Does climate variability influence the demography of wild primates? Evidence from long-term life-history data in seven species.

PubMed

Campos, Fernando A; Morris, William F; Alberts, Susan C; Altmann, Jeanne; Brockman, Diane K; Cords, Marina; Pusey, Anne; Stoinski, Tara S; Strier, Karen B; Fedigan, Linda M

2017-11-01

Earth's rapidly changing climate creates a growing need to understand how demographic processes in natural populations are affected by climate variability, particularly among organisms threatened by extinction. Long-term, large-scale, and cross-taxon studies of vital rate variation in relation to climate variability can be particularly valuable because they can reveal environmental drivers that affect multiple species over extensive regions. Few such data exist for animals with slow life histories, particularly in the tropics, where climate variation over large-scale space is asynchronous. As our closest relatives, nonhuman primates are especially valuable as a resource to understand the roles of climate variability and climate change in human evolutionary history. Here, we provide the first comprehensive investigation of vital rate variation in relation to climate variability among wild primates. We ask whether primates are sensitive to global changes that are universal (e.g., higher temperature, large-scale climate oscillations) or whether they are more sensitive to global change effects that are local (e.g., more rain in some places), which would complicate predictions of how primates in general will respond to climate change. To address these questions, we use a database of long-term life-history data for natural populations of seven primate species that have been studied for 29-52 years to investigate associations between vital rate variation, local climate variability, and global climate oscillations. Associations between vital rates and climate variability varied among species and depended on the time windows considered, highlighting the importance of temporal scale in detection of such effects. We found strong climate signals in the fertility rates of three species. However, survival, which has a greater impact on population growth, was little affected by climate variability. Thus, we found evidence for demographic buffering of life histories, but also evidence of mechanisms by which climate change could affect the fates of wild primates. © 2017 John Wiley & Sons Ltd.

Selection of climate change scenario data for impact modelling.

PubMed

Sloth Madsen, M; Maule, C Fox; MacKellar, N; Olesen, J E; Christensen, J Hesselbjerg

2012-01-01

Impact models investigating climate change effects on food safety often need detailed climate data. The aim of this study was to select climate change projection data for selected crop phenology and mycotoxin impact models. Using the ENSEMBLES database of climate model output, this study illustrates how the projected climate change signal of important variables as temperature, precipitation and relative humidity depends on the choice of the climate model. Using climate change projections from at least two different climate models is recommended to account for model uncertainty. To make the climate projections suitable for impact analysis at the local scale a weather generator approach was adopted. As the weather generator did not treat all the necessary variables, an ad-hoc statistical method was developed to synthesise realistic values of missing variables. The method is presented in this paper, applied to relative humidity, but it could be adopted to other variables if needed.

Climate variability drives population cycling and synchrony

Treesearch

Lars Y. Pomara; Benjamin Zuckerberg

2017-01-01

Aim There is mounting concern that climate change will lead to the collapse of cyclic population dynamics, yet the influence of climate variability on population cycling remains poorly understood. We hypothesized that variability in survival and fecundity, driven by climate variability at different points in the life cycle, scales up from...

Sources and Impacts of Modeled and Observed Low-Frequency Climate Variability

NASA Astrophysics Data System (ADS)

Parsons, Luke Alexander

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.

Statistical structure of intrinsic climate variability under global warming

NASA Astrophysics Data System (ADS)

Zhu, Xiuhua; Bye, John; Fraedrich, Klaus

2017-04-01

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.

Climate-Driven Crop Yield and Yield Variability and Climate Change Impacts on the U.S. Great Plains Agricultural Production.

PubMed

Kukal, Meetpal S; Irmak, Suat

2018-02-22

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.

Reconstruction of Past Mediterranean Climate

NASA Astrophysics Data System (ADS)

García-Herrera, Ricardo; Luterbacher, Jürg; Lionello, Piero; Gonzáles-Rouco, Fidel; Ribera, Pedro; Rodó, Xavier; Kull, Christoph; Zerefos, Christos

2007-02-01

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.

Exploring objective climate classification for the Himalayan arc and adjacent regions using gridded data sources

NASA Astrophysics Data System (ADS)

Forsythe, N.; Blenkinsop, S.; Fowler, H. J.

2015-05-01

A three-step climate classification was applied to a spatial domain covering the Himalayan arc and adjacent plains regions using input data from four global meteorological reanalyses. Input variables were selected based on an understanding of the climatic drivers of regional water resource variability and crop yields. Principal component analysis (PCA) of those variables and k-means clustering on the PCA outputs revealed a reanalysis ensemble consensus for eight macro-climate zones. Spatial statistics of input variables for each zone revealed consistent, distinct climatologies. This climate classification approach has potential for enhancing assessment of climatic influences on water resources and food security as well as for characterising the skill and bias of gridded data sets, both meteorological reanalyses and climate models, for reproducing subregional climatologies. Through their spatial descriptors (area, geographic centroid, elevation mean range), climate classifications also provide metrics, beyond simple changes in individual variables, with which to assess the magnitude of projected climate change. Such sophisticated metrics are of particular interest for regions, including mountainous areas, where natural and anthropogenic systems are expected to be sensitive to incremental climate shifts.

Climatic extremes improve predictions of spatial patterns of tree species

USGS Publications Warehouse

Zimmermann, N.E.; Yoccoz, N.G.; Edwards, T.C.; Meier, E.S.; Thuiller, W.; Guisan, Antoine; Schmatz, D.R.; Pearman, P.B.

2009-01-01

Understanding niche evolution, dynamics, and the response of species to climate change requires knowledge of the determinants of the environmental niche and species range limits. Mean values of climatic variables are often used in such analyses. In contrast, the increasing frequency of climate extremes suggests the importance of understanding their additional influence on range limits. Here, we assess how measures representing climate extremes (i.e., interannual variability in climate parameters) explain and predict spatial patterns of 11 tree species in Switzerland. We find clear, although comparably small, improvement (+20% in adjusted D2, +8% and +3% in cross-validated True Skill Statistic and area under the receiver operating characteristics curve values) in models that use measures of extremes in addition to means. The primary effect of including information on climate extremes is a correction of local overprediction and underprediction. Our results demonstrate that measures of climate extremes are important for understanding the climatic limits of tree species and assessing species niche characteristics. The inclusion of climate variability likely will improve models of species range limits under future conditions, where changes in mean climate and increased variability are expected.

Climate variability and demand growth as drivers of water scarcity in the Turkwel river basin: a bottom-up risk assessment of a data-sparse basin in Kenya

NASA Astrophysics Data System (ADS)

Hirpa, F. A.; Dyer, E.; Hope, R.; Dadson, S. J.

2017-12-01

Sustainable water management and allocation are essential for maintaining human well-being, sustaining healthy ecosystems, and supporting steady economic growth. The Turkwel river basin, located in north-western Kenya, experiences a high level of water scarcity due to its arid climate, high rainfall variability, and rapidly growing water demand. However, due to sparse hydro-climatic data and limited literature, the water resources system of the basin has been poorly understood. Here we apply a bottom-up climate risk assessment method to estimate the resilience of the basin's water resources system to growing demand and climate stressors. First, using a water resource system model and historical climate data, we construct a climate risk map that depicts the way in which the system responds to climate change and variability. Then we develop a set of water demand scenarios to identify the conditions that potentially lead to the risk of unmet water demand and groundwater depletion. Finally, we investigate the impact of climate change and variability by stress testing these development scenarios against historically strong El Niño/Southern Oscillation (ENSO) years and future climate projections from multiple Global Circulation Models (GCMs). The results reveal that climate variability and increased water demand are the main drivers of water scarcity in the basin. Our findings show that increases in water demand due to expanded irrigation and population growth exert the strongest influence on the ability of the system to meet water resource supply requirements, and in all cases considered increase the impacts of droughts caused by future climate variability. Our analysis illustrates the importance of combining analysis of future climate risks with other development decisions that affect water resources planning. Policy and investment decisions which maximise water use efficiency in the present day are likely to impart resilience to climate change and variability under a wide range of future scenarios and therefore constitute low regret measures for climate adaptation.

Flexible stocking as a strategy for enhancing ranch profitability in the face of a changing and variable climate

USDA-ARS?s Scientific Manuscript database

Predicted climate change impacts include increased weather variability and increased occurrences of extreme events such as drought. Such climate changes potentially affect cattle performance as well as pasture and range productivity. These climate induced risks are often coupled with variable market...

Linking global climate and temperature variability to widespread amphibian declines putatively caused by disease.

PubMed

Rohr, Jason R; Raffel, Thomas R

2010-05-04

The role of global climate change in the decline of biodiversity and the emergence of infectious diseases remains controversial, and the effect of climatic variability, in particular, has largely been ignored. For instance, it was recently revealed that the proposed link between climate change and widespread amphibian declines, putatively caused by the chytrid fungus Batrachochytrium dendrobatidis (Bd), was tenuous because it was based on a temporally confounded correlation. Here we provide temporally unconfounded evidence that global El Niño climatic events drive widespread amphibian losses in genus Atelopus via increased regional temperature variability, which can reduce amphibian defenses against pathogens. Of 26 climate variables tested, only factors associated with temperature variability could account for the spatiotemporal patterns of declines thought to be associated with Bd. Climatic predictors of declines became significant only after controlling for a pattern consistent with epidemic spread (by temporally detrending the data). This presumed spread accounted for 59% of the temporal variation in amphibian losses, whereas El Niño accounted for 59% of the remaining variation. Hence, we could account for 83% of the variation in declines with these two variables alone. Given that global climate change seems to increase temperature variability, extreme climatic events, and the strength of Central Pacific El Niño episodes, climate change might exacerbate worldwide enigmatic declines of amphibians, presumably by increasing susceptibility to disease. These results suggest that changes to temperature variability associated with climate change might be as significant to biodiversity losses and disease emergence as changes to mean temperature.

Climate Prediction Center - Outreach: 41st Annual Climate Diagnostics &

Science.gov Websites

the University of Maine Climate Change Institute and School of Earth and Climate Sciences and is co (drought, heat waves, severe weather, tropical cyclones) in the framework of climate variability and change and including the use of paleoclimate data. Arctic climate variability and change, and linkages to

Post-Fire Recovery of Eco-Hydrologic Behavior Given Historic and Projected Climate Variability in California Mediterranean Type Environments

NASA Astrophysics Data System (ADS)

Seaby, L. P.; Tague, C. L.; Hope, A. S.

2006-12-01

The Mediterranean type environments (MTEs) of California are characterized by a distinct wet and dry season and high variability in inter-annual climate. Water limitation in MTEs makes eco-hydrological processes highly sensitive to both climate variability and frequent fire disturbance. This research modeled post-fire eco- hydrologic behavior under historical and moderate and extreme scenarios of future climate in a semi-arid chaparral dominated southern California MTE. We used a physically-based, spatially-distributed, eco- hydrological model (RHESSys - Regional Hydro-Ecologic Simulation System), to capture linkages between water and vegetation response to the combined effects of fire and historic and future climate variability. We found post-fire eco-hydrologic behavior to be strongly influenced by the episodic nature of MTE climate, which intensifies under projected climate change. Higher rates of post-fire net primary productivity were found under moderate climate change, while more extreme climate change produced water stressed conditions which were less favorable for vegetation productivity. Precipitation variability in the historic record follows the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO), and these inter-annual climate characteristics intensify under climate change. Inter-annual variation in streamflow follows these precipitation patterns. Post-fire streamflow and carbon cycling trajectories are strongly dependent on climate characteristics during the first 5 years following fire, and historic intra-climate variability during this period tends to overwhelm longer term trends and variation that might be attributable to climate change. Results have implications for water resource availability, vegetation type conversion from shrubs to grassland, and changes in ecosystem structure and function.

Climate | National Oceanic and Atmospheric Administration

Science.gov Websites

to help people understand and prepare for climate variability and change. Climate. NOAA From to help people understand and prepare for climate variability and change. LATEST FEATURES // Ocean Jump to Content Enter Search Terms Weather Climate Oceans & Coasts Fisheries Satellites

Climate change

USGS Publications Warehouse

Cronin, Thomas M.

2016-01-01

Climate change (including climate variability) refers to regional or global changes in mean climate state or in patterns of climate variability over decades to millions of years often identified using statistical methods and sometimes referred to as changes in long-term weather conditions (IPCC, 2012). Climate is influenced by changes in continent-ocean configurations due to plate tectonic processes, variations in Earth’s orbit, axial tilt and precession, atmospheric greenhouse gas (GHG) concentrations, solar variability, volcanism, internal variability resulting from interactions between the atmosphere, oceans and ice (glaciers, small ice caps, ice sheets, and sea ice), and anthropogenic activities such as greenhouse gas emissions and land use and their effects on carbon cycling.

Means and extremes: building variability into community-level climate change experiments.

PubMed

Thompson, Ross M; Beardall, John; Beringer, Jason; Grace, Mike; Sardina, Paula

2013-06-01

Experimental studies assessing climatic effects on ecological communities have typically applied static warming treatments. Although these studies have been informative, they have usually failed to incorporate either current or predicted future, patterns of variability. Future climates are likely to include extreme events which have greater impacts on ecological systems than changes in means alone. Here, we review the studies which have used experiments to assess impacts of temperature on marine, freshwater and terrestrial communities, and classify them into a set of 'generations' based on how they incorporate variability. The majority of studies have failed to incorporate extreme events. In terrestrial ecosystems in particular, experimental treatments have reduced temperature variability, when most climate models predict increased variability. Marine studies have tended to not concentrate on changes in variability, likely in part because the thermal mass of oceans will moderate variation. In freshwaters, climate change experiments have a much shorter history than in the other ecosystems, and have tended to take a relatively simple approach. We propose a new 'generation' of climate change experiments using down-scaled climate models which incorporate predicted changes in climatic variability, and describe a process for generating data which can be applied as experimental climate change treatments. © 2013 John Wiley & Sons Ltd/CNRS.

Separating out the influence of climatic trend, fluctuations, and extreme events on crop yield: a case study in Hunan Province, China

NASA Astrophysics Data System (ADS)

Wang, Zhu; Shi, Peijun; Zhang, Zhao; Meng, Yongchang; Luan, Yibo; Wang, Jiwei

2017-09-01

Separating out the influence of climatic trend, fluctuations and extreme events on crop yield is of paramount importance to climate change adaptation, resilience, and mitigation. Previous studies lack systematic and explicit assessment of these three fundamental aspects of climate change on crop yield. This research attempts to separate out the impacts on rice yields of climatic trend (linear trend change related to mean value), fluctuations (variability surpassing the "fluctuation threshold" which defined as one standard deviation (1 SD) of the residual between the original data series and the linear trend value for each climatic variable), and extreme events (identified by absolute criterion for each kind of extreme events related to crop yield). The main idea of the research method was to construct climate scenarios combined with crop system simulation model. Comparable climate scenarios were designed to express the impact of each climate change component and, were input to the crop system model (CERES-Rice), which calculated the related simulated yield gap to quantify the percentage impacts of climatic trend, fluctuations, and extreme events. Six Agro-Meteorological Stations (AMS) in Hunan province were selected to study the quantitatively impact of climatic trend, fluctuations and extreme events involving climatic variables (air temperature, precipitation, and sunshine duration) on early rice yield during 1981-2012. The results showed that extreme events were found to have the greatest impact on early rice yield (-2.59 to -15.89%). Followed by climatic fluctuations with a range of -2.60 to -4.46%, and then the climatic trend (4.91-2.12%). Furthermore, the influence of climatic trend on early rice yield presented "trade-offs" among various climate variables and AMS. Climatic trend and extreme events associated with air temperature showed larger effects on early rice yield than other climatic variables, particularly for high-temperature events (-2.11 to -12.99%). Finally, the methodology use to separate out the influences of the climatic trend, fluctuations, and extreme events on crop yield was proved to be feasible and robust. Designing different climate scenarios and feeding them into a crop system model is a potential way to evaluate the quantitative impact of each climate variable.

Timing of climate variability and grassland productivity

PubMed Central

Craine, Joseph M.; Nippert, Jesse B.; Elmore, Andrew J.; Skibbe, Adam M.; Hutchinson, Stacy L.; Brunsell, Nathaniel A.

2012-01-01

Future climates are forecast to include greater precipitation variability and more frequent heat waves, but the degree to which the timing of climate variability impacts ecosystems is uncertain. In a temperate, humid grassland, we examined the seasonal impacts of climate variability on 27 y of grass productivity. Drought and high-intensity precipitation reduced grass productivity only during a 110-d period, whereas high temperatures reduced productivity only during 25 d in July. The effects of drought and heat waves declined over the season and had no detectable impact on grass productivity in August. If these patterns are general across ecosystems, predictions of ecosystem response to climate change will have to account not only for the magnitude of climate variability but also for its timing. PMID:22331914

Framework for a U.S. Geological Survey Hydrologic Climate-Response Program in Maine

USGS Publications Warehouse

Hodgkins, Glenn A.; Lent, Robert M.; Dudley, Robert W.; Schalk, Charles W.

2009-01-01

This report presents a framework for a U.S. Geological Survey (USGS) hydrologic climate-response program designed to provide early warning of changes in the seasonal water cycle of Maine. Climate-related hydrologic changes on Maine's rivers and lakes in the winter and spring during the last century are well documented, and several river and lake variables have been shown to be sensitive to air-temperature changes. Monitoring of relevant hydrologic data would provide important baseline information against which future climate change can be measured. The framework of the hydrologic climate-response program presented here consists of four major parts: (1) identifying homogeneous climate-response regions; (2) identifying hydrologic components and key variables of those components that would be included in a hydrologic climate-response data network - as an example, streamflow has been identified as a primary component, with a key variable of streamflow being winter-spring streamflow timing; the data network would be created by maintaining existing USGS data-collection stations and establishing new ones to fill data gaps; (3) regularly updating historical trends of hydrologic data network variables; and (4) establishing basins for process-based studies. Components proposed for inclusion in the hydrologic climate-response data network have at least one key variable for which substantial historical data are available. The proposed components are streamflow, lake ice, river ice, snowpack, and groundwater. The proposed key variables of each component have extensive historical data at multiple sites and are expected to be responsive to climate change in the next few decades. These variables are also important for human water use and (or) ecosystem function. Maine would be divided into seven climate-response regions that follow major river-basin boundaries (basins subdivided to hydrologic units with 8-digit codes or larger) and have relatively homogeneous climates. Key hydrologic variables within each climate-response region would be analyzed regularly to maintain up-to-date analyses of year-to-year variability, decadal variability, and longer term trends. Finally, one basin in each climate-response region would be identified for process-based hydrologic and ecological studies.

The Climate Variability & Predictability (CVP) Program at NOAA - Recent Program Advancements in Understanding AMOC

NASA Astrophysics Data System (ADS)

Lucas, S. E.

2016-12-01

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.

Analyzing the responses of species assemblages to climate change across the Great Basin, USA.

NASA Astrophysics Data System (ADS)

Henareh Khalyani, A.; Falkowski, M. J.; Crookston, N.; Yousef, F.

2016-12-01

The potential impacts of climate change on the future distribution of tree species in not well understood. Climate driven changes in tree species distribution could cause significant changes in realized species niches, potentially resulting in the loss of ecotonal species as well as the formation on novel assemblages of overlapping tree species. In an effort to gain a better understating of how the geographic distribution of tree species may respond to climate change, we model the potential future distribution of 50 different tree species across 70 million ha in the Great Basin, USA. This is achieved by leveraging a species realized niche model based on non-parametric analysis of species occurrences across climatic, topographic, and edaphic variables. Spatially explicit, high spatial resolution (30 m) climate variables (e.g., precipitation, and minimum, maximum, and mean temperature) and associated climate indices were generated on an annual basis between 1981-2010 by integrating climate station data with digital elevation data (Shuttle Radar Topographic Mission (SRTM) data) in a thin plate spline interpolation algorithm (ANUSPLIN). Bioclimate models of species niches in in the cotemporary period and three following 30 year periods were then generated by integrating the climate variables, soil data, and CMIP 5 general circulation model projections. Our results suggest that local scale contemporary variations in species realized niches across space are influenced by edaphic and topographic variables as well as climatic variables. The local variability in soil properties and topographic variability across space also affect the species responses to climate change through time and potential formation of species assemblages in future. The results presented here in will aid in the development of adaptive forest management techniques aimed at mitigating negative impacts of climate change on forest composition, structure, and function.

Impacts of climate change and variability on transportation systems and infrastructure : Gulf Coast study, phase 2 : task 2 : climate variability and change in Mobile, Alabama.

DOT National Transportation Integrated Search

2012-09-01

Despite increasing confidence in global climate change projections in recent years, projections of : climate effects at local scales remains scarce. Location-specific risks to transportation systems : imposed by changes in climate are not yet well kn...

Coral Records of 20th Century Central Tropical Pacific SST and Salinity: Signatures of Natural and Anthropogenic Climate Change

NASA Astrophysics Data System (ADS)

Nurhati, I. S.; Cobb, K.; Di Lorenzo, E.

2011-12-01

Accurate forecasts of regional climate changes in many regions of the world largely depend on quantifying anthropogenic trends in tropical Pacific climate against its rich background of interannual to decadal-scale climate variability. However, the strong natural climate variability combined with limited instrumental climate datasets have obscured potential anthropogenic climate signals in the region. Here, we present coral-based sea-surface temperature (SST) and salinity proxy records over the 20th century (1898-1998) from the central tropical Pacific - a region sensitive to El Niño-Southern Oscillation (ENSO) whose variability strongly impacts the global climate. The SST and salinity proxy records are reconstructed via coral Sr/Ca and the oxygen isotopic composition of seawater (δ18Osw), respectively. On interannual (2-7yr) timescales, the SST proxy record tracks both eastern- and central-Pacific flavors of ENSO variability (R=0.65 and R=0.67, respectively). Interannual-scale salinity variability in our coral record highlights profound differences in precipitation and ocean advections during the two flavors of ENSO. On decadal (8yr-lowpassed) timescales, the central tropical Pacific SST and salinity proxy records are controlled by different sets of dynamics linked to the leading climate modes of North Pacific climate variability. Decadal-scale central tropical Pacific SST is highly correlated to the recently discovered North Pacific Gyre Oscillation (NPGO; R=-0.85), reflecting strong dynamical links between the central Pacific warming mode and extratropical decadal climate variability. Whereas decadal-scale salinity variations in the central tropical Pacific are significantly correlated with the Pacific Decadal Oscillation (PDO; R=0.54), providing a better understanding on low-frequency salinity variability in the region. Having characterized natural climate variability in this region, the coral record shows a +0.5°C warming trend throughout the last century. However, the most prominent feature of the new coral records is an unprecedented freshening trend since the mid-20th century, in line with global climate models (GCMs) projections of enhanced hydrological patterns (wet areas are getting wetter and vice versa) under greenhouse forcing. Taken together, the coral records provide key constraints on tropical Pacific climate trends that may improve regional climate projections in areas affected by tropical Pacific climate variability.
Central Tropical Pacific SST and Salinity Proxy Records

Does internal climate variability overwhelm climate change signals in streamflow? The upper Po and Rhone basin case studies.

