Hydrological response of karst systems to large-scale climate variability for different catchments of the French karst observatory network INSU/CNRS SNO KARST

NASA Astrophysics Data System (ADS)

Massei, Nicolas; Labat, David; Jourde, Hervé; Lecoq, Nicolas; Mazzilli, Naomi

2017-04-01

The french karst observatory network SNO KARST is a national initiative from the National Institute for Earth Sciences and Astronomy (INSU) of the National Center for Scientific Research (CNRS). It is also part of the new french research infrastructure for the observation of the critical zone OZCAR. SNO KARST is composed by several karst sites distributed over conterminous France which are located in different physiographic and climatic contexts (Mediterranean, Pyrenean, Jura mountain, western and northwestern shore near the Atlantic or the English Channel). This allows the scientific community to develop advanced research and experiments dedicated to improve understanding of the hydrological functioning of karst catchments. Here we used several sites of SNO KARST in order to assess the hydrological response of karst catchments to long-term variation of large-scale atmospheric circulation. Using NCEP reanalysis products and karst discharge, we analyzed the links between large-scale circulation and karst water resources variability. As karst hydrosystems are highly heterogeneous media, they behave differently across different time-scales : we explore the large-scale/local-scale relationships according to time-scales using a wavelet multiresolution approach of both karst hydrological variables and large-scale climate fields such as sea level pressure (SLP). The different wavelet components of karst discharge in response to the corresponding wavelet component of climate fields are either 1) compared to physico-chemical/geochemical responses at karst springs, or 2) interpreted in terms of hydrological functioning by comparing discharge wavelet components to internal components obtained from precipitation/discharge models using the KARSTMOD conceptual modeling platform of SNO KARST.

The causality analysis of climate change and large-scale human crisis

PubMed Central

Zhang, David D.; Lee, Harry F.; Wang, Cong; Li, Baosheng; Pei, Qing; Zhang, Jane; An, Yulun

2011-01-01

Recent studies have shown strong temporal correlations between past climate changes and societal crises. However, the specific causal mechanisms underlying this relation have not been addressed. We explored quantitative responses of 14 fine-grained agro-ecological, socioeconomic, and demographic variables to climate fluctuations from A.D. 1500–1800 in Europe. Results show that cooling from A.D. 1560–1660 caused successive agro-ecological, socioeconomic, and demographic catastrophes, leading to the General Crisis of the Seventeenth Century. We identified a set of causal linkages between climate change and human crisis. Using temperature data and climate-driven economic variables, we simulated the alternation of defined “golden” and “dark” ages in Europe and the Northern Hemisphere during the past millennium. Our findings indicate that climate change was the ultimate cause, and climate-driven economic downturn was the direct cause, of large-scale human crises in preindustrial Europe and the Northern Hemisphere. PMID:21969578

The causality analysis of climate change and large-scale human crisis.

PubMed

Zhang, David D; Lee, Harry F; Wang, Cong; Li, Baosheng; Pei, Qing; Zhang, Jane; An, Yulun

2011-10-18

Recent studies have shown strong temporal correlations between past climate changes and societal crises. However, the specific causal mechanisms underlying this relation have not been addressed. We explored quantitative responses of 14 fine-grained agro-ecological, socioeconomic, and demographic variables to climate fluctuations from A.D. 1500-1800 in Europe. Results show that cooling from A.D. 1560-1660 caused successive agro-ecological, socioeconomic, and demographic catastrophes, leading to the General Crisis of the Seventeenth Century. We identified a set of causal linkages between climate change and human crisis. Using temperature data and climate-driven economic variables, we simulated the alternation of defined "golden" and "dark" ages in Europe and the Northern Hemisphere during the past millennium. Our findings indicate that climate change was the ultimate cause, and climate-driven economic downturn was the direct cause, of large-scale human crises in preindustrial Europe and the Northern Hemisphere.

The global climate of December 1992-February 1993. Part 2: Large-scale variability across the tropical western Pacific during TOGA COARE

NASA Technical Reports Server (NTRS)

Gutzler, D. S.; Kiladis, G. N.; Meehl, G. A.; Weickmann, K. M.; Wheeler, M.

1994-01-01

Recently, scientists from more than a dozen countries carried out the field phase of a project called the Coupled-Atmosphere Response Experiment (COARE), devoted to describing the ocean-atmosphere system of the western Pacific near-equatorial warm pool. The project was conceived, organized, and funded under the auspices of the International Tropical Ocean Global Atmosphere (TOGA) Program. Although COARE consisted of several field phases, including a year-long atmospheric enhanced monitoring period (1 July 1992 -- 30 June 1993), the heart of COARE was its four-month Intensive Observation Period (IOP) extending from 1 Nov. 1992 through 28 Feb. 1993. An overview of large-scale variability during COARE is presented. The weather and climate observed in the IOP is placed into context with regard to large-scale, low-frequency fluctuations of the ocean-atmosphere system. Aspects of tropical variability beginning in Aug. 1992 and extending through Mar. 1993, with some sounding data for Apr. 1993 are considered. Variability over the large-scale sounding array (LSA) and the intensive flux array (IFA) is emphasized.

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.

Land use and climate affect Black Tern, Northern Harrier, and Marsh Wren abundance in the Prairie Pothole Region of the United States

USGS Publications Warehouse

Forcey, Greg M.; Thogmartin, Wayne E.; Linz, George M.; McKann, Patrick C.

2014-01-01

Bird populations are influenced by many environmental factors at both large and small scales. Our study evaluated the influences of regional climate and land-use variables on the Northern Harrier (Circus cyaneus), Black Tern (Childonias niger), and Marsh Wren (Cistothorus palustris) in the prairie potholes of the upper Midwest of the United States. These species were chosen because their diverse habitat preference represent the spectrum of habitat conditions present in the Prairie Potholes, ranging from open prairies to dense cattail marshes. We evaluated land-use covariates at three logarithmic spatial scales (1,000 ha, 10,000 ha, and 100,000 ha) and constructed models a priori using information from published habitat associations and climatic influences. The strongest influences on the abundance of each of the three species were the percentage of wetland area across all three spatial scales and precipitation in the year preceding that when bird surveys were conducted. Even among scales ranging over three orders of magnitude the influence of spatial scale was small, as models with the same variables expressed at different scales were often in the best model subset. Examination of the effects of large-scale environmental variables on wetland birds elucidated relationships overlooked in many smaller-scale studies, such as the influences of climate and habitat variables at landscape scales. Given the spatial variation in the abundance of our focal species within the prairie potholes, our model predictions are especially useful for targeting locations, such as northeastern South Dakota and central North Dakota, where management and conservation efforts would be optimally beneficial. This modeling approach can also be applied to other species and geographic areas to focus landscape conservation efforts and subsequent small-scale studies, especially in constrained economic climates.

Range-wide reproductive consequences of ocean climate variability for the seabird Cassin's Auklet.

PubMed

Wolf, Shaye G; Sydeman, William J; Hipfner, J Mark; Abraham, Christine L; Tershy, Bernie R; Croll, Donald A

2009-03-01

We examine how ocean climate variability influences the reproductive phenology and demography of the seabird Cassin's Auklet (Ptychoramphus aleuticus) across approximately 2500 km of its breeding range in the oceanographically dynamic California Current System along the west coast of North America. Specifically, we determine the extent to which ocean climate conditions and Cassin's Auklet timing of breeding and breeding success covary across populations in British Columbia, central California, and northern Mexico over six years (2000-2005) and test whether auklet timing of breeding and breeding success are similarly related to local and large-scale ocean climate indices across populations. Local ocean foraging environments ranged from seasonally variable, high-productivity environments in the north to aseasonal, low-productivity environments to the south, but covaried similarly due to the synchronizing effects of large-scale climate processes. Auklet timing of breeding in the southern population did not covary with populations to the north and was not significantly related to local oceanographic conditions, in contrast to northern populations, where timing of breeding appears to be influenced by oceanographic cues that signal peaks in prey availability. Annual breeding success covaried similarly across populations and was consistently related to local ocean climate conditions across this system. Overall, local ocean climate indices, particularly sea surface height, better explained timing of breeding and breeding success than a large-scale climate index by better representing heterogeneity in physical processes important to auklets and their prey. The significant, consistent relationships we detected between Cassin's Auklet breeding success and ocean climate conditions across widely spaced populations indicate that Cassin's Auklets are susceptible to climate change across the California Current System, especially by the strengthening of climate processes that synchronize oceanographic conditions. Auklet populations in the northern and central regions of this ecosystem may be more sensitive to changes in the timing and variability of ocean climate conditions since they appear to time breeding to take advantage of seasonal productivity peaks.

Variability of tropical cyclone rapid intensification in the North Atlantic and its relationship with climate variations

NASA Astrophysics Data System (ADS)

Wang, Chunzai; Wang, Xidong; Weisberg, Robert H.; Black, Michael L.

2017-12-01

The paper uses observational data from 1950 to 2014 to investigate rapid intensification (RI) variability of tropical cyclones (TCs) in the North Atlantic and its relationships with large-scale climate variations. RI is defined as a TC intensity increase of at least 15.4 m/s (30 knots) in 24 h. The seasonal RI distribution follows the seasonal TC distribution, with the highest number in September. Although an RI event can occur anywhere over the tropical North Atlantic (TNA), there are three regions of maximum RI occurrence: (1) the western TNA of 12°N-18°N and 60°W-45°W, (2) the Gulf of Mexico and the western Caribbean Sea, and (3) the open ocean southeast and east of Florida. RI events also show a minimum value in the eastern Caribbean Sea north of South America—a place called a hurricane graveyard due to atmospheric divergence and subsidence. On longer time scales, RI displays both interannual and multidecadal variability, but RI does not show a long-term trend due to global warming. The top three climate indices showing high correlations with RI are the June-November ENSO and Atlantic warm pool indices, and the January-March North Atlantic oscillation index. It is found that variabilities of vertical wind shear and TC heat potential are important for TC RI in the hurricane main development region, whereas relative humidity at 500 hPa is the main factor responsible for TC RI in the eastern TNA. However, the large-scale oceanic and atmospheric variables analyzed in this study do not show an important role in TC RI in the Gulf of Mexico and the open ocean southeast and east of Florida. This suggests that other factors such as small-scale changes of oceanic and atmospheric variables or TC internal processes may be responsible for TC RI in these two regions. Additionally, the analyses indicate that large-scale atmospheric and oceanic variables are not critical to TC genesis and formation; however, once a tropical depression forms, large-scale climate variations play a role in TC intensification.

Groundwater Variability in a Sandstone Catchment and Linkages with Large-scale Climatic Circulatio

NASA Astrophysics Data System (ADS)

Hannah, D. M.; Lavers, D. A.; Bradley, C.

2015-12-01

Groundwater is a crucial water resource that sustains river ecosystems and provides public water supply. Furthermore, during periods of prolonged high rainfall, groundwater-dominated catchments can be subject to protracted flooding. Climate change and associated projected increases in the frequency and intensity of hydrological extremes have implications for groundwater levels. This study builds on previous research undertaken on a Chalk catchment by investigating groundwater variability in a UK sandstone catchment: the Tern in Shropshire. In contrast to the Chalk, sandstone is characterised by a more lagged response to precipitation inputs; and, as such, it is important to determine the groundwater behaviour and its links with the large-scale climatic circulation to improve process understanding of recharge, groundwater level and river flow responses to hydroclimatological drivers. Precipitation, river discharge and groundwater levels for borehole sites in the Tern basin over 1974-2010 are analysed as the target variables; and we use monthly gridded reanalysis data from the Twentieth Century Reanalysis Project (20CR). First, groundwater variability is evaluated and associations with precipitation / discharge are explored using monthly concurrent and lagged correlation analyses. Second, gridded 20CR reanalysis data are used in composite and correlation analyses to identify the regions of strongest climate-groundwater association. Results show that reasonably strong climate-groundwater connections exist in the Tern basin, with a several months lag. These lags are associated primarily with the time taken for recharge waters to percolate through to the groundwater table. The uncovered patterns improve knowledge of large-scale climate forcing of groundwater variability and may provide a basis to inform seasonal prediction of groundwater levels, which would be useful for strategic water resource planning.

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

Climate, Water, and Human Health: Large Scale Hydroclimatic Controls in Forecasting Cholera Epidemics

NASA Astrophysics Data System (ADS)

Akanda, A. S.; Jutla, A. S.; Islam, S.

2009-12-01

Despite ravaging the continents through seven global pandemics in past centuries, the seasonal and interannual variability of cholera outbreaks remain a mystery. Previous studies have focused on the role of various environmental and climatic factors, but provided little or no predictive capability. Recent findings suggest a more prominent role of large scale hydroclimatic extremes - droughts and floods - and attempt to explain the seasonality and the unique dual cholera peaks in the Bengal Delta region of South Asia. We investigate the seasonal and interannual nature of cholera epidemiology in three geographically distinct locations within the region to identify the larger scale hydroclimatic controls that can set the ecological and environmental ‘stage’ for outbreaks and have significant memory on a seasonal scale. Here we show that two distinctly different, pre and post monsoon, cholera transmission mechanisms related to large scale climatic controls prevail in the region. An implication of our findings is that extreme climatic events such as prolonged droughts, record floods, and major cyclones may cause major disruption in the ecosystem and trigger large epidemics. We postulate that a quantitative understanding of the large-scale hydroclimatic controls and dominant processes with significant system memory will form the basis for forecasting such epidemic outbreaks. A multivariate regression method using these predictor variables to develop probabilistic forecasts of cholera outbreaks will be explored. Forecasts from such a system with a seasonal lead-time are likely to have measurable impact on early cholera detection and prevention efforts in endemic regions.

New Perspectives on the Role of Internal Variability in Regional Climate Change and Climate Model Evaluation

NASA Astrophysics Data System (ADS)

Deser, C.

2017-12-01

Natural climate variability occurs over a wide range of time and space scales as a result of processes intrinsic to the atmosphere, the ocean, and their coupled interactions. Such internally generated climate fluctuations pose significant challenges for the identification of externally forced climate signals such as those driven by volcanic eruptions or anthropogenic increases in greenhouse gases. This challenge is exacerbated for regional climate responses evaluated from short (< 50 years) data records. The limited duration of the observations also places strong constraints on how well the spatial and temporal characteristics of natural climate variability are known, especially on multi-decadal time scales. The observational constraints, in turn, pose challenges for evaluation of climate models, including their representation of internal variability and assessing the accuracy of their responses to natural and anthropogenic radiative forcings. A promising new approach to climate model assessment is the advent of large (10-100 member) "initial-condition" ensembles of climate change simulations with individual models. Such ensembles allow for accurate determination, and straightforward separation, of externally forced climate signals and internal climate variability on regional scales. The range of climate trajectories in a given model ensemble results from the fact that each simulation represents a particular sequence of internal variability superimposed upon a common forced response. This makes clear that nature's single realization is only one of many that could have unfolded. This perspective leads to a rethinking of approaches to climate model evaluation that incorporate observational uncertainty due to limited sampling of internal variability. Illustrative examples across a range of well-known climate phenomena including ENSO, volcanic eruptions, and anthropogenic climate change will be discussed.

Do GCM's Predict the Climate.... Or the Low Frequency Weather?

NASA Astrophysics Data System (ADS)

Lovejoy, S.; Varon, D.; Schertzer, D. J.

2011-12-01

Over twenty-five years ago, a three-regime scaling model was proposed describing the statistical variability of the atmosphere over time scales ranging from weather scales out to ≈ 100 kyrs. Using modern in situ data reanalyses, monthly surface series (at 5ox5o), 8 "multiproxy" (yearly) series of the Northern hemisphere from 1500- 1980, and GRIP and Vostok paleotemperatures at 5.2 and ≈ 100 year resolutions (over the past 91-420 kyrs), we refine the model and show how it can be understood with the help of new developments in nonlinear dynamics, especially multifractals and cascades. In a scaling range, mean fluctuations in state variables such as temperature ΔT ≈ ΔtH the where Δt is the duration. At small (weather) scales the fluctuation exponents are generally H>0; they grow with scale. At longer scales Δt >τw (≈ 10 days) they change sign, the fluctuations decrease with scale; this is the low variability, "low frequency weather" regime the spectrum is a relatively flat "plateau", it's variability is that of the usual idea of "long term weather statistics". Finally for longer times, Δt>τc ≈ 10 - 100 years, again H>0, the variability again increases with scale. This is the true climate regime. These scaling regimes allow us to objectively define the weather as fluctuations over periods <τw, "climate states", as fluctuations at scale τc and "climate change" as the fluctuations at longer periods >τc). We show that the intermediate regime is the result of the weather regime undergoing a "dimensional transition": at temporal scales longer than the typical lifetime of planetary structures (τw), the spatial degrees of freedom are rapidly quenched, only the temporal degrees of freedom are important. This low frequency weather regime has statistical properties well reproduced not only by weather cascade models, but also by control runs (i.e. without climate forcing) of GCM's (including IPSL and ECHAM GCM's). In order for GCM's to go beyond simply predicting this low frequency weather so as to predict the climate, they need appropriate climate forcings and/ or new internal mechanisms of variability. We examine this using wavelet analyses of forced and unforced GCM outputs, including the ECHO-G simulation used in the Millenium project. For example, we find that climate scenarios with large CO2 increases do give rise to a climate regime but that Hc>1 i.e. much larger than that of natural variability which for temperatures has Hc≈0.4. In comparison, the (largely volcanic) forcing of the ECHO-G Millenium simulation is fairly realistic (Hc≈0.4), although it is not clear that this mechanism can explain the even lower frequency variability found in the paleotemperature series, nor is it clear that this is compatible with low frequency solar or orbital forcings.

Large-scale climatic phenomena drive fluctuations in macroinvertebrate assemblages in lowland tropical streams, Costa Rica: The importance of ENSO events in determining long-term (15y) patterns

PubMed Central

Ramírez, Alonso; Pringle, Catherine M.

2018-01-01

Understanding how environmental variables influence the distribution and density of organisms over relatively long temporal scales is a central question in ecology given increased climatic variability (e.g., precipitation, ENSO events). The primary goal of our study was to evaluate long-term (15y time span) patterns of climate, as well as environmental parameters in two Neotropical streams in lowland Costa Rica, to assess potential effects on aquatic macroinvertebrates. We also examined the relative effects of an 8y whole-stream P-enrichment experiment on macroinvertebrate assemblages against the backdrop of this long-term study. Climate, environmental variables and macroinvertebrate samples were measured monthly for 7y and then quarterly for an additional 8y in each stream. Temporal patterns in climatic and environmental variables showed high variability over time, without clear inter-annual or intra-annual patterns. Macroinvertebrate richness and abundance decreased with increasing discharge and was positively related to the number of days since the last high discharge event. Findings show that fluctuations in stream physicochemistry and macroinvertebrate assemblage structure are ultimately the result of large-scale climatic phenomena, such as ENSO events, while the 8y P-enrichment did not appear to affect macroinvertebrates. Our study demonstrates that Neotropical lowland streams are highly dynamic and not as stable as is commonly presumed, with high intra- and inter-annual variability in environmental parameters that change the structure and composition of freshwater macroinvertebrate assemblages. PMID:29420548

Hierarchical stochastic modeling of large river ecosystems and fish growth across spatio-temporal scales and climate models: the Missouri River endangered pallid sturgeon example

USGS Publications Warehouse

Wildhaber, Mark L.; Wikle, Christopher K.; Moran, Edward H.; Anderson, Christopher J.; Franz, Kristie J.; Dey, Rima

2017-01-01

We present a hierarchical series of spatially decreasing and temporally increasing models to evaluate the uncertainty in the atmosphere – ocean global climate model (AOGCM) and the regional climate model (RCM) relative to the uncertainty in the somatic growth of the endangered pallid sturgeon (Scaphirhynchus albus). For effects on fish populations of riverine ecosystems, cli- mate output simulated by coarse-resolution AOGCMs and RCMs must be downscaled to basins to river hydrology to population response. One needs to transfer the information from these climate simulations down to the individual scale in a way that minimizes extrapolation and can account for spatio-temporal variability in the intervening stages. The goal is a framework to determine whether, given uncertainties in the climate models and the biological response, meaningful inference can still be made. The non-linear downscaling of climate information to the river scale requires that one realistically account for spatial and temporal variability across scale. Our down- scaling procedure includes the use of fixed/calibrated hydrological flow and temperature models coupled with a stochastically parameterized sturgeon bioenergetics model. We show that, although there is a large amount of uncertainty associated with both the climate model output and the fish growth process, one can establish significant differences in fish growth distributions between models, and between future and current climates for a given model.

Space-time dependence between energy sources and climate related energy production

NASA Astrophysics Data System (ADS)

Engeland, Kolbjorn; Borga, Marco; Creutin, Jean-Dominique; Ramos, Maria-Helena; Tøfte, Lena; Warland, Geir

2014-05-01

The European Renewable Energy Directive adopted in 2009 focuses on achieving a 20% share of renewable energy in the EU overall energy mix by 2020. A major part of renewable energy production is related to climate, called "climate related energy" (CRE) production. CRE production systems (wind, solar, and hydropower) are characterized by a large degree of intermittency and variability on both short and long time scales due to the natural variability of climate variables. The main strategies to handle the variability of CRE production include energy-storage, -transport, -diversity and -information (smart grids). The three first strategies aim to smooth out the intermittency and variability of CRE production in time and space whereas the last strategy aims to provide a more optimal interaction between energy production and demand, i.e. to smooth out the residual load (the difference between demand and production). In order to increase the CRE share in the electricity system, it is essential to understand the space-time co-variability between the weather variables and CRE production under both current and future climates. This study presents a review of the literature that searches to tackle these problems. It reveals that the majority of studies deals with either a single CRE source or with the combination of two CREs, mostly wind and solar. This may be due to the fact that the most advanced countries in terms of wind equipment have also very little hydropower potential (Denmark, Ireland or UK, for instance). Hydropower is characterized by both a large storage capacity and flexibility in electricity production, and has therefore a large potential for both balancing and storing energy from wind- and solar-power. Several studies look at how to better connect regions with large share of hydropower (e.g., Scandinavia and the Alps) to regions with high shares of wind- and solar-power (e.g., green battery North-Sea net). Considering time scales, various studies consider wind and solar power production and their co-fluctuation at small time scales. The multi-scale nature of the variability is less studied, i.e., the potential adverse or favorable co-fluctuation at intermediate time scales involving water scarcity or abundance, is less present in the literature.Our review points out that it could be especially interesting to promote research on how the pronounced large-scale fluctuations in inflow to hydropower (intra-annual run-off) and smaller scale fluctuations in wind- and solar-power interact in an energy system. There is a need to better represent the profound difference between wind-, solar- and hydro-energy sources. On the one hand, they are all directly linked to the 2-D horizontal dynamics of meteorology. On the other hand, the branching structure of hydrological systems transforms this variability and governs the complex combination of natural inflows and reservoir storage.Finally, we note that the CRE production is, in addition to weather, also influenced by the energy system and market, i.e., the energy transport and demand across scales as well as changes of market regulation. The CRE production system lies thus in this nexus between climate, energy systems and market regulations. The work presented is part of the FP7 project COMPLEX (Knowledge based climate mitigation systems for a low carbon economy; http://www.complex.ac.uk)

Creation of Synthetic Surface Temperature and Precipitation Ensembles Through A Computationally Efficient, Mixed Method Approach

NASA Astrophysics Data System (ADS)

Hartin, C.; Lynch, C.; Kravitz, B.; Link, R. P.; Bond-Lamberty, B. P.

2017-12-01

Typically, uncertainty quantification of internal variability relies on large ensembles of climate model runs under multiple forcing scenarios or perturbations in a parameter space. Computationally efficient, standard pattern scaling techniques only generate one realization and do not capture the complicated dynamics of the climate system (i.e., stochastic variations with a frequency-domain structure). In this study, we generate large ensembles of climate data with spatially and temporally coherent variability across a subselection of Coupled Model Intercomparison Project Phase 5 (CMIP5) models. First, for each CMIP5 model we apply a pattern emulation approach to derive the model response to external forcing. We take all the spatial and temporal variability that isn't explained by the emulator and decompose it into non-physically based structures through use of empirical orthogonal functions (EOFs). Then, we perform a Fourier decomposition of the EOF projection coefficients to capture the input fields' temporal autocorrelation so that our new emulated patterns reproduce the proper timescales of climate response and "memory" in the climate system. Through this 3-step process, we derive computationally efficient climate projections consistent with CMIP5 model trends and modes of variability, which address a number of deficiencies inherent in the ability of pattern scaling to reproduce complex climate model behavior.

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.

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.

Towards an automatic statistical model for seasonal precipitation prediction and its application to Central and South Asian headwater catchments

NASA Astrophysics Data System (ADS)

Gerlitz, Lars; Gafurov, Abror; Apel, Heiko; Unger-Sayesteh, Katy; Vorogushyn, Sergiy; Merz, Bruno

2016-04-01

Statistical climate forecast applications typically utilize a small set of large scale SST or climate indices, such as ENSO, PDO or AMO as predictor variables. If the predictive skill of these large scale modes is insufficient, specific predictor variables such as customized SST patterns are frequently included. Hence statistically based climate forecast models are either based on a fixed number of climate indices (and thus might not consider important predictor variables) or are highly site specific and barely transferable to other regions. With the aim of developing an operational seasonal forecast model, which is easily transferable to any region in the world, we present a generic data mining approach which automatically selects potential predictors from gridded SST observations and reanalysis derived large scale atmospheric circulation patterns and generates robust statistical relationships with posterior precipitation anomalies for user selected target regions. Potential predictor variables are derived by means of a cellwise correlation analysis of precipitation anomalies with gridded global climate variables under consideration of varying lead times. Significantly correlated grid cells are subsequently aggregated to predictor regions by means of a variability based cluster analysis. Finally for every month and lead time, an individual random forest based forecast model is automatically calibrated and evaluated by means of the preliminary generated predictor variables. The model is exemplarily applied and evaluated for selected headwater catchments in Central and South Asia. Particularly the for winter and spring precipitation (which is associated with westerly disturbances in the entire target domain) the model shows solid results with correlation coefficients up to 0.7, although the variability of precipitation rates is highly underestimated. Likewise for the monsoonal precipitation amounts in the South Asian target areas a certain skill of the model could be detected. The skill of the model for the dry summer season in Central Asia and the transition seasons over South Asia is found to be low. A sensitivity analysis by means on well known climate indices reveals the major large scale controlling mechanisms for the seasonal precipitation climate of each target area. For the Central Asian target areas, both, the El Nino Southern Oscillation and the North Atlantic Oscillation are identified as important controlling factors for precipitation totals during moist spring season. Drought conditions are found to be triggered by a warm ENSO phase in combination with a positive phase of the NAO. For the monsoonal summer precipitation amounts over Southern Asia, the model suggests a distinct negative response to El Nino events.

Response of wheat yield in Spain to large-scale patterns

NASA Astrophysics Data System (ADS)

Hernandez-Barrera, Sara; Rodriguez-Puebla, Concepcion

2016-04-01

Crops are vulnerable to extreme climate conditions as drought, heat stress and frost risk. In previous study we have quantified the influence of these climate conditions for winter wheat in Spain (Hernandez-Barrera et al. 2015). The climate extremes respond to large-scale atmospheric and oceanic patterns. Therefore, a question emerges in our investigation: How large-scale patterns affect wheat yield? Obtaining and understanding these relationships require different approaches. In this study, we first obtained the leading mode of observed wheat yield variability to characterize the common variability over different provinces in Spain. Then, the wheat variability is related to different modes of mean sea level pressure, jet stream and sea surface temperature by using Partial Least-Squares, which captures the relevant climate drivers accounting for variations in wheat yield from sowing to harvesting. We used the ERA-Interim reanalysis data and the Extended Reconstructed Sea Surface Temperature (SST) (ERSST v3b). The derived model provides insight about the teleconnections between wheat yield and atmospheric and oceanic circulations, which is considered to project the wheat yield trend under global warming using outputs of twelve climate models corresponding to the Coupled Models Intercomparison Project phase 5 (CMIP5). Hernandez-Barrera S., C. Rodríguez-Puebla and A.J. Challinor. Effects of diurnal temperature range and drought on wheat yield in Spain. Theoretical and Applied Climatology (submitted)

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

PubMed Central

Hoffmann, Holger; Zhao, Gang; Asseng, Senthold; Bindi, Marco; Biernath, Christian; Constantin, Julie; Coucheney, Elsa; Dechow, Rene; Doro, Luca; Eckersten, Henrik; Gaiser, Thomas; Grosz, Balázs; Heinlein, Florian; Kassie, Belay T.; Kersebaum, Kurt-Christian; Klein, Christian; Kuhnert, Matthias; Lewan, Elisabet; Moriondo, Marco; Nendel, Claas; Priesack, Eckart; Raynal, Helene; Roggero, Pier P.; Rötter, Reimund P.; Siebert, Stefan; Specka, Xenia; Tao, Fulu; Teixeira, Edmar; Trombi, Giacomo; Wallach, Daniel; Weihermüller, Lutz; Yeluripati, Jagadeesh; Ewert, Frank

2016-01-01

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations. PMID:27055028

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations.

PubMed

Hoffmann, Holger; Zhao, Gang; Asseng, Senthold; Bindi, Marco; Biernath, Christian; Constantin, Julie; Coucheney, Elsa; Dechow, Rene; Doro, Luca; Eckersten, Henrik; Gaiser, Thomas; Grosz, Balázs; Heinlein, Florian; Kassie, Belay T; Kersebaum, Kurt-Christian; Klein, Christian; Kuhnert, Matthias; Lewan, Elisabet; Moriondo, Marco; Nendel, Claas; Priesack, Eckart; Raynal, Helene; Roggero, Pier P; Rötter, Reimund P; Siebert, Stefan; Specka, Xenia; Tao, Fulu; Teixeira, Edmar; Trombi, Giacomo; Wallach, Daniel; Weihermüller, Lutz; Yeluripati, Jagadeesh; Ewert, Frank

2016-01-01

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

An analytical approach to separate climate and human contributions to basin streamflow variability

NASA Astrophysics Data System (ADS)

Li, Changbin; Wang, Liuming; Wanrui, Wang; Qi, Jiaguo; Linshan, Yang; Zhang, Yuan; Lei, Wu; Cui, Xia; Wang, Peng

2018-04-01

Climate variability and anthropogenic regulations are two interwoven factors in the ecohydrologic system across large basins. Understanding the roles that these two factors play under various hydrologic conditions is of great significance for basin hydrology and sustainable water utilization. In this study, we present an analytical approach based on coupling water balance method and Budyko hypothesis to derive effectiveness coefficients (ECs) of climate change, as a way to disentangle contributions of it and human activities to the variability of river discharges under different hydro-transitional situations. The climate dominated streamflow change (ΔQc) by EC approach was compared with those deduced by the elasticity method and sensitivity index. The results suggest that the EC approach is valid and applicable for hydrologic study at large basin scale. Analyses of various scenarios revealed that contributions of climate change and human activities to river discharge variation differed among the regions of the study area. Over the past several decades, climate change dominated hydro-transitions from dry to wet, while human activities played key roles in the reduction of streamflow during wet to dry periods. Remarkable decline of discharge in upstream was mainly due to human interventions, although climate contributed more to runoff increasing during dry periods in the semi-arid downstream. Induced effectiveness on streamflow changes indicated a contribution ratio of 49% for climate and 51% for human activities at the basin scale from 1956 to 2015. The mathematic derivation based simple approach, together with the case example of temporal segmentation and spatial zoning, could help people understand variation of river discharge with more details at a large basin scale under the background of climate change and human regulations.

Decadal-Scale Forecasting of Climate Drivers for Marine Applications.

PubMed

Salinger, J; Hobday, A J; Matear, R J; O'Kane, T J; Risbey, J S; Dunstan, P; Eveson, J P; Fulton, E A; Feng, M; Plagányi, É E; Poloczanska, E S; Marshall, A G; Thompson, P A

Climate influences marine ecosystems on a range of time scales, from weather-scale (days) through to climate-scale (hundreds of years). Understanding of interannual to decadal climate variability and impacts on marine industries has received less attention. Predictability up to 10 years ahead may come from large-scale climate modes in the ocean that can persist over these time scales. In Australia the key drivers of climate variability affecting the marine environment are the Southern Annular Mode, the Indian Ocean Dipole, the El Niño/Southern Oscillation, and the Interdecadal Pacific Oscillation, each has phases that are associated with different ocean circulation patterns and regional environmental variables. The roles of these drivers are illustrated with three case studies of extreme events-a marine heatwave in Western Australia, a coral bleaching of the Great Barrier Reef, and flooding in Queensland. Statistical and dynamical approaches are described to generate forecasts of climate drivers that can subsequently be translated to useful information for marine end users making decisions at these time scales. Considerable investment is still needed to support decadal forecasting including improvement of ocean-atmosphere models, enhancement of observing systems on all scales to support initiation of forecasting models, collection of important biological data, and integration of forecasts into decision support tools. Collaboration between forecast developers and marine resource sectors-fisheries, aquaculture, tourism, biodiversity management, infrastructure-is needed to support forecast-based tactical and strategic decisions that reduce environmental risk over annual to decadal time scales. © 2016 Elsevier Ltd. All rights reserved.

Large-scale Meteorological Patterns Associated with Extreme Precipitation Events over Portland, OR

NASA Astrophysics Data System (ADS)

Aragon, C.; Loikith, P. C.; Lintner, B. R.; Pike, M.

2017-12-01

Extreme precipitation events can have profound impacts on human life and infrastructure, with broad implications across a range of stakeholders. Changes to extreme precipitation events are a projected outcome of climate change that warrants further study, especially at regional- to local-scales. While global climate models are generally capable of simulating mean climate at global-to-regional scales with reasonable skill, resiliency and adaptation decisions are made at local-scales where most state-of-the-art climate models are limited by coarse resolution. Characterization of large-scale meteorological patterns associated with extreme precipitation events at local-scales can provide climatic information without this scale limitation, thus facilitating stakeholder decision-making. This research will use synoptic climatology as a tool by which to characterize the key large-scale meteorological patterns associated with extreme precipitation events in the Portland, Oregon metro region. Composite analysis of meteorological patterns associated with extreme precipitation days, and associated watershed-specific flooding, is employed to enhance understanding of the climatic drivers behind such events. The self-organizing maps approach is then used to characterize the within-composite variability of the large-scale meteorological patterns associated with extreme precipitation events, allowing us to better understand the different types of meteorological conditions that lead to high-impact precipitation events and associated hydrologic impacts. A more comprehensive understanding of the meteorological drivers of extremes will aid in evaluation of the ability of climate models to capture key patterns associated with extreme precipitation over Portland and to better interpret projections of future climate at impact-relevant scales.

Synoptic circulation and temperature pattern during severe wildland fires

Treesearch

Warren E. Heilman

1996-01-01

Large-scale changes in the atmosphere associated with a globally changed climate and changes in climatic variability may have important regional impacts on the frequency and severity of wildland fires in the future.

Large and local-scale influences on physical and chemical characteristics of coastal waters of Western Europe during winter

NASA Astrophysics Data System (ADS)

Tréguer, Paul; Goberville, Eric; Barrier, Nicolas; L'Helguen, Stéphane; Morin, Pascal; Bozec, Yann; Rimmelin-Maury, Peggy; Czamanski, Marie; Grossteffan, Emilie; Cariou, Thierry; Répécaud, Michel; Quéméner, Loic

2014-11-01

There is now a strong scientific consensus that coastal marine systems of Western Europe are highly sensitive to the combined effects of natural climate variability and anthropogenic climate change. However, it still remains challenging to assess the spatial and temporal scales at which climate influence operates. While large-scale hydro-climatic indices, such as the North Atlantic Oscillation (NAO) or the East Atlantic Pattern (EAP) and the weather regimes such as the Atlantic Ridge (AR), are known to be relevant predictors of physical processes, changes in coastal waters can also be related to local hydro-meteorological and geochemical forcing. Here, we study the temporal variability of physical and chemical characteristics of coastal waters located at about 48°N over the period 1998-2013 using (1) sea surface temperature, (2) sea surface salinity and (3) nutrient concentration observations for two coastal sites located at the outlet of the Bay of Brest and off Roscoff, (4) river discharges of the major tributaries close to these two sites and (5) regional and local precipitation data over the region of interest. Focusing on the winter months, we characterize the physical and chemical variability of these coastal waters and document changes in both precipitation and river runoffs. Our study reveals that variability in coastal waters is connected to the large-scale North Atlantic atmospheric circulation but is also partly explained by local river influences. Indeed, while the NAO is strongly related to changes in sea surface temperature at the Brest and Roscoff sites, the EAP and the AR have a major influence on precipitations, which in turn modulate river discharges that impact sea surface salinity at the scale of the two coastal stations.

Reassessing regime shifts in the North Pacific: incremental climate change and commercial fishing are necessary for explaining decadal-scale biological variability.

PubMed

Litzow, Michael A; Mueter, Franz J; Hobday, Alistair J

2014-01-01

In areas of the North Pacific that are largely free of overfishing, climate regime shifts - abrupt changes in modes of low-frequency climate variability - are seen as the dominant drivers of decadal-scale ecological variability. We assessed the ability of leading modes of climate variability [Pacific Decadal Oscillation (PDO), North Pacific Gyre Oscillation (NPGO), Arctic Oscillation (AO), Pacific-North American Pattern (PNA), North Pacific Index (NPI), El Niño-Southern Oscillation (ENSO)] to explain decadal-scale (1965-2008) patterns of climatic and biological variability across two North Pacific ecosystems (Gulf of Alaska and Bering Sea). Our response variables were the first principle component (PC1) of four regional climate parameters [sea surface temperature (SST), sea level pressure (SLP), freshwater input, ice cover], and PCs 1-2 of 36 biological time series [production or abundance for populations of salmon (Oncorhynchus spp.), groundfish, herring (Clupea pallasii), shrimp, and jellyfish]. We found that the climate modes alone could not explain ecological variability in the study region. Both linear models (for climate PC1) and generalized additive models (for biology PC1-2) invoking only the climate modes produced residuals with significant temporal trends, indicating that the models failed to capture coherent patterns of ecological variability. However, when the residual climate trend and a time series of commercial fishery catches were used as additional candidate variables, resulting models of biology PC1-2 satisfied assumptions of independent residuals and out-performed models constructed from the climate modes alone in terms of predictive power. As measured by effect size and Akaike weights, the residual climate trend was the most important variable for explaining biology PC1 variability, and commercial catch the most important variable for biology PC2. Patterns of climate sensitivity and exploitation history for taxa strongly associated with biology PC1-2 suggest plausible mechanistic explanations for these modeling results. Our findings suggest that, even in the absence of overfishing and in areas strongly influenced by internal climate variability, climate regime shift effects can only be understood in the context of other ecosystem perturbations. © 2013 John Wiley & Sons Ltd.

Information transfer across the scales of climate variability: The effect of the 7-8 year cycle on the annual and interannual scales

NASA Astrophysics Data System (ADS)

Palus, Milan; Jajcay, Nikola; Hlinka, Jaroslav; Kravtsov, Sergey; Tsonis, Anastasios

2016-04-01

Complexity of the climate system stems not only from the fact that it is variable over a huge range of spatial and temporal scales, but also from the nonlinear character of the climate system that leads to interactions of dynamics across scales. The dynamical processes on large time scales influence variability on shorter time scales. This nonlinear phenomenon of cross-scale causal interactions can be observed due to the recently introduced methodology [1] which starts with a wavelet decomposition of a multi-scale signal into quasi-oscillatory modes of a limited bandwidth, described using their instantaneous phases and amplitudes. Then their statistical associations are tested in order to search for interactions across time scales. An information-theoretic formulation of the generalized, nonlinear Granger causality [2] uncovers causal influence and information transfer from large-scale modes of climate variability with characteristic time scales from years to almost a decade to regional temperature variability on short time scales. In analyses of air temperature records from various European locations, a quasioscillatory phenomenon with the period around 7-8 years has been identified as the factor influencing variability of surface air temperature (SAT) on shorter time scales. Its influence on the amplitude of the SAT annual cycle was estimated in the range 0.7-1.4 °C and the effect on the overall variability of the SAT anomalies (SATA) leads to the changes 1.5-1.7 °C in the annual SATA means. The strongest effect of the 7-8 year cycle was observed in the winter SATA means where it reaches 4-5 °C in central European station and reanalysis data [3]. This study is supported by the Ministry of Education, Youth and Sports of the Czech Republic within the Program KONTAKT II, Project No. LH14001. [1] M. Palus, Phys. Rev. Lett. 112 078702 (2014) [2] M. Palus, M. Vejmelka, Phys. Rev. E 75, 056211 (2007) [3] N. Jajcay, J. Hlinka, S. Kravtsov, A. A. Tsonis, M. Palus, Time-scales of the European surface air temperature variability: The role of the 7-8 year cycle. Geophys. Res. Lett., in press, DOI: 10.1002/2015GL067325

Flood events across the North Atlantic region - past development and future perspectives

NASA Astrophysics Data System (ADS)

Matti, Bettina; Dieppois, Bastien; Lawler, Damian; Dahlke, Helen E.; Lyon, Steve W.

2016-04-01

Flood events have a large impact on humans, both socially and economically. An increase in winter and spring flooding across much of northern Europe in recent years opened up the question of changing underlying hydro-climatic drivers of flood events. Predicting the manifestation of such changes is difficult due to the natural variability and fluctuations in northern hydrological systems caused by large-scale atmospheric circulations, especially under altered climate conditions. Improving knowledge on the complexity of these hydrological systems and their interactions with climate is essential to be able to determine drivers of flood events and to predict changes in these drivers under altered climate conditions. This is particularly true for the North Atlantic region where both physical catchment properties and large-scale atmospheric circulations have a profound influence on floods. This study explores changes in streamflow across North Atlantic region catchments. An emphasis is placed on high-flow events, namely the timing and magnitude of past flood events, and selected flood percentiles were tested for stationarity by applying a flood frequency analysis. The issue of non-stationarity of flood return periods is important when linking streamflow to large-scale atmospheric circulations. Natural fluctuations in these circulations are found to have a strong influence on the outcome causing natural variability in streamflow records. Long time series and a multi-temporal approach allows for determining drivers of floods and linking streamflow to large-scale atmospheric circulations. Exploring changes in selected hydrological signatures consistency was found across much of the North Atlantic region suggesting a shift in flow regime. The lack of an overall regional pattern suggests that how catchments respond to changes in climatic drivers is strongly influenced by their physical characteristics. A better understanding of hydrological response to climate drivers is essential for example for forecasting purposes.

Large-scale assessment of present day and future groundwater recharge and its sensitivity to climate variability in Europe's karst regions

NASA Astrophysics Data System (ADS)

Hartmann, A. J.; Gleeson, T. P.; Wagener, T.; Wada, Y.

2016-12-01

Karst aquifers in Europe are an important source of fresh water contributing up to half of the total drinking water supply in some countries. Karstic groundwater recharge is one of the most important components of the water balance of karst systems as it feeds the karst aquifers. Presently available large-scale hydrological models do not consider karst heterogeneity adequately. Projections of current and potential future groundwater recharge of Europe's karst aquifers are therefore unclear. In this study we compare simulations of present (1991-2010) and future (2080-2099) recharge using two different models to simulate groundwater recharge processes. One model includes karst processes (subsurface heterogeneity, lateral flow and concentrated recharge), while the other is based on the conceptual understanding of common hydrological systems (homogeneous subsurface, saturation excess overland flow). Both models are driven by the bias-corrected 5 GCMs of the ISI-MIP project (RCP8.5). To further assess sensitivity of groundwater recharge to climate variability, we calculate the elasticity of recharge rates to annual precipitation, temperature and average intensity of rainfall events, which is the median change of recharge that corresponds to the median change of these climate variables within the present and future time period, respectively. Our model comparison shows that karst regions over Europe have enhanced recharge rates with greater inter-annual variability compared to those with more homogenous subsurface properties. Furthermore, the heterogeneous representation shows stronger elasticity concerning climate variability than the homogeneous subsurface representation. This difference tends to increase towards the future. Our results suggest that water management in regions with heterogeneous subsurface can expect a higher water availability than estimated by most of the current large-scale simulations, while measures should be taken to prepare for increasingly variable groundwater recharge rates.

Regression-based season-ahead drought prediction for southern Peru conditioned on large-scale climate variables

NASA Astrophysics Data System (ADS)

Mortensen, Eric; Wu, Shu; Notaro, Michael; Vavrus, Stephen; Montgomery, Rob; De Piérola, José; Sánchez, Carlos; Block, Paul

2018-01-01

Located at a complex topographic, climatic, and hydrologic crossroads, southern Peru is a semiarid region that exhibits high spatiotemporal variability in precipitation. The economic viability of the region hinges on this water, yet southern Peru is prone to water scarcity caused by seasonal meteorological drought. Meteorological droughts in this region are often triggered during El Niño episodes; however, other large-scale climate mechanisms also play a noteworthy role in controlling the region's hydrologic cycle. An extensive season-ahead precipitation prediction model is developed to help bolster the existing capacity of stakeholders to plan for and mitigate deleterious impacts of drought. In addition to existing climate indices, large-scale climatic variables, such as sea surface temperature, are investigated to identify potential drought predictors. A principal component regression framework is applied to 11 potential predictors to produce an ensemble forecast of regional January-March precipitation totals. Model hindcasts of 51 years, compared to climatology and another model conditioned solely on an El Niño-Southern Oscillation index, achieve notable skill and perform better for several metrics, including ranked probability skill score and a hit-miss statistic. The information provided by the developed model and ancillary modeling efforts, such as extending the lead time of and spatially disaggregating precipitation predictions to the local level as well as forecasting the number of wet-dry days per rainy season, may further assist regional stakeholders and policymakers in preparing for drought.

Palaeoclimate dynamics : a voyage through scales

NASA Astrophysics Data System (ADS)

Crucifix, Michel; Mitsui, Takahito

2015-04-01

Our knowledge of climate dynamics depends on indirect observations of past climate evolution, as well as on what can be inferred from theoretical arguments. At the scale of the Cenozoic, it is common to define a framework of nested time scales, the longest time scale of interest being related to the slow tectonic evolution, then variability associated with or controlled by the astronomical forcing, and finally the fastest dynamics associated with the natural modes of variability of the ocean and the atmosphere. For example, in a model, the astronomical modes of variability may be simulated with deterministic equations under fixed boundary conditions representing the tectonic state, and associated with stochastic parameterisations of the ocean-atmosphere (chaotic) modes of motion. Bifurcations or, more generally, qualitative changes in climate dynamics may be scanned by changing slowly the tectonic state, in order to provide explanations to observed changes in regimes such as the appearance of ice ages and their changes in length or amplitude. The above framework, largely theorized by B. Saltzman, may still be partly justified but is in need of a review. We address here specifically three questions: To what extent astronomical variability interacts with natural modes of ocean - atmosphere variability ? Specifically, how does millennial variability (e.g.: Dansgaard-Oeschger events) fit the Saltzman scheme ? The astronomical forcing is quasi-periodic, and we recently showed that it may produce somewhat counter-intuitive dynamics associated with the emergence of strange non-chaotic attractors. What are the consequences on the spectrum of climate variability ? What are the effects of centennial climate variability on the slow variability of climate ? These three questions are addressed by reference to recently published material, with the objective of emphasising research questions to be explored in the near future.

Internal Variability-Generated Uncertainty in East Asian Climate Projections Estimated with 40 CCSM3 Ensembles.

PubMed

Yao, Shuai-Lei; Luo, Jing-Jia; Huang, Gang

2016-01-01

Regional climate projections are challenging because of large uncertainty particularly stemming from unpredictable, internal variability of the climate system. Here, we examine the internal variability-induced uncertainty in precipitation and surface air temperature (SAT) trends during 2005-2055 over East Asia based on 40 member ensemble projections of the Community Climate System Model Version 3 (CCSM3). The model ensembles are generated from a suite of different atmospheric initial conditions using the same SRES A1B greenhouse gas scenario. We find that projected precipitation trends are subject to considerably larger internal uncertainty and hence have lower confidence, compared to the projected SAT trends in both the boreal winter and summer. Projected SAT trends in winter have relatively higher uncertainty than those in summer. Besides, the lower-level atmospheric circulation has larger uncertainty than that in the mid-level. Based on k-means cluster analysis, we demonstrate that a substantial portion of internally-induced precipitation and SAT trends arises from internal large-scale atmospheric circulation variability. These results highlight the importance of internal climate variability in affecting regional climate projections on multi-decadal timescales.

Regional hydro-climatic impacts of contemporary Amazonian deforestation

NASA Astrophysics Data System (ADS)

Khanna, Jaya

More than 17% of the Amazon rainforest has been cleared in the past three decades triggering important climatological and societal impacts. This thesis is devoted to identifying and explaining the regional hydroclimatic impacts of this change employing multidecadal satellite observations and numerical simulations providing an integrated perspective on this topic. The climatological nature of this study motivated the implementation and application of a cloud detection technique to a new geostationary satellite dataset. The resulting sub daily, high spatial resolution, multidecadal time series facilitated the detection of trends and variability in deforestation triggered cloud cover changes. The analysis was complemented by satellite precipitation, reanalysis and ground based datasets and attribution with the variable resolution Ocean-Land-Atmosphere-Model. Contemporary Amazonian deforestation affects spatial scales of hundreds of kilometers. But, unlike the well-studied impacts of a few kilometers scale deforestation, the climatic response to contemporary, large scale deforestation is neither well observed nor well understood. Employing satellite datasets, this thesis shows a transition in the regional hydroclimate accompanying increasing scales of deforestation, with downwind deforested regions receiving 25% more and upwind deforested regions receiving 25% less precipitation from the deforested area mean. Simulations robustly reproduce these shifts when forced with increasing deforestation alone, suggesting a negligible role of large-scale decadal climate variability in causing the shifts. Furthermore, deforestation-induced surface roughness variations are found necessary to reproduce the observed spatial patterns in recent times illustrating the strong scale-sensitivity of the climatic response to Amazonian deforestation. This phenomenon, inconsequential during the wet season, is found to substantially affect the regional hydroclimate in the local dry and parts of transition seasons, hence occurring in atmospheric conditions otherwise less conducive to thermal convection. Evidence of this phenomenon is found at two large scale deforested areas considered in this thesis. Hence, the 'dynamical' mechanism, which affects the seasons most important for regional ecology, emerges as an impactful convective triggering mechanism. The phenomenon studied in this thesis provides context for thinking about the climate of a future, more patchily forested Amazonia, by articulating relationships between climate and spatial scales of deforestation.

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).

Paleoclimate

USGS Publications Warehouse

Bartlein, Patrick J.; Hostetler, Steven W.; Alder, Jay R.; Ohring, G.

2014-01-01

As host to one of the major continental-scale ice sheets, and with considerable spatial variability of climate related to its physiography and location, North America has experienced a wide range of climates over time. The aim of this chapter is to review the history of those climate variations, focusing in particular on the continental-scale climatic variations between the Last Glacial Maximum (LGM, ca. 21,000 years ago or 21 ka) and the present, which were as large in amplitude as any experienced over a similar time span during the past several million years. As background to that discussion, the climatic variations over the Cenozoic (the past 65.5 Myr, or 65.5 Ma to present) that led ultimately to the onset of Northern Hemisphere glaciation at 2.59 Ma will also be discussed. Superimposed on the large-amplitude, broad-scale variations from the LGM to present, are climatic variations on millennial-to-decadal scales, and these will be reviewed in particular for the Holocene (11.7 ka to present) and the past millennium.

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.

Unravelling connections between river flow and large-scale climate: experiences from Europe

NASA Astrophysics Data System (ADS)

Hannah, D. M.; Kingston, D. G.; Lavers, D.; Stagge, J. H.; Tallaksen, L. M.

2016-12-01

The United Nations has identified better knowledge of large-scale water cycle processes as essential for socio-economic development and global water-food-energy security. In this context, and given the ever-growing concerns about climate change/ variability and human impacts on hydrology, there is an urgent research need: (a) to quantify space-time variability in regional river flow, and (b) to improve hydroclimatological understanding of climate-flow connections as a basis for identifying current and future water-related issues. In this paper, we draw together studies undertaken at the pan-European scale: (1) to evaluate current methods for assessing space-time dynamics for different streamflow metrics (annual regimes, low flows and high flows) and for linking flow variability to atmospheric drivers (circulation indices, air-masses, gridded climate fields and vapour flux); and (2) to propose a plan for future research connecting streamflow and the atmospheric conditions in Europe and elsewhere. We believe this research makes a useful, unique contribution to the literature through a systematic inter-comparison of different streamflow metrics and atmospheric descriptors. In our findings, we highlight the need to consider appropriate atmospheric descriptors (dependent on the target flow metric and region of interest) and to develop analytical techniques that best characterise connections in the ocean-atmosphere-land surface process chain. We call for the need to consider not only atmospheric interactions, but also the role of the river basin-scale terrestrial hydrological processes in modifying the climate signal response of river flows.

Recent changes in county-level corn yield variability in the United States from observations and crop models

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

Leng, Guoyong

The United States is responsible for 35% and 60% of global corn supply and exports. Enhanced supply stability through a reduction in the year-to-year variability of US corn yield would greatly benefit global food security. Important in this regard is to understand how corn yield variability has evolved geographically in the history and how it relates to climatic and non-climatic factors. Results showed that year-to-year variation of US corn yield has decreased significantly during 1980-2010, mainly in Midwest Corn Belt, Nebraska and western arid regions. Despite the country-scale decreasing variability, corn yield variability exhibited an increasing trend in South Dakota,more » Texas and Southeast growing regions, indicating the importance of considering spatial scales in estimating yield variability. The observed pattern is partly reproduced by process-based crop models, simulating larger areas experiencing increasing variability and underestimating the magnitude of decreasing variability. And 3 out of 11 models even produced a differing sign of change from observations. Hence, statistical model which produces closer agreement with observations is used to explore the contribution of climatic and non-climatic factors to the changes in yield variability. It is found that climate variability dominate the change trends of corn yield variability in the Midwest Corn Belt, while the ability of climate variability in controlling yield variability is low in southeastern and western arid regions. Irrigation has largely reduced the corn yield variability in regions (e.g. Nebraska) where separate estimates of irrigated and rain-fed corn yield exist, demonstrating the importance of non-climatic factors in governing the changes in corn yield variability. The results highlight the distinct spatial patterns of corn yield variability change as well as its influencing factors at the county scale. I also caution the use of process-based crop models, which have substantially underestimated the change trend of corn yield variability, in projecting its future changes.« less

The frequency response of a coupled ice sheet-ice shelf-ocean system to climate forcing variability

NASA Astrophysics Data System (ADS)

Goldberg, D.; Snow, K.; Jordan, J. R.; Holland, P.; Arthern, R. J.

