spread of different CORDEX models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EnMan..57..976K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EnMan..57..976K"><span>Farmers' Perceptions of Climate Variability and Factors Influencing Adaptation: Evidence from Anhui and Jiangsu, China</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kibue, Grace Wanjiru; Liu, Xiaoyu; Zheng, Jufeng; zhang, Xuhui; Pan, Genxing; Li, Lianqing; Han, Xiaojun</p> <p>2016-05-01</p> <p>Impacts of climate variability and climate change are on the rise in China posing great threat to agriculture and rural livelihoods. Consequently, China is undertaking research to find solutions of confronting climate change and variability. However, most studies of climate change and variability in China largely fail to address farmers' perceptions of climate variability and adaptation. Yet, without an understanding of farmers' perceptions, strategies are unlikely to be effective. We conducted questionnaire surveys of farmers in two farming regions, Yifeng, Jiangsu and Qinxi, Anhui achieving 280 and 293 responses, respectively. Additionally, we used climatological data to corroborate the farmers' perceptions of climate variability. We found that farmers' were aware of climate variability such that were consistent with climate records. However, perceived impacts of climate variability differed between the two regions and were influenced by farmers' characteristics. In addition, the vast majorities of farmers were yet to make adjustments in their farming practices as a result of numerous challenges. These challenges included socioeconomic and socio-cultural barriers. Results of logit modeling showed that farmers are more likely to adapt to climate variability if contact with extension services, frequency of seeking information, household heads' education, and climate variability perceptions are improved. These results suggest the need for policy makers to understand farmers' perceptions of climate variability and change in order to formulate policies that foster adaptation, and ultimately protect China's agricultural assets.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26796698','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26796698"><span>Farmers' Perceptions of Climate Variability and Factors Influencing Adaptation: Evidence from Anhui and Jiangsu, China.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kibue, Grace Wanjiru; Liu, Xiaoyu; Zheng, Jufeng; Zhang, Xuhui; Pan, Genxing; Li, Lianqing; Han, Xiaojun</p> <p>2016-05-01</p> <p>Impacts of climate variability and climate change are on the rise in China posing great threat to agriculture and rural livelihoods. Consequently, China is undertaking research to find solutions of confronting climate change and variability. However, most studies of climate change and variability in China largely fail to address farmers' perceptions of climate variability and adaptation. Yet, without an understanding of farmers' perceptions, strategies are unlikely to be effective. We conducted questionnaire surveys of farmers in two farming regions, Yifeng, Jiangsu and Qinxi, Anhui achieving 280 and 293 responses, respectively. Additionally, we used climatological data to corroborate the farmers' perceptions of climate variability. We found that farmers' were aware of climate variability such that were consistent with climate records. However, perceived impacts of climate variability differed between the two regions and were influenced by farmers' characteristics. In addition, the vast majorities of farmers were yet to make adjustments in their farming practices as a result of numerous challenges. These challenges included socioeconomic and socio-cultural barriers. Results of logit modeling showed that farmers are more likely to adapt to climate variability if contact with extension services, frequency of seeking information, household heads' education, and climate variability perceptions are improved. These results suggest the need for policy makers to understand farmers' perceptions of climate variability and change in order to formulate policies that foster adaptation, and ultimately protect China's agricultural assets.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017NatCo...814991L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017NatCo...814991L"><span>On the discrepancy between observed and CMIP5 multi-model simulated Barents Sea winter sea ice decline</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Dawei; Zhang, Rong; Knutson, Thomas R.</p> <p>2017-04-01</p> <p>This study aims to understand the relative roles of external forcing versus internal climate variability in causing the observed Barents Sea winter sea ice extent (SIE) decline since 1979. We identify major discrepancies in the spatial patterns of winter Northern Hemisphere sea ice concentration trends over the satellite period between observations and CMIP5 multi-model mean externally forced response. The CMIP5 externally forced decline in Barents Sea winter SIE is much weaker than that observed. Across CMIP5 ensemble members, March Barents Sea SIE trends have little correlation with global mean surface air temperature trends, but are strongly anti-correlated with trends in Atlantic heat transport across the Barents Sea Opening (BSO). Further comparison with control simulations from coupled climate models suggests that enhanced Atlantic heat transport across the BSO associated with regional internal variability may have played a leading role in the observed decline in winter Barents Sea SIE since 1979.