PubMed

Fatichi, S; Rimkus, S; Burlando, P; Bordoy, R

2014-09-15

Projections of climate change effects in streamflow are increasingly required to plan water management strategies. These projections are however largely uncertain due to the spread among climate model realizations, internal climate variability, and difficulties in transferring climate model results at the spatial and temporal scales required by catchment hydrology. A combination of a stochastic downscaling methodology and distributed hydrological modeling was used in the ACQWA project to provide projections of future streamflow (up to year 2050) for the upper Po and Rhone basins, respectively located in northern Italy and south-western Switzerland. Results suggest that internal (stochastic) climate variability is a fundamental source of uncertainty, typically comparable or larger than the projected climate change signal. Therefore, climate change effects in streamflow mean, frequency, and seasonality can be masked by natural climatic fluctuations in large parts of the analyzed regions. An exception to the overwhelming role of stochastic variability is represented by high elevation catchments fed by glaciers where streamflow is expected to be considerably reduced due to glacier retreat, with consequences appreciable in the main downstream rivers in August and September. Simulations also identify regions (west upper Rhone and Toce, Ticino river basins) where a strong precipitation increase in the February to April period projects streamflow beyond the range of natural climate variability during the melting season. This study emphasizes the importance of including internal climate variability in climate change analyses, especially when compared to the limited uncertainty that would be accounted for by few deterministic projections. The presented results could be useful in guiding more specific impact studies, although design or management decisions should be better based on reliability and vulnerability criteria as suggested by recent literature. Copyright © 2013 Elsevier B.V. All rights reserved.

Resilience, rapid transitions and regime shifts: fingerprinting the responses of Lake Żabińskie (NE Poland) to climate variability and human disturbance since 1000 AD

NASA Astrophysics Data System (ADS)

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

2016-04-01

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.

Beyond a Climate-Centric View of Plant Distribution: Edaphic Variables Add Value to Distribution Models

PubMed Central

Beauregard, Frieda; de Blois, Sylvie

2014-01-01

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential for non-climate aspects of the environment to pose a constraint to range expansion under climate change. PMID:24658097

Beyond a climate-centric view of plant distribution: edaphic variables add value to distribution models.

PubMed

Beauregard, Frieda; de Blois, Sylvie

2014-01-01

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55,000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential for non-climate aspects of the environment to pose a constraint to range expansion under climate change.

A method for screening climate change-sensitive infectious diseases.

PubMed

Wang, Yunjing; Rao, Yuhan; Wu, Xiaoxu; Zhao, Hainan; Chen, Jin

2015-01-14

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.

A Method for Screening Climate Change-Sensitive Infectious Diseases

PubMed Central

Wang, Yunjing; Rao, Yuhan; Wu, Xiaoxu; Zhao, Hainan; Chen, Jin

2015-01-01

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

Impacts of Considering Climate Variability on Investment Decisions in Ethiopia

NASA Astrophysics Data System (ADS)

Strzepek, K.; Block, P.; Rosegrant, M.; Diao, X.

2005-12-01

In Ethiopia, climate extremes, inducing droughts or floods, are not unusual. Monitoring the effects of these extremes, and climate variability in general, is critical for economic prediction and assessment of the country's future welfare. The focus of this study involves adding climate variability to a deterministic, mean climate-driven agro-economic model, in an attempt to understand its effects and degree of influence on general economic prediction indicators for Ethiopia. Four simulations are examined, including a baseline simulation and three investment strategies: simulations of irrigation investment, roads investment, and a combination investment of both irrigation and roads. The deterministic model is transformed into a stochastic model by dynamically adding year-to-year climate variability through climate-yield factors. Nine sets of actual, historic, variable climate data are individually assembled and implemented into the 12-year stochastic model simulation, producing an ensemble of economic prediction indicators. This ensemble allows for a probabilistic approach to planning and policy making, allowing decision makers to consider risk. The economic indicators from the deterministic and stochastic approaches, including rates of return to investments, are significantly different. The predictions of the deterministic model appreciably overestimate the future welfare of Ethiopia; the predictions of the stochastic model, utilizing actual climate data, tend to give a better semblance of what may be expected. Inclusion of climate variability is vital for proper analysis of the predictor values from this agro-economic model.

Disease in a more variable and unpredictable climate

NASA Astrophysics Data System (ADS)

McMahon, T. A.; Raffel, T.; Rohr, J. R.; Halstead, N.; Venesky, M.; Romansic, J.

2014-12-01

Global climate change is shifting the dynamics of infectious diseases of humans and wildlife with potential adverse consequences for disease control. Despite this, the role of global climate change in the decline of biodiversity and the emergence of infectious diseases remains controversial. Climate change is expected to increase climate variability in addition to increasing mean temperatures, making climate less predictable. However, few empirical or theoretical studies have considered the effects of climate variability or predictability on disease, despite it being likely that hosts and parasites will have differential responses to climatic shifts. Here we present a theoretical framework for how temperature variation and its predictability influence disease risk by affecting host and parasite acclimation responses. Laboratory experiments and field data on disease-associated frog declines in Latin America support this framework and provide evidence that unpredictable temperature fluctuations, on both monthly and diurnal timescales, decrease frog resistance to the pathogenic chytrid fungus Batrachochytrium dendrobatidis (Bd). Furthermore, the pattern of temperature-dependent growth of the fungus on frogs was inconsistent with the pattern of Bd growth in culture, emphasizing the importance of accounting for the host-parasite interaction when predicting climate-dependent disease dynamics. Consistent with our laboratory experiments, increased regional temperature variability associated with global El Niño climatic events was the best predictor of widespread amphibian losses in the genus Atelopus. Thus, incorporating the effects of small-scale temporal variability in climate can greatly improve our ability to predict the effects of climate change on disease.

Centennial-scale Holocene climate variations amplified by Antarctic Ice Sheet discharge

NASA Astrophysics Data System (ADS)

Bakker, Pepijn; Clark, Peter U.; Golledge, Nicholas R.; Schmittner, Andreas; Weber, Michael E.

2017-01-01

Proxy-based indicators of past climate change show that current global climate models systematically underestimate Holocene-epoch climate variability on centennial to multi-millennial timescales, with the mismatch increasing for longer periods. Proposed explanations for the discrepancy include ocean-atmosphere coupling that is too weak in models, insufficient energy cascades from smaller to larger spatial and temporal scales, or that global climate models do not consider slow climate feedbacks related to the carbon cycle or interactions between ice sheets and climate. Such interactions, however, are known to have strongly affected centennial- to orbital-scale climate variability during past glaciations, and are likely to be important in future climate change. Here we show that fluctuations in Antarctic Ice Sheet discharge caused by relatively small changes in subsurface ocean temperature can amplify multi-centennial climate variability regionally and globally, suggesting that a dynamic Antarctic Ice Sheet may have driven climate fluctuations during the Holocene. We analysed high-temporal-resolution records of iceberg-rafted debris derived from the Antarctic Ice Sheet, and performed both high-spatial-resolution ice-sheet modelling of the Antarctic Ice Sheet and multi-millennial global climate model simulations. Ice-sheet responses to decadal-scale ocean forcing appear to be less important, possibly indicating that the future response of the Antarctic Ice Sheet will be governed more by long-term anthropogenic warming combined with multi-centennial natural variability than by annual or decadal climate oscillations.

The Dynamics of Vulnerability and Implications for Climate Change Adaptation: Lessons from Urban Water Management

NASA Astrophysics Data System (ADS)

Dilling, L.; Daly, M.; Travis, W.; Wilhelmi, O.; Klein, R.; Kenney, D.; Ray, A. J.; Miller, K.

2013-12-01

Recent reports and scholarship have suggested that adapting to current climate variability may represent a "no regrets" strategy for adapting to climate change. Filling "adaptation deficits" and other approaches that rely on addressing current vulnerabilities are of course helpful for responding to current climate variability, but we find here that they are not sufficient for adapting to climate change. First, following a comprehensive review and unique synthesis of the natural hazards and climate adaptation literatures, we advance six reasons why adapting to climate variability is not sufficient for adapting to climate change: 1) Vulnerability is different at different levels of exposure; 2) Coping with climate variability is not equivalent to adaptation to longer term change; 3) The socioeconomic context for vulnerability is constantly changing; 4) The perception of risk associated with climate variability does not necessarily promote adaptive behavior in the face of climate change; 5) Adaptations made to short term climate variability may reduce the flexibility of the system in the long term; and 6) Adaptive actions may shift vulnerabilities to other parts of the system or to other people. Instead we suggest that decision makers faced with choices to adapt to climate change must consider the dynamics of vulnerability in a connected system-- how choices made in one part of the system might impact other valued outcomes or even create new vulnerabilities. Furthermore we suggest that rather than expressing climate change adaptation as an extension of adaptation to climate variability, the research and practice communities would do well to articulate adaptation as an imperfect policy, with tradeoffs and consequences and that decisions be prioritized to preserve flexibility be revisited often as climate change unfolds. We then present the results of a number of empirical studies of decision making for drought in urban water systems in the United States to understand: a) the variety of actions taken; b) the limitations of actions available to water managers; and c) the effectiveness of actions taken to date. Time permitting, we briefly present the results of 3 in-depth case studies of drought response and current perception of preparedness with respect to future drought and climate change among urban water system managers. We examine the role of governance, system connectivity, public perceptions and other factors in driving decision making and outcomes.

Selecting climate change scenarios using impact-relevant sensitivities

Treesearch

Julie A. Vano; John B. Kim; David E. Rupp; Philip W. Mote

2015-01-01

Climate impact studies often require the selection of a small number of climate scenarios. Ideally, a subset would have simulations that both (1) appropriately represent the range of possible futures for the variable/s most important to the impact under investigation and (2) come from global climate models (GCMs) that provide plausible results for future climate in the...

Ad hoc committee on global climate issues: Annual report

USGS Publications Warehouse

Gerhard, L.C.; Hanson, B.M.B.

2000-01-01

The AAPG Ad Hoc Committee on Global Climate Issues has studied the supposition of human-induced climate change since the committee's inception in January 1998. This paper details the progress and findings of the committee through June 1999. At that time there had been essentially no geologic input into the global climate change debate. The following statements reflect the current state of climate knowledge from the geologic perspective as interpreted by the majority of the committee membership. The committee recognizes that new data could change its conclusions. The earth's climate is constantly changing owing to natural variability in earth processes. Natural climate variability over recent geological time is greater than reasonable estimates of potential human-induced greenhouse gas changes. Because no tool is available to test the supposition of human-induced climate change and the range of natural variability is so great, there is no discernible human influence on global climate at this time.

Assessing performance and seasonal bias of pollen-based climate reconstructions in a perfect model world

NASA Astrophysics Data System (ADS)

Trachsel, M.; Rehfeld, K.; Telford, R.; Laepple, T.

2017-12-01

Reconstructions of summer, winter or annual mean temperatures based on the species composition of bio-indicators such as pollen are routinely used in climate model-proxy data comparison studies. Most reconstruction algorithms exploit the joint distribution of modern spatial climate and species distribution for the development of the reconstructions. They rely on the space-for-time substitution and the specific assumption that environmental variables other than those reconstructed are not important or that their relationship with the reconstructed variable(s) should be the same in the past as in the modern spatial calibration dataset. Here we test the implications of this "correlative uniformitarianism" assumption on climate reconstructions in an ideal model world, in which climate and vegetation are known at all times. The alternate reality is a climate simulation of the last 6000 years with dynamic vegetation. Transient changes of plant functional types are considered as surrogate pollen counts and allow us to establish, apply and evaluate transfer functions in the modeled world. We find that the transfer function cross validation r2 is of limited use to identify reconstructible climate variables, as it only relies on the modern spatial climate-vegetation relationship. However, ordination approaches that assess the amount of fossil vegetation variance explained by the reconstructions are promising. We show that correlations between climate variables in the modern climate-vegetation relationship are systematically extended into the reconstructions. Summer temperatures, the most prominent driving variable for modeled vegetation change in the Northern Hemisphere, are accurately reconstructed. However, the amplitude of the model winter and mean annual temperature cooling between the mid-Holocene and present day is overestimated and similar to the summer trend in magnitude. This effect occurs because temporal changes of a dominant climate variable are imprinted on a less important variable, leading to reconstructions biased towards the dominant variable's trends. Our results, although based on a model vegetation that is inevitably simpler than reality, indicate that reconstructions of multiple climate variables based on modern spatial bio-indicator datasets should be treated with caution.

Spatial variability in forest growth—climate relationships in the Olympic Mountains, Washington.

Treesearch

Jill M. Nakawatase; David L. Peterson

2006-01-01

For many Pacific Northwest forests, little is known about the spatial and temporal variability in tree growth - climate relationships, yet it is this information that is needed to predict how forests will respond to future climatic change. We studied the effects of climatic variability on forest growth at 74 plots in the western and northeastern Olympic Mountains....

Frontiers in Decadal Climate Variability: Proceedings of a Workshop

DOE Office of Scientific and Technical Information (OSTI.GOV)

Purcell, Amanda

A number of studies indicate an apparent slowdown in the overall rise in global average surface temperature between roughly 1998 and 2014. Most models did not predict such a slowdown--a fact that stimulated a lot of new research on variability of Earth's climate system. At a September 2015 workshop, leading scientists gathered to discuss current understanding of climate variability on decadal timescales (10 to 30 years) and whether and how prediction of it might be improved. Many researchers have focused their attention on the climate system itself, which is known to vary across seasons, decades, and other timescales. Several naturalmore » variables produce "ups and downs" in the climate system, which are superimposed on the long-term warming trend due to human influence. Understanding decadal climate variability is important not only for assessing global climate change but also for improving decision making related to infrastructure, water resources, agriculture, energy, and other realms. Like the well-studied El Nino and La Nina interannual variations, decadal climate variability is associated with specific regional patterns of temperature and precipitation, such as heat waves, cold spells, and droughts. Several participants shared research that assesses decadal predictive capability of current models.« less

Joint effects of climate variability and socioecological factors on dengue transmission: epidemiological evidence.

PubMed

Akter, Rokeya; Hu, Wenbiao; Naish, Suchithra; Banu, Shahera; Tong, Shilu

2017-06-01

To assess the epidemiological evidence on the joint effects of climate variability and socioecological factors on dengue transmission. Following PRISMA guidelines, a detailed literature search was conducted in PubMed, Web of Science and Scopus. Peer-reviewed, freely available and full-text articles, considering both climate and socioecological factors in relation to dengue, published in English from January 1993 to October 2015 were included in this review. Twenty studies have met the inclusion criteria and assessed the impact of both climatic and socioecological factors on dengue dynamics. Among those, four studies have further investigated the relative importance of climate variability and socioecological factors on dengue transmission. A few studies also developed predictive models including both climatic and socioecological factors. Due to insufficient data, methodological issues and contextual variability of the studies, it is hard to draw conclusion on the joint effects of climate variability and socioecological factors on dengue transmission. Future research should take into account socioecological factors in combination with climate variables for a better understanding of the complex nature of dengue transmission as well as for improving the predictive capability of dengue forecasting models, to develop effective and reliable early warning systems. © 2017 John Wiley & Sons Ltd.

Spatial Models for Prediction and Early Warning of Aedes aegypti Proliferation from Data on Climate Change and Variability in Cuba.

PubMed

Ortiz, Paulo L; Rivero, Alina; Linares, Yzenia; Pérez, Alina; Vázquez, Juan R

2015-04-01

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.

Application of Climate Assessment Tool (CAT) to estimate climate variability impacts on nutrient loading from local watersheds

Treesearch

Ying Ouyang; Prem B. Parajuli; Gary Feng; Theodor D. Leininger; Yongshan Wan; Padmanava Dash

2018-01-01

A vast amount of future climate scenario datasets, created by climate models such as general circulation models (GCMs), have been used in conjunction with watershed models to project future climate variability impact on hydrological processes and water quality. However, these low spatial-temporal resolution datasets are often difficult to downscale spatially and...

Selecting Populations for Non-Analogous Climate Conditions Using Universal Response Functions: The Case of Douglas-Fir in Central Europe.

PubMed

Chakraborty, Debojyoti; Wang, Tongli; Andre, Konrad; Konnert, Monika; Lexer, Manfred J; Matulla, Christoph; Schueler, Silvio

2015-01-01

Identifying populations within tree species potentially adapted to future climatic conditions is an important requirement for reforestation and assisted migration programmes. Such populations can be identified either by empirical response functions based on correlations of quantitative traits with climate variables or by climate envelope models that compare the climate of seed sources and potential growing areas. In the present study, we analyzed the intraspecific variation in climate growth response of Douglas-fir planted within the non-analogous climate conditions of Central and continental Europe. With data from 50 common garden trials, we developed Universal Response Functions (URF) for tree height and mean basal area and compared the growth performance of the selected best performing populations with that of populations identified through a climate envelope approach. Climate variables of the trial location were found to be stronger predictors of growth performance than climate variables of the population origin. Although the precipitation regime of the population sources varied strongly none of the precipitation related climate variables of population origin was found to be significant within the models. Overall, the URFs explained more than 88% of variation in growth performance. Populations identified by the URF models originate from western Cascades and coastal areas of Washington and Oregon and show significantly higher growth performance than populations identified by the climate envelope approach under both current and climate change scenarios. The URFs predict decreasing growth performance at low and middle elevations of the case study area, but increasing growth performance on high elevation sites. Our analysis suggests that population recommendations based on empirical approaches should be preferred and population selections by climate envelope models without considering climatic constrains of growth performance should be carefully appraised before transferring populations to planting locations with novel or dissimilar climate.

Climate Controls AM Fungal Distributions from Global to Local Scales

NASA Astrophysics Data System (ADS)

Kivlin, S. N.; Hawkes, C.; Muscarella, R.; Treseder, K. K.; Kazenel, M.; Lynn, J.; Rudgers, J.

2016-12-01

Arbuscular mycorrhizal (AM) fungi have key functions in terrestrial biogeochemical processes; thus, determining the relative importance of climate, edaphic factors, and plant community composition on their geographic distributions can improve predictions of their sensitivity to global change. Local adaptation by AM fungi to plant hosts, soil nutrients, and climate suggests that all of these factors may control fungal geographic distributions, but their relative importance is unknown. We created species distribution models for 142 AM fungal taxa at the global scale with data from GenBank. We compared climate variables (BioClim and soil moisture), edaphic variables (phosphorus, carbon, pH, and clay content), and plant variables using model selection on models with (1) all variables, (2) climatic variables only (including soil moisture) and (3) resource-related variables only (all other soil parameters and NPP) using the MaxEnt algorithm evaluated with ENMEval. We also evaluated whether drivers of AM fungal distributions were phylogenetically conserved. To test whether global correlates of AM fungal distributions were reflected at local scales, we then surveyed AM fungi in nine plant hosts along three elevation gradients in the Upper Gunnison Basin, Colorado, USA. At the global scale, the distributions of 55% of AM fungal taxa were affected by both climate and soil resources, whereas 16% were only affected by climate and 29% were only affected by soil resources. Even for AM fungi that were affected by both climate and resources, the effects of climatic variables nearly always outweighed those of resources. Soil moisture and isothermality were the main climatic and NPP and soil carbon the main resource related factors influencing AM fungal distributions. Distributions of closely related AM fungal taxa were similarly affected by climate, but not by resources. Local scale surveys of AM fungi across elevations confirmed that climate was a key driver of AM fungal composition and root colonization, with weaker influences of plant identity and soil nutrients. These two studies across scales suggest prevailing effects of climate on AM fungal distributions. Thus, incorporating climate when forecasting future ranges of AM fungi will enhance predictions of AM fungal abundance and associated ecosystem functions.

Climate and Southern Africa's Water-Energy-Food Nexus

NASA Astrophysics Data System (ADS)

Conway, D.; Osborn, T.; Dorling, S.; Ringler, C.; Lankford, B.; Dalin, C.; Thurlow, J.; Zhu, T.; Deryng, D.; Landman, W.; Archer van Garderen, E.; Krueger, T.; Lebek, K.

2014-12-01

Numerous challenges coalesce to make Southern Africa emblematic of the connections between climate and the water-energy-food nexus. Rainfall and river flows in the region show high levels of variability across a range of spatial and temporal scales. Physical and socioeconomic exposure to climate variability and change is high, for example, the contribution of electricity produced from hydroelectric sources is over 30% in Madagascar and Zimbabwe and almost 100% in the DRC, Lesotho, Malawi, and Zambia. The region's economy is closely linked with that of the rest of the African continent and climate-sensitive food products are an important item of trade. Southern Africa's population is concentrated in regions exposed to high levels of hydro-meteorological variability, and will increase rapidly over the next four decades. The capacity to manage the effects of climate variability tends, however, to be low. Moreover, with climate change annual precipitation levels, soil moisture and runoff are likely to decrease and rising temperatures will increase evaporative demand. Despite high levels of hydro-meteorological variability, the sectoral and cross-sectoral water-energy-food linkages with climate in Southern Africa have not been considered in detail. Lack of data and questionable reliability are compounded by complex dynamic relationships. We review the role of climate in Southern Africa's nexus, complemented by empirical analysis of national level data on climate, water resources, crop and energy production, and economic activity. Our aim is to examine the role of climate variability as a driver of production fluctuations in the nexus, and to improve understanding of the magnitude and temporal dimensions of their interactions. We first consider national level exposure of food, water and energy production to climate in aggregate economic terms and then examine the linkages between interannual and multi-year climate variability and economic activity, focusing on food and hydropower production. We then review the potential for connecting areas with robust seasonal climate forecasting skill with key precursors of economic output and conclude by identifying knowledge gaps in our understanding of regional and national economic linkages in the climate and water-energy-food nexus.