2017-12-01

Changes at the West Antarctic ice-ocean boundary in recent decades has triggered significant increases in the regions contribution to global sea-level rise, coincident with large scale, and in some cases potentially unstable, grounding line retreat. Much of the induced change is thought to be driven by fluctuations in the oceanic heat available at the ice-ocean boundary, transported on-shelf via warm Circumpolar Deep Water (CDW). However, the processes in which ocean heat drives ice-sheet loss remains poorly understood, with observational studies routinely hindered by the extreme environment notorious to the Antarctic region. In this study we apply a novel synchronous coupled ice-ocean model, developed within the MITgcm, and are thus able to provide detailed insight into the impacts of short time scale (interannual to decadal) climate variability and feedbacks within the ice-ocean system. Feedbacks and response are assessed in an idealised ice-sheet/ocean-cavity configuration in which the far field ocean condition is adjusted to emulate periodic climate variability patterns. We reveal a non-linear response of the ice-sheet to periodic variations in thermocline depth. These non-linearities illustrate the heightened sensitivity of fast flowing ice-shelves to periodic perturbations in heat fluxes occurring at interannual and decadal time scales. The results thus highlight how small perturbations in variable climate forcing, like that of ENSO, may trigger large changes in ice-sheet response.

Modeling distributional changes in winter precipitation of Canada using Bayesian spatiotemporal quantile regression subjected to different teleconnections

NASA Astrophysics Data System (ADS)

Tan, Xuezhi; Gan, Thian Yew; Chen, Shu; Liu, Bingjun

2018-05-01

Climate change and large-scale climate patterns may result in changes in probability distributions of climate variables that are associated with changes in the mean and variability, and severity of extreme climate events. In this paper, we applied a flexible framework based on the Bayesian spatiotemporal quantile (BSTQR) model to identify climate changes at different quantile levels and their teleconnections to large-scale climate patterns such as El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO) and Pacific-North American (PNA). Using the BSTQR model with time (year) as a covariate, we estimated changes in Canadian winter precipitation and their uncertainties at different quantile levels. There were some stations in eastern Canada showing distributional changes in winter precipitation such as an increase in low quantiles but a decrease in high quantiles. Because quantile functions in the BSTQR model vary with space and time and assimilate spatiotemporal precipitation data, the BSTQR model produced much spatially smoother and less uncertain quantile changes than the classic regression without considering spatiotemporal correlations. Using the BSTQR model with five teleconnection indices (i.e., SOI, PDO, PNA, NP and NAO) as covariates, we investigated effects of large-scale climate patterns on Canadian winter precipitation at different quantile levels. Winter precipitation responses to these five teleconnections were found to occur differently at different quantile levels. Effects of five teleconnections on Canadian winter precipitation were stronger at low and high than at medium quantile levels.

Quantifying Livestock Heat Stress Impacts in the Sahel

NASA Astrophysics Data System (ADS)

Broman, D.; Rajagopalan, B.; Hopson, T. M.

2014-12-01

Livestock heat stress, especially in regions of the developing world with limited adaptive capacity, has a largely unquantified impact on food supply. Though dominated by ambient air temperature, relative humidity, wind speed, and solar radiation all affect heat stress, which can decrease livestock growth, milk production, reproduction rates, and mortality. Indices like the thermal-humidity index (THI) are used to quantify the heat stress experienced from climate variables. Livestock experience differing impacts at different index critical thresholds that are empirically determined and specific to species and breed. This lack of understanding has been highlighted in several studies with a limited knowledge of the critical thresholds of heat stress in native livestock breeds, as well as the current and future impact of heat stress,. As adaptation and mitigation strategies to climate change depend on a solid quantitative foundation, this knowledge gap has limited such efforts. To address the lack of study, we have investigated heat stress impacts in the pastoral system of Sub-Saharan West Africa. We used a stochastic weather generator to quantify both the historic and future variability of heat stress. This approach models temperature, relative humidity, and precipitation, the climate variables controlling heat stress. Incorporating large-scale climate as covariates into this framework provides a better historical fit and allows us to include future CMIP5 GCM projections to examine the climate change impacts on heat stress. Health and production data allow us to examine the influence of this variability on livestock directly, and are considered in conjunction with the confounding impacts of fodder and water access. This understanding provides useful information to decision makers looking to mitigate the impacts of climate change and can provide useful seasonal forecasts of heat stress risk. A comparison of the current and future heat stress conditions based on climate variables for West Africa will be presented, An assessment of current and future risk was obtained by linking climatic heat stress to cattle health and production. Seasonal forecasts of heat stress are also provided by modeling the heat stress climate variables using persistent large-scale climate features.

Regionally synchronous fires in interior British Columbia, Canada, driven by interannual climate variability and weakly associated with large-scale climate patterns between AD 1600-1900

NASA Astrophysics Data System (ADS)

Harvey, J. E.; Smith, D. J.

2016-12-01

We investigated the influence of climate variability on forest fire occurrence in west central British Columbia (BC), Canada, between AD 1600 and 1900. Fire history was reconstructed at 8 sites in the Cariboo-Chilcotin region and we identified 46 local (fires that affected 1 site) and 16 moderate (fires that affected 2 sites) fires. Preexisting fire history data collected from nearby sites was incorporated to identify 17 regionally synchronous fire years (fires that affected ³ 3 sites). Interannual and multidecadal relationships between fire occurrence and the Palmer Drought Severity Index (PDSI), El Nino Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and the Pacific North American (PNA) pattern were examined, in addition to the effects of phase interactions between ENSO and PDO. We examined multiple reconstructions of PDO and ENSO and utilized three methodological approaches to characterize climate-fire relationships. We found that the influence of interannual climate expressed as PDSI, increasingly synchronized the occurrence of of fires from local to regional fires. Regional fires were associated with anomalously dry, warm conditions in the year of the fire and in years preceding the fire. We also identified an association between local fires and antecedent moisture conditions, where wetter and cooler conditions persisted 2-3 years prior to fire. This finding suggests that moisture-driven fine fuel development and proximity to grasslands could function as key determinants of local (small-scale) fire history parameters. The relationships we identified between regional fires and ENSO, PDO and PNA suggest that large-scale patterns of climate variability exert a weak and/or inconsistent influence over fire activity in west central BC between AD 1600-1900. The strongest relationships between regional fires and large-scale climate patterns were identified when ENSO and PDO were both in positive phases. We also documented a relationship between regional fires and positive years of the PNA pattern. Our findings suggest that long-term fire planning using predictions of large scale climate patterns may be limited in west central BC, however, the consideration of additive phases of ENSO and PDO, and the PNA pattern, may be effective and has been suggested by others in the inland Pacific Northwest.

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.

Assessing Lebanon's wildfire potential in association with current and future climatic conditions

Treesearch

George H. Mitri; Mireille G. Jazi; David McWethy

2015-01-01

The increasing occurrence and extent of large-scale wildfires in the Mediterranean have been linked to extended periods of warm and dry weather. We set out to assess Lebanon's wildfire potential in association with current and future climatic conditions. The Keetch-Byram Drought Index (KBDI) was the primary climate variable used in our evaluation of climate/fire...

Millennial-scale climate variations recorded in Early Pliocene colour reflectance time series from the lacustrine Ptolemais Basin (NW Greece)

NASA Astrophysics Data System (ADS)

Steenbrink, J.; Kloosterboer-van Hoeve, M. L.; Hilgen, F. J.

2003-03-01

Quaternary climate proxy records show compelling evidence for climate variability on time scales of a few thousand years. The causes for these millennial-scale or sub-Milankovitch cycles are still poorly understood, not least due to the complex feedback mechanisms of large ice sheets during the Quaternary. We present evidence of millennial-scale climate variability in Early Pliocene lacustrine sediments from the intramontane Ptolemais Basin in northwestern Greece. The sediments are well exposed in a series of open-pit lignite mines and exhibit a distinct millennial-scale sedimentary cyclicity of alternating lignites and lacustrine marl beds that resulted from precession-induced variations in climate. The higher-frequency, millennial-scale cyclicity is particularly prominent within the grey-coloured marl segment of individual cycles. A stratigraphic interval of ˜115 ka, covering five precession-induced sedimentary cycles, was studied in nine parallel sections from two open-pit lignite mines located several km apart. High-resolution colour reflectance records were used to quantify the within-cycle variability and to determine its lateral continuity. Much of the within-cycle variability could be correlated between the parallel sections, even in fine detail, which suggests that these changes reflect basin-wide variations in environmental conditions related to (regional) climate fluctuations. Interbedded volcanic ash beds demonstrate the synchronicity of these fluctuations and spectral analysis of the reflectance time series shows a significant concentration of within-cycle variability at periods of ˜11, ˜5.5 and ˜2 ka. The occurrence of variability at such time scales at times before the intensification of the Northern Hemisphere glaciation suggests that they cannot solely have resulted from internal ice-sheet dynamics. Possible candidates include harmonics or combination tones of the main orbital cycles, variations in solar output or periodic motions of the Earth and Moon.

Statistical Downscaling in Multi-dimensional Wave Climate Forecast

NASA Astrophysics Data System (ADS)

Camus, P.; Méndez, F. J.; Medina, R.; Losada, I. J.; Cofiño, A. S.; Gutiérrez, J. M.

2009-04-01

Wave climate at a particular site is defined by the statistical distribution of sea state parameters, such as significant wave height, mean wave period, mean wave direction, wind velocity, wind direction and storm surge. Nowadays, long-term time series of these parameters are available from reanalysis databases obtained by numerical models. The Self-Organizing Map (SOM) technique is applied to characterize multi-dimensional wave climate, obtaining the relevant "wave types" spanning the historical variability. This technique summarizes multi-dimension of wave climate in terms of a set of clusters projected in low-dimensional lattice with a spatial organization, providing Probability Density Functions (PDFs) on the lattice. On the other hand, wind and storm surge depend on instantaneous local large-scale sea level pressure (SLP) fields while waves depend on the recent history of these fields (say, 1 to 5 days). Thus, these variables are associated with large-scale atmospheric circulation patterns. In this work, a nearest-neighbors analog method is used to predict monthly multi-dimensional wave climate. This method establishes relationships between the large-scale atmospheric circulation patterns from numerical models (SLP fields as predictors) with local wave databases of observations (monthly wave climate SOM PDFs as predictand) to set up statistical models. A wave reanalysis database, developed by Puertos del Estado (Ministerio de Fomento), is considered as historical time series of local variables. The simultaneous SLP fields calculated by NCEP atmospheric reanalysis are used as predictors. Several applications with different size of sea level pressure grid and with different temporal domain resolution are compared to obtain the optimal statistical model that better represents the monthly wave climate at a particular site. In this work we examine the potential skill of this downscaling approach considering perfect-model conditions, but we will also analyze the suitability of this methodology to be used for seasonal forecast and for long-term climate change scenario projection of wave climate.

Tropical warming and the dynamics of endangered primates.

PubMed

Wiederholt, Ruscena; Post, Eric

2010-04-23

Many primate species are severely threatened, but little is known about the effects of global warming and the associated intensification of El Niño events on primate populations. Here, we document the influences of the El Niño southern oscillation (ENSO) and hemispheric climatic variability on the population dynamics of four genera of ateline (neotropical, large-bodied) primates. All ateline genera experienced either an immediate or a lagged negative effect of El Niño events. ENSO events were also found to influence primate resource levels through neotropical arboreal phenology. Furthermore, frugivorous primates showed a high degree of interspecific population synchrony over large scales across Central and South America attributable to the recent trends in large-scale climate. These results highlight the role of large-scale climatic variation and trends in ateline primate population dynamics, and emphasize that global warming could pose additional threats to the persistence of multiple species of endangered primates.

The scaling of population persistence with carrying capacity does not asymptote in populations of a fish experiencing extreme climate variability.

PubMed

White, Richard S A; Wintle, Brendan A; McHugh, Peter A; Booker, Douglas J; McIntosh, Angus R

2017-06-14

Despite growing concerns regarding increasing frequency of extreme climate events and declining population sizes, the influence of environmental stochasticity on the relationship between population carrying capacity and time-to-extinction has received little empirical attention. While time-to-extinction increases exponentially with carrying capacity in constant environments, theoretical models suggest increasing environmental stochasticity causes asymptotic scaling, thus making minimum viable carrying capacity vastly uncertain in variable environments. Using empirical estimates of environmental stochasticity in fish metapopulations, we showed that increasing environmental stochasticity resulting from extreme droughts was insufficient to create asymptotic scaling of time-to-extinction with carrying capacity in local populations as predicted by theory. Local time-to-extinction increased with carrying capacity due to declining sensitivity to demographic stochasticity, and the slope of this relationship declined significantly as environmental stochasticity increased. However, recent 1 in 25 yr extreme droughts were insufficient to extirpate populations with large carrying capacity. Consequently, large populations may be more resilient to environmental stochasticity than previously thought. The lack of carrying capacity-related asymptotes in persistence under extreme climate variability reveals how small populations affected by habitat loss or overharvesting, may be disproportionately threatened by increases in extreme climate events with global warming. © 2017 The Author(s).

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

Drought change in the middle reach of the Yellow River since the late Ming Dynasty and their correlation with runoff

NASA Astrophysics Data System (ADS)

Chen, F.

2017-12-01

Because of the reported decreasing trends in precipitation and streamflow in north-central China (Starting point of Ancient Silk Road), it is essential to understand long-term in water resource availability in this area. Thus, this research presents a new February-August PDSI reconstruction spanning CE 1615-2013 for the southern edge of the Gobi Desert under a highly variable arid and semi-arid climate in northern China. In addition to this new PDSI reconstruction, some previously published annual precipitation/PDSI reconstructions from the neighbouring regions were also used to infer the large-scale hydro-climatic signal of the middle reach of the Yellow River. Spatial correlation analyses with gridded precipitation data showed that the tree-ring records were indeed able to capture much of the regional interannual hydro-climatic signal variability. Using principal component analyses on the reconstructions and documentary records, many large-scale dry and flood events were found during the period AD 1615-2006. Many of these dry events have had profound impacts on the people of the study area over the past several centuries. Temporal correlations among the reconstruction and climatic indices, such as the El Niño-Southern Oscillation, demonstrate that water availability is influenced by tropical and high-latitude forcings in the Pacific rim. Continued work in this direction should enable us to understand better the hydrological change under global warming and the past climate variability of the silk road over long temporal and large spatial scales.

Changes in Intense Precipitation Events in West Africa and the central U.S. under Global Warming

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

Cook, Kerry H.; Vizy, Edward

The purpose of the proposed project is to improve our understanding of the physical processes and large-scale connectivity of changes in intense precipitation events (high rainfall rates) under global warming in West Africa and the central U.S., including relationships with low-frequency modes of variability. This is in response to the requested subject area #2 “simulation of climate extremes under a changing climate … to better quantify the frequency, duration, and intensity of extreme events under climate change and elucidate the role of low frequency climate variability in modulating extremes.” We will use a regional climate model and emphasize an understandingmore » of the physical processes that lead to an intensification of rainfall. The project objectives are as follows: 1. Understand the processes responsible for simulated changes in warm-season rainfall intensity and frequency over West Africa and the Central U.S. associated with greenhouse gas-induced global warming 2. Understand the relationship between changes in warm-season rainfall intensity and frequency, which generally occur on regional space scales, and the larger-scale global warming signal by considering modifications of low-frequency modes of variability. 3. Relate changes simulated on regional space scales to global-scale theories of how and why atmospheric moisture levels and rainfall should change as climate warms.« less

Biological communities in San Francisco Bay track large-scale climate forcing over the North Pacific

NASA Astrophysics Data System (ADS)

Cloern, James E.; Hieb, Kathryn A.; Jacobson, Teresa; Sansó, Bruno; Di Lorenzo, Emanuele; Stacey, Mark T.; Largier, John L.; Meiring, Wendy; Peterson, William T.; Powell, Thomas M.; Winder, Monika; Jassby, Alan D.

2010-11-01

Long-term observations show that fish and plankton populations in the ocean fluctuate in synchrony with large-scale climate patterns, but similar evidence is lacking for estuaries because of shorter observational records. Marine fish and invertebrates have been sampled in San Francisco Bay since 1980 and exhibit large, unexplained population changes including record-high abundances of common species after 1999. Our analysis shows that populations of demersal fish, crabs and shrimp covary with the Pacific Decadal Oscillation (PDO) and North Pacific Gyre Oscillation (NPGO), both of which reversed signs in 1999. A time series model forced by the atmospheric driver of NPGO accounts for two-thirds of the variability in the first principal component of species abundances, and generalized linear models forced by PDO and NPGO account for most of the annual variability of individual species. We infer that synchronous shifts in climate patterns and community variability in San Francisco Bay are related to changes in oceanic wind forcing that modify coastal currents, upwelling intensity, surface temperature, and their influence on recruitment of marine species that utilize estuaries as nursery habitat. Ecological forecasts of estuarine responses to climate change must therefore consider how altered patterns of atmospheric forcing across ocean basins influence coastal oceanography as well as watershed hydrology.

The Role of Arctic Sea Ice in Last Millennium Climate Variability: Model-Proxy Comparisons Using Ensemble Members and Novel Model Experiments.

NASA Astrophysics Data System (ADS)

Gertler, C. G.; Monier, E.; Prinn, R. G.

2016-12-01

Variability in sea ice extent is a prominent feature of forced simulations of the last millennium and reconstructions of paleoclimate using proxy records. The rapid 20th century decline in sea ice extent is most likely due to greenhouse gas forcing, but the accuracy of future projections depend on the characterization of natural variability. Declining sea ice extent affects regional climate and society, but also plays a large role in Arctic amplification, with implications for mid-latitude circulation and even large-scale climate oscillations. To characterize the effects of natural and anthropogenic climate forcing on sea ice and the related changes in large-scale atmospheric circulation, a combination of instrumental record, paleoclimate reconstructions, and general circulation models can be employed to recreate sea ice extents and the corresponding atmosphere-ocean states. Model output from the last millennium ensemble (LME) is compared to a proxy-based sea ice reconstruction and a global proxy network using a variety of statistical and data assimilation techniques. Further model runs using the Community Earth Systems Model (CESM) are performed with the same inputs as LME but forced with experimental sea ice extents, and results are contextualized within the larger ensemble by a variety of metrics.

Groundwater level responses to precipitation variability in Mediterranean insular aquifers

NASA Astrophysics Data System (ADS)

Lorenzo-Lacruz, Jorge; Garcia, Celso; Morán-Tejeda, Enrique

2017-09-01

Groundwater is one of the largest and most important sources of fresh water on many regions under Mediterranean climate conditions, which are exposed to large precipitation variability that includes frequent meteorological drought episodes, and present high evapotranspiration rates and water demand during the dry season. The dependence on groundwater increases in those areas with predominant permeable lithologies, contributing to aquifer recharge and the abundance of ephemeral streams. The increasing pressure of tourism on water resources in many Mediterranean coastal areas, and uncertainty related to future precipitation and water availability, make it urgent to understand the spatio-temporal response of groundwater bodies to precipitation variability, if sustainable use of the resource is to be achieved. We present an assessment of the response of aquifers to precipitation variability based on correlations between the Standardized Precipitation Index (SPI) at various time scales and the Standardized Groundwater Index (SGI) across a Mediterranean island. We detected three main responses of aquifers to accumulated precipitation anomalies: (i) at short time scales of the SPI (<6 months); (ii) at medium time scales (6-24 months); and at long time scales (>24 months). The differing responses were mainly explained by differences in lithology and the percentage of highly permeable rock strata in the aquifer recharge areas. We also identified differences in the months and seasons when aquifer storages are more dependent on precipitation; these were related to climate seasonality and the degree of aquifer exploitation or underground water extraction. The recharge of some aquifers, especially in mountainous areas, is related to precipitation variability within a limited spatial extent, whereas for aquifers located in the plains, precipitation variability influence much larger areas; the topography and geological structure of the island explain these differences. Results indicate large spatial variability in the response of aquifers to precipitation in a very small area, highlighting the importance of having high spatial resolution hydro-climatic databases available to enable full understanding of the effects of climate variability on scarce water resources.

Assessing millennial-scale variability during the Holocene: A perspective from the western tropical Pacific

NASA Astrophysics Data System (ADS)

Khider, D.; Jackson, C. S.; Stott, L. D.

2014-03-01

We investigate the relationship between tropical Pacific and Southern Ocean variability during the Holocene using the stable oxygen isotope and magnesium/calcium records of cooccurring planktonic and benthic foraminifera from a marine sediment core collected in the western equatorial Pacific. The planktonic record exhibits millennial-scale sea surface temperature (SST) oscillations over the Holocene of 0.5°C while the benthic δ18Oc document 0.10‰ millennial-scale changes of Upper Circumpolar Deep Water (UCDW), a water mass which outcrops in the Southern Ocean. Solar forcing as an explanation for millennial-scale SST variability requires (1) a large climate sensitivity and (2) a long 400 year delayed response, suggesting that if solar forcing is the cause of the variability, it would need to be considerably amplified by processes within the climate system at least at the core location. We also explore the possibility that SST variability arose from volcanic forcing using a simple red noise model. Our best estimates of volcanic forcing falls short of reproducing the amplitude of observed SST variations although it produces power at low-frequency similar to that observed in the MD81 record. Although we cannot totally discount the volcanic and solar forcing hypotheses, we are left to consider that the most plausible source for Holocene millennial-scale variability lies within the climate system itself. In particular, UCDW variability coincided with deep North Atlantic changes, indicating a role for the deep ocean in Holocene millennial-scale variability.

Planetary boundary layer as an essential component of the earth's climate system

NASA Astrophysics Data System (ADS)

Davy, Richard; Esau, Igor

2015-04-01

Following the traditional engineering approach proposed by Prandtl, the turbulent planetary boundary layers (PBLs) are considered in the climate science as complex, non-linear, essential but nevertheless subordinated components of the earth's climate system. Correspondingly, the temperature variations, dT - a popular and practically important measure of the climate variability, are seen as the system's response to the external heat forcing, Q, e.g. in the energy balance model of the type dT=Q/C (1). The moderation of this response by non-linear feedbacks embedded in the effective heat capacity, C, are to a large degree overlooked. The effective heat capacity is globally determined by the depth of the ocean mixed layer (on multi-decadal and longer time scales) but regionally, over the continents, C is much smaller and determined (on decadal time scales) by the depth, h, of the PBL. The present understanding of the climatological features of turbulent boundary layers is set by the works of Frankignoul & Hasselmann (1976) and Manabe & Stauffer (1980). The former explained how large-scale climate anomalies could be generated in the case of a large C (in the sea surface temperature) by the delta-correlated stochastic forcing (white noise). The latter demonstrated that the climate response to a given forcing is moderated by the depth, h, so that in the shallow PBL the signal should be significantly amplified. At present there are more than 3000 publications (ISI Web of Knowledge) which detail this understanding but the physical mechanisms, which control the boundary layer depth, and statistical relationships between the turbulent and climatological measures remain either unexplored or incorrectly attributed. In order to identify the climatic role of the PBL, the relationships between the PBL depth, h, - as the integral measure of the turbulent processes and micro-circulations due to the surface heterogeneity - and the climatic variability (variations and trends) of temperature have to be established. These relationships are necessary to complete the model (1) where the relationships between temperature variability, dT, and heat forcing, Q, are intensively studied. We demonstrate that the statistical dependences between dT and h becomes the primary factor in controlling the climate features of the earth's climate system when h is shallow (less than about 500 m). Such conditions are found in the cold (with negative surface heat balance on average) and dry (with large-scale air subsidence) climates. To get those climates and their variations correct, the climate models must be able to reproduce the shallow stably-stratified PBL. We show that the present-day CMIP-5 models are systematically and strongly biased towards producing deeper PBLs (between 20-50% deeper than observed) in this part of the parameter space which leads to large errors (around 15 K) and a damped variability of the surface temperatures under these conditions. More generally, this bias indicates that the models represent the earth's cooling processes incorrectly, which may be a part of the puzzle of the observed "hiatus" (or pause) in global warming. Frankignoul, C. & K. Hasselmann, 1977: Stochastic climate models. Part 2, Application to sea-surface temperature anomalies and thermocline variability, Tellus, 29, 289-305. Manabe, S. & R. Stouffer, 1980: Sensitivity of a Global Climate Model to an increase of CO2 concentration in the atmosphere, Journal of Geophysical Research, 85(C10): 5529-5554.

Assessing the climate-scale variability of atmospheric rivers affecting western North America

NASA Astrophysics Data System (ADS)

Gershunov, Alexander; Shulgina, Tamara; Ralph, F. Martin; Lavers, David A.; Rutz, Jonathan J.

2017-08-01

A new method for automatic detection of atmospheric rivers (ARs) is developed and applied to an atmospheric reanalysis, yielding an extensive catalog of ARs land-falling along the west coast of North America during 1948-2017. This catalog provides a large array of variables that can be used to examine AR cases and their climate-scale variability in exceptional detail. The new record of AR activity, as presented, validated and examined here, provides a perspective on the seasonal cycle and the interannual-interdecadal variability of AR activity affecting the hydroclimate of western North America. Importantly, AR intensity does not exactly follow the climatological pattern of AR frequency. Strong links to hydroclimate are demonstrated using a high-resolution precipitation data set. We describe the seasonal progression of AR activity and diagnose linkages with climate variability expressed in Pacific sea surface temperatures, revealing links to Pacific decadal variability, recent regional anomalies, as well as a generally rising trend in land-falling AR activity. The latter trend is consistent with a long-term increase in vapor transport from the warming North Pacific onto the North American continent. The new catalog provides unprecedented opportunities to study the climate-scale behavior and predictability of ARs affecting western North America.

Linking the climatic and geochemical controls on global soil carbon cycling

NASA Astrophysics Data System (ADS)

Doetterl, Sebastian; Stevens, Antoine; Six, Johan; Merckx, Roel; Van Oost, Kristof; Casanova Pinto, Manuel; Casanova-Katny, Angélica; Muñoz, Cristina; Boudin, Mathieu; Zagal Venegas, Erick; Boeckx, Pascal

2015-04-01

Climatic and geochemical parameters are regarded as the primary controls for soil organic carbon (SOC) storage and turnover. However, due to the difference in scale between climate and geochemical-related soil research, the interaction of these key factors for SOC dynamics have rarely been assessed. Across a large geochemical and climatic transect in similar biomes in Chile and the Antarctic Peninsula we show how abiotic geochemical soil features describing soil mineralogy and weathering pose a direct control on SOC stocks, concentration and turnover and are central to explaining soil C dynamics at larger scales. Precipitation and temperature had an only indirect control by regulating geochemistry. Soils with high SOC content have low specific potential CO2 respiration rates, but a large fraction of SOC that is stabilized via organo-mineral interactions. The opposite was observed for soils with low SOC content. The observed differences for topsoil SOC stocks along this transect of similar biomes but differing geo-climatic site conditions are of the same magnitude as differences observed for topsoil SOC stocks across all major global biomes. Using precipitation and a set of abiotic geochemical parameters describing soil mineralogy and weathering status led to predictions of high accuracy (R2 0.53-0.94) for different C response variables. Partial correlation analyses revealed that the strength of the correlation between climatic predictors and SOC response variables decreased by 51 - 83% when controlling for geochemical predictors. In contrast, controlling for climatic variables did not result in a strong decrease in the strength of the correlations of between most geochemical variables and SOC response variables. In summary, geochemical parameters describing soil mineralogy and weathering were found to be essential for accurate predictions of SOC stocks and potential CO2 respiration, while climatic factors were of minor importance as a direct control, but are important through governing soil weathering and geochemistry. In conclusion, we pledge for a stronger implementation of geochemical soil properties to predict SOC stocks on a global scale. Understanding the effects of climate (temperature and precipitation) change on SOC dynamics also requires good understanding of the relationship between climate and soil geochemistry.

Improving plot- and regional-scale crop models for simulating impacts of climate variability and extremes

NASA Astrophysics Data System (ADS)

Tao, F.; Rötter, R.

2013-12-01

Many studies on global climate report that climate variability is increasing with more frequent and intense extreme events1. There are quite large uncertainties from both the plot- and regional-scale models in simulating impacts of climate variability and extremes on crop development, growth and productivity2,3. One key to reducing the uncertainties is better exploitation of experimental data to eliminate crop model deficiencies and develop better algorithms that more adequately capture the impacts of extreme events, such as high temperature and drought, on crop performance4,5. In the present study, in a first step, the inter-annual variability in wheat yield and climate from 1971 to 2012 in Finland was investigated. Using statistical approaches the impacts of climate variability and extremes on wheat growth and productivity were quantified. In a second step, a plot-scale model, WOFOST6, and a regional-scale crop model, MCWLA7, were calibrated and validated, and applied to simulate wheat growth and yield variability from 1971-2012. Next, the estimated impacts of high temperature stress, cold damage, and drought stress on crop growth and productivity based on the statistical approaches, and on crop simulation models WOFOST and MCWLA were compared. Then, the impact mechanisms of climate extremes on crop growth and productivity in the WOFOST model and MCWLA model were identified, and subsequently, the various algorithm and impact functions were fitted against the long-term crop trial data. Finally, the impact mechanisms, algorithms and functions in WOFOST model and MCWLA model were improved to better simulate the impacts of climate variability and extremes, particularly high temperature stress, cold damage and drought stress for location-specific and large area climate impact assessments. Our studies provide a good example of how to improve, in parallel, the plot- and regional-scale models for simulating impacts of climate variability and extremes, as needed for better informed decision-making on adaptation strategies. References 1. Coumou, D. & Rahmstorf, S. A decade of extremes. Nature Clim. Change, 2, 491-496 (2012). 2. Rötter, R. P., Carter, T. R., Olesen, J. E. & Porter, J. R. Crop-climate models need an overhaul. Nature Clim. Change, 1, 175-177 (2011). 3. Asseng, S. et al., Uncertainty in simulating wheat yields under climate change. Nature Clim. Change. 10.1038/nclimate1916. (2013). 4. Porter, J.R., & Semenov, M., Crop responses to climatic variation . Trans. R. Soc. B., 360, 2021-2035 (2005). 5. Porter, J.R. & Christensen, S. Deconstructing crop processes and models via identities. Plant, Cell and Environment . doi: 10.1111/pce.12107 (2013). 6. Boogaard, H.L., van Diepen C.A., Rötter R.P., Cabrera J.M. & van Laar H.H. User's guide for the WOFOST 7.1 crop growth simulation model and Control Center 1.5, Alterra, Wageningen, The Netherlands. (1998) 7. Tao, F. & Zhang, Z. Climate change, wheat productivity and water use in the North China Plain: a new super-ensemble-based probabilistic projection. Agric. Forest Meteorol., 170, 146-165. (2013).

Millennial-scale Climate Variations Recorded As Far Back As The Early Pliocene

NASA Astrophysics Data System (ADS)

Steenbrink, J.; Hilgen, F. J.; Lourens, L. J.

Quaternary climate proxy records show compelling evidence for climate variability on time scales of a few thousand years. The causes for these millennial-scale or sub- Milankovitch cycles are yet poorly understood, not in the least due to the complex feedback mechanisms of large ice-sheets during the Quaternary. We present evidence of millennial-scale climate variability in Early Pliocene lacustrine sediments from the intramontane Ptolemais Basin in northwestern Greece. The sediments are well ex- posed in a series of open-pit lignite mines and exhibit a distinct m-scale sedimentary cyclicity of alternating lignites and lacustrine marl beds that result from precession- induced variations in climate. A higher-frequency cyclicity is particular prominent within the marl segment of individual cycles. A stratigraphic interval of~115 kyr, cov- ering five precession-induced sedimentary cycles, was studied in nine parallel sections from two quarries located several km apart. Colour reflectance records were used to quantify the within-cycle variability and to determine its lateral continuity. Much of the within-cycle variability could be correlated between the parallel sections, even in fine detail, which suggests that these changes reflect basin-wide variations in environ- mental conditions related to (regional) climate fluctuations. Interbedded volcanic ash beds demonstrate the synchronicity of these fluctuations and spectral analysis of the reflectance time series shows a significant concentration of variability at periods of ~11,~5.5 and~2 kyr. Their occurrence at times before the intensification of the North- ern Hemisphere glaciation suggests that they cannot solely have resulted from internal ice-sheet dynamics. Possible candidates include harmonics or combination tones of the main orbital cycles, variations in solar output or periodic motions of the Earth and moon.

Uncertainties in Past and Future Global Water Availability

NASA Astrophysics Data System (ADS)

Sheffield, J.; Kam, J.

2014-12-01

Understanding how water availability changes on inter-annual to decadal time scales and how it may change in the future under climate change are a key part of understanding future stresses on water and food security. Historic evaluations of water availability on regional to global scales are generally based on large-scale model simulations with their associated uncertainties, in particular for long-term changes. Uncertainties are due to model errors and missing processes, parameter uncertainty, and errors in meteorological forcing data. Recent multi-model inter-comparisons and impact studies have highlighted large differences for past reconstructions, due to different simplifying assumptions in the models or the inclusion of physical processes such as CO2 fertilization. Modeling of direct anthropogenic factors such as water and land management also carry large uncertainties in their physical representation and from lack of socio-economic data. Furthermore, there is little understanding of the impact of uncertainties in the meteorological forcings that underpin these historic simulations. Similarly, future changes in water availability are highly uncertain due to climate model diversity, natural variability and scenario uncertainty, each of which dominates at different time scales. In particular, natural climate variability is expected to dominate any externally forced signal over the next several decades. We present results from multi-land surface model simulations of the historic global availability of water in the context of natural variability (droughts) and long-term changes (drying). The simulations take into account the impact of uncertainties in the meteorological forcings and the incorporation of water management in the form of reservoirs and irrigation. The results indicate that model uncertainty is important for short-term drought events, and forcing uncertainty is particularly important for long-term changes, especially uncertainty in precipitation due to reduced gauge density in recent years. We also discuss uncertainties in future projections from these models as driven by bias-corrected and downscaled CMIP5 climate projections, in the context of the balance between climate model robustness and climate model diversity.

A review of the South American monsoon history as recorded in stable isotopic proxies over the past two millennia

NASA Astrophysics Data System (ADS)

Vuille, M.; Burns, S. J.; Taylor, B. L.; Cruz, F. W.; Bird, B. W.; Abbott, M. B.; Kanner, L. C.; Cheng, H.; Novello, V. F.

2012-08-01

We review the history of the South American summer monsoon (SASM) over the past ~2000 yr based on high-resolution stable isotope proxies from speleothems, ice cores and lake sediments. Our review is complemented by an analysis of an isotope-enabled atmospheric general circulation model (GCM) for the past 130 yr. Proxy records from the monsoon belt in the tropical Andes and SE Brazil show a very coherent behavior over the past 2 millennia with significant decadal to multidecadal variability superimposed on large excursions during three key periods: the Medieval Climate Anomaly (MCA), the Little Ice Age (LIA) and the current warm period (CWP). We interpret these three periods as times when the SASM's mean state was significantly weakened (MCA and CWP) and strengthened (LIA), respectively. During the LIA each of the proxy archives considered contains the most negative δ18O values recorded during the entire record length. On the other hand, the monsoon strength is currently rather weak in a 2000-yr historical perspective, rivaled only by the low intensity during the MCA. Our climatic interpretation of these archives is consistent with our isotope-based GCM analysis, which suggests that these sites are sensitive recorders of large-scale monsoon variations. We hypothesize that these centennial-scale climate anomalies were at least partially driven by temperature changes in the Northern Hemisphere and in particular over the North Atlantic, leading to a latitudinal displacement of the ITCZ and a change in monsoon intensity (amount of rainfall upstream over the Amazon Basin). This interpretation is supported by several independent records from different proxy archives and modeling studies. Although ENSO is the main forcing for δ18O variability over tropical South America on interannual time scales, our results suggest that its influence may be significantly modulated by North Atlantic climate variability on longer time scales. Finally, our analyses indicate that isotopic proxies, because of their ability to integrate climatic information on large spatial scales, could complement more traditional proxies such as tree rings or documentary evidence. Future climate reconstruction efforts could potentially benefit from including isotopic proxies as large-scale predictors in order to better constrain past changes in the atmospheric circulation.

Centennial-scale winter climate variability over the last two millennia in the northern Gulf of Mexico based on paired δ18O and Mg/Ca in Globorotalia truncatulinoides

NASA Astrophysics Data System (ADS)

Fortiz, V.; Thirumalai, K.; Richey, J. N.; Quinn, T. M.

2014-12-01

We present a replicated record of paired foraminiferal δ18O and Mg/Ca variations in multi-cores collected from the Garrison Basin (26º43'N, 93º55'W) in the northern Gulf of Mexico (GOM). Using δ18O (sea surface temperature, SST; sea surface salinity, SSS proxy) and Mg/Ca (SST proxy) variations in non-encrusted planktic foraminifer Globorotalia truncatulinoides we produce time series spanning the last two millennia that is characterized by centennial-scale climate variability. We interpret geochemical variations in G. truncatulinoides to reflect winter climate variability because data from a sediment trap, located ~350 km east of the core site, reveal that annual flux of G. truncatulinoides is heavily weighted towards winter (peak production in January-February; Spear et al., 2011). Similar centennial-scale variability is also observed in the foraminiferal geochemistry of Globigerinoides ruber in the same multi-cores, which likely reflect mean annual climate variations. Our replicated results and comparisons to other SST reconstructions from the region lend confidence that the northern GOM surface ocean underwent large, centennial-scale variability, most likely dominated by changes in winter climate. This variability occurred in a time period where climate forcing is small and background conditions are similar to pre-industrial times. References: Spear, J.W.; Poore, R.Z., and Quinn, T.M., 2011, Globorotalia truncatulinoides (dextral) Mg/Ca as a proxy for Gulf of Mexico winter mixed-layer temperature: Evidence from a sediment trap in the northern Gulf of Mexico. Marine Micropaleontology, 80, 53-61.

Two Centuries of Climate Variability From a Gulf of Papua Coral Confirms a Coherent, Widespread Multidecadal Signal

NASA Astrophysics Data System (ADS)

Cole, J. E.; Lough, J.; Reed, E. V.; Schrag, D. P.

2016-12-01

The Indo-Pacific warm pool is intimately involved with large-scale climate variability on seasonal to secular time scales. The lack of long instrumental observations in this region has motivated paleoclimatic analyses using diverse proxy data sources. We present here new multicentury paleoclimate records from a Gulf of Papua coral that capture past variability with a Pacific-wide signature. We have developed stable isotope, Sr/Ca, skeletal density, and luminescence data from a coral core recovered at Bramble Cay, Australia (9°S, 144°E). The geochemical records span CE 1775-1993 and are dominated by low-frequency (decade-century scale) variability that is consistent with records from other proxies in the same region, and with other coral records from far-flung sites across the southwest Pacific. Unlike in many Pacific coral records, we observe no strong trend towards warmer conditions. Although skeletal density bands are clearly visible, they show inconsistent seasonal phasing with the geochemical tracers of sea surface temperature (SST; Sr/Ca and oxygen isotope content), and skeletal density does not correlate with these tracers on longer time scales. In this coral, density banding must be controlled by a more complex mix of internal and/or external factors. Luminescent banding and reconstructed salinity provide similar histories, suggesting a common hydroclimatic signal with significant variability at periods of decades and longer. The strong low-frequency behavior in these new climate records of SST and hydroclimate, from a remote region of the Indo-Pacific, confirms an important source of internal climate variability, on a poorly documented time scale, from a region with far-reaching climatic importance.

Uncertainties in Future Regional Sea Level Trends: How to Deal with the Internal Climate Variability?

NASA Astrophysics Data System (ADS)

Becker, M.; Karpytchev, M.; Hu, A.; Deser, C.; Lennartz-Sassinek, S.

2017-12-01

Today, the Climate models (CM) are the main tools for forecasting sea level rise (SLR) at global and regional scales. The CM forecasts are accompanied by inherent uncertainties. Understanding and reducing these uncertainties is becoming a matter of increasing urgency in order to provide robust estimates of SLR impact on coastal societies, which need sustainable choices of climate adaptation strategy. These CM uncertainties are linked to structural model formulation, initial conditions, emission scenario and internal variability. The internal variability is due to complex non-linear interactions within the Earth Climate System and can induce diverse quasi-periodic oscillatory modes and long-term persistences. To quantify the effects of internal variability, most studies used multi-model ensembles or sea level projections from a single model ran with perturbed initial conditions. However, large ensembles are not generally available, or too small, and computationally expensive. In this study, we use a power-law scaling of sea level fluctuations, as observed in many other geophysical signals and natural systems, which can be used to characterize the internal climate variability. From this specific statistical framework, we (1) use the pre-industrial control run of the National Center for Atmospheric Research Community Climate System Model (NCAR-CCSM) to test the robustness of the power-law scaling hypothesis; (2) employ the power-law statistics as a tool for assessing the spread of regional sea level projections due to the internal climate variability for the 21st century NCAR-CCSM; (3) compare the uncertainties in predicted sea level changes obtained from a NCAR-CCSM multi-member ensemble simulations with estimates derived for power-law processes, and (4) explore the sensitivity of spatial patterns of the internal variability and its effects on regional sea level projections.

Role of the North Atlantic Ocean in Low Frequency Climate Variability

NASA Astrophysics Data System (ADS)

Danabasoglu, G.; Yeager, S. G.; Kim, W. M.; Castruccio, F. S.

2017-12-01

The Atlantic Ocean is a unique basin with its extensive, North - South overturning circulation, referred to as the Atlantic meridional overturning circulation (AMOC). AMOC is thought to represent the dynamical memory of the climate system, playing an important role in decadal and longer time scale climate variability as well as prediction of the earth's future climate on these time scales via its large heat and salt transports. This oceanic memory is communicated to the atmosphere primarily through the influence of persistent sea surface temperature (SST) variations. Indeed, many modeling studies suggest that ocean circulation, i.e., AMOC, is largely responsible for the creation of coherent SST variability in the North Atlantic, referred to as Atlantic Multidecadal Variability (AMV). AMV has been linked to many (multi)decadal climate variations in, e.g., Sahel and Brazilian rainfall, Atlantic hurricane activity, and Arctic sea-ice extent. In the absence of long, continuous observations, much of the evidence for the ocean's role in (multi)decadal variability comes from model simulations. Although models tend to agree on the role of the North Atlantic Oscillation in creating the density anomalies that proceed the changes in ocean circulation, model fidelity in representing variability characteristics, mechanisms, and air-sea interactions remains a serious concern. In particular, there is increasing evidence that models significantly underestimate low frequency variability in the North Atlantic compared to available observations. Such model deficiencies can amplify the relative influence of external or stochastic atmospheric forcing in generating (multi)decadal variability, i.e., AMV, at the expense of ocean dynamics. Here, a succinct overview of the current understanding of the (North) Atlantic Ocean's role on the regional and global climate, including some outstanding questions, will be presented. In addition, a few examples of the climate impacts of the AMV via atmospheric teleconnections from a set of coupled simulations, also considering the relative roles of its tropical and extratropical components, will be highlighted.

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.

Impacts of climate variability and future climate change on harmful algal blooms and human health.

PubMed

Moore, Stephanie K; Trainer, Vera L; Mantua, Nathan J; Parker, Micaela S; Laws, Edward A; Backer, Lorraine C; Fleming, Lora E

2008-11-07

Anthropogenically-derived increases in atmospheric greenhouse gas concentrations have been implicated in recent climate change, and are projected to substantially impact the climate on a global scale in the future. For marine and freshwater systems, increasing concentrations of greenhouse gases are expected to increase surface temperatures, lower pH, and cause changes to vertical mixing, upwelling, precipitation, and evaporation patterns. The potential consequences of these changes for harmful algal blooms (HABs) have received relatively little attention and are not well understood. Given the apparent increase in HABs around the world and the potential for greater problems as a result of climate change and ocean acidification, substantial research is needed to evaluate the direct and indirect associations between HABs, climate change, ocean acidification, and human health. This research will require a multidisciplinary approach utilizing expertise in climatology, oceanography, biology, epidemiology, and other disciplines. We review the interactions between selected patterns of large-scale climate variability and climate change, oceanic conditions, and harmful algae.

Impacts of climate variability and future climate change on harmful algal blooms and human health

PubMed Central

Moore, Stephanie K; Trainer, Vera L; Mantua, Nathan J; Parker, Micaela S; Laws, Edward A; Backer, Lorraine C; Fleming, Lora E

2008-01-01

Anthropogenically-derived increases in atmospheric greenhouse gas concentrations have been implicated in recent climate change, and are projected to substantially impact the climate on a global scale in the future. For marine and freshwater systems, increasing concentrations of greenhouse gases are expected to increase surface temperatures, lower pH, and cause changes to vertical mixing, upwelling, precipitation, and evaporation patterns. The potential consequences of these changes for harmful algal blooms (HABs) have received relatively little attention and are not well understood. Given the apparent increase in HABs around the world and the potential for greater problems as a result of climate change and ocean acidification, substantial research is needed to evaluate the direct and indirect associations between HABs, climate change, ocean acidification, and human health. This research will require a multidisciplinary approach utilizing expertise in climatology, oceanography, biology, epidemiology, and other disciplines. We review the interactions between selected patterns of large-scale climate variability and climate change, oceanic conditions, and harmful algae. PMID:19025675

Signal to noise quantification of regional climate projections

NASA Astrophysics Data System (ADS)

Li, S.; Rupp, D. E.; Mote, P.

2016-12-01

One of the biggest challenges in interpreting climate model outputs for impacts studies and adaptation planning is understanding the sources of disagreement among models (which is often used imperfectly as a stand-in for system uncertainty). Internal variability is a primary source of uncertainty in climate projections, especially for precipitation, for which models disagree about even the sign of changes in large areas like the continental US. Taking advantage of a large initial-condition ensemble of regional climate simulations, this study quantifies the magnitude of changes forced by increasing greenhouse gas concentrations relative to internal variability. Results come from a large initial-condition ensemble of regional climate model simulations generated by weather@home, a citizen science computing platform, where the western United States climate was simulated for the recent past (1985-2014) and future (2030-2059) using a 25-km horizontal resolution regional climate model (HadRM3P) nested in global atmospheric model (HadAM3P). We quantify grid point level signal-to-noise not just in temperature and precipitation responses, but also the energy and moisture flux terms that are related to temperature and precipitation responses, to provide important insights regarding uncertainty in climate change projections at local and regional scales. These results will aid modelers in determining appropriate ensemble sizes for different climate variables and help users of climate model output with interpreting climate model projections.

Recent surface cooling in the Yellow and East China Seas and the associated North Pacific climate regime shift

NASA Astrophysics Data System (ADS)

Kim, Yong Sun; Jang, Chan Joo; Yeh, Sang-Wook

2018-03-01

The Yellow and East China Seas (YECS) are widely believed to have experienced robust, basin-scale warming over the last few decades. However, the warming reached a peak in the late 1990s, followed by a significant cooling trend. In this study, we investigated the characteristics of this low-frequency sea surface temperature (SST) variance and its dynamic relationship with large-scale climate variability through cyclostationary orthogonal function analysis for the 1982-2014 period. Both regressed surface winds on the primary mode of the YECS SST and trends in air-sea heat fluxes demonstrate that the intensification of the northerly winds in winter contribute largely to the recent cooling trend by increasing heat loss to the atmosphere. As a localized oceanic response to these winds, the upwind flow seems to bring warm waters and partially counteracts the basin-scale cooling, thus contributing to a weakening of the cooling trend along the central trough of the Yellow Sea. In the context of the large-scale climate variabilities, a strong relationship between the YECS SST variability and Pacific Decadal Oscillation (PDO) became weak considerably during the recent cooling period after the late 1990s as the PDO signals appeared to be confined within the eastern basin of the North Pacific in association with the regime shift. In addition to this decoupling of the YECS SST from the PDO, the intensifying Siberian High pressure system likely caused the enhanced northerly winds, leading to the recent cooling trend. These findings highlight relative roles of the PDO and the Siberian High in shaping the YECS SST variance through the changes in the large-scale atmospheric circulation and attendant oceanic advection.

Global Climate Change: Valuable Insights from Concordant and Discordant Ice Core Histories

NASA Astrophysics Data System (ADS)

Mosley-Thompson, E.; Thompson, L. G.; Porter, S. E.; Goodwin, B. P.; Wilson, A. B.

2014-12-01

Earth's ice cover is responding to the ongoing large-scale warming driven in part by anthropogenic forces. The highest tropical and subtropical ice fields are dramatically shrinking and/or thinning and unique climate histories archived therein are now threatened, compromised or lost. Many ice fields in higher latitudes are also experiencing and recording climate system changes although these are often manifested in less evident and spectacular ways. The Antarctic Peninsula (AP) has experienced a rapid, widespread and dramatic warming over the last 60 years. Carefully selected ice fields in the AP allow reconstruction of long histories of key climatic variables. As more proxy climate records are recovered it is clear they reflect a combination of expected and unexpected responses to seemingly similar climate forcings. Recently acquired temperature and precipitation histories from the Bruce Plateau are examined within the context provided by other cores recently collected in the AP. Understanding the differences and similarities among these records provides a better understanding of the forces driving climate variability in the AP over the last century. The Arctic is also rapidly warming. The δ18O records from the Bona-Churchill and Mount Logan ice cores from southeast Alaska and southwest Yukon Territory, respectively, do not record this strong warming. The Aleutian Low strongly influences moisture transport to this geographically complex region, yet its interannual variability is preserved differently in these cores located just 110 km apart. Mount Logan is very sensitive to multi-decadal to multi-centennial climate shifts in the tropical Pacific while low frequency variability on Bona-Churchill is more strongly connected to Western Arctic sea ice extent. There is a natural tendency to focus more strongly on commonalities among records, particularly on regional scales. However, it is also important to investigate seemingly poorly correlated records, particularly those from geographically complex settings that appear to be dominated by similar large-scale climatological processes. Better understanding of the spatially and temporally diverse responses in such regions will expand our understanding of the mechanisms forcing climate variability in meteorologically complex environments.

Shifts in tree functional composition amplify the response of forest biomass to climate

NASA Astrophysics Data System (ADS)

Zhang, Tao; Niinemets, Ülo; Sheffield, Justin; Lichstein, Jeremy W.

2018-04-01

Forests have a key role in global ecosystems, hosting much of the world’s terrestrial biodiversity and acting as a net sink for atmospheric carbon. These and other ecosystem services that are provided by forests may be sensitive to climate change as well as climate variability on shorter time scales (for example, annual to decadal). Previous studies have documented responses of forest ecosystems to climate change and climate variability, including drought-induced increases in tree mortality rates. However, relationships between forest biomass, tree species composition and climate variability have not been quantified across a large region using systematically sampled data. Here we use systematic forest inventories from the 1980s and 2000s across the eastern USA to show that forest biomass responds to decadal-scale changes in water deficit, and that this biomass response is amplified by concurrent changes in community-mean drought tolerance, a functionally important aspect of tree species composition. The amplification of the direct effects of water stress on biomass occurs because water stress tends to induce a shift in tree species composition towards species that are more tolerant to drought but are slower growing. These results demonstrate concurrent changes in forest species composition and biomass carbon storage across a large, systematically sampled region, and highlight the potential for climate-induced changes in forest ecosystems across the world, resulting from both direct effects of climate on forest biomass and indirect effects mediated by shifts in species composition.

Shifts in tree functional composition amplify the response of forest biomass to climate.

PubMed

Zhang, Tao; Niinemets, Ülo; Sheffield, Justin; Lichstein, Jeremy W

2018-04-05

Forests have a key role in global ecosystems, hosting much of the world's terrestrial biodiversity and acting as a net sink for atmospheric carbon. These and other ecosystem services that are provided by forests may be sensitive to climate change as well as climate variability on shorter time scales (for example, annual to decadal). Previous studies have documented responses of forest ecosystems to climate change and climate variability, including drought-induced increases in tree mortality rates. However, relationships between forest biomass, tree species composition and climate variability have not been quantified across a large region using systematically sampled data. Here we use systematic forest inventories from the 1980s and 2000s across the eastern USA to show that forest biomass responds to decadal-scale changes in water deficit, and that this biomass response is amplified by concurrent changes in community-mean drought tolerance, a functionally important aspect of tree species composition. The amplification of the direct effects of water stress on biomass occurs because water stress tends to induce a shift in tree species composition towards species that are more tolerant to drought but are slower growing. These results demonstrate concurrent changes in forest species composition and biomass carbon storage across a large, systematically sampled region, and highlight the potential for climate-induced changes in forest ecosystems across the world, resulting from both direct effects of climate on forest biomass and indirect effects mediated by shifts in species composition.

How resilient are ecosystems in adapting to climate variability

NASA Astrophysics Data System (ADS)

Savenije, Hubert H. G.

2015-04-01

The conclusion often drawn in the media is that ecosystems may perish as a result of climate change. Although climatic trends may indeed lead to shifts in ecosystem composition, the challenge to adjust to climatic variability - even if there is no trend - is larger, particularly in semi-arid or topical climates where climatic variability is large compared to temperate climates. How do ecosystems buffer for climatic variability? The most powerful mechanism is to invest in root zone storage capacity, so as to guarantee access to water and nutrients during period of drought. This investment comes at a cost of having less energy available to invest in growth or formation of fruits. Ecosystems are expected to create sufficient buffer to overcome critical periods of drought, but not more than is necessary to survive or reproduce. Based on this concept, a methodology has been developed to estimate ecosystem root zone storage capacity at local, regional and global scale. These estimates correspond well with estimates made by combining soil and ecosystem information, but are more accurate and more detailed. The methodology shows that ecosystems have intrinsic capacity to adjust to climatic variability and hence have a high resilience to both climatic variability and climatic trends.

A south equatorial African precipitation dipole and the associated atmospheric circulation

NASA Astrophysics Data System (ADS)

Dezfuli, A. K.; Zaitchik, B.; Gnanadesikan, A.