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC41A0994B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC41A0994B"><span>The Impact of Low-Level Cloud Feedback on Persistent Changes in Atmospheric Circulation in the Pacific</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Burgman, R.; Kirtman, B. P.; Clement, A. C.; Vazquez, H.</p> <p>2017-12-01</p> <p>Recent studies suggest that low clouds in the Pacific play an important role in the observed decadal climate variability and future climate change. In this study, we implement a novel modeling experiment designed to isolate how interactions between local and remote feedbacks associated with low cloud, SSTs, and the largescale circulation play a significant role in the observed persistence of tropical Pacific SST and associated North American drought. The modeling approach involves the incorporation of observed patterns of satellite-derived shortwave cloud radiative effect (SWCRE) into the coupled model framework and is ideally suited for examining the role of local and large-scale coupled feedbacks and ocean heat transport in Pacific decadal variability. We show that changes in SWCRE forcing in eastern subtropical Pacific alone reproduces much of the observed changes in SST and atmospheric circulation over the past 16 years, including the observed changes in precipitation over much of the Western Hemisphere.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016NatSR...621251D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016NatSR...621251D"><span>The Footprint of the Inter-decadal Pacific Oscillation in Indian Ocean Sea Surface Temperatures</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dong, Lu; Zhou, Tianjun; Dai, Aiguo; Song, Fengfei; Wu, Bo; Chen, Xiaolong</p> <p>2016-02-01</p> <p>Superimposed on a pronounced warming trend, the Indian Ocean (IO) sea surface temperatures (SSTs) also show considerable decadal variations that can cause regional climate oscillations around the IO. However, the mechanisms of the IO decadal variability remain unclear. Here we perform numerical experiments using a state-of-the-art, fully coupled climate model in which the external forcings with or without the observed SSTs in the tropical eastern Pacific Ocean (TEP) are applied for 1871-2012. Both the observed timing and magnitude of the IO decadal variations are well reproduced in those experiments with the TEP SSTs prescribed to observations. Although the external forcings account for most of the warming trend, the decadal variability in IO SSTs is dominated by internal variability that is induced by the TEP SSTs, especially the Inter-decadal Pacific Oscillation (IPO). The IPO weakens (enhances) the warming of the external forcings by about 50% over the IO during IPO’s cold (warm) phase, which contributes about 10% to the recent global warming hiatus since 1999. The decadal variability in IO SSTs is modulated by the IPO-induced atmospheric adjustment through changing surface heat fluxes, sea surface height and thermocline depth.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26884089','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26884089"><span>The Footprint of the Inter-decadal Pacific Oscillation in Indian Ocean Sea Surface Temperatures.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Dong, Lu; Zhou, Tianjun; Dai, Aiguo; Song, Fengfei; Wu, Bo; Chen, Xiaolong</p> <p>2016-02-17</p> <p>Superimposed on a pronounced warming trend, the Indian Ocean (IO) sea surface temperatures (SSTs) also show considerable decadal variations that can cause regional climate oscillations around the IO. However, the mechanisms of the IO decadal variability remain unclear. Here we perform numerical experiments using a state-of-the-art, fully coupled climate model in which the external forcings with or without the observed SSTs in the tropical eastern Pacific Ocean (TEP) are applied for 1871-2012. Both the observed timing and magnitude of the IO decadal variations are well reproduced in those experiments with the TEP SSTs prescribed to observations. Although the external forcings account for most of the warming trend, the decadal variability in IO SSTs is dominated by internal variability that is induced by the TEP SSTs, especially the Inter-decadal Pacific Oscillation (IPO). The IPO weakens (enhances) the warming of the external forcings by about 50% over the IO during IPO's cold (warm) phase, which contributes about 10% to the recent global warming hiatus since 1999. The decadal variability in IO SSTs is modulated by the IPO-induced atmospheric adjustment through changing surface heat fluxes, sea surface height and thermocline depth.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.B32D..07M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.B32D..07M"><span>Climate induced changes in biome distribution, NPP and hydrology for potential vegetation of the Upper Midwest U.S</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Motew, M.; Kucharik, C. J.