The CESM Large Ensemble Project: Inspiring New Ideas and Understanding

NASA Astrophysics Data System (ADS)

Kay, J. E.; Deser, C.

2016-12-01

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.

Influence of climate variability on acute myocardial infarction mortality in Havana, 2001-2012.

PubMed

Rivero, Alina; Bolufé, Javier; Ortiz, Paulo L; Rodríguez, Yunisleydi; Reyes, María C

2015-04-01

Death from acute myocardial infarction is due to many factors; influences on risk to the individual include habits, lifestyle and behavior, as well as weather, climate and other environmental components. Changing climate patterns make it especially important to understand how climatic variability may influence acute myocardial infarction mortality. Describe the relationship between climate variability and acute myocardial infarction mortality during the period 2001-2012 in Havana. An ecological time-series study was conducted. The universe comprised 23,744 deaths from acute myocardial infarction (ICD-10: I21-I22) in Havana residents from 2001 to 2012. Climate variability and seasonal anomalies were described using the Bultó-1 bioclimatic index (comprising variables of temperature, humidity, precipitation, and atmospheric pressure), along with series analysis to determine different seasonal-to-interannual climate variation signals. The role played by climate variables in acute myocardial infarction mortality was determined using factor analysis. The Mann-Kendall and Pettitt statistical tests were used for trend analysis with a significance level of 5%. The strong association between climate variability conditions described using the Bultó-1 bioclimatic index and acute myocardial infarctions accounts for the marked seasonal pattern in AMI mortality. The highest mortality rate occurred during the dry season, i.e., the winter months in Cuba (November-April), with peak numbers in January, December and March. The lowest mortality coincided with the rainy season, i.e., the summer months (May-October). A downward trend in total number of deaths can be seen starting with the change point in April 2009. Climate variability is inversely associated with an increase in acute myocardial infarction mortality as is shown by the Bultó-1 index. This inverse relationship accounts for acute myocardial infarction mortality's seasonal pattern.

Short-term climate change impacts on Mara basin hydrology

NASA Astrophysics Data System (ADS)

Demaria, E. M.; Roy, T.; Valdés, J. B.; Lyon, B.; Valdés-Pineda, R.; Serrat-Capdevila, A.; Durcik, M.; Gupta, H.

2017-12-01

The predictability of climate diminishes significantly at shorter time scales (e.g. decadal). Both natural variability as well as sampling variability of climate can obscure or enhance climate change signals in these shorter scales. Therefore, in order to assess the impacts of climate change on basin hydrology, it is important to design climate projections with exhaustive climate scenarios. In this study, we first create seasonal climate scenarios by combining (1) synthetic precipitation projections generated from a Vector Auto-Regressive (VAR) model using the University of East Anglia Climate Research Unit (UEA-CRU) data with (2) seasonal trends calculated from 31 models in the Coupled Model Intercomparison Project Phase 5 (CMIP). The seasonal climate projections are then disaggregated to daily level using the Agricultural Modern-Era Retrospective Analysis for Research and Applications (AgMERRA) data. The daily climate data are then bias-corrected and used as forcings to the land-surface model, Variable Infiltration Capacity (VIC), to generate different hydrological projections for the Mara River basin in East Africa, which are then evaluated to study the hydrologic changes in the basin in the next three decades (2020-2050).

Vulnerability of breeding waterbirds to climate change in the Prairie Pothole Region, U.S.A.

PubMed

Steen, Valerie; Skagen, Susan K; Noon, Barry R

2014-01-01

The Prairie Pothole Region (PPR) of the north-central U.S. and south-central Canada contains millions of small prairie wetlands that provide critical habitat to many migrating and breeding waterbirds. Due to their small size and the relatively dry climate of the region, these wetlands are considered at high risk for negative climate change effects as temperatures increase. To estimate the potential impacts of climate change on breeding waterbirds, we predicted current and future distributions of species common in the PPR using species distribution models (SDMs). We created regional-scale SDMs for the U.S. PPR using Breeding Bird Survey occurrence records for 1971-2011 and wetland, upland, and climate variables. For each species, we predicted current distribution based on climate records for 1981-2000 and projected future distributions to climate scenarios for 2040-2049. Species were projected to, on average, lose almost half their current habitat (-46%). However, individual species projections varied widely, from +8% (Upland Sandpiper) to -100% (Wilson's Snipe). Variable importance ranks indicated that land cover (wetland and upland) variables were generally more important than climate variables in predicting species distributions. However, climate variables were relatively more important during a drought period. Projected distributions of species responses to climate change contracted within current areas of distribution rather than shifting. Given the large variation in species-level impacts, we suggest that climate change mitigation efforts focus on species projected to be the most vulnerable by enacting targeted wetland management, easement acquisition, and restoration efforts.

Climate controls the distribution of a widespread invasive species: Implications for future range expansion

USGS Publications Warehouse

McDowell, W.G.; Benson, A.J.; Byers, J.E.

2014-01-01

1. Two dominant drivers of species distributions are climate and habitat, both of which are changing rapidly. Understanding the relative importance of variables that can control distributions is critical, especially for invasive species that may spread rapidly and have strong effects on ecosystems. 2. Here, we examine the relative importance of climate and habitat variables in controlling the distribution of the widespread invasive freshwater clam Corbicula fluminea, and we model its future distribution under a suite of climate scenarios using logistic regression and maximum entropy modelling (MaxEnt). 3. Logistic regression identified climate variables as more important than habitat variables in controlling Corbicula distribution. MaxEnt modelling predicted Corbicula's range expansion westward and northward to occupy half of the contiguous United States. By 2080, Corbicula's potential range will expand 25–32%, with more than half of the continental United States being climatically suitable. 4. Our combination of multiple approaches has revealed the importance of climate over habitat in controlling Corbicula's distribution and validates the climate-only MaxEnt model, which can readily examine the consequences of future climate projections. 5. Given the strong influence of climate variables on Corbicula's distribution, as well as Corbicula's ability to disperse quickly and over long distances, Corbicula is poised to expand into New England and the northern Midwest of the United States. Thus, the direct effects of climate change will probably be compounded by the addition of Corbicula and its own influences on ecosystem function.

Violence and Disorder, School Climate, and PBIS: The Relationship among School Climate, Student Outcomes, and the Use of Positive Behavioral Interventions and Supports

ERIC Educational Resources Information Center

Eacho, Thomas Christopher

2013-01-01

The primary purpose of this study was to examine the relationship between school climate and student outcome variables. The secondary purpose was to examine the relationship between the use of Positive Behavioral Interventions and Supports (PBIS) and the same student outcome variables. Variables depicting student perceptions of school climate,…

Quantitative approaches in climate change ecology

PubMed Central

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

2011-01-01

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.

Using physiology to understand climate-driven changes in disease and their implications for conservation.

PubMed

Rohr, Jason R; Raffel, Thomas R; Blaustein, Andrew R; Johnson, Pieter T J; Paull, Sara H; Young, Suzanne

2013-01-01

Controversy persists regarding the contributions of climate change to biodiversity losses, through its effects on the spread and emergence of infectious diseases. One of the reasons for this controversy is that there are few mechanistic studies that explore the links among climate change, infectious disease, and declines of host populations. Given that host-parasite interactions are generally mediated by physiological responses, we submit that physiological models could facilitate the prediction of how host-parasite interactions will respond to climate change, and might offer theoretical and terminological cohesion that has been lacking in the climate change-disease literature. We stress that much of the work on how climate influences host-parasite interactions has emphasized changes in climatic means, despite a hallmark of climate change being changes in climatic variability and extremes. Owing to this gap, we highlight how temporal variability in weather, coupled with non-linearities in responses to mean climate, can be used to predict the effects of climate on host-parasite interactions. We also discuss the climate variability hypothesis for disease-related declines, which posits that increased unpredictable temperature variability might provide a temporary advantage to pathogens because they are smaller and have faster metabolisms than their hosts, allowing more rapid acclimatization following a temperature shift. In support of these hypotheses, we provide case studies on the role of climatic variability in host population declines associated with the emergence of the infectious diseases chytridiomycosis, withering syndrome, and malaria. Finally, we present a mathematical model that provides the scaffolding to integrate metabolic theory, physiological mechanisms, and large-scale spatiotemporal processes to predict how simultaneous changes in climatic means, variances, and extremes will affect host-parasite interactions. However, several outstanding questions remain to be answered before investigators can accurately predict how changes in climatic means and variances will affect infectious diseases and the conservation status of host populations.

Using physiology to understand climate-driven changes in disease and their implications for conservation

PubMed Central

Rohr, Jason R.; Raffel, Thomas R.; Blaustein, Andrew R.; Johnson, Pieter T. J.; Paull, Sara H.; Young, Suzanne

2013-01-01

Controversy persists regarding the contributions of climate change to biodiversity losses, through its effects on the spread and emergence of infectious diseases. One of the reasons for this controversy is that there are few mechanistic studies that explore the links among climate change, infectious disease, and declines of host populations. Given that host–parasite interactions are generally mediated by physiological responses, we submit that physiological models could facilitate the prediction of how host–parasite interactions will respond to climate change, and might offer theoretical and terminological cohesion that has been lacking in the climate change–disease literature. We stress that much of the work on how climate influences host–parasite interactions has emphasized changes in climatic means, despite a hallmark of climate change being changes in climatic variability and extremes. Owing to this gap, we highlight how temporal variability in weather, coupled with non-linearities in responses to mean climate, can be used to predict the effects of climate on host–parasite interactions. We also discuss the climate variability hypothesis for disease-related declines, which posits that increased unpredictable temperature variability might provide a temporary advantage to pathogens because they are smaller and have faster metabolisms than their hosts, allowing more rapid acclimatization following a temperature shift. In support of these hypotheses, we provide case studies on the role of climatic variability in host population declines associated with the emergence of the infectious diseases chytridiomycosis, withering syndrome, and malaria. Finally, we present a mathematical model that provides the scaffolding to integrate metabolic theory, physiological mechanisms, and large-scale spatiotemporal processes to predict how simultaneous changes in climatic means, variances, and extremes will affect host–parasite interactions. However, several outstanding questions remain to be answered before investigators can accurately predict how changes in climatic means and variances will affect infectious diseases and the conservation status of host populations. PMID:27293606

Climate and climate variability of the wind power resources in the Great Lakes region of the United States

Treesearch

X. Li; S. Zhong; X. Bian; W.E. Heilman

2010-01-01

The climate and climate variability of low-level winds over the Great Lakes region of the United States is examined using 30 year (1979-2008) wind records from the recently released North American Regional Reanalysis (NARR), a three-dimensional, high-spatial and temporal resolution, and dynamically consistent climate data set. The analyses focus on spatial distribution...

Processes Understanding of Decadal Climate Variability

NASA Astrophysics Data System (ADS)

Prömmel, Kerstin; Cubasch, Ulrich

2016-04-01

The realistic representation of decadal climate variability in the models is essential for the quality of decadal climate predictions. Therefore, the understanding of those processes leading to decadal climate variability needs to be improved. Several of these processes are already included in climate models but their importance has not yet completely been clarified. The simulation of other processes requires sometimes a higher resolution of the model or an extension by additional subsystems. This is addressed within one module of the German research program "MiKlip II - Decadal Climate Predictions" (http://www.fona-miklip.de/en/) with a focus on the following processes. Stratospheric processes and their impact on the troposphere are analysed regarding the climate response to aerosol perturbations caused by volcanic eruptions and the stratospheric decadal variability due to solar forcing, climate change and ozone recovery. To account for the interaction between changing ozone concentrations and climate a computationally efficient ozone chemistry module is developed and implemented in the MiKlip prediction system. The ocean variability and air-sea interaction are analysed with a special focus on the reduction of the North Atlantic cold bias. In addition, the predictability of the oceanic carbon uptake with a special emphasis on the underlying mechanism is investigated. This addresses a combination of physical, biological and chemical processes.

Forecasting Glacier Evolution and Hindcasting Paleoclimates In Light of Mass Balance Nonlinearities

NASA Astrophysics Data System (ADS)

Malone, A.; Doughty, A. M.; MacAyeal, D. R.

2016-12-01

Glaciers are commonly used barometers of present and past climate change, with their variations often being linked to shifts in the mean climate. Climate variability within a unchanging mean state, however, can produce short term mass balance and glacier length anomalies, complicating this linkage. Also, the mass balance response to this variability can be nonlinear, possibly impacting the longer term state of the glacier. We propose a conceptual model to understand these nonlinearities and quantify their impacts on the longer term mass balance and glacier length. The relationship between mass balance and elevation, i.e. the vertical balance profile (VBP), illuminates these nonlinearities (Figure A). The VBP, given here for a wet tropical glacier, is piecewise, which can lead to different mass balance responses to climate anomalies of similar magnitude but opposite sign. We simulate the mass balance response to climate variability by vertically (temperature anomalies) and horizontally (precipitation anomalies) transposing the VBP for the mean climate (Figure A). The resulting anomalous VBP is the superposition of the two translations. We drive a 1-D flowline model with 10,000 years of anomalous VBPs. The aggregate VBP for the mean climate including variability differs from the VBP for the mean climate excluding variability, having a higher equilibrium line altitude (ELA) and a negative mass balance (Figure B). Accordingly, the glacier retreats, and the equilibrium glacier length for the aggregate VBP is the same as the mean length from the 10,000 year flowline simulation (Figure C). The magnitude of the VBP shift and glacier retreat increases with greater temperature variability and larger discontinuities in the VBP slope. These results highlight the importance of both the climate mean and variability in determining the longer term state of the glacier. Thus, forecasting glacier evolution or hindcasting past climates should also include representation of climate variability.

Climate Variability and Ecosystem Response

Treesearch

David Greenland; Lloyd W. Swift; [Editors

1990-01-01

Nine papers describe studies of climate variability and ecosystem response. The studies were conducted at LTER (Long-Term Ecological Research) sites representing forest, agricultural, and aquatic ecosystems and systems in which extreme climates limit vegetational cover. An overview paper prepared by the LTER Climate Committee stresses the importance of (1) clear...

Atmospheric Teleconnection and Climate Variability: Affecting Rice Productivity of Bihar, India

NASA Astrophysics Data System (ADS)

Saini, A.

2017-12-01

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.

Building Training Curricula for Accelerating the Use of NOAA Climate Products and Tools

NASA Astrophysics Data System (ADS)

Timofeyeva-Livezey, M. M.; Meyers, J. C.; Stevermer, A.; Abshire, W. E.; Beller-Simms, N.; Herring, D.

2016-12-01

The National Oceanic and Atmospheric Administration (NOAA) plays a leading role in U.S. intergovernmental efforts on the Climate Data Initiative and the Climate Resilience Toolkit (CRT). CRT (http://toolkit.climate.gov/) is a valuable resource that provides tools, information, and subject matter expertise to decision makers in various sectors, such as agriculture, water resources and transportation, to help them build resilience to our changing climate. In order to make best use of the toolkit and all the resources within it, a training component is critical. The training section helps building users' understanding of the data, science, and impacts of climate variability and change. CRT identifies five steps in building resilience that includes use of appropriate tools to support decision makers depending on their needs. One tool that can be potentially integrated into CRT is NOAA's Local Climate Analysis Tool (LCAT), which provides access to trusted NOAA data and scientifically-sound analysis techniques for doing regional and local climate studies on climate variability and climate change. However, in order for LCAT to be used effectively, we have found an iterative learning approach using specific examples to train users. For example, for LCAT application in analysis of water resources, we use existing CRT case studies for Arizona and Florida water supply users. The Florida example demonstrates primary sensitivity to climate variability impacts, whereas the Arizona example takes into account longer- term climate change. The types of analyses included in LCAT are time series analysis of local climate and the estimated rate of change in the local climate. It also provides a composite analysis to evaluate the relationship between local climate and climate variability events such as El Niño Southern Oscillation, the Pacific North American Index, and other modes of climate variability. This paper will describe the development of a training module for use of LCAT and its integration into CRT. An iterative approach was used that incorporates specific examples of decision making while working with subject matter experts within the water supply community. The recommended strategy is to use a "stepping stone" learning structure to build users knowledge of best practices for use of LCAT.

Leveraging federal science data and tools to help communities & business build climate resilience

NASA Astrophysics Data System (ADS)

Herring, D.

2016-12-01

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.

A new statistical tool for NOAA local climate studies

NASA Astrophysics Data System (ADS)

Timofeyeva, M. M.; Meyers, J. C.; Hollingshead, A.

2011-12-01

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.

Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables

NASA Astrophysics Data System (ADS)

Jones, Philip D.; Harpham, Colin; Troccoli, Alberto; Gschwind, Benoit; Ranchin, Thierry; Wald, Lucien; Goodess, Clare M.; Dorling, Stephen

2017-07-01

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 ftp://ecem.climate.copernicus.eu. The benefit of performing bias adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against gridded observational fields.

Multi-Wheat-Model Ensemble Responses to Interannual Climate Variability

NASA Technical Reports Server (NTRS)

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

2016-01-01

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.

Response-Guided Community Detection: Application to Climate Index Discovery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bello, Gonzalo; Angus, Michael; Pedemane, Navya

Discovering climate indices-time series that summarize spatiotemporal climate patterns-is a key task in the climate science domain. In this work, we approach this task as a problem of response-guided community detection; that is, identifying communities in a graph associated with a response variable of interest. To this end, we propose a general strategy for response-guided community detection that explicitly incorporates information of the response variable during the community detection process, and introduce a graph representation of spatiotemporal data that leverages information from multiple variables. We apply our proposed methodology to the discovery of climate indices associated with seasonal rainfall variability.more » Our results suggest that our methodology is able to capture the underlying patterns known to be associated with the response variable of interest and to improve its predictability compared to existing methodologies for data-driven climate index discovery and official forecasts.« less

Human Responses to Climate Variability: The Case of South Africa

NASA Astrophysics Data System (ADS)

Oppenheimer, M.; Licker, R.; Mastrorillo, M.; Bohra-Mishra, P.; Estes, L. D.; Cai, R.

2014-12-01

Climate variability has been associated with a range of societal and individual outcomes including migration, violent conflict, changes in labor productivity, and health impacts. Some of these may be direct responses to changes in mean temperature or precipitation or extreme events, such as displacement of human populations by tropical cyclones. Others may be mediated by a variety of biological, social, or ecological factors such as migration in response to long-term changes in crops yields. Research is beginning to elucidate and distinguish the many channels through which climate variability may influence human behavior (ranging from the individual to the collective, societal level) in order to better understand how to improve resilience in the face of current variability as well as future climate change. Using a variety of data sets from South Africa, we show how climate variability has influenced internal (within country) migration in recent history. We focus on South Africa as it is a country with high levels of internal migration and dramatic temperature and precipitation changes projected for the 21st century. High poverty rates and significant levels of rain-fed, smallholder agriculture leave large portions of South Africa's population base vulnerable to future climate change. In this study, we utilize two complementary statistical models - one micro-level model, driven by individual and household level survey data, and one macro-level model, driven by national census statistics. In both models, we consider the effect of climate on migration both directly (with gridded climate reanalysis data) and indirectly (with agricultural production statistics). With our historical analyses of climate variability, we gain insights into how the migration decisions of South Africans may be influenced by future climate change. We also offer perspective on the utility of micro and macro level approaches in the study of climate change and human migration.

Central Tropical Pacific Variability And ENSO Response To Changing Climate Boundary Conditions: Evidence From Individual Line Island Foraminifera

NASA Astrophysics Data System (ADS)

Rustic, G. T.; Polissar, P. J.; Ravelo, A. C.; White, S. M.

2017-12-01

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.

Assessing effects of variation in global climate data sets on spatial predictions from climate envelope models

USGS Publications Warehouse

Romañach, Stephanie; Watling, James I.; Fletcher, Robert J.; Speroterra, Carolina; Bucklin, David N.; Brandt, Laura A.; Pearlstine, Leonard G.; Escribano, Yesenia; Mazzotti, Frank J.

2014-01-01

Climate change poses new challenges for natural resource managers. Predictive modeling of species–environment relationships using climate envelope models can enhance our understanding of climate change effects on biodiversity, assist in assessment of invasion risk by exotic organisms, and inform life-history understanding of individual species. While increasing interest has focused on the role of uncertainty in future conditions on model predictions, models also may be sensitive to the initial conditions on which they are trained. Although climate envelope models are usually trained using data on contemporary climate, we lack systematic comparisons of model performance and predictions across alternative climate data sets available for model training. Here, we seek to fill that gap by comparing variability in predictions between two contemporary climate data sets to variability in spatial predictions among three alternative projections of future climate. Overall, correlations between monthly temperature and precipitation variables were very high for both contemporary and future data. Model performance varied across algorithms, but not between two alternative contemporary climate data sets. Spatial predictions varied more among alternative general-circulation models describing future climate conditions than between contemporary climate data sets. However, we did find that climate envelope models with low Cohen's kappa scores made more discrepant spatial predictions between climate data sets for the contemporary period than did models with high Cohen's kappa scores. We suggest conservation planners evaluate multiple performance metrics and be aware of the importance of differences in initial conditions for spatial predictions from climate envelope models.

Disease and thermal acclimation in a more variable and unpredictable climate

NASA Astrophysics Data System (ADS)

Raffel, Thomas R.; Romansic, John M.; Halstead, Neal T.; McMahon, Taegan A.; Venesky, Matthew D.; Rohr, Jason R.