2013-12-01

South Equatorial Africa (SEA) is a climatically diverse region that includes a dramatic topographic and vegetation contrast between the lowland, humid Congo basin to the west and the East African Plateau to the east. Due to lack of conventional weather data and a tendency for researchers to treat East and western Africa as separate regions, dynamics of the atmospheric water cycle across SEA have received relatively little attention, particularly at subseasonal timescales. Both western and eastern sectors of SEA are affected by large-scale drivers of the water cycle associated with Atlantic variability (western sector), Indian Ocean variability (eastern sector) and Pacific variability (both sectors). However, a specific characteristic of SEA is strong heterogeneity in interannual rainfall variability that cannot be explained by large-scale climatic phenomena. For this reason, this study examines regional climate dynamics on daily time-scale with a focus on the role that the abrupt topographic contrast between the lowland Congo and the East African highlands plays in driving rainfall behavior on short timescales. Analysis of daily precipitation data during November-March reveals a zonally-oriented dipole mode over SEA that explains the leading pattern of weather-scale precipitation variability in the region. The separating longitude of the two poles is coincident with the zonal variation of topography. An anomalous counter-clockwise atmospheric circulation associated with the dipole mode appears over the entire SEA. The circulation is triggered by its low-level westerly component, which is in turn generated by an interhemispheric pressure gradient. These enhanced westerlies hit the East African highlands and produce topographically-driven low-level convergence and convection that further intensifies the circulation. Recent studies have shown that under climate change the position and intensity of subtropical highs in both hemispheres and the intensity of precipitation over equatorial Africa are projected to change. Both of these trends have implications for the manner in which large-scale dynamics will interact with regional topography, affecting the intensity and frequency of the dipole mode characterized in this study and the occurrence of extreme wet and dry spells in the region.

Climate variability rather than overstocking causes recent large scale cover changes of Tibetan pastures.

PubMed

Lehnert, L W; Wesche, K; Trachte, K; Reudenbach, C; Bendix, J

2016-04-13

The Tibetan Plateau (TP) is a globally important "water tower" that provides water for nearly 40% of the world's population. This supply function is claimed to be threatened by pasture degradation on the TP and the associated loss of water regulation functions. However, neither potential large scale degradation changes nor their drivers are known. Here, we analyse trends in a high-resolution dataset of grassland cover to determine the interactions among vegetation dynamics, climate change and human impacts on the TP. The results reveal that vegetation changes have regionally different triggers: While the vegetation cover has increased since the year 2000 in the north-eastern part of the TP due to an increase in precipitation, it has declined in the central and western parts of the TP due to rising air temperature and declining precipitation. Increasing livestock numbers as a result of land use changes exacerbated the negative trends but were not their exclusive driver. Thus, we conclude that climate variability instead of overgrazing has been the primary cause for large scale vegetation cover changes on the TP since the new millennium. Since areas of positive and negative changes are almost equal in extent, pasture degradation is not generally proceeding.

High-Resolution Climate Data Visualization through GIS- and Web-based Data Portals

NASA Astrophysics Data System (ADS)

WANG, X.; Huang, G.

2017-12-01

Sound decisions on climate change adaptation rely on an in-depth assessment of potential climate change impacts at regional and local scales, which usually requires finer resolution climate projections at both spatial and temporal scales. However, effective downscaling of global climate projections is practically difficult due to the lack of computational resources and/or long-term reference data. Although a large volume of downscaled climate data has been make available to the public, how to understand and interpret the large-volume climate data and how to make use of the data to drive impact assessment and adaptation studies are still challenging for both impact researchers and decision makers. Such difficulties have become major barriers preventing informed climate change adaptation planning at regional scales. Therefore, this research will explore new GIS- and web-based technologies to help visualize the large-volume regional climate data with high spatiotemporal resolutions. A user-friendly public data portal, named Climate Change Data Portal (CCDP, http://ccdp.network), will be established to allow intuitive and open access to high-resolution regional climate projections at local scales. The CCDP offers functions of visual representation through geospatial maps and data downloading for a variety of climate variables (e.g., temperature, precipitation, relative humidity, solar radiation, and wind) at multiple spatial resolutions (i.e., 25 - 50 km) and temporal resolutions (i.e., annual, seasonal, monthly, daily, and hourly). The vast amount of information the CCDP encompasses can provide a crucial basis for assessing impacts of climate change on local communities and ecosystems and for supporting better decision making under a changing climate.

A global classification of coastal flood hazard climates associated with large-scale oceanographic forcing.

PubMed

Rueda, Ana; Vitousek, Sean; Camus, Paula; Tomás, Antonio; Espejo, Antonio; Losada, Inigo J; Barnard, Patrick L; Erikson, Li H; Ruggiero, Peter; Reguero, Borja G; Mendez, Fernando J

2017-07-11

Coastal communities throughout the world are exposed to numerous and increasing threats, such as coastal flooding and erosion, saltwater intrusion and wetland degradation. Here, we present the first global-scale analysis of the main drivers of coastal flooding due to large-scale oceanographic factors. Given the large dimensionality of the problem (e.g. spatiotemporal variability in flood magnitude and the relative influence of waves, tides and surge levels), we have performed a computer-based classification to identify geographical areas with homogeneous climates. Results show that 75% of coastal regions around the globe have the potential for very large flooding events with low probabilities (unbounded tails), 82% are tide-dominated, and almost 49% are highly susceptible to increases in flooding frequency due to sea-level rise.

Signatures of large-scale and local climates on the demography of white-tailed ptarmigan in Rocky Mountain National Park, Colorado, USA.

PubMed

Wang, Guiming; Hobbs, N Thompson; Galbraith, Hector; Giesen, Kenneth M

2002-09-01

Global climate change may impact wildlife populations by affecting local weather patterns, which, in turn, can impact a variety of ecological processes. However, it is not clear that local variations in ecological processes can be explained by large-scale patterns of climate. The North Atlantic oscillation (NAO) is a large-scale climate phenomenon that has been shown to influence the population dynamics of some animals. Although effects of the NAO on vertebrate population dynamics have been studied, it remains uncertain whether it broadly predicts the impact of weather on species. We examined the ability of local weather data and the NAO to explain the annual variation in population dynamics of white-tailed ptarmigan ( Lagopus leucurus) in Rocky Mountain National Park, USA. We performed canonical correlation analysis on the demographic subspace of ptarmigan and local-climate subspace defined by the empirical orthogonal function (EOF) using data from 1975 to 1999. We found that two subspaces were significantly correlated on the first canonical variable. The Pearson correlation coefficient of the first EOF values of the demographic and local-climate subspaces was significant. The population density and the first EOF of local-climate subspace influenced the ptarmigan population with 1-year lags in the Gompertz model. However, the NAO index was neither related to the first two EOF of local-climate subspace nor to the first EOF of the demographic subspace of ptarmigan. Moreover, the NAO index was not a significant term in the Gompertz model for the ptarmigan population. Therefore, local climate had stronger signature on the demography of ptarmigan than did a large-scale index, i.e., the NAO index. We conclude that local responses of wildlife populations to changing climate may not be adequately explained by models that project large-scale climatic patterns.

Hydrologic effects of large southwestern USA wildfires significantly increase regional water supply: fact or fiction?

NASA Astrophysics Data System (ADS)

Wine, M. L.; Cadol, D.

2016-08-01

In recent years climate change and historic fire suppression have increased the frequency of large wildfires in the southwestern USA, motivating study of the hydrological consequences of these wildfires at point and watershed scales, typically over short periods of time. These studies have revealed that reduced soil infiltration capacity and reduced transpiration due to tree canopy combustion increase streamflow at the watershed scale. However, the degree to which these local increases in runoff propagate to larger scales—relevant to urban and agricultural water supply—remains largely unknown, particularly in semi-arid mountainous watersheds co-dominated by winter snowmelt and the North American monsoon. To address this question, we selected three New Mexico watersheds—the Jemez (1223 km2), Mogollon (191 km2), and Gila (4807 km2)—that together have been affected by over 100 wildfires since 1982. We then applied climate-driven linear models to test for effects of fire on streamflow metrics after controlling for climatic variability. Here we show that, after controlling for climatic and snowpack variability, significantly more streamflow discharged from the Gila watershed for three to five years following wildfires, consistent with increased regional water yield due to enhanced infiltration-excess overland flow and groundwater recharge at the large watershed scale. In contrast, we observed no such increase in discharge from the Jemez watershed following wildfires. Fire regimes represent a key difference between the contrasting responses of the Jemez and Gila watersheds with the latter experiencing more frequent wildfires, many caused by lightning strikes. While hydrologic dynamics at the scale of large watersheds were previously thought to be climatically dominated, these results suggest that if one fifth or more of a large watershed has been burned in the previous three to five years, significant increases in water yield can be expected.

Quantifying Tropical Glacier Mass Balance Sensitivity to Climate Change Through Regional-Scale Modeling and The Randolph Glacier Inventory

NASA Astrophysics Data System (ADS)

Malone, A.

2017-12-01

Quantifying mass balance sensitivity to climate change is essential for forecasting glacier evolution and deciphering climate signals embedded in archives of past glacier changes. Ideally, these quantifications result from decades of field measurement, remote sensing, and a hierarchy modeling approach, but in data-sparse regions, such as the Himalayas and tropical Andes, regional-scale modeling rooted in first principles provides a first-order picture. Previous regional-scaling modeling studies have applied a surface energy and mass balance approach in order to quantify equilibrium line altitude sensitivity to climate change. In this study, an expanded regional-scale surface energy and mass balance model is implemented to quantify glacier-wide mass balance sensitivity to climate change for tropical Andean glaciers. Data from the Randolph Glacier Inventory are incorporated, and additional physical processes are included, such as a dynamic albedo and cloud-dependent atmospheric emissivity. The model output agrees well with the limited mass balance records for tropical Andean glaciers. The dominant climate variables driving interannual mass balance variability differ depending on the climate setting. For wet tropical glaciers (annual precipitation >0.75 m y-1), temperature is the dominant climate variable. Different hypotheses for the processes linking wet tropical glacier mass balance variability to temperature are evaluated. The results support the hypothesis that glacier-wide mass balance on wet tropical glaciers is largely dominated by processes at the lowest elevation where temperature plays a leading role in energy exchanges. This research also highlights the transient nature of wet tropical glaciers - the vast majority of tropical glaciers and a vital regional water resource - in an anthropogenic warming world.

Macroweather Predictions and Climate Projections using Scaling and Historical Observations

NASA Astrophysics Data System (ADS)

Hébert, R.; Lovejoy, S.; Del Rio Amador, L.

2017-12-01

There are two fundamental time scales that are pertinent to decadal forecasts and multidecadal projections. The first is the lifetime of planetary scale structures, about 10 days (equal to the deterministic predictability limit), and the second is - in the anthropocene - the scale at which the forced anthropogenic variability exceeds the internal variability (around 16 - 18 years). These two time scales define three regimes of variability: weather, macroweather and climate that are respectively characterized by increasing, decreasing and then increasing varibility with scale.We discuss how macroweather temperature variability can be skilfully predicted to its theoretical stochastic predictability limits by exploiting its long-range memory with the Stochastic Seasonal and Interannual Prediction System (StocSIPS). At multi-decadal timescales, the temperature response to forcing is approximately linear and this can be exploited to make projections with a Green's function, or Climate Response Function (CRF). To make the problem tractable, we exploit the temporal scaling symmetry and restrict our attention to global mean forcing and temperature response using a scaling CRF characterized by the scaling exponent H and an inner scale of linearity τ. An aerosol linear scaling factor α and a non-linear volcanic damping exponent ν were introduced to account for the large uncertainty in these forcings. We estimate the model and forcing parameters by Bayesian inference using historical data and these allow us to analytically calculate a median (and likely 66% range) for the transient climate response, and for the equilibrium climate sensitivity: 1.6K ([1.5,1.8]K) and 2.4K ([1.9,3.4]K) respectively. Aerosol forcing typically has large uncertainty and we find a modern (2005) forcing very likely range (90%) of [-1.0, -0.3] Wm-2 with median at -0.7 Wm-2. Projecting to 2100, we find that to keep the warming below 1.5 K, future emissions must undergo cuts similar to Representative Concentration Pathway (RCP) 2.6 for which the probability to remain under 1.5 K is 48%. RCP 4.5 and RCP 8.5-like futures overshoot with very high probability. This underscores that over the next century, the state of the environment will be strongly influenced by past, present and future economical policies.

Driving terrestrial ecosystem models from space

NASA Technical Reports Server (NTRS)

Waring, R. H.

1993-01-01

Regional air pollution, land-use conversion, and projected climate change all affect ecosystem processes at large scales. Changes in vegetation cover and growth dynamics can impact the functioning of ecosystems, carbon fluxes, and climate. As a result, there is a need to assess and monitor vegetation structure and function comprehensively at regional to global scales. To provide a test of our present understanding of how ecosystems operate at large scales we can compare model predictions of CO2, O2, and methane exchange with the atmosphere against regional measurements of interannual variation in the atmospheric concentration of these gases. Recent advances in remote sensing of the Earth's surface are beginning to provide methods for estimating important ecosystem variables at large scales. Ecologists attempting to generalize across landscapes have made extensive use of models and remote sensing technology. The success of such ventures is dependent on merging insights and expertise from two distinct fields. Ecologists must provide the understanding of how well models emulate important biological variables and their interactions; experts in remote sensing must provide the biophysical interpretation of complex optical reflectance and radar backscatter data.

Inter-annual and decadal changes in teleconnections drive continental-scale synchronization of tree reproduction.

PubMed

Ascoli, Davide; Vacchiano, Giorgio; Turco, Marco; Conedera, Marco; Drobyshev, Igor; Maringer, Janet; Motta, Renzo; Hacket-Pain, Andrew

2017-12-20

Climate teleconnections drive highly variable and synchronous seed production (masting) over large scales. Disentangling the effect of high-frequency (inter-annual variation) from low-frequency (decadal trends) components of climate oscillations will improve our understanding of masting as an ecosystem process. Using century-long observations on masting (the MASTREE database) and data on the Northern Atlantic Oscillation (NAO), we show that in the last 60 years both high-frequency summer and spring NAO, and low-frequency winter NAO components are highly correlated to continent-wide masting in European beech and Norway spruce. Relationships are weaker (non-stationary) in the early twentieth century. This finding improves our understanding on how climate variation affects large-scale synchronization of tree masting. Moreover, it supports the connection between proximate and ultimate causes of masting: indeed, large-scale features of atmospheric circulation coherently drive cues and resources for masting, as well as its evolutionary drivers, such as pollination efficiency, abundance of seed dispersers, and natural disturbance regimes.

Downscaling GCM Output with Genetic Programming Model

NASA Astrophysics Data System (ADS)

Shi, X.; Dibike, Y. B.; Coulibaly, P.

2004-05-01

Climate change impact studies on watershed hydrology require reliable data at appropriate spatial and temporal resolution. However, the outputs of the current global climate models (GCMs) cannot be used directly because GCM do not provide hourly or daily precipitation and temperature reliable enough for hydrological modeling. Nevertheless, we can get more reliable data corresponding to future climate scenarios derived from GCM outputs using the so called 'downscaling techniques'. This study applies Genetic Programming (GP) based technique to downscale daily precipitation and temperature values at the Chute-du-Diable basin of the Saguenay watershed in Canada. In applying GP downscaling technique, the objective is to find a relationship between the large-scale predictor variables (NCEP data which provide daily information concerning the observed large-scale state of the atmosphere) and the predictand (meteorological data which describes conditions at the site scale). The selection of the most relevant predictor variables is achieved using the Pearson's coefficient of determination ( R2) (between the large-scale predictor variables and the daily meteorological data). In this case, the period (1961 - 2000) is identified to represent the current climate condition. For the forty years of data, the first 30 years (1961-1990) are considered for calibrating the models while the remaining ten years of data (1991-2000) are used to validate those models. In general, the R2 between the predictor variables and each predictand is very low in case of precipitation compared to that of maximum and minimum temperature. Moreover, the strength of individual predictors varies for every month and for each GP grammar. Therefore, the most appropriate combination of predictors has to be chosen by looking at the output analysis of all the twelve months and the different GP grammars. During the calibration of the GP model for precipitation downscaling, in addition to the mean daily precipitation and daily precipitation variability for each month, monthly average dry and wet-spell lengths are also considered as performance criteria. For the cases of Tmax and Tmin, means and variances of these variables corresponding to each month were considered as performance criteria. The GP downscaling results show satisfactory agreement between the observed daily temperature (Tmax and Tmin) and the simulated temperature. However, the downscaling results for the daily precipitation still require some improvement - suggesting further investigation of other grammars. KEY WORDS: Climate change; GP downscaling; GCM.

ASSOCIATIONS BETWEEN NAO VARIBILITY AND U.S. MID-ATLANTIC REGION HYDROCLIMATOLOGY

EPA Science Inventory

Variability in the climate of the US Mid-Atlantic Region is associated with larger scale variability in the El Nino-Southern Oscillation (ENSO), the Pacific North American (PNA) teleconnection pattern, and the North Atlantic Oscillation (NAO). Collectively, these three large-scal...

Effects of local and large-scale climate patterns on estuarine resident fishes: The example of Pomatoschistus microps and Pomatoschistus minutus

NASA Astrophysics Data System (ADS)

Nyitrai, Daniel; Martinho, Filipe; Dolbeth, Marina; Rito, João; Pardal, Miguel A.

2013-12-01

Large-scale and local climate patterns are known to influence several aspects of the life cycle of marine fish. In this paper, we used a 9-year database (2003-2011) to analyse the populations of two estuarine resident fishes, Pomatoschistus microps and Pomatoschistus minutus, in order to determine their relationships with varying environmental stressors operating over local and large scales. This study was performed in the Mondego estuary, Portugal. Firstly, the variations in abundance, growth, population structure and secondary production were evaluated. These species appeared in high densities in the beginning of the study period, with subsequent occasional high annual density peaks, while their secondary production was lower in dry years. The relationships between yearly fish abundance and the environmental variables were evaluated separately for both species using Spearman correlation analysis, considering the yearly abundance peaks for the whole population, juveniles and adults. Among the local climate patterns, precipitation, river runoff, salinity and temperature were used in the analyses, and North Atlantic Oscillation (NAO) index and sea surface temperature (SST) were tested as large-scale factors. For P. microps, precipitation and NAO were the significant factors explaining abundance of the whole population, the adults and the juveniles as well. Regarding P. minutus, for the whole population, juveniles and adults river runoff was the significant predictor. The results for both species suggest a differential influence of climate patterns on the various life cycle stages, confirming also the importance of estuarine resident fishes as indicators of changes in local and large-scale climate patterns, related to global climate change.

Synoptic-scale circulation patterns during summer derived from tree rings in mid-latitude Asia

NASA Astrophysics Data System (ADS)

Seim, Andrea; Schultz, Johannes A.; Leland, Caroline; Davi, Nicole; Byambasuren, Oyunsanaa; Liang, Eryuan; Wang, Xiaochun; Beck, Christoph; Linderholm, Hans W.; Pederson, Neil

2017-09-01

Understanding past and recent climate and atmospheric circulation variability is vital for regions that are affected by climate extremes. In mid-latitude Asia, however, the synoptic climatology is complex and not yet fully understood. The aim of this study was to investigate dominant synoptic-scale circulation patterns during the summer season using a multi-species tree-ring width (TRW) network comprising 78 sites from mid-latitude Asia. For each TRW chronology, we calculated an atmospheric circulation tree-ring index (ACTI), based on 1000 hPa geopotential height data, to directly link tree growth to 13 summertime weather types and their associated local climate conditions for the period 1871-1993. Using the ACTI, three groups of similarly responding tree-ring sites can be associated with distinct large-scale atmospheric circulation patterns: 1. growth of drought sensitive trees is positively affected by a cyclone over northern Russia; 2. temperature sensitive trees show positive associations to a cyclone over northwestern Russia and an anticyclone over Mongolia; 3. trees at two high elevation sites show positive relations to a zonal cyclone extending from mid-latitude Eurasia to the West Pacific. The identified synoptic-scale circulation patterns showed spatiotemporal variability in their intensity and position, causing temporally varying climate conditions in mid-latitude Asia. Our results highlight that for regions with less pronounced atmospheric action centers during summer such as the occurrence of large-scale cyclones and anticyclones, synoptic-scale circulation patterns can be extracted and linked to the Northern Hemisphere circulation system. Thus, we provide a new and solid envelope for climate studies covering the past to the future.

Information transfer across the scales of climate data variability

NASA Astrophysics Data System (ADS)

Palus, Milan; Jajcay, Nikola; Hartman, David; Hlinka, Jaroslav

2015-04-01

Multitude of scales characteristic of the climate system variability requires innovative approaches in analysis of instrumental time series. We present a methodology which starts with a wavelet decomposition of a multi-scale signal into quasi-oscillatory modes of a limited band-with, described using their instantaneous phases and amplitudes. Then their statistical associations are tested in order to search for interactions across time scales. In particular, an information-theoretic formulation of the generalized, nonlinear Granger causality is applied together with surrogate data testing methods [1]. The method [2] uncovers causal influence (in the Granger sense) and information transfer from large-scale modes of climate variability with characteristic time scales from years to almost a decade to regional temperature variability on short time scales. In analyses of daily mean surface air temperature from various European locations an information transfer from larger to smaller scales has been observed as the influence of the phase of slow oscillatory phenomena with periods around 7-8 years on amplitudes of the variability characterized by smaller temporal scales from a few months to annual and quasi-biennial scales [3]. In sea surface temperature data from the tropical Pacific area an influence of quasi-oscillatory phenomena with periods around 4-6 years on the variability on and near the annual scale has been observed. This study is supported by the Ministry of Education, Youth and Sports of the Czech Republic within the Program KONTAKT II, Project No. LH14001. [1] M. Palus, M. Vejmelka, Phys. Rev. E 75, 056211 (2007) [2] M. Palus, Entropy 16(10), 5263-5289 (2014) [3] M. Palus, Phys. Rev. Lett. 112, 078702 (2014)

Multi-time-scale hydroclimate dynamics of a regional watershed and links to large-scale atmospheric circulation: Application to the Seine river catchment, France

NASA Astrophysics Data System (ADS)

Massei, N.; Dieppois, B.; Hannah, D. M.; Lavers, D. A.; Fossa, M.; Laignel, B.; Debret, M.

2017-03-01

In the present context of global changes, considerable efforts have been deployed by the hydrological scientific community to improve our understanding of the impacts of climate fluctuations on water resources. Both observational and modeling studies have been extensively employed to characterize hydrological changes and trends, assess the impact of climate variability or provide future scenarios of water resources. In the aim of a better understanding of hydrological changes, it is of crucial importance to determine how and to what extent trends and long-term oscillations detectable in hydrological variables are linked to global climate oscillations. In this work, we develop an approach associating correlation between large and local scales, empirical statistical downscaling and wavelet multiresolution decomposition of monthly precipitation and streamflow over the Seine river watershed, and the North Atlantic sea level pressure (SLP) in order to gain additional insights on the atmospheric patterns associated with the regional hydrology. We hypothesized that: (i) atmospheric patterns may change according to the different temporal wavelengths defining the variability of the signals; and (ii) definition of those hydrological/circulation relationships for each temporal wavelength may improve the determination of large-scale predictors of local variations. The results showed that the links between large and local scales were not necessarily constant according to time-scale (i.e. for the different frequencies characterizing the signals), resulting in changing spatial patterns across scales. This was then taken into account by developing an empirical statistical downscaling (ESD) modeling approach, which integrated discrete wavelet multiresolution analysis for reconstructing monthly regional hydrometeorological processes (predictand: precipitation and streamflow on the Seine river catchment) based on a large-scale predictor (SLP over the Euro-Atlantic sector). This approach basically consisted in three steps: 1 - decomposing large-scale climate and hydrological signals (SLP field, precipitation or streamflow) using discrete wavelet multiresolution analysis, 2 - generating a statistical downscaling model per time-scale, 3 - summing up all scale-dependent models in order to obtain a final reconstruction of the predictand. The results obtained revealed a significant improvement of the reconstructions for both precipitation and streamflow when using the multiresolution ESD model instead of basic ESD. In particular, the multiresolution ESD model handled very well the significant changes in variance through time observed in either precipitation or streamflow. For instance, the post-1980 period, which had been characterized by particularly high amplitudes in interannual-to-interdecadal variability associated with alternating flood and extremely low-flow/drought periods (e.g., winter/spring 2001, summer 2003), could not be reconstructed without integrating wavelet multiresolution analysis into the model. In accordance with previous studies, the wavelet components detected in SLP, precipitation and streamflow on interannual to interdecadal time-scales could be interpreted in terms of influence of the Gulf-Stream oceanic front on atmospheric circulation.

Application of regional climate models to the Indian winter monsoon over the western Himalayas.

PubMed

Dimri, A P; Yasunari, T; Wiltshire, A; Kumar, P; Mathison, C; Ridley, J; Jacob, D

2013-12-01

The Himalayan region is characterized by pronounced topographic heterogeneity and land use variability from west to east, with a large variation in regional climate patterns. Over the western part of the region, almost one-third of the annual precipitation is received in winter during cyclonic storms embedded in westerlies, known locally as the western disturbance. In the present paper, the regional winter climate over the western Himalayas is analyzed from simulations produced by two regional climate models (RCMs) forced with large-scale fields from ERA-Interim. The analysis was conducted by the composition of contrasting (wet and dry) winter precipitation years. The findings showed that RCMs could simulate the regional climate of the western Himalayas and represent the atmospheric circulation during extreme precipitation years in accordance with observations. The results suggest the important role of topography in moisture fluxes, transport and vertical flows. Dynamical downscaling with RCMs represented regional climates at the mountain or even event scale. However, uncertainties of precipitation scale and liquid-solid precipitation ratios within RCMs are still large for the purposes of hydrological and glaciological studies. Copyright © 2013 Elsevier B.V. All rights reserved.

Spatial Covariability of Temperature and Hydroclimate as a Function of Timescale During the Common Era

NASA Astrophysics Data System (ADS)

McKay, N.

2017-12-01

As timescale increases from years to centuries, the spatial scale of covariability in the climate system is hypothesized to increase as well. Covarying spatial scales are larger for temperature than for hydroclimate, however, both aspects of the climate system show systematic changes on large-spatial scales on orbital to tectonic timescales. The extent to which this phenomenon is evident in temperature and hydroclimate at centennial timescales is largely unknown. Recent syntheses of multidecadal to century-scale variability in hydroclimate during the past 2k in the Arctic, North America, and Australasia show little spatial covariability in hydroclimate during the Common Era. To determine 1) the evidence for systematic relationships between the spatial scale of climate covariability as a function of timescale, and 2) whether century-scale hydroclimate variability deviates from the relationship between spatial covariability and timescale, we quantify this phenomenon during the Common Era by calculating the e-folding distance in large instrumental and paleoclimate datasets. We calculate this metric of spatial covariability, at different timescales (1, 10 and 100-yr), for a large network of temperature and precipitation observations from the Global Historical Climatology Network (n=2447), from v2.0.0 of the PAGES2k temperature database (n=692), and from moisture-sensitive paleoclimate records North America, the Arctic, and the Iso2k project (n = 328). Initial results support the hypothesis that the spatial scale of covariability is larger for temperature, than for precipitation or paleoclimate hydroclimate indicators. Spatially, e-folding distances for temperature are largest at low latitudes and over the ocean. Both instrumental and proxy temperature data show clear evidence for increasing spatial extent as a function of timescale, but this phenomenon is very weak in the hydroclimate data analyzed here. In the proxy hydroclimate data, which are predominantly indicators of effective moisture, e-folding distance increases from annual to decadal timescales, but does not continue to increase to centennial timescales. Future work includes examining additional instrumental and proxy datasets of moisture variability, and extending the analysis to millennial timescales of variability.

Characterizing and understanding the climatic determinism of high- to low-frequency variations in precipitation in northwestern France using a coupled wavelet multiresolution/statistical downscaling approach

NASA Astrophysics Data System (ADS)

Massei, Nicolas; Dieppois, Bastien; Hannah, David; Lavers, David; Fossa, Manuel; Laignel, Benoit; Debret, Maxime

2017-04-01

Geophysical signals oscillate over several time-scales that explain different amount of their overall variability and may be related to different physical processes. Characterizing and understanding such variabilities in hydrological variations and investigating their determinism is one important issue in a context of climate change, as these variabilities can be occasionally superimposed to long-term trend possibly due to climate change. It is also important to refine our understanding of time-scale dependent linkages between large-scale climatic variations and hydrological responses on the regional or local-scale. Here we investigate such links by conducting a wavelet multiresolution statistical dowscaling approach of precipitation in northwestern France (Seine river catchment) over 1950-2016 using sea level pressure (SLP) and sea surface temperature (SST) as indicators of atmospheric and oceanic circulations, respectively. Previous results demonstrated that including multiresolution decomposition in a statistical downscaling model (within a so-called multiresolution ESD model) using SLP as large-scale predictor greatly improved simulation of low-frequency, i.e. interannual to interdecadal, fluctuations observed in precipitation. Building on these results, continuous wavelet transform of simulated precipiation using multiresolution ESD confirmed the good performance of the model to better explain variability at all time-scales. A sensitivity analysis of the model to the choice of the scale and wavelet function used was also tested. It appeared that whatever the wavelet used, the model performed similarly. The spatial patterns of SLP found as the best predictors for all time-scales, which resulted from the wavelet decomposition, revealed different structures according to time-scale, showing possible different determinisms. More particularly, some low-frequency components ( 3.2-yr and 19.3-yr) showed a much wide-spread spatial extentsion across the Atlantic. Moreover, in accordance with other previous studies, the wavelet components detected in SLP and precipitation on interannual to interdecadal time-scales could be interpreted in terms of influence of the Gulf-Stream oceanic front on atmospheric circulation. Current works are now conducted including SST over the Atlantic in order to get further insights into this mechanism.

Using satellite-based measurements to explore spatiotemporal scales and variability of drivers of new particle formation

EPA Science Inventory

New particle formation (NPF) can potentially alter regional climate by increasing aerosol particle (hereafter particle) number concentrations and ultimately cloud condensation nuclei. The large scales on which NPF is manifest indicate potential to use satellite-based (inherently ...

Spatial synchrony of local populations has increased in association with the recent Northern Hemisphere climate trend.

PubMed

Post, Eric; Forchhammer, Mads C

2004-06-22

According to ecological theory, populations whose dynamics are entrained by environmental correlation face increased extinction risk as environmental conditions become more synchronized spatially. This prediction is highly relevant to the study of ecological consequences of climate change. Recent empirical studies have indicated, for example, that large-scale climate synchronizes trophic interactions and population dynamics over broad spatial scales in freshwater and terrestrial systems. Here, we present an analysis of century-scale, spatially replicated data on local weather and the population dynamics of caribou in Greenland. Our results indicate that spatial autocorrelation in local weather has increased with large-scale climatic warming. This increase in spatial synchrony of environmental conditions has been matched, in turn, by an increase in the spatial synchrony of local caribou populations toward the end of the 20th century. Our results indicate that spatial synchrony in environmental conditions and the populations influenced by them are highly variable through time and can increase with climatic warming. We suggest that if future warming can increase population synchrony, it may also increase extinction risk.

Interannual Variability in the Position and Strength of the East Asian Jet Stream and Its Relation to Large - scale Circulation

NASA Astrophysics Data System (ADS)

Chan, Duo; Zhang, Yang; Wu, Qigang

2013-04-01

East Asian Jet Stream (EASJ) is charactered by obvious interannual variability in strength and position (latitude), with wide impacts on East Asian climate in all seasons. In this study, two indices are established to measure the interannual variability in intensity and position of EAJS. Possible causing factors, including both local signals and non-local large-scale circulation, are examined using NCAP-NCAR reanalysis data to investigate their relations with jet variation. Our analysis shows that the relationship between the interannual variations of EASJ and these factors depends on seasons. In the summer, both the intensity and position of EASJ are closely related to the meridional gradient of local surface temperature, but display no apparent relationship with the larg-scale circulation. In cold seasons (autumn, winter and spring), both the local factor and the large-scale circulation, i.e. the Pacific/North American teleconnection pattern (PNA), play important roles in the interannual variability of the jet intensity. The variability in the jet position, however, is more correlated to the Arctic Oscillation (AO), especially in winter. Diagnostic analysis indicates that transient eddy activity plays an important role in connecting the interannual variability of EASJ position with AO.

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.

Climate and wildfires in the North American boreal forest.

PubMed

Macias Fauria, Marc; Johnson, E A

2008-07-12

The area burned in the North American boreal forest is controlled by the frequency of mid-tropospheric blocking highs that cause rapid fuel drying. Climate controls the area burned through changing the dynamics of large-scale teleconnection patterns (Pacific Decadal Oscillation/El Niño Southern Oscillation and Arctic Oscillation, PDO/ENSO and AO) that control the frequency of blocking highs over the continent at different time scales. Changes in these teleconnections may be caused by the current global warming. Thus, an increase in temperature alone need not be associated with an increase in area burned in the North American boreal forest. Since the end of the Little Ice Age, the climate has been unusually moist and variable: large fire years have occurred in unusual years, fire frequency has decreased and fire-climate relationships have occurred at interannual to decadal time scales. Prolonged and severe droughts were common in the past and were partly associated with changes in the PDO/ENSO system. Under these conditions, large fire years become common, fire frequency increases and fire-climate relationships occur at decadal to centennial time scales. A suggested return to the drier climate regimes of the past would imply major changes in the temporal dynamics of fire-climate relationships and in area burned, a reduction in the mean age of the forest, and changes in species composition of the North American boreal forest.

On unravelling mechanism of interplay between cloud and large scale circulation: a grey area in climate science

NASA Astrophysics Data System (ADS)

De, S.; Agarwal, N. K.; Hazra, Anupam; Chaudhari, Hemantkumar S.; Sahai, A. K.

2018-04-01

The interaction between cloud and large scale circulation is much less explored area in climate science. Unfolding the mechanism of coupling between these two parameters is imperative for improved simulation of Indian summer monsoon (ISM) and to reduce imprecision in climate sensitivity of global climate model. This work has made an effort to explore this mechanism with CFSv2 climate model experiments whose cloud has been modified by changing the critical relative humidity (CRH) profile of model during ISM. Study reveals that the variable CRH in CFSv2 has improved the nonlinear interactions between high and low frequency oscillations in wind field (revealed as internal dynamics of monsoon) and modulates realistically the spatial distribution of interactions over Indian landmass during the contrasting monsoon season compared to the existing CRH profile of CFSv2. The lower tropospheric wind error energy in the variable CRH simulation of CFSv2 appears to be minimum due to the reduced nonlinear convergence of error to the planetary scale range from long and synoptic scales (another facet of internal dynamics) compared to as observed from other CRH experiments in normal and deficient monsoons. Hence, the interplay between cloud and large scale circulation through CRH may be manifested as a change in internal dynamics of ISM revealed from scale interactive quasi-linear and nonlinear kinetic energy exchanges in frequency as well as in wavenumber domain during the monsoon period that eventually modify the internal variance of CFSv2 model. Conversely, the reduced wind bias and proper modulation of spatial distribution of scale interaction between the synoptic and low frequency oscillations improve the eastward and northward extent of water vapour flux over Indian landmass that in turn give feedback to the realistic simulation of cloud condensates attributing improved ISM rainfall in CFSv2.

State of the Art in Large-Scale Soil Moisture Monitoring

NASA Technical Reports Server (NTRS)

Ochsner, Tyson E.; Cosh, Michael Harold; Cuenca, Richard H.; Dorigo, Wouter; Draper, Clara S.; Hagimoto, Yutaka; Kerr, Yan H.; Larson, Kristine M.; Njoku, Eni Gerald; Small, Eric E.;

Climate Drivers of Alaska Summer Stream Temperature

NASA Astrophysics Data System (ADS)

Bieniek, P.; Bhatt, U. S.; Plumb, E. W.; Thoman, R.; Trammell, E. J.

2016-12-01

The temperature of the water in lakes, rivers and streams has wide ranging impacts from local water quality and fish habitats to global climate change. Salmon fisheries in Alaska, a critical source of food in many subsistence communities, are sensitive to large-scale climate variability and river and stream temperatures have also been linked with salmon production in Alaska. Given current and projected climate change, understanding the mechanisms that link the large-scale climate and river and stream temperatures is essential to better understand the changes that may occur with aquatic life in Alaska's waterways on which subsistence users depend. An analysis of Alaska stream temperatures in the context of reanalysis, downscaled, station and other climate data is undertaken in this study to fill that need. Preliminary analysis identified eight stream observation sites with sufficiently long (>15 years) data available for climate-scale analysis in Alaska with one station, Terror Creek in Kodiak, having a 30-year record. Cross-correlation of summer (June-August) water temperatures between the stations are generally high even though they are spread over a large geographic region. Correlation analysis of the Terror Creek summer observations with seasonal sea surface temperatures (SSTs) in the North Pacific broadly resembles the SST anomaly fields typically associated with the Pacific Decadal Oscillation (PDO). A similar result was found for the remaining stations and in both cases PDO-like correlation patterns also occurred in the preceding spring. These preliminary results demonstrate that there is potential to diagnose the mechanisms that link the large-scale climate system and Alaska stream temperatures.

Hydrometeorological variability on a large french catchment and its relation to large-scale circulation across temporal scales

NASA Astrophysics Data System (ADS)

Massei, Nicolas; Dieppois, Bastien; Fritier, Nicolas; Laignel, Benoit; Debret, Maxime; Lavers, David; Hannah, David

2015-04-01

In the present context of global changes, considerable efforts have been deployed by the hydrological scientific community to improve our understanding of the impacts of climate fluctuations on water resources. Both observational and modeling studies have been extensively employed to characterize hydrological changes and trends, assess the impact of climate variability or provide future scenarios of water resources. In the aim of a better understanding of hydrological changes, it is of crucial importance to determine how and to what extent trends and long-term oscillations detectable in hydrological variables are linked to global climate oscillations. In this work, we develop an approach associating large-scale/local-scale correlation, enmpirical statistical downscaling and wavelet multiresolution decomposition of monthly precipitation and streamflow over the Seine river watershed, and the North Atlantic sea level pressure (SLP) in order to gain additional insights on the atmospheric patterns associated with the regional hydrology. We hypothesized that: i) atmospheric patterns may change according to the different temporal wavelengths defining the variability of the signals; and ii) definition of those hydrological/circulation relationships for each temporal wavelength may improve the determination of large-scale predictors of local variations. The results showed that the large-scale/local-scale links were not necessarily constant according to time-scale (i.e. for the different frequencies characterizing the signals), resulting in changing spatial patterns across scales. This was then taken into account by developing an empirical statistical downscaling (ESD) modeling approach which integrated discrete wavelet multiresolution analysis for reconstructing local hydrometeorological processes (predictand : precipitation and streamflow on the Seine river catchment) based on a large-scale predictor (SLP over the Euro-Atlantic sector) on a monthly time-step. This approach basically consisted in 1- decomposing both signals (SLP field and precipitation or streamflow) using discrete wavelet multiresolution analysis and synthesis, 2- generating one statistical downscaling model per time-scale, 3- summing up all scale-dependent models in order to obtain a final reconstruction of the predictand. The results obtained revealed a significant improvement of the reconstructions for both precipitation and streamflow when using the multiresolution ESD model instead of basic ESD ; in addition, the scale-dependent spatial patterns associated to the model matched quite well those obtained from scale-dependent composite analysis. In particular, the multiresolution ESD model handled very well the significant changes in variance through time observed in either prepciptation or streamflow. For instance, the post-1980 period, which had been characterized by particularly high amplitudes in interannual-to-interdecadal variability associated with flood and extremely low-flow/drought periods (e.g., winter 2001, summer 2003), could not be reconstructed without integrating wavelet multiresolution analysis into the model. Further investigations would be required to address the issue of the stationarity of the large-scale/local-scale relationships and to test the capability of the multiresolution ESD model for interannual-to-interdecadal forecasting. In terms of methodological approach, further investigations may concern a fully comprehensive sensitivity analysis of the modeling to the parameter of the multiresolution approach (different families of scaling and wavelet functions used, number of coefficients/degree of smoothness, etc.).

An integrated, indicator framework for assessing large-scale variations and change in seasonal timing and phenology (Invited)

NASA Astrophysics Data System (ADS)

Betancourt, J. L.; Weltzin, J. F.

2013-12-01

As part of an effort to develop an Indicator System for the National Climate Assessment (NCA), the Seasonality and Phenology Indicators Technical Team (SPITT) proposed an integrated, continental-scale framework for understanding and tracking seasonal timing in physical and biological systems. The framework shares several metrics with the EPA's National Climate Change Indicators. The SPITT framework includes a comprehensive suite of national indicators to track conditions, anticipate vulnerabilities, and facilitate intervention or adaptation to the extent possible. Observed, modeled, and forecasted seasonal timing metrics can inform a wide spectrum of decisions on federal, state, and private lands in the U.S., and will be pivotal for international efforts to mitigation and adaptation. Humans use calendars both to understand the natural world and to plan their lives. Although the seasons are familiar concepts, we lack a comprehensive understanding of how variability arises in the timing of seasonal transitions in the atmosphere, and how variability and change translate and propagate through hydrological, ecological and human systems. For example, the contributions of greenhouse warming and natural variability to secular trends in seasonal timing are difficult to disentangle, including earlier spring transitions from winter (strong westerlies) to summer (weak easterlies) patterns of atmospheric circulation; shifts in annual phasing of daily temperature means and extremes; advanced timing of snow and ice melt and soil thaw at higher latitudes and elevations; and earlier start and longer duration of the growing and fire seasons. The SPITT framework aims to relate spatiotemporal variability in surface climate to (1) large-scale modes of natural climate variability and greenhouse gas-driven climatic change, and (2) spatiotemporal variability in hydrological, ecological and human responses and impacts. The hierarchical framework relies on ground and satellite observations, and includes metrics of surface climate seasonality, seasonality of snow and ice, land surface phenology, ecosystem disturbance seasonality, and organismal phenology. Recommended metrics met the following requirements: (a) easily measured by day-of-year, number of days, or in the case of species migrations, by the latitude of the observation on a given date; (b) are observed or can be calculated across a high density of locations in many different regions of the U.S.; and (c) unambiguously describe both spatial and temporal variability and trends in seasonal timing that are climatically driven. The SPITT framework is meant to provide climatic and strategic guidance for the growth and application of seasonal timing and phenological monitoring efforts. The hope is that additional national indicators based on observed phenology, or evidence-based algorithms calibrated with observational data, will evolve with sustained and broad-scale monitoring of climatically sensitive species and ecological processes.

Extreme Events in China under Climate Change: Uncertainty and related impacts (CSSP-FOREX)

NASA Astrophysics Data System (ADS)

Leckebusch, Gregor C.; Befort, Daniel J.; Hodges, Kevin I.

2016-04-01

Suitable adaptation strategies or the timely initiation of related mitigation efforts in East Asia will strongly depend on robust and comprehensive information about future near-term as well as long-term potential changes in the climate system. Therefore, understanding the driving mechanisms associated with the East Asian climate is of major importance. The FOREX project (Fostering Regional Decision Making by the Assessment of Uncertainties of Future Regional Extremes and their Linkage to Global Climate System Variability for China and East Asia) focuses on the investigation of extreme wind and rainfall related events over Eastern Asia and their possible future changes. Here, analyses focus on the link between local extreme events and their driving weather systems. This includes the coupling between local rainfall extremes and tropical cyclones, the Meiyu frontal system, extra-tropical teleconnections and monsoonal activity. Furthermore, the relation between these driving weather systems and large-scale variability modes, e.g. NAO, PDO, ENSO is analysed. Thus, beside analysing future changes of local extreme events, the temporal variability of their driving weather systems and related large-scale variability modes will be assessed in current CMIP5 global model simulations to obtain more robust results. Beyond an overview of FOREX itself, first results regarding the link between local extremes and their steering weather systems based on observational and reanalysis data are shown. Special focus is laid on the contribution of monsoonal activity, tropical cyclones and the Meiyu frontal system on the inter-annual variability of the East Asian summer rainfall.

Climate-driven vital rates do not always mean climate-driven population.

PubMed

Tavecchia, Giacomo; Tenan, Simone; Pradel, Roger; Igual, José-Manuel; Genovart, Meritxell; Oro, Daniel

2016-12-01

Current climatic changes have increased the need to forecast population responses to climate variability. A common approach to address this question is through models that project current population state using the functional relationship between demographic rates and climatic variables. We argue that this approach can lead to erroneous conclusions when interpopulation dispersal is not considered. We found that immigration can release the population from climate-driven trajectories even when local vital rates are climate dependent. We illustrated this using individual-based data on a trans-equatorial migratory seabird, the Scopoli's shearwater Calonectris diomedea, in which the variation of vital rates has been associated with large-scale climatic indices. We compared the population annual growth rate λ i , estimated using local climate-driven parameters with ρ i , a population growth rate directly estimated from individual information and that accounts for immigration. While λ i varied as a function of climatic variables, reflecting the climate-dependent parameters, ρ i did not, indicating that dispersal decouples the relationship between population growth and climate variables from that between climatic variables and vital rates. Our results suggest caution when assessing demographic effects of climatic variability especially in open populations for very mobile organisms such as fish, marine mammals, bats, or birds. When a population model cannot be validated or it is not detailed enough, ignoring immigration might lead to misleading climate-driven projections. © 2016 John Wiley & Sons Ltd.

Climatic and Catchment-Scale Predictors of Chinese Stream Insect Richness Differ between Taxonomic Groups

PubMed Central

Tonkin, Jonathan D.; Shah, Deep Narayan; Kuemmerlen, Mathias; Li, Fengqing; Cai, Qinghua; Haase, Peter; Jähnig, Sonja C.

2015-01-01

Little work has been done on large-scale patterns of stream insect richness in China. We explored the influence of climatic and catchment-scale factors on stream insect (Ephemeroptera, Plecoptera, Trichoptera; EPT) richness across mid-latitude China. We assessed the predictive ability of climatic, catchment land cover and physical structure variables on genus richness of EPT, both individually and combined, in 80 mid-latitude Chinese streams, spanning a 3899-m altitudinal gradient. We performed analyses using boosted regression trees and explored the nature of their influence on richness patterns. The relative importance of climate, land cover, and physical factors on stream insect richness varied considerably between the three orders, and while important for Ephemeroptera and Plecoptera, latitude did not improve model fit for any of the groups. EPT richness was linked with areas comprising high forest cover, elevation and slope, large catchments and low temperatures. Ephemeroptera favoured areas with high forest cover, medium-to-large catchment sizes, high temperature seasonality, and low potential evapotranspiration. Plecoptera richness was linked with low temperature seasonality and annual mean, and high slope, elevation and warm-season rainfall. Finally, Trichoptera favoured high elevation areas, with high forest cover, and low mean annual temperature, seasonality and aridity. Our findings highlight the variable role that catchment land cover, physical properties and climatic influences have on stream insect richness. This is one of the first studies of its kind in Chinese streams, thus we set the scene for more in-depth assessments of stream insect richness across broader spatial scales in China, but stress the importance of improving data availability and consistency through time. PMID:25909190

A Generalized Framework for Non-Stationary Extreme Value Analysis

NASA Astrophysics Data System (ADS)

Ragno, E.; Cheng, L.; Sadegh, M.; AghaKouchak, A.

2017-12-01

Empirical trends in climate variables including precipitation, temperature, snow-water equivalent at regional to continental scales are evidence of changes in climate over time. The evolving climate conditions and human activity-related factors such as urbanization and population growth can exert further changes in weather and climate extremes. As a result, the scientific community faces an increasing demand for updated appraisal of the time-varying climate extremes. The purpose of this study is to offer a robust and flexible statistical tool for non-stationary extreme value analysis which can better characterize the severity and likelihood of extreme climatic variables. This is critical to ensure a more resilient environment in a changing climate. Following the positive feedback on the first version of Non-Stationary Extreme Value Analysis (NEVA) Toolbox by Cheng at al. 2014, we present an improved version, i.e. NEVA2.0. The upgraded version herein builds upon a newly-developed hybrid evolution Markov Chain Monte Carlo (MCMC) approach for numerical parameters estimation and uncertainty assessment. This addition leads to a more robust uncertainty estimates of return levels, return periods, and risks of climatic extremes under both stationary and non-stationary assumptions. Moreover, NEVA2.0 is flexible in incorporating any user-specified covariate other than the default time-covariate (e.g., CO2 emissions, large scale climatic oscillation patterns). The new feature will allow users to examine non-stationarity of extremes induced by physical conditions that underlie the extreme events (e.g. antecedent soil moisture deficit, large-scale climatic teleconnections, urbanization). In addition, the new version offers an option to generate stationary and/or non-stationary rainfall Intensity - Duration - Frequency (IDF) curves that are widely used for risk assessment and infrastructure design. Finally, a Graphical User Interface (GUI) of the package is provided, making NEVA accessible to a broader audience.

Non-stationarity of extreme weather events in a changing climate - an application to long-term droughts in the US Southwest

NASA Astrophysics Data System (ADS)

Grossmann, I.

2013-12-01

Return periods of many extreme weather events are not stationary over time, given increasing risks due to global warming and multidecadal variability resulting from large scale climate patterns. This is problematic as extreme weather events and long-term climate risks such as droughts are typically conceptualized via measures such as return periods that implicitly assume non-stationarity. I briefly review these problems and present an application to the non-stationarity of droughts in the US Southwest. The US Southwest relies on annual precipitation maxima during winter and the North American Monsoon (NAM), both of which vary with large-scale climate patterns, in particular ENSO, the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO). The latter two exhibit variability on longer (multi-decadal) time scales in addition to short-term variations. The region is also part of the subtropical belt projected to become more arid in a warming climate. The possible multidecadal impacts of the PDO on precipitation in the study region are analyzed with a focus on Arizona and New Mexico, using GPCC and CRU data since 1900. The projected impacts of the PDO on annual precipitation during the next three decades with GPCC data are similar in scale to the impacts of global warming on precipitation according to the A1B scenario and the CMIP2 multi-model means, while the combined impact of the PDO and AMO is about 19% larger. The effects according to the CRU dataset are about half as large as the projected global warming impacts. Given the magnitude of the projected impacts from both multidecadal variability and global warming, water management needs to explicitly incorporate both of these trends into long-term planning. Multi-decadal variability could be incorporated into the concept of return periods by presenting return periods as time-varying or as conditional on the respective 'phase' of relevant multidecadal patterns and on global warming. Problems in detecting the PDO signal and potential solutions are also discussed. We find that the long-term effect of the PDO can be more clearly separated from short-term variability by considering return periods of multi-year drought measures rather than return periods of simple drought measures that are more affected by short-term variations.

Ability of an ensemble of regional climate models to reproduce weather regimes over Europe-Atlantic during the period 1961-2000

NASA Astrophysics Data System (ADS)

Sanchez-Gomez, Emilia; Somot, S.; Déqué, M.

2009-10-01

One of the main concerns in regional climate modeling is to which extent limited-area regional climate models (RCM) reproduce the large-scale atmospheric conditions of their driving general circulation model (GCM). In this work we investigate the ability of a multi-model ensemble of regional climate simulations to reproduce the large-scale weather regimes of the driving conditions. The ensemble consists of a set of 13 RCMs on a European domain, driven at their lateral boundaries by the ERA40 reanalysis for the time period 1961-2000. Two sets of experiments have been completed with horizontal resolutions of 50 and 25 km, respectively. The spectral nudging technique has been applied to one of the models within the ensemble. The RCMs reproduce the weather regimes behavior in terms of composite pattern, mean frequency of occurrence and persistence reasonably well. The models also simulate well the long-term trends and the inter-annual variability of the frequency of occurrence. However, there is a non-negligible spread among the models which is stronger in summer than in winter. This spread is due to two reasons: (1) we are dealing with different models and (2) each RCM produces an internal variability. As far as the day-to-day weather regime history is concerned, the ensemble shows large discrepancies. At daily time scale, the model spread has also a seasonal dependence, being stronger in summer than in winter. Results also show that the spectral nudging technique improves the model performance in reproducing the large-scale of the driving field. In addition, the impact of increasing the number of grid points has been addressed by comparing the 25 and 50 km experiments. We show that the horizontal resolution does not affect significantly the model performance for large-scale circulation.

Climate variability rather than overstocking causes recent large scale cover changes of Tibetan pastures

PubMed Central

Lehnert, L. W.; Wesche, K.; Trachte, K.; Reudenbach, C.; Bendix, J.

2016-01-01

The Tibetan Plateau (TP) is a globally important “water tower” that provides water for nearly 40% of the world’s population. This supply function is claimed to be threatened by pasture degradation on the TP and the associated loss of water regulation functions. However, neither potential large scale degradation changes nor their drivers are known. Here, we analyse trends in a high-resolution dataset of grassland cover to determine the interactions among vegetation dynamics, climate change and human impacts on the TP. The results reveal that vegetation changes have regionally different triggers: While the vegetation cover has increased since the year 2000 in the north-eastern part of the TP due to an increase in precipitation, it has declined in the central and western parts of the TP due to rising air temperature and declining precipitation. Increasing livestock numbers as a result of land use changes exacerbated the negative trends but were not their exclusive driver. Thus, we conclude that climate variability instead of overgrazing has been the primary cause for large scale vegetation cover changes on the TP since the new millennium. Since areas of positive and negative changes are almost equal in extent, pasture degradation is not generally proceeding. PMID:27073126

Climate variability rather than overstocking causes recent large scale cover changes of Tibetan pastures

NASA Astrophysics Data System (ADS)

Lehnert, L. W.; Wesche, K.; Trachte, K.; Reudenbach, C.; Bendix, J.

2016-04-01

The Tibetan Plateau (TP) is a globally important “water tower” that provides water for nearly 40% of the world’s population. This supply function is claimed to be threatened by pasture degradation on the TP and the associated loss of water regulation functions. However, neither potential large scale degradation changes nor their drivers are known. Here, we analyse trends in a high-resolution dataset of grassland cover to determine the interactions among vegetation dynamics, climate change and human impacts on the TP. The results reveal that vegetation changes have regionally different triggers: While the vegetation cover has increased since the year 2000 in the north-eastern part of the TP due to an increase in precipitation, it has declined in the central and western parts of the TP due to rising air temperature and declining precipitation. Increasing livestock numbers as a result of land use changes exacerbated the negative trends but were not their exclusive driver. Thus, we conclude that climate variability instead of overgrazing has been the primary cause for large scale vegetation cover changes on the TP since the new millennium. Since areas of positive and negative changes are almost equal in extent, pasture degradation is not generally proceeding.