</p> <p>2011-12-01</p> <p>While much attention is focused on future impacts of climate change on ecosystems, much can be learned about the previous interactions of ecosystems with recent climate change. In this study, we investigated the impacts of climate change on potential vegetation distributions (i.e. grasses, trees, and shrubs) and carbon and water cycling across the Upper Midwest USA from 1948-2007 using the Agro-IBIS dynamic vegetation model. We drove the model using a historical, gridded daily climate data set (temperature, precipitation, humidity, solar radiation, and wind speed) at a spatial resolution of 5 min x 5 min. While trends in climate variables exhibited heterogeneous spatial patterns over the study period, the overall impact of climate change on vegetation productivity was positive. We observed total increases in net primary productivity (NPP) ranging from 20-150 g C m-2, based on linear regression analysis. We determined that increased summer relative humidity, increased annual precipitation and decreased mean maximum summer temperatures were key variables contributing to these positive trends, likely through a reduction in soil moisture stress (e.g., increased available water) and heat stress. Model simulations also illustrated an increase in annual drainage throughout the region of 20-140 mm yr-1, driven by substantial increases in annual precipitation. Evapotranspiration had a highly variable spatial trend over the 60-year period, with total change over the study period ranging between -100 and +100 mm yr-1. We also analyzed potential changes in plant functional type (PFT) distributions at the biome level, but hypothesize that the model may be unable to adequately capture competitive interactions among PFTs as well as the dynamics between upper and lower canopies consisting of trees, grasses and shrubs. An analysis of the bioclimatic envelopes for PFTs common to the region revealed no significant change to the boreal conifer tree climatic domain over the study period, yet did reveal a slightly expanded domain for temperate deciduous broadleaf trees. The location of the Tension Zone, a broad ecotone dividing mixed forests in the north and southern hardwood forests and prairies in the south, was not observed to shift using analyses of both meteorological variables and through the results of simulated vegetation distributions. In general, our results supported the idea that climate change is spatially variable in nature, having significant effects on ecosystem structure and function. Our analysis also revealed interesting relationships among the key climatic quantities driving plant productivity and hydrology in the region. Most notably, while the model suggested that potential biome and PFT distributions have not likely shifted significantly in the past 60 years, climate change has contributed to substantial changes in coupled carbon, water, and energy exchange in natural ecosystems of the Upper Midwest US. We conclude that incorporating recent, high-resolution climate records into ecological studies offers valuable insight into the heterogeneous nature of climate change and its impacts on ecosystems at the local level.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JGRD..12210939W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JGRD..12210939W"><span>Exploring a Variable-Resolution Approach for Simulating Regional Climate in the Rocky Mountain Region Using the VR-CESM</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wu, Chenglai; Liu, Xiaohong; Lin, Zhaohui; Rhoades, Alan M.; Ullrich, Paul A.; Zarzycki, Colin M.; Lu, Zheng; Rahimi-Esfarjani, Stefan R.</p> <p>2017-10-01</p> <p>The reliability of climate simulations and projections, particularly in the regions with complex terrains, is greatly limited by the model resolution. In this study we evaluate the variable-resolution Community Earth System Model (VR-CESM) with a high-resolution (0.125°) refinement over the Rocky Mountain region. The VR-CESM results are compared with observations, as well as CESM simulation at a quasi-uniform 1° resolution (UNIF) and Canadian Regional Climate Model version 5 (CRCM5) simulation at a 0.11° resolution. We find that VR-CESM is effective at capturing the observed spatial patterns of temperature, precipitation, and snowpack in the Rocky Mountains with the performance comparable to CRCM5, while UNIF is unable to do so. VR-CESM and CRCM5 simulate better the seasonal variations of precipitation than UNIF, although VR-CESM still overestimates winter precipitation whereas CRCM5 and UNIF underestimate it. All simulations distribute more winter precipitation along the windward (west) flanks of mountain ridges with the greatest overestimation in VR-CESM. VR-CESM simulates much greater snow water equivalent peaks than CRCM5 and UNIF, although the peaks are still 10-40% less than observations. Moreover, the frequency of heavy precipitation events (daily precipitation ≥ 25 mm) in VR-CESM and CRCM5 is comparable to observations, whereas the same events in UNIF are an order of magnitude less frequent. In addition, VR-CESM captures the observed occurrence frequency and seasonal variation of rain-on-snow days and performs better than UNIF and CRCM5. These results demonstrate the VR-CESM's capability in regional climate modeling over the mountainous regions and its promising applications for climate change studies.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1998JGR...103.5973R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1998JGR...103.5973R"><span>Regional model simulations of New Zealand climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Renwick, James A.; Katzfey, Jack J.; Nguyen, Kim C.; McGregor, John L.