2013-02-01

Global climate change is shifting the distribution of infectious diseases of humans and wildlife with potential adverse consequences for disease control. As well as increasing mean temperatures, climate change is expected to increase climate variability, making climate less predictable. However, few empirical or theoretical studies have considered the effects of climate variability or predictability on disease, despite it being likely that hosts and parasites will have differential responses to climatic shifts. Here we present a theoretical framework for how temperature variation and its predictability influence disease risk by affecting host and parasite acclimation responses. Laboratory experiments conducted in 80 independent incubators, and field data on disease-associated frog declines in Latin America, support the framework and provide evidence that unpredictable temperature fluctuations, on both monthly and diurnal timescales, decrease frog resistance to the pathogenic chytrid fungus Batrachochytrium dendrobatidis. Furthermore, the pattern of temperature-dependent growth of the fungus on frogs was opposite to the pattern of growth in culture, emphasizing the importance of accounting for the host-parasite interaction when predicting climate-dependent disease dynamics. If similar acclimation responses influence other host-parasite systems, as seems likely, then present models, which generally ignore small-scale temporal variability in climate, might provide poor predictions for climate effects on disease.

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.

PubMed

Yousefpour, Rasoul; Temperli, Christian; Bugmann, Harald; Elkin, Che; Hanewinkel, Marc; Meilby, Henrik; Jacobsen, Jette Bredahl; Thorsen, Bo Jellesmark

2013-06-15

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.

[Modelling the effect of local climatic variability on dengue transmission in Medellin (Colombia) by means of time series analysis].

PubMed

Rúa-Uribe, Guillermo L; Suárez-Acosta, Carolina; Chauca, José; Ventosilla, Palmira; Almanza, Rita

2013-09-01

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.

Towards the Prediction of Decadal to Centennial Climate Processes in the Coupled Earth System Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Zhengyu; Kutzbach, J.; Jacob, R.

2011-12-05

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

Multi-objective optimization for evaluation of simulation fidelity for precipitation, cloudiness and insolation in regional climate models

NASA Astrophysics Data System (ADS)

Lee, H.

2016-12-01

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.

The response of the southwest Western Australian wave climate to Indian Ocean climate variability

NASA Astrophysics Data System (ADS)

Wandres, Moritz; Pattiaratchi, Charitha; Hetzel, Yasha; Wijeratne, E. M. S.

2018-03-01

Knowledge of regional wave climates is critical for coastal planning, management, and protection. In order to develop a regional wave climate, it is important to understand the atmospheric systems responsible for wave generation. This study examines the variability of the southwest Western Australian (SWWA) shelf and nearshore wind wave climate and its relationship to southern hemisphere climate variability represented by various atmospheric indices: the southern oscillation index (SOI), the Southern Annular Mode (SAM), the Indian Ocean Dipole Mode Index (DMI), the Indian Ocean Subtropical Dipole (IOSD), the latitudinal position of the subtropical high-pressure ridge (STRP), and the corresponding intensity of the subtropical ridge (STRI). A 21-year wave hindcast (1994-2014) of the SWWA continental shelf was created using the third generation wave model Simulating WAves Nearshore (SWAN), to analyse the seasonal and inter-annual wave climate variability and its relationship to the atmospheric regime. Strong relationships between wave heights and the STRP and the STRI, a moderate correlation between the wave climate and the SAM, and no significant correlation between SOI, DMI, and IOSD and the wave climate were found. Strong spatial, seasonal, and inter-annual variability, as well as seasonal longer-term trends in the mean wave climate were studied and linked to the latitudinal changes in the subtropical high-pressure ridge and the Southern Ocean storm belt. As the Southern Ocean storm belt and the subtropical high-pressure ridge shifted southward (northward) wave heights on the SWWA shelf region decreased (increased). The wave height anomalies appear to be driven by the same atmospheric conditions that influence rainfall variability in SWWA.

Association between climate variability and malaria epidemics in the East African highlands.

PubMed

Zhou, Guofa; Minakawa, Noboru; Githeko, Andrew K; Yan, Guiyun

2004-02-24

The causes of the recent reemergence of Plasmodium falciparum epidemic malaria in the East African highlands are controversial. Regional climate changes have been invoked as a major factor; however, assessing the impact of climate in malaria resurgence is difficult due to high spatial and temporal climate variability and the lack of long-term data series on malaria cases from different sites. Climate variability, defined as short-term fluctuations around the mean climate state, may be epidemiologically more relevant than mean temperature change, but its effects on malaria epidemics have not been rigorously examined. Here we used nonlinear mixed-regression model to investigate the association between autoregression (number of malaria outpatients during the previous time period), seasonality and climate variability, and the number of monthly malaria outpatients of the past 10-20 years in seven highland sites in East Africa. The model explained 65-81% of the variance in the number of monthly malaria outpatients. Nonlinear and synergistic effects of temperature and rainfall on the number of malaria outpatients were found in all seven sites. The net variance in the number of monthly malaria outpatients caused by autoregression and seasonality varied among sites and ranged from 18 to 63% (mean=38.6%), whereas 12-63% (mean=36.1%) of variance is attributed to climate variability. Our results suggest that there was a high spatial variation in the sensitivity of malaria outpatient number to climate fluctuations in the highlands, and that climate variability played an important role in initiating malaria epidemics in the East African highlands.

NUTRItion and CLIMate (NUTRICLIM): investigating the relationship between climate variables and childhood malnutrition through agriculture, an exploratory study in Burkina Faso.

PubMed

Sorgho, Raissa; Franke, Jonas; Simboro, Seraphin; Phalkey, Revati; Saeurborn, Rainer

Malnutrition remains a leading cause of death in children in low- and middle-income countries; this will be aggravated by climate change. Annually, 6.9 million deaths of children under 5 were attributable directly or indirectly to malnutrition. Although these figures have recently decreased, evidence shows that a world with a medium climate (local warming up to 3-4 °C) will create an additional 25.2 million malnourished children. This proof of concept study explores the relationships between childhood malnutrition (more specifically stunting), regional agricultural yields, and climate variables through the use of remote sensing (RS) satellite imaging along with algorithms to predict the effect of climate variability on agricultural yields and on malnutrition of children under 5. The success of this proof of purpose study, NUTRItion and CLIMate (NUTRICLIM), should encourage researchers to apply both concept and tools to study of the link between weather variability, crop yield, and malnutrition on a larger scale. It would also allow for linking such micro-level data to climate models and address the challenge of projecting the additional impact of childhood malnutrition from climate change to various policy relevant time horizons.

Climate change and occurrence of diarrheal diseases: evolving facts from Nepal.

PubMed

Bhandari, G P; Gurung, S; Dhimal, M; Bhusal, C L

2012-09-01

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.

Predicting phenology by integrating ecology, evolution and climate science

USGS Publications Warehouse

Pau, Stephanie; Wolkovich, Elizabeth M.; Cook, Benjamin I.; Davies, T. Jonathan; Kraft, Nathan J.B.; Bolmgren, Kjell; Betancourt, Julio L.; Cleland, Elsa E.

2011-01-01

Forecasting how species and ecosystems will respond to climate change has been a major aim of ecology in recent years. Much of this research has focused on phenology — the timing of life-history events. Phenology has well-demonstrated links to climate, from genetic to landscape scales; yet our ability to explain and predict variation in phenology across species, habitats and time remains poor. Here, we outline how merging approaches from ecology, climate science and evolutionary biology can advance research on phenological responses to climate variability. Using insight into seasonal and interannual climate variability combined with niche theory and community phylogenetics, we develop a predictive approach for species' reponses to changing climate. Our approach predicts that species occupying higher latitudes or the early growing season should be most sensitive to climate and have the most phylogenetically conserved phenologies. We further predict that temperate species will respond to climate change by shifting in time, while tropical species will respond by shifting space, or by evolving. Although we focus here on plant phenology, our approach is broadly applicable to ecological research of plant responses to climate variability.

Assessing performance and seasonal bias of pollen-based climate reconstructions in a perfect model world

NASA Astrophysics Data System (ADS)

Rehfeld, Kira; Trachsel, Mathias; Telford, Richard J.; Laepple, Thomas

2016-12-01

Reconstructions of summer, winter or annual mean temperatures based on the species composition of bio-indicators such as pollen, foraminifera or chironomids are routinely used in climate model-proxy data comparison studies. Most reconstruction algorithms exploit the joint distribution of modern spatial climate and species distribution for the development of the reconstructions. They rely on the space-for-time substitution and the specific assumption that environmental variables other than those reconstructed are not important or that their relationship with the reconstructed variable(s) should be the same in the past as in the modern spatial calibration dataset. Here we test the implications of this "correlative uniformitarianism" assumption on climate reconstructions in an ideal model world, in which climate and vegetation are known at all times. The alternate reality is a climate simulation of the last 6000 years with dynamic vegetation. Transient changes of plant functional types are considered as surrogate pollen counts and allow us to establish, apply and evaluate transfer functions in the modeled world. We find that in our model experiments the transfer function cross validation r2 is of limited use to identify reconstructible climate variables, as it only relies on the modern spatial climate-vegetation relationship. However, ordination approaches that assess the amount of fossil vegetation variance explained by the reconstructions are promising. We furthermore show that correlations between climate variables in the modern climate-vegetation relationship are systematically extended into the reconstructions. Summer temperatures, the most prominent driving variable for modeled vegetation change in the Northern Hemisphere, are accurately reconstructed. However, the amplitude of the model winter and mean annual temperature cooling between the mid-Holocene and present day is overestimated and similar to the summer trend in magnitude. This effect occurs because temporal changes of a dominant climate variable, such as summer temperatures in the model's Arctic, are imprinted on a less important variable, leading to reconstructions biased towards the dominant variable's trends. Our results, although based on a model vegetation that is inevitably simpler than reality, indicate that reconstructions of multiple climate variables based on modern spatial bio-indicator datasets should be treated with caution. Expert knowledge on the ecophysiological drivers of the proxies, as well as statistical methods that go beyond the cross validation on modern calibration datasets, are crucial to avoid misinterpretation.

Effects of short-term variability of meteorological variables on soil temperature in permafrost regions

NASA Astrophysics Data System (ADS)

Beer, Christian; Porada, Philipp; Ekici, Altug; Brakebusch, Matthias

2018-03-01

Effects of the short-term temporal variability of meteorological variables on soil temperature in northern high-latitude regions have been investigated. For this, a process-oriented land surface model has been driven using an artificially manipulated climate dataset. Short-term climate variability mainly impacts snow depth, and the thermal diffusivity of lichens and bryophytes. These impacts of climate variability on insulating surface layers together substantially alter the heat exchange between atmosphere and soil. As a result, soil temperature is 0.1 to 0.8 °C higher when climate variability is reduced. Earth system models project warming of the Arctic region but also increasing variability of meteorological variables and more often extreme meteorological events. Therefore, our results show that projected future increases in permafrost temperature and active-layer thickness in response to climate change will be lower (i) when taking into account future changes in short-term variability of meteorological variables and (ii) when representing dynamic snow and lichen and bryophyte functions in land surface models.

Accounting for multiple climate components when estimating climate change exposure and velocity

USGS Publications Warehouse

Nadeau, Christopher P.; Fuller, Angela K.

2015-01-01

The effect of anthropogenic climate change on organisms will likely be related to climate change exposure and velocity at local and regional scales. However, common methods to estimate climate change exposure and velocity ignore important components of climate that are known to affect the ecology and evolution of organisms.We develop a novel index of climate change (climate overlap) that simultaneously estimates changes in the means, variation and correlation between multiple weather variables. Specifically, we estimate the overlap between multivariate normal probability distributions representing historical and current or projected future climates. We provide methods for estimating the statistical significance of climate overlap values and methods to estimate velocity using climate overlap.We show that climates have changed significantly across 80% of the continental United States in the last 32 years and that much of this change is due to changes in the variation and correlation between weather variables (two statistics that are rarely incorporated into climate change studies). We also show that projected future temperatures are predicted to be locally novel (<1·5% overlap) across most of the global land surface and that exposure is likely to be highest in areas with low historical climate variation. Last, we show that accounting for changes in the variation and correlation between multiple weather variables can dramatically affect velocity estimates; mean velocity estimates in the continental United States were between 3·1 and 19·0 km yr−1when estimated using climate overlap compared to 1·4 km yr−1 when estimated using traditional methods.Our results suggest that accounting for changes in the means, variation and correlation between multiple weather variables can dramatically affect estimates of climate change exposure and velocity. These climate components are known to affect the ecology and evolution of organisms, but are ignored by most measures of climate change. We conclude with a set of future directions and recommend future work to determine which measures of climate change exposure and velocity are most related to biological responses to climate change.

Transferability of optimally-selected climate models in the quantification of climate change impacts on hydrology

NASA Astrophysics Data System (ADS)

Chen, Jie; Brissette, François P.; Lucas-Picher, Philippe

2016-11-01

Given the ever increasing number of climate change simulations being carried out, it has become impractical to use all of them to cover the uncertainty of climate change impacts. Various methods have been proposed to optimally select subsets of a large ensemble of climate simulations for impact studies. However, the behaviour of optimally-selected subsets of climate simulations for climate change impacts is unknown, since the transfer process from climate projections to the impact study world is usually highly non-linear. Consequently, this study investigates the transferability of optimally-selected subsets of climate simulations in the case of hydrological impacts. Two different methods were used for the optimal selection of subsets of climate scenarios, and both were found to be capable of adequately representing the spread of selected climate model variables contained in the original large ensemble. However, in both cases, the optimal subsets had limited transferability to hydrological impacts. To capture a similar variability in the impact model world, many more simulations have to be used than those that are needed to simply cover variability from the climate model variables' perspective. Overall, both optimal subset selection methods were better than random selection when small subsets were selected from a large ensemble for impact studies. However, as the number of selected simulations increased, random selection often performed better than the two optimal methods. To ensure adequate uncertainty coverage, the results of this study imply that selecting as many climate change simulations as possible is the best avenue. Where this was not possible, the two optimal methods were found to perform adequately.

Global variation in thermal tolerances and vulnerability of endotherms to climate change

PubMed Central

Khaliq, Imran; Hof, Christian; Prinzinger, Roland; Böhning-Gaese, Katrin; Pfenninger, Markus

2014-01-01

The relationships among species' physiological capacities and the geographical variation of ambient climate are of key importance to understanding the distribution of life on the Earth. Furthermore, predictions of how species will respond to climate change will profit from the explicit consideration of their physiological tolerances. The climatic variability hypothesis, which predicts that climatic tolerances are broader in more variable climates, provides an analytical framework for studying these relationships between physiology and biogeography. However, direct empirical support for the hypothesis is mostly lacking for endotherms, and few studies have tried to integrate physiological data into assessments of species' climatic vulnerability at the global scale. Here, we test the climatic variability hypothesis for endotherms, with a comprehensive dataset on thermal tolerances derived from physiological experiments, and use these data to assess the vulnerability of species to projected climate change. We find the expected relationship between thermal tolerance and ambient climatic variability in birds, but not in mammals—a contrast possibly resulting from different adaptation strategies to ambient climate via behaviour, morphology or physiology. We show that currently most of the species are experiencing ambient temperatures well within their tolerance limits and that in the future many species may be able to tolerate projected temperature increases across significant proportions of their distributions. However, our findings also underline the high vulnerability of tropical regions to changes in temperature and other threats of anthropogenic global changes. Our study demonstrates that a better understanding of the interplay among species' physiology and the geography of climate change will advance assessments of species' vulnerability to climate change. PMID:25009066

Association of genetic and phenotypic variability with geography and climate in three southern California oaks.

PubMed

Riordan, Erin C; Gugger, Paul F; Ortego, Joaquín; Smith, Carrie; Gaddis, Keith; Thompson, Pam; Sork, Victoria L

2016-01-01

Geography and climate shape the distribution of organisms, their genotypes, and their phenotypes. To understand historical and future evolutionary and ecological responses to climate, we compared the association of geography and climate of three oak species (Quercus engelmannii, Quercus berberidifolia, and Quercus cornelius-mulleri) in an environmentally heterogeneous region of southern California at three organizational levels: regional species distributions, genetic variation, and phenotypic variation. We identified climatic variables influencing regional distribution patterns using species distribution models (SDMs), and then tested whether those individual variables are important in shaping genetic (microsatellite) and phenotypic (leaf morphology) variation. We estimated the relative contributions of geography and climate using multivariate redundancy analyses (RDA) with variance partitioning. The modeled distribution of each species was influenced by climate differently. Our analysis of genetic variation using RDA identified small but significant associations between genetic variation with climate and geography in Q. engelmannii and Q. cornelius-mulleri, but not in Q. berberidifolia, and climate explained more of the variation. Our analysis of phenotypic variation in Q. engelmannii indicated that climate had more impact than geography, but not in Q. berberidifolia. Throughout our analyses, we did not find a consistent pattern in effects of individual climatic variables. Our comparative analysis illustrates that climate influences tree response at all organizational levels, but the important climate factors vary depending on the level and on the species. Because of these species-specific and level-specific responses, today's sympatric species are unlikely to have similar distributions in the future. © 2016 Botanical Society of America.

Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability?

NASA Astrophysics Data System (ADS)

Aalbers, Emma E.; Lenderink, Geert; van Meijgaard, Erik; van den Hurk, Bart J. J. M.

2018-06-01

High-resolution climate information provided by e.g. regional climate models (RCMs) is valuable for exploring the changing weather under global warming, and assessing the local impact of climate change. While there is generally more confidence in the representativeness of simulated processes at higher resolutions, internal variability of the climate system—`noise', intrinsic to the chaotic nature of atmospheric and oceanic processes—is larger at smaller spatial scales as well, limiting the predictability of the climate signal. To quantify the internal variability and robustly estimate the climate signal, large initial-condition ensembles of climate simulations conducted with a single model provide essential information. We analyze a regional downscaling of a 16-member initial-condition ensemble over western Europe and the Alps at 0.11° resolution, similar to the highest resolution EURO-CORDEX simulations. We examine the strength of the forced climate response (signal) in mean and extreme daily precipitation with respect to noise due to internal variability, and find robust small-scale geographical features in the forced response, indicating regional differences in changes in the probability of events. However, individual ensemble members provide only limited information on the forced climate response, even for high levels of global warming. Although the results are based on a single RCM-GCM chain, we believe that they have general value in providing insight in the fraction of the uncertainty in high-resolution climate information that is irreducible, and can assist in the correct interpretation of fine-scale information in multi-model ensembles in terms of a forced response and noise due to internal variability.

Vulnerability of Breeding Waterbirds to Climate Change in the Prairie Pothole Region, U.S.A

PubMed Central

Steen, Valerie; Skagen, Susan K.; Noon, Barry R.

2014-01-01

The Prairie Pothole Region (PPR) of the north-central U.S. and south-central Canada contains millions of small prairie wetlands that provide critical habitat to many migrating and breeding waterbirds. Due to their small size and the relatively dry climate of the region, these wetlands are considered at high risk for negative climate change effects as temperatures increase. To estimate the potential impacts of climate change on breeding waterbirds, we predicted current and future distributions of species common in the PPR using species distribution models (SDMs). We created regional-scale SDMs for the U.S. PPR using Breeding Bird Survey occurrence records for 1971–2011 and wetland, upland, and climate variables. For each species, we predicted current distribution based on climate records for 1981–2000 and projected future distributions to climate scenarios for 2040–2049. Species were projected to, on average, lose almost half their current habitat (-46%). However, individual species projections varied widely, from +8% (Upland Sandpiper) to -100% (Wilson's Snipe). Variable importance ranks indicated that land cover (wetland and upland) variables were generally more important than climate variables in predicting species distributions. However, climate variables were relatively more important during a drought period. Projected distributions of species responses to climate change contracted within current areas of distribution rather than shifting. Given the large variation in species-level impacts, we suggest that climate change mitigation efforts focus on species projected to be the most vulnerable by enacting targeted wetland management, easement acquisition, and restoration efforts. PMID:24927165

Vulnerability of breeding waterbirds to climate change in the Prairie Pothole Region, U.S.A.

USGS Publications Warehouse

Steen, Valerie; Skagen, Susan K.; Noon, Barry R.

2014-01-01

The Prairie Pothole Region (PPR) of the north-central U.S. and south-central Canada contains millions of small prairie wetlands that provide critical habitat to many migrating and breeding waterbirds. Due to their small size and the relatively dry climate of the region, these wetlands are considered at high risk for negative climate change effects as temperatures increase. To estimate the potential impacts of climate change on breeding waterbirds, we predicted current and future distributions of species common in the PPR using species distribution models (SDMs). We created regional-scale SDMs for the U.S. PPR using Breeding Bird Survey occurrence records for 1971–2011 and wetland, upland, and climate variables. For each species, we predicted current distribution based on climate records for 1981–2000 and projected future distributions to climate scenarios for 2040–2049. Species were projected to, on average, lose almost half their current habitat (-46%). However, individual species projections varied widely, from +8% (Upland Sandpiper) to -100% (Wilson's Snipe). Variable importance ranks indicated that land cover (wetland and upland) variables were generally more important than climate variables in predicting species distributions. However, climate variables were relatively more important during a drought period. Projected distributions of species responses to climate change contracted within current areas of distribution rather than shifting. Given the large variation in species-level impacts, we suggest that climate change mitigation efforts focus on species projected to be the most vulnerable by enacting targeted wetland management, easement acquisition, and restoration efforts.

Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability?

NASA Astrophysics Data System (ADS)

Aalbers, Emma E.; Lenderink, Geert; van Meijgaard, Erik; van den Hurk, Bart J. J. M.