Patterns of soil community structure differ by scale and ecosystem type along a large-scale precipitation gradient

USDA-ARS?s Scientific Manuscript database

Climate models predict increased variability in precipitation regimes, which will likely increase frequency/duration of drought. Reductions in soil moisture affect physical and chemical characteristics of the soil habitat and can influence soil organisms such as mites and nematodes. These organisms ...

Learning, climate and the evolution of cultural capacity.

PubMed

Whitehead, Hal

2007-03-21

Patterns of environmental variation influence the utility, and thus evolution, of different learning strategies. I use stochastic, individual-based evolutionary models to assess the relative advantages of 15 different learning strategies (genetic determination, individual learning, vertical social learning, horizontal/oblique social learning, and contingent combinations of these) when competing in variable environments described by 1/f noise. When environmental variation has little effect on fitness, then genetic determinism persists. When environmental variation is large and equal over all time-scales ("white noise") then individual learning is adaptive. Social learning is advantageous in "red noise" environments when variation over long time-scales is large. Climatic variability increases with time-scale, so that short-lived organisms should be able to rely largely on genetic determination. Thermal climates usually are insufficiently red for social learning to be advantageous for species whose fitness is very determined by temperature. In contrast, population trajectories of many species, especially large mammals and aquatic carnivores, are sufficiently red to promote social learning in their predators. The ocean environment is generally redder than that on land. Thus, while individual learning should be adaptive for many longer-lived organisms, social learning will often be found in those dependent on the populations of other species, especially if they are marine. This provides a potential explanation for the evolution of a prevalence of social learning, and culture, in humans and cetaceans.

Global weather and local butterflies: variable responses to a large-scale climate pattern along an elevational gradient.

PubMed

Pardikes, Nicholas A; Shapiro, Arthur M; Dyer, Lee A; Forister, Matthew L

2015-11-01

Understanding the spatial and temporal scales at which environmental variation affects populations of plants and animals is an important goal for modern population biology, especially in the context of shifting climatic conditions. The El Niño Southern Oscillation (ENSO) generates climatic extremes of interannual variation, and has been shown to have significant effects on the diversity and abundance of a variety of terrestrial taxa. However, studies that have investigated the influence of such large-scale climate phenomena have often been limited in spatial and taxonomic scope. We used 23 years (1988-2010) of a long-term butterfly monitoring data set to explore associations between variation in population abundance of 28 butterfly species and variation in ENSO-derived sea surface temperature anomalies (SSTA) across 10 sites that encompass an elevational range of 2750 m in the Sierra Nevada mountain range of California. Our analysis detected a positive, regional effect of increased SSTA on butterfly abundance (wetter and warmer years predict more butterfly observations), yet the influence of SSTA on butterfly abundances varied along the elevational gradient, and also differed greatly among the 28 species. Migratory species had the strongest relationships with ENSO-derived SSTA, suggesting that large-scale climate indices are particularly valuable for understanding biotic-abiotic relationships of the most mobile species. In general, however, the ecological effects of large-scale climatic factors are context dependent between sites and species. Our results illustrate the power of long-term data sets for revealing pervasive yet subtle climatic effects, but also caution against expectations derived from exemplar species or single locations in the study of biotic-abiotic interactions.

The influence of Seychelles Dome on the large scale Tropical Variability

NASA Astrophysics Data System (ADS)

Manola, Iris; Selten, Frank; Hazeleger, Wilco

2013-04-01

The Seychelles Dome (SD) is the thermocline ridge just South of the equator in the Western Indian Ocean basin. It is characterized by strong atmospheric convection and a shallow thermocline and is associated with large intraseasonal convection and SST variability (Harrison and Vecchi 2001). The SD is influenced by surface and subsurface processes, such as air-sea fluxes, Ekman upwelling from wind stress curl, ocean dynamics (vertical mixing) and oceanic Rossby waves from southeastern Indian Ocean. The favoring season for a strong SD is the boreal winter, where the thermocline is most shallow. Then the southeasterly trade winds converge with the northwesterly monsoonal winds over the intertropical convergence zone and cause cyclonic wind stress curl that drives Ekman divergence and a ridging of the thermocline. It is found that the subseasonal and interranual variability of the SD is influenced by large scale events, such as the Indian Ocean Dipole (IOD), the ENSO and the Madden-Julian Oscillation (MJO) (Tozuka et al., 2010, Lloyd and Vecchi, 2010). The SD is enhanced by cooling events in the Western Indian Ocean and easterly winds that raise the thermocline and increase the upwelling. This can be associated with a strong Walker circulation, like negative IOD conditions or La Nina-like conditions. So far the studies focus on the origins of the SD variability, but the influence of the SD itself on regional or large scale climate is largely unknown. In this study we focus on the influence of the SD variations on the large scale tropical circulation. We analyze the covariance of the SD variations and the tropical circulation in a 200 year control imulation of the climate model EC-EARTH and perform idealized SST forced simulations to study the character of the atmospheric response and its relation to ENSO, IOD and MJO. References -Harrison, D. E. and G. A. Vecchi, 2001: January 1999 Indian Ocean cooling event. Geophys. Res. Lett., 28, 3717-3720. -Lloyd, I. D., and G. A. Vecchi, 2010: Submonthly Indian Ocean cooling events and their interaction with large-scale conditions. J. Climate, 23, 700-716. -Tozuka, T., T. Yokoi, and T. Yamagata, 2010: A modeling study of interannual variations of the Seychelles Dome. J. Geophys. Res., 115, C04005, doi:10.1029/2009JC005547.

Statistical Compression of Wind Speed Data

NASA Astrophysics Data System (ADS)

Tagle, F.; Castruccio, S.; Crippa, P.; Genton, M.

2017-12-01

In this work we introduce a lossy compression approach that utilizes a stochastic wind generator based on a non-Gaussian distribution to reproduce the internal climate variability of daily wind speed as represented by the CESM Large Ensemble over Saudi Arabia. Stochastic wind generators, and stochastic weather generators more generally, are statistical models that aim to match certain statistical properties of the data on which they are trained. They have been used extensively in applications ranging from agricultural models to climate impact studies. In this novel context, the parameters of the fitted model can be interpreted as encoding the information contained in the original uncompressed data. The statistical model is fit to only 3 of the 30 ensemble members and it adequately captures the variability of the ensemble in terms of seasonal internannual variability of daily wind speed. To deal with such a large spatial domain, it is partitioned into 9 region, and the model is fit independently to each of these. We further discuss a recent refinement of the model, which relaxes this assumption of regional independence, by introducing a large-scale component that interacts with the fine-scale regional effects.

Contributions of meteorology to the phenology of cyanobacterial blooms: implications for future climate change.

PubMed

Zhang, Min; Duan, Hongtao; Shi, Xiaoli; Yu, Yang; Kong, Fanxiang

2012-02-01

Cyanobacterial blooms are often a result of eutrophication. Recently, however, their expansion has also been found to be associated with changes in climate. To elucidate the effects of climatic variables on the expansion of cyanobacterial blooms in Taihu, China, we analyzed the relationships between climatic variables and bloom events which were retrieved by satellite images. We then assessed the contribution of each climate variable to the phenology of blooms using multiple regression models. Our study demonstrates that retrieving ecological information from satellite images is meritorious for large-scale and long-term ecological research in freshwater ecosystems. Our results show that the phenological changes of blooms at an inter-annual scale are strongly linked to climate in Taihu during the past 23 yr. Cyanobacterial blooms occur earlier and last longer with the increase of temperature, sunshine hours, and global radiation and the decrease of wind speed. Furthermore, the duration increases when the daily averages of maximum, mean, and minimum temperature each exceed 20.3 °C, 16.7 °C, and 13.7 °C, respectively. Among these factors, sunshine hours and wind speed are the primary contributors to the onset of the blooms, explaining 84.6% of their variability over the past 23 yr. These factors are also good predictors of the variability in the duration of annual blooms and determined 58.9% of the variability in this parameter. Our results indicate that when nutrients are in sufficiently high quantities to sustain the formation of cyanobacterial blooms, climatic variables become crucial in predicting cyanobacterial bloom events. Climate changes should be considered when we evaluate how much the amount of nutrients should be reduced in Taihu for lake management. Copyright © 2011 Elsevier Ltd. All rights reserved.

Role of Internal Variability in Surface Temperature and Precipitation Change Uncertainties over India.

NASA Astrophysics Data System (ADS)

Achutarao, K. M.; Singh, R.

2017-12-01

There are various sources of uncertainty in model projections of future climate change. These include differences in the formulation of climate models, internal variability, and differences in scenarios. Internal variability in a climate system represents the unforced change due to the chaotic nature of the climate system and is considered irreducible (Deser et al., 2012). Internal variability becomes important at regional scales where it can dominate forced changes. Therefore it needs to be carefully assessed in future projections. In this study we segregate the role of internal variability in the future temperature and precipitation projections over the Indian region. We make use of the Coupled Model Inter-comparison Project - phase 5 (CMIP5; Taylor et al., 2012) database containing climate model simulations carried out by various modeling centers around the world. While the CMIP5 experimental protocol recommended producing numerous ensemble members, only a handful of the modeling groups provided multiple realizations. Having a small number of realizations is a limitation in producing a quantification of internal variability. We therefore exploit the Community Earth System Model Large Ensemble (CESM-LE; Kay et al., 2014) dataset which contains a 40 member ensemble of a single model- CESM1 (CAM5) to explore the role of internal variability in Future Projections. Surface air temperature and precipitation change projections over regional and sub-regional scale are analyzed under the IPCC emission scenario (RCP8.5) for different seasons and homogeneous climatic zones over India. We analyze the spread in projections due to internal variability in the CESM-LE and CMIP5 datasets over these regions.

Observed Differences between North American Snow Extent and Snow Depth Variability

NASA Astrophysics Data System (ADS)

Ge, Y.; Gong, G.

2006-12-01

Snow extent and snow depth are two related characteristics of a snowpack, but they need not be mutually consistent. Differences between these two variables at local scales are readily apparent. However at larger scales which interact with atmospheric circulation and climate, snow extent is typically the variable used, while snow depth is often assumed to be minor and/or mutually consistent compared to snow extent, though this is rarely verified. In this study, a new regional/continental-scale gridded dataset derived from field observations is utilized to quantitatively evaluate the relationship between snow extent and snow depth over North America. Various statistical methods are applied to assess the mutual consistency of monthly snow depth vs. snow extent, including correlations, composites and principal components. Results indicate that snow depth variations are significant in their own rights, and that depth and extent anomalies are largely unrelated, especially over broad high latitude regions north of the snowline. In the vicinity of the snowline, where precipitation and ablation can affect both snow extent and snow depth, the two variables vary concurrently, especially in autumn and spring. It is also found that deeper winter snow translates into larger snow-covered area in the subsequent spring/summer season, which suggests a possible influence of winter snow depth on summer climate. The observed lack of mutual consistency at continental/regional scales suggests that snowpack depth variations may be of sufficiently large magnitude, spatial scope and temporal duration to influence regional-hemispheric climate, in a manner unrelated to the more extensively studied snow extent variations.

Understanding Climate Variability of Urban Ecosystems Through the Lens of Citizen Science

NASA Astrophysics Data System (ADS)

Ripplinger, J.; Jenerette, D.; Wang, J.; Chandler, M.; Ge, C.; Koutzoukis, S.

2017-12-01

The Los Angeles megacity is vulnerable to climate warming - a process that locally exacerbates the urban heat island effect as it intensifies with size and density of the built-up area. We know that large-scale drivers play a role, but in order to understand local-scale climate variation, more research is needed on the biophysical and sociocultural processes driving the urban climate system. In this study, we work with citizen scientists to deploy a high-density network of microsensors across a climate gradient to characterize geographic variation in neighborhood meso- and micro-climates. This research asks: How do urbanization, global climate, and vegetation interact across multiple scales to affect local-scale experiences of temperature? Additionally, citizen scientist-led efforts generated research questions focused on examining microclimatic differences among yard groundcover types (rock mulch vs. lawn vs. artificial turf) and also on variation in temperature related to tree cover. Combining sensor measurements with Weather Research and Forecasting (WRF) spatial models and satellite-based temperature, we estimate spatially-explicit maps of land surface temperature and air temperature to illustrate the substantial difference between surface and air urban heat island intensities and the variable degree of coupling between land surface and air temperature in urban areas. Our results show a strong coupling between air temperature variation and landcover for neighborhoods, with significant detectable signatures from tree cover and impervious surface. Temperature covaried most strongly with urbanization intensity at nighttime during peak summer season, when daily mean air temperature ranged from 12.8C to 30.4C across all groundcover types. The combined effects of neighborhood geography and vegetation determine where and how temperature and tree canopy vary within a city. This citizen science-enabled research shows how large-scale climate drivers and urbanization intensity jointly influence the nature and magnitude of coupling between air temperature and tree cover, and demonstrate how urban vegetation provides an important ecosystem service in cities by decreasing the intensity of local urban heat islands.

Assessing the influence of watershed characteristics on chlorophyll a in waterbodies at global and regional scales

USGS Publications Warehouse

Woelmer, Whitney; Kao, Yu-Chun; Bunnell, David B.; Deines, Andrew M.; Bennion, David; Rogers, Mark W.; Brooks, Colin N.; Sayers, Michael J.; Banach, David M.; Grimm, Amanda G.; Shuchman, Robert A.

2016-01-01

Prediction of primary production of lentic water bodies (i.e., lakes and reservoirs) is valuable to researchers and resource managers alike, but is very rarely done at the global scale. With the development of remote sensing technologies, it is now feasible to gather large amounts of data across the world, including understudied and remote regions. To determine which factors were most important in explaining the variation of chlorophyll a (Chl-a), an indicator of primary production in water bodies, at global and regional scales, we first developed a geospatial database of 227 water bodies and watersheds with corresponding Chl-a, nutrient, hydrogeomorphic, and climate data. Then we used a generalized additive modeling approach and developed model selection criteria to select models that most parsimoniously related Chl-a to predictor variables for all 227 water bodies and for 51 lakes in the Laurentian Great Lakes region in the data set. Our best global model contained two hydrogeomorphic variables (water body surface area and the ratio of watershed to water body surface area) and a climate variable (average temperature in the warmest model selection criteria to select models that most parsimoniously related Chl-a to predictor variables quarter) and explained ~ 30% of variation in Chl-a. Our regional model contained one hydrogeomorphic variable (flow accumulation) and the same climate variable, but explained substantially more variation (58%). Our results indicate that a regional approach to watershed modeling may be more informative to predicting Chl-a, and that nearly a third of global variability in Chl-a may be explained using hydrogeomorphic and climate variables.

Scales of variability of black carbon plumes and their dependence on resolution of ECHAM6-HAM

NASA Astrophysics Data System (ADS)

Weigum, Natalie; Stier, Philip; Schutgens, Nick; Kipling, Zak

2015-04-01

Prediction of the aerosol effect on climate depends on the ability of three-dimensional numerical models to accurately estimate aerosol properties. However, a limitation of traditional grid-based models is their inability to resolve variability on scales smaller than a grid box. Past research has shown that significant aerosol variability exists on scales smaller than these grid-boxes, which can lead to discrepancies between observations and aerosol models. The aim of this study is to understand how a global climate model's (GCM) inability to resolve sub-grid scale variability affects simulations of important aerosol features. This problem is addressed by comparing observed black carbon (BC) plume scales from the HIPPO aircraft campaign to those simulated by ECHAM-HAM GCM, and testing how model resolution affects these scales. This study additionally investigates how model resolution affects BC variability in remote and near-source regions. These issues are examined using three different approaches: comparison of observed and simulated along-flight-track plume scales, two-dimensional autocorrelation analysis, and 3-dimensional plume analysis. We find that the degree to which GCMs resolve variability can have a significant impact on the scales of BC plumes, and it is important for models to capture the scales of aerosol plume structures, which account for a large degree of aerosol variability. In this presentation, we will provide further results from the three analysis techniques along with a summary of the implication of these results on future aerosol model development.

The Role of Climate Covariability on Crop Yields in the Conterminous United States

DOE PAGES

Leng, Guoyong; Zhang, Xuesong; Huang, Maoyi; ...

2016-09-12

The covariability of temperature (T), precipitation (P) and radiation (R) is an important aspect in understanding the climate influence on crop yields. Here in this paper, we analyze county-level corn and soybean yields and observed climate for the period 1983–2012 to understand how growing-season (June, July and August) mean T, P and R influence crop yields jointly and in isolation across the CONterminous United States (CONUS). Results show that nationally averaged corn and soybean yields exhibit large interannual variability of 21% and 22%, of which 35% and 32% can be significantly explained by T and P, respectively. By including R,more » an additional of 5% in variability can be explained for both crops. Using partial regression analyses, we find that studies that ignore the covariability among T, P, and R can substantially overestimate the sensitivity of crop yields to a single climate factor at the county scale. Further analyses indicate large spatial variation in the relative contributions of different climate variables to the variability of historical corn and soybean yields. Finally, the structure of the dominant climate factors did not change substantially over 1983–2012, confirming the robustness of the findings, which have important implications for crop yield prediction and crop model validations.« less

Greenhouse gas scenario sensitivity and uncertainties in precipitation projections for central Belgium

NASA Astrophysics Data System (ADS)

Van Uytven, E.; Willems, P.

2018-03-01

Climate change impact assessment on meteorological variables involves large uncertainties as a result of incomplete knowledge on the future greenhouse gas concentrations and climate model physics, next to the inherent internal variability of the climate system. Given that the alteration in greenhouse gas concentrations is the driver for the change, one expects the impacts to be highly dependent on the considered greenhouse gas scenario (GHS). In this study, we denote this behavior as GHS sensitivity. Due to the climate model related uncertainties, this sensitivity is, at local scale, not always that strong as expected. This paper aims to study the GHS sensitivity and its contributing role to climate scenarios for a case study in Belgium. An ensemble of 160 CMIP5 climate model runs is considered and climate change signals are studied for precipitation accumulation, daily precipitation intensities and wet day frequencies. This was done for the different seasons of the year and the scenario periods 2011-2040, 2031-2060, 2051-2081 and 2071-2100. By means of variance decomposition, the total variance in the climate change signals was separated in the contribution of the differences in GHSs and the other model-related uncertainty sources. These contributions were found dependent on the variable and season. Following the time of emergence concept, the GHS uncertainty contribution is found dependent on the time horizon and increases over time. For the most distinct time horizon (2071-2100), the climate model uncertainty accounts for the largest uncertainty contribution. The GHS differences explain up to 18% of the total variance in the climate change signals. The results point further at the importance of the climate model ensemble design, specifically the ensemble size and the combination of climate models, whereupon climate scenarios are based. The numerical noise, introduced at scales smaller than the skillful scale, e.g. at local scale, was not considered in this study.

Regime Behavior in Paleo-Reconstructed Streamflow: Attributions to Atmospheric Dynamics, Synoptic Circulation and Large-Scale Climate Teleconnection Patterns

NASA Astrophysics Data System (ADS)

Ravindranath, A.; Devineni, N.

2017-12-01

Studies have shown that streamflow behavior and dynamics have a significant link with climate and climate variability. Patterns of persistent regime behavior from extended streamflow records in many watersheds justify investigating large-scale climate mechanisms as potential drivers of hydrologic regime behavior and streamflow variability. Understanding such streamflow-climate relationships is crucial to forecasting/simulation systems and the planning and management of water resources. In this study, hidden Markov models are used with reconstructed streamflow to detect regime-like behaviors - the hidden states - and state transition phenomena. Individual extreme events and their spatial variability across the basin are then verified with the identified states. Wavelet analysis is performed to examine the signals over time in the streamflow records. Joint analyses of the climatic data in the 20th century and the identified states are undertaken to better understand the hydroclimatic connections within the basin as well as important teleconnections that influence water supply. Compositing techniques are used to identify atmospheric circulation patterns associated with identified states of streamflow. The grouping of such synoptic patterns and their frequency are then examined. Sliding time-window correlation analysis and cross-wavelet spectral analysis are performed to establish the synchronicity of basin flows to the identified synoptic and teleconnection patterns. The Missouri River Basin (MRB) is examined in this study, both as a means of better understanding the synoptic climate controls in this important watershed and as a case study for the techniques developed here. Initial wavelet analyses of reconstructed streamflow at major gauges in the MRB show multidecadal cycles in regime behavior.

Web based visualization of large climate data sets

USGS Publications Warehouse

Alder, Jay R.; Hostetler, Steven W.

2015-01-01

We have implemented the USGS National Climate Change Viewer (NCCV), which is an easy-to-use web application that displays future projections from global climate models over the United States at the state, county and watershed scales. We incorporate the NASA NEX-DCP30 statistically downscaled temperature and precipitation for 30 global climate models being used in the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), and hydrologic variables we simulated using a simple water-balance model. Our application summarizes very large, complex data sets at scales relevant to resource managers and citizens and makes climate-change projection information accessible to users of varying skill levels. Tens of terabytes of high-resolution climate and water-balance data are distilled to compact binary format summary files that are used in the application. To alleviate slow response times under high loads, we developed a map caching technique that reduces the time it takes to generate maps by several orders of magnitude. The reduced access time scales to >500 concurrent users. We provide code examples that demonstrate key aspects of data processing, data exporting/importing and the caching technique used in the NCCV.

Climate-driven C4 plant distributions in China: divergence in C4 taxa

PubMed Central

Wang, Renzhong; Ma, Linna

2016-01-01

There have been debates on the driving factors of C4 plant expansion, such as PCO2 decline in the late Micocene and warmer climate and precipitation at large-scale modern ecosystems. These disputes are mainly due to the lack of direct evidence and extensive data analysis. Here we use mass flora data to explore the driving factors of C4 distribution and divergent patterns for different C4 taxa at continental scale in China. The results display that it is mean annual climate variables driving C4 distribution at present-day vegetation. Mean annual temperature is the critical restriction of total C4 plants and the precipitation gradients seem to have much less impact. Grass and sedge C4 plants are largely restricted to mean annual temperature and precipitation respectively, while Chenopod C4 plants are strongly restricted by aridity in China. Separate regression analysis can succeed to detect divergences of climate distribution patterns of C4 taxa at global scale. PMID:27302686

Hydrocentric view of Agro-ecosystem Resiliency to Extreme Hydrometeorological and Climate Events in the High Plains, US.

NASA Astrophysics Data System (ADS)

Munoz-Arriola, Francisco; Sharma, Ashutosh; Werner, Katherine; Chacon, Juan-Carlos; Corzo, Gerald; Goyal, Manish-Kumar

2017-04-01

An increasing incidence of Hydrometeorological and Climate Extreme Events (EHCEs) is challenging food, water, and ecosystem services security at local to global contexts. This study aims to understand how a large-scale representation of agroecosystems and ecosystems respond to EHCE in the Northern Highplains, US. To track such responses the Variable Infiltration Capacity model (VIC) Land Surface Hydrology model was used and two experiments were implemented. The first experiment uses the LAI MODIS15A2 product to capture dynamic responses of vegetation with a time span from 2000 to 2013. The second experiment used a climatological fixed seasonal cycle calculated as the average from the 2000-2013 dynamic MODIS15A2 product to isolate vegetation from soil physical responses. Based on the analyses of multiple hydrological variables and state variables and high-level organization of agroecosystems and ecosystems, we evidence how the influence of droughts and anomalously wet conditions affect hydrological resilience at large scale.

Explaining European fungal fruiting phenology with climate variability.

PubMed

Andrew, Carrie; Heegaard, Einar; Høiland, Klaus; Senn-Irlet, Beatrice; Kuyper, Thomas W; Krisai-Greilhuber, Irmgard; Kirk, Paul M; Heilmann-Clausen, Jacob; Gange, Alan C; Egli, Simon; Bässler, Claus; Büntgen, Ulf; Boddy, Lynne; Kauserud, Håvard

2018-06-01

Here we assess the impact of geographically dependent (latitude, longitude, and altitude) changes in bioclimatic (temperature, precipitation, and primary productivity) variability on fungal fruiting phenology across Europe. Two main nutritional guilds of fungi, saprotrophic and ectomycorrhizal, were further separated into spring and autumn fruiters. We used a path analysis to investigate how biogeographic patterns in fungal fruiting phenology coincided with seasonal changes in climate and primary production. Across central to northern Europe, mean fruiting varied by approximately 25 d, primarily with latitude. Altitude affected fruiting by up to 30 d, with spring delays and autumnal accelerations. Fruiting was as much explained by the effects of bioclimatic variability as by their large-scale spatial patterns. Temperature drove fruiting of autumnal ectomycorrhizal and saprotrophic groups as well as spring saprotrophic groups, while primary production and precipitation were major drivers for spring-fruiting ectomycorrhizal fungi. Species-specific phenology predictors were not stable, instead deviating from the overall mean. There is significant likelihood that further climatic change, especially in temperature, will impact fungal phenology patterns at large spatial scales. The ecological implications are diverse, potentially affecting food webs (asynchrony), nutrient cycling and the timing of nutrient availability in ecosystems. © 2018 by the Ecological Society of America.

State dependence of climatic instability over the past 720,000 years from Antarctic ice cores and climate modeling.

PubMed

Kawamura, Kenji; Abe-Ouchi, Ayako; Motoyama, Hideaki; Ageta, Yutaka; Aoki, Shuji; Azuma, Nobuhiko; Fujii, Yoshiyuki; Fujita, Koji; Fujita, Shuji; Fukui, Kotaro; Furukawa, Teruo; Furusaki, Atsushi; Goto-Azuma, Kumiko; Greve, Ralf; Hirabayashi, Motohiro; Hondoh, Takeo; Hori, Akira; Horikawa, Shinichiro; Horiuchi, Kazuho; Igarashi, Makoto; Iizuka, Yoshinori; Kameda, Takao; Kanda, Hiroshi; Kohno, Mika; Kuramoto, Takayuki; Matsushi, Yuki; Miyahara, Morihiro; Miyake, Takayuki; Miyamoto, Atsushi; Nagashima, Yasuo; Nakayama, Yoshiki; Nakazawa, Takakiyo; Nakazawa, Fumio; Nishio, Fumihiko; Obinata, Ichio; Ohgaito, Rumi; Oka, Akira; Okuno, Jun'ichi; Okuyama, Junichi; Oyabu, Ikumi; Parrenin, Frédéric; Pattyn, Frank; Saito, Fuyuki; Saito, Takashi; Saito, Takeshi; Sakurai, Toshimitsu; Sasa, Kimikazu; Seddik, Hakime; Shibata, Yasuyuki; Shinbori, Kunio; Suzuki, Keisuke; Suzuki, Toshitaka; Takahashi, Akiyoshi; Takahashi, Kunio; Takahashi, Shuhei; Takata, Morimasa; Tanaka, Yoichi; Uemura, Ryu; Watanabe, Genta; Watanabe, Okitsugu; Yamasaki, Tetsuhide; Yokoyama, Kotaro; Yoshimori, Masakazu; Yoshimoto, Takayasu

2017-02-01

Climatic variabilities on millennial and longer time scales with a bipolar seesaw pattern have been documented in paleoclimatic records, but their frequencies, relationships with mean climatic state, and mechanisms remain unclear. Understanding the processes and sensitivities that underlie these changes will underpin better understanding of the climate system and projections of its future change. We investigate the long-term characteristics of climatic variability using a new ice-core record from Dome Fuji, East Antarctica, combined with an existing long record from the Dome C ice core. Antarctic warming events over the past 720,000 years are most frequent when the Antarctic temperature is slightly below average on orbital time scales, equivalent to an intermediate climate during glacial periods, whereas interglacial and fully glaciated climates are unfavourable for a millennial-scale bipolar seesaw. Numerical experiments using a fully coupled atmosphere-ocean general circulation model with freshwater hosing in the northern North Atlantic showed that climate becomes most unstable in intermediate glacial conditions associated with large changes in sea ice and the Atlantic Meridional Overturning Circulation. Model sensitivity experiments suggest that the prerequisite for the most frequent climate instability with bipolar seesaw pattern during the late Pleistocene era is associated with reduced atmospheric CO 2 concentration via global cooling and sea ice formation in the North Atlantic, in addition to extended Northern Hemisphere ice sheets.

State dependence of climatic instability over the past 720,000 years from Antarctic ice cores and climate modeling

PubMed Central

Kawamura, Kenji; Abe-Ouchi, Ayako; Motoyama, Hideaki; Ageta, Yutaka; Aoki, Shuji; Azuma, Nobuhiko; Fujii, Yoshiyuki; Fujita, Koji; Fujita, Shuji; Fukui, Kotaro; Furukawa, Teruo; Furusaki, Atsushi; Goto-Azuma, Kumiko; Greve, Ralf; Hirabayashi, Motohiro; Hondoh, Takeo; Hori, Akira; Horikawa, Shinichiro; Horiuchi, Kazuho; Igarashi, Makoto; Iizuka, Yoshinori; Kameda, Takao; Kanda, Hiroshi; Kohno, Mika; Kuramoto, Takayuki; Matsushi, Yuki; Miyahara, Morihiro; Miyake, Takayuki; Miyamoto, Atsushi; Nagashima, Yasuo; Nakayama, Yoshiki; Nakazawa, Takakiyo; Nakazawa, Fumio; Nishio, Fumihiko; Obinata, Ichio; Ohgaito, Rumi; Oka, Akira; Okuno, Jun’ichi; Okuyama, Junichi; Oyabu, Ikumi; Parrenin, Frédéric; Pattyn, Frank; Saito, Fuyuki; Saito, Takashi; Saito, Takeshi; Sakurai, Toshimitsu; Sasa, Kimikazu; Seddik, Hakime; Shibata, Yasuyuki; Shinbori, Kunio; Suzuki, Keisuke; Suzuki, Toshitaka; Takahashi, Akiyoshi; Takahashi, Kunio; Takahashi, Shuhei; Takata, Morimasa; Tanaka, Yoichi; Uemura, Ryu; Watanabe, Genta; Watanabe, Okitsugu; Yamasaki, Tetsuhide; Yokoyama, Kotaro; Yoshimori, Masakazu; Yoshimoto, Takayasu

2017-01-01

Climatic variabilities on millennial and longer time scales with a bipolar seesaw pattern have been documented in paleoclimatic records, but their frequencies, relationships with mean climatic state, and mechanisms remain unclear. Understanding the processes and sensitivities that underlie these changes will underpin better understanding of the climate system and projections of its future change. We investigate the long-term characteristics of climatic variability using a new ice-core record from Dome Fuji, East Antarctica, combined with an existing long record from the Dome C ice core. Antarctic warming events over the past 720,000 years are most frequent when the Antarctic temperature is slightly below average on orbital time scales, equivalent to an intermediate climate during glacial periods, whereas interglacial and fully glaciated climates are unfavourable for a millennial-scale bipolar seesaw. Numerical experiments using a fully coupled atmosphere-ocean general circulation model with freshwater hosing in the northern North Atlantic showed that climate becomes most unstable in intermediate glacial conditions associated with large changes in sea ice and the Atlantic Meridional Overturning Circulation. Model sensitivity experiments suggest that the prerequisite for the most frequent climate instability with bipolar seesaw pattern during the late Pleistocene era is associated with reduced atmospheric CO2 concentration via global cooling and sea ice formation in the North Atlantic, in addition to extended Northern Hemisphere ice sheets. PMID:28246631

Climate variability and the European agricultural production

NASA Astrophysics Data System (ADS)

Guimarães Nobre, Gabriela; Hunink, Johannes E.; Baruth, Bettina; Aerts, Jeroen C. J. H.; Ward, Philip J.

2017-04-01

By 2050, the global demand for maize, wheat and other major crops is expected to grow sharply. To meet this challenge, agricultural systems have to increase substantially their production. However, the expanding world population, coupled with a decline of arable land per person, and the variability in global climate, are obstacles to achieving the increasing demand. Creating a resilient agriculture system requires the incorporation of preparedness measures against weather-related events, which can trigger disruptive risks such as droughts. This study examines the influence of large-scale climate variability on agriculture production applying a robust decision-making tool named fast-and-frugal trees (FFT). We created FFTs using a dataset of crop production and indices of climate variability: the El Niño Southern Oscillation (SOI) and the North Atlantic Oscillation (NAO). Our main goal is to predict the occurrence of below-average crop production, using these two indices at different lead times. Initial results indicated that SOI and NAO have strong links with European low sugar beet production. For some areas, the FFTs were able to detect below-average productivity events six months before harvesting with hit rate and predictive positive value higher than 70%. We found that shorter lead times, such as three months before harvesting, have the highest predictive skill. Additionally, we observed that the responses of low production events to the phases of the NAO and SOI vary spatially and seasonally. Through the comprehension of the relationship between large scale climate variability and European drought related agricultural impact, this study reflects on how this information could potentially improve the management of the agricultural sector by coupling the findings with seasonal forecasting system of crop production.

Persistent millennial-scale shifts in moisture regimes in western Canada during the past six millennia

PubMed Central

Cumming, Brian F.; Laird, Kathleen R.; Bennett, Joseph R.; Smol, John P.; Salomon, Anne K.

2002-01-01

Inferences of past climatic conditions from a sedimentary record from Big Lake, British Columbia, Canada, over the past 5,500 years show strong millennial-scale patterns, which oscillate between periods of wet and drier climatic conditions. Higher frequency decadal- to centennial-scale fluctuations also occur within the dominant millennial-scale patterns. These changes in climatic conditions are based on estimates of changes in lake depth and salinity inferred from diatom assemblages in a well dated sediment core. After periods of relative stability, abrupt shifts in diatom assemblages and inferred climatic conditions occur approximately every 1,220 years. The correspondence of these shifts to millennial-scale variations in records of glacial expansion/recession and ice-rafting events in the Atlantic suggest that abrupt millennial-scale shifts are important to understanding climatic variability in North America during the mid- to late Holocene. Unfortunately, the spatial patterns and mechanisms behind these large and abrupt swings are poorly understood. Similar abrupt and prolonged changes in climatic conditions today could pose major societal challenges for many regions. PMID:12461174

Persistent millennial-scale shifts in moisture regimes in western Canada during the past six millennia.

PubMed

Cumming, Brian F; Laird, Kathleen R; Bennett, Joseph R; Smol, John P; Salomon, Anne K

2002-12-10

Inferences of past climatic conditions from a sedimentary record from Big Lake, British Columbia, Canada, over the past 5,500 years show strong millennial-scale patterns, which oscillate between periods of wet and drier climatic conditions. Higher frequency decadal- to centennial-scale fluctuations also occur within the dominant millennial-scale patterns. These changes in climatic conditions are based on estimates of changes in lake depth and salinity inferred from diatom assemblages in a well dated sediment core. After periods of relative stability, abrupt shifts in diatom assemblages and inferred climatic conditions occur approximately every 1,220 years. The correspondence of these shifts to millennial-scale variations in records of glacial expansionrecession and ice-rafting events in the Atlantic suggest that abrupt millennial-scale shifts are important to understanding climatic variability in North America during the mid- to late Holocene. Unfortunately, the spatial patterns and mechanisms behind these large and abrupt swings are poorly understood. Similar abrupt and prolonged changes in climatic conditions today could pose major societal challenges for many regions.

Sensitivity of proxies on non-linear interactions in the climate system

PubMed Central

Schultz, Johannes A.; Beck, Christoph; Menz, Gunter; Neuwirth, Burkhard; Ohlwein, Christian; Philipp, Andreas

2015-01-01

Recent climate change is affecting the earth system to an unprecedented extent and intensity and has the potential to cause severe ecological and socioeconomic consequences. To understand natural and anthropogenic induced processes, feedbacks, trends, and dynamics in the climate system, it is also essential to consider longer timescales. In this context, annually resolved tree-ring data are often used to reconstruct past temperature or precipitation variability as well as atmospheric or oceanic indices such as the North Atlantic Oscillation (NAO) or the Atlantic Multidecadal Oscillation (AMO). The aim of this study is to assess weather-type sensitivity across the Northern Atlantic region based on two tree-ring width networks. Our results indicate that nonstationarities in superordinate space and time scales of the climate system (here synoptic- to global scale, NAO, AMO) can affect the climate sensitivity of tree-rings in subordinate levels of the system (here meso- to synoptic scale, weather-types). This scale bias effect has the capability to impact even large multiproxy networks and the ability of these networks to provide information about past climate conditions. To avoid scale biases in climate reconstructions, interdependencies between the different scales in the climate system must be considered, especially internal ocean/atmosphere dynamics. PMID:26686001

Statistical downscaling of daily precipitation over Llobregat river basin in Catalonia (Spain) using three downscaling methods.

NASA Astrophysics Data System (ADS)

Ballinas, R.; Versini, P.-A.; Sempere, D.; Escaler, I.

2009-09-01

Any long-term change in the patterns of average weather in a global or regional scale is called climate change. It may cause a progressive increase of atmospheric temperature and consequently may change the amount, frequency and intensity of precipitation. All these changes of meteorological parameters may modify the water cycle: run-off, infiltration, aquifer recharge, etc. Recent studies in Catalonia foresee changes in hydrological systems caused by climate change. This will lead to alterations in the hydrological cycle that could impact in land use, in the regimen of water extractions, in the hydrological characteristics of the territory and reduced groundwater recharge. Besides, can expect a loss of flow in rivers. In addition to possible increases in the frequency of extreme rainfall, being necessary to modify the design of infrastructure. Because this, it work focuses on studying the impacts of climate change in one of the most important basins in Catalonia, the Llobregat River Basin. The basin is the hub of the province of Barcelona. It is a highly populated and urbanized catchment, where water resources are used for different purposes, as drinking water production, agricultural irrigation, industry and hydro-electrical energy production. In consequence, many companies and communities depend on these resources. To study the impact of climate change in the Llobregat basin, storms (frequency, intensity) mainly, we will need regional climate change information. A regional climate is determined by interactions at large, regional and local scales. The general circulation models (GCMs) are run at too coarse resolution to permit accurate description of these regional and local interactions. So far, they have been unable to provide consistent estimates of climate change on a local scale. Several regionalization techniques have been developed to bridge the gap between the large-scale information provided by GCMs and fine spatial scales required for regional and environmental impact studies. Downscaling methods to assess the effect of large-scale circulations on local parameters have. Statistical downscaling methods are based on the view that regional climate can be conditioned by two factors: large-scale climatic state and regional/local features. Local climate information is derived by first developing a statistical model which relates large-scale variables or "predictors" for which GCMs are trustable to regional or local surface "predictands" for which models are less skilful. The main advantage of these methods is that they are computationally inexpensive, and can be applied to outputs from different GCM experiments. Three statistical downscaling methods are applied: Analogue method, Delta Change and Direct Forcing. These methods have been used to determine daily precipitation projections at rain gauge location to study the intensity, frequency and variability of storms in a context of climate change in the Llobregat River Basin in Catalonia, Spain. This work is part of the European project "Water Change" (included in the LIFE + Environment Policy and Governance program). It deals with Medium and long term water resources modelling as a tool for planning and global change adaptation. Two stakeholders involved in the project provided the historical time series: Catalan Water Agency (ACA) and the State Meteorological Agency (AEMET).

Influence of land use and climate on wetland breeding birds in the Prairie Pothole region of Canada

USGS Publications Warehouse

Forcey, G.M.; Linz, G.M.; Thogmartin, W.E.; Bleier, W.J.

2007-01-01

Bird populations are influenced by a variety of factors at both small and large scales that range from the presence of suitable nesting habitat, predators, and food supplies to climate conditions and land-use patterns. We evaluated the influences of regional climate and land-use variables on wetland breeding birds in the Canada section of Bird Conservation Region 11 (CA-BCR11), the Prairie Potholes. We used bird abundance data from the North American Breeding Bird Survey, land-use data from the Prairie Farm Rehabilitation Administration, and weather data from the National Climatic Data and Information Archive to model effects of regional environmental variables on bird abundance. Models were constructed a priori using information from published habitat associations in the literature, and fitting was performed with WinBUGS using Markov chain Monte Carlo techniques. Both land-use and climate variables contributed to predicting bird abundance in CA-BCR11, although climate predictors contributed the most to improving model fit. Examination of regional effects of climate and land use on wetland birds in CA-BCR11 revealed relationships with environmental covariates that are often overlooked by small-scale habitat studies. Results from these studies can be used to improve conservation and management planning for regional populations of avifauna. ?? 2007 NRC.

Climate limits across space and time on European forest structure

NASA Astrophysics Data System (ADS)

Moreno, A. L. S.; Neumann, M.; Hasenauer, H.

2017-12-01

The impact climate has on forests has been extensively studied. However, the large scale effect climate has on forest structures, such as average diameters, heights and basal area are understudied in a spatially explicit manner. The limits, tipping points and thresholds that climate places on forest structures dictate the services a forest may provide, the vulnerability of a forest to mortality and the potential value of the timber there within. The majority of current research either investigates climate impacts on forest pools and fluxes, on a tree physiological scale or on case studies that are used to extrapolate results and potential impacts. A spatially explicit study on how climate affects forest structure over a large region would give valuable information to stakeholders who are more concerned with ecosystem services that cannot be described by pools and fluxes but require spatially explicit information - such as biodiversity, habitat suitability, and market values. In this study, we quantified the limits that climate (maximum, minimum temperature and precipitation) places on 3 forest structures, diameter at breast height, height, and basal area throughout Europe. Our results show clear climatic zones of high and low upper limits for each forest structure variable studied. We also spatially analyzed how climate restricts the potential bio-physical upper limits and creates tipping points of each forest structure variable and which climate factors are most limiting. Further, we demonstrated how the climate change has affected 8 individual forests across Europe and then the continent as a whole. We find that diameter, height and basal area are limited by climate in different ways and that areas may have high upper limits in one structure and low upper limits in another limitted by different climate variables. We also found that even though individual forests may have increased their potential upper limit forest structure values, European forests as a whole have lost, on average, 5.0%, 1.7% and 6.5% in potential mean forest diameter, height and basal area, respectively.

A new large initial condition ensemble to assess avoided impacts in a climate mitigation scenario

NASA Astrophysics Data System (ADS)

Sanderson, B. M.; Tebaldi, C.; Knutti, R.; Oleson, K. W.

2014-12-01

It has recently been demonstrated that when considering timescales of up to 50 years, natural variability may play an equal role to anthropogenic forcing on subcontinental trends for a variety of climate indicators. Thus, for many questions assessing climate impacts on such time and spatial scales, it has become clear that a significant number of ensemble members may be required to produce robust statistics (and especially so for extreme events). However, large ensemble experiments to date have considered the role of variability in a single scenario, leaving uncertain the relationship between the forced climate trajectory and the variability about that path. To address this issue, we present a new, publicly available, 15 member initial condition ensemble of 21st century climate projections for the RCP 4.5 scenario using the CESM1.1 Earth System Model, which we propose as a companion project to the existing 40 member CESM large ensemble which uses the higher greenhouse gas emission future of RCP8.5. This provides a valuable data set for assessing what societal and ecological impacts might be avoided through a moderate mitigation strategy in contrast to a fossil fuel intensive future. We present some early analyses of these combined ensembles to assess to what degree the climate variability can be considered to combine linearly with the underlying forced response. In regions where there is no detectable relationship between the mean state and the variability about the mean trajectory, then linear assumptions can be trivially exploited to utilize a single ensemble or control simulation to characterize the variability in any scenario of interest. We highlight regions where there is a detectable nonlinearity in extreme event frequency, how far in the future they will be manifested and propose mechanisms to account for these effects.

The Response of African Land Surface Phenology to Large Scale Climate Oscillations

NASA Technical Reports Server (NTRS)

Brown, Molly E.; de Beurs, Kirsten; Vrieling, Anton

2010-01-01

Variations in agricultural production due to rainfall and temperature fluctuations are a primary cause of food insecurity on the African continent. Analysis of changes in phenology can provide quantitative information on the effect of climate variability on growing seasons in agricultural regions. Using a robust statistical methodology, we describe the relationship between phenology metrics derived from the 26 year AVHRR NDVI record and the North Atlantic Oscillation index (NAO), the Indian Ocean Dipole (IOD), the Pacific Decadal Oscillation (PDO), and the Multivariate ENSO Index (MEI). We map the most significant positive and negative correlation for the four climate indices in Eastern, Western and Southern Africa between two phenological metrics and the climate indices. Our objective is to provide evidence of whether climate variability captured in the four indices has had a significant impact on the vegetative productivity of Africa during the past quarter century. We found that the start of season and cumulative NDVI were significantly affected by large scale variations in climate. The particular climate index and the timing showing highest correlation depended heavily on the region examined. In Western Africa the cumulative NDVI correlates with PDO in September-November. In Eastern Africa the start of the June-October season strongly correlates with PDO in March-May, while the PDO in December-February correlates with the start of the February-June season. The cumulative NDVI over this last season relates to the MEI of March-May. For Southern Africa, high correlations exist between SOS and NAO of September-November, and cumulative NDVI and MEI of March-May. The research shows that climate indices can be used to anticipate late start and variable vigor in the growing season of sensitive agricultural regions in Africa.

From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact

PubMed Central

Baron, Christian; Sultan, Benjamin; Balme, Maud; Sarr, Benoit; Traore, Seydou; Lebel, Thierry; Janicot, Serge; Dingkuhn, Michael

2005-01-01

General circulation models (GCM) are increasingly capable of making relevant predictions of seasonal and long-term climate variability, thus improving prospects of predicting impact on crop yields. This is particularly important for semi-arid West Africa where climate variability and drought threaten food security. Translating GCM outputs into attainable crop yields is difficult because GCM grid boxes are of larger scale than the processes governing yield, involving partitioning of rain among runoff, evaporation, transpiration, drainage and storage at plot scale. This study analyses the bias introduced to crop simulation when climatic data is aggregated spatially or in time, resulting in loss of relevant variation. A detailed case study was conducted using historical weather data for Senegal, applied to the crop model SARRA-H (version for millet). The study was then extended to a 10°N–17° N climatic gradient and a 31 year climate sequence to evaluate yield sensitivity to the variability of solar radiation and rainfall. Finally, a down-scaling model called LGO (Lebel–Guillot–Onibon), generating local rain patterns from grid cell means, was used to restore the variability lost by aggregation. Results indicate that forcing the crop model with spatially aggregated rainfall causes yield overestimations of 10–50% in dry latitudes, but nearly none in humid zones, due to a biased fraction of rainfall available for crop transpiration. Aggregation of solar radiation data caused significant bias in wetter zones where radiation was limiting yield. Where climatic gradients are steep, these two situations can occur within the same GCM grid cell. Disaggregation of grid cell means into a pattern of virtual synoptic stations having high-resolution rainfall distribution removed much of the bias caused by aggregation and gave realistic simulations of yield. It is concluded that coupling of GCM outputs with plot level crop models can cause large systematic errors due to scale incompatibility. These errors can be avoided by transforming GCM outputs, especially rainfall, to simulate the variability found at plot level. PMID:16433096

Interpretation of Recent Temperature Trends in California

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

Duffy, P B; Bonfils, C; Lobell, D

2007-09-21

Regional-scale climate change and associated societal impacts result from large-scale (e.g. well-mixed greenhouse gases) and more local (e.g. land-use change) 'forcing' (perturbing) agents. It is essential to understand these forcings and climate responses to them, in order to predict future climate and societal impacts. California is a fine example of the complex effects of multiple climate forcings. The State's natural climate is diverse, highly variable, and strongly influenced by ENSO. Humans are perturbing this complex system through urbanization, irrigation, and emission of multiple types of aerosols and greenhouse gases. Despite better-than-average observational coverage, we are only beginning to understand themore » manifestations of these forcings in California's temperature record.« less

Sustainable management for rangelands in a variable climate: evidence and insights from northern Australia.

PubMed

O'Reagain, P J; Scanlan, J C

2013-03-01

Inter-annual rainfall variability is a major challenge to sustainable and productive grazing management on rangelands. In Australia, rainfall variability is particularly pronounced and failure to manage appropriately leads to major economic loss and environmental degradation. Recommended strategies to manage sustainably include stocking at long-term carrying capacity (LTCC) or varying stock numbers with forage availability. These strategies are conceptually simple but difficult to implement, given the scale and spatial heterogeneity of grazing properties and the uncertainty of the climate. This paper presents learnings and insights from northern Australia gained from research and modelling on managing for rainfall variability. A method to objectively estimate LTCC in large, heterogeneous paddocks is discussed, and guidelines and tools to tactically adjust stocking rates are presented. The possible use of seasonal climate forecasts (SCF) in management is also considered. Results from a 13-year grazing trial in Queensland show that constant stocking at LTCC was far more profitable and largely maintained land condition compared with heavy stocking (HSR). Variable stocking (VAR) with or without the use of SCF was marginally more profitable, but income variability was greater and land condition poorer than constant stocking at LTCC. Two commercial scale trials in the Northern Territory with breeder cows highlighted the practical difficulties of variable stocking and provided evidence that heavier pasture utilisation rates depress reproductive performance. Simulation modelling across a range of regions in northern Australia also showed a decline in resource condition and profitability under heavy stocking rates. Modelling further suggested that the relative value of variable v. constant stocking depends on stocking rate and land condition. Importantly, variable stocking may possibly allow slightly higher stocking rates without pasture degradation. Enterprise-level simulations run for breeder herds nevertheless show that poor economic performance can occur under constant stocking and even under variable stocking in some circumstances. Modelling and research results both suggest that a form of constrained flexible stocking should be applied to manage for climate variability. Active adaptive management and research will be required as future climate changes make managing for rainfall variability increasingly challenging.

Trend and variability in western and central Africa streamflow, and major drivers of variability between 1950 and 2005

NASA Astrophysics Data System (ADS)

Dieppois, B.; Sidibe, M.; Mahe, G. M.; Paturel, J. E.; Anifowose, B. A.; Lawler, D.; Amoussou, E.

2017-12-01

Unprecedented drought episodes that struck western and central Africa between the late 1960s and 1980s, triggered many studies investigating rainfall variability and its impacts on water resources and food production systems. However, most studies were focused at the catchment scale. In this study, we aim at investigating the key large-scale controls determining and modulating climate-river flows relationships at the subcontinental scale between 1950 and 2005. Using the first complete monthly streamflow data set (1950-2005) over western and central Africa, streamflow trend and variability are seasonally assessed at this subcontinental scale and compared to those observed in other hydroclimatic variables (precipitation, temperature and potential evapotranspiration). Long-term trends and variability in streamflow are mainly consistent with trends in rainfall. In particular, the recent post-1990s partial recovery in Sahel rainfall could have, at least partially, positively impacted river flows (e.g. the Senegal and Niger rivers). However, these relationships may have been moderated by: i) changes in land use; and ii) contributions from groundwater resources. In addition, the time-evolution of river flows is shown to be primarily driven by very strong decadal fluctuations, which can be interpreted as modulations in the baseflow, as determined using multi-temporal trend and continuous wavelet analysis. These decadal fluctuations, which are also significantly detected in rainfall, are likely related to large-scale sea-surface temperature (SST) anomaly patterns (such as the tropical Atlantic SST variability, the Atlantic Multidecadal Oscillation, the Interdecadal Pacific Oscillation and the Pacific Decadal Oscillation), which are together modulating the West African monsoon. Furthermore, influences of the catchment properties (e.g. size, vegetation and land use cover, soil properties, direction of stream flow across climate zones) on these decadal fluctuations in river flows have been examined. This study therefore aims to improve the ability of current global to regional climate models to simulate such ranges of variability and understand regional hydroclimate, as a means for improving the development of future scenarios for water resources in western and central Africa.

Large-scale disturbance legacies and the climate sensitivity of primary Picea abies forests.

PubMed

Schurman, Jonathan S; Trotsiuk, Volodymyr; Bače, Radek; Čada, Vojtěch; Fraver, Shawn; Janda, Pavel; Kulakowski, Dominik; Labusova, Jana; Mikoláš, Martin; Nagel, Thomas A; Seidl, Rupert; Synek, Michal; Svobodová, Kristýna; Chaskovskyy, Oleh; Teodosiu, Marius; Svoboda, Miroslav

2018-05-01

Determining the drivers of shifting forest disturbance rates remains a pressing global change issue. Large-scale forest dynamics are commonly assumed to be climate driven, but appropriately scaled disturbance histories are rarely available to assess how disturbance legacies alter subsequent disturbance rates and the climate sensitivity of disturbance. We compiled multiple tree ring-based disturbance histories from primary Picea abies forest fragments distributed throughout five European landscapes spanning the Bohemian Forest and the Carpathian Mountains. The regional chronology includes 11,595 tree cores, with ring dates spanning the years 1750-2000, collected from 560 inventory plots in 37 stands distributed across a 1,000 km geographic gradient, amounting to the largest disturbance chronology yet constructed in Europe. Decadal disturbance rates varied significantly through time and declined after 1920, resulting in widespread increases in canopy tree age. Approximately 75% of current canopy area recruited prior to 1900. Long-term disturbance patterns were compared to an historical drought reconstruction, and further linked to spatial variation in stand structure and contemporary disturbance patterns derived from LANDSAT imagery. Historically, decadal Palmer drought severity index minima corresponded to higher rates of canopy removal. The severity of contemporary disturbances increased with each stand's estimated time since last major disturbance, increased with mean diameter, and declined with increasing within-stand structural variability. Reconstructed spatial patterns suggest that high small-scale structural variability has historically acted to reduce large-scale susceptibility and climate sensitivity of disturbance. Reduced disturbance rates since 1920, a potential legacy of high 19th century disturbance rates, have contributed to a recent region-wide increase in disturbance susceptibility. Increasingly common high-severity disturbances throughout primary Picea forests of Central Europe should be reinterpreted in light of both legacy effects (resulting in increased susceptibility) and climate change (resulting in increased exposure to extreme events). © 2018 John Wiley & Sons Ltd.

Relating large-scale climate variability to local species abundance: ENSO forcing and shrimp in Breton Sound, Louisiana, USA

USGS Publications Warehouse

Piazza, Bryan P.; LaPeyre, Megan K.; Keim, B.D.