</p> <p>1998-03-01</p> <p>Simulation of New Zealand climate is examined through the use of a regional climate model nested within the output of the Commonwealth Scientific and Industrial Research Organisation nine-level general circulation model (GCM). R21 resolution GCM output is used to drive a regional model run at 125 km grid spacing over the Australasian region. The 125 km run is used in turn to drive a simulation at 50 km resolution over New Zealand. Simulations with a full seasonal cycle are performed for 10 model years. The focus is on the quality of the simulation of present-day climate, but results of a doubled-CO2 run are discussed briefly. Spatial patterns of mean simulated precipitation and surface temperatures improve markedly as horizontal resolution is increased, through the better resolution of the country's orography. However, increased horizontal resolution leads to a positive bias in precipitation. At 50 km resolution, simulated frequency distributions of daily maximum/minimum temperatures are statistically similar to those of observations at many stations, while frequency distributions of daily precipitation appear to be statistically different to those of observations at most stations. Modeled daily precipitation variability at 125 km resolution is considerably less than observed, but is comparable to, or exceeds, observed variability at 50 km resolution. The sensitivity of the simulated climate to changes in the specification of the land surface is discussed briefly. Spatial patterns of the frequency of extreme temperatures and precipitation are generally well modeled. Under a doubling of CO2, the frequency of precipitation extremes changes only slightly at most locations, while air frosts become virtually unknown except at high-elevation sites.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMPA33B..06W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMPA33B..06W"><span>The Economic Value of Climate Science</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wielicki, B. A.; Cooke, R.; Young, D. F.; Mlynczak, M. G.</p> <p>2012-12-01</p> <p>While demonstrating the economic value of science is challenging, it can be more direct for some Earth observations. For example, suppose a climate science mission can yield decisive information on climate change within a shortened time frame. How much should society be willing to pay for this knowledge today? The US interagency memo on the social cost of carbon (SCC) provides a standard for valuing damages from carbon emissions. We illustrate how value of information (VOI) calculations can be used to monetize the relative value of different climate observations. We follow the SCC, stipulating uncertainty in climate sensitivity, using discount rates of 2.5%, 3% and 5%, and using one of the Integrated Assessment Models sanctioned in SCC (DICE, Nordhaus 2008). We consider three mitigation scenarios: Business as Usual (BAU), a moderate response (DICE Optimal), and a strong response (Stern). To illustrate results, suppose that we would switch from BAU to the Stern emissions path if we learn with 90% confidence that the decadal rate of temperature change reaches or exceeds 0.2 C/decade. Under the SCC assumptions, the year in which this happens, if it happens, depends on uncertain climate sensitivity and on the emissions path. The year in which we become 90% certain also depends on our Earth observations, their accuracy, and their completeness. The resolving power of a climate observing system cannot exceed climate system natural variability. All climate observations add noise to natural variability caused by observing limitations, including calibration errors and space/time sampling uncertainty. The basic concept is that more accurate observations can advance the time for societal decisions. The economic value of the resulting averted damages depends on the discount rate, and the years in which the damages occur. A new climate observation would be economically justified if the net present value (NPV) of the difference in averted damages, relative to the existing systems, exceeds the NPV of the system costs. We present illustrative results comparing the proposed CLARREO advance in satellite absolute calibration for climate change records to an existing system for detecting decadal temperature change and cloud feedback (i.e. climate sensitivity uncertainty). While CLARREO is used as an example, the value should be considered as relevant to an improved climate observing system, since societal decisions are unlikely to be based on one or a few observations. The VOI is found to depend on the required confidence level, the trigger value at which we would abandon the BAU emissions path, the path to which we switch, and the date at which the new system is launched. The VOI of CLARREO in this decision context is the surfeit of NPV of averted damages, relative to the existing system. Over all it is in the order of tens of trillions of US dollars. Among the noteworthy conclusions are (1) switching to either the DICE optimal or Stern emissions paths makes only a modest difference in the VOI of CLARREO, (2) raising the trigger value from 0.2C to 0.3C/decade, increases the VOI of CLARREO, while increasing the total NPV of climate damages, and (3) the choice of discount rate affects the VOI by a factor ~ 5. The results conclude that the economic value of advanced climate observing systems is dramatically larger than their cost, and argues for the continual enhancement of the SCC assessment process.