2017-09-01

High-resolution climate information provided by e.g. regional climate models (RCMs) is valuable for exploring the changing weather under global warming, and assessing the local impact of climate change. While there is generally more confidence in the representativeness of simulated processes at higher resolutions, internal variability of the climate system—`noise', intrinsic to the chaotic nature of atmospheric and oceanic processes—is larger at smaller spatial scales as well, limiting the predictability of the climate signal. To quantify the internal variability and robustly estimate the climate signal, large initial-condition ensembles of climate simulations conducted with a single model provide essential information. We analyze a regional downscaling of a 16-member initial-condition ensemble over western Europe and the Alps at 0.11° resolution, similar to the highest resolution EURO-CORDEX simulations. We examine the strength of the forced climate response (signal) in mean and extreme daily precipitation with respect to noise due to internal variability, and find robust small-scale geographical features in the forced response, indicating regional differences in changes in the probability of events. However, individual ensemble members provide only limited information on the forced climate response, even for high levels of global warming. Although the results are based on a single RCM-GCM chain, we believe that they have general value in providing insight in the fraction of the uncertainty in high-resolution climate information that is irreducible, and can assist in the correct interpretation of fine-scale information in multi-model ensembles in terms of a forced response and noise due to internal variability.

How does complex terrain influence responses of carbon and water cycle processes to climate variability and climate change?

EPA Science Inventory

We are pursuing the ambitious goal of understanding how complex terrain influences the responses of carbon and water cycle processes to climate variability and climate change. Our studies take place in H.J. Andrews Experimental Forest, an LTER (Long Term Ecological Research) site...

Enhancing seasonal climate prediction capacity for the Pacific countries

NASA Astrophysics Data System (ADS)

Kuleshov, Y.; Jones, D.; Hendon, H.; Charles, A.; Cottrill, A.; Lim, E.-P.; Langford, S.; de Wit, R.; Shelton, K.

2012-04-01

Seasonal and inter-annual climate variability is a major factor in determining the vulnerability of many Pacific Island Countries to climate change and there is need to improve weekly to seasonal range climate prediction capabilities beyond what is currently available from statistical models. In the seasonal climate prediction project under the Australian Government's Pacific Adaptation Strategy Assistance Program (PASAP), we describe a comprehensive project to strengthen the climate prediction capacities in National Meteorological Services in 14 Pacific Island Countries and East Timor. The intent is particularly to reduce the vulnerability of current services to a changing climate, and improve the overall level of information available assist with managing climate variability. Statistical models cannot account for aspects of climate variability and change that are not represented in the historical record. In contrast, dynamical physics-based models implicitly include the effects of a changing climate whatever its character or cause and can predict outcomes not seen previously. The transition from a statistical to a dynamical prediction system provides more valuable and applicable climate information to a wide range of climate sensitive sectors throughout the countries of the Pacific region. In this project, we have developed seasonal climate outlooks which are based upon the current dynamical model POAMA (Predictive Ocean-Atmosphere Model for Australia) seasonal forecast system. At present, meteorological services of the Pacific Island Countries largely employ statistical models for seasonal outlooks. Outcomes of the PASAP project enhanced capabilities of the Pacific Island Countries in seasonal prediction providing National Meteorological Services with an additional tool to analyse meteorological variables such as sea surface temperatures, air temperature, pressure and rainfall using POAMA outputs and prepare more accurate seasonal climate outlooks.

Climate Variability and Sugarcane Yield in Louisiana.

NASA Astrophysics Data System (ADS)

Greenland, David

2005-11-01

This paper seeks to understand the role that climate variability has on annual yield of sugarcane in Louisiana. Unique features of sugarcane growth in Louisiana and nonclimatic, yield-influencing factors make this goal an interesting and challenging one. Several methods of seeking and establishing the relations between yield and climate variables are employed. First, yield climate relations were investigated at a single research station where crop variety and growing conditions could be held constant and yield relations could be established between a predominant older crop variety and a newer one. Interviews with crop experts and a literature survey were used to identify potential climatic factors that control yield. A statistical analysis was performed using statewide yield data from the American Sugar Cane League from 1963 to 2002 and a climate database. Yield values for later years were adjusted downward to form an adjusted yield dataset. The climate database was principally constructed from daily and monthly values of maximum and minimum temperature and daily and monthly total precipitation for six cooperative weather-reporting stations representative of the area of sugarcane production. The influence of 74 different, though not independent, climate-related variables on sugarcane yield was investigated. The fact that a climate signal exists is demonstrated by comparing mean values of the climate variables corresponding to the upper and lower third of adjusted yield values. Most of these mean-value differences show an intuitively plausible difference between the high- and low-yield years. The difference between means of the climate variables for years corresponding to the upper and lower third of annual yield values for 13 of the variables is statistically significant at or above the 90% level. A correlation matrix was used to identify the variables that had the largest influence on annual yield. Four variables [called here critical climatic variables (CCV)], mean maximum August temperature, mean minimum February temperature, soil water surplus between April and September, and occurrence of autumn (fall) hurricanes, were built into a model to simulate adjusted yield values. The CCV model simulates the yield value with an rmse of 5.1 t ha-1. The mean of the adjusted yield data over the study period was 60.4 t ha-1, with values for the highest and lowest years being 73.1 and 50.6 t ha-1, respectively, and a standard deviation of 5.9 t ha-1. Presumably because of the almost constant high water table and soil water availability, higher precipitation totals, which are inversely related to radiation and temperature, tend to have a negative effect on the yields. Past trends in the values of critical climatic variables and general projections of future climate suggest that, with respect to the climatic environment and as long as land drainage is continued and maintained, future levels of sugarcane yield will rise in Louisiana.

Climate variability and human impact in South America during the last 2000 years: synthesis and perspectives from pollen records

NASA Astrophysics Data System (ADS)

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.

2016-02-01

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.

Quantifying climate-growth relationships at the stand level in a mature mixed-species conifer forest.

PubMed

Teets, Aaron; Fraver, Shawn; Weiskittel, Aaron R; Hollinger, David Y

2018-03-11

A range of environmental factors regulate tree growth; however, climate is generally thought to most strongly influence year-to-year variability in growth. Numerous dendrochronological (tree-ring) studies have identified climate factors that influence year-to-year variability in growth for given tree species and location. However, traditional dendrochronology methods have limitations that prevent them from adequately assessing stand-level (as opposed to species-level) growth. We argue that stand-level growth analyses provide a more meaningful assessment of forest response to climate fluctuations, as well as the management options that may be employed to sustain forest productivity. Working in a mature, mixed-species stand at the Howland Research Forest of central Maine, USA, we used two alternatives to traditional dendrochronological analyses by (1) selecting trees for coring using a stratified (by size and species), random sampling method that ensures a representative sample of the stand, and (2) converting ring widths to biomass increments, which once summed, produced a representation of stand-level growth, while maintaining species identities or canopy position if needed. We then tested the relative influence of seasonal climate variables on year-to-year variability in the biomass increment using generalized least squares regression, while accounting for temporal autocorrelation. Our results indicate that stand-level growth responded most strongly to previous summer and current spring climate variables, resulting from a combination of individualistic climate responses occurring at the species- and canopy-position level. Our climate models were better fit to stand-level biomass increment than to species-level or canopy-position summaries. The relative growth responses (i.e., percent change) predicted from the most influential climate variables indicate stand-level growth varies less from to year-to-year than species-level or canopy-position growth responses. By assessing stand-level growth response to climate, we provide an alternative perspective on climate-growth relationships of forests, improving our understanding of forest growth dynamics under a fluctuating climate. © 2018 John Wiley & Sons Ltd.

Slowing down of North Pacific climate variability and its implications for abrupt ecosystem change.

PubMed

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.

Analysis of the Relationship Between Climate and NDVI Variability at Global Scales

NASA Technical Reports Server (NTRS)

Zeng, Fan-Wei; Collatz, G. James; Pinzon, Jorge; Ivanoff, Alvaro

2011-01-01

interannual variability in modeled (CASA) C flux is in part caused by interannual variability in Normalized Difference Vegetation Index (NDVI) Fraction of Photosynthetically Active Radiation (FPAR). This study confirms a mechanism producing variability in modeled NPP: -- NDVI (FPAR) interannual variability is strongly driven by climate; -- The climate driven variability in NDVI (FPAR) can lead to much larger fluctuation in NPP vs. the NPP computed from FPAR climatology

Terrestrial essential climate variables (ECVs) at a glance

USGS Publications Warehouse

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.

Climate variability has a stabilizing effect on the coexistence of prairie grasses

PubMed Central

Adler, Peter B.; HilleRisLambers, Janneke; Kyriakidis, Phaedon C.; Guan, Qingfeng; Levine, Jonathan M.

2006-01-01

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

Effects of climatic factors and ecosystem responses on the inter-annual variability of evapotranspiration in a coniferous plantation in subtropical China.

PubMed

Xu, Mingjie; Wen, Xuefa; Wang, Huimin; Zhang, Wenjiang; Dai, Xiaoqin; Song, Jie; Wang, Yidong; Fu, Xiaoli; Liu, Yunfen; Sun, Xiaomin; Yu, Guirui

2014-01-01

Because evapotranspiration (ET) is the second largest component of the water cycle and a critical process in terrestrial ecosystems, understanding the inter-annual variability of ET is important in the context of global climate change. Eight years of continuous eddy covariance measurements (2003-2010) in a subtropical coniferous plantation were used to investigate the impacts of climatic factors and ecosystem responses on the inter-annual variability of ET. The mean and standard deviation of annual ET for 2003-2010 were 786.9 and 103.4 mm (with a coefficient of variation of 13.1%), respectively. The inter-annual variability of ET was largely created in three periods: March, May-June, and October, which are the transition periods between seasons. A set of look-up table approaches were used to separate the sources of inter-annual variability of ET. The annual ETs were calculated by assuming that (a) both the climate and ecosystem responses among years are variable (Vcli-eco), (b) the climate is variable but the ecosystem responses are constant (Vcli), and (c) the climate is constant but ecosystem responses are variable (Veco). The ETs that were calculated under the above assumptions suggested that the inter-annual variability of ET was dominated by ecosystem responses and that there was a negative interaction between the effects of climate and ecosystem responses. These results suggested that for long-term predictions of water and energy balance in global climate change projections, the ecosystem responses must be taken into account to better constrain the uncertainties associated with estimation.

Effects of Climatic Factors and Ecosystem Responses on the Inter-Annual Variability of Evapotranspiration in a Coniferous Plantation in Subtropical China

PubMed Central

Xu, Mingjie; Wen, Xuefa; Wang, Huimin; Zhang, Wenjiang; Dai, Xiaoqin; Song, Jie; Wang, Yidong; Fu, Xiaoli; Liu, Yunfen; Sun, Xiaomin; Yu, Guirui

2014-01-01

Because evapotranspiration (ET) is the second largest component of the water cycle and a critical process in terrestrial ecosystems, understanding the inter-annual variability of ET is important in the context of global climate change. Eight years of continuous eddy covariance measurements (2003–2010) in a subtropical coniferous plantation were used to investigate the impacts of climatic factors and ecosystem responses on the inter-annual variability of ET. The mean and standard deviation of annual ET for 2003–2010 were 786.9 and 103.4 mm (with a coefficient of variation of 13.1%), respectively. The inter-annual variability of ET was largely created in three periods: March, May–June, and October, which are the transition periods between seasons. A set of look-up table approaches were used to separate the sources of inter-annual variability of ET. The annual ETs were calculated by assuming that (a) both the climate and ecosystem responses among years are variable (Vcli-eco), (b) the climate is variable but the ecosystem responses are constant (Vcli), and (c) the climate is constant but ecosystem responses are variable (Veco). The ETs that were calculated under the above assumptions suggested that the inter-annual variability of ET was dominated by ecosystem responses and that there was a negative interaction between the effects of climate and ecosystem responses. These results suggested that for long-term predictions of water and energy balance in global climate change projections, the ecosystem responses must be taken into account to better constrain the uncertainties associated with estimation. PMID:24465610

Selecting Populations for Non-Analogous Climate Conditions Using Universal Response Functions: The Case of Douglas-Fir in Central Europe

PubMed Central

Chakraborty, Debojyoti; Wang, Tongli; Andre, Konrad; Konnert, Monika; Lexer, Manfred J.; Matulla, Christoph; Schueler, Silvio

2015-01-01

Identifying populations within tree species potentially adapted to future climatic conditions is an important requirement for reforestation and assisted migration programmes. Such populations can be identified either by empirical response functions based on correlations of quantitative traits with climate variables or by climate envelope models that compare the climate of seed sources and potential growing areas. In the present study, we analyzed the intraspecific variation in climate growth response of Douglas-fir planted within the non-analogous climate conditions of Central and continental Europe. With data from 50 common garden trials, we developed Universal Response Functions (URF) for tree height and mean basal area and compared the growth performance of the selected best performing populations with that of populations identified through a climate envelope approach. Climate variables of the trial location were found to be stronger predictors of growth performance than climate variables of the population origin. Although the precipitation regime of the population sources varied strongly none of the precipitation related climate variables of population origin was found to be significant within the models. Overall, the URFs explained more than 88% of variation in growth performance. Populations identified by the URF models originate from western Cascades and coastal areas of Washington and Oregon and show significantly higher growth performance than populations identified by the climate envelope approach under both current and climate change scenarios. The URFs predict decreasing growth performance at low and middle elevations of the case study area, but increasing growth performance on high elevation sites. Our analysis suggests that population recommendations based on empirical approaches should be preferred and population selections by climate envelope models without considering climatic constrains of growth performance should be carefully appraised before transferring populations to planting locations with novel or dissimilar climate. PMID:26288363

LAMPPOST: A Mnemonic Device for Teaching Climate Variables

ERIC Educational Resources Information Center

Fahrer, Chuck; Harris, Dan

2004-01-01

This article introduces the word "LAMPPOST" as a mnemonic device to aid in the instruction of climate variables. It provides instructors with a framework for discussing climate patterns that is based on eight variables: latitude, altitude, maritime influence and continentality, pressure systems, prevailing winds, ocean currents, storms, and…

A Generalized Stability Analysis of the AMOC in Earth System Models: Implication for Decadal Variability and Abrupt Climate Change

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fedorov, Alexey V.

2015-01-14

The central goal of this research project was to understand the mechanisms of decadal and multi-decadal variability of the Atlantic Meridional Overturning Circulation (AMOC) as related to climate variability and abrupt climate change within a hierarchy of climate models ranging from realistic ocean models to comprehensive Earth system models. Generalized Stability Analysis, a method that quantifies the transient and asymptotic growth of perturbations in the system, is one of the main approaches used throughout this project. The topics we have explored range from physical mechanisms that control AMOC variability to the factors that determine AMOC predictability in the Earth systemmore » models, to the stability and variability of the AMOC in past climates.« less

Climate Variability, Climate Change and Social Vulnerability in the Semi-arid Tropics

NASA Astrophysics Data System (ADS)

Ribot, Jesse C.; Rocha Magalhaes, Antonio; Panagides, Stahis

1996-06-01

Climate changes can trigger events that lead to mass migration, hunger, and even famine. Rather than focus on the impacts that result from climatic fluctuations, the authors look at the underlying conditions that cause social vulnerability. Once we understand why individuals, households, nations, and regions are vulnerable, and how they have buffered themselves against climatic and environmental shifts, then present and future vulnerability can be redressed. By using case studies from across the globe, the authors explore past experiences with climate variability, and the likely effects of--and the possible policy responses to--the types of climatic events that global warming might bring.

The value of seasonal forecasting and crop mix adaptation to climate variability for agriculture under climate change

NASA Astrophysics Data System (ADS)

Choi, H. S.; Schneider, U.; Schmid, E.; Held, H.

2012-04-01

Changes to climate variability and frequency of extreme weather events are expected to impose damages to the agricultural sector. Seasonal forecasting and long range prediction skills have received attention as an option to adapt to climate change because seasonal climate and yield predictions could improve farmers' management decisions. The value of seasonal forecasting skill is assessed with a crop mix adaptation option in Spain where drought conditions are prevalent. Yield impacts of climate are simulated for six crops (wheat, barely, cotton, potato, corn and rice) with the EPIC (Environmental Policy Integrated Climate) model. Daily weather data over the period 1961 to 1990 are used and are generated by the regional climate model REMO as reference period for climate projection. Climate information and its consequent yield variability information are given to the stochastic agricultural sector model to calculate the value of climate information in the agricultural market. Expected consumers' market surplus and producers' revenue is compared with and without employing climate forecast information. We find that seasonal forecasting benefits not only consumers but also producers if the latter adopt a strategic crop mix. This mix differs from historical crop mixes by having higher shares of crops which fare relatively well under climate change. The corresponding value of information is highly sensitive to farmers' crop mix choices.

Examining for any impact of climate change on the association between seasonality and hospitalization for mania.

PubMed

Parker, Gordon B; Hadzi-Pavlovic, Dusan; Graham, Rebecca K

2017-01-15

Studies have established higher rates of hospitalization for mania in spring and summer and posit various explanatory climatic variables. As the earth's climate is changing, we pursue whether this is reflected in the yearly seasonal variation in hospitalizations for mania. This would be indicated by the presence of secular changes in both the hospitalization seasonal pattern and climatic variables, and associations between both variable sets. Data were obtained for 21,882 individuals hospitalized to psychiatric hospitals in the Australian state of New South Wales (NSW) over a 14-year period (2000-2014) with ICD-diagnosed mania - and with NSW population figures and salient climatic variables collected for the same period. Regression analyses were conducted to examine the predictive value of climate variables on hospital admissions. Data quantified a peak for manic admissions in spring of the southern hemisphere, in the months of October and November. There was a significant linear increase in manic admissions (0.5%/year) over the 14-year time period, with significant variation across years. In terms of climatic variables, there was a significant linear trend over the interval for solar radiation, although the trend indicated a decrease rather than an increase. Seasonal variation in admissions was most closely associated with two climate variables - evaporation in the current month and temperature in the previous month. Hospitalization rates do not necessarily provide an accurate estimate of the onset of manic episodes and findings may be limited to the southern hemisphere, or New South Wales. While overall findings do not support the hypothesis that climate change is leading to a higher seasonal impact for manic hospital admissions in the southern hemisphere, analyses identified two climate/weather variables - evaporation and temperature - that may account for the yearly spring excess. Copyright © 2016 Elsevier B.V. All rights reserved.

Evaluating the Contribution of Natural Variability and Climate Model Response to Uncertainty in Projections of Climate Change Impacts on U.S. Air Quality

EPA Science Inventory

We examine the effects of internal variability and model response in projections of climate impacts on U.S. ground-level ozone across the 21st century using integrated global system modeling and global atmospheric chemistry simulations. The impact of climate change on air polluti...

Development, malaria and adaptation to climate change: a case study from India.

PubMed

Garg, Amit; Dhiman, R C; Bhattacharya, Sumana; Shukla, P R

2009-05-01

India has reasons to be concerned about climate change. Over 650 million people depend on climate-sensitive sectors, such as rain-fed agriculture and forestry, for livelihood and over 973 million people are exposed to vector borne malarial parasites. Projection of climatic factors indicates a wider exposure to malaria for the Indian population in the future. If precautionary measures are not taken and development processes are not managed properly some developmental activities, such as hydro-electric dams and irrigation canal systems, may also exacerbate breeding grounds for malaria. This article integrates climate change and developmental variables in articulating a framework for integrated impact assessment and adaptation responses, with malaria incidence in India as a case study. The climate change variables include temperature, rainfall, humidity, extreme events, and other secondary variables. Development variables are income levels, institutional mechanisms to implement preventive measures, infrastructure development that could promote malarial breeding grounds, and other policies. The case study indicates that sustainable development variables may sometimes reduce the adverse impacts on the system due to climate change alone, while it may sometimes also exacerbate these impacts if the development variables are not managed well and therefore they produce a negative impact on the system. The study concludes that well crafted and well managed developmental policies could result in enhanced resilience of communities and systems, and lower health impacts due to climate change.

Development, Malaria and Adaptation to Climate Change: A Case Study from India

NASA Astrophysics Data System (ADS)

Garg, Amit; Dhiman, R. C.; Bhattacharya, Sumana; Shukla, P. R.

2009-05-01

India has reasons to be concerned about climate change. Over 650 million people depend on climate-sensitive sectors, such as rain-fed agriculture and forestry, for livelihood and over 973 million people are exposed to vector borne malarial parasites. Projection of climatic factors indicates a wider exposure to malaria for the Indian population in the future. If precautionary measures are not taken and development processes are not managed properly some developmental activities, such as hydro-electric dams and irrigation canal systems, may also exacerbate breeding grounds for malaria. This article integrates climate change and developmental variables in articulating a framework for integrated impact assessment and adaptation responses, with malaria incidence in India as a case study. The climate change variables include temperature, rainfall, humidity, extreme events, and other secondary variables. Development variables are income levels, institutional mechanisms to implement preventive measures, infrastructure development that could promote malarial breeding grounds, and other policies. The case study indicates that sustainable development variables may sometimes reduce the adverse impacts on the system due to climate change alone, while it may sometimes also exacerbate these impacts if the development variables are not managed well and therefore they produce a negative impact on the system. The study concludes that well crafted and well managed developmental policies could result in enhanced resilience of communities and systems, and lower health impacts due to climate change.

A New High Resolution Climate Dataset for Climate Change Impacts Assessments in New England

NASA Astrophysics Data System (ADS)

Komurcu, M.; Huber, M.

2016-12-01

Assessing regional impacts of climate change (such as changes in extreme events, land surface hydrology, water resources, energy, ecosystems and economy) requires much higher resolution climate variables than those available from global model projections. While it is possible to run global models in higher resolution, the high computational cost associated with these simulations prevent their use in such manner. To alleviate this problem, dynamical downscaling offers a method to deliver higher resolution climate variables. As part of an NSF EPSCoR funded interdisciplinary effort to assess climate change impacts on New Hampshire ecosystems, hydrology and economy (the New Hampshire Ecosystems and Society project), we create a unique high-resolution climate dataset for New England. We dynamically downscale global model projections under a high impact emissions scenario using the Weather Research and Forecasting model (WRF) with three nested grids of 27, 9 and 3 km horizontal resolution with the highest resolution innermost grid focusing over New England. We prefer dynamical downscaling over other methods such as statistical downscaling because it employs physical equations to progressively simulate climate variables as atmospheric processes interact with surface processes, emissions, radiation, clouds, precipitation and other model components, hence eliminates fix relationships between variables. In addition to simulating mean changes in regional climate, dynamical downscaling also allows for the simulation of climate extremes that significantly alter climate change impacts. We simulate three time slices: 2006-2015, 2040-2060 and 2080-2100. This new high-resolution climate dataset (with more than 200 variables saved in hourly (six hourly) intervals for the highest resolution domain (outer two domains)) along with model input and restart files used in our WRF simulations will be publicly available for use to the broader scientific community to support in-depth climate change impacts assessments for New England. We present results focusing on future changes in New England extreme events.