2010-01-01

Climate creates environmental constraints (filters) that affect the abundance and distribution of species. In estuaries, these constraints often result from variability in water flow properties and environmental conditions (i.e. water flow, salinity, water temperature) and can have significant effects on the abundance and distribution of commercially important nekton species. We investigated links between large-scale climate variability and juvenile brown shrimp Farfantepenaeus aztecus abundance in Breton Sound estuary, Louisiana (USA). Our goals were to (1) determine if a teleconnection exists between local juvenile brown shrimp abundance and the El Niño Southern Oscillation (ENSO) and (2) relate that linkage to environmental constraints that may affect juvenile brown shrimp recruitment to, and survival in, the estuary. Our results identified a teleconnection between winter ENSO conditions and juvenile brown shrimp abundance in Breton Sound estuary the following spring. The physical connection results from the impact of ENSO on winter weather conditions in Breton Sound (air pressure, temperature, and precipitation). Juvenile brown shrimp abundance effects lagged ENSO by 3 mo: lower than average abundances of juvenile brown shrimp were caught in springs following winter El Niño events, and higher than average abundances of brown shrimp were caught in springs following La Niña winters. Salinity was the dominant ENSO-forced environmental filter for juvenile brown shrimp. Spring salinity was cumulatively forced by winter river discharge, winter wind forcing, and spring precipitation. Thus, predicting brown shrimp abundance requires incorporating climate variability into models.

A global perspective on Glacial- to Interglacial variability change

NASA Astrophysics Data System (ADS)

Rehfeld, Kira; Münch, Thomas; Ho, Sze Ling; Laepple, Thomas

2017-04-01

Changes in climate variability are more important for society than changes in the mean state alone. While we will be facing a large-scale shift of the mean climate in the future, its implications for climate variability are not well constrained. Here we quantify changes in temperature variability as climate shifted from the Last Glacial cold to the Holocene warm period. Greenland ice core oxygen isotope records provide evidence of this climatic shift, and are used as reference datasets in many palaeoclimate studies worldwide. A striking feature in these records is pronounced millennial variability in the Glacial, and a distinct reduction in variance in the Holocene. We present quantitative estimates of the change in variability on 500- to 1500-year timescales based on a global compilation of high-resolution proxy records for temperature which span both the Glacial and the Holocene. The estimates are derived based on power spectral analysis, and corrected using estimates of the proxy signal-to-noise ratios. We show that, on a global scale, variability at the Glacial maximum is five times higher than during the Holocene, with a possible range of 3-10 times. The spatial pattern of the variability change is latitude-dependent. While the tropics show no changes in variability, mid-latitude changes are higher. A slight overall reduction in variability in the centennial to millennial range is found in Antarctica. The variability decrease in the Greenland ice core oxygen isotope records is larger than in any other proxy dataset. These results therefore contradict the view of a globally quiescent Holocene following the instable Glacial, and imply that, in terms of centennial to millennial temperature variability, the two states may be more similar than previously thought.

An observational and modeling study of the regional impacts of climate variability

NASA Astrophysics Data System (ADS)

Horton, Radley M.

Climate variability has large impacts on humans and their agricultural systems. Farmers are at the center of this agricultural network, but it is often agricultural planners---regional planners, extension agents, commodity groups and cooperatives---that translate climate information for users. Global climate models (GCMs) are a leading tool for understanding and predicting climate and climate change. Armed with climate projections and forecasts, agricultural planners adapt their decision-making to optimize outcomes. This thesis explores what GCMs can, and cannot, tell us about climate variability and change at regional scales. The question is important, since high-quality regional climate projections could assist farmers and regional planners in key management decisions, contributing to better agricultural outcomes. To answer these questions, climate variability and its regional impacts are explored in observations and models for the current and future climate. The goals are to identify impacts of observed variability, assess model simulation of variability, and explore how climate variability and its impacts may change under enhanced greenhouse warming. Chapter One explores how well Goddard Institute for Space Studies (GISS) atmospheric models, forced by historical sea surface temperatures (SST), simulate climatology and large-scale features during the exceptionally strong 1997--1999 El Nino Southern Oscillation (ENSO) cycle. Reasonable performance in this 'proof of concept' test is considered a minimum requirement for further study of variability in models. All model versions produce appropriate local changes with ENSO, indicating that with correct ocean temperatures these versions are capable of simulating the large-scale effects of ENSO around the globe. A high vertical resolution model (VHR) provides the best simulation. Evidence is also presented that SST anomalies outside the tropical Pacific may play a key role in generating remote teleconnections even during El Nino events. Based on the results from Chapter One, the analysis is expanded in several ways in Chapter Two. To gain a more complete and statistically meaningful understanding of ENSO, a 25 year time period is used instead of a single event. To gain a fuller understanding of climate variability, additional patterns are analyzed. Finally analysis is conducted at the regional scales that are of interest to farmers and agricultural planners. Key findings are that GISS ModelE can reproduce: (1) the spatial pattern associated with two additional related modes, the Arctic Oscillation (AO) and the North Atlantic Oscillation (NAO); (2) rainfall patterns in Indonesia; and (3) dynamical features such as sea level pressure (SLP) gradients and wind in the study regions. When run in coupled mode, the same model reproduces similar modes spatially but with reduced variance and weak teleconnections. Since Chapter Two identified Western Indonesia as the region where GCMs hold the most promise for agricultural applications, in Chapter Three a finer spatial and temporal scale analysis of ENSO's effects is presented. Agricultural decision-making is also linked to ENSO's climate effects. Early rainy season precipitation and circulation, and same-season planting and harvesting dates, are shown to be sensitive to ENSO. The locus of ENSO convergence and rainfall anomalies is shown to be near the axis of rainy season establishment, defined as the 6--8 mm/day isohyet, an approximate threshold for irrigated rice cultivation. As the axis tracks south and east between October and January, so do ENSO anomalies. Circulation anomalies associated with ENSO are shown to be similar to those associated with rainfall anomalies, suggesting that long lead-time ENSO forecasts may allow more adaptation than 'wait and see' methods, with little loss of forecast skill. Additional findings include: (1) rice and corn yields are lower (higher) during dry (wet) trimesters and El Nino (La Nina) years; and (2) a statistically significant negative relationship exists between malaria cases and ENSO. The final chapter adds climate change to the climate variability story. Under high CO2, the model able to capture ENSO dynamics---an atmospheric model coupled to the Cane-Zebiak ocean model ('C4' here)---generates more El Nino-like mean conditions in the tropical Pacific. These changes produce a 4x larger increase in maximum precipitation with warming in C4 than an atmospheric model with a slab ocean (Q4), dramatically enhancing the Pacific Hadley and Walker circulations, and through positive feedbacks, increasing the global temperature. Near Nordeste warming alone (Q4) produces added rainfall, which in C4 is partially cancelled out by El Nino-like changes in the Walker Cell. Both Q4 and C4 produce small changes in Indonesia, although C4 generates large circulation and precipitation anomalies over the Western Indian Ocean. C4 changes in the midlatitudes produce a very strong Pacific North American pattern (PNA) response that dominates a small positive AO change associated with Q4. These PNA changes produce increased rainfall over the Southeastern United States (SEUS) in C4. AO and NAO-like variability are also found to increase with enhanced CO2. This thesis highlights how climate variability influences regional climate variability, with an emphasis on four regions: Nordeste, Brazil, Western Indonesia, the Southeastern United States (SEUS), and the Mediterranean. It links El Nino-driven delay in the onset of rainy season drivers in Western Indonesia to decision-making about when to plant the year's largest crop. In a coupled configuration, the GISS GCM produces strong El Nino-like changes with global warming. This result suggests that the impacts---climatological and agricultural---of climate change may ultimately exceed the impacts of current variability. Somewhat paradoxically, these results indicate that one of the central manifestations of climate change is likely to be changes in patterns of climate variability and their regional impacts.

Decoding the spatial signatures of multi-scale climate variability - a climate network perspective

NASA Astrophysics Data System (ADS)

Donner, R. V.; Jajcay, N.; Wiedermann, M.; Ekhtiari, N.; Palus, M.

2017-12-01

During the last years, the application of complex networks as a versatile tool for analyzing complex spatio-temporal data has gained increasing interest. Establishing this approach as a new paradigm in climatology has already provided valuable insights into key spatio-temporal climate variability patterns across scales, including novel perspectives on the dynamics of the El Nino Southern Oscillation or the emergence of extreme precipitation patterns in monsoonal regions. In this work, we report first attempts to employ network analysis for disentangling multi-scale climate variability. Specifically, we introduce the concept of scale-specific climate networks, which comprises a sequence of networks representing the statistical association structure between variations at distinct time scales. For this purpose, we consider global surface air temperature reanalysis data and subject the corresponding time series at each grid point to a complex-valued continuous wavelet transform. From this time-scale decomposition, we obtain three types of signals per grid point and scale - amplitude, phase and reconstructed signal, the statistical similarity of which is then represented by three complex networks associated with each scale. We provide a detailed analysis of the resulting connectivity patterns reflecting the spatial organization of climate variability at each chosen time-scale. Global network characteristics like transitivity or network entropy are shown to provide a new view on the (global average) relevance of different time scales in climate dynamics. Beyond expected trends originating from the increasing smoothness of fluctuations at longer scales, network-based statistics reveal different degrees of fragmentation of spatial co-variability patterns at different scales and zonal shifts among the key players of climate variability from tropically to extra-tropically dominated patterns when moving from inter-annual to decadal scales and beyond. The obtained results demonstrate the potential usefulness of systematically exploiting scale-specific climate networks, whose general patterns are in line with existing climatological knowledge, but provide vast opportunities for further quantifications at local, regional and global scales that are yet to be explored.

Spatio-temporal variability of dryness/wetness in the middle and lower reaches of the Yangtze River Basin and correlation with large-scale climatic factors

NASA Astrophysics Data System (ADS)

Chen, Xinchi; Zhang, Liping; Zou, Lei; Shan, Lijie; She, Dunxian

2018-02-01

The middle and lower reaches of the Yangtze River Basin (MLYR) are greatly affected by frequent drought/flooding events and abrupt alternations of these events in China. The purpose of this study is to analyze the spatial and temporal variability of dryness/wetness based on the data obtained from 75 meteorological stations in the MLYR for the period 1960-2015 and investigate the correlations between dryness/wetness and atmospheric circulation factors. The empirical orthogonal function method was applied in this study based on the monthly Standardized Precipitation Index at a 12-month time scale. The first leading pattern captured the same characteristics of dryness/wetness over the entire MLYR area and accounted for 40.87% of the total variance. Both the second and third leading patterns manifested as regional features of variability over the entire MLYR. The cross-wavelet transform method was applied to explore the potential relationship between the three leading patterns and the large-scale climate factors, and finally the relationships between drought/wetness events and climate factors were also analyzed. Our results indicated that the main patterns of dryness/wetness were primarily associated with the Niño 3.4, Indian Ocean Dipole, Southern Oscillation Index and Northern Oscillation Index, with the first pattern exhibiting noticeable periods and remarkable changes in phase with the indices.

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.

The Mediterranean Sea regime shift at the end of the 1980s, and intriguing parallelisms with other European basins.

PubMed

Conversi, Alessandra; Fonda Umani, Serena; Peluso, Tiziana; Molinero, Juan Carlos; Santojanni, Alberto; Edwards, Martin

2010-05-19

Regime shifts are abrupt changes encompassing a multitude of physical properties and ecosystem variables, which lead to new regime conditions. Recent investigations focus on the changes in ecosystem diversity and functioning associated to such shifts. Of particular interest, because of the implication on climate drivers, are shifts that occur synchronously in separated basins. In this work we analyze and review long-term records of Mediterranean ecological and hydro-climate variables and find that all point to a synchronous change in the late 1980s. A quantitative synthesis of the literature (including observed oceanic data, models and satellite analyses) shows that these years mark a major change in Mediterranean hydrographic properties, surface circulation, and deep water convection (the Eastern Mediterranean Transient). We provide novel analyses that link local, regional and basin scale hydrological properties with two major indicators of large scale climate, the North Atlantic Oscillation index and the Northern Hemisphere Temperature index, suggesting that the Mediterranean shift is part of a large scale change in the Northern Hemisphere. We provide a simplified scheme of the different effects of climate vs. temperature on pelagic ecosystems. Our results show that the Mediterranean Sea underwent a major change at the end of the 1980s that encompassed atmospheric, hydrological, and ecological systems, for which it can be considered a regime shift. We further provide evidence that the local hydrography is linked to the larger scale, northern hemisphere climate. These results suggest that the shifts that affected the North, Baltic, Black and Mediterranean (this work) Seas at the end of the 1980s, that have been so far only partly associated, are likely linked as part a northern hemisphere change. These findings bear wide implications for the development of climate change scenarios, as synchronous shifts may provide the key for distinguishing local (i.e., basin) anthropogenic drivers, such as eutrophication or fishing, from larger scale (hemispheric) climate drivers.

North Atlantic sub-decadal variability in climate models

NASA Astrophysics Data System (ADS)

Reintges, Annika; Martin, Thomas; Latif, Mojib; Park, Wonsun

2017-04-01

The North Atlantic Oscillation (NAO) is the dominant variability mode for the winter climate of the North Atlantic sector. During a positive (negative) NAO phase, the sea level pressure (SLP) difference between the subtropical Azores high and the subpolar Icelandic low is anomalously strong (weak). This affects, for example, temperature, precipitation, wind, and surface heat flux over the North Atlantic, and over large parts of Europe. In observations we find enhanced sub-decadal variability of the NAO index that goes along with a dipolar sea surface temperature (SST) pattern. The corresponding SLP and SST patterns are reproduced in a control experiment of the Kiel Climate Model (KCM). Large-scale air-sea interaction is suggested to be essential for the North Atlantic sub-decadal variability in the KCM. The Atlantic Meridional Overturning Circulation (AMOC) plays a key role, setting the timescale of the variability by providing a delayed negative feedback to the NAO. The interplay of the NAO and the AMOC on the sub-decadal timescale is further investigated in the CMIP5 model ensemble. For example, the average CMIP5 model AMOC pattern associated with sub-decadal variability is characterized by a deep-reaching dipolar structure, similar to the KCM's sub-decadal AMOC variability pattern. The results suggest that dynamical air-sea interactions are crucial to generate enhanced sub-decadal variability in the North Atlantic climate.

Last millennium Northern Hemisphere summer temperatures from tree rings: Part II, spatially resolved reconstructions

NASA Astrophysics Data System (ADS)

Anchukaitis, Kevin J.; Wilson, Rob; Briffa, Keith R.; Büntgen, Ulf; Cook, Edward R.; D'Arrigo, Rosanne; Davi, Nicole; Esper, Jan; Frank, David; Gunnarson, Björn E.; Hegerl, Gabi; Helama, Samuli; Klesse, Stefan; Krusic, Paul J.; Linderholm, Hans W.; Myglan, Vladimir; Osborn, Timothy J.; Zhang, Peng; Rydval, Milos; Schneider, Lea; Schurer, Andrew; Wiles, Greg; Zorita, Eduardo

2017-05-01

Climate field reconstructions from networks of tree-ring proxy data can be used to characterize regional-scale climate changes, reveal spatial anomaly patterns associated with atmospheric circulation changes, radiative forcing, and large-scale modes of ocean-atmosphere variability, and provide spatiotemporal targets for climate model comparison and evaluation. Here we use a multiproxy network of tree-ring chronologies to reconstruct spatially resolved warm season (May-August) mean temperatures across the extratropical Northern Hemisphere (40-90°N) using Point-by-Point Regression (PPR). The resulting annual maps of temperature anomalies (750-1988 CE) reveal a consistent imprint of volcanism, with 96% of reconstructed grid points experiencing colder conditions following eruptions. Solar influences are detected at the bicentennial (de Vries) frequency, although at other time scales the influence of insolation variability is weak. Approximately 90% of reconstructed grid points show warmer temperatures during the Medieval Climate Anomaly when compared to the Little Ice Age, although the magnitude varies spatially across the hemisphere. Estimates of field reconstruction skill through time and over space can guide future temporal extension and spatial expansion of the proxy network.

Intraseasonal and Interannual Variability of Mars Present Climate

NASA Astrophysics Data System (ADS)

Hollingsworth, Jeffery L.; Bridger, Alison F. C.; Haberle, Robert M.

1996-01-01

This is a Final Report for a Joint Research Interchange (JRI) between NASA Ames Research Center and San Jose State University, Department of Meteorology. The focus of this JRI has been to investigate the nature of intraseasonal and interannual variability of Mars'present climate. We have applied a three-dimensional climate model based on the full hydrostatic primitive equations to determine the spatial, but primarily, the temporal structures of the planet's large-scale circulation as it evolves during a given seasonal advance, and, over multi-annual cycles. The particular climate model applies simplified physical parameterizations and is computationally efficient. It could thus easily be integrated in a perpetual season or advancing season configuration, as well as over many Mars years. We have assessed both high and low-frequency components of the circulation (i.e., motions having periods of Omicron(2-10 days) or greater than Omicron(10 days), respectively). Results from this investigation have explored the basic issue whether Mars' climate system is naturally 'chaotic' associated with nonlinear interactions of the large-scale circulation-regardless of any allowance for year-to-year variations in external forcing mechanisms. Titles of papers presented at scientific conferences and a manuscript to be submitted to the scientific literature are provided. An overview of a areas for further investigation is also presented.

Century long observation constrained global dynamic downscaling and hydrologic implication

NASA Astrophysics Data System (ADS)

Kim, H.; Yoshimura, K.; Chang, E.; Famiglietti, J. S.; Oki, T.

2012-12-01

It has been suggested that greenhouse gas induced warming climate causes the acceleration of large scale hydrologic cycles, and, indeed, many regions on the Earth have been suffered by hydrologic extremes getting more frequent. However, historical observations are not able to provide enough information in comprehensive manner to understand their long-term variability and/or global distributions. In this study, a century long high resolution global climate data is developed in order to break through existing limitations. 20th Century Reanalysis (20CR) which has relatively low spatial resolution (~2.0°) and longer term availability (140 years) is dynamically downscaled into global T248 (~0.5°) resolution using Experimental Climate Prediction Center (ECPC) Global Spectral Model (GSM) by spectral nudging data assimilation technique. Also, Global Precipitation Climatology Centre (GPCC) and Climate Research Unit (CRU) observational data are adopted to reduce model dependent uncertainty. Downscaled product successfully represents realistic geographical detail keeping low frequency signal in mean state and spatiotemporal variability, while previous bias correction method fails to reproduce high frequency variability. Newly developed data is used to investigate how long-term large scale terrestrial hydrologic cycles have been changed globally and how they have been interacted with various climate modes, such as El-Niño Southern Oscillation (ENSO) and Atlantic Multidecadal Oscillation (AMO). As a further application, it will be used to provide atmospheric boundary condition of multiple land surface models in the Global Soil Wetness Project Phase 3 (GSWP3).

United States Temperature and Precipitation Extremes: Phenomenology, Large-Scale Organization, Physical Mechanisms and Model Representation

NASA Astrophysics Data System (ADS)

Black, R. X.

2017-12-01

We summarize results from a project focusing on regional temperature and precipitation extremes over the continental United States. Our project introduces a new framework for evaluating these extremes emphasizing their (a) large-scale organization, (b) underlying physical sources (including remote-excitation and scale-interaction) and (c) representation in climate models. Results to be reported include the synoptic-dynamic behavior, seasonality and secular variability of cold waves, dry spells and heavy rainfall events in the observational record. We also study how the characteristics of such extremes are systematically related to Northern Hemisphere planetary wave structures and thus planetary- and hemispheric-scale forcing (e.g., those associated with major El Nino events and Arctic sea ice change). The underlying physics of event onset are diagnostically quantified for different categories of events. Finally, the representation of these extremes in historical coupled climate model simulations is studied and the origins of model biases are traced using new metrics designed to assess the large-scale atmospheric forcing of local extremes.

Challenges to Progress in Studies of Climate-Tectonic-Erosion Interactions

NASA Astrophysics Data System (ADS)

Burbank, D. W.

2016-12-01

Attempts to unravel the relative importance of climate and tectonics in modulating topography and erosion should compare relevant data sets at comparable temporal and spatial scales. Given that such data are uncommonly available, how can we compare diverse data sets in a robust fashion? Many erosion-rate studies rely on detrital cosmogenic nuclides. What time scales can such data address, and what landscape conditions do they require to provide accurate representations of long-term erosion rates? To what extent do large-scale, but infrequent erosional events impact long-term rates? Commonly, long-term erosion rates are deduced from thermochronologic data. What types of data are needed to test for consistency of rates across a given interval or change in rates through time? Similarly, spatial and temporal variability in precipitation or tectonics requires averaging across appropriate scales. How are such data obtained in deforming mountain belts, and how do we assess their reliability? This study describes the character and temporal duration of key variables that are needed to examine climate-tectonic-erosion interactions, explores the strengths and weaknesses of several study areas, and suggests the types of data requirements that will underpin enlightening "tests" of hypotheses related to the mutual impacts of climate, tectonics, and erosion.

Enhanced recharge rates and altered recharge sensitivity to climate variability through subsurface heterogeneity

NASA Astrophysics Data System (ADS)

Hartmann, Andreas; Gleeson, Tom; Wada, Yoshihide; Wagener, Thorsten

2017-04-01

Karst aquifers in Europe are an important source of fresh water contributing up to half of the total drinking water supply in some countries. Karstic groundwater recharge is one of the most important components of the water balance of karst systems as it feeds the karst aquifers. Presently available large-scale hydrological models do not consider karst heterogeneity adequately. Projections of current and potential future groundwater recharge of Europe's karst aquifers are therefore unclear. In this study we compare simulations of present (1991-2010) and future (2080-2099) recharge using two different models to simulate groundwater recharge processes. One model includes karst processes (subsurface heterogeneity, lateral flow and concentrated recharge), while the other is based on the conceptual understanding of common hydrological systems (homogeneous subsurface, saturation excess overland flow). Both models are driven by the bias-corrected 5 GCMs of the ISI-MIP project (RCP8.5). To further assess sensitivity of groundwater recharge to climate variability, we calculate the elasticity of recharge rates to annual precipitation, temperature and average intensity of rainfall events, which is the median change of recharge that corresponds to the median change of these climate variables within the present and future time period, respectively. Our model comparison shows that karst regions over Europe have enhanced recharge rates with greater inter-annual variability compared to those with more homogenous subsurface properties. Furthermore, the heterogeneous representation shows stronger elasticity concerning climate variability than the homogeneous subsurface representation. This difference tends to increase towards the future. Our results suggest that water management in regions with heterogeneous subsurface can expect a higher water availability than estimated by most of the current large-scale simulations, while measures should be taken to prepare for increasingly variable groundwater recharge rates.

Evaluating sub-seasonal skill in probabilistic forecasts of Atmospheric Rivers and associated extreme events

NASA Astrophysics Data System (ADS)

Subramanian, A. C.; Lavers, D.; Matsueda, M.; Shukla, S.; Cayan, D. R.; Ralph, M.

2017-12-01

Atmospheric rivers (ARs) - elongated plumes of intense moisture transport - are a primary source of hydrological extremes, water resources and impactful weather along the West Coast of North America and Europe. There is strong demand in the water management, societal infrastructure and humanitarian sectors for reliable sub-seasonal forecasts, particularly of extreme events, such as floods and droughts so that actions to mitigate disastrous impacts can be taken with sufficient lead-time. Many recent studies have shown that ARs in the Pacific and the Atlantic are modulated by large-scale modes of climate variability. Leveraging the improved understanding of how these large-scale climate modes modulate the ARs in these two basins, we use the state-of-the-art multi-model forecast systems such as the North American Multi-Model Ensemble (NMME) and the Subseasonal-to-Seasonal (S2S) database to help inform and assess the probabilistic prediction of ARs and related extreme weather events over the North American and European West Coasts. We will present results from evaluating probabilistic forecasts of extreme precipitation and AR activity at the sub-seasonal scale. In particular, results from the comparison of two winters (2015-16 and 2016-17) will be shown, winters which defied canonical El Niño teleconnection patterns over North America and Europe. We further extend this study to analyze probabilistic forecast skill of AR events in these two basins and the variability in forecast skill during certain regimes of large-scale climate modes.

North Atlantic climate variability: The role of the North Atlantic Oscillation

NASA Astrophysics Data System (ADS)

Hurrell, James W.; Deser, Clara

2009-08-01

Marine ecosystems are undergoing rapid change at local and global scales. To understand these changes, including the relative roles of natural variability and anthropogenic effects, and to predict the future state of marine ecosystems requires quantitative understanding of the physics, biogeochemistry and ecology of oceanic systems at mechanistic levels. Central to this understanding is the role played by dominant patterns or "modes" of atmospheric and oceanic variability, which orchestrate coherent variations in climate over large regions with profound impacts on ecosystems. We review the spatial structure of extratropical climate variability over the Northern Hemisphere and, specifically, focus on modes of climate variability over the extratropical North Atlantic. A leading pattern of weather and climate variability over the Northern Hemisphere is the North Atlantic Oscillation (NAO). The NAO refers to a redistribution of atmospheric mass between the Arctic and the subtropical Atlantic, and swings from one phase to another producing large changes in surface air temperature, winds, storminess and precipitation over the Atlantic as well as the adjacent continents. The NAO also affects the ocean through changes in heat content, gyre circulations, mixed layer depth, salinity, high latitude deep water formation and sea ice cover. Thus, indices of the NAO have become widely used to document and understand how this mode of variability alters the structure and functioning of marine ecosystems. There is no unique way, however, to define the NAO. Several approaches are discussed including both linear (e.g., principal component analysis) and nonlinear (e.g., cluster analysis) techniques. The former, which have been most widely used, assume preferred atmospheric circulation states come in pairs, in which anomalies of opposite polarity have the same spatial structure. In contrast, nonlinear techniques search for recurrent patterns of a specific amplitude and sign. They reveal, for instance, spatial asymmetries between different phases of the NAO that are likely important for ecological studies. It also follows that there is no universally accepted index to describe the temporal evolution of the NAO. Several of the most common measures are presented and compared. All reveal that there is no preferred time scale of variability for the NAO: large changes occur from one winter to the next and from one decade to the next. There is also a large amount of within-season variability in the patterns of atmospheric circulation of the North Atlantic, so that most winters cannot be characterized solely by a canonical NAO structure. A better understanding of how the NAO responds to external forcing, including sea surface temperature changes in the tropics, stratospheric influences, and increasing greenhouse gas concentrations, is crucial to the current debate on climate variability and change.

North Atlantic climate variability: The role of the North Atlantic Oscillation

NASA Astrophysics Data System (ADS)

Hurrell, James W.; Deser, Clara

2010-02-01

Marine ecosystems are undergoing rapid change at local and global scales. To understand these changes, including the relative roles of natural variability and anthropogenic effects, and to predict the future state of marine ecosystems requires quantitative understanding of the physics, biogeochemistry and ecology of oceanic systems at mechanistic levels. Central to this understanding is the role played by dominant patterns or "modes" of atmospheric and oceanic variability, which orchestrate coherent variations in climate over large regions with profound impacts on ecosystems. We review the spatial structure of extratropical climate variability over the Northern Hemisphere and, specifically, focus on modes of climate variability over the extratropical North Atlantic. A leading pattern of weather and climate variability over the Northern Hemisphere is the North Atlantic Oscillation (NAO). The NAO refers to a redistribution of atmospheric mass between the Arctic and the subtropical Atlantic, and swings from one phase to another producing large changes in surface air temperature, winds, storminess and precipitation over the Atlantic as well as the adjacent continents. The NAO also affects the ocean through changes in heat content, gyre circulations, mixed layer depth, salinity, high latitude deep water formation and sea ice cover. Thus, indices of the NAO have become widely used to document and understand how this mode of variability alters the structure and functioning of marine ecosystems. There is no unique way, however, to define the NAO. Several approaches are discussed including both linear (e.g., principal component analysis) and nonlinear (e.g., cluster analysis) techniques. The former, which have been most widely used, assume preferred atmospheric circulation states come in pairs, in which anomalies of opposite polarity have the same spatial structure. In contrast, nonlinear techniques search for recurrent patterns of a specific amplitude and sign. They reveal, for instance, spatial asymmetries between different phases of the NAO that are likely important for ecological studies. It also follows that there is no universally accepted index to describe the temporal evolution of the NAO. Several of the most common measures are presented and compared. All reveal that there is no preferred time scale of variability for the NAO: large changes occur from one winter to the next and from one decade to the next. There is also a large amount of within-season variability in the patterns of atmospheric circulation of the North Atlantic, so that most winters cannot be characterized solely by a canonical NAO structure. A better understanding of how the NAO responds to external forcing, including sea surface temperature changes in the tropics, stratospheric influences, and increasing greenhouse gas concentrations, is crucial to the current debate on climate variability and change.

How well do the GCMs/RCMs capture the multi-scale temporal variability of precipitation in the Southwestern United States?

NASA Astrophysics Data System (ADS)

Jiang, Peng; Gautam, Mahesh R.; Zhu, Jianting; Yu, Zhongbo

2013-02-01

SummaryMulti-scale temporal variability of precipitation has an established relationship with floods and droughts. In this paper, we present the diagnostics on the ability of 16 General Circulation Models (GCMs) from Bias Corrected and Downscaled (BCSD) World Climate Research Program's (WCRP's) Coupled Model Inter-comparison Project Phase 3 (CMIP3) projections and 10 Regional Climate Models (RCMs) that participated in the North American Regional Climate Change Assessment Program (NARCCAP) to represent multi-scale temporal variability determined from the observed station data. Four regions (Los Angeles, Las Vegas, Tucson, and Cimarron) in the Southwest United States are selected as they represent four different precipitation regions classified by clustering method. We investigate how storm properties and seasonal, inter-annual, and decadal precipitation variabilities differed between GCMs/RCMs and observed records in these regions. We find that current GCMs/RCMs tend to simulate longer storm duration and lower storm intensity compared to those from observed records. Most GCMs/RCMs fail to produce the high-intensity summer storms caused by local convective heat transport associated with the summer monsoon. Both inter-annual and decadal bands are present in the GCM/RCM-simulated precipitation time series; however, these do not line up to the patterns of large-scale ocean oscillations such as El Nino/La Nina Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). Our results show that the studied GCMs/RCMs can capture long-term monthly mean as the examined data is bias-corrected and downscaled, but fail to simulate the multi-scale precipitation variability including flood generating extreme events, which suggests their inadequacy for studies on floods and droughts that are strongly associated with multi-scale temporal precipitation variability.

Basin-scale heterogeneity in Antarctic precipitation and its impact on surface mass variability

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

Fyke, Jeremy; Lenaerts, Jan T. M.; Wang, Hailong

Annually averaged precipitation in the form of snow, the dominant term of the Antarctic Ice Sheet surface mass balance, displays large spatial and temporal variability. Here we present an analysis of spatial patterns of regional Antarctic precipitation variability and their impact on integrated Antarctic surface mass balance variability simulated as part of a preindustrial 1800-year global, fully coupled Community Earth System Model simulation. Correlation and composite analyses based on this output allow for a robust exploration of Antarctic precipitation variability. We identify statistically significant relationships between precipitation patterns across Antarctica that are corroborated by climate reanalyses, regional modeling and icemore » core records. These patterns are driven by variability in large-scale atmospheric moisture transport, which itself is characterized by decadal- to centennial-scale oscillations around the long-term mean. We suggest that this heterogeneity in Antarctic precipitation variability has a dampening effect on overall Antarctic surface mass balance variability, with implications for regulation of Antarctic-sourced sea level variability, detection of an emergent anthropogenic signal in Antarctic mass trends and identification of Antarctic mass loss accelerations.« less

Basin-scale heterogeneity in Antarctic precipitation and its impact on surface mass variability

DOE PAGES

Fyke, Jeremy; Lenaerts, Jan T. M.; Wang, Hailong

2017-11-15

Annually averaged precipitation in the form of snow, the dominant term of the Antarctic Ice Sheet surface mass balance, displays large spatial and temporal variability. Here we present an analysis of spatial patterns of regional Antarctic precipitation variability and their impact on integrated Antarctic surface mass balance variability simulated as part of a preindustrial 1800-year global, fully coupled Community Earth System Model simulation. Correlation and composite analyses based on this output allow for a robust exploration of Antarctic precipitation variability. We identify statistically significant relationships between precipitation patterns across Antarctica that are corroborated by climate reanalyses, regional modeling and icemore » core records. These patterns are driven by variability in large-scale atmospheric moisture transport, which itself is characterized by decadal- to centennial-scale oscillations around the long-term mean. We suggest that this heterogeneity in Antarctic precipitation variability has a dampening effect on overall Antarctic surface mass balance variability, with implications for regulation of Antarctic-sourced sea level variability, detection of an emergent anthropogenic signal in Antarctic mass trends and identification of Antarctic mass loss accelerations.« less

South American Climate Variability: Remote and Regional Forcing Processes of the Holocene and the LGM

NASA Astrophysics Data System (ADS)

Cook, K. H.

2006-12-01

An overview of concepts used in studying climate variability is provided as an introduction. Internally generated variability is the result of interactions within a system, while externally forced variability arises when some factor outside of the system causes a change. Distinguishing between the two requires a definition of the boundaries of "the system" considered. Climate variability is also classified according to space and time scales, for example, regional to global space scales and/or intraseasonal, seasonal, interannual, decadal, and millennial time scales. Any of these variability signatures may be internally generated or externally forced. A discussion of some of the climate forcing factors and physical processes thought to be relevant in determining climate variations of the past 20,000 years over South America is presented. An exhaustive treatment is not practical, and there are still many unknowns. Prominent in the literature are studies that discuss the influence of the ITCZ on South American precipitation. Other investigations focus on the South American monsoon dynamics. The physical processes that support these two precipitation systems are quite different, so the modes of variability that they exhibit also differ and it is important to clearly distinguish between them. The ITCZ is zonally elongated, formed by meridional convergence in the tropics. It is largely a structure of the atmosphere over the ocean, and persists throughout the year. Its position and strength vary with SST gradients and the vertical stability of the atmosphere. In contrast, a monsoon system is seasonal, and arises because of the different heat capacities of the land and ocean. It is influenced by land surface features such as vegetation and topography, and SSTs in the vicinity of the continent. Monsoon systems may also vary due to remote and/or large-scale forcing factors such as global sea surface temperature distributions and Hadley and Walker circulations. An example for the LGM climate of South America is presented to distinguish between the variations of ITCZ and monsoon dynamics. Another example presented concerns remote forcing of South American climate from an "intercontinental teleconnection" from Africa. GCM simulations show that summertime precipitation rates in Brazil's Nordeste region would be significantly greater in the absence of the African continent, and precipitation rates over the Amazon basin would be smaller. The generation of a Walker circulation by heating over southern Africa is the cause, and the effect is amplified by land surface feedbacks over South America. The teleconnection is sensitive to the distance between the two continents, to the strength and position of the heating over Africa, and the land surface characteristics over both South America and Africa. The east/west circulation influences the north/south position of the Atlantic ITCZ when asymmetry in surface conditions over Africa displaces the meridional convergence.

The environmental context for the origins of modern human diversity: a synthesis of regional variability in African climate 150,000-30,000 years ago.

PubMed

Blome, Margaret Whiting; Cohen, Andrew S; Tryon, Christian A; Brooks, Alison S; Russell, Joellen

2012-05-01

We synthesize African paleoclimate from 150 to 30 ka (thousand years ago) using 85 diverse datasets at a regional scale, testing for coherence with North Atlantic glacial/interglacial phases and northern and southern hemisphere insolation cycles. Two major determinants of circum-African climate variability over this time period are supported by principal components analysis: North Atlantic sea surface temperature (SST) variations and local insolation maxima. North Atlantic SSTs correlated with the variability found in most circum-African SST records, whereas the variability of the majority of terrestrial temperature and precipitation records is explained by local insolation maxima, particularly at times when solar radiation was intense and highly variable (e.g., 150-75 ka). We demonstrate that climates varied with latitude, such that periods of relatively increased aridity or humidity were asynchronous across the northern, eastern, tropical and southern portions of Africa. Comparisons of the archaeological, fossil, or genetic records with generalized patterns of environmental change based solely on northern hemisphere glacial/interglacial cycles are therefore imprecise. We compare our refined climatic framework to a database of 64 radiometrically-dated paleoanthropological sites to test hypotheses of demographic response to climatic change among African hominin populations during the 150-30 ka interval. We argue that at a continental scale, population and climate changes were asynchronous and likely occurred under different regimes of climate forcing, creating alternating opportunities for migration into adjacent regions. Our results suggest little relation between large scale demographic and climate change in southern Africa during this time span, but strongly support the hypothesis of hominin occupation of the Sahara during discrete humid intervals ~135-115 ka and 105-75 ka. Hominin populations in equatorial and eastern Africa may have been buffered from the extremes of climate change by locally steep altitudinal and rainfall gradients and the complex and variable effects of increased aridity on human habitat suitability in the tropics. Our data are consistent with hominin migrations out of Africa through varying exit points from ~140-80 ka. Copyright © 2012 Elsevier Ltd. All rights reserved.

Sensitivity of Water Scarcity Events to ENSO-Driven Climate Variability at the Global Scale

NASA Technical Reports Server (NTRS)

Veldkamp, T. I. E.; Eisner, S.; Wada, Y.; Aerts, J. C. J. H.; Ward, P. J.

2015-01-01

Globally, freshwater shortage is one of the most dangerous risks for society. Changing hydro-climatic and socioeconomic conditions have aggravated water scarcity over the past decades. A wide range of studies show that water scarcity will intensify in the future, as a result of both increased consumptive water use and, in some regions, climate change. Although it is well-known that El Niño- Southern Oscillation (ENSO) affects patterns of precipitation and drought at global and regional scales, little attention has yet been paid to the impacts of climate variability on water scarcity conditions, despite its importance for adaptation planning. Therefore, we present the first global-scale sensitivity assessment of water scarcity to ENSO, the most dominant signal of climate variability. We show that over the time period 1961-2010, both water availability and water scarcity conditions are significantly correlated with ENSO-driven climate variability over a large proportion of the global land area (> 28.1 %); an area inhabited by more than 31.4% of the global population. We also found, however, that climate variability alone is often not enough to trigger the actual incidence of water scarcity events. The sensitivity of a region to water scarcity events, expressed in terms of land area or population exposed, is determined by both hydro-climatic and socioeconomic conditions. Currently, the population actually impacted by water scarcity events consists of 39.6% (CTA: consumption-to-availability ratio) and 41.1% (WCI: water crowding index) of the global population, whilst only 11.4% (CTA) and 15.9% (WCI) of the global population is at the same time living in areas sensitive to ENSO-driven climate variability. These results are contrasted, however, by differences in growth rates found under changing socioeconomic conditions, which are relatively high in regions exposed to water scarcity events. Given the correlations found between ENSO and water availability and scarcity conditions, and the relative developments of water scarcity impacts under changing socioeconomic conditions, we suggest that there is potential for ENSO-based adaptation and risk reduction that could be facilitated by more research on this emerging topic.

Relation between climatic factors, diet and reproductive parameters of Little Terns over a decade

NASA Astrophysics Data System (ADS)

Ramos, Jaime A.; Pedro, Patrícia; Matos, Antonio; Paiva, Vitor H.

2013-11-01

We used 10 years of data on clutch size, egg size and diet, and 8 years of data on timing of laying on Little Terns (Sternula albifrons) breeding in Ria Formosa lagoon system, Algarve, Portugal to assess whether diet acts as an important intermediary between climatic conditions and breeding parameters. We used Generalized Linear Models to relate (1) the relative occurrence and size of the main prey species, sand smelts (Atherina spp.), with environmental variables, a large-scale climate variable, the North Atlantic Oscillation (NAO) index, and a local scale variable, the sea-surface temperature (SST), and (2) the respective effects of sand smelts relative occurrence, NAO index and SST on Little Tern breeding parameters. The diet of Little Terns was dominated by sand smelts, with a frequency occurrence of over 60% in all years. The winter SST (February) was negatively associated with the relative occurrence of sand smelts in the diet of Little Terns during the breeding season which, in turn, was positively associated with Little Tern clutch size. Our results suggest that negative NAO conditions in the Atlantic Ocean, often associated with rougher sea conditions (greater vertical mixing, stronger winds and lower SST) were related with earlier breeding, and lower SST in the surroundings of the colony during winter-spring favour the abundance of prey fish for Little Terns as well as their reproductive parameters. Climate patterns at both large and local scales are likely to change in the future, which may have important implications for estuarine seabirds in Southern Europe.

Seasonal prediction of US summertime ozone using statistical analysis of large scale climate patterns.

PubMed

Shen, Lu; Mickley, Loretta J

2017-03-07

We develop a statistical model to predict June-July-August (JJA) daily maximum 8-h average (MDA8) ozone concentrations in the eastern United States based on large-scale climate patterns during the previous spring. We find that anomalously high JJA ozone in the East is correlated with these springtime patterns: warm tropical Atlantic and cold northeast Pacific sea surface temperatures (SSTs), as well as positive sea level pressure (SLP) anomalies over Hawaii and negative SLP anomalies over the Atlantic and North America. We then develop a linear regression model to predict JJA MDA8 ozone from 1980 to 2013, using the identified SST and SLP patterns from the previous spring. The model explains ∼45% of the variability in JJA MDA8 ozone concentrations and ∼30% variability in the number of JJA ozone episodes (>70 ppbv) when averaged over the eastern United States. This seasonal predictability results from large-scale ocean-atmosphere interactions. Warm tropical Atlantic SSTs can trigger diabatic heating in the atmosphere and influence the extratropical climate through stationary wave propagation, leading to greater subsidence, less precipitation, and higher temperatures in the East, which increases surface ozone concentrations there. Cooler SSTs in the northeast Pacific are also associated with more summertime heatwaves and high ozone in the East. On average, models participating in the Atmospheric Model Intercomparison Project fail to capture the influence of this ocean-atmosphere interaction on temperatures in the eastern United States, implying that such models would have difficulty simulating the interannual variability of surface ozone in this region.

Seasonal prediction of US summertime ozone using statistical analysis of large scale climate patterns

PubMed Central

Mickley, Loretta J.

2017-01-01

We develop a statistical model to predict June–July–August (JJA) daily maximum 8-h average (MDA8) ozone concentrations in the eastern United States based on large-scale climate patterns during the previous spring. We find that anomalously high JJA ozone in the East is correlated with these springtime patterns: warm tropical Atlantic and cold northeast Pacific sea surface temperatures (SSTs), as well as positive sea level pressure (SLP) anomalies over Hawaii and negative SLP anomalies over the Atlantic and North America. We then develop a linear regression model to predict JJA MDA8 ozone from 1980 to 2013, using the identified SST and SLP patterns from the previous spring. The model explains ∼45% of the variability in JJA MDA8 ozone concentrations and ∼30% variability in the number of JJA ozone episodes (>70 ppbv) when averaged over the eastern United States. This seasonal predictability results from large-scale ocean–atmosphere interactions. Warm tropical Atlantic SSTs can trigger diabatic heating in the atmosphere and influence the extratropical climate through stationary wave propagation, leading to greater subsidence, less precipitation, and higher temperatures in the East, which increases surface ozone concentrations there. Cooler SSTs in the northeast Pacific are also associated with more summertime heatwaves and high ozone in the East. On average, models participating in the Atmospheric Model Intercomparison Project fail to capture the influence of this ocean–atmosphere interaction on temperatures in the eastern United States, implying that such models would have difficulty simulating the interannual variability of surface ozone in this region. PMID:28223483

Studies of regional-scale climate variability and change. Hidden Markov models and coupled ocean-atmosphere modes

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

Ghil, M.; Kravtsov, S.; Robertson, A. W.

2008-10-14

This project was a continuation of previous work under DOE CCPP funding, in which we had developed a twin approach of probabilistic network (PN) models (sometimes called dynamic Bayesian networks) and intermediate-complexity coupled ocean-atmosphere models (ICMs) to identify the predictable modes of climate variability and to investigate their impacts on the regional scale. We had developed a family of PNs (similar to Hidden Markov Models) to simulate historical records of daily rainfall, and used them to downscale GCM seasonal predictions. Using an idealized atmospheric model, we had established a novel mechanism through which ocean-induced sea-surface temperature (SST) anomalies might influencemore » large-scale atmospheric circulation patterns on interannual and longer time scales; we had found similar patterns in a hybrid coupled ocean-atmosphere-sea-ice model. The goal of the this continuation project was to build on these ICM results and PN model development to address prediction of rainfall and temperature statistics at the local scale, associated with global climate variability and change, and to investigate the impact of the latter on coupled ocean-atmosphere modes. Our main results from the grant consist of extensive further development of the hidden Markov models for rainfall simulation and downscaling together with the development of associated software; new intermediate coupled models; a new methodology of inverse modeling for linking ICMs with observations and GCM results; and, observational studies of decadal and multi-decadal natural climate results, informed by ICM results.« less

Applying an economical scale-aware PDF-based turbulence closure model in NOAA NCEP GCMs

NASA Astrophysics Data System (ADS)

Belochitski, A.; Krueger, S. K.; Moorthi, S.; Bogenschutz, P.; Pincus, R.

2016-12-01

A novel unified representation of sub-grid scale (SGS) turbulence, cloudiness, and shallow convection is being implemented into the NOAA NCEP Global Forecasting System (GFS) general circulation model. The approach, known as Simplified High Order Closure (SHOC), is based on predicting a joint PDF of SGS thermodynamic variables and vertical velocity and using it to diagnose turbulent diffusion coefficients, SGS fluxes, condensation and cloudiness. Unlike other similar methods, only one new prognostic variable, turbulent kinetic energy (TKE), needs to be intoduced, making the technique computationally efficient.SHOC is now incorporated into a version of GFS, as well as into the next generation of the NCEP global model - NOAA Environmental Modeling System (NEMS). Turbulent diffusion coefficients computed by SHOC are now used in place of those produced by the boundary layer turbulence and shallow convection parameterizations. Large scale microphysics scheme is no longer used to calculate cloud fraction or the large-scale condensation/deposition. Instead, SHOC provides these variables. Radiative transfer parameterization uses cloudiness computed by SHOC.Outstanding problems include high level tropical cloud fraction being too high in SHOC runs, possibly related to the interaction of SHOC with condensate detrained from deep convection.Future work will consist of evaluating model performance and tuning the physics if necessary, by performing medium-range NWP forecasts with prescribed initial conditions, and AMIP-type climate tests with prescribed SSTs. Depending on the results, the model will be tuned or parameterizations modified. Next, SHOC will be implemented in the NCEP CFS, and tuned and evaluated for climate applications - seasonal prediction and long coupled climate runs. Impact of new physics on ENSO, MJO, ISO, monsoon variability, etc will be examined.

Normal forms for reduced stochastic climate models

PubMed Central

Majda, Andrew J.; Franzke, Christian; Crommelin, Daan

2009-01-01

The systematic development of reduced low-dimensional stochastic climate models from observations or comprehensive high-dimensional climate models is an important topic for atmospheric low-frequency variability, climate sensitivity, and improved extended range forecasting. Here techniques from applied mathematics are utilized to systematically derive normal forms for reduced stochastic climate models for low-frequency variables. The use of a few Empirical Orthogonal Functions (EOFs) (also known as Principal Component Analysis, Karhunen–Loéve and Proper Orthogonal Decomposition) depending on observational data to span the low-frequency subspace requires the assessment of dyad interactions besides the more familiar triads in the interaction between the low- and high-frequency subspaces of the dynamics. It is shown below that the dyad and multiplicative triad interactions combine with the climatological linear operator interactions to simultaneously produce both strong nonlinear dissipation and Correlated Additive and Multiplicative (CAM) stochastic noise. For a single low-frequency variable the dyad interactions and climatological linear operator alone produce a normal form with CAM noise from advection of the large scales by the small scales and simultaneously strong cubic damping. These normal forms should prove useful for developing systematic strategies for the estimation of stochastic models from climate data. As an illustrative example the one-dimensional normal form is applied below to low-frequency patterns such as the North Atlantic Oscillation (NAO) in a climate model. The results here also illustrate the short comings of a recent linear scalar CAM noise model proposed elsewhere for low-frequency variability. PMID:19228943

Multifarious anchovy and sardine regimes in the Humboldt Current System during the last 150 years.

PubMed

Salvatteci, Renato; Field, David; Gutiérrez, Dimitri; Baumgartner, Tim; Ferreira, Vicente; Ortlieb, Luc; Sifeddine, Abdel; Grados, Daniel; Bertrand, Arnaud

2018-03-01

The Humboldt Current System (HCS) has the highest production of forage fish in the world, although it is highly variable and the future of the primary component, anchovy, is uncertain in the context of global warming. Paradigms based on late 20th century observations suggest that large-scale forcing controls decadal-scale fluctuations of anchovy and sardine across different boundary currents of the Pacific. We develop records of anchovy and sardine fluctuations since 1860 AD using fish scales from multiple sites containing laminated sediments and compare them with Pacific basin-scale and regional indices of ocean climate variability. Our records reveal two main anchovy and sardine phases with a timescale that is not consistent with previously proposed periodicities. Rather, the regime shifts in the HCS are related to 3D habitat changes driven by changes in upwelling intensity from both regional and large-scale forcing. Moreover, we show that a long-term increase in coastal upwelling translates via a bottom-up mechanism to top predators suggesting that the warming climate, at least up to the start of the 21st century, was favorable for fishery productivity in the HCS. © 2017 John Wiley & Sons Ltd.

Regional Community Climate Simulations with variable resolution meshes in the Community Earth System Model

NASA Astrophysics Data System (ADS)

Zarzycki, C. M.; Gettelman, A.; Callaghan, P.

2017-12-01

Accurately predicting weather extremes such as precipitation (floods and droughts) and temperature (heat waves) requires high resolution to resolve mesoscale dynamics and topography at horizontal scales of 10-30km. Simulating such resolutions globally for climate scales (years to decades) remains computationally impractical. Simulating only a small region of the planet is more tractable at these scales for climate applications. This work describes global simulations using variable-resolution static meshes with multiple dynamical cores that target the continental United States using developmental versions of the Community Earth System Model version 2 (CESM2). CESM2 is tested in idealized, aquaplanet and full physics configurations to evaluate variable mesh simulations against uniform high and uniform low resolution simulations at resolutions down to 15km. Different physical parameterization suites are also evaluated to gauge their sensitivity to resolution. Idealized variable-resolution mesh cases compare well to high resolution tests. More recent versions of the atmospheric physics, including cloud schemes for CESM2, are more stable with respect to changes in horizontal resolution. Most of the sensitivity is due to sensitivity to timestep and interactions between deep convection and large scale condensation, expected from the closure methods. The resulting full physics model produces a comparable climate to the global low resolution mesh and similar high frequency statistics in the high resolution region. Some biases are reduced (orographic precipitation in the western United States), but biases do not necessarily go away at high resolution (e.g. summertime JJA surface Temp). The simulations are able to reproduce uniform high resolution results, making them an effective tool for regional climate studies and are available in CESM2.

Interannual rainfall variability and SOM-based circulation classification

NASA Astrophysics Data System (ADS)

Wolski, Piotr; Jack, Christopher; Tadross, Mark; van Aardenne, Lisa; Lennard, Christopher

2018-01-01

Self-Organizing Maps (SOM) based classifications of synoptic circulation patterns are increasingly being used to interpret large-scale drivers of local climate variability, and as part of statistical downscaling methodologies. These applications rely on a basic premise of synoptic climatology, i.e. that local weather is conditioned by the large-scale circulation. While it is clear that this relationship holds in principle, the implications of its implementation through SOM-based classification, particularly at interannual and longer time scales, are not well recognized. Here we use a SOM to understand the interannual synoptic drivers of climate variability at two locations in the winter and summer rainfall regimes of South Africa. We quantify the portion of variance in seasonal rainfall totals that is explained by year to year differences in the synoptic circulation, as schematized by a SOM. We furthermore test how different spatial domain sizes and synoptic variables affect the ability of the SOM to capture the dominant synoptic drivers of interannual rainfall variability. Additionally, we identify systematic synoptic forcing that is not captured by the SOM classification. The results indicate that the frequency of synoptic states, as schematized by a relatively disaggregated SOM (7 × 9) of prognostic atmospheric variables, including specific humidity, air temperature and geostrophic winds, captures only 20-45% of interannual local rainfall variability, and that the residual variance contains a strong systematic component. Utilising a multivariate linear regression framework demonstrates that this residual variance can largely be explained using synoptic variables over a particular location; even though they are used in the development of the SOM their influence, however, diminishes with the size of the SOM spatial domain. The influence of the SOM domain size, the choice of SOM atmospheric variables and grid-point explanatory variables on the levels of explained variance, is consistent with the general understanding of the dominant processes and atmospheric variables that affect rainfall variability at a particular location.

Distant Influence of Kuroshio Eddies on North Pacific Weather Patterns?

PubMed

Ma, Xiaohui; Chang, Ping; Saravanan, R; Montuoro, Raffaele; Hsieh, Jen-Shan; Wu, Dexing; Lin, Xiaopei; Wu, Lixin; Jing, Zhao

2015-12-04

High-resolution satellite measurements of surface winds and sea-surface temperature (SST) reveal strong coupling between meso-scale ocean eddies and near-surface atmospheric flow over eddy-rich oceanic regions, such as the Kuroshio and Gulf Stream, highlighting the importance of meso-scale oceanic features in forcing the atmospheric planetary boundary layer (PBL). Here, we present high-resolution regional climate modeling results, supported by observational analyses, demonstrating that meso-scale SST variability, largely confined in the Kuroshio-Oyashio confluence region (KOCR), can further exert a significant distant influence on winter rainfall variability along the U.S. Northern Pacific coast. The presence of meso-scale SST anomalies enhances the diabatic conversion of latent heat energy to transient eddy energy, intensifying winter cyclogenesis via moist baroclinic instability, which in turn leads to an equivalent barotropic downstream anticyclone anomaly with reduced rainfall. The finding points to the potential of improving forecasts of extratropical winter cyclones and storm systems and projections of their response to future climate change, which are known to have major social and economic impacts, by improving the representation of ocean eddy-atmosphere interaction in forecast and climate models.