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20160003531','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20160003531"><span>Interannual to Decadal Variability of Ocean Evaporation as Viewed from Climate Reanalyses</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Robertson, Franklin R.; Bosilovich, Michael G.; Roberts, Jason B.; Wang, Hailan</p> <p>2015-01-01</p> <p>Questions we'll address: Given the uncoupled framework of "AMIP" (Atmosphere Model Inter-comparison Project) experiments, what can they tell us regarding evaporation variability? Do Reduced Observations Reanalyses (RedObs) using Surface Fluxes and Clouds (SFC) pressure (and wind) provide a more realistic picture of evaporation variability? What signals of interannual variability (e.g. El Nino/Southern Oscillation (ENSO)) and decadal variability (Interdecadal Pacific Oscillation (IPO)) are detectable with this hierarchy of evaporation estimates?</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A31K..03O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A31K..03O"><span>Climate Projections from the NARCliM Project: Bayesian Model Averaging of Maximum Temperature Projections</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Olson, R.; Evans, J. P.; Fan, Y.</p> <p>2015-12-01</p> <p>NARCliM (NSW/ACT Regional Climate Modelling Project) is a regional climate project for Australia and the surrounding region. It dynamically downscales 4 General Circulation Models (GCMs) using three Regional Climate Models (RCMs) to provide climate projections for the CORDEX-AustralAsia region at 50 km resolution, and for south-east Australia at 10 km resolution. The project differs from previous work in the level of sophistication of model selection. Specifically, the selection process for GCMs included (i) conducting literature review to evaluate model performance, (ii) analysing model independence, and (iii) selecting models that span future temperature and precipitation change space. RCMs for downscaling the GCMs were chosen based on their performance for several precipitation events over South-East Australia, and on model independence.Bayesian Model Averaging (BMA) provides a statistically consistent framework for weighing the models based on their likelihood given the available observations. These weights are used to provide probability distribution functions (pdfs) for model projections. We develop a BMA framework for constructing probabilistic climate projections for spatially-averaged variables from the NARCliM project. The first step in the procedure is smoothing model output in order to exclude the influence of internal climate variability. Our statistical model for model-observations residuals is a homoskedastic iid process. Comparing RCMs with Australian Water Availability Project (AWAP) observations is used to determine model weights through Monte Carlo integration. Posterior pdfs of statistical parameters of model-data residuals are obtained using Markov Chain Monte Carlo. The uncertainty in the properties of the model-data residuals is fully accounted for when constructing the projections. We present the preliminary results of the BMA analysis for yearly maximum temperature for New South Wales state planning regions for the period 2060-2079.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009NW.....96...93W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009NW.....96...93W"><span>Does weather shape rodents? Climate related changes in morphology of two heteromyid species</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wolf, Mosheh; Friggens, Michael; Salazar-Bravo, Jorge</p> <p>2009-01-01</p> <p>Geographical variation in morphometric characters in heteromyid rodents has often correlated with climate gradients. Here, we used the long-term database of rodents trapped in the Sevilleta National Wildlife Refuge in New Mexico, USA to test whether significant annual changes in external morphometric characters are observed in a region with large variations in temperature and precipitation. We looked at the relationships between multiple temperature and precipitation variables and a number of morphological traits (body mass, body, tail, hind leg, and ear length) for two heteromyid rodents, Dipodomys merriami and Perognathus flavescens. Because these rodents can live multiple years in the wild, the climate variables for the year of the capture and the previous 2 years were included in the analyses. Using multiple linear regressions, we found that all of our morphometric traits, with the exception of tail length in D. merriami, had a significant relationship with one or more of the climate variables used. Our results demonstrate that effects of climate change on morphological traits occur over short periods, even in noninsular mammal populations. It is unclear, though, whether these changes are the result of morphological plasticity or natural selection.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ClDy...49.1181V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ClDy...49.1181V"><span>Comparison of full field and anomaly initialisation for decadal climate prediction: towards an optimal consistency between the ocean and sea-ice anomaly initialisation state</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Volpi, Danila; Guemas, Virginie; Doblas-Reyes, Francisco J.