Phylogenetic assemblage structure of North American trees is more strongly shaped by glacial-interglacial climate variability in gymnosperms than in angiosperms.

PubMed

Ma, Ziyu; Sandel, Brody; Svenning, Jens-Christian

2016-05-01

How fast does biodiversity respond to climate change? The relationship of past and current climate with phylogenetic assemblage structure helps us to understand this question. Studies of angiosperm tree diversity in North America have already suggested effects of current water-energy balance and tropical niche conservatism. However, the role of glacial-interglacial climate variability remains to be determined, and little is known about any of these relationships for gymnosperms. Moreover, phylogenetic endemism, the concentration of unique lineages in restricted ranges, may also be related to glacial-interglacial climate variability and needs more attention. We used a refined phylogeny of both angiosperms and gymnosperms to map phylogenetic diversity, clustering and endemism of North American trees in 100-km grid cells, and climate change velocity since Last Glacial Maximum together with postglacial accessibility to recolonization to quantify glacial-interglacial climate variability. We found: (1) Current climate is the dominant factor explaining the overall patterns, with more clustered angiosperm assemblages toward lower temperature, consistent with tropical niche conservatism. (2) Long-term climate stability is associated with higher angiosperm endemism, while higher postglacial accessibility is linked to to more phylogenetic clustering and endemism in gymnosperms. (3) Factors linked to glacial-interglacial climate change have stronger effects on gymnosperms than on angiosperms. These results suggest that paleoclimate legacies supplement current climate in shaping phylogenetic patterns in North American trees, and especially so for gymnosperms.

Linking the variability of atmospheric carbon monoxide to climate modes in the Southern Hemisphere

NASA Astrophysics Data System (ADS)

Buchholz, Rebecca; Monks, Sarah; Hammerling, Dorit; Worden, Helen; Deeter, Merritt; Emmons, Louisa; Edwards, David

2017-04-01

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.

Large-Scale Circulation and Climate Variability. Chapter 5

NASA Technical Reports Server (NTRS)

Perlwitz, J.; Knutson, T.; Kossin, J. P.; LeGrande, A. N.

2017-01-01

The causes of regional climate trends cannot be understood without considering the impact of variations in large-scale atmospheric circulation and an assessment of the role of internally generated climate variability. There are contributions to regional climate trends from changes in large-scale latitudinal circulation, which is generally organized into three cells in each hemisphere-Hadley cell, Ferrell cell and Polar cell-and which determines the location of subtropical dry zones and midlatitude jet streams. These circulation cells are expected to shift poleward during warmer periods, which could result in poleward shifts in precipitation patterns, affecting natural ecosystems, agriculture, and water resources. In addition, regional climate can be strongly affected by non-local responses to recurring patterns (or modes) of variability of the atmospheric circulation or the coupled atmosphere-ocean system. These modes of variability represent preferred spatial patterns and their temporal variation. They account for gross features in variance and for teleconnections which describe climate links between geographically separated regions. Modes of variability are often described as a product of a spatial climate pattern and an associated climate index time series that are identified based on statistical methods like Principal Component Analysis (PC analysis), which is also called Empirical Orthogonal Function Analysis (EOF analysis), and cluster analysis.

Describing rainfall in northern Australia using multiple climate indices

NASA Astrophysics Data System (ADS)

Wilks Rogers, Cassandra Denise; Beringer, Jason

2017-02-01

Savanna landscapes are globally extensive and highly sensitive to climate change, yet the physical processes and climate phenomena which affect them remain poorly understood and therefore poorly represented in climate models. Both human populations and natural ecosystems are highly susceptible to precipitation variation in these regions due to the effects on water and food availability and atmosphere-biosphere energy fluxes. Here we quantify the relationship between climate phenomena and historical rainfall variability in Australian savannas and, in particular, how these relationships changed across a strong rainfall gradient, namely the North Australian Tropical Transect (NATT). Climate phenomena were described by 16 relevant climate indices and correlated against precipitation from 1900 to 2010 to determine the relative importance of each climate index on seasonal, annual and decadal timescales. Precipitation trends, climate index trends and wet season characteristics have also been investigated using linear statistical methods. In general, climate index-rainfall correlations were stronger in the north of the NATT where annual rainfall variability was lower and a high proportion of rainfall fell during the wet season. This is consistent with a decreased influence of the Indian-Australian monsoon from the north to the south. Seasonal variation was most strongly correlated with the Australian Monsoon Index, whereas yearly variability was related to a greater number of climate indices, predominately the Tasman Sea and Indonesian sea surface temperature indices (both of which experienced a linear increase over the duration of the study) and the El Niño-Southern Oscillation indices. These findings highlight the importance of understanding the climatic processes driving variability and, subsequently, the importance of understanding the relationships between rainfall and climatic phenomena in the Northern Territory in order to project future rainfall patterns in the region.

Impacts of climate change and internal climate variability on french rivers streamflows

NASA Astrophysics Data System (ADS)

Dayon, Gildas; Boé, Julien; Martin, Eric

2016-04-01

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

Climate variations of Central Asia on orbital to millennial timescales.

PubMed

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

2016-11-11

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.

Climate variations of Central Asia on orbital to millennial timescales

PubMed Central

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

2016-01-01

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

Screening variability and change of soil moisture under wide-ranging climate conditions: Snow dynamics effects.

PubMed

Verrot, Lucile; Destouni, Georgia

2015-01-01

Soil moisture influences and is influenced by water, climate, and ecosystem conditions, affecting associated ecosystem services in the landscape. This paper couples snow storage-melting dynamics with an analytical modeling approach to screening basin-scale, long-term soil moisture variability and change in a changing climate. This coupling enables assessment of both spatial differences and temporal changes across a wide range of hydro-climatic conditions. Model application is exemplified for two major Swedish hydrological basins, Norrström and Piteälven. These are located along a steep temperature gradient and have experienced different hydro-climatic changes over the time period of study, 1950-2009. Spatially, average intra-annual variability of soil moisture differs considerably between the basins due to their temperature-related differences in snow dynamics. With regard to temporal change, the long-term average state and intra-annual variability of soil moisture have not changed much, while inter-annual variability has changed considerably in response to hydro-climatic changes experienced so far in each basin.

Why inputs matter: Selection of climatic variables for species distribution modelling in the Himalayan region

NASA Astrophysics Data System (ADS)

Bobrowski, Maria; Schickhoff, Udo

2017-04-01

Betula utilis is a major constituent of alpine treeline ecotones in the western and central Himalayan region. The objective of this study is to provide first time analysis of the potential distribution of Betula utilis in the subalpine and alpine belts of the Himalayan region using species distribution modelling. Using Generalized Linear Models (GLM) we aim at examining climatic factors controlling the species distribution under current climate conditions. Furthermore we evaluate the prediction ability of climate data derived from different statistical methods. GLMs were created using least correlated bioclimatic variables derived from two different climate models: 1) interpolated climate data (i.e. Worldclim, Hijmans et al., 2005) and 2) quasi-mechanistical statistical downscaling (i.e. Chelsa; Karger et al., 2016). Model accuracy was evaluated by the ability to predict the potential species distribution range. We found that models based on variables of Chelsa climate data had higher predictive power, whereas models using Worldclim climate data consistently overpredicted the potential suitable habitat for Betula utilis. Although climatic variables of Worldclim are widely used in modelling species distribution, our results suggest to treat them with caution when remote regions like the Himalayan mountains are in focus. Unmindful usage of climatic variables for species distribution models potentially cause misleading projections and may lead to wrong implications and recommendations for nature conservation. References: Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. & Jarvis, A. (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965-1978. Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N., Linder, H.P. & Kessler, M. (2016) Climatologies at high resolution for the earth land surface areas. arXiv:1607.00217 [physics].

Managing living marine resources in a dynamic environment: The role of seasonal to decadal climate forecasts

NASA Astrophysics Data System (ADS)

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.

2017-03-01

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.

Skilful multi-year predictions of tropical trans-basin climate variability

PubMed Central

Chikamoto, Yoshimitsu; Timmermann, Axel; Luo, Jing-Jia; Mochizuki, Takashi; Kimoto, Masahide; Watanabe, Masahiro; Ishii, Masayoshi; Xie, Shang-Ping; Jin, Fei-Fei

2015-01-01

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

Skilful multi-year predictions of tropical trans-basin climate variability.

PubMed

Chikamoto, Yoshimitsu; Timmermann, Axel; Luo, Jing-Jia; Mochizuki, Takashi; Kimoto, Masahide; Watanabe, Masahiro; Ishii, Masayoshi; Xie, Shang-Ping; Jin, Fei-Fei

2015-04-21

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.

Semi-arid vegetation response to antecedent climate and water balance windows

USGS Publications Warehouse

Thoma, David P.; Munson, Seth M.; Irvine, Kathryn M.; Witwicki, Dana L.; Bunting, Erin

2016-01-01

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.

Regionally heterogeneous paleoenvironmental responses in the West African and South American monsoon systems on glacial to millennial timescales

NASA Astrophysics Data System (ADS)

Shanahan, T. M.; Hughen, K. A.; van Mooy, B.; Overpeck, J. T.; Baker, P. A.; Fritz, S.; Peck, J. A.; Scholz, C. A.; King, J. W.

2008-12-01

Although millennial-scale paleoenvironmental changes have been well characterized for high latitude sites, short-term climate variability in the tropics is less well understood. While the Intertropical Convergence Zone may act as an integrator of tropical climate changes, regional factors also play an important role in controlling the tropical response to climate forcing. Understanding these influences, and how they modulate the response to global climate forcing under different mean climate states is thus important for assessing how the tropics may respond to future climate change. Here, we examine new centennial-resolution records of paleoenvironmental change from isotopic and relative abundance data from molecular biomarkers in sediment cores from Lake Bosumtwi and Lake Titicaca. We assess the relative response of the West African and South American monsoon systems to millennial and suborbital-scale climate variability over the last ca. 30,000 years. While there is evidence for synchronous climate variability in the two systems, the dominant paleoenvironmental changes appear largely decoupled, highlighting the importance of regional climatology in controlling the response to climate forcing in tropical regions.

Climate Variability and Human Migration in the Netherlands, 1865–1937

PubMed Central

Jennings, Julia A.; Gray, Clark L.

2014-01-01

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

AMOC decadal variability in Earth system models: Mechanisms and climate impacts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fedorov, Alexey

This is the final report for the project titled "AMOC decadal variability in Earth system models: Mechanisms and climate impacts". The central goal of this one-year research project was to understand the mechanisms of decadal and multi-decadal variability of the Atlantic Meridional Overturning Circulation (AMOC) within a hierarchy of climate models ranging from realistic ocean GCMs to Earth system models. The AMOC is a key element of ocean circulation responsible for oceanic transport of heat from low to high latitudes and controlling, to a large extent, climate variations in the North Atlantic. The questions of the AMOC stability, variability andmore » predictability, directly relevant to the questions of climate predictability, were at the center of the research work.« less

Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD

PubMed Central

Lorenz, David J.; Nieto-Lugilde, Diego; Blois, Jessica L.; Fitzpatrick, Matthew C.; Williams, John W.

2016-01-01

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

Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD.

PubMed

Lorenz, David J; Nieto-Lugilde, Diego; Blois, Jessica L; Fitzpatrick, Matthew C; Williams, John W

2016-07-05

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.

Climate and health: observation and modeling of malaria in the Ferlo (Senegal).

PubMed

Diouf, Ibrahima; Deme, Abdoulaye; Ndione, Jacques-André; Gaye, Amadou Thierno; Rodríguez-Fonseca, Belén; Cissé, Moustapha

2013-01-01

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.

Climatic variability effects on summer cropping systems of the Iberian Peninsula

NASA Astrophysics Data System (ADS)

Capa-Morocho, M.; Rodríguez-Fonseca, B.; Ruiz-Ramos, M.

2012-04-01

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.

The relative impacts of climate and land-use change on conterminous United States bird species from 2001 to 2075

USGS Publications Warehouse

Sohl, Terry L.

2014-01-01

Species distribution models often use climate data to assess contemporary and/or future ranges for animal or plant species. Land use and land cover (LULC) data are important predictor variables for determining species range, yet are rarely used when modeling future distributions. In this study, maximum entropy modeling was used to construct species distribution maps for 50 North American bird species to determine relative contributions of climate and LULC for contemporary (2001) and future (2075) time periods. Species presence data were used as a dependent variable, while climate, LULC, and topographic data were used as predictor variables. Results varied by species, but in general, measures of model fit for 2001 indicated significantly poorer fit when either climate or LULC data were excluded from model simulations. Climate covariates provided a higher contribution to 2001 model results than did LULC variables, although both categories of variables strongly contributed. The area deemed to be "suitable" for 2001 species presence was strongly affected by the choice of model covariates, with significantly larger ranges predicted when LULC was excluded as a covariate. Changes in species ranges for 2075 indicate much larger overall range changes due to projected climate change than due to projected LULC change. However, the choice of study area impacted results for both current and projected model applications, with truncation of actual species ranges resulting in lower model fit scores and increased difficulty in interpreting covariate impacts on species range. Results indicate species-specific response to climate and LULC variables; however, both climate and LULC variables clearly are important for modeling both contemporary and potential future species ranges.

The Relative Impacts of Climate and Land-Use Change on Conterminous United States Bird Species from 2001 to 2075

PubMed Central

Sohl, Terry L.

2014-01-01

Species distribution models often use climate data to assess contemporary and/or future ranges for animal or plant species. Land use and land cover (LULC) data are important predictor variables for determining species range, yet are rarely used when modeling future distributions. In this study, maximum entropy modeling was used to construct species distribution maps for 50 North American bird species to determine relative contributions of climate and LULC for contemporary (2001) and future (2075) time periods. Species presence data were used as a dependent variable, while climate, LULC, and topographic data were used as predictor variables. Results varied by species, but in general, measures of model fit for 2001 indicated significantly poorer fit when either climate or LULC data were excluded from model simulations. Climate covariates provided a higher contribution to 2001 model results than did LULC variables, although both categories of variables strongly contributed. The area deemed to be “suitable” for 2001 species presence was strongly affected by the choice of model covariates, with significantly larger ranges predicted when LULC was excluded as a covariate. Changes in species ranges for 2075 indicate much larger overall range changes due to projected climate change than due to projected LULC change. However, the choice of study area impacted results for both current and projected model applications, with truncation of actual species ranges resulting in lower model fit scores and increased difficulty in interpreting covariate impacts on species range. Results indicate species-specific response to climate and LULC variables; however, both climate and LULC variables clearly are important for modeling both contemporary and potential future species ranges. PMID:25372571

The Climate Variability & Predictability (CVP) Program at NOAA - Recent Program Advancements

NASA Astrophysics Data System (ADS)

Lucas, S. E.; Todd, J. F.

2015-12-01

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.

Do bioclimate variables improve performance of climate envelope models?

USGS Publications Warehouse

Watling, James I.; Romañach, Stephanie S.; Bucklin, David N.; Speroterra, Carolina; Brandt, Laura A.; Pearlstine, Leonard G.; Mazzotti, Frank J.

2012-01-01

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.

Building Climate Resilience in the Blue Nile/Abay Highlands: A Role for Earth System Sciences

PubMed Central

Zaitchik, Benjamin F.; Simane, Belay; Habib, Shahid; Anderson, Martha C.; Ozdogan, Mutlu; Foltz, Jeremy D.

2012-01-01

The Blue Nile (Abay) Highlands of Ethiopia are characterized by significant interannual climate variability, complex topography and associated local climate contrasts, erosive rains and erodible soils, and intense land pressure due to an increasing population and an economy that is almost entirely dependent on smallholder, low-input agriculture. As a result, these highland zones are highly vulnerable to negative impacts of climate variability. As patterns of variability and precipitation intensity alter under anthropogenic climate change, there is concern that this vulnerability will increase, threatening economic development and food security in the region. In order to overcome these challenges and to enhance sustainable development in the context of climate change, it is necessary to establish climate resilient development strategies that are informed by best-available Earth System Science (ESS) information. This requirement is complicated by the fact that climate projections for the Abay Highlands contain significant and perhaps irreducible uncertainties. A critical challenge for ESS, then, is to generate and to communicate meaningful information for climate resilient development in the context of a highly uncertain climate forecast. Here we report on a framework for applying ESS to climate resilient development in the Abay Highlands, with a focus on the challenge of reducing land degradation. PMID:22470302

Final Report: Closeout of the Award NO. DE-FG02-98ER62618 (M.S. Fox-Rabinovitz, P.I.)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fox-Rabinovitz, M. S.

The final report describes the study aimed at exploring the variable-resolution stretched-grid (SG) approach to decadal regional climate modeling using advanced numerical techniques. The obtained results have shown that variable-resolution SG-GCMs using stretched grids with fine resolution over the area(s) of interest, is a viable established approach to regional climate modeling. The developed SG-GCMs have been extensively used for regional climate experimentation. The SG-GCM simulations are aimed at studying the U.S. regional climate variability with an emphasis on studying anomalous summer climate events, the U.S. droughts and floods.

The role of internal climate variability for interpreting climate change scenarios

NASA Astrophysics Data System (ADS)

Maraun, Douglas

2013-04-01

When communicating information on climate change, the use of multi-model ensembles has been advocated to sample uncertainties over a range as wide as possible. To meet the demand for easily accessible results, the ensemble is often summarised by its multi-model mean signal. In rare cases, additional uncertainty measures are given to avoid loosing all information on the ensemble spread, e.g., the highest and lowest projected values. Such approaches, however, disregard the fundamentally different nature of the different types of uncertainties and might cause wrong interpretations and subsequently wrong decisions for adaptation. Whereas scenario and climate model uncertainties are of epistemic nature, i.e., caused by an in principle reducible lack of knowledge, uncertainties due to internal climate variability are aleatory, i.e., inherently stochastic and irreducible. As wisely stated in the proverb "climate is what you expect, weather is what you get", a specific region will experience one stochastic realisation of the climate system, but never exactly the expected climate change signal as given by a multi model mean. Depending on the meteorological variable, region and lead time, the signal might be strong or weak compared to the stochastic component. In cases of a low signal-to-noise ratio, even if the climate change signal is a well defined trend, no trends or even opposite trends might be experienced. Here I propose to use the time of emergence (TOE) to quantify and communicate when climate change trends will exceed the internal variability. The TOE provides a useful measure for end users to assess the time horizon for implementing adaptation measures. Furthermore, internal variability is scale dependent - the more local the scale, the stronger the influence of internal climate variability. Thus investigating the TOE as a function of spatial scale could help to assess the required spatial scale for implementing adaptation measures. I exemplify this proposal with a recently published study on the TOE for mean and heavy precipitation trends in Europe. In some regions trends emerge only late in the 21st century or even later, suggesting that in these regions adaptation to internal variability rather than to climate change is required. Yet in other regions the climate change signal is strong, urging for timely adaptation. Douglas Maraun, When at what scale will trends in European mean and heavy precipitation emerge? Env. Res. Lett., in press, 2013.

The rise of the mediocre forest: why chronically stressed trees may better survive extreme episodic climate variability

Treesearch

Steven G. McNulty; Johnny L. Boggs; Ge Sun

2014-01-01

Anthropogenic climate change is a relatively new phenomenon, largely occurring over the past 150 years, and much of the discussion on climate change impacts to forests has focused on long-term shifts in temperature and precipitation. However, individual trees respond to the much shorter impacts of climate variability. Historically, fast growing, fully canopied, non-...

Climatic influences on fire regimes in montane forests of the southern Cascades, California, USA

Treesearch

A. H. Taylor; V. Trouet; C. N. Skinner

2008-01-01

he relationship between climate variability and fire extent was examined in montane and upper montane forests in the southern Cascades. Fire occurrence and extent were reconstructed for seven sites and related to measures of reconstructed climate for the period 1700 to 1900. The climate variables included the Palmer Drought Severity Index (PDSI), summer temperature (...

Climate signals derived from cell anatomy of Scots pine in NE Germany.

PubMed

Liang, Wei; Heinrich, Ingo; Simard, Sonia; Helle, Gerhard; Liñán, Isabel Dorado; Heinken, Thilo

2013-08-01

Tree-ring chronologies of Pinus sylvestris L. from latitudinal and altitudinal limits of the species distribution have been widely used for climate reconstructions, but there are many sites within the temperate climate zone, as is the case in northeastern Germany, at which there is little evidence of a clear climate signal in the chronologies. In this study, we developed long chronologies of several cell structure variables (e.g., average lumen area and cell wall thickness) from P. sylvestris growing in northeastern Germany and investigated the influence of climate on ring widths and cell structure variables. We found significant correlations between cell structure variables and temperature, and between tree-ring width and relative humidity and vapor pressure, respectively, enabling the development of robust reconstructions from temperate sites that have not yet been realized. Moreover, it has been shown that it may not be necessary to detrend chronologies of cell structure variables and thus low-frequency climate signals may be retrieved from longer cell structure chronologies. The relatively extensive resource of archaeological material of P. sylvestris covering approximately the last millennium may now be useful for climate reconstructions in northeastern Germany and other sites in the temperate climate zone.

Impacts of Climate Trends and Variability on Livestock Production in Brazil

NASA Astrophysics Data System (ADS)

Cohn, A.; Munger, J.; Gibbs, H.

2015-12-01

Cattle systems of Brazil are of major economic and environmental importance. They occupy ¼ of the land surface of the country, account for over 15 billion USD of annual revenue through the sale of beef, leather, and milk, are closely associated with deforestation, and have been projected to substantially grow in the coming decades. Sustainable intensification of production in the sector could help to limit environmental harm from increased production, but productivity growth could be inhibited by climate change. Gauging the potential future impacts of climate change on the Brazilian livestock sector can be aided by examining past evidence of the link between climate and cattle production and productivity. We use statistical techniques to investigate the contribution of climate variability and climate change to variability in cattle system output in Brazil's municipalities over the period 1974 to 2013. We find significant impacts of both temperature and precipitation variability and temperature trends on municipality-level exports and the production of both milk and beef. Pasture productivity, represented by a vegetation index, also varies significantly with climate shocks. In some regions, losses from exposure to climate trends were of comparable magnitude to technology and/or market-driven productivity gains over the study period.