Solar radiation variability over La Réunion island and associated larger-scale dynamics

NASA Astrophysics Data System (ADS)

Mialhe, Pauline; Morel, Béatrice; Pohl, Benjamin; Bessafi, Miloud; Chabriat, Jean-Pierre

2017-04-01

This study aims to examine the solar radiation variability over La Réunion island and its relationship with large-scale circulation. The Satellite Application Facility on Climate Monitoring (CM SAF) produces a Shortwave Incoming Solar radiation (SIS) data record called Solar surfAce RAdiation Heliosat - East (SARAH-E). A comparison to in situ observations from Météo-France measurements networks quantifies the skill of SARAH-E grids which we use as dataset. First step of the work, irradiance mean cycles are calculated to describe the diurnal-seasonal SIS behaviour over La Réunion island. By analogy with the climate anomalies, instantaneous deviations are computed after removal of the mean states. Finally, we associate these anomalies with larger-scale atmospheric dynamics into the South West Indian Ocean by applying multivariate clustering analyses (Hierarchical Ascending Classification, k-means).

Ocean-atmosphere forcing of centennial hydroclimatic variability in the Pacific Northwest

USGS Publications Warehouse

Steinman, Byron A.; Abbott, Mark B.; Mann, Michael E.; Ortiz, Joseph D.; Feng, Song; Pompeani, David P.; Stansell, Nathan D.; Anderson, Lesleigh; Finney, Bruce P.; Bird, Broxton W.

2014-01-01

Reconstructing centennial timescale hydroclimate variability during the late Holocene is critically important for understanding large-scale patterns of drought and their relationship with climate dynamics. We present sediment oxygen isotope records spanning the last two millennia from 10 lakes, as well as climate model simulations, indicating that the Little Ice Age was dry relative to the Medieval Climate Anomaly in much of the Pacific Northwest of North America. This pattern is consistent with observed associations between the El Niño Southern Oscillation (ENSO), the Northern Annular Mode and drought as well as with proxy-based reconstructions of Pacific ocean-atmosphere variations over the past 1000 years. The large amplitude of centennial variability indicated by the lake data suggests that regional hydroclimate is characterized by longer-term shifts in ENSO-like dynamics, and that an improved understanding of the centennial timescale relationship between external forcing and drought conditions is necessary for projecting future hydroclimatic conditions in western North America.

Regional climate model sensitivity to domain size

NASA Astrophysics Data System (ADS)

Leduc, Martin; Laprise, René

2009-05-01

Regional climate models are increasingly used to add small-scale features that are not present in their lateral boundary conditions (LBC). It is well known that the limited area over which a model is integrated must be large enough to allow the full development of small-scale features. On the other hand, integrations on very large domains have shown important departures from the driving data, unless large scale nudging is applied. The issue of domain size is studied here by using the “perfect model” approach. This method consists first of generating a high-resolution climatic simulation, nicknamed big brother (BB), over a large domain of integration. The next step is to degrade this dataset with a low-pass filter emulating the usual coarse-resolution LBC. The filtered nesting data (FBB) are hence used to drive a set of four simulations (LBs for Little Brothers), with the same model, but on progressively smaller domain sizes. The LB statistics for a climate sample of four winter months are compared with BB over a common region. The time average (stationary) and transient-eddy standard deviation patterns of the LB atmospheric fields generally improve in terms of spatial correlation with the reference (BB) when domain gets smaller. The extraction of the small-scale features by using a spectral filter allows detecting important underestimations of the transient-eddy variability in the vicinity of the inflow boundary, which can penalize the use of small domains (less than 100 × 100 grid points). The permanent “spatial spin-up” corresponds to the characteristic distance that the large-scale flow needs to travel before developing small-scale features. The spin-up distance tends to grow in size at higher levels in the atmosphere.

Improving preparedness of farmers to Climate Variability: A case study of Vidarbha region of Maharashtra, India

NASA Astrophysics Data System (ADS)

Swami, D.; Parthasarathy, D.; Dave, P.

2016-12-01

A key objective of the ongoing research is to understand the risk and vulnerability of agriculture and farming communities with respect to multiple climate change attributes, particularly monsoon variability and hydrology such as ground water availability. Climate Variability has always been a feature affecting Indian agriculture but the nature and characteristics of this variability is not well understood. Indian monsoon patterns are highly variable and most of the studies focus on larger domain such as Central India or Western coast (Ghosh et al., 2009) but district level analysis is missing i.e. the linkage between agriculture and climate variables at finer scale has not been investigated comprehensively. For example, Eastern Vidarbha region in Maharashtra is considered as one of the most agriculturally sensitive region in India, where every year a large number of farmers commit suicide. The main reasons for large number of suicides are climate related stressors such as droughts, hail storms, and monsoon variability aggravated with poor socio-economic conditions. Present study has tried to explore the areas in Vidarbha region of Maharashtra where famers and crop productivity, specifically cotton, sorghum, is highly vulnerable to monsoon variability, hydrological and socio-economic variables which are further modelled to determine the maximal contributing factor towards crops and farmers' vulnerability. After analysis using primary and secondary data, it will aid in decision making regarding field operations such as time of sowing, harvesting and irrigation requirements by optimizing the cropping pattern with climatic, hydrological and socio-economic variables. It also suggests the adaptation strategies to farmers regarding different types of cropping and water harvesting practices, optimized dates and timings for harvesting, sowing, water and nutrient requirements of particular crops according to the specific region. Primarily along with secondary analysis captured here can be highly beneficial for the farmers and policy makers while formulating agricultural policies related to climate change.

Future projection of Indian summer monsoon variability under climate change scenario: An assessment from CMIP5 climate models

NASA Astrophysics Data System (ADS)

Sharmila, S.; Joseph, S.; Sahai, A. K.; Abhilash, S.; Chattopadhyay, R.

2015-01-01

In this study, the impact of enhanced anthropogenic greenhouse gas emissions on the possible future changes in different aspects of daily-to-interannual variability of Indian summer monsoon (ISM) is systematically assessed using 20 coupled models participated in the Coupled Model Inter-comparison Project Phase 5. The historical (1951-1999) and future (2051-2099) simulations under the strongest Representative Concentration Pathway have been analyzed for this purpose. A few reliable models are selected based on their competence in simulating the basic features of present-climate ISM variability. The robust and consistent projections across the selected models suggest substantial changes in the ISM variability by the end of 21st century indicating strong sensitivity of ISM to global warming. On the seasonal scale, the all-India summer monsoon mean rainfall is likely to increase moderately in future, primarily governed by enhanced thermodynamic conditions due to atmospheric warming, but slightly offset by weakened large scale monsoon circulation. It is projected that the rainfall magnitude will increase over core monsoon zone in future climate, along with lengthening of the season due to late withdrawal. On interannual timescales, it is speculated that severity and frequency of both strong monsoon (SM) and weak monsoon (WM) might increase noticeably in future climate. Substantial changes in the daily variability of ISM are also projected, which are largely associated with the increase in heavy rainfall events and decrease in both low rain-rate and number of wet days during future monsoon. On the subseasonal scale, the model projections depict considerable amplification of higher frequency (below 30 day mode) components; although the dominant northward propagating 30-70 day mode of monsoon intraseasonal oscillations may not change appreciably in a warmer climate. It is speculated that the enhanced high frequency mode of monsoon ISOs due to increased GHG induced warming may notably modulate the ISM rainfall in future climate. Both extreme wet and dry episodes are likely to intensify and regionally extend in future climate with enhanced propensity of short active and long break spells. The SM (WM) could also be more wet (dry) in future due to the increment in longer active (break) spells. However, future changes in the spatial pattern during active/break phase of SM and WM are geographically inconsistent among the models. The results point out the growing climate-related vulnerability over Indian subcontinent, and further suggest the requisite of profound adaptation measures and better policy making in future.

Land change variability and human-environment dynamics in the United States Great Plains

USGS Publications Warehouse

Drummond, M.A.; Auch, Roger F.; Karstensen, K.A.; Sayler, K. L.; Taylor, Janis L.; Loveland, Thomas R.

2012-01-01

Land use and land cover changes have complex linkages to climate variability and change, biophysical resources, and socioeconomic driving forces. To assess these land change dynamics and their causes in the Great Plains, we compare and contrast contemporary changes across 16 ecoregions using Landsat satellite data and statistical analysis. Large-area change analysis of agricultural regions is often hampered by change detection error and the tendency for land conversions to occur at the local-scale. To facilitate a regional-scale analysis, a statistical sampling design of randomly selected 10 km × 10 km blocks is used to efficiently identify the types and rates of land conversions for four time intervals between 1973 and 2000, stratified by relatively homogenous ecoregions. Nearly 8% of the overall Great Plains region underwent land-use and land-cover change during the study period, with a substantial amount of ecoregion variability that ranged from less than 2% to greater than 13%. Agricultural land cover declined by more than 2% overall, with variability contingent on the differential characteristics of regional human–environment systems. A large part of the Great Plains is in relatively stable land cover. However, other land systems with significant biophysical and climate limitations for agriculture have high rates of land change when pushed by economic, policy, technology, or climate forcing factors. The results indicate the regionally based potential for land cover to persist or fluctuate as land uses are adapted to spatially and temporally variable forcing factors.

Assessing global vegetation activity using spatio-temporal Bayesian modelling

NASA Astrophysics Data System (ADS)

Mulder, Vera L.; van Eck, Christel M.; Friedlingstein, Pierre; Regnier, Pierre A. G.

2016-04-01

This work demonstrates the potential of modelling vegetation activity using a hierarchical Bayesian spatio-temporal model. This approach allows modelling changes in vegetation and climate simultaneous in space and time. Changes of vegetation activity such as phenology are modelled as a dynamic process depending on climate variability in both space and time. Additionally, differences in observed vegetation status can be contributed to other abiotic ecosystem properties, e.g. soil and terrain properties. Although these properties do not change in time, they do change in space and may provide valuable information in addition to the climate dynamics. The spatio-temporal Bayesian models were calibrated at a regional scale because the local trends in space and time can be better captured by the model. The regional subsets were defined according to the SREX segmentation, as defined by the IPCC. Each region is considered being relatively homogeneous in terms of large-scale climate and biomes, still capturing small-scale (grid-cell level) variability. Modelling within these regions is hence expected to be less uncertain due to the absence of these large-scale patterns, compared to a global approach. This overall modelling approach allows the comparison of model behavior for the different regions and may provide insights on the main dynamic processes driving the interaction between vegetation and climate within different regions. The data employed in this study encompasses the global datasets for soil properties (SoilGrids), terrain properties (Global Relief Model based on SRTM DEM and ETOPO), monthly time series of satellite-derived vegetation indices (GIMMS NDVI3g) and climate variables (Princeton Meteorological Forcing Dataset). The findings proved the potential of a spatio-temporal Bayesian modelling approach for assessing vegetation dynamics, at a regional scale. The observed interrelationships of the employed data and the different spatial and temporal trends support our hypothesis. That is, the change of vegetation in space and time may be better understood when modelling vegetation change as both a dynamic and multivariate process. Therefore, future research will focus on a multivariate dynamical spatio-temporal modelling approach. This ongoing research is performed within the context of the project "Global impacts of hydrological and climatic extremes on vegetation" (project acronym: SAT-EX) which is part of the Belgian research programme for Earth Observation Stereo III.

Volcanic influence on centennial to millennial Holocene Greenland temperature change.

PubMed

Kobashi, Takuro; Menviel, Laurie; Jeltsch-Thömmes, Aurich; Vinther, Bo M; Box, Jason E; Muscheler, Raimund; Nakaegawa, Toshiyuki; Pfister, Patrik L; Döring, Michael; Leuenberger, Markus; Wanner, Heinz; Ohmura, Atsumu

2017-05-03

Solar variability has been hypothesized to be a major driver of North Atlantic millennial-scale climate variations through the Holocene along with orbitally induced insolation change. However, another important climate driver, volcanic forcing has generally been underestimated prior to the past 2,500 years partly owing to the lack of proper proxy temperature records. Here, we reconstruct seasonally unbiased and physically constrained Greenland Summit temperatures over the Holocene using argon and nitrogen isotopes within trapped air in a Greenland ice core (GISP2). We show that a series of volcanic eruptions through the Holocene played an important role in driving centennial to millennial-scale temperature changes in Greenland. The reconstructed Greenland temperature exhibits significant millennial correlations with K + and Na + ions in the GISP2 ice core (proxies for atmospheric circulation patterns), and δ 18 O of Oman and Chinese Dongge cave stalagmites (proxies for monsoon activity), indicating that the reconstructed temperature contains hemispheric signals. Climate model simulations forced with the volcanic forcing further suggest that a series of large volcanic eruptions induced hemispheric-wide centennial to millennial-scale variability through ocean/sea-ice feedbacks. Therefore, we conclude that volcanic activity played a critical role in driving centennial to millennial-scale Holocene temperature variability in Greenland and likely beyond.

Risk of large-scale fires in boreal forests of Finland under changing climate

NASA Astrophysics Data System (ADS)

Lehtonen, I.; Venäläinen, A.; Kämäräinen, M.; Peltola, H.; Gregow, H.

2016-01-01

The target of this work was to assess the impact of projected climate change on forest-fire activity in Finland with special emphasis on large-scale fires. In addition, we were particularly interested to examine the inter-model variability of the projected change of fire danger. For this purpose, we utilized fire statistics covering the period 1996-2014 and consisting of almost 20 000 forest fires, as well as daily meteorological data from five global climate models under representative concentration pathway RCP4.5 and RCP8.5 scenarios. The model data were statistically downscaled onto a high-resolution grid using the quantile-mapping method before performing the analysis. In examining the relationship between weather and fire danger, we applied the Canadian fire weather index (FWI) system. Our results suggest that the number of large forest fires may double or even triple during the present century. This would increase the risk that some of the fires could develop into real conflagrations which have become almost extinct in Finland due to active and efficient fire suppression. However, the results reveal substantial inter-model variability in the rate of the projected increase of forest-fire danger, emphasizing the large uncertainty related to the climate change signal in fire activity. We moreover showed that the majority of large fires in Finland occur within a relatively short period in May and June due to human activities and that FWI correlates poorer with the fire activity during this time of year than later in summer when lightning is a more important cause of fires.

South American Monsoon variability during the past 2,000 years from stable isotopic proxies and model simulations

NASA Astrophysics Data System (ADS)

Vuille, M.; Cruz, F. W.; Abbott, M.; Bird, B. W.; Burns, S. J.; Cheng, H.; Colose, C. M.; Kanner, L. C.; LeGrande, A. N.; Novello, V. F.; Taylor, B. L.

2012-12-01

The rapidly growing number of high-resolution stable isotopic proxies from speleothems, ice cores and lake sediments, located in the South American summer monsoon (SASM) belt, will soon allow for a comprehensive analysis of climate variability in the South American tropics and subtropics over the past ~ 2000 years. In combination with isotope-enabled General Circulation Models (GCMs) this offers new prospects for better understanding the spatiotemporal dynamics of the South American monsoon system and for diagnosing its sensitivities to external forcing mechanisms (solar, volcanic) and modes of ocean-atmosphere variability (e.g. ENSO and AMO). In this presentation we will discuss the rationale for interpreting isotopic excursions recorded in various proxies from the Andes, northeastern and southeastern Brazil as indicative of changes in monsoon intensity. We will focus on the past 2 millenia when isotopic proxies from the SASM region show a very coherent behavior regardless of the type of archive or their location. All proxies exhibit significant decadal to multidecadal variability, superimposed on large excursions during three key periods, the Medieval Climate Anomaly (MCA), the Little Ice Age (LIA) and the Current Warm Period (CWP). We interpret these three periods as times when the SASM mean state was significantly weakened (MCA and CWP) and strengthened (LIA), respectively. During the LIA each of the proxy archives considered contains the most negative delta-18O values recorded during the entire record length. On the other hand the monsoon strength is currently rather weak in a 2000- year historical perspective, rivaled only by the low intensity during the MCA. One interpretation of these centennial-scale climate anomalies suggests that they were at least partially driven by temperature changes in the northern hemisphere and in particular over the North Atlantic, leading to a latitudinal displacement of the ITCZ and a change in monsoon intensity and degree of rainout upstream of the proxy locations, over the tropical continent. This interpretation is supported by several independent proxy archives and modeling studies. One question that remains, however, is how ENSO, arguably the main forcing factor for delta-18O variability over tropical South America on interannual time scales, interacts with and may be modulated by low-frequency North Atlantic forcing. Our analysis also implies that isotopic proxies, because of their ability to integrate climatic information on large spatial scales, are complementary to more traditional proxies such as tree rings or historical archives, which record in-situ climate variations. Future climate reconstructions therefore should make an effort to include isotopic proxies as large-scale predictors in order to better constrain past changes in the atmospheric circulation.

A crisis in the making: responses of Amazonian forests to land use and climate change.

PubMed

Laurance, W F

1998-10-01

At least three global-change phenomena are having major impacts on Amazonian forests: (1) accelerating deforestation and logging; (2) rapidly changing patterns of forest loss; and (3) interactions between human land-use and climatic variability. Additional alterations caused by climatic change, rising concentrations of atmospheric carbon dioxide, mining, overhunting and other large-scale phenomena could also have important effects on the Amazon ecosystem. Consequently, decisions regarding Amazon forest use in the next decade are crucial to its future existence.

Recent advances in research on climate and human conflict

NASA Astrophysics Data System (ADS)

Hsiang, S. M.

2014-12-01

A rapidly growing body of empirical, quantitative research examines whether rates of human conflict can be systematically altered by climatic changes. We discuss recent advances in this field, including Bayesian meta-analyses of the effect of temperature and rainfall on current and future large-scale conflicts, the impact of climate variables on gang violence and suicides in Mexico, and probabilistic projections of personal violence and property crime in the United States under RCP scenarios. Criticisms of this research field will also be explained and addressed.

Drought Persistence Errors in Global Climate Models

NASA Astrophysics Data System (ADS)

Moon, H.; Gudmundsson, L.; Seneviratne, S. I.

2018-04-01

The persistence of drought events largely determines the severity of socioeconomic and ecological impacts, but the capability of current global climate models (GCMs) to simulate such events is subject to large uncertainties. In this study, the representation of drought persistence in GCMs is assessed by comparing state-of-the-art GCM model simulations to observation-based data sets. For doing so, we consider dry-to-dry transition probabilities at monthly and annual scales as estimates for drought persistence, where a dry status is defined as negative precipitation anomaly. Though there is a substantial spread in the drought persistence bias, most of the simulations show systematic underestimation of drought persistence at global scale. Subsequently, we analyzed to which degree (i) inaccurate observations, (ii) differences among models, (iii) internal climate variability, and (iv) uncertainty of the employed statistical methods contribute to the spread in drought persistence errors using an analysis of variance approach. The results show that at monthly scale, model uncertainty and observational uncertainty dominate, while the contribution from internal variability is small in most cases. At annual scale, the spread of the drought persistence error is dominated by the statistical estimation error of drought persistence, indicating that the partitioning of the error is impaired by the limited number of considered time steps. These findings reveal systematic errors in the representation of drought persistence in current GCMs and suggest directions for further model improvement.

iClimate: a climate data and analysis portal

NASA Astrophysics Data System (ADS)

Goodman, P. J.; Russell, J. L.; Merchant, N.; Miller, S. J.; Juneja, A.

2015-12-01

We will describe a new climate data and analysis portal called iClimate that facilitates direct comparisons between available climate observations and climate simulations. Modeled after the successful iPlant Collaborative Discovery Environment (www.iplantcollaborative.org) that allows plant scientists to trade and share environmental, physiological and genetic data and analyses, iClimate provides an easy-to-use platform for large-scale climate research, including the storage, sharing, automated preprocessing, analysis and high-end visualization of large and often disparate observational and model datasets. iClimate will promote data exploration and scientific discovery by providing: efficient and high-speed transfer of data from nodes around the globe (e.g. PCMDI and NASA); standardized and customized data/model metrics; efficient subsampling of datasets based on temporal period, geographical region or variable; and collaboration tools for sharing data, workflows, analysis results, and data visualizations with collaborators or with the community at large. We will present iClimate's capabilities, and demonstrate how it will simplify and enhance the ability to do basic or cutting-edge climate research by professionals, laypeople and students.

Structural overshoot of tree growth with climate variability and the global spectrum of drought-induced forest dieback.

PubMed

Jump, Alistair S; Ruiz-Benito, Paloma; Greenwood, Sarah; Allen, Craig D; Kitzberger, Thomas; Fensham, Rod; Martínez-Vilalta, Jordi; Lloret, Francisco

2017-09-01

Ongoing climate change poses significant threats to plant function and distribution. Increased temperatures and altered precipitation regimes amplify drought frequency and intensity, elevating plant stress and mortality. Large-scale forest mortality events will have far-reaching impacts on carbon and hydrological cycling, biodiversity, and ecosystem services. However, biogeographical theory and global vegetation models poorly represent recent forest die-off patterns. Furthermore, as trees are sessile and long-lived, their responses to climate extremes are substantially dependent on historical factors. We show that periods of favourable climatic and management conditions that facilitate abundant tree growth can lead to structural overshoot of aboveground tree biomass due to a subsequent temporal mismatch between water demand and availability. When environmental favourability declines, increases in water and temperature stress that are protracted, rapid, or both, drive a gradient of tree structural responses that can modify forest self-thinning relationships. Responses ranging from premature leaf senescence and partial canopy dieback to whole-tree mortality reduce canopy leaf area during the stress period and for a lagged recovery window thereafter. Such temporal mismatches of water requirements from availability can occur at local to regional scales throughout a species geographical range. As climate change projections predict large future fluctuations in both wet and dry conditions, we expect forests to become increasingly structurally mismatched to water availability and thus overbuilt during more stressful episodes. By accounting for the historical context of biomass development, our approach can explain previously problematic aspects of large-scale forest mortality, such as why it can occur throughout the range of a species and yet still be locally highly variable, and why some events seem readily attributable to an ongoing drought while others do not. This refined understanding can facilitate better projections of structural overshoot responses, enabling improved prediction of changes in forest distribution and function from regional to global scales. © 2017 John Wiley & Sons Ltd.

Structural overshoot of tree growth with climate variability and the global spectrum of drought-induced forest dieback

USGS Publications Warehouse

Jump, Alistair S.; Ruiz-Benito, Paloma; Greenwood, Sarah; Allen, Craig D.; Kitzberger, Thomas; Fensham, Rod; Martínez-Vilalta, Jordi; Lloret, Francisco

2017-01-01

Ongoing climate change poses significant threats to plant function and distribution. Increased temperatures and altered precipitation regimes amplify drought frequency and intensity, elevating plant stress and mortality. Large-scale forest mortality events will have far-reaching impacts on carbon and hydrological cycling, biodiversity, and ecosystem services. However, biogeographical theory and global vegetation models poorly represent recent forest die-off patterns. Furthermore, as trees are sessile and long-lived, their responses to climate extremes are substantially dependent on historical factors. We show that periods of favourable climatic and management conditions that facilitate abundant tree growth can lead to structural overshoot of aboveground tree biomass due to a subsequent temporal mismatch between water demand and availability. When environmental favourability declines, increases in water and temperature stress that are protracted, rapid, or both, drive a gradient of tree structural responses that can modify forest self-thinning relationships. Responses ranging from premature leaf senescence and partial canopy dieback to whole-tree mortality reduce canopy leaf area during the stress period and for a lagged recovery window thereafter. Such temporal mismatches of water requirements from availability can occur at local to regional scales throughout a species geographical range. As climate change projections predict large future fluctuations in both wet and dry conditions, we expect forests to become increasingly structurally mismatched to water availability and thus overbuilt during more stressful episodes. By accounting for the historical context of biomass development, our approach can explain previously problematic aspects of large-scale forest mortality, such as why it can occur throughout the range of a species and yet still be locally highly variable, and why some events seem readily attributable to an ongoing drought while others do not. This refined understanding can facilitate better projections of structural overshoot responses, enabling improved prediction of changes in forest distribution and function from regional to global scales.

A global assessment of climate-water quality relationships in large rivers: an elasticity perspective.

PubMed

Jiang, Jiping; Sharma, Ashish; Sivakumar, Bellie; Wang, Peng

2014-01-15

To uncover climate-water quality relationships in large rivers on a global scale, the present study investigates the climate elasticity of river water quality (CEWQ) using long-term monthly records observed at 14 large rivers. Temperature and precipitation elasticities of 12 water quality parameters, highlighted by N- and P-nutrients, are assessed. General observations on elasticity values show the usefulness of this approach to describe the magnitude of stream water quality responses to climate change, which improves that of simple statistical correlation. Sensitivity type, intensity and variability rank of CEWQ are reported and specific characteristics and mechanism of elasticity of nutrient parameters are also revealed. Among them, the performance of ammonia, total phosphorus-air temperature models, and nitrite, orthophosphorus-precipitation models are the best. Spatial and temporal assessment shows that precipitation elasticity is more variable in space than temperature elasticity and that seasonal variation is more evident for precipitation elasticity than for temperature elasticity. Moreover, both anthropogenic activities and environmental factors are found to impact CEWQ for select variables. The major relationships that can be inferred include: (1) human population has a strong linear correlation with temperature elasticity of turbidity and total phosphorus; and (2) latitude has a strong linear correlation with precipitation elasticity of turbidity and N nutrients. As this work improves our understanding of the relation between climate factors and surface water quality, it is potentially helpful for investigating the effect of climate change on water quality in large rivers, such as on the long-term change of nutrient concentrations. © 2013.

Storm-tracks interannual variability and large-scale climate modes

NASA Astrophysics Data System (ADS)

Liberato, Margarida L. R.; Trigo, Isabel F.; Trigo, Ricardo M.

2013-04-01

In this study we focus on the interannual variability and observed changes in northern hemisphere mid-latitude storm-tracks and relate them to large scale atmospheric circulation variability modes. Extratropical storminess, cyclones dominant paths, frequency and intensity have long been the object of climatological studies. The analysis of storm characteristics and historical trends presented here is based on the cyclone detecting and tracking algorithm first developed for the Mediterranean region (Trigo et al. 1999) and recently extended to a larger Euro-Atlantic region (Trigo 2006). The objective methodology, which identifies and follows individual lows as minima in SLP fields, fulfilling a set of conditions regarding the central pressure and the pressure gradient, is applied to the northern hemisphere 6-hourly geopotential data at 1000 hPa from the 20th Century Reanalyses (20CRv2) project and from reanalyses datasets provided by the European Centre for Medium-Range Weather Forecasts (ECMWF): ERA-40 and ERA Interim reanalyses. First, we assess the interannual variability and cyclone frequency trends for each of the datasets, for the 20th century and for the period between 1958 and 2002 using the highest spatial resolution available (1.125° x 1.125°) from the ERA-40 data. Results show that winter variability of storm paths, cyclone frequency and travel times is in agreement with the reported variability in a number of large-scale climate patterns (including the North Atlantic Oscillation, the East Atlantic Pattern and the Scandinavian Pattern). In addition, three storm-track databases are built spanning the common available extended winter seasons from October 1979 to March 2002. Although relatively short, this common period allows a comparison of systems represented in reanalyses datasets with distinct horizontal resolutions. This exercise is mostly focused on the key areas of cyclogenesis and cyclolysis and main cyclone characteristics over the northern hemisphere. Trigo IF., TD Davies, GR Bigg (1999) Objective climatology of cyclones in the Mediterranean region. J. Climate 12: 1685-1696. Trigo IF (2006) Climatology and interannual variability of storm-tracks in the Euro-Atlantic sector: a comparison between ERA-40 and NCEP/NCAR reanalyses. Clim. Dyn. 26: 127-143.

Quantifying the Influence of Dynamics Across Scales on Regional Climate Uncertainty in Western North America

NASA Astrophysics Data System (ADS)

Goldenson, Naomi L.

Uncertainties in climate projections at the regional scale are inevitably larger than those for global mean quantities. Here, focusing on western North American regional climate, several approaches are taken to quantifying uncertainties starting with the output of global climate model projections. Internal variance is found to be an important component of the projection uncertainty up and down the west coast. To quantify internal variance and other projection uncertainties in existing climate models, we evaluate different ensemble configurations. Using a statistical framework to simultaneously account for multiple sources of uncertainty, we find internal variability can be quantified consistently using a large ensemble or an ensemble of opportunity that includes small ensembles from multiple models and climate scenarios. The latter offers the advantage of also producing estimates of uncertainty due to model differences. We conclude that climate projection uncertainties are best assessed using small single-model ensembles from as many model-scenario pairings as computationally feasible. We then conduct a small single-model ensemble of simulations using the Model for Prediction Across Scales with physics from the Community Atmosphere Model Version 5 (MPAS-CAM5) and prescribed historical sea surface temperatures. In the global variable resolution domain, the finest resolution (at 30 km) is in our region of interest over western North America and upwind over the northeast Pacific. In the finer-scale region, extreme precipitation from atmospheric rivers (ARs) is connected to tendencies in seasonal snowpack in mountains of the Northwest United States and California. In most of the Cascade Mountains, winters with more AR days are associated with less snowpack, in contrast to the northern Rockies and California's Sierra Nevadas. In snowpack observations and reanalysis of the atmospheric circulation, we find similar relationships between frequency of AR events and winter season snowpack in the western United States. In spring, however, there is not a clear relationship between number of AR days and seasonal mean snowpack across the model ensemble, so caution is urged in interpreting the historical record in the spring season. Finally, the representation of the El Nino Southern Oscillation (ENSO)--an important source of interannual climate predictability in some regions--is explored in a large single-model ensemble using ensemble Empirical Orthogonal Functions (EOFs) to find modes of variance across the entire ensemble at once. The leading EOF is ENSO. The principal components (PCs) of the next three EOFs exhibit a lead-lag relationship with the ENSO signal captured in the first PC. The second PC, with most of its variance in the summer season, is the most strongly cross-correlated with the first. This approach offers insight into how the model considered represents this important atmosphere-ocean interaction. Taken together these varied approaches quantify the implications of climate projections regionally, identify processes that make snowpack water resources vulnerable, and seek insight into how to better simulate the large-scale climate modes controlling regional variability.

Capturing subregional variability in regional-scale climate change vulnerability assessments of natural resources

Treesearch

Polly C. Buotte; David L. Peterson; Kevin S. McKelvey; Jeffrey A. Hicke

2016-01-01

Natural resource vulnerability to climate change can depend on the climatology and ecological conditions at a particular site. Here we present a conceptual framework for incorporating spatial variability in natural resource vulnerability to climate change in a regional-scale assessment. The framework was implemented in the first regional-scale vulnerability...

Substantial large-scale feedbacks between natural aerosols and climate

NASA Astrophysics Data System (ADS)

Scott, C. E.; Arnold, S. R.; Monks, S. A.; Asmi, A.; Paasonen, P.; Spracklen, D. V.

2018-01-01

The terrestrial biosphere is an important source of natural aerosol. Natural aerosol sources alter climate, but are also strongly controlled by climate, leading to the potential for natural aerosol-climate feedbacks. Here we use a global aerosol model to make an assessment of terrestrial natural aerosol-climate feedbacks, constrained by observations of aerosol number. We find that warmer-than-average temperatures are associated with higher-than-average number concentrations of large (>100 nm diameter) particles, particularly during the summer. This relationship is well reproduced by the model and is driven by both meteorological variability and variability in natural aerosol from biogenic and landscape fire sources. We find that the calculated extratropical annual mean aerosol radiative effect (both direct and indirect) is negatively related to the observed global temperature anomaly, and is driven by a positive relationship between temperature and the emission of natural aerosol. The extratropical aerosol-climate feedback is estimated to be -0.14 W m-2 K-1 for landscape fire aerosol, greater than the -0.03 W m-2 K-1 estimated for biogenic secondary organic aerosol. These feedbacks are comparable in magnitude to other biogeochemical feedbacks, highlighting the need for natural aerosol feedbacks to be included in climate simulations.

Synchronous centennial-scale variability in abundance of remote sardine populations in the Pacific

NASA Astrophysics Data System (ADS)

Kuwae, M.; Takashige, S.; Yamamoto, M.; Sagawa, T.; Takeoka, H.

2012-12-01

A number of studies have identified evidence for connections between Pacific climate decadal variability and variations in Pacific marine ecosystems which are typically shown in abundance of remote sardine and anchovy species off Japan, California, Peru, and Chile as well as Alaska salmon species. The variations in climate indices and abundance of sardine and anchovy species most likely have 50-70 year cycles and therefore these natural perturbations in climates and Pacific ecosystems should be considered for developing predictive models of fisheries productions and the managements. Despite the importance of natural perturbations for long-term predictions, one issue, whether synchronous centennial-variations in remote Pacific fisheries productions in response to climate variability exists in the past, has not been questioned, because there has never been long-term reconstructed time series in the western North Pacific. Here we present well preserved, fossil fish scale-based abundance record of Japanese sardine over the last 1100 years reconstructed from a seasonal anoxic basin in the western Seto Inland Sea near their spawning areas in the western North Pacific. A comparison of our record with other previous records clearly showed centennial-scale variations in abundance of sardine species off Japan, California, and Chile, characterized by centennial-scale alternations between low abundance regimes and high abundance regimes in which multidecadal fluctuations with large amplitudes occurred once or several times. High abundance regimes from 1450 to 1650 AD and after 1800 AD and a low abundance regime from 1650 to 1800 AD corresponded to low frequency patterns of PDO index reconstructed from tree-ring records in North America. This indicates that connections between Pacific climate variability and variations in Pacific marine ecosystems exist not only on multidecadal timescales but on centennial timescales. Three to four hundred-yr periodicity of the Pacific climate-ecosystem dynamics suggests possibility of a change into a century-long, low sardine abundance regime in the next 100 years.

Climate drives inter-annual variability in probability of high severity fire occurrence in the western United States

NASA Astrophysics Data System (ADS)

Keyser, Alisa; Westerling, Anthony LeRoy

2017-05-01

A long history of fire suppression in the western United States has significantly changed forest structure and ecological function, leading to increasingly uncharacteristic fires in terms of size and severity. Prior analyses of fire severity in California forests showed that time since last fire and fire weather conditions predicted fire severity very well, while a larger regional analysis showed that topography and climate were important predictors of high severity fire. There has not yet been a large-scale study that incorporates topography, vegetation and fire-year climate to determine regional scale high severity fire occurrence. We developed models to predict the probability of high severity fire occurrence for the western US. We predict high severity fire occurrence with some accuracy, and identify the relative importance of predictor classes in determining the probability of high severity fire. The inclusion of both vegetation and fire-year climate predictors was critical for model skill in identifying fires with high fractional fire severity. The inclusion of fire-year climate variables allows this model to forecast inter-annual variability in areas at future risk of high severity fire, beyond what slower-changing fuel conditions alone can accomplish. This allows for more targeted land management, including resource allocation for fuels reduction treatments to decrease the risk of high severity fire.

Assessing the Role of Climate Variability on Liver Fluke Risk in the UK Through Mechanistic Hydro-Epidemiological Modelling

NASA Astrophysics Data System (ADS)

Beltrame, L.; Dunne, T.; Rose, H.; Walker, J.; Morgan, E.; Vickerman, P.; Wagener, T.

2016-12-01

Liver fluke is a flatworm parasite infecting grazing animals worldwide. In the UK, it causes considerable production losses to cattle and sheep industries and costs farmers millions of pounds each year due to reduced growth rates and lower milk yields. Large part of the parasite life-cycle takes place outside of the host, with its survival and development strongly controlled by climatic and hydrologic conditions. Evidence of climate-driven changes in the distribution and seasonality of fluke disease already exists, as the infection is increasingly expanding to new areas and becoming a year-round problem. Therefore, it is crucial to assess current and potential future impacts of climate variability on the disease to guide interventions at the farm scale and mitigate risk. Climate-based fluke risk models have been available since the 1950s, however, they are based on empirical relationships derived between historical climate and incidence data, and thus are unlikely to be robust for simulating risk under changing conditions. Moreover, they are not dynamic, but estimate risk over large regions in the UK based on monthly average climate conditions, so they do not allow investigating the effects of climate variability for supporting farmers' decisions. In this study, we introduce a mechanistic model for fluke, which represents habitat suitability for disease development at 25m resolution with a daily time step, explicitly linking the parasite life-cycle to key hydro-climate conditions. The model is used on a case study in the UK and sensitivity analysis is performed to better understand the role of climate variability on the space-time dynamics of the disease, while explicitly accounting for uncertainties. Comparisons are presented with experts' knowledge and a widely used empirical model.

Impact of climate variability on runoff in the north-central United States

USGS Publications Warehouse

Ryberg, Karen R.; Lin, Wei; Vecchia, Aldo V.

2014-01-01

Large changes in runoff in the north-central United States have occurred during the past century, with larger floods and increases in runoff tending to occur from the 1970s to the present. The attribution of these changes is a subject of much interest. Long-term precipitation, temperature, and streamflow records were used to compare changes in precipitation and potential evapotranspiration (PET) to changes in runoff within 25 stream basins. The basins studied were organized into four groups, each one representing basins similar in topography, climate, and historic patterns of runoff. Precipitation, PET, and runoff data were adjusted for near-decadal scale variability to examine longer-term changes. A nonlinear water-balance analysis shows that changes in precipitation and PET explain the majority of multidecadal spatial/temporal variability of runoff and flood magnitudes, with precipitation being the dominant driver. Historical changes in climate and runoff in the region appear to be more consistent with complex transient shifts in seasonal climatic conditions than with gradual climate change. A portion of the unexplained variability likely stems from land-use change.

Carboniferous climate teleconnections archived in coupled bioapatite δ18OPO4 and 87Sr/86Sr records from the epicontinental Donets Basin, Ukraine

NASA Astrophysics Data System (ADS)

Montañez, Isabel P.; Osleger, Dillon J.; Chen, Jitao; Wortham, Barbara E.; Stamm, Robert G.; Nemyrovska, Tamara I.; Griffin, Julie M.; Poletaev, Vladislav I.; Wardlaw, Bruce R.

2018-06-01

Reconstructions of paleo-seawater chemistry are largely inferred from biogenic records of epicontinental seas. Recent studies provide considerable evidence for large-scale spatial and temporal variability in the environmental dynamics of these semi-restricted seas that leads to the decoupling of epicontinental isotopic records from those of the open ocean. We present conodont apatite δ18OPO4 and 87Sr/86Sr records spanning 24 Myr of the late Mississippian through Pennsylvanian derived from the U-Pb calibrated cyclothemic succession of the Donets Basin, eastern Ukraine. On a 2 to 6 Myr-scale, systematic fluctuations in bioapatite δ18OPO4 and 87Sr/86Sr broadly follow major shifts in the Donets onlap-offlap history and inferred regional climate, but are distinct from contemporaneous more open-water δ18OPO4 and global seawater Sr isotope trends. A -1 to -6‰ offset in Donets δ18OPO4 values from those of more open-water conodonts and greater temporal variability in δ18OPO4 and 87Sr/86Sr records are interpreted to primarily record climatically driven changes in local environmental processes in the Donets sea. Systematic isotopic shifts associated with Myr-scale sea-level fluctuations, however, indicate an extrabasinal driver. We propose a mechanistic link to glacioeustasy through a teleconnection between high-latitude ice changes and atmospheric pCO2 and regional monsoonal circulation in the Donets region. Inferred large-magnitude changes in Donets seawater salinity and temperature, not archived in the more open-water or global contemporaneous records, indicate a modification of the global climate signal in the epicontinental sea through amplification or dampening of the climate signal by local and regional environmental processes. This finding of global climate change filtered through local processes has implications for the use of conodont δ18OPO4 and 87Sr/86Sr values as proxies of paleo-seawater composition, mean temperature, and glacioeustasy.

Carboniferous climate teleconnections archived in coupled bioapatite δ18OPO4 and 87Sr/86Sr records from the epicontinental Donets Basin, Ukraine

USGS Publications Warehouse

Montanez, Isabel P.; Osleger, Dillon J.; Chen, J.-H.; Wortham, Barbara E.; Stamm, Robert G.; Nemyrovska, Tamara I.; Griffin, Julie M.; Poletaev, Vladislav I.; Wardlaw, Bruce R.

2018-01-01

Reconstructions of paleo-seawater chemistry are largely inferred from biogenic records of epicontinental seas. Recent studies provide considerable evidence for large-scale spatial and temporal variability in the environmental dynamics of these semi-restricted seas that leads to the decoupling of epicontinental isotopic records from those of the open ocean. We present conodont apatite δ18OPO4 and 87Sr/86Sr records spanning 24 Myr of the late Mississippian through Pennsylvanian derived from the U–Pb calibrated cyclothemic succession of the Donets Basin, eastern Ukraine. On a 2 to 6 Myr-scale, systematic fluctuations in bioapatite δ18OPO4 and 87Sr/86Sr broadly follow major shifts in the Donets onlap–offlap history and inferred regional climate, but are distinct from contemporaneous more open-water δ18OPO4 and global seawater Sr isotope trends. A −1 to −6‰ offset in Donets δ18OPO4 values from those of more open-water conodonts and greater temporal variability in δ18OPO4 and 87Sr/86Sr records are interpreted to primarily record climatically driven changes in local environmental processes in the Donets sea. Systematic isotopic shifts associated with Myr-scale sea-level fluctuations, however, indicate an extrabasinal driver. We propose a mechanistic link to glacioeustasy through a teleconnection between high-latitude ice changes and atmospheric pCO2 and regional monsoonal circulation in the Donets region. Inferred large-magnitude changes in Donets seawater salinity and temperature, not archived in the more open-water or global contemporaneous records, indicate a modification of the global climate signal in the epicontinental sea through amplification or dampening of the climate signal by local and regional environmental processes. This finding of global climate change filtered through local processes has implications for the use of conodont δ18OPO4 and 87Sr/86Sr values as proxies of paleo-seawater composition, mean temperature, and glacioeustasy.

Uncertainties in data-model comparisons: Spatio-temporal scales for past climates

NASA Astrophysics Data System (ADS)

Lohmann, G.

2016-12-01

Data-model comparisons are hindered by uncertainties like varying reservoir ages or potential seasonality bias of the recorder systems, but also due to the models' difficulty to represent the spatio-temporal variability patterns. For the Holocene we detect a sensitivity to horizontal resolution in the atmosphere, the representation of atmospheric dynamics, as well as the dynamics of the western boundary currents in the ocean. These features can create strong spatial heterogeneity in the North Atlantic and Pacific Oceans over long timescales (unlike a diffusive spatio-temporal scale separation). Futhermore, it is shown that such non-linear mechanisms could create a non-trivial response to seasonal insolation forcing via an atmospheric bridge inducing non-uniform temperature anomalies over the northern continents on multi-millennial time scales. Through the fluctuation-dissipation-theorem, climate variability and sensitivity are ultimately coupled. It is argued that some obvious biases between models and data may be linked to the missing key persistent component of the atmospheric dynamics, the North Atlantic blocking activity. It is shown that blocking is also linked to Atlantic multidecadal ocean variability and to extreme events. Interestingly, several proxies provide a measure of the frequency of extreme events, and a proper representation is a true challenge for climate models. Finally, case studies from deep paleo are presented in which changes in land-sea distribution or subscale parameterizations can cause relatively large effects on surface temperature. Such experiments can explore the phase space of solutions, but show the limitation of past climates to constrain climate sensitivity.

Response of Tropical Forests to Intense Climate Variability and Rainfall Anomaly over the Last Decade

NASA Astrophysics Data System (ADS)

Saatchi, S.; Asefi, S.

2012-04-01

During the last decade, strong precipitation anomalies resulted from increased sea surface temperature in the tropical Atlantic, have caused extensive drying trends in rainforests of western Amazonia, exerting water stress, tree mortality, biomass loss, and large-scale fire disturbance. In contrast, there have been no reports on large-scale disturbance in rainforests of west and central Africa, though being exposed to similar intensity of climate variability. Using data from Tropical Rainfall Mapping Mission (TRMM) (1999-2010), and time series of rainfall observations from meteorological stations (1971-2000), we show that both Amazonian and African rainforest experienced strong precipitation anomalies from 2005-2010. We monitored the response of forest to the climate variability by analyzing the canopy water content observed by SeaWinds Ku-band Scatterometer (QSCAT) (1999-2009) and found that more than 70 million ha of forests in western Amazonia experienced a strong water deficit during the dry season of 2005 and a closely corresponding decline in canopy backscatter that persisted until the next major drought in 2010. This decline in backscatter has been attributed to loss of canopy water content and large-scale tree mortality corroborated by ground and airborne observations. However, no strong impacts was observed on tropical forests of Africa, suggesting that the African rainforest may have more resilience to droughts. We tested this hypothesis by examining the seasonal rainfall patterns, maximum water deficit, and the surface temperature variations. Results show that there is a complex pattern of low annual rainfall, moderate seasonality, and lower surface temperature in Central Africa compared to Amazonia, indicating potentially a lower evapotranspiration circumventing strong water deficits

Response of Tropical Forests to Intense Climate Variability and Rainfall Anomaly of Last Decade

NASA Astrophysics Data System (ADS)

Saatchi, S. S.; Asefi Najafabady, S.

2011-12-01

During the last decade, strong precipitation anomalies resulted from increased sea surface temperature in the tropical Atlantic, have caused extensive drying trends in rainforests of western Amazonia, exerting water stress, tree mortality, biomass loss, and large-scale fire disturbance. In contrast, there have been no reports on large-scale disturbance in rainforests of west and central Africa, though being exposed to similar intensity of climate variability. Using data from Tropical Rainfall Mapping Mission (TRMM) (1999-2010), and time series of rainfall observations from meteorological stations (1971-2000), we show that both Amazonian and African rainforest experienced strong precipitation anomalies from 2005-2010. We monitored the response of forest to the climate variability by analyzing the canopy water content observed by SeaWinds Ku-band Scatterometer (QSCAT) (1999-2009) and found that more than 70 million ha of forests in western Amazonia experienced a strong water deficit during the dry season of 2005 and a closely corresponding decline in canopy backscatter that persisted until the next major drought in 2010. This decline in backscatter has been attributed to loss of canopy water content and large-scale tree mortality corroborated by ground and airborne observations. However, no strong impacts was observed on tropical forests of Africa, suggesting that the African rainforest may have more resilience to droughts. We tested this hypothesis by examining the seasonal rainfall patterns, maximum water deficit, and the surface temperature variations. Results show that there is a complex pattern of low annual rainfall, moderate seasonality, and lower surface temperature in Central Africa compared to Amazonia, indicating potentially a lower evapotranspiration circumventing strong water deficits.

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.

Fire activity as a function of fire–weather seasonal severity and antecedent climate across spatial scales in southern Europe and Pacific western USA

USGS Publications Warehouse

Urbieta, Itziar R.; Zavala, Gonzalo; Bedia, Joaquin; Gutierrez, Jose M.; San Miguel-Ayanz, Jesus; Camia, Andrea; Keeley, Jon E.; Moreno, Jose M.

2015-01-01

Climate has a strong influence on fire activity, varying across time and space. We analyzed the relationships between fire–weather conditions during the main fire season and antecedent water-balance conditions and fires in two Mediterranean-type regions with contrasted management histories: five southern countries of the European Union (EUMED)(all fires); the Pacific western coast of the USA (California and Oregon, PWUSA)(national forest fires). Total number of fires (≥1 ha), number of large fires (≥100 ha) and area burned were related to mean seasonal fire weather index (FWI), number of days over the 90th percentile of the FWI, and to the standardized precipitation-evapotranspiration index (SPEI) from the preceding 3 (spring) or 8 (autumn through spring) months. Calculations were made at three spatial aggregations in each area, and models related first-difference (year-to-year change) of fires and FWI/climate variables to minimize autocorrelation. An increase in mean seasonal FWI resulted in increases in the three fire variables across spatial scales in both regions. SPEI contributed little to explain fires, with few exceptions. Negative water-balance (dry) conditions from autumn through spring (SPEI8) were generally more important than positive conditions (moist) in spring (SPEI3), both of which contributed positively to fires. The R2 of the models generally improved with increasing area of aggregation. For total number of fires and area burned, the R2 of the models tended to decrease with increasing mean seasonal FWI. Thus, fires were more susceptible to change with climate variability in areas with less amenable conditions for fires (lower FWI) than in areas with higher mean FWI values. The relationships were similar in both regions, albeit weaker in PWUSA, probably due to the wider latitudinal gradient covered in PWUSA than in EUMED. The large variance explained by some of the models indicates that large-scale seasonal forecast could help anticipating fire activity in the investigated areas.

Fire activity as a function of fire-weather seasonal severity and antecedent climate across spatial scales in southern Europe and Pacific western USA

NASA Astrophysics Data System (ADS)

Urbieta, Itziar R.; Zavala, Gonzalo; Bedia, Joaquín; Gutiérrez, José M.; San Miguel-Ayanz, Jesús; Camia, Andrea; Keeley, Jon E.; Moreno, José M.

2015-11-01

Climate has a strong influence on fire activity, varying across time and space. We analyzed the relationships between fire-weather conditions during the main fire season and antecedent water-balance conditions and fires in two Mediterranean-type regions with contrasted management histories: five southern countries of the European Union (EUMED)(all fires); the Pacific western coast of the USA (California and Oregon, PWUSA)(national forest fires). Total number of fires (≥1 ha), number of large fires (≥100 ha) and area burned were related to mean seasonal fire weather index (FWI), number of days over the 90th percentile of the FWI, and to the standardized precipitation-evapotranspiration index (SPEI) from the preceding 3 (spring) or 8 (autumn through spring) months. Calculations were made at three spatial aggregations in each area, and models related first-difference (year-to-year change) of fires and FWI/climate variables to minimize autocorrelation. An increase in mean seasonal FWI resulted in increases in the three fire variables across spatial scales in both regions. SPEI contributed little to explain fires, with few exceptions. Negative water-balance (dry) conditions from autumn through spring (SPEI8) were generally more important than positive conditions (moist) in spring (SPEI3), both of which contributed positively to fires. The R2 of the models generally improved with increasing area of aggregation. For total number of fires and area burned, the R2 of the models tended to decrease with increasing mean seasonal FWI. Thus, fires were more susceptible to change with climate variability in areas with less amenable conditions for fires (lower FWI) than in areas with higher mean FWI values. The relationships were similar in both regions, albeit weaker in PWUSA, probably due to the wider latitudinal gradient covered in PWUSA than in EUMED. The large variance explained by some of the models indicates that large-scale seasonal forecast could help anticipating fire activity in the investigated areas.

Regional reanalysis without local data: Exploiting the downscaling paradigm

NASA Astrophysics Data System (ADS)

von Storch, Hans; Feser, Frauke; Geyer, Beate; Klehmet, Katharina; Li, Delei; Rockel, Burkhardt; Schubert-Frisius, Martina; Tim, Nele; Zorita, Eduardo

2017-08-01

This paper demonstrates two important aspects of regional dynamical downscaling of multidecadal atmospheric reanalysis. First, that in this way skillful regional descriptions of multidecadal climate variability may be constructed in regions with little or no local data. Second, that the concept of large-scale constraining allows global downscaling, so that global reanalyses may be completed by additions of consistent detail in all regions of the world. Global reanalyses suffer from inhomogeneities. However, their large-scale componenst are mostly homogeneous; Therefore, the concept of downscaling may be applied to homogeneously complement the large-scale state of the reanalyses with regional detail—wherever the condition of homogeneity of the description of large scales is fulfilled. Technically, this can be done by dynamical downscaling using a regional or global climate model, which's large scales are constrained by spectral nudging. This approach has been developed and tested for the region of Europe, and a skillful representation of regional weather risks—in particular marine risks—was identified. We have run this system in regions with reduced or absent local data coverage, such as Central Siberia, the Bohai and Yellow Sea, Southwestern Africa, and the South Atlantic. Also, a global simulation was computed, which adds regional features to prescribed global dynamics. Our cases demonstrate that spatially detailed reconstructions of the climate state and its change in the recent three to six decades add useful supplementary information to existing observational data for midlatitude and subtropical regions of the world.

Relationships between large-scale circulation patterns and carbon dioxide exchange by a deciduous forest

NASA Astrophysics Data System (ADS)

Zhang, Jingyong; Wu, Lingyun; Huang, Gang; Notaro, Michael

2011-02-01

In this study, we focus on a deciduous forest in central Massachusetts and investigate the relationships between global climate indices and CO2 exchange using eddy-covariance flux measurements from 1992 to 2007. Results suggest that large-scale circulation patterns influence the annual CO2 exchange in the forest through their effects on the local surface climate. Annual gross ecosystem exchange (GEE) in the forest is closely associated with spring El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO), previous fall Atlantic Multidecadal Oscillation (AMO), and previous winter East Pacific-North Pacific (EP-NP) pattern. Annual net ecosystem exchange (NEE) responds to previous fall AMO and PDO, while annual respiration (R) is impacted by previous fall ENSO and Pacific/North American Oscillation (PNA). Regressions based on these relationships are developed to simulate the annual GEE, NEE, and R. To avoid problems of multicollinearity, we compute a "Composite Index for GEE (CIGEE)" based on a linear combination of spring ENSO and PDO, fall AMO, and winter EP-NP and a "Composite Index for R (CIR)" based on a linear combination of fall ENSO and PNA. CIGEE, CIR, and fall AMO and PDO can explain 41, 27, and 40% of the variance of the annual GEE, R, and NEE, respectively. We further apply the methodology to two other northern midlatitude forests and find that interannual variabilities in NEE of the two forests are largely controlled by large-scale circulation patterns. This study suggests that global climate indices provide the potential for predicting CO2 exchange variability in the northern midlatitude forests.

Impacts of large-scale climatic disturbances on the terrestrial carbon cycle.

PubMed

Erbrecht, Tim; Lucht, Wolfgang

2006-07-27

The amount of carbon dioxide in the atmosphere steadily increases as a consequence of anthropogenic emissions but with large interannual variability caused by the terrestrial biosphere. These variations in the CO2 growth rate are caused by large-scale climate anomalies but the relative contributions of vegetation growth and soil decomposition is uncertain. We use a biogeochemical model of the terrestrial biosphere to differentiate the effects of temperature and precipitation on net primary production (NPP) and heterotrophic respiration (Rh) during the two largest anomalies in atmospheric CO2 increase during the last 25 years. One of these, the smallest atmospheric year-to-year increase (largest land carbon uptake) in that period, was caused by global cooling in 1992/93 after the Pinatubo volcanic eruption. The other, the largest atmospheric increase on record (largest land carbon release), was caused by the strong El Niño event of 1997/98. We find that the LPJ model correctly simulates the magnitude of terrestrial modulation of atmospheric carbon anomalies for these two extreme disturbances. The response of soil respiration to changes in temperature and precipitation explains most of the modelled anomalous CO2 flux. Observed and modelled NEE anomalies are in good agreement, therefore we suggest that the temporal variability of heterotrophic respiration produced by our model is reasonably realistic. We therefore conclude that during the last 25 years the two largest disturbances of the global carbon cycle were strongly controlled by soil processes rather then the response of vegetation to these large-scale climatic events.