</p> <p>2017-08-01</p> <p>Decadal prediction exploits sources of predictability from both the internal variability through the initialisation of the climate model from observational estimates, and the external radiative forcings. When a model is initialised with the observed state at the initial time step (Full Field Initialisation—FFI), the forecast run drifts towards the biased model climate. Distinguishing between the climate signal to be predicted and the model drift is a challenging task, because the application of a-posteriori bias correction has the risk of removing part of the variability signal. The anomaly initialisation (AI) technique aims at addressing the drift issue by answering the following question: if the model is allowed to start close to its own attractor (i.e. its biased world), but the phase of the simulated variability is constrained toward the contemporaneous observed one at the initialisation time, does the prediction skill improve? The relative merits of the FFI and AI techniques applied respectively to the ocean component and the ocean and sea ice components simultaneously in the EC-Earth global coupled model are assessed. For both strategies the initialised hindcasts show better skill than historical simulations for the ocean heat content and AMOC along the first two forecast years, for sea ice and PDO along the first forecast year, while for AMO the improvements are statistically significant for the first two forecast years. The AI in the ocean and sea ice components significantly improves the skill of the Arctic sea surface temperature over the FFI.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ChJOL..35...23Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ChJOL..35...23Y"><span>Climate variability and predictability associated with the Indo-Pacific Oceanic Channel Dynamics in the CCSM4 Coupled System Model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yuan, Dongliang; Xu, Peng; Xu, Tengfei</p> <p>2017-01-01</p> <p>An experiment using the Community Climate System Model (CCSM4), a participant of the Coupled Model Intercomparison Project phase-5 (CMIP5), is analyzed to assess the skills of this model in simulating and predicting the climate variabilities associated with the oceanic channel dynamics across the Indo-Pacific Oceans. The results of these analyses suggest that the model is able to reproduce the observed lag correlation between the oceanic anomalies in the southeastern tropical Indian Ocean and those in the cold tongue in the eastern equatorial Pacific Ocean at a time lag of 1 year. This success may be largely attributed to the successful simulation of the interannual variations of the Indonesian Throughflow, which carries the anomalies of the Indian Ocean Dipole (IOD) into the western equatorial Pacific Ocean to produce subsurface temperature anomalies, which in turn propagate to the eastern equatorial Pacific to generate ENSO. This connection is termed the "oceanic channel dynamics" and is shown to be consistent with the observational analyses. However, the model simulates a weaker connection between the IOD and the interannual variability of the Indonesian Throughflow transport than found in the observations. In addition, the model overestimates the westerly wind anomalies in the western-central equatorial Pacific in the year following the IOD, which forces unrealistic upwelling Rossby waves in the western equatorial Pacific and downwelling Kelvin waves in the east. This assessment suggests that the CCSM4 coupled climate system has underestimated the oceanic channel dynamics and overestimated the atmospheric bridge processes.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29345639','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29345639"><span>Emergent constraint on equilibrium climate sensitivity from global temperature variability.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Cox, Peter M; Huntingford, Chris; Williamson, Mark S</p> <p>2018-01-17</p> <p>Equilibrium climate sensitivity (ECS) remains one of the most important unknowns in climate change science. ECS is defined as the global mean warming that would occur if the atmospheric carbon dioxide (CO 2 ) concentration were instantly doubled and the climate were then brought to equilibrium with that new level of CO 2 . Despite its rather idealized definition, ECS has continuing relevance for international climate change agreements, which are often framed in terms of stabilization of global warming relative to the pre-industrial climate. However, the 'likely' range of ECS as stated by the Intergovernmental Panel on Climate Change (IPCC) has remained at 1.5-4.5 degrees Celsius for more than 25 years. The possibility of a value of ECS towards the upper end of this range reduces the feasibility of avoiding 2 degrees Celsius of global warming, as required by the Paris Agreement. Here we present a new emergent constraint on ECS that yields a central estimate of 2.8 degrees Celsius with 66 per cent confidence limits (equivalent to the IPCC 'likely' range) of 2.2-3.4 degrees Celsius. Our approach is to focus on the variability of temperature about long-term historical warming, rather than on the warming trend itself. We use an ensemble of climate models to define an emergent relationship between ECS and a theoretically informed metric of global temperature variability. This metric of variability can also be calculated from observational records of global warming, which enables tighter constraints to be placed on ECS, reducing the probability of ECS being less than 1.5 degrees Celsius to less than 3 per cent, and the probability of ECS exceeding 4.5 degrees Celsius to less than 1 per cent.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li class="active"><span>25</span></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_25 --> <div class="footer-extlink text-muted" style="margin-bottom:1rem; text-align:center;">Some links on this page may take you to non-federal websites. 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