Violence Prevention and School Climate Reform. School Climate Brief, Number 5

ERIC Educational Resources Information Center

Nader, Kathleen

2012-01-01

Research has demonstrated that a positive school climate is an essential part of violence prevention. Many factors influence the association between school climate and behavioral outcomes. Positive school climate alone cannot prevent all variables that may contribute to the expression of aggression. Nevertheless, positive school climates influence…

Desert grassland responses to climate and soil moisture suggest divergent vulnerabilities across the southwestern US

USGS Publications Warehouse

Gremer, Jennifer; Bradford, John B.; Munson, Seth M.; Duniway, Michael C.

2015-01-01

Climate change predictions include warming and drying trends, which are expected to be particularly pronounced in the southwestern United States. In this region, grassland dynamics are tightly linked to available moisture, yet it has proven difficult to resolve what aspects of climate drive vegetation change. In part, this is because it is unclear how heterogeneity in soils affects plant responses to climate. Here, we combine climate and soil properties with a mechanistic soil water model to explain temporal fluctuations in perennial grass cover, quantify where and the degree to which incorporating soil water dynamics enhances our ability to understand temporal patterns, and explore the potential consequences of climate change by assessing future trajectories of important climate and soil water variables. Our analyses focused on long-term (20 to 56 years) perennial grass dynamics across the Colorado Plateau, Sonoran, and Chihuahuan Desert regions. Our results suggest that climate variability has negative effects on grass cover, and that precipitation subsidies that extend growing seasons are beneficial. Soil water metrics, including the number of dry days and availability of water from deeper (>30 cm) soil layers, explained additional grass cover variability. While individual climate variables were ranked as more important in explaining grass cover, collectively soil water accounted for 40 to 60% of the total explained variance. Soil water conditions were more useful for understanding the responses of C3 than C4 grass species. Projections of water balance variables under climate change indicate that conditions that currently support perennial grasses will be less common in the future, and these altered conditions will be more pronounced in the Chihuahuan Desert and Colorado Plateau. We conclude that incorporating multiple aspects of climate and accounting for soil variability can improve our ability to understand patterns, identify areas of vulnerability, and predict the future of desert grasslands.

Desert grassland responses to climate and soil moisture suggest divergent vulnerabilities across the southwestern United States.

PubMed

Gremer, Jennifer R; Bradford, John B; Munson, Seth M; Duniway, Michael C

2015-11-01

Climate change predictions include warming and drying trends, which are expected to be particularly pronounced in the southwestern United States. In this region, grassland dynamics are tightly linked to available moisture, yet it has proven difficult to resolve what aspects of climate drive vegetation change. In part, this is because it is unclear how heterogeneity in soils affects plant responses to climate. Here, we combine climate and soil properties with a mechanistic soil water model to explain temporal fluctuations in perennial grass cover, quantify where and the degree to which incorporating soil water dynamics enhances our ability to understand temporal patterns, and explore the potential consequences of climate change by assessing future trajectories of important climate and soil water variables. Our analyses focused on long-term (20-56 years) perennial grass dynamics across the Colorado Plateau, Sonoran, and Chihuahuan Desert regions. Our results suggest that climate variability has negative effects on grass cover, and that precipitation subsidies that extend growing seasons are beneficial. Soil water metrics, including the number of dry days and availability of water from deeper (>30 cm) soil layers, explained additional grass cover variability. While individual climate variables were ranked as more important in explaining grass cover, collectively soil water accounted for 40-60% of the total explained variance. Soil water conditions were more useful for understanding the responses of C3 than C4 grass species. Projections of water balance variables under climate change indicate that conditions that currently support perennial grasses will be less common in the future, and these altered conditions will be more pronounced in the Chihuahuan Desert and Colorado Plateau. We conclude that incorporating multiple aspects of climate and accounting for soil variability can improve our ability to understand patterns, identify areas of vulnerability, and predict the future of desert grasslands. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

High-resolution regional climate model evaluation using variable-resolution CESM over California

NASA Astrophysics Data System (ADS)

Huang, X.; Rhoades, A.; Ullrich, P. A.; Zarzycki, C. M.

2015-12-01

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.

Rainfall variability and extremes over southern Africa: assessment of a climate model to reproduce daily extremes

NASA Astrophysics Data System (ADS)

Williams, C.; Kniveton, D.; Layberry, R.

2009-04-01

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 role of climate variability in extreme floods in Europe

NASA Astrophysics Data System (ADS)

Guimarães Nobre, Gabriela; Aerts, Jeroen C. J. H.; Jongman, Brenden; Ward, Philip J.

2017-04-01

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 .

Establishing a Real-Money Prediction Market for Climate on Decadal Horizons

NASA Astrophysics Data System (ADS)

Roulston, M. S.; Hand, D. J.; Harding, D. W.

2016-12-01

A plan to establish a not-for-profit prediction market that will allow participants to bet on the value of selected climate variables decades into the future will be presented. It is hoped that this market will provide an objective measure of the consensus view on climate change, including information concerning the uncertainty of climate projections. The proposed design of the market and the definition of the climate variables underlying the contracts will be discussed, as well as relevant regulatory and legal issues.

Trends and Controls of inter-annual Variability in the Carbon Budget of Terrestrial Ecosystems

NASA Astrophysics Data System (ADS)

Cescatti, A.; Marcolla, B.

2014-12-01

The climate sensitivity of the terrestrial carbon budget will substantially affect the sign and strength of the land-climate feedbacks and the future climate trajectories. Current trends in the inter-annual variability of terrestrial carbon fluxes (IAV) may contribute to clarify the relative role of physical and biological controls of ecosystem responses to climate change. For this purpose we investigated how recent climate variability has impacted the carbon fluxes at long-term FLUXNET sites. Using a novel method, the IAV has been factored out in climate induced variability (physical control), variability due to changes in ecosystem functioning (biological control) and the interaction of the two terms. The relative control of the main climatic drivers (temperature, water availability) on the physical and biological sources of IAV has been investigated using both site level fluxes and global gridded products generated from the up-scaling of flux data. Results of this analysis highlight the fundamental role of precipitation trends on the pattern of IAV in the last 30 years. Our findings on the spatial/temporal trends of IAV have been finally confirmed using the signal derived from the global network of atmospheric CO2 concentrations measurements.

Pollen-based continental climate reconstructions at 6 and 21 ka: a global synthesis

USGS Publications Warehouse

Bartlein, P.J.; Harrison, S.P.; Brewer, Sandra; Connor, S.; Davis, B.A.S.; Gajewski, K.; Guiot, J.; Harrison-Prentice, T. I.; Henderson, A.; Peyron, O.; Prentice, I.C.; Scholze, M.; Seppa, H.; Shuman, B.; Sugita, S.; Thompson, R.S.; Viau, A.E.; Williams, J.; Wu, H.

2010-01-01

Subfossil pollen and plant macrofossil data derived from 14C-dated sediment profiles can provide quantitative information on glacial and interglacial climates. The data allow climate variables related to growing-season warmth, winter cold, and plant-available moisture to be reconstructed. Continental-scale reconstructions have been made for the mid-Holocene (MH, around 6 ka) and Last Glacial Maximum (LGM, around 21 ka), allowing comparison with palaeoclimate simulations currently being carried out as part of the fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change. The synthesis of the available MH and LGM climate reconstructions and their uncertainties, obtained using modern-analogue, regression and model-inversion techniques, is presented for four temperature variables and two moisture variables. Reconstructions of the same variables based on surface-pollen assemblages are shown to be accurate and unbiased. Reconstructed LGM and MH climate anomaly patterns are coherent, consistent between variables, and robust with respect to the choice of technique. They support a conceptual model of the controls of Late Quaternary climate change whereby the first-order effects of orbital variations and greenhouse forcing on the seasonal cycle of temperature are predictably modified by responses of the atmospheric circulation and surface energy balance.

Quantifying the increasing sensitivity of power systems to climate variability

NASA Astrophysics Data System (ADS)

Bloomfield, H. C.; Brayshaw, D. J.; Shaffrey, L. C.; Coker, P. J.; Thornton, H. E.

2016-12-01

Large quantities of weather-dependent renewable energy generation are expected in power systems under climate change mitigation policies, yet little attention has been given to the impact of long term climate variability. By combining state-of-the-art multi-decadal meteorological records with a parsimonious representation of a power system, this study characterises the impact of year-to-year climate variability on multiple aspects of the power system of Great Britain (including coal, gas and nuclear generation), demonstrating why multi-decadal approaches are necessary. All aspects of the example system are impacted by inter-annual climate variability, with the impacts being most pronounced for baseload generation. The impacts of inter-annual climate variability increase in a 2025 wind-power scenario, with a 4-fold increase in the inter-annual range of operating hours for baseload such as nuclear. The impacts on peak load and peaking-plant are comparably small. Less than 10 years of power supply and demand data are shown to be insufficient for providing robust power system planning guidance. This suggests renewable integration studies—widely used in policy, investment and system design—should adopt a more robust approach to climate characterisation.

Exploring the impact of climate variability during the Last Glacial Maximum on the pattern of human occupation of Iberia.

PubMed

Burke, Ariane; Levavasseur, Guillaume; James, Patrick M A; Guiducci, Dario; Izquierdo, Manuel Arturo; Bourgeon, Lauriane; Kageyama, Masa; Ramstein, Gilles; Vrac, Mathieu

2014-08-01

The Last Glacial Maximum (LGM) was a global climate event, which had significant repercussions for the spatial distribution and demographic history of prehistoric populations. In Eurasia, the LGM coincides with a potential bottleneck for modern humans and may mark the divergence date for Asian and European populations (Keinan et al., 2007). In this research, the impact of climate variability on human populations in the Iberian Peninsula during the Last Glacial Maximum (LGM) is examined with the aid of downscaled high-resolution (16 × 16 km) numerical climate experiments. Human sensitivity to short time-scale (inter-annual) climate variability during this key time period, which follows the initial modern human colonisation of Eurasia and the extinction of the Neanderthals, is tested using the spatial distribution of archaeological sites. Results indicate that anatomically modern human populations responded to small-scale spatial patterning in climate variability, specifically inter-annual variability in precipitation levels as measured by the standard precipitation index. Climate variability at less than millennial scale, therefore, is shown to be an important component of ecological risk, one that played a role in regulating the spatial behaviour of prehistoric human populations and consequently affected their social networks. Copyright © 2014 Elsevier Ltd. All rights reserved.

Local air temperature tolerance: a sensible basis for estimating climate variability

NASA Astrophysics Data System (ADS)

Kärner, Olavi; Post, Piia

2016-11-01

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.

Climate mode links to atmospheric carbon monoxide over fire regions

NASA Astrophysics Data System (ADS)

Buchholz, R. R.; Hammerling, D.; Worden, H. M.; Monks, S. A.; Edwards, D. P.; Deeter, M. N.; Emmons, L. K.

2017-12-01

Fire is a strong contributor to variability in atmospheric carbon monoxide (CO), particularly for the Southern Hemisphere and tropics. The magnitude of emissions, such as CO, from biomass burning are related to climate through both the availability and dryness of fuel. We investigate this link between CO and climate using satellite measured CO and climate indices. Interannual variability in satellite-measured CO is determined for the time period covering 2001-2016. We use MOPITT total column retrievals and focus on biomass burning regions of the Southern Hemisphere and tropics. In each of the regions, 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 Antarctic Oscillation (AAO). Step-wise forward and backward regression combined with the Bayesian Information Criterion is used to select the best predictive 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. Generally, over 50% of the variability can be explained, with over 70% for the Maritime Southeast Asia and North Australasia regions. To help interpret variability, we draw on the chemistry-climate model CAM-chem, which provides information on source contributions and the relative influence of emissions and meteorology. Our results have implications for applications such as air quality forecasting and verifying climate-chemistry models.

Alternating high and low climate variability: The context of natural selection and speciation in Plio-Pleistocene hominin evolution.

PubMed

Potts, Richard; Faith, J Tyler

2015-10-01

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.

Changes in rainfed and irrigated crop yield response to climate in the western US

NASA Astrophysics Data System (ADS)

Li, X.; Troy, T. J.

2018-06-01

As the global population increases and the climate changes, ensuring a secure food supply is increasingly important. One strategy is irrigation, which allows for crops to be grown outside their optimal climate growing regions and which buffers against climate variability. Although irrigation is a positive climate adaptation mechanism for agriculture, it has a potentially negative effect on water resources as it can lead to groundwater depletion and diminished surface water supplies. This study quantifies how crop yields are affected by climate variability and extremes and the impact of irrigation on crop yield increases under various growing-season climate conditions. To do this, we use historical climate data and county-level rainfed and irrigated crop yields for maize, soybean, winter and spring wheat over the US to analyze the relationship between climate, crop yields, and irrigation. We find that there are optimal climates, specific to each crop, where irrigation provides a benefit and other conditions where irrigation proves to have marginal, if any, benefits. Furthermore, the relationship between crop yields and climate has changed over the last decades, with a changing sensitivity in the relationship of soybean and winter wheat yields to certain climate variables, like crop reference evapotranspiration. These two conclusions have important implications for agricultural and water resource system planning, as it implies there are more optimal climate conditions where irrigation is particularly productive and regions where irrigation should be reconsidered as there is not a significant agricultural benefit and the water could be used more productively.

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

NASA Technical Reports Server (NTRS)

Stackhouse, Paul W., Jr.; Chandler, William S.; Hoell, James M.; Westberg, David; Zhang, Taiping

2015-01-01

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.

Testing competing forms of the Milankovitch hypothesis: A multivariate approach

NASA Astrophysics Data System (ADS)

Kaufmann, Robert K.; Juselius, Katarina

2016-02-01

We test competing forms of the Milankovitch hypothesis by estimating the coefficients and diagnostic statistics for a cointegrated vector autoregressive model that includes 10 climate variables and four exogenous variables for solar insolation. The estimates are consistent with the physical mechanisms postulated to drive glacial cycles. They show that the climate variables are driven partly by solar insolation, determining the timing and magnitude of glaciations and terminations, and partly by internal feedback dynamics, pushing the climate variables away from equilibrium. We argue that the latter is consistent with a weak form of the Milankovitch hypothesis and that it should be restated as follows: internal climate dynamics impose perturbations on glacial cycles that are driven by solar insolation. Our results show that these perturbations are likely caused by slow adjustment between land ice volume and solar insolation. The estimated adjustment dynamics show that solar insolation affects an array of climate variables other than ice volume, each at a unique rate. This implies that previous efforts to test the strong form of the Milankovitch hypothesis by examining the relationship between solar insolation and a single climate variable are likely to suffer from omitted variable bias.

Ground Water and Climate Change

NASA Technical Reports Server (NTRS)

Taylor, Richard G.; Scanlon, Bridget; Doell, Petra; Rodell, Matt; van Beek, Rens; Wada, Yoshihide; Longuevergne, Laurent; Leblanc, Marc; Famiglietti, James S.; Edmunds, Mike;

Ground water and climate change

USGS Publications Warehouse

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

2012-01-01

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.

Holocene climate variability in Texas, USA: An integration of existing paleoclimate data and modeling with a new, high-resolution speleothem record

NASA Astrophysics Data System (ADS)

Wong, Corinne I.; Banner, Jay L.; Musgrove, MaryLynn

2015-11-01

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. We synthesize existing paleoclimate proxy data and climate simulations to provide an overview of climate variability in Texas during the Holocene. Conditions became progressively warmer and drier transitioning from the early to mid Holocene, culminating between 7 and 3 ka (thousand years ago), and were more variable during the late Holocene. The timing and relative magnitude of Holocene climate variability, however, is poorly constrained owing to considerable variability among the different records. To help address this, we present a new speleothem (NBJ) reconstruction from a central Texas cave that comprises the highest resolution proxy record to date, spanning the mid to late Holocene. NBJ trace-element concentrations indicate variable moisture conditions with no clear temporal trend. There is a decoupling between NBJ growth rate, trace-element concentrations, and δ18O values, which indicate that (i) the often direct relation between speleothem growth rate and moisture availability is likely complicated by changes in the overlying ecosystem that affect subsurface CO2 production, and (ii) speleothem δ18O variations likely 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.

Effects of Global Change on U.S. Urban Areas: Vulnerabilities, Impacts, and Adaptation

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Wilbanks, Thomas J.; Kirshen, Paul; Romero-Lnkao, Patricia; Rosenzweig, Cynthia; Ruth, Matthias; Solecki, William; Tarr, Joel

2007-01-01

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.

Changing precipitation in western Europe, climate change or natural variability?

NASA Astrophysics Data System (ADS)

Aalbers, Emma; Lenderink, Geert; van Meijgaard, Erik; van den Hurk, Bart

2017-04-01

Multi-model RCM-GCM ensembles provide high resolution climate projections, valuable for among others climate impact assessment studies. While the application of multiple models (both GCMs and RCMs) provides a certain robustness with respect to model uncertainty, the interpretation of differences between ensemble members - the combined result of model uncertainty and natural variability of the climate system - is not straightforward. Natural variability is intrinsic to the climate system, and a potentially large source of uncertainty in climate change projections, especially for projections on the local to regional scale. To quantify the natural variability and get a robust estimate of the forced climate change response (given a certain model and forcing scenario), large ensembles of climate model simulations of the same model provide essential information. While for global climate models (GCMs) a number of such large single model ensembles exists and have been analyzed, for regional climate models (RCMs) the number and size of single model ensembles is limited, and the predictability of the forced climate response at the local to regional scale is still rather uncertain. We present a regional downscaling of a 16-member single model ensemble over western Europe and the Alps at a resolution of 0.11 degrees (˜12km), similar to the highest resolution EURO-CORDEX simulations. This 16-member ensemble was generated by the GCM EC-EARTH, which was downscaled with the RCM RACMO for the period 1951-2100. This single model ensemble has been investigated in terms of the ensemble mean response (our estimate of the forced climate response), as well as the difference between the ensemble members, which measures natural variability. We focus on the response in seasonal mean and extreme precipitation (seasonal maxima and extremes with a return period up to 20 years) for the near to far future. For most precipitation indices we can reliably determine the climate change signal, given the applied model chain and forcing scenario. However, the analysis also shows how limited the information in single ensemble members is on the local scale forced climate response, even for high levels of global warming when the forced response has emerged from natural variability. Analysis and application of multi-model ensembles like EURO-CORDEX should go hand-in-hand with single model ensembles, like the one presented here, to be able to correctly interpret the fine-scale information in terms of a forced signal and random noise due to natural variability.

Social vulnerability and climate variability in southern Brazil: a TerraPop case study

NASA Astrophysics Data System (ADS)

Adamo, S. B.; Fitch, C. A.; Kugler, T.; Doxsey-Whitfield, E.

2014-12-01

Climate variability is an inherent characteristic of the Earth's climate, including but not limited to climate change. It affects and impacts human society in different ways, depending on the underlying socioeconomic vulnerability of specific places, social groups, households and individuals. This differential vulnerability presents spatial and temporal variations, and is rooted in historical patterns of development and relations between human and ecological systems. This study aims to assess the impact of climate variability on livelihoods and well-being, as well as their changes over time and across space, and for rural and urban populations. The geographic focus is Southern Brazil-the states of Parana, Santa Catarina and Rio Grande do Sul-- and the objectives include (a) to identify and map critical areas or hotspots of exposure to climate variability (temperature and precipitation), and (b) to identify internal variation or differential vulnerability within these areas and its evolution over time (1980-2010), using newly available integrated data from the Terra Populus project. These data include geo-referenced climate and agricultural data, and data describing demographic and socioeconomic characteristics of individuals, households and places.

Virtual water trade in the Roman Mediterranean

NASA Astrophysics Data System (ADS)

Dermody, Brian; van Beek, Rens; Meeks, Elijah; Klein Goldewijk, Kees; Scheidel, Walter; van der Velde, Ype; Bierkens, Marc; Wassen, Martin; Dekker, Stefan

2015-04-01

The Romans were perhaps the most impressive exponents of water resource management in pre-industrial times with irrigation and virtual water trade facilitating unprecedented urbanisation and socio-economic stability for hundreds of years in a region of highly variable climate. To understand Roman water resource management in response to urbanisation and climate variability, a Virtual Water Network of the Roman World was developed. Using this network we found that irrigation and virtual water trade increased Roman resilience to inter-annual climate variability. However, urbanisation and population growth arising from virtual water trade likely pushed the Empire closer to the boundary of its water resources, led to an increase in import costs, and eroded its resilience to climate variability in the long term. Our newest findings also assess the impact that persistent climate change associated with Holocene climate anomalies had on Roman water resource management. Specifically we assess the impact of the change in climate from the Roman Warm Period to the Dark Ages Cold Period on the Roman food supply and whether it could have contributed to the fall of the Western Roman Empire.

Improving the Use of Species Distribution Models in Conservation Planning and Management under Climate Change

PubMed Central

Porfirio, Luciana L.; Harris, Rebecca M. B.; Lefroy, Edward C.; Hugh, Sonia; Gould, Susan F.; Lee, Greg; Bindoff, Nathaniel L.; Mackey, Brendan

2014-01-01

Choice of variables, climate models and emissions scenarios all influence the results of species distribution models under future climatic conditions. However, an overview of applied studies suggests that the uncertainty associated with these factors is not always appropriately incorporated or even considered. We examine the effects of choice of variables, climate models and emissions scenarios can have on future species distribution models using two endangered species: one a short-lived invertebrate species (Ptunarra Brown Butterfly), and the other a long-lived paleo-endemic tree species (King Billy Pine). We show the range in projected distributions that result from different variable selection, climate models and emissions scenarios. The extent to which results are affected by these choices depends on the characteristics of the species modelled, but they all have the potential to substantially alter conclusions about the impacts of climate change. We discuss implications for conservation planning and management, and provide recommendations to conservation practitioners on variable selection and accommodating uncertainty when using future climate projections in species distribution models. PMID:25420020

Changes in climate variability with reference to land quality and agriculture in Scotland.