Impacts of large-scale climatic disturbances on the terrestrial carbon cycle

PubMed Central

Erbrecht, Tim; Lucht, Wolfgang

2006-01-01

Background The amount of carbon dioxide in the atmosphere steadily increases as a consequence of anthropogenic emissions but with large interannual variability caused by the terrestrial biosphere. These variations in the CO2 growth rate are caused by large-scale climate anomalies but the relative contributions of vegetation growth and soil decomposition is uncertain. We use a biogeochemical model of the terrestrial biosphere to differentiate the effects of temperature and precipitation on net primary production (NPP) and heterotrophic respiration (Rh) during the two largest anomalies in atmospheric CO2 increase during the last 25 years. One of these, the smallest atmospheric year-to-year increase (largest land carbon uptake) in that period, was caused by global cooling in 1992/93 after the Pinatubo volcanic eruption. The other, the largest atmospheric increase on record (largest land carbon release), was caused by the strong El Niño event of 1997/98. Results We find that the LPJ model correctly simulates the magnitude of terrestrial modulation of atmospheric carbon anomalies for these two extreme disturbances. The response of soil respiration to changes in temperature and precipitation explains most of the modelled anomalous CO2 flux. Conclusion Observed and modelled NEE anomalies are in good agreement, therefore we suggest that the temporal variability of heterotrophic respiration produced by our model is reasonably realistic. We therefore conclude that during the last 25 years the two largest disturbances of the global carbon cycle were strongly controlled by soil processes rather then the response of vegetation to these large-scale climatic events. PMID:16930463

Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth

NASA Astrophysics Data System (ADS)

Alessandri, Andrea; Catalano, Franco; De Felice, Matteo; Van Den Hurk, Bart; Doblas Reyes, Francisco; Boussetta, Souhail; Balsamo, Gianpaolo; Miller, Paul

2016-04-01

The EC-Earth earth system model has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning retrospective predictions at the decadal (5-years), seasonal and sub-seasonal time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and sub-seasonal time-scales. Significant improvements of the prediction of 2m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste Brazil and South East Asia, mainly related to improved performance in the surface evapotranspiration.

Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth

NASA Astrophysics Data System (ADS)

Alessandri, A.; Catalano, F.; De Felice, M.; van den Hurk, B.; Doblas-Reyes, F. J.; Boussetta, S.; Balsamo, G.; Miller, P. A.

2016-12-01

The European consortium earth system model (EC-Earth; http://www.ec-earth.org) has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (20th Century) simulations and retrospective predictions to the decadal (5-years), seasonal and weather time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste Brazil and South East Asia, mainly related to improved performance in the surface evapotranspiration.

Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth

NASA Astrophysics Data System (ADS)

Alessandri, Andrea; Catalano, Franco; De Felice, Matteo; Van Den Hurk, Bart; Doblas Reyes, Francisco; Boussetta, Souhail; Balsamo, Gianpaolo; Miller, Paul A.

2017-08-01

The EC-Earth earth system model has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (twentieth century) simulations and retrospective predictions to the decadal (5-years), seasonal and weather time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2 m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste Brazil and South East Asia, mainly related to improved performance in the surface evapotranspiration.

Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth

NASA Astrophysics Data System (ADS)

Alessandri, Andrea; Catalano, Franco; De Felice, Matteo; Van Den Hurk, Bart; Doblas Reyes, Francisco; Boussetta, Souhail; Balsamo, Gianpaolo; Miller, Paul A.

2017-04-01

The EC-Earth earth system model has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (20th Century) simulations and retrospective predictions to the decadal (5-years), seasonal and weather time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste Brazil and South East Asia, mainly related to improved performance in the surface evapotranspiration.

Cholera and shigellosis in Bangladesh: similarities and differences in population dynamics under climate forcing

NASA Astrophysics Data System (ADS)

Pascual, M.; Cash, B.; Reiner, R.; King, A.; Emch, M.; Yunus, M.; Faruque, A. S.

2012-12-01

The influence of climate variability on the population dynamics of infectious diseases is considered a large scale, regional, phenomenon, and as such, has been previously addressed for cholera with temporal models that do not incorporate fine-scale spatial structure. In our previous work, evidence for a role of ENSO (El Niño Southern Oscillation) on cholera in Bangladesh was elucidated, and shown to influence the regional climate through precipitation. With a probabilistic spatial model for cholera dynamics in the megacity of Dhaka, we found that the action of climate variability (ENSO and flooding) is localized: there is a climate-sensitive urban core that acts to propagate risk to the rest of the city. Here, we consider long-term surveillance data for shigellosis, another diarrheal disease that coexists with cholera in Bangladesh. We compare the patterns of association with climate variables for these two diseases in a rural setting, as well as the spatial structure in their spatio-temporal dynamics in an urban one. Evidence for similar patterns is presented, and discussed in the context of the differences in the routes of transmission of the two diseases and the proposed role of an environmental reservoir in cholera. The similarities provide evidence for a more general influence of hydrology and of socio-economic factors underlying human susceptibility and sanitary conditions.

Evaluating the fidelity of CMIP5 models in producing large-scale meteorological patterns over the Northwestern United States

NASA Astrophysics Data System (ADS)

Lintner, B. R.; Loikith, P. C.; Pike, M.; Aragon, C.

2017-12-01

Climate change information is increasingly required at impact-relevant scales. However, most state-of-the-art climate models are not of sufficiently high spatial resolution to resolve features explicitly at such scales. This challenge is particularly acute in regions of complex topography, such as the Pacific Northwest of the United States. To address this scale mismatch problem, we consider large-scale meteorological patterns (LSMPs), which can be resolved by climate models and associated with the occurrence of local scale climate and climate extremes. In prior work, using self-organizing maps (SOMs), we computed LSMPs over the northwestern United States (NWUS) from daily reanalysis circulation fields and further related these to the occurrence of observed extreme temperatures and precipitation: SOMs were used to group LSMPs into 12 nodes or clusters spanning the continuum of synoptic variability over the regions. Here this observational foundation is utilized as an evaluation target for a suite of global climate models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5). Evaluation is performed in two primary ways. First, daily model circulation fields are assigned to one of the 12 reanalysis nodes based on minimization of the mean square error. From this, a bulk model skill score is computed measuring the similarity between the model and reanalysis nodes. Next, SOMs are applied directly to the model output and compared to the nodes obtained from reanalysis. Results reveal that many of the models have LSMPs analogous to the reanalysis, suggesting that the models reasonably capture observed daily synoptic states.

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.

Interannual and spatial variability of maple syrup yield as related to climatic factors

PubMed Central

Houle, Daniel

2014-01-01

Sugar maple syrup production is an important economic activity for eastern Canada and the northeastern United States. Since annual variations in syrup yield have been related to climate, there are concerns about the impacts of climatic change on the industry in the upcoming decades. Although the temporal variability of syrup yield has been studied for specific sites on different time scales or for large regions, a model capable of accounting for both temporal and regional differences in yield is still lacking. In the present study, we studied the factors responsible for interregional and interannual variability in maple syrup yield over the 2001–2012 period, by combining the data from 8 Quebec regions (Canada) and 10 U.S. states. The resulting model explained 44.5% of the variability in yield. It includes the effect of climatic conditions that precede the sapflow season (variables from the previous growing season and winter), the effect of climatic conditions during the current sapflow season, and terms accounting for intercountry and temporal variability. Optimal conditions for maple syrup production appear to be spatially restricted by less favourable climate conditions occurring during the growing season in the north, and in the south, by the warmer winter and earlier spring conditions. This suggests that climate change may favor maple syrup production northwards, while southern regions are more likely to be negatively affected by adverse spring conditions. PMID:24949244

Improved spectral comparisons of paleoclimate models and observations via proxy system modeling: Implications for multi-decadal variability

NASA Astrophysics Data System (ADS)

Dee, S. G.; Parsons, L. A.; Loope, G. R.; Overpeck, J. T.; Ault, T. R.; Emile-Geay, J.

2017-10-01

The spectral characteristics of paleoclimate observations spanning the last millennium suggest the presence of significant low-frequency (multi-decadal to centennial scale) variability in the climate system. Since this low-frequency climate variability is critical for climate predictions on societally-relevant scales, it is essential to establish whether General Circulation models (GCMs) are able to simulate it faithfully. Recent studies find large discrepancies between models and paleoclimate data at low frequencies, prompting concerns surrounding the ability of GCMs to predict long-term, high-magnitude variability under greenhouse forcing (Laepple and Huybers, 2014a, 2014b). However, efforts to ground climate model simulations directly in paleoclimate observations are impeded by fundamental differences between models and the proxy data: proxy systems often record a multivariate and/or nonlinear response to climate, precluding a direct comparison to GCM output. In this paper we bridge this gap via a forward proxy modeling approach, coupled to an isotope-enabled GCM. This allows us to disentangle the various contributions to signals embedded in ice cores, speleothem calcite, coral aragonite, tree-ring width, and tree-ring cellulose. The paper addresses the following questions: (1) do forward-modeled ;pseudoproxies; exhibit variability comparable to proxy data? (2) if not, which processes alter the shape of the spectrum of simulated climate variability, and are these processes broadly distinguishable from climate? We apply our method to representative case studies, and broaden these insights with an analysis of the PAGES2k database (PAGES2K Consortium, 2013). We find that current proxy system models (PSMs) can help resolve model-data discrepancies on interannual to decadal timescales, but cannot account for the mismatch in variance on multi-decadal to centennial timescales. We conclude that, specific to this set of PSMs and isotope-enabled model, the paleoclimate record may exhibit larger low-frequency variability than GCMs currently simulate, indicative of incomplete physics and/or forcings.

Impacts of Changing Climatic Drivers and Land use features on Future Stormwater Runoff in the Northwest Florida Basin: A Large-Scale Hydrologic Modeling Assessment

NASA Astrophysics Data System (ADS)

Khan, M.; Abdul-Aziz, O. I.

2017-12-01

Potential changes in climatic drivers and land cover features can significantly influence the stormwater budget in the Northwest Florida Basin. We investigated the hydro-climatic and land use sensitivities of stormwater runoff by developing a large-scale process-based rainfall-runoff model for the large basin by using the EPA Storm Water Management Model (SWMM 5.1). Climatic and hydrologic variables, as well as land use/cover features were incorporated into the model to account for the key processes of coastal hydrology and its dynamic interactions with groundwater and sea levels. We calibrated and validated the model by historical daily streamflow observations during 2009-2012 at four major rivers in the basin. Downscaled climatic drivers (precipitation, temperature, solar radiation) projected by twenty GCMs-RCMs under CMIP5, along with the projected future land use/cover features were also incorporated into the model. The basin storm runoff was then simulated for the historical (2000s = 1976-2005) and two future periods (2050s = 2030-2059, and 2080s = 2070-2099). Comparative evaluation of the historical and future scenarios leads to important guidelines for stormwater management in Northwest Florida and similar regions under a changing climate and environment.

Trees tell of past climates: but are they speaking less clearly today?

PubMed Central

Briffa, K. R.

1998-01-01

The annual growth of trees, as represented by a variety of ring-width, densitometric, or chemical parameters, represents a combined record of different environmental forcings, one of which is climate. Along with climate, relatively large-scale positive growth influences such as hypothesized 'fertilization' due to increased levels of atmospheric carbon dioxide or various nitrogenous compounds, or possibly deleterious effects of 'acid rain' or increased ultra-violet radiation, might all be expected to exert some influence on recent tree growth rates. Inferring the details of past climate variability from tree-ring data remains a largely empirical exercise, but one that goes hand-in-hand with the development of techniques that seek to identify and isolate the confounding influence of local and larger-scale non-climatic factors. By judicious sampling, and the use of rigorous statistical procedures, dendroclimatology has provided unique insight into the nature of past climate variability, but most significantly at interannual, decadal, and centennial timescales. Here, examples are shown that illustrate the reconstruction of annually resolved patterns of past summer temperature around the Northern Hemisphere, as well as some more localized reconstructions, but ones which span 1000 years or more. These data provide the means of exploring the possible role of different climate forcings; for example, they provide evidence of the large-scale effects of explosive volcanic eruptions on regional and hemispheric temperatures during the last 400 years. However, a dramatic change in the sensitivity of hemispheric tree-growth to temperature forcing has become apparent during recent decades, and there is additional evidence of major tree-growth (and hence, probably, ecosystem biomass) increases in the northern boreal forests, most clearly over the last century. These possibly anthropogenically related changes in the ecology of tree growth have important implications for modelling future atmospheric CO2 concentrations. Also, where dendroclimatology is concerned to reconstruct longer (increasingly above centennial) temperature histories, such alterations of 'normal' (pre-industrial) tree-growth rates and climate-growth relationships must be accounted for in our attempts to translate the evidence of past tree growth changes.

Towards a Unified Framework in Hydroclimate Extremes Prediction in Changing Climate

NASA Astrophysics Data System (ADS)

Moradkhani, H.; Yan, H.; Zarekarizi, M.; Bracken, C.

2016-12-01

Spatio-temporal analysis and prediction of hydroclimate extremes are of paramount importance in disaster mitigation and emergency management. The IPCC special report on managing the risks of extreme events and disasters emphasizes that the global warming would change the frequency, severity, and spatial pattern of extremes. In addition to climate change, land use and land cover changes also influence the extreme characteristics at regional scale. Therefore, natural variability and anthropogenic changes to the hydroclimate system result in nonstationarity in hydroclimate variables. In this presentation recent advancements in developing and using Bayesian approaches to account for non-stationarity in hydroclimate extremes are discussed. Also, implications of these approaches in flood frequency analysis, treatment of spatial dependence, the impact of large-scale climate variability, the selection of cause-effect covariates, with quantification of model errors in extreme prediction is explained. Within this framework, the applicability and usefulness of the ensemble data assimilation for extreme flood predictions is also introduced. Finally, a practical and easy to use approach for better communication with decision-makers and emergency managers is presented.

Modeling responses of large-river fish populations to global climate change through downscaling and incorporation of predictive uncertainty

USGS Publications Warehouse

Wildhaber, Mark L.; Wikle, Christopher K.; Anderson, Christopher J.; Franz, Kristie J.; Moran, Edward H.; Dey, Rima; Mader, Helmut; Kraml, Julia

2012-01-01

Climate change operates over a broad range of spatial and temporal scales. Understanding its effects on ecosystems requires multi-scale models. For understanding effects on fish populations of riverine ecosystems, climate predicted by coarse-resolution Global Climate Models must be downscaled to Regional Climate Models to watersheds to river hydrology to population response. An additional challenge is quantifying sources of uncertainty given the highly nonlinear nature of interactions between climate variables and community level processes. We present a modeling approach for understanding and accomodating uncertainty by applying multi-scale climate models and a hierarchical Bayesian modeling framework to Midwest fish population dynamics and by linking models for system components together by formal rules of probability. The proposed hierarchical modeling approach will account for sources of uncertainty in forecasts of community or population response. The goal is to evaluate the potential distributional changes in an ecological system, given distributional changes implied by a series of linked climate and system models under various emissions/use scenarios. This understanding will aid evaluation of management options for coping with global climate change. In our initial analyses, we found that predicted pallid sturgeon population responses were dependent on the climate scenario considered.

Trends and natural variability of North American spring onset as evaluated by a new gridded dataset of spring indices

USGS Publications Warehouse

Ault, Toby R.; Schwartz, Mark D.; Zurita-Milla, Raul; Weltzin, Jake F.; Betancourt, Julio L.

2015-01-01

Climate change is expected to modify the timing of seasonal transitions this century, impacting wildlife migrations, ecosystem function, and agricultural activity. Tracking seasonal transitions in a consistent manner across space and through time requires indices that can be used for monitoring and managing biophysical and ecological systems during the coming decades. Here a new gridded dataset of spring indices is described and used to understand interannual, decadal, and secular trends across the coterminous United States. This dataset is derived from daily interpolated meteorological data, and the results are compared with historical station data to ensure the trends and variations are robust. Regional trends in the first leaf index range from 20.8 to 21.6 days decade21, while first bloom index trends are between20.4 and 21.2 for most regions. However, these trends are modulated by interannual to multidecadal variations, which are substantial throughout the regions considered here. These findings emphasize the important role large-scale climate modes of variability play in modulating spring onset on interannual to multidecadal time scales. Finally, there is some potential for successful subseasonal forecasts of spring onset, as indices from most regions are significantly correlated with antecedent large-scale modes of variability.

Synchronous multi-decadal climate variability of the whole Pacific areas revealed in tree rings since 1567

NASA Astrophysics Data System (ADS)

Fang, Keyan; Cook, Edward; Guo, Zhengtang; Chen, Deliang; Ou, Tinghai; Zhao, Yan

2018-02-01

Oceanic and atmospheric patterns play a crucial role in modulating climate variability from interannual to multi-decadal timescales by causing large-scale co-varying climate changes. The brevity of the existing instrumental records hinders the ability to recognize climate patterns before the industrial era, which can be alleviated using proxies. Unfortunately, proxy based reconstructions of oceanic and atmospheric modes of the past millennia often have modest agreements with each other before the instrumental period, raising questions about the robustness of the reconstructions. To ensure the stability of climate signals in proxy data through time, we first identified tree-ring datasets from distant regions containing coherent variations in Asia and North America, and then interpreted their climate information. We found that the multi-decadal covarying climate patterns of the middle and high latitudinal regions around the northern Pacific Ocean agreed quite well with the climate reconstructions of the tropical and southern Pacific areas. This indicates a synchronous variability at the multi-decadal timescale of the past 430 years for the entire Pacific Ocean. This pattern is closely linked to the dominant mode of the Pacific sea surface temperature (SST) after removing the warming trend. This Pacific multi-decadal SST variability resembles the Interdecadal Pacific Oscillation.

Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America

PubMed Central

Wang, Tongli; Hamann, Andreas; Spittlehouse, Dave; Carroll, Carlos

2016-01-01

Large volumes of gridded climate data have become available in recent years including interpolated historical data from weather stations and future predictions from general circulation models. These datasets, however, are at various spatial resolutions that need to be converted to scales meaningful for applications such as climate change risk and impact assessments or sample-based ecological research. Extracting climate data for specific locations from large datasets is not a trivial task and typically requires advanced GIS and data management skills. In this study, we developed a software package, ClimateNA, that facilitates this task and provides a user-friendly interface suitable for resource managers and decision makers as well as scientists. The software locally downscales historical and future monthly climate data layers into scale-free point estimates of climate values for the entire North American continent. The software also calculates a large number of biologically relevant climate variables that are usually derived from daily weather data. ClimateNA covers 1) 104 years of historical data (1901–2014) in monthly, annual, decadal and 30-year time steps; 2) three paleoclimatic periods (Last Glacial Maximum, Mid Holocene and Last Millennium); 3) three future periods (2020s, 2050s and 2080s); and 4) annual time-series of model projections for 2011–2100. Multiple general circulation models (GCMs) were included for both paleo and future periods, and two representative concentration pathways (RCP4.5 and 8.5) were chosen for future climate data. PMID:27275583

Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America.

PubMed

Wang, Tongli; Hamann, Andreas; Spittlehouse, Dave; Carroll, Carlos

2016-01-01

Large volumes of gridded climate data have become available in recent years including interpolated historical data from weather stations and future predictions from general circulation models. These datasets, however, are at various spatial resolutions that need to be converted to scales meaningful for applications such as climate change risk and impact assessments or sample-based ecological research. Extracting climate data for specific locations from large datasets is not a trivial task and typically requires advanced GIS and data management skills. In this study, we developed a software package, ClimateNA, that facilitates this task and provides a user-friendly interface suitable for resource managers and decision makers as well as scientists. The software locally downscales historical and future monthly climate data layers into scale-free point estimates of climate values for the entire North American continent. The software also calculates a large number of biologically relevant climate variables that are usually derived from daily weather data. ClimateNA covers 1) 104 years of historical data (1901-2014) in monthly, annual, decadal and 30-year time steps; 2) three paleoclimatic periods (Last Glacial Maximum, Mid Holocene and Last Millennium); 3) three future periods (2020s, 2050s and 2080s); and 4) annual time-series of model projections for 2011-2100. Multiple general circulation models (GCMs) were included for both paleo and future periods, and two representative concentration pathways (RCP4.5 and 8.5) were chosen for future climate data.

The Calibration and Characterization of Earth Remote Sensing and Environmental Monitoring Instruments. Chapter 10

NASA Technical Reports Server (NTRS)

Butler, James J.; Johnson, B. Carol; Barnes, Robert A.

2005-01-01

The use of remote sensing instruments on orbiting satellite platforms in the study of Earth Science and environmental monitoring was officially inaugurated with the April 1, 1960 launch of the Television Infrared Observation Satellite (TIROS) [1]. The first TIROS accommodated two television cameras and operated for only 78 days. However, the TIROS program, in providing in excess of 22,000 pictures of the Earth, achieved its primary goal of providing Earth images from a satellite platform to aid in identifying and monitoring meteorological processes. This marked the beginning of what is now over four decades of Earth observations from satellite platforms. reflected and emitted radiation from the Earth using instruments on satellite platforms. These measurements are input to climate models, and the model results are analyzed in an effort to detect short and long-term changes and trends in the Earth's climate and environment, to identify the cause of those changes, and to predict or influence future changes. Examples of short-term climate change events include the periodic appearance of the El Nino-Southern Oscillation (ENSO) in the tropical Pacific Ocean [2] and the spectacular eruption of Mount Pinatubo on the Philippine island of Luzon in 1991. Examples of long term climate change events, which are more subtle to detect, include the destruction of coral reefs, the disappearance of glaciers, and global warming. Climatic variability can be both large and small scale and can be caused by natural or anthropogenic processes. The periodic El Nino event is an example of a natural process which induces significant climatic variability over a wide range of the Earth. A classic example of a large scale anthropogenic influence on climate is the well-documented rapid increase of atmospheric carbon dioxide occurring since the beginning of the Industrial Revolution [3]. An example of the study of a small-scale anthropogenic influence in climate variability is the Atlanta Land-use Analysis Temperature and Air-quality (ATLANTA) project [4]. This project has found that the replacement of trees and vegetation with concrete and asphalt in Atlanta, Georgia, and its environs has created a microclimate capable of producing wind and thunderstorms. A key objective of climate research is to be able to distinguish the natural versus human roles in climate change and to clearly communicate those findings to those who shape and direct environmental policy.

The Sun as a variable star: Solar and stellar irradiance variations; Colloquium of the International Astronomical Union, 143rd, Boulder, CO, Jun. 20-25, 1993

NASA Technical Reports Server (NTRS)

Pap, Judit M. (Editor); Froehlich, Claus (Editor); Hudson, Hugh S. (Editor); Tobiska, W. Kent (Editor)

1994-01-01

Variations in solar and stellar irradiances have long been of interest. An International Astronomical Union (IAU) colloquium reviewed such relevant subjects as observations, theoretical interpretations, and empirical and physical models, with a special emphasis on climatic impact of solar irradiance variability. Specific topics discussed included: (1) General Reviews on Observations of Solar and Stellar Irradiance Variability; (2) Observational Programs for Solar and Stellar Irradiance Variability; (3) Variability of Solar and Stellar Irradiance Related to the Network, Active Regions (Sunspots and Plages), and Large-Scale Magnetic Structures; (4) Empirical Models of Solar Total and Spectral Irradiance Variability; (5) Solar and Stellar Oscillations, Irradiance Variations and their Interpretations; and (6) The Response of the Earth's Atmosphere to Solar Irradiance Variations and Sun-Climate Connections.

Importance of scale, land cover, and weather on the abundance of bird species in a managed forest

USGS Publications Warehouse

Grinde, Alexis R.; Hiemi, Gerald J.; Sturtevant, Brian R.; Panci, Hannah; Thogmartin, Wayne E.; Wolter, Peter

2017-01-01

Climate change and habitat loss are projected to be the two greatest drivers of biodiversity loss over the coming century. While public lands have the potential to increase regional resilience of bird populations to these threats, long-term data are necessary to document species responses to changes in climate and habitat to better understand population vulnerabilities. We used generalized linear mixed models to determine the importance of stand-level characteristics, multi-scale land cover, and annual weather factors to the abundance of 61 bird species over a 20-year time frame in Chippewa National Forest, Minnesota, USA. Of the 61 species modeled, we were able to build final models with R-squared values that ranged from 26% to 69% for 37 species; the remaining 24 species models had issues with convergence or low explanatory power (R-squared < 20%). Models for the 37 species show that stand-level characteristics, land cover factors, and annual weather effects on species abundance were species-specific and varied within guilds. Forty-one percent of the final species models included stand-level characteristics, 92% included land cover variables at the 200 m scale, 51% included land cover variables at the 500 m scale, 46% included land cover variables at the 1000 m scale, and 38% included weather variables in best models. Three species models (8%) included significant weather and land cover interaction terms. Overall, models indicated that aboveground tree biomass and land cover variables drove changes in the majority of species. Of those species models including weather variables, more included annual variation in precipitation or drought than temperature. Annual weather variability was significantly more likely to impact abundance of species associated with deciduous forests and bird species that are considered climate sensitive. The long-term data and models we developed are particularly suited to informing science-based adaptive forest management plans that incorporate climate sensitivity, aim to conserve large areas of forest habitat, and maintain an historical mosaic of cover types for conserving a diverse and abundant avian assemblage.

Climate forecasting services: coming down from the ivory tower

NASA Astrophysics Data System (ADS)

Doblas-Reyes, F. J.; Caron, L. P.; Cortesi, N.; Soret, A.; Torralba, V.; Turco, M.; González Reviriego, N.; Jiménez, I.; Terrado, M.

2016-12-01

Subseasonal-to-seasonal (S2S) climate forecasts are increasingly used across a range of application areas (energy, water management, agriculture, health, insurance) through tailored services using the climate services paradigm. In this contribution we show the value of climate forecasting services through several examples of their application in the energy, reinsurance and agriculture sectors. Climate services aim at making climate information action oriented. In a climate forecasting context the task starts with the identification of climate variables, thresholds and events relevant to the users. These elements are then analysed to determine whether they can be both reliably and skilfully predicted at appropriate time scales. In this contribution we assess climate predictions of precipitation, temperature and wind indices from state-of-the-art operational multi-model forecast systems and if they respond to the expectations and requests from a range of users. This requires going beyond the more traditional assessment of monthly mean values to include assessments of global forecast quality of the frequency of warm, cold, windy and wet extremes (e.g. [1], [2]), as well as of using tools like the Euro-Atlantic weather regimes [3]. The forecast quality of extremes is generally similar to or slightly lower than that of monthly or seasonal averages, but offers a kind of information closer to what some users require. In addition to considering local climate variables, we also explore the use of large-scale climate indices, such as ENSO and NAO, that are associated with large regional synchronous variations of wind or tropical storm frequency. These indices help illustrating the relative merits of climate forecast information to users and are the cornerstone of climate stories that engage them in the co-production of climate information. [1] Doblas-Reyes et al, WIREs, 2013 [2] Pepler et al, Weather and Climate Extremes, 2015 [3] Pavan and Doblas-Reyes, Clim Dyn, 2013

Large Scale Relationship between Aquatic Insect Traits and Climate.

PubMed

Bhowmik, Avit Kumar; Schäfer, Ralf B

2015-01-01

Climate is the predominant environmental driver of freshwater assemblage pattern on large spatial scales, and traits of freshwater organisms have shown considerable potential to identify impacts of climate change. Although several studies suggest traits that may indicate vulnerability to climate change, the empirical relationship between freshwater assemblage trait composition and climate has been rarely examined on large scales. We compared the responses of the assumed climate-associated traits from six grouping features to 35 bioclimatic indices (~18 km resolution) for five insect orders (Diptera, Ephemeroptera, Odonata, Plecoptera and Trichoptera), evaluated their potential for changing distribution pattern under future climate change and identified the most influential bioclimatic indices. The data comprised 782 species and 395 genera sampled in 4,752 stream sites during 2006 and 2007 in Germany (~357,000 km² spatial extent). We quantified the variability and spatial autocorrelation in the traits and orders that are associated with the combined and individual bioclimatic indices. Traits of temperature preference grouping feature that are the products of several other underlying climate-associated traits, and the insect order Ephemeroptera exhibited the strongest response to the bioclimatic indices as well as the highest potential for changing distribution pattern. Regarding individual traits, insects in general and ephemeropterans preferring very cold temperature showed the highest response, and the insects preferring cold and trichopterans preferring moderate temperature showed the highest potential for changing distribution. We showed that the seasonal radiation and moisture are the most influential bioclimatic aspects, and thus changes in these aspects may affect the most responsive traits and orders and drive a change in their spatial distribution pattern. Our findings support the development of trait-based metrics to predict and detect climate-related changes of freshwater assemblages.

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.

Constraints on Variability of Brightness and Surface Magnetism on Time Scales of Decades to Centuries in the Sun and Sun-Like Stars: A Source of Potential Terrestrial Climate Variability

NASA Technical Reports Server (NTRS)

Baliunas, Sallie L.; Sharber, James (Technical Monitor)

2001-01-01

These four points summarize our work to date. (1) Conciliation of solar and stellar photometric variability. Previous research by us and colleagues suggested that the Sun might at present be showing unusually low photometric variability compared to other sun-like stars. Those early results would question the suitability of the technique of using sun-like stars as proxies for solar irradiance change on time scales of decades to centuries. However, our results indicate the contrary: the Sun's observed short-term (seasonal) and longterm (year-to-year) brightness variations closely agree with observed brightness variations in stars of similar mass and age. (2) We have demonstrated an inverse correlation between the global temperature of the terrestrial lower troposphere, inferred from the NASA Microwave Sounding Unit (MSU) radiometers, and the total area of the Sun covered by coronal holes from January 1979 to present (up to May 2000). Variable fluxes of either solar charged particles or cosmic rays, or both, may influence the terrestrial tropospheric temperature. The geographical pattern of the correlation is consistent with our interpretation of an extra-terrestrial charged particle forcing. (3) Possible climate mechanism amplifying the impact of solar ultraviolet irradiance variations. The key points of our proposed climate hypersensitivity mechanism are: (a) The Sun is more variable in the UV (ultraviolet) than in the visible. However, the increased UV irradiance is mainly absorbed in the lower stratosphere/upper troposphere rather than at the surface. (b) Absorption in the stratosphere raises the temperature moderately around the vicinity of the tropopause, and tends to stabilize the atmosphere against vertical convective/diffusive transport, thus decreasing the flux of heat and moisture carried upward from surface. (c) The decrease in the upward convection of heat and moisture tends to raise the surface temperature because a drier upper atmosphere becomes less cloudy, which in turn allows more solar radiation to reach the Earth's surface. (4) Natural variability in an ocean-atmosphere climate model. We use a 14-region, 6-layer, global thermo-hydrodynamic ocean-atmosphere model to study natural climate variability. All the numerical experiments were performed with no change in the prescribed external boundary conditions (except for the seasonal cycle of the Sun's tilt angle). Therefore, the observed inter-annual variability is of an internal kind. The model results are helpful toward the understanding of the role of nonlinearity in climate change. We have demonstrated a range of possible climate behaviors using our newly developed ocean-atmosphere model. These include climate configurations with no interannual variability, with multi-year periodicities, with continuous chaos, or with chaotically occuring transitions between two discrete substrates. These possible modes of climate behavior are all possible for the real climate, as well as the model. We have shown that small temporary climate influences can trigger shifts both in the mean climate, and among these different types of behavior. Such shifts are not only theoretically plausible, as shown here and elsewhere; they are omnipresent in the climate record on time scales from several years to the age of the Earth. This has two apparently opposite implications for the possibility of anthropogenic global warming. First, any warming which might occur as a result of human influence would be only a fraction of the small-to-large unpredictable natural changes and changes which result from other external causes. On the other hand, small temporary influences such as human influence do have the potential of causing large permanent shifts in mean climate and interannual variability.

Patterns of variations in large pelagic fish: A comparative approach between the Indian and the Atlantic Oceans

NASA Astrophysics Data System (ADS)

Corbineau, A.; Rouyer, T.; Fromentin, J.-M.; Cazelles, B.; Fonteneau, A.; Ménard, F.

2010-07-01

Catch data of large pelagic fish such as tuna, swordfish and billfish are highly variable ranging from short to long term. Based on fisheries data, these time series are noisy and reflect mixed information on exploitation (targeting, strategy, fishing power), population dynamics (recruitment, growth, mortality, migration, etc.), and environmental forcing (local conditions or dominant climate patterns). In this work, we investigated patterns of variation of large pelagic fish (i.e. yellowfin tuna, bigeye tuna, swordfish and blue marlin) in Japanese longliners catch data from 1960 to 2004. We performed wavelet analyses on the yearly time series of each fish species in each biogeographic province of the tropical Indian and Atlantic Oceans. In addition, we carried out cross-wavelet analyses between these biological time series and a large-scale climatic index, i.e. the Southern Oscillation Index (SOI). Results showed that the biogeographic province was the most important factor structuring the patterns of variability of Japanese catch time series. Relationships between the SOI and the fish catches in the Indian and Atlantic Oceans also pointed out the role of climatic variability for structuring patterns of variation of catch time series. This work finally confirmed that Japanese longline CPUE data poorly reflect the underlying population dynamics of tunas.

Poleward energy transport: is the standard definition physically relevant at all time scales?

NASA Astrophysics Data System (ADS)

Liang, Minyi; Czaja, Arnaud; Graversen, Rune; Tailleux, Remi

2018-03-01

Poleward energy transport in the atmosphere and oceans constitutes an important branch of the global energy budget, and its role in the climate system has been the subject of many studies. In the atmosphere, the transport is affected by "eddies" and large scale meridional cells, both with zero net mass transport across latitude circles, but also partly by processes associated with a net transport of mass across latitude circles. The latter must cease to operate in steady state, but they may be significant when time variability of the heat budget is considered. Indeed, examination of reanalysis data on short (daily to monthly) timescales shows that mass variations on these timescales result in surprisingly large fluctuations (in excess of 10^{15} W = 1 PW) in the poleward heat transport. These fluctuations are referred to as "extensive", for they primarily alter the mass integrated energy of the region considered, but not its averaged value. It is suggested that extensive fluctuations mask more meaningful climate signals present in the heat transport variability on monthly and interannual timescales, and a new formulation is proposed to isolate the latter. This new formulation is applied successfully to reanalysis data and climate model simulations.

Poleward Energy Transport: Is the Standard Definition Physically Relevant at All Time Scales?

NASA Astrophysics Data System (ADS)

Liang, M.; Czaja, A.; Graversen, R.; Tailleux, R.

2017-12-01

Poleward energy transport in the atmosphere and oceans constitutes an important branch of the global energy budget, and its role in the climate system has been the subject of many studies. In the atmosphere, the transport is affected by ''eddies'' and large scale meridional cells, both with zero net mass transport across latitude circles, but also partly by processes associated with a net transport of mass across latitude circles. The latter must cease to operate in steady state, but they may be significant when time variability of the heat budget is considered. Indeed, examination of reanalysis data on short (daily to monthly) timescales shows that mass variations on these timescales result in surprisingly large fluctuations (in excess of 1015W = 1PW) in the poleward heat transport. These fluctuations are referred to as ''extensive'', for they primarily alter the mass integrated energy of the region considered, but not its averaged value. It is suggested that extensive fluctuations mask more meaningful climate signals present in the heat transport variability, and a new formulation is proposed to isolate the latter. This new formulation is applied successfully to reanalysis data and climate model simulations.

Improving Synoptic and Intra-Seasonnal Variability in CFS via a Better Representation of Organized Convection

NASA Astrophysics Data System (ADS)

Khouider, B.; Goswami, B. B.; Majda, A.; Krishna, R. P. M. M.; Mukhopadhyay, P.

2016-12-01

Improvements in the capability of climate models to realistically capture the synoptic and intra-seasonnal variability, associated with tropical rainfall, are conditioned by improvement in the representation of the subgrid variability due to organized convection and the underlying two-way interactions through multiple scales and thus breaking with the quasi-equilibrium bottleneck. By design, the stochastic multi-cloud model (SMCM) mimics the life cycle of organized tropical convective systems and the interactions of the associated cloud types with each other and with large scales, as it is observed. It is based a lattice particle interaction model for predefined microscopic (subgrid) sites that make random transitions from one cloud type to another conditional to the large scale state. In return the SMCM provides the cloud type area fractions on the form of a Markov chain model which can be run in parallel with the climate model without any significant computational overhead. The SMCM was previously successfully tested in both reduced complexity tropical models and an aquaplanet global atmospheric model. Here, we report for the first time the results of its implementation in the fully coupled NCEP climate model (CFSv2) through the used of prescribed vertical profiles of heating and drying obtained from observations. While many known biases in CFSv2 have been slightly improved there are no noticeable degradation in the simulated mean climatology. Nonetheless, comparison with observations show that the improvements in terms of synoptic and intra-seasonnal variability are spectacular, despite the fact that CFSv2 is one of the best models in this regard. In particular, while CFSv2 exaggerates the intra-seasonnal variance at the expense of the synoptic contribution, the CFS-SMCM shows a good balance between the two as in the observations.

Scaling and contextualizing climate-conflict nexus in historical agrarian China

NASA Astrophysics Data System (ADS)

Lee, Harry F.

2017-04-01

This study examines climate-conflict nexus in historical agrarian China in multi-scalar and contextualized approach, illustrating what and how socio-political factors could significantly mediate the climate-violent link in pre-industrial society. Previous empirical large-N studies show that violent conflict in historical agrarian society was triggered by climate-induced food scarcity. The relationship was valid in China, Europe, and various geographic regions in the Northern Hemisphere in pre-industrial era. Nevertheless, the observed relationship has only been verified at a macro level (long-term variability of the nexus is emphasized and data over large area are aggregated), and somewhat generalized in nature (only physical environmental factors are controlled). Three inter-related issues remain unresolved: First, the key explanatory variable of violent conflicts may change substantially at different spatio-temporal scales. It is necessary to check whether the climate-conflict nexus is valid at a micro level (about short-term variability of the nexus and data in finer spatial resolution), and explore how the nexus changes along various spatio-temporal dimensions. Second, as the climate-conflict nexus has only been demonstrated in a broad sense, it is necessary to check whether and how the nexus is mediated by local socio-political context. More non-climatic factors pertinent to the cause and distribution of conflicts (e.g., governance, adaptive mechanisms, etc.) should be considered. Third, the methodology applied in the previous studies assumes spatially-independent observations and linear relationship, which may simplify the climate-conflict link. Moreover, the solitary reliance on quantitative methods may neglect those non-quantifiable socio-political dynamics which mediates the climate-conflict nexus. I plan to address the above issues by using disaggregated spatial analysis and in-depth case studies, with close attention to local and temporal differences and non-linear nature of the climate-conflict link. China will be chosen as study area. Study period will be delimited to AD1-1911. This study represents pioneering research which systematically examines the climate-conflict nexus in pre-industrial society over extended period in multi-scalar and contextualized perspective. By comparing and evaluating the climate-conflict link along various spatio-temporal dimensions and in different socio-political context, it may help to deepen the theoretical understanding of, and also resolve the current debate over, the climate-conflict relationship. Given the large potential changes in climatic regimes projected in coming decades, the findings in this study may have important implications for the social impact of climate change in tropical countries that are in some ways similar to pre-industrial society.

Ocean-atmosphere forcing of South American tropical paleoclimate, LGM to present

NASA Astrophysics Data System (ADS)

Baker, P. A.; Fritz, S. C.; Dwyer, G. S.; Rigsby, C. A.; Silva, C. G.; Burns, S. J.

2012-12-01

Because of many recent terrestrial paleoclimatic and marine paleoceanographic records, late Quaternary South American tropical paleoclimate is as well understood as that anywhere in the world. While lessons learned from the recent instrumental record of climate are informative, this record is too short to capture much of the lower frequency variability encountered in the paleoclimate records and much of the observed paleoclimate is without modern analogue. This paleoclimate is known to be regionally variable with significant differences both north and south of the equator and between the western high Andes and eastern lowlands of the Amazon and Nordeste Brazil. Various extrinsic forcing mechanisms affected climate throughout the period, including global concentrations of GHGs, Northern Hemisphere ice sheet forcing, seasonal insolation forcing of the South American summer monsoon (SASM), millennial-scale Atlantic forcing, and Pacific forcing of the large-scale Walker circulation. The magnitude of the climate response to these forcings varied temporally, largely because of the varying amplitude of the forcing itself. For example, during the last glacial, large-amplitude north Atlantic forcing during Heinrich 1 and the LGM itself, led to wet (dry) conditions south (north) of the equator. During the Holocene, Atlantic forcing was lower amplitude, thus seasonal insolation forcing generally predominated with a weaker-than-normal SASM during the early Holocene resulting in dry conditions in the south-western tropics and wet conditions in the eastern lowlands and Nordeste; in the late Holocene seasonal insolation reached a maximum in the southern tropics and climate conditions reversed.

Elucidating Critical Zone Process Interactions with an Integrated Hydrology Model in a Headwaters Research Catchment

NASA Astrophysics Data System (ADS)

Collins, C.; Maxwell, R. M.

2017-12-01

Providence Creek (P300) watershed is an alpine headwaters catchment located at the Southern Sierra Critical Zone Observatory (SSCZO). Evidence of groundwater-dependent vegetation and drought-induced tree mortality at P300 along with the effect of subsurface characterization on mountain ecohydrology motivates this study. A hyper resolution integrated hydrology model of this site, along with extensive instrumentation, provides an opportunity to study the effects of lateral groundwater flow on vegetation's tolerance to drought. ParFlow-CLM is a fully integrated surface-subsurface model that is driven with reconstructed meteorology, such as the North American Land Data Assimilation System project phase 2 (NLDAS-2) dataset. However, large-scale data products mute orographic effects on climate at smaller scales. Climate variables often do not behave uniformly in highly heterogeneous mountain regions. Therefore, forcing physically-based integrated hydrologic models—especially of mountain headwaters catchments—with a large-scale data product is a major challenge. Obtaining reliable observations in complex terrain is challenging and while climate data products introduce uncertainties likewise, documented discrepancies between several data products and P300 observations suggest these data products may suffice. To tackle these issues, a suite of simulations was run to parse out (1) the effects of climate data source (data products versus observations) and (2) the effects of climate data spatial variability. One tool for evaluating the effect of climate data on model outputs is the relationship between latent head flux (LH) and evapotranspiration (ET) partitioning with water table depth (WTD). This zone of LH sensitivity to WTD is referred to as the "critical zone." Preliminary results suggest that these critical zone relationships are preserved despite forcing albeit significant shifts in magnitude. These results demonstrate that integrated hydrology models are sensitive to climate data thereby impacting the accuracy of hydrologic modeling of headwaters catchments used for water management and planning purposes and exploring the effects of climate change perturbations.

The trend of the multi-scale temporal variability of precipitation in Colorado River Basin

NASA Astrophysics Data System (ADS)

Jiang, P.; Yu, Z.

2011-12-01

Hydrological problems like estimation of flood and drought frequencies under future climate change are not well addressed as a result of the disability of current climate models to provide reliable prediction (especially for precipitation) shorter than 1 month. In order to assess the possible impacts that multi-scale temporal distribution of precipitation may have on the hydrological processes in Colorado River Basin (CRB), a comparative analysis of multi-scale temporal variability of precipitation as well as the trend of extreme precipitation is conducted in four regions controlled by different climate systems. Multi-scale precipitation variability including within-storm patterns and intra-annual, inter-annual and decadal variabilities will be analyzed to explore the possible trends of storm durations, inter-storm periods, average storm precipitation intensities and extremes under both long-term natural climate variability and human-induced warming. Further more, we will examine the ability of current climate models to simulate the multi-scale temporal variability and extremes of precipitation. On the basis of these analyses, a statistical downscaling method will be developed to disaggregate the future precipitation scenarios which will provide a more reliable and finer temporal scale precipitation time series for hydrological modeling. Analysis results and downscaling results will be presented.

Distant Influence of Kuroshio Eddies on North Pacific Weather Patterns?

PubMed Central

Ma, Xiaohui; Chang, Ping; Saravanan, R.; Montuoro, Raffaele; Hsieh, Jen-Shan; Wu, Dexing; Lin, Xiaopei; Wu, Lixin; Jing, Zhao

2015-01-01

High-resolution satellite measurements of surface winds and sea-surface temperature (SST) reveal strong coupling between meso-scale ocean eddies and near-surface atmospheric flow over eddy-rich oceanic regions, such as the Kuroshio and Gulf Stream, highlighting the importance of meso-scale oceanic features in forcing the atmospheric planetary boundary layer (PBL). Here, we present high-resolution regional climate modeling results, supported by observational analyses, demonstrating that meso-scale SST variability, largely confined in the Kuroshio-Oyashio confluence region (KOCR), can further exert a significant distant influence on winter rainfall variability along the U.S. Northern Pacific coast. The presence of meso-scale SST anomalies enhances the diabatic conversion of latent heat energy to transient eddy energy, intensifying winter cyclogenesis via moist baroclinic instability, which in turn leads to an equivalent barotropic downstream anticyclone anomaly with reduced rainfall. The finding points to the potential of improving forecasts of extratropical winter cyclones and storm systems and projections of their response to future climate change, which are known to have major social and economic impacts, by improving the representation of ocean eddy–atmosphere interaction in forecast and climate models. PMID:26635077

Got spirit? The spiritual climate scale, psychometric properties, benchmarking data and future directions.

PubMed

Doram, Keith; Chadwick, Whitney; Bokovoy, Joni; Profit, Jochen; Sexton, Janel D; Sexton, J Bryan

2017-02-11

Organizations that encourage the respectful expression of diverse spiritual views have higher productivity and performance, and support employees with greater organizational commitment and job satisfaction. Within healthcare, there is a paucity of studies which define or intervene on the spiritual needs of healthcare workers, or examine the effects of a pro-spirituality environment on teamwork and patient safety. Our objective was to describe a novel survey scale for evaluating spiritual climate in healthcare workers, evaluate its psychometric properties, provide benchmarking data from a large faith-based healthcare system, and investigate relationships between spiritual climate and other predictors of patient safety and job satisfaction. Cross-sectional survey study of US healthcare workers within a large, faith-based health system. Seven thousand nine hundred twenty three of 9199 eligible healthcare workers across 325 clinical areas within 16 hospitals completed our survey in 2009 (86% response rate). The spiritual climate scale exhibited good psychometric properties (internal consistency: Cronbach α = .863). On average 68% (SD 17.7) of respondents of a given clinical area expressed good spiritual climate, although assessments varied widely (14 to 100%). Spiritual climate correlated positively with teamwork climate (r = .434, p < .001) and safety climate (r = .489, p < .001). Healthcare workers reporting good spiritual climate were less likely to have intentions to leave, to be burned out, or to experience disruptive behaviors in their unit and more likely to have participated in executive rounding (p < .001 for each variable). The spiritual climate scale exhibits good psychometric properties, elicits results that vary widely by clinical area, and aligns well with other culture constructs that have been found to correlate with clinical and organizational outcomes.

Downscaling of Global Climate Change Estimates to Regional Scales: An Application to Iberian Rainfall in Wintertime.

NASA Astrophysics Data System (ADS)

von Storch, Hans; Zorita, Eduardo; Cubasch, Ulrich

1993-06-01

A statistical strategy to deduct regional-scale features from climate general circulation model (GCM) simulations has been designed and tested. The main idea is to interrelate the characteristic patterns of observed simultaneous variations of regional climate parameters and of large-scale atmospheric flow using the canonical correlation technique.The large-scale North Atlantic sea level pressure (SLP) is related to the regional, variable, winter (DJF) mean Iberian Peninsula rainfall. The skill of the resulting statistical model is shown by reproducing, to a good approximation, the winter mean Iberian rainfall from 1900 to present from the observed North Atlantic mean SLP distributions. It is shown that this observed relationship between these two variables is not well reproduced in the output of a general circulation model (GCM).The implications for Iberian rainfall changes as the response to increasing atmospheric greenhouse-gas concentrations simulated by two GCM experiments are examined with the proposed statistical model. In an instantaneous `2 C02' doubling experiment, using the simulated change of the mean North Atlantic SLP field to predict Iberian rainfall yields, there is an insignificant increase of area-averaged rainfall of 1 mm/month, with maximum values of 4 mm/month in the northwest of the peninsula. In contrast, for the four GCM grid points representing the Iberian Peninsula, the change is 10 mm/month, with a minimum of 19 mm/month in the southwest. In the second experiment, with the IPCC scenario A ("business as usual") increase Of C02, the statistical-model results partially differ from the directly simulated rainfall changes: in the experimental range of 100 years, the area-averaged rainfall decreases by 7 mm/month (statistical model), and by 9 mm/month (GCM); at the same time the amplitude of the interdecadal variability is quite different.

Recent Naval Postgraduate School Publications.

DTIC Science & Technology

1982-04-01

477 p. Haney, R L; et al.; eds. Ocean models for climate research: A workshop Sponsored by the U.S. Committee for the Global Atmos. Hes. Program. Nat... climate variability Oceanus, vol. 21, no. 4, p. 33-39, (1978). Williams, R T A review of theoretical models of atmospheric frontogenesis Chapman Conf...structure in large-scale optimization models Symp. 9 n Computer-Assisted Analysis and Model Simpification, Boulder, Colo., Mar. 24, 1980. Brown, G G

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...

Objective spatiotemporal proxy-model comparisons of the Asian monsoon for the last millennium

NASA Astrophysics Data System (ADS)

Anchukaitis, K. J.; Cook, E. R.; Ammann, C. M.; Buckley, B. M.; D'Arrigo, R. D.; Jacoby, G.; Wright, W. E.; Davi, N.; Li, J.

2008-12-01

The Asian monsoon system can be studied using a complementary proxy/simulation approach which evaluates climate models using estimates of past precipitation and temperature, and which subsequently applies the best understanding of the physics of the climate system as captured in general circulation models to evaluate the broad-scale dynamics behind regional paleoclimate reconstructions. Here, we use a millennial-length climate field reconstruction of monsoon season summer (JJA) drought, developed from tree- ring proxies, with coupled climate simulations from NCAR CSM1.4 and CCSM3 to evaluate the cause of large- scale persistent droughts over the last one thousand years. Direct comparisons are made between the external forced response within the climate model and the spatiotemporal field reconstruction. In order to identify patterns of drought associated with internal variability in the climate system, we use a model/proxy analog technique which objectively selects epochs in the model that most closely reproduce those observed in the reconstructions. The concomitant ocean-atmosphere dynamics are then interpreted in order to identify and understand the internal climate system forcing of low frequency monsoon variability. We examine specific periods of extensive or intensive regional drought in the 15th, 17th, and 18th centuries, many of which are coincident with major cultural changes in the region.

Spatio-Temporal Variability of Groundwater Storage in India

NASA Technical Reports Server (NTRS)

Bhanja, Soumendra; Rodell, Matthew; Li, Bailing; Mukherjee, Abhijit

2016-01-01

Groundwater level measurements from 3907 monitoring wells, distributed within 22 major river basins of India, are assessed to characterize their spatial and temporal variability. Ground water storage (GWS) anomalies (relative to the long-term mean) exhibit strong seasonality, with annual maxima observed during the monsoon season and minima during pre-monsoon season. Spatial variability of GWS anomalies increases with the extent of measurements, following the power law relationship, i.e., log-(spatial variability) is linearly dependent on log-(spatial extent).In addition, the impact of well spacing on spatial variability and the power law relationship is investigated. We found that the mean GWS anomaly sampled at a 0.25 degree grid scale closes to unweighted average over all wells. The absolute error corresponding to each basin grows with increasing scale, i.e., from 0.25 degree to 1 degree. It was observed that small changes in extent could create very large changes in spatial variability at large grid scales. Spatial variability of GWS anomaly has been found to vary with climatic conditions. To our knowledge, this is the first study of the effects of well spacing on groundwater spatial variability. The results may be useful for interpreting large scale groundwater variations from unevenly spaced or sparse groundwater well observations or for siting and prioritizing wells in a network for groundwater management. The output of this study could be used to maintain a cost effective groundwater monitoring network in the study region and the approach can also be used in other parts of the globe.

Spatio-temporal variability of groundwater storage in India.

PubMed

Bhanja, Soumendra N; Rodell, Matthew; Li, Bailing; Mukherjee, Abhijit

2017-01-01

Groundwater level measurements from 3907 monitoring wells, distributed within 22 major river basins of India, are assessed to characterize their spatial and temporal variability. Groundwater storage (GWS) anomalies (relative to the long-term mean) exhibit strong seasonality, with annual maxima observed during the monsoon season and minima during pre-monsoon season. Spatial variability of GWS anomalies increases with the extent of measurements, following the power law relationship, i.e., log-(spatial variability) is linearly dependent on log-(spatial extent). In addition, the impact of well spacing on spatial variability and the power law relationship is investigated. We found that the mean GWS anomaly sampled at a 0.25 degree grid scale closes to unweighted average over all wells. The absolute error corresponding to each basin grows with increasing scale, i.e., from 0.25 degree to 1 degree. It was observed that small changes in extent could create very large changes in spatial variability at large grid scales. Spatial variability of GWS anomaly has been found to vary with climatic conditions. To our knowledge, this is the first study of the effects of well spacing on groundwater spatial variability. The results may be useful for interpreting large scale groundwater variations from unevenly spaced or sparse groundwater well observations or for siting and prioritizing wells in a network for groundwater management. The output of this study could be used to maintain a cost effective groundwater monitoring network in the study region and the approach can also be used in other parts of the globe.