PubMed

Brown, Iain; Castellazzi, Marie

2015-06-01

Classification and mapping of land capability represents an established format for summarising spatial information on land quality and land-use potential. By convention, this information incorporates bioclimatic constraints through the use of a long-term average. However, climate change means that land capability classification should also have a dynamic temporal component. Using an analysis based upon Land Capability for Agriculture in Scotland, it is shown that this dynamism not only involves the long-term average but also shorter term spatiotemporal patterns, particularly through changes in interannual variability. Interannual and interdecadal variations occur both in the likelihood of land being in prime condition (top three capability class divisions) and in class volatility from year to year. These changing patterns are most apparent in relation to the west-east climatic gradient which is mainly a function of precipitation regime and soil moisture. Analysis is also extended into the future using climate results for the 2050s from a weather generator which show a complex interaction between climate interannual variability and different soil types for land quality. In some locations, variability of land capability is more likely to decrease because the variable climatic constraints are relaxed and the dominant constraint becomes intrinsic soil properties. Elsewhere, climatic constraints will continue to be influential. Changing climate variability has important implications for land-use planning and agricultural management because it modifies local risk profiles in combination with the current trend towards agricultural intensification and specialisation.

Intensified Indian Ocean climate variability during the Last Glacial Maximum

NASA Astrophysics Data System (ADS)

Thirumalai, K.; DiNezro, P.; Tierney, J. E.; Puy, M.; Mohtadi, M.

2017-12-01

Climate models project increased year-to-year climate variability in the equatorial Indian Ocean in response to greenhouse gas warming. This response has been attributed to changes in the mean climate of the Indian Ocean associated with the zonal sea-surface temperature (SST) gradient. According to these studies, air-sea coupling is enhanced due to a stronger SST gradient driving anomalous easterlies that shoal the thermocline in the eastern Indian Ocean. We propose that this relationship between the variability and the zonal SST gradient is consistent across different mean climate states. We test this hypothesis using simulations of past and future climate performed with the Community Earth System Model Version 1 (CESM1). We constrain the realism of the model for the Last Glacial Maximum (LGM) where CESM1 simulates a mean climate consistent with a stronger SST gradient, agreeing with proxy reconstructions. CESM1 also simulates a pronounced increase in seasonal and interannual variability. We develop new estimates of climate variability on these timescales during the LGM using δ18O analysis of individual foraminifera (IFA). IFA data generated from four different cores located in the eastern Indian Ocean indicate a marked increase in δ18O-variance during the LGM as compared to the late Holocene. Such a significant increase in the IFA-δ18O variance strongly supports the modeling simulations. This agreement further supports the dynamics linking year-to-year variability and an altered SST gradient, increasing our confidence in model projections.

The future demographic niche of a declining grassland bird fails to shift poleward in response to climate change

USGS Publications Warehouse

McCauley, Lisa A.; Ribic, Christine; Pomara, Lars Y.; Zuckerberg, Benjamin

2017-01-01

ContextTemperate grasslands and their dependent species are exposed to high variability in weather and climate due to the lack of natural buffers such as forests. Grassland birds are particularly vulnerable to this variability, yet have failed to shift poleward in response to recent climate change like other bird species in North America. However, there have been few studies examining the effect of weather on grassland bird demography and consequent influence of climate change on population persistence and distributional shifts.ObjectivesThe goal of this study was to estimate the vulnerability of Henslow’s Sparrow (Ammodramus henslowii), an obligate grassland bird that has been declining throughout much of its range, to past and future climatic variability.MethodsWe conducted a demographic meta-analysis from published studies and quantified the relationship between nest success rates and variability in breeding season climate. We projected the climate-demography relationships spatially, throughout the breeding range, and temporally, from 1981 to 2050. These projections were used to evaluate population dynamics by implementing a spatially explicit population model.ResultsWe uncovered a climate-demography linkage for Henslow’s Sparrow with summer precipitation, and to a lesser degree, temperature positively affecting nest success. We found that future climatic conditions—primarily changes in precipitation—will likely contribute to reduced population persistence and a southwestward range contraction.ConclusionsFuture distributional shifts in response to climate change may not always be poleward and assessing projected changes in precipitation is critical for grassland bird conservation and climate change adaptation.

Sensitivity of river fishes to climate change: The role of hydrological stressors on habitat range shifts.

PubMed

Segurado, Pedro; Branco, Paulo; Jauch, Eduardo; Neves, Ramiro; Ferreira, M Teresa

2016-08-15

Climate change will predictably change hydrological patterns and processes at the catchment scale, with impacts on habitat conditions for fish. The main goal of this study is to assess how shifts in fish habitat favourability under climate change scenarios are affected by hydrological stressors. The interplay between climate and hydrological stressors has important implications in river management under climate change because management actions to control hydrological parameters are more feasible than controlling climate. This study was carried out in the Tamega catchment of the Douro basin. A set of hydrological stressor variables were generated through a process-based modelling based on current climate data (2008-2014) and also considering a high-end future climate change scenario. The resulting parameters, along with climatic and site-descriptor variables were used as explanatory variables in empirical habitat models for nine fish species using boosted regression trees. Models were calibrated for the whole Douro basin using 254 fish sampling sites and predictions under future climate change scenarios were made for the Tamega catchment. Results show that models using climatic variables but not hydrological stressors produce more stringent predictions of future favourability, predicting more distribution contractions or stronger range shifts. The use of hydrological stressors strongly influences projections of habitat favourability shifts; the integration of these stressors in the models thinned shifts in range due to climate change. Hydrological stressors were retained in the models for most species and had a high importance, demonstrating that it is important to integrate hydrology in studies of impacts of climate change on freshwater fishes. This is a relevant result because it means that management actions to control hydrological parameters in rivers will have an impact on the effects of climate change and may potentially be helpful to mitigate its negative effects on fish populations and assemblages. Copyright © 2016 Elsevier B.V. All rights reserved.

Relationship of suicide rates with climate and economic variables in Europe during 2000-2012.

PubMed

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

2016-01-01

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.

National climate assessment technical report on the impacts of climate and land use and land cover change

Treesearch

Thomas Loveland; Rezaul Mahmood; Toral Patel-Weynand; Krista Karstensen; Kari Beckendorf; Norman Bliss; Andrew Carleton

2012-01-01

This technical report responds to the recognition by the U.S. Global Change Research Program (USGCRP) and the National Climate Assessment (NCA) of the importance of understanding how land use and land cover (LULC) affects weather and climate variability and change and how that variability and change affects LULC. Current published, peer-reviewed, scientific literature...

Chapter 2: Effects of climatic variability and change. In Effects of Climate Variability and Change on Forest Ecosystems: A Comprehensive Science Synthesis for the U.S. Forest Sector; General Technical Report PNW-GTR-870, Washington DC

EPA Science Inventory

Climate profoundly shapes forests. Forest species composition, productivity, availability of goods and services, disturbance regimes, and location on the landscape are all regulated by climate. Much research attention has focused on the problem of predicting the response of fores...

Earth System Science Education Centered on Natural Climate Variability

NASA Astrophysics Data System (ADS)

Ramirez, P. C.; Ladochy, S.; Patzert, W. C.; Willis, J. K.

2009-12-01

Several new courses and many educational activities related to climate change are available to teachers and students of all grade levels. However, not all new discoveries in climate research have reached the science education community. In particular, effective learning tools explaining natural climate change are scarce. For example, the Pacific Decadal Oscillation (PDO) is a main cause of natural climate variability spanning decades. While most educators are familiar with the shorter-temporal events impacting climate, El Niño and La Niña, very little has trickled into the climate change curriculum on the PDO. We have developed two online educational modules, using an Earth system science approach, on the PDO and its role in climate change and variability. The first concentrates on the discovery of the PDO through records of salmon catch in the Pacific Northwest and Alaska. We present the connection between salmon abundance in the North Pacific to changing sea surface temperature patterns associated with the PDO. The connection between sea surface temperatures and salmon abundance led to the discovery of the PDO. Our activity also lets students explore the role of salmon in the economy and culture of the Pacific Northwest and Alaska and the environmental requirements for salmon survival. The second module is based on the climate of southern California and how changes in the Pacific Ocean , such as the PDO and ENSO (El Niño-Southern Oscillation), influence regional climate variability. PDO and ENSO signals are evident in the long-term temperature and precipitation record of southern California. Students are guided in the module to discover the relationships between Pacific Ocean conditions and southern California climate variability. The module also provides information establishing the relationship between climate change and variability and the state's water, energy, agriculture, wildfires and forestry, air quality and health issues. Both modules will be reviewed for inclusion on the ESSEA (Earth Systems Science Education Alliance) course module list. ESSEA is a NSF-funded organization dedicated to K-12 online Earth system science education.

Rainfall variability and extremes over southern Africa: Assessment of a climate model to reproduce daily extremes

NASA Astrophysics Data System (ADS)

Williams, C. J. R.; Kniveton, D. R.; Layberry, R.

2009-04-01

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.

Quantitative Analysis of Relevant Soil, Land-use and Climate Characteristics on Landscape Degradation in Hungary

NASA Astrophysics Data System (ADS)

Kertesz, Adam; Mika, Janos; Jakab, Gergely; Palinkas, Melinda

2017-04-01

The objective of our research is to survey degradation processes acting in each micro-region of Hungary in connection with geographical and climatic characteristics. A survey of land degradation processes has been carried out at medium scale (1:50 000) to identify the affected areas of the region. Over 18,000 rectangles of Hungary have been digitally characterised for several types of land degradation. Water-flow type gully erosion and soil-loss (RUSLE, 2015: Esdac-data) are studied for dependent variables in this study. USDA textural classes, available water capacity, bulk density, clay content, coarse fragments, silt content, sand content, soil parent material, soil texture, land-use type (Corine, 2012) are used for non-climatic variables. Some of these characteristics are quantified in a non-scalable way, so the first step was to arrange these qualitative codes or pseudo-numbers into monotonous order for including them into the following multi-regression analyses. Data available from the CarpatClim Project (www.carpatclim-eu.org/pages/home) for 1961-2010 are also used in their 50 years averages is seasonal and annual resolution. The selected variables from this gridded data set are global radiation, daily mean temperature, maximum and minimum temperature, number of extreme cold days (< 20 C), precipitation, extreme wet days (>20 mm), days with utilizable precipitation (>1mm/d), potential evapotranspiration, Palmer Index (PDSI), Palfai Index (PAI), relative humidity and wind speed at 10 m height. The gully erosion processes strongly depend on the investigated non-climatic variables, mostly on parent material and slope. The group of further climatic factors is formed by winter relative humidity, wind speed and all-year round Palmer index. Besides leading role of the above non-climatic factors, additional effects of the significant climate variables are difficult to interpret. Nevertheless, the partial effects of these climate variables are combined with future climate scenarios available from GCM and RCM studies for Hungary. The real climate change effects may likely be stronger, than those obtained by this combination, due to inter-dependences between the non-climatic factors and climate variations. The study has been supported by the OTKA-K108755 project.

Water management to cope with and adapt to climate variability and change.

NASA Astrophysics Data System (ADS)

Hamdy, A.; Trisorio-Liuzzi, G.

2009-04-01

In many parts of the world, variability in climatic conditions is already resulting in major impacts. These impacts are wide ranging and the link to water management problems is obvious and profound. The know-how and the available information undoubtedly indicate that climate change will lead to an intensification of the global hydrological cycle and can have major impacts on regional water resources, affecting both ground and surface water supply for sectorial water uses and, in particular, the irrigation field imposing notable negative effects on food security and poverty alleviation programs in most arid and semi-arid developing countries. At the United Nations Millennium Summit, in September 2000, world leaders adopted the Millennium Development Declaration. From this declaration, the IWRM was recognised as the key concept the water sector should be using for water related development and measures and, hence, for achieving the water related MDG's. However, the potential impacts of climate change and increasing climate variability are not sufficiently addressed in the IWRM plans. Indeed, only a very limited IWRM national plans have been prepared, coping with climate variability and changes. This is mainly due to the lack of operational instruments to deal with climate change and climate variability issues. This is particularly true in developing countries where the financial, human and ecological impacts are potentially greatest and where water resources may be already highly stressed, but the capacity to cope and adapt is weakest. Climate change has now brought realities including mainly rising temperatures and increasing frequency of floods and droughts that present new challenges to be addressed by the IWRM practice. There are already several regional and international initiatives underway that focus on various aspects of water resources management those to be linked with climate changes and vulnerability issues. This is the way where the water resources management and climate scientist communities are engaged in a process for building confidence and understanding, identifying options and defining the water resources management strategies which to cope with impacts of climate variability and change.

Smallholder agriculture in India and adaptation to current and future climate variability and climate change

NASA Astrophysics Data System (ADS)

Murari, K. K.; Jayaraman, T.

2014-12-01

Modeling studies have indicated that global warming, in many regions, will increase the exposure of major crops to rainfall and temperature stress, leading to lower crop yields. Climate variability alone has a potential to decrease yield to an extent comparable to or greater than yield reductions expected due to rising temperature. For India, where agriculture is important, both in terms of food security as well as a source of livelihoods to a majority of its population, climate variability and climate change are subjects of serious concern. There is however a need to distinguish the impact of current climate variability and climate change on Indian agriculture, especially in relation to their socioeconomic impact. This differentiation is difficult to determine due to the secular trend of increasing production and yield of the past several decades. The current research in this aspect is in an initial stage and requires a multi-disciplinary effort. In this study, we assess the potential differential impacts of environmental stress and shock across different socioeconomic strata of the rural population, using village level survey data. The survey data from eight selected villages, based on the Project on Agrarian Relations in India conducted by the Foundation for Agrarian Studies, indicated that income from crop production of the top 20 households (based on the extent of operational land holding, employment of hired labour and asset holdings) is a multiple of the mean income of the village. In sharp contrast, the income of the bottom 20 households is a fraction of the mean and sometimes negative, indicating a net loss from crop production. The considerable differentials in output and incomes suggest that small and marginal farmers are far more susceptible to climate variability and climate change than the other sections. Climate change is effectively an immediate threat to small and marginal farmers, which is driven essentially by socioeconomic conditions. The impact of climate variability on smallholder agriculture in the present can therefore provide important insights into the nature of its vulnerability to future climate change.

Climate change and forest diseases

Treesearch

R.N. Sturrock; Susan Frankel; A. V. Brown; Paul Hennon; J. T. Kliejunas; K. J. Lewis; J. J. Worrall; A. J. Woods

2011-01-01

As climate changes, the effects of forest diseases on forest ecosystems will change. We review knowledge of relationships between climate variables and several forest diseases, as well as current evidence of how climate, host and pathogen interactions are responding or might respond to climate change. Many forests can be managed to both adapt to climate change and...

Intercomparison of model response and internal variability across climate model ensembles

NASA Astrophysics Data System (ADS)

Kumar, Devashish; Ganguly, Auroop R.

2017-10-01

Characterization of climate uncertainty at regional scales over near-term planning horizons (0-30 years) is crucial for climate adaptation. Climate internal variability (CIV) dominates climate uncertainty over decadal prediction horizons at stakeholders' scales (regional to local). In the literature, CIV has been characterized indirectly using projections of climate change from multi-model ensembles (MME) instead of directly using projections from multiple initial condition ensembles (MICE), primarily because adequate number of initial condition (IC) runs were not available for any climate model. Nevertheless, the recent availability of significant number of IC runs from one climate model allows for the first time to characterize CIV directly from climate model projections and perform a sensitivity analysis to study the dominance of CIV compared to model response variability (MRV). Here, we measure relative agreement (a dimensionless number with values ranging between 0 and 1, inclusive; a high value indicates less variability and vice versa) among MME and MICE and find that CIV is lower than MRV for all projection time horizons and spatial resolutions for precipitation and temperature. However, CIV exhibits greater dominance over MRV for seasonal and annual mean precipitation at higher latitudes where signals of climate change are expected to emerge sooner. Furthermore, precipitation exhibits large uncertainties and a rapid decline in relative agreement from global to continental, regional, or local scales for MICE compared to MME. The fractional contribution of uncertainty due to CIV is invariant for precipitation and decreases for temperature as lead time progresses towards the end of the century.

Adaptation with climate uncertainty: An examination of agricultural land use in the United States

USGS Publications Warehouse

Mu, Jianhong E.; McCarl, Bruce A.; Sleeter, Benjamin M.; Abatzoglou, John T.; Zhang, Hongliang

2018-01-01

This paper examines adaptation responses to climate change through adjustment of agricultural land use. The climate drivers we examine are changes in long-term climate normals (e.g., 10-year moving averages) and changes in inter-annual climate variability. Using US county level data over 1982 to 2012 from Census of Agriculture, we find that impacts of long-term climate normals are as important as that of inter-annual climate variability. Projecting into the future, we find projected climate change will lead to an expansion in crop land share across the northern and interior western United States with decreases in the south. We also find that grazing land share increases in southern regions and Inland Pacific Northwest and declines in the northern areas. However, the extent to which the adaptation potential would be is dependent on the climate model, emission scenario and time horizon under consideration.

Climate variability and causes: from the perspective of the Tharaka people of eastern Kenya

NASA Astrophysics Data System (ADS)

Recha, Charles W.; Makokha, George L.; Shisanya, Chris A.

2017-12-01

The study assessed community understanding of climate variability in semi-arid Tharaka sub-county, Kenya. The study used four focus group discussions (FGD) ( N = 48) and a household survey ( N = 326) to obtain information from four agro-ecological zones (AEZs). The results were synthesized and descriptively presented. People in Tharaka sub-county are familiar with the term climate change and associate it with environmental degradation. There are, however, misconceptions and gaps in understanding the causes of climate change. There was a mismatch between community and individual perception of onset and cessation of rainfall—evidence that analysis of the impact of climate change should take into account the scale of interaction. To improve climate change knowledge, there is a need for climate change education by scientific institutions—to provide information on local climatic conditions and global and regional drivers of climate change to local communities.

Climate models predict increasing temperature variability in poor countries.

PubMed

Bathiany, Sebastian; Dakos, Vasilis; Scheffer, Marten; Lenton, Timothy M

2018-05-01

Extreme events such as heat waves are among the most challenging aspects of climate change for societies. We show that climate models consistently project increases in temperature variability in tropical countries over the coming decades, with the Amazon as a particular hotspot of concern. During the season with maximum insolation, temperature variability increases by ~15% per degree of global warming in Amazonia and Southern Africa and by up to 10%°C -1 in the Sahel, India, and Southeast Asia. Mechanisms include drying soils and shifts in atmospheric structure. Outside the tropics, temperature variability is projected to decrease on average because of a reduced meridional temperature gradient and sea-ice loss. The countries that have contributed least to climate change, and are most vulnerable to extreme events, are projected to experience the strongest increase in variability. These changes would therefore amplify the inequality associated with the impacts of a changing climate.

Climate models predict increasing temperature variability in poor countries

PubMed Central

Dakos, Vasilis; Scheffer, Marten

2018-01-01

Extreme events such as heat waves are among the most challenging aspects of climate change for societies. We show that climate models consistently project increases in temperature variability in tropical countries over the coming decades, with the Amazon as a particular hotspot of concern. During the season with maximum insolation, temperature variability increases by ~15% per degree of global warming in Amazonia and Southern Africa and by up to 10%°C−1 in the Sahel, India, and Southeast Asia. Mechanisms include drying soils and shifts in atmospheric structure. Outside the tropics, temperature variability is projected to decrease on average because of a reduced meridional temperature gradient and sea-ice loss. The countries that have contributed least to climate change, and are most vulnerable to extreme events, are projected to experience the strongest increase in variability. These changes would therefore amplify the inequality associated with the impacts of a changing climate. PMID:29732409

Climate variability and human impact on the environment in South America during the last 2000 years: synthesis and perspectives

NASA Astrophysics Data System (ADS)

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.

2015-07-01

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.

Evaluation of climatic changes in South-Asia

NASA Astrophysics Data System (ADS)

Kjellstrom, Erik; Rana, Arun; Grigory, Nikulin; Renate, Wilcke; Hansson, Ulf; Kolax, Michael

2016-04-01

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.

An Integrated Hydro-Economic Model for Economy-Wide Climate Change Impact Assessment for Zambia

NASA Astrophysics Data System (ADS)

Zhu, T.; Thurlow, J.; Diao, X.

2008-12-01

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.

Climate SPHINX: High-resolution present-day and future climate simulations with an improved representation of small-scale variability

NASA Astrophysics Data System (ADS)

Davini, Paolo; von Hardenberg, Jost; Corti, Susanna; Subramanian, Aneesh; Weisheimer, Antje; Christensen, Hannah; Juricke, Stephan; Palmer, Tim

2016-04-01

The PRACE Climate SPHINX project investigates the sensitivity of climate simulations to model resolution and stochastic parameterization. The EC-Earth Earth-System Model is used to explore the impact of stochastic physics in 30-years climate integrations as a function of model resolution (from 80km up to 16km for the atmosphere). The experiments include more than 70 simulations in both a historical scenario (1979-2008) and a climate change projection (2039-2068), using RCP8.5 CMIP5 forcing. A total amount of 20 million core hours will be used at end of the project (March 2016) and about 150 TBytes of post-processed data will be available to the climate community. Preliminary results show a clear improvement in the representation of climate variability over the Euro-Atlantic following resolution increase. More specifically, the well-known atmospheric blocking negative bias over Europe is definitely resolved. High resolution runs also show improved fidelity in representation of tropical variability - such as the MJO and its propagation - over the low resolution simulations. It is shown that including stochastic parameterization in the low resolution runs help to improve some of the aspects of the MJO propagation further. These findings show the importance of representing the impact of small scale processes on the large scale climate variability either explicitly (with high resolution simulations) or stochastically (in low resolution simulations).

Pronounced differences between observed and CMIP5-simulated multidecadal climate variability in the twentieth century

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.