Historical drought patterns over Canada and their teleconnections with large-scale climate signals

NASA Astrophysics Data System (ADS)

Asong, Zilefac Elvis; Wheater, Howard Simon; Bonsal, Barrie; Razavi, Saman; Kurkute, Sopan

2018-06-01

Drought is a recurring extreme climate event and among the most costly natural disasters in the world. This is particularly true over Canada, where drought is both a frequent and damaging phenomenon with impacts on regional water resources, agriculture, industry, aquatic ecosystems, and health. However, nationwide drought assessments are currently lacking and impacted by limited ground-based observations. This study provides a comprehensive analysis of historical droughts over the whole of Canada, including the role of large-scale teleconnections. Drought events are characterized by the Standardized Precipitation Evapotranspiration Index (SPEI) over various temporal scales (1, 3, 6, and 12 consecutive months, 6 months from April to September, and 12 months from October to September) applied to different gridded monthly data sets for the period 1950-2013. The Mann-Kendall test, rotated empirical orthogonal function, continuous wavelet transform, and wavelet coherence analyses are used, respectively, to investigate the trend, spatio-temporal patterns, periodicity, and teleconnectivity of drought events. Results indicate that southern (northern) parts of the country experienced significant trends towards drier (wetter) conditions although substantial variability exists. Two spatially well-defined regions with different temporal evolution of droughts were identified - the Canadian Prairies and northern central Canada. The analyses also revealed the presence of a dominant periodicity of between 8 and 32 months in the Prairie region and between 8 and 40 months in the northern central region. These cycles of low-frequency variability are found to be associated principally with the Pacific-North American (PNA) and Multivariate El Niño/Southern Oscillation Index (MEI) relative to other considered large-scale climate indices. This study is the first of its kind to identify dominant periodicities in drought variability over the whole of Canada in terms of when the drought events occur, their duration, and how often they occur.

Contrasting scaling properties of interglacial and glacial climates

PubMed Central

Shao, Zhi-Gang; Ditlevsen, Peter D.

2016-01-01

Understanding natural climate variability is essential for assessments of climate change. This is reflected in the scaling properties of climate records. The scaling exponents of the interglacial and the glacial climates are fundamentally different. The Holocene record is monofractal, with a scaling exponent H∼0.7. On the contrary, the glacial record is multifractal, with a significantly higher scaling exponent H∼1.2, indicating a longer persistence time and stronger nonlinearities in the glacial climate. The glacial climate is dominated by the strong multi-millennial Dansgaard–Oeschger (DO) events influencing the long-time correlation. However, by separately analysing the last glacial maximum lacking DO events, here we find the same scaling for that period as for the full glacial period. The unbroken scaling thus indicates that the DO events are part of the natural variability and not externally triggered. At glacial time scales, there is a scale break to a trivial scaling, contrasting the DO events from the similarly saw-tooth-shaped glacial cycles. PMID:26980084

Individual-scale inference to anticipate climate-change vulnerability of biodiversity.

PubMed

Clark, James S; Bell, David M; Kwit, Matthew; Stine, Anne; Vierra, Ben; Zhu, Kai

2012-01-19

Anticipating how biodiversity will respond to climate change is challenged by the fact that climate variables affect individuals in competition with others, but interest lies at the scale of species and landscapes. By omitting the individual scale, models cannot accommodate the processes that determine future biodiversity. We demonstrate how individual-scale inference can be applied to the problem of anticipating vulnerability of species to climate. The approach places climate vulnerability in the context of competition for light and soil moisture. Sensitivities to climate and competition interactions aggregated from the individual tree scale provide estimates of which species are vulnerable to which variables in different habitats. Vulnerability is explored in terms of specific demographic responses (growth, fecundity and survival) and in terms of the synthetic response (the combination of demographic rates), termed climate tracking. These indices quantify risks for individuals in the context of their competitive environments. However, by aggregating in specific ways (over individuals, years, and other input variables), we provide ways to summarize and rank species in terms of their risks from climate change.

Direct and indirect climate controls predict heterogeneous early-mid 21st century wildfire burned area across western and boreal North America

PubMed Central

Falk, Donald A.; Westerling, Anthony L.; Swetnam, Thomas W.

2017-01-01

Predicting wildfire under future conditions is complicated by complex interrelated drivers operating across large spatial scales. Annual area burned (AAB) is a useful index of global wildfire activity. Current and antecedent seasonal climatic conditions, and the timing of snowpack melt, have been suggested as important drivers of AAB. As climate warms, seasonal climate and snowpack co-vary in intricate ways, influencing fire at continental and sub-continental scales. We used independent records of seasonal climate and snow cover duration (last date of permanent snowpack, LDPS) and cell-based Structural Equation Models (SEM) to separate direct (climatic) and indirect (snow cover) effects on relative changes in AAB under future climatic scenarios across western and boreal North America. To isolate seasonal climate variables with the greatest effect on AAB, we ran multiple regression models of log-transformed AAB on seasonal climate variables and LDPS. We used the results of multiple regressions to project future AAB using GCM ensemble climate variables and LDPS, and validated model predictions with recent AAB trends. Direct influences of spring and winter temperatures on AAB are larger and more widespread than the indirect effect mediated by changes in LDPS in most areas. Despite significant warming trends and reductions in snow cover duration, projected responses of AAB to early-mid 21st century are heterogeneous across the continent. Changes in AAB range from strongly increasing (one order of magnitude increases in AAB) to moderately decreasing (more than halving of baseline AAB). Annual wildfire area burned in coming decades is likely to be highly geographically heterogeneous, reflecting interacting regional and seasonal climate drivers of fire occurrence and spread. PMID:29244839

Direct and indirect climate controls predict heterogeneous early-mid 21st century wildfire burned area across western and boreal North America.

PubMed

Kitzberger, Thomas; Falk, Donald A; Westerling, Anthony L; Swetnam, Thomas W

2017-01-01

Predicting wildfire under future conditions is complicated by complex interrelated drivers operating across large spatial scales. Annual area burned (AAB) is a useful index of global wildfire activity. Current and antecedent seasonal climatic conditions, and the timing of snowpack melt, have been suggested as important drivers of AAB. As climate warms, seasonal climate and snowpack co-vary in intricate ways, influencing fire at continental and sub-continental scales. We used independent records of seasonal climate and snow cover duration (last date of permanent snowpack, LDPS) and cell-based Structural Equation Models (SEM) to separate direct (climatic) and indirect (snow cover) effects on relative changes in AAB under future climatic scenarios across western and boreal North America. To isolate seasonal climate variables with the greatest effect on AAB, we ran multiple regression models of log-transformed AAB on seasonal climate variables and LDPS. We used the results of multiple regressions to project future AAB using GCM ensemble climate variables and LDPS, and validated model predictions with recent AAB trends. Direct influences of spring and winter temperatures on AAB are larger and more widespread than the indirect effect mediated by changes in LDPS in most areas. Despite significant warming trends and reductions in snow cover duration, projected responses of AAB to early-mid 21st century are heterogeneous across the continent. Changes in AAB range from strongly increasing (one order of magnitude increases in AAB) to moderately decreasing (more than halving of baseline AAB). Annual wildfire area burned in coming decades is likely to be highly geographically heterogeneous, reflecting interacting regional and seasonal climate drivers of fire occurrence and spread.

The susceptibility of large river basins to orogenic and climatic drivers

NASA Astrophysics Data System (ADS)

Haedke, Hanna; Wittmann, Hella; von Blanckenburg, Friedhelm

2017-04-01

Large rivers are known to buffer pulses in sediment production driven by changes in climate as sediment is transported through lowlands. Our new dataset of in situ cosmogenic nuclide concentration and chemical composition of 62 sandy bedload samples from the world largest rivers integrates over 25% of Earth's terrestrial surface, distributed over a variety of climatic zones across all continents, and represents the millennial-scale denudation rate of the sediment's source area. We can show that these denudation rates do not respond to climatic forcing, but faithfully record orogenic forcing, when analyzed with respective variables representing orogeny (strain rate, relief, bouguer anomaly, free-air anomaly), and climate (runoff, temperature, precipitation) and basin properties (floodplain response time, drainage area). In contrast to this orogenic forcing of denudation rates, elemental bedload chemistry from the fine-grained portion of the same samples correlates with climate-related variables (precipitation, runoff) and floodplain response times. It is also well-known from previous compilations of river-gauged sediment loads that the short-term basin-integrated sediment export is also climatically controlled. The chemical composition of detrital sediment shows a climate control that can originate in the rivers source area, but this signal is likely overprinted during transfer through the lowlands because we also find correlation with floodplain response times. At the same time, cosmogenic nuclides robustly preserve the orogenic forcing of the source area denudation signal through of the floodplain buffer. Conversely, previous global compilations of cosmogenic nuclides in small river basins show the preservation of climate drivers in their analysis, but these are buffered in large lowland rivers. Hence, we can confirm the assumption that cosmogenic nuclides in large rivers are poorly susceptible to climate changes, but are at the same time highly suited to detect changes in orogenic forcing in their paleo sedimentary records.

Towards a More Biologically-meaningful Climate Characterization: Variability in Space and Time at Multiple Scales

NASA Astrophysics Data System (ADS)

Christianson, D. S.; Kaufman, C. G.; Kueppers, L. M.; Harte, J.

2013-12-01

Sampling limitations and current modeling capacity justify the common use of mean temperature values in summaries of historical climate and future projections. However, a monthly mean temperature representing a 1-km2 area on the landscape is often unable to capture the climate complexity driving organismal and ecological processes. Estimates of variability in addition to mean values are more biologically meaningful and have been shown to improve projections of range shifts for certain species. Historical analyses of variance and extreme events at coarse spatial scales, as well as coarse-scale projections, show increasing temporal variability in temperature with warmer means. Few studies have considered how spatial variance changes with warming, and analysis for both temporal and spatial variability across scales is lacking. It is unclear how the spatial variability of fine-scale conditions relevant to plant and animal individuals may change given warmer coarse-scale mean values. A change in spatial variability will affect the availability of suitable habitat on the landscape and thus, will influence future species ranges. By characterizing variability across both temporal and spatial scales, we can account for potential bias in species range projections that use coarse climate data and enable improvements to current models. In this study, we use temperature data at multiple spatial and temporal scales to characterize spatial and temporal variability under a warmer climate, i.e., increased mean temperatures. Observational data from the Sierra Nevada (California, USA), experimental climate manipulation data from the eastern and western slopes of the Rocky Mountains (Colorado, USA), projected CMIP5 data for California (USA) and observed PRISM data (USA) allow us to compare characteristics of a mean-variance relationship across spatial scales ranging from sub-meter2 to 10,000 km2 and across temporal scales ranging from hours to decades. Preliminary spatial analysis at fine-spatial scales (sub-meter to 10-meter) shows greater temperature variability with warmer mean temperatures. This is inconsistent with the inherent assumption made in current species distribution models that fine-scale variability is static, implying that current projections of future species ranges may be biased -- the direction and magnitude requiring further study. While we focus our findings on the cross-scaling characteristics of temporal and spatial variability, we also compare the mean-variance relationship between 1) experimental climate manipulations and observed conditions and 2) temporal versus spatial variance, i.e., variability in a time-series at one location vs. variability across a landscape at a single time. The former informs the rich debate concerning the ability to experimentally mimic a warmer future. The latter informs space-for-time study design and analyses, as well as species persistence via a combined spatiotemporal probability of suitable future habitat.

Environment and host as large-scale controls of ectomycorrhizal fungi.

PubMed

van der Linde, Sietse; Suz, Laura M; Orme, C David L; Cox, Filipa; Andreae, Henning; Asi, Endla; Atkinson, Bonnie; Benham, Sue; Carroll, Christopher; Cools, Nathalie; De Vos, Bruno; Dietrich, Hans-Peter; Eichhorn, Johannes; Gehrmann, Joachim; Grebenc, Tine; Gweon, Hyun S; Hansen, Karin; Jacob, Frank; Kristöfel, Ferdinand; Lech, Paweł; Manninger, Miklós; Martin, Jan; Meesenburg, Henning; Merilä, Päivi; Nicolas, Manuel; Pavlenda, Pavel; Rautio, Pasi; Schaub, Marcus; Schröck, Hans-Werner; Seidling, Walter; Šrámek, Vít; Thimonier, Anne; Thomsen, Iben Margrete; Titeux, Hugues; Vanguelova, Elena; Verstraeten, Arne; Vesterdal, Lars; Waldner, Peter; Wijk, Sture; Zhang, Yuxin; Žlindra, Daniel; Bidartondo, Martin I

2018-06-06

Explaining the large-scale diversity of soil organisms that drive biogeochemical processes-and their responses to environmental change-is critical. However, identifying consistent drivers of belowground diversity and abundance for some soil organisms at large spatial scales remains problematic. Here we investigate a major guild, the ectomycorrhizal fungi, across European forests at a spatial scale and resolution that is-to our knowledge-unprecedented, to explore key biotic and abiotic predictors of ectomycorrhizal diversity and to identify dominant responses and thresholds for change across complex environmental gradients. We show the effect of 38 host, environment, climate and geographical variables on ectomycorrhizal diversity, and define thresholds of community change for key variables. We quantify host specificity and reveal plasticity in functional traits involved in soil foraging across gradients. We conclude that environmental and host factors explain most of the variation in ectomycorrhizal diversity, that the environmental thresholds used as major ecosystem assessment tools need adjustment and that the importance of belowground specificity and plasticity has previously been underappreciated.

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.

Continental-scale temperature covariance in proxy reconstructions and climate models

NASA Astrophysics Data System (ADS)

Hartl-Meier, Claudia; Büntgen, Ulf; Smerdon, Jason; Zorita, Eduardo; Krusic, Paul; Ljungqvist, Fredrik; Schneider, Lea; Esper, Jan

2017-04-01

Inter-continental temperature variability over the past millennium has been reported to be more coherent in climate model simulations than in multi-proxy-based reconstructions, a finding that undermines the representation of spatial variability in either of these approaches. We assess the covariance of summer temperatures among Northern Hemisphere continents by comparing tree-ring based temperature reconstructions with state-of-the-art climate model simulations over the past millennium. We find inter-continental temperature covariance to be larger in tree-ring-only reconstructions compared to those derived from multi-proxy networks, thus enhancing the agreement between proxy- and model-based spatial representations. A detailed comparison of simulated temperatures, however, reveals substantial spread among the models. Over the past millennium, inter-continental temperature correlations are driven by the cooling after major volcanic eruptions in 1257, 1452, 1601, and 1815. The coherence of these synchronizing events appears to be elevated in several climate simulations relative to their own covariance baselines and the proxy reconstructions, suggesting these models overestimate the amplitude of cooling in response to volcanic forcing at large spatial scales.

Analysis of the historical precipitation in the South East Iberian Peninsula at different spatio-temporal scale. Study of the meteorological drought

NASA Astrophysics Data System (ADS)

Fernández-Chacón, Francisca; Pulido-Velazquez, David; Jiménez-Sánchez, Jorge; Luque-Espinar, Juan Antonio

2017-04-01

Precipitation is a fundamental climate variable that has a pronounced spatial and temporal variability on a global scale, as well as at regional and sub-regional scales. Due to its orographic complexity and its latitude the Iberian Peninsula (IP), located to the west of the Mediterranean Basin between the Atlantic Ocean and the Mediterranean Sea, has a complex climate. Over the peninsula there are strong north-south and east-west gradients, as a consequence of the different low-frequency atmospheric patterns, and he overlap of these over the year will be determinants in the variability of climatic variables. In the southeast of the Iberian Peninsula dominates a dry Mediterranean climate, the precipitation is characterized as being an intermittent and discontinuous variable. In this research information coming from the Spain02 v4 database was used to study the South East (SE) IP for the 1971-2010 period with a spatial resolution of 0.11 x 0.11. We analysed precipitation at different time scale (daily, monthly, seasonal, annual,…) to study the spatial distribution and temporal tendencies. The high spatial, intra-annual and inter-annual climatic variability observed makes it necessary to propose a climatic regionalization. In addition, for the identified areas and subareas of homogeneous climate we have analysed the evolution of the meteorological drought for the same period at different time scales. The standardized precipitation index has been used at 12, 24 and 48 month temporal scale. The climatic complexity of the area determines a high variability in the drought characteristics, duration, intensity and frequency in the different climatic areas. This research has been supported by the GESINHIMPADAPT project (CGL2013-48424-C2-2-R) with Spanish MINECO funds. We would also like to thank Spain02 project for the data provided for this study.

Climate variability drives recent tree mortality in Europe.

PubMed

Neumann, Mathias; Mues, Volker; Moreno, Adam; Hasenauer, Hubert; Seidl, Rupert

2017-11-01

Tree mortality is an important process in forest ecosystems, frequently hypothesized to be highly climate sensitive. Yet, tree death remains one of the least understood processes of forest dynamics. Recently, changes in tree mortality have been observed in forests around the globe, which could profoundly affect ecosystem functioning and services provisioning to society. We describe continental-scale patterns of recent tree mortality from the only consistent pan-European forest monitoring network, identifying recent mortality hotspots in southern and northern Europe. Analyzing 925,462 annual observations of 235,895 trees between 2000 and 2012, we determine the influence of climate variability and tree age on interannual variation in tree mortality using Cox proportional hazard models. Warm summers as well as high seasonal variability in precipitation increased the likelihood of tree death. However, our data also suggest that reduced cold-induced mortality could compensate increased mortality related to peak temperatures in a warming climate. Besides climate variability, age was an important driver of tree mortality, with individual mortality probability decreasing with age over the first century of a trees life. A considerable portion of the observed variation in tree mortality could be explained by satellite-derived net primary productivity, suggesting that widely available remote sensing products can be used as an early warning indicator of widespread tree mortality. Our findings advance the understanding of patterns of large-scale tree mortality by demonstrating the influence of seasonal and diurnal climate variation, and highlight the potential of state-of-the-art remote sensing to anticipate an increased likelihood of tree mortality in space and time. © 2017 John Wiley & Sons Ltd.

Regression tree modeling of forest NPP using site conditions and climate variables across eastern USA

NASA Astrophysics Data System (ADS)

Kwon, Y.

2013-12-01

As evidence of global warming continue to increase, being able to predict forest response to climate changes, such as expected rise of temperature and precipitation, will be vital for maintaining the sustainability and productivity of forests. To map forest species redistribution by climate change scenario has been successful, however, most species redistribution maps lack mechanistic understanding to explain why trees grow under the novel conditions of chaining climate. Distributional map is only capable of predicting under the equilibrium assumption that the communities would exist following a prolonged period under the new climate. In this context, forest NPP as a surrogate for growth rate, the most important facet that determines stand dynamics, can lead to valid prediction on the transition stage to new vegetation-climate equilibrium as it represents changes in structure of forest reflecting site conditions and climate factors. The objective of this study is to develop forest growth map using regression tree analysis by extracting large-scale non-linear structures from both field-based FIA and remotely sensed MODIS data set. The major issue addressed in this approach is non-linear spatial patterns of forest attributes. Forest inventory data showed complex spatial patterns that reflect environmental states and processes that originate at different spatial scales. At broad scales, non-linear spatial trends in forest attributes and mixture of continuous and discrete types of environmental variables make traditional statistical (multivariate regression) and geostatistical (kriging) models inefficient. It calls into question some traditional underlying assumptions of spatial trends that uncritically accepted in forest data. To solve the controversy surrounding the suitability of forest data, regression tree analysis are performed using Software See5 and Cubist. Four publicly available data sets were obtained: First, field-based Forest Inventory and Analysis (USDA, Forest Service) data set for the 31 eastern most United States. Second, 8-day composite of MODIS Land Cover, FPAR, LAI and GPP/NPP data were obtained from Jan 2001 to Dec 2004 (total 182 composite) and each product were filtered by pixel-level quality assurance data to select best quality pixels. Third, 30-year averaged climate data were collected from National Oceanic and Atmospheric Administration (NOAA) and five climatic variables were obtained: Monthly temperature, precipitation, annual heating and cooling days, and annual frost-free days. Forth, topographic data were obtained from digital elevation model (1km by 1km). This research will provide a better understanding of large-scale forest responses to environmental factors that will be beneficial for the development of important forest management applications.

Revealing The Impact Of Climate Variability On The Wind Resource Using Data Mining Techniques

NASA Astrophysics Data System (ADS)

Clifton, A.; Lundquist, J. K.

2011-12-01

Wind turbines harvest energy from the wind. Winds at heights where industrial-scale turbines operate, up to 200 m above ground, experience a complex interaction between the atmosphere and the Earth's surface. Previous studies for a variety of locations have shown that the wind resource varies over time. In some locations, this variability can be related to large-scale climate oscillations as revealed in climate indices such as the El-Nino-Southern Oscillation (ENSO). These indices can be used to quantify climate change in the past, and can also be extracted from models of future climate. Understanding the correlation between climate indices and wind resources therefore allows us to understand how climate change may influence wind energy production. We present a new methodology for assessing relevant climate modes of oscillation at a given site in order to quantify future wind resource variability. We demonstrate the method on a 14-year record of 10-minute averaged wind speed and wind direction data from several levels of an 80m tower at the National Renewable Energy Laboratory (NREL) National Wind Technology Center near Boulder, Colorado. Data mining techniques (based on k-means clustering) identify 4 major groups of wind speed and direction. After removing annual means, each cluster was compared to a series of climate indices, including the Arctic Oscillation (AO) and Multivariate ENSO Index (MEI). Statistically significant relationships emerge between individual clusters and climate indices. At this location, this result is consistent with the MEI's relationship with other meteorological parameters, such as precipitation, in the Rocky Mountain Region. The presentation will illustrate these relationships between wind resource at this location and other relevant climate indices, and suggest how these relationships can provide a foundation for quantifying the potential future variability of wind energy production at this site and others.

Inter-model variability in hydrological extremes projections for Amazonian sub-basins

NASA Astrophysics Data System (ADS)

Andres Rodriguez, Daniel; Garofolo, Lucas; Lázaro de Siqueira Júnior, José; Samprogna Mohor, Guilherme; Tomasella, Javier

2014-05-01

Irreducible uncertainties due to knowledge's limitations, chaotic nature of climate system and human decision-making process drive uncertainties in Climate Change projections. Such uncertainties affect the impact studies, mainly when associated to extreme events, and difficult the decision-making process aimed at mitigation and adaptation. However, these uncertainties allow the possibility to develop exploratory analyses on system's vulnerability to different sceneries. The use of different climate model's projections allows to aboard uncertainties issues allowing the use of multiple runs to explore a wide range of potential impacts and its implications for potential vulnerabilities. Statistical approaches for analyses of extreme values are usually based on stationarity assumptions. However, nonstationarity is relevant at the time scales considered for extreme value analyses and could have great implications in dynamic complex systems, mainly under climate change transformations. Because this, it is required to consider the nonstationarity in the statistical distribution parameters. We carried out a study of the dispersion in hydrological extremes projections using climate change projections from several climate models to feed the Distributed Hydrological Model of the National Institute for Spatial Research, MHD-INPE, applied in Amazonian sub-basins. This model is a large-scale hydrological model that uses a TopModel approach to solve runoff generation processes at the grid-cell scale. MHD-INPE model was calibrated for 1970-1990 using observed meteorological data and comparing observed and simulated discharges by using several performance coeficients. Hydrological Model integrations were performed for present historical time (1970-1990) and for future period (2010-2100). Because climate models simulate the variability of the climate system in statistical terms rather than reproduce the historical behavior of climate variables, the performances of the model's runs during the historical period, when feed with climate model data, were tested using descriptors of the Flow Duration Curves. The analyses of projected extreme values were carried out considering the nonstationarity of the GEV distribution parameters and compared with extremes events in present time. Results show inter-model variability in a broad dispersion on projected extreme's values. Such dispersion implies different degrees of socio-economic impacts associated to extreme hydrological events. Despite the no existence of one optimum result, this variability allows the analyses of adaptation strategies and its potential vulnerabilities.

Comparison of Grid Nudging and Spectral Nudging Techniques for Dynamical Climate Downscaling within the WRF Model

NASA Astrophysics Data System (ADS)

Fan, X.; Chen, L.; Ma, Z.

2010-12-01

Climate downscaling has been an active research and application area in the past several decades focusing on regional climate studies. Dynamical downscaling, in addition to statistical methods, has been widely used in downscaling as the advanced modern numerical weather and regional climate models emerge. The utilization of numerical models enables that a full set of climate variables are generated in the process of downscaling, which are dynamically consistent due to the constraints of physical laws. While we are generating high resolution regional climate, the large scale climate patterns should be retained. To serve this purpose, nudging techniques, including grid analysis nudging and spectral nudging, have been used in different models. There are studies demonstrating the benefit and advantages of each nudging technique; however, the results are sensitive to many factors such as nudging coefficients and the amount of information to nudge to, and thus the conclusions are controversy. While in a companion work of developing approaches for quantitative assessment of the downscaled climate, in this study, the two nudging techniques are under extensive experiments in the Weather Research and Forecasting (WRF) model. Using the same model provides fair comparability. Applying the quantitative assessments provides objectiveness of comparison. Three types of downscaling experiments were performed for one month of choice. The first type is serving as a base whereas the large scale information is communicated through lateral boundary conditions only; the second is using the grid analysis nudging; and the third is using spectral nudging. Emphases are given to the experiments of different nudging coefficients and nudging to different variables in the grid analysis nudging; while in spectral nudging, we focus on testing the nudging coefficients, different wave numbers on different model levels to nudge.

Leishmaniasis and Climate Change—Case Study: Argentina

PubMed Central

Salomón, Oscar Daniel; Quintana, María Gabriela; Mastrángelo, Andrea Verónica; Fernández, María Soledad

2012-01-01

Vector-borne diseases closely associated with the environment, such as leishmaniases, have been a usual argument about the deleterious impact of climate change on public health. From the biological point of view interaction of different variables has different and even conflicting effects on the survival of vectors and the probability transmission of pathogens. The results on ecoepidemiology of leishmaniasis in Argentina related to climate variables at different scales of space and time are presented. These studies showed that the changes in transmission due to change or increase in frequency and intensity of climatic instability were expressed through changes in the probability of vector-human reservoir effective contacts. These changes of contact in turn are modulated by both direct effects on the biology and ecology of the organisms involved, as by perceptions and changes in the behavior of the human communities at risk. Therefore, from the perspective of public health and state policy, and taking into account the current nonlinear increased velocity of climate change, we concluded that discussing the uncertainties of large-scale models will have lower impact than to develop-validate mitigation strategies to be operative at local level, and compatibles with sustainable development, conservation biodiversity, and respect for cultural diversity. PMID:22685477

Phase I of a National Phenological Assessment

NASA Astrophysics Data System (ADS)

Betancourt, J. L.; Henebry, G. M.

2009-12-01

Phenology is the gateway to climatic effects on both managed and unmanaged ecosystems. Adaptation to climatic variability and change will require integration of phenological data and models with climatic forecasts at seasonal to decadal timescales. We propose a scoping study to identify, formulate, and refine approaches to the first National Phenological Assessment (NPA) for the U.S. The NPA should be viewed as a data product of the USA-National Phenology Network that will help guide future phenological monitoring and research at the national level. We envision three main objectives for the first NPA: 1) Establish a suite of indicators of phenological change (IPCs) at regional to continental scales, following the Heinz Center model for such national assessments; 2) Using sufficiently long and broad-scale time series of IPCs and legacy phenological data, assess phenological responses to what many scientists are calling the early stages of anthropogenic climate change, specifically the abrupt advance in spring onset in the late 1970’s/early 1980’s 3) Project large-scale phenological changes into 21st Century using GCM and RCM model realizations. Toward this end we see the following tasks as critical preliminary work to plan the first NPA: a) Identify, evaluate, and refine IPCs based on indices developed from standard weather observations, streamflow and other hydrological observations (e.g., center of mass, lake freeze/thaw, etc.), plant and animal phenology observations from legacy datasets, remote sensing datastreams, flux tower observations, and GCM and RCM model realizations; b) Evaluate covariability between IPCs, legacy phenological data, and large-scale modes of climate variability to help detection and attribution of supposed secular trends and development of short and long-lead forecasts for phenological variations; c) identify, evaluate, and refine optimal methods for quantifying what constitutes significant statistical and ecological change in phenological indicators, given uncertainties in both data and methods and defined range of natural variability; d) identify, evaluate, and refine key questions of natural resource managers regarding phenological indicators for monitoring and adaptive management of habitats and wildlife, given the spectrum of management objectives on federal, state, and private lands.

Using nudging to improve global-regional dynamic consistency in limited-area climate modeling: What should we nudge?

NASA Astrophysics Data System (ADS)

Omrani, Hiba; Drobinski, Philippe; Dubos, Thomas

2015-03-01

Regional climate modelling sometimes requires that the regional model be nudged towards the large-scale driving data to avoid the development of inconsistencies between them. These inconsistencies are known to produce large surface temperature and rainfall artefacts. Therefore, it is essential to maintain the synoptic circulation within the simulation domain consistent with the synoptic circulation at the domain boundaries. Nudging techniques, initially developed for data assimilation purposes, are increasingly used in regional climate modeling and offer a workaround to this issue. In this context, several questions on the "optimal" use of nudging are still open. In this study we focus on a specific question which is: What variable should we nudge? in order to maintain the consistencies between the regional model and the driving fields as much as possible. For that, a "Big Brother Experiment", where a reference atmospheric state is known, is conducted using the weather research and forecasting (WRF) model over the Euro-Mediterranean region. A set of 22 3-month simulations is performed with different sets of nudged variables and nudging options (no nudging, indiscriminate nudging, spectral nudging) for summer and winter. The results show that nudging clearly improves the model capacity to reproduce the reference fields. However the skill scores depend on the set of variables used to nudge the regional climate simulations. Nudging the tropospheric horizontal wind is by far the key variable to nudge to simulate correctly surface temperature and wind, and rainfall. To a lesser extent, nudging tropospheric temperature also contributes to significantly improve the simulations. Indeed, nudging tropospheric wind or temperature directly impacts the simulation of the tropospheric geopotential height and thus the synoptic scale atmospheric circulation. Nudging moisture improves the precipitation but the impact on the other fields (wind and temperature) is not significant. As an immediate consequence, nudging tropospheric wind, temperature and moisture in WRF gives by far the best results with respect to the Big-Brother simulation. However, we noticed that a residual bias of the geopotential height persists due to a negative surface pressure anomaly which suggests that surface pressure is the missing quantity to nudge. Nudging the geopotential has no discernible effect. Finally, it should be noted that the proposed strategy ensures a dynamical consistency between the driving field and the simulated small-scale field but it does not ensure the best "observed" fine scale field because of the possible impact of incorrect driving large-scale field.

Analysis of the Effect of Interior Nudging on Temperature and Precipitation Distributions of Multi-year Regional Climate Simulations

NASA Astrophysics Data System (ADS)

Nolte, C. G.; Otte, T. L.; Bowden, J. H.; Otte, M. J.

2010-12-01

There is disagreement in the regional climate modeling community as to the appropriateness of the use of internal nudging. Some investigators argue that the regional model should be minimally constrained and allowed to respond to regional-scale forcing, while others have noted that in the absence of interior nudging, significant large-scale discrepancies develop between the regional model solution and the driving coarse-scale fields. These discrepancies lead to reduced confidence in the ability of regional climate models to dynamically downscale global climate model simulations under climate change scenarios, and detract from the usability of the regional simulations for impact assessments. The advantages and limitations of interior nudging schemes for regional climate modeling are investigated in this study. Multi-year simulations using the WRF model driven by reanalysis data over the continental United States at 36km resolution are conducted using spectral nudging, grid point nudging, and for a base case without interior nudging. The means, distributions, and inter-annual variability of temperature and precipitation will be evaluated in comparison to regional analyses.

A new reference frame for astronomically-tuned Plio-Pleistocene climate variability derived from a benthic oxygen isotope splice of the Mediterranean

NASA Astrophysics Data System (ADS)

Lourens, L. J.; Ziegler, M.; Konijnendijk, T. Y. M.; Hilgen, F. J.; Bos, R.; Beekvelt, B.; van Loevezijn, A.; Collin, S.

2017-12-01

The astronomical theory of climate has revolutionized our understanding of past climate change and the development of highly accurate geologic time scales for the entire Cenozoic. Most of this understanding has come from the construction of astronomically tuned global ocean benthic foraminiferal oxygen isotope (δ18O) stacked record, derived by the international drilling operations of DSDP, ODP and IODP. The tuning includes fixed phase relationships between the obliquity and precession cycles and the inferred high-latitude climate, i.e. glacial-interglacial, response, which hark back to SPECMAP, using simple ice sheet models and a limited number of radiometric dates. This approach was largely implemented in the widely applied LR04 stack, though LR04 assumed shorter response times for the smaller ice caps during the Pliocene. In the past decades, an astronomically calibrated time scale for the Pliocene and Pleistocene of the Mediterranean has been developed, which has become the reference for the standard Geologic Time Scale. Typical of the Mediterranean marine sediments are the cyclic lithological alternations, reflecting the interference between obliquity and precession-paced low latitude climate variability, such as the African monsoon. Here we present the first benthic foraminiferal based oxygen isotope record of the Mediterranean reference scale, which strikingly mirrors the LR04. We will use this record to discuss the assumed open ocean glacial-interglacial related phase relations over the past 5.3 million years.

Climatic and Landscape Influences on Fire Regimes from 1984 to 2010 in the Western United States

PubMed Central

Liu, Zhihua; Wimberly, Michael C.

2015-01-01

An improved understanding of the relative influences of climatic and landscape controls on multiple fire regime components is needed to enhance our understanding of modern fire regimes and how they will respond to future environmental change. To address this need, we analyzed the spatio-temporal patterns of fire occurrence, size, and severity of large fires (> 405 ha) in the western United States from 1984–2010. We assessed the associations of these fire regime components with environmental variables, including short-term climate anomalies, vegetation type, topography, and human influences, using boosted regression tree analysis. Results showed that large fire occurrence, size, and severity each exhibited distinctive spatial and spatio-temporal patterns, which were controlled by different sets of climate and landscape factors. Antecedent climate anomalies had the strongest influences on fire occurrence, resulting in the highest spatial synchrony. In contrast, climatic variability had weaker influences on fire size and severity and vegetation types were the most important environmental determinants of these fire regime components. Topography had moderately strong effects on both fire occurrence and severity, and human influence variables were most strongly associated with fire size. These results suggest a potential for the emergence of novel fire regimes due to the responses of fire regime components to multiple drivers at different spatial and temporal scales. Next-generation approaches for projecting future fire regimes should incorporate indirect climate effects on vegetation type changes as well as other landscape effects on multiple components of fire regimes. PMID:26465959

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.

Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches

PubMed Central

Delerce, Sylvain; Dorado, Hugo; Grillon, Alexandre; Rebolledo, Maria Camila; Prager, Steven D.; Patiño, Victor Hugo; Garcés Varón, Gabriel; Jiménez, Daniel

2016-01-01

Seasonal and inter-annual climate variability have become important issues for farmers, and climate change has been shown to increase them. Simultaneously farmers and agricultural organizations are increasingly collecting observational data about in situ crop performance. Agriculture thus needs new tools to cope with changing environmental conditions and to take advantage of these data. Data mining techniques make it possible to extract embedded knowledge associated with farmer experiences from these large observational datasets in order to identify best practices for adapting to climate variability. We introduce new approaches through a case study on irrigated and rainfed rice in Colombia. Preexisting observational datasets of commercial harvest records were combined with in situ daily weather series. Using Conditional Inference Forest and clustering techniques, we assessed the relationships between climatic factors and crop yield variability at the local scale for specific cultivars and growth stages. The analysis showed clear relationships in the various location-cultivar combinations, with climatic factors explaining 6 to 46% of spatiotemporal variability in yield, and with crop responses to weather being non-linear and cultivar-specific. Climatic factors affected cultivars differently during each stage of development. For instance, one cultivar was affected by high nighttime temperatures in the reproductive stage but responded positively to accumulated solar radiation during the ripening stage. Another was affected by high nighttime temperatures during both the vegetative and reproductive stages. Clustering of the weather patterns corresponding to individual cropping events revealed different groups of weather patterns for irrigated and rainfed systems with contrasting yield levels. Best-suited cultivars were identified for some weather patterns, making weather-site-specific recommendations possible. This study illustrates the potential of data mining for adding value to existing observational data in agriculture by allowing embedded knowledge to be quickly leveraged. It generates site-specific information on cultivar response to climatic factors and supports on-farm management decisions for adaptation to climate variability. PMID:27560980

Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches.

PubMed

Delerce, Sylvain; Dorado, Hugo; Grillon, Alexandre; Rebolledo, Maria Camila; Prager, Steven D; Patiño, Victor Hugo; Garcés Varón, Gabriel; Jiménez, Daniel

2016-01-01

Seasonal and inter-annual climate variability have become important issues for farmers, and climate change has been shown to increase them. Simultaneously farmers and agricultural organizations are increasingly collecting observational data about in situ crop performance. Agriculture thus needs new tools to cope with changing environmental conditions and to take advantage of these data. Data mining techniques make it possible to extract embedded knowledge associated with farmer experiences from these large observational datasets in order to identify best practices for adapting to climate variability. We introduce new approaches through a case study on irrigated and rainfed rice in Colombia. Preexisting observational datasets of commercial harvest records were combined with in situ daily weather series. Using Conditional Inference Forest and clustering techniques, we assessed the relationships between climatic factors and crop yield variability at the local scale for specific cultivars and growth stages. The analysis showed clear relationships in the various location-cultivar combinations, with climatic factors explaining 6 to 46% of spatiotemporal variability in yield, and with crop responses to weather being non-linear and cultivar-specific. Climatic factors affected cultivars differently during each stage of development. For instance, one cultivar was affected by high nighttime temperatures in the reproductive stage but responded positively to accumulated solar radiation during the ripening stage. Another was affected by high nighttime temperatures during both the vegetative and reproductive stages. Clustering of the weather patterns corresponding to individual cropping events revealed different groups of weather patterns for irrigated and rainfed systems with contrasting yield levels. Best-suited cultivars were identified for some weather patterns, making weather-site-specific recommendations possible. This study illustrates the potential of data mining for adding value to existing observational data in agriculture by allowing embedded knowledge to be quickly leveraged. It generates site-specific information on cultivar response to climatic factors and supports on-farm management decisions for adaptation to climate variability.

Strategies for Interactive Visualization of Large Scale Climate Simulations

NASA Astrophysics Data System (ADS)

Xie, J.; Chen, C.; Ma, K.; Parvis

2011-12-01

With the advances in computational methods and supercomputing technology, climate scientists are able to perform large-scale simulations at unprecedented resolutions. These simulations produce data that are time-varying, multivariate, and volumetric, and the data may contain thousands of time steps with each time step having billions of voxels and each voxel recording dozens of variables. Visualizing such time-varying 3D data to examine correlations between different variables thus becomes a daunting task. We have been developing strategies for interactive visualization and correlation analysis of multivariate data. The primary task is to find connection and correlation among data. Given the many complex interactions among the Earth's oceans, atmosphere, land, ice and biogeochemistry, and the sheer size of observational and climate model data sets, interactive exploration helps identify which processes matter most for a particular climate phenomenon. We may consider time-varying data as a set of samples (e.g., voxels or blocks), each of which is associated with a vector of representative or collective values over time. We refer to such a vector as a temporal curve. Correlation analysis thus operates on temporal curves of data samples. A temporal curve can be treated as a two-dimensional function where the two dimensions are time and data value. It can also be treated as a point in the high-dimensional space. In this case, to facilitate effective analysis, it is often necessary to transform temporal curve data from the original space to a space of lower dimensionality. Clustering and segmentation of temporal curve data in the original or transformed space provides us a way to categorize and visualize data of different patterns, which reveals connection or correlation of data among different variables or at different spatial locations. We have employed the power of GPU to enable interactive correlation visualization for studying the variability and correlations of a single or a pair of variables. It is desired to create a succinct volume classification that summarizes the connection among all correlation volumes with respect to various reference locations. Providing a reference location must correspond to a voxel position, the number of correlation volumes equals the total number of voxels. A brute-force solution takes all correlation volumes as the input and classifies their corresponding voxels according to their correlation volumes' distance. For large-scale time-varying multivariate data, calculating all these correlation volumes on-the-fly and analyzing the relationships among them is not feasible. We have developed a sampling-based approach for volume classification in order to reduce the computation cost of computing the correlation volumes. Users are able to employ their domain knowledge in selecting important samples. The result is a static view that captures the essence of correlation relationships; i.e., for all voxels in the same cluster, their corresponding correlation volumes are similar. This sampling-based approach enables us to obtain an approximation of correlation relations in a cost-effective manner, thus leading to a scalable solution to investigate large-scale data sets. These techniques empower climate scientists to study large data from their simulations.

REGRESSION MODELS THAT RELATE STREAMS TO WATERSHEDS: COPING WITH NUMEROUS, COLLINEAR PEDICTORS

EPA Science Inventory

GIS efforts can produce a very large number of watershed variables (climate, land use/land cover and topography, all defined for multiple areas of influence) that could serve as candidate predictors in a regression model of reach-scale stream features. Invariably, many of these ...

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.

Cloudy Windows: What GCM Ensembles, Reanalyses and Observations Tell Us About Uncertainty in Greenland's Future Climate and Surface Melting

NASA Astrophysics Data System (ADS)

Reusch, D. B.

2016-12-01

Any analysis that wants to use a GCM-based scenario of future climate benefits from knowing how much uncertainty the GCM's inherent variability adds to the development of climate change predictions. This is extra relevant in the polar regions due to the potential of global impacts (e.g., sea level rise) from local (ice sheet) climate changes such as more frequent/intense surface melting. High-resolution, regional-scale models using GCMs for boundary/initial conditions in future scenarios inherit a measure of GCM-derived externally-driven uncertainty. We investigate these uncertainties for the Greenland ice sheet using the 30-member CESM1.0-CAM5-BGC Large Ensemble (CESMLE) for recent (1981-2000) and future (2081-2100, RCP 8.5) decades. Recent simulations are skill-tested against the ERA-Interim reanalysis and AWS observations with results informing future scenarios. We focus on key variables influencing surface melting through decadal climatologies, nonlinear analysis of variability with self-organizing maps (SOMs), regional-scale modeling (Polar WRF), and simple melt models. Relative to the ensemble average, spatially averaged climatological July temperature anomalies over a Greenland ice-sheet/ocean domain are mostly between +/- 0.2 °C. The spatial average hides larger local anomalies of up to +/- 2 °C. The ensemble average itself is 2 °C cooler than ERA-Interim. SOMs extend our diagnostics by providing a concise, objective summary of model variability as a set of generalized patterns. For CESMLE, the SOM patterns summarize the variability of multiple realizations of climate. Changes in pattern frequency by ensemble member show the influence of initial conditions. For example, basic statistical analysis of pattern frequency yields interquartile ranges of 2-4% for individual patterns across the ensemble. In climate terms, this tells us about climate state variability through the range of the ensemble, a potentially significant source of melt-prediction uncertainty. SOMs can also capture the different trajectories of climate due to intramodel variability over time. Polar WRF provides higher resolution regional modeling with improved, polar-centric model physics. Simple melt models allow us to characterize impacts of the upstream uncertainties on estimates of surface melting.

Water management in the Roman world

NASA Astrophysics Data System (ADS)

Dermody, Brian J.; van Beek, Rens L. P. H.; Meeks, Elijah; Klein Goldewijk, Kees; Bierkens, Marc F. P.; Scheidel, Walter; Wassen, Martin J.; van der Velde, Ype; Dekker, Stefan C.

2014-05-01

Climate variability can have extreme impacts on societies in regions that are water-limited for agriculture. A society's ability to manage its water resources in such environments is critical to its long-term viability. Water management can involve improving agricultural yields through in-situ irrigation or redistributing water resources through trade in food. Here, we explore how such water management strategies affected the resilience of the Roman Empire to climate variability in the water-limited region of the Mediterranean. Using the large-scale hydrological model PCR-GLOBWB and estimates of landcover based on the Historical Database of the Global Environment (HYDE) we generate potential agricultural yield maps under variable climate. HYDE maps of population density in conjunction with potential yield estimates are used to develop maps of agricultural surplus and deficit. The surplus and deficit regions are abstracted to nodes on a water redistribution network based on the Stanford Geospatial Network Model of the Roman World (ORBIS). This demand-driven, water redistribution network allows us to quantitatively explore how water management strategies such as irrigation and food trade improved the resilience of the Roman Empire to climate variability.

Coordinated Parameterization Development and Large-Eddy Simulation for Marine and Arctic Cloud-Topped Boundary Layers

NASA Technical Reports Server (NTRS)

Bretherton, Christopher S.

2002-01-01

The goal of this project was to compare observations of marine and arctic boundary layers with: (1) parameterization systems used in climate and weather forecast models; and (2) two and three dimensional eddy resolving (LES) models for turbulent fluid flow. Based on this comparison, we hoped to better understand, predict, and parameterize the boundary layer structure and cloud amount, type, and thickness as functions of large scale conditions that are predicted by global climate models. The principal achievements of the project were as follows: (1) Development of a novel boundary layer parameterization for large-scale models that better represents the physical processes in marine boundary layer clouds; and (2) Comparison of column output from the ECMWF global forecast model with observations from the SHEBA experiment. Overall the forecast model did predict most of the major precipitation events and synoptic variability observed over the year of observation of the SHEBA ice camp.

Science and Strategic - Climate Implications

NASA Astrophysics Data System (ADS)

Tindall, J. A.; Moran, E. H.

2008-12-01

Energy of weather systems greatly exceeds energy produced and used by humans. Variation in this energy causes climate variability potentially resulting in local, national, and/or global catastrophes beyond our ability to deter the loss of life and economic destabilization. Large scale natural disasters routinely result in shortages of water, disruption of energy supplies, and destruction of infrastructure. The resulting unforeseen and disastrous events occurring beyond national emergency preparation, as related to climate variability, could insight civil unrest due to dwindling and/or inaccessible resources necessary for survival. Lack of these necessary resources in impacted countries often leads to wars. Climate change coupled with population growth, which exposes more of the population to potential risks associated with climate and environmental change, demands faster technological response. Understanding climate/associated environmental changes, the relation to human activity and behavior, and including this in national and international emergency/security management plans would alleviate shortcomings in our present and future technological status. The scale of environmental change will determine the potential magnitude of civil unrest at the local, national, and/or global level along with security issues at each level. Commonly, security issues related to possible civil unrest owing to temporal environmental change is not part of a short and/or long-term strategy, yet recent large-scale disasters are reminders that system failures (as in hurricane Katrina) include acknowledged breaches to individual, community, and infrastructure security. Without advance planning and management concerning environmental change, oncoming and climate related events will intensify the level of devastation and human catastrophe. Depending upon the magnitude and period of catastrophic events and/or environmental changes, destabilization of agricultural systems, energy supplies, and other lines of commodities often results in severely unbalanced supply and demand ratios, which eventually affect the entire global community. National economies potentially risk destabilization, which is especially important since economics plays a major role in strategic planning. This presentation will address these issues and the role that science can play in human sustainability and local, national, and international security.

Decadal changes in the frequency of major floods in near-natural catchments across North America and Europe

NASA Astrophysics Data System (ADS)

Hodgkins, Glenn A.; Hannaford, Jamie; Whitfield, Paul H.; Burn, Donald H.; Fleig, Anne; Stahl, Kerstin; Renard, Benjamin; Korhonen, Johanna; Murphy, Conor; Crochet, Philippe; Wilson, Donna; Madsen, Henrik

2013-04-01

Recent major floods in North America and Europe have received much press, with some concluding that these floods are more frequent in recent years as a result of anthropogenic warming. There has therefore been considerable scientific effort invested in establishing whether observed flood records show evidence of trends or variability in flood frequency, and to determine whether these patterns can be linked to climatic changes. However, the river catchments used in many published studies are influenced by direct human alteration such as reservoir regulation and urbanisation, which can confound the interpretation of climate-driven variability. Furthermore, a majority of previous studies have analysed changes in low magnitude floods, such as the annual peak flow, at a national scale. Few studies are known that have analysed changes in large floods (greater than 25-year floods) on a continental scale. To fill this research gap, the current study is analysing flood flows from reference hydrologic networks (RHNs) or RHN-like gauges across a large study domain embracing North America and much of Europe. RHNs comprise gauging stations with minimally disturbed catchment conditions, which have a near-natural flow regime and provide good quality data; RHN analyses thus allow hydro-climatic variability to be distinguished from direct artificial disturbances or data inhomogeneities. One of the key innovations in this study is the definition of an RHN-like network on a continental scale. The network incorporates existing, well-established RHNs in Canada, the US, the UK, Ireland and Norway, alongside RHN-like catchments from Europe (France, Switzerland, Iceland, Denmark, Sweden, Finland), which have been incorporated in the network following a major effort to ensure RHN-like status of candidate gauges through consultation with local experts. As the aim of the study is to examine long-term variability in the number of major floods, annual exceedances of 25-, 50-, and 100-year floods during the last 40-100 years are estimated for all study gauges across North America and Europe. These are then pooled, and regional and continental flood frequency time series computed, including separate groups for different types of hydrological regime (pluvial, nival, mixed etc). Preliminary results will be presented, focusing on whether there is evidence for interdecadal variability in the occurrence of flooding at the large scale in Europe and North America. The unique intercontinental dataset is an example of successful international collaboration on hydro-climatic data exchange, which is potentially a step towards establishing RHN-like networks on a global scale. Such networks will make a valuable contribution to the understanding of hydrological change in future.

Extratropical Respones to Amazon Deforestation

NASA Astrophysics Data System (ADS)

Badger, A.; Dirmeyer, P.

2014-12-01

Land-use change (LUC) is known to impact local climate conditions through modifications of land-atmosphere interactions. Large-scale LUC, such as Amazon deforestation, could have a significant effect on the local and regional climates. The question remains as to what the global impact of large-scale LUC could be, as previous modeling studies have shown non-local responses due to Amazon deforestation. A common shortcoming in many previous modeling studies is the use of prescribed ocean conditions, which can act as a boundary condition to dampen the global response with respect to changes in the mean and variability. Using fully coupled modeling simulations with the Community Earth System Model version 1.2.0, the Amazon rainforest has been replaced with a distribution of representative tropical crops. Through the modifications of local land-atmosphere interactions, a significant change in the region, both at the surface and throughout the atmosphere, can be quantified. Accompanying these local changes are significant changes to the atmospheric circulation across all scales, thus modifying regional climates in other locales. Notable impacts include significant changes in precipitation, surface fluxes, basin-wide sea surface temperatures and ENSO behavior.

Statistical analysis of corn yields responding to climate variability at various spatio-temporal resolutions

NASA Astrophysics Data System (ADS)

Jiang, H.; Lin, T.

2017-12-01

Rain-fed corn production systems are subject to sub-seasonal variations of precipitation and temperature during the growing season. As each growth phase has varied inherent physiological process, plants necessitate different optimal environmental conditions during each phase. However, this temporal heterogeneity towards climate variability alongside the lifecycle of crops is often simplified and fixed as constant responses in large scale statistical modeling analysis. To capture the time-variant growing requirements in large scale statistical analysis, we develop and compare statistical models at various spatial and temporal resolutions to quantify the relationship between corn yield and weather factors for 12 corn belt states from 1981 to 2016. The study compares three spatial resolutions (county, agricultural district, and state scale) and three temporal resolutions (crop growth phase, monthly, and growing season) to characterize the effects of spatial and temporal variability. Our results show that the agricultural district model together with growth phase resolution can explain 52% variations of corn yield caused by temperature and precipitation variability. It provides a practical model structure balancing the overfitting problem in county specific model and weak explanation power in state specific model. In US corn belt, precipitation has positive impact on corn yield in growing season except for vegetative stage while extreme heat attains highest sensitivity from silking to dough phase. The results show the northern counties in corn belt area are less interfered by extreme heat but are more vulnerable to water deficiency.

From the Last Interglacial to the Anthropocene: Modelling a Complete Glacial Cycle (PalMod)

NASA Astrophysics Data System (ADS)

Brücher, Tim; Latif, Mojib

2017-04-01

We will give a short overview and update on the current status of the national climate modelling initiative PalMod (Paleo Modelling, www.palmod.de). PalMod focuses on the understanding of the climate system dynamics and its variability during the last glacial cycle. The initiative is funded by the German Federal Ministry of Education and Research (BMBF) and its specific topics are: (i) to identify and quantify the relative contributions of the fundamental processes which determined the Earth's climate trajectory and variability during the last glacial cycle, (ii) to simulate with comprehensive Earth System Models (ESMs) the climate from the peak of the last interglacial - the Eemian warm period - up to the present, including the changes in the spectrum of variability, and (iii) to assess possible future climate trajectories beyond this century during the next millennia with sophisticated ESMs tested in such a way. The research is intended to be conducted over a period of 10 years, but with shorter funding cycles. PalMod kicked off in February 2016. The first phase focuses on the last deglaciation (app. the last 23.000 years). From the ESM perspective PalMod pushes forward model development by coupling ESM with dynamical ice sheet models. Computer scientists work on speeding up climate models using different concepts (like parallelisation in time) and one working group is dedicated to perform a comprehensive data synthesis to validate model performance. The envisioned approach is innovative in three respects. First, the consortium aims at simulating a full glacial cycle in transient mode and with comprehensive ESMs which allow full interactions between the physical and biogeochemical components of the Earth system, including ice sheets. Second, we shall address climate variability during the last glacial cycle on a large range of time scales, from interannual to multi-millennial, and attempt to quantify the relative contributions of external forcing and processes internal to the Earth system to climate variability at different time scales. Third, in order to achieve a higher level of understanding of natural climate variability at time scales of millennia, its governing processes and implications for the future climate, we bring together three different research communities: the Earth system modeling community, the proxy data community and the computational science community. The consortium consists of 18 partners including all major modelling centers within Germany. The funding comprises approximately 65 PostDoc positions and more than 120 scientists are involved. PalMod is coordinated at the Helmholtz Centre for Ocean Research Kiel (GEOMAR).

A multi-scale comparison of trait linkages to environmental and spatial variables in fish communities across a large freshwater lake.

PubMed

Strecker, Angela L; Casselman, John M; Fortin, Marie-Josée; Jackson, Donald A; Ridgway, Mark S; Abrams, Peter A; Shuter, Brian J

2011-07-01

Species present in communities are affected by the prevailing environmental conditions, and the traits that these species display may be sensitive indicators of community responses to environmental change. However, interpretation of community responses may be confounded by environmental variation at different spatial scales. Using a hierarchical approach, we assessed the spatial and temporal variation of traits in coastal fish communities in Lake Huron over a 5-year time period (2001-2005) in response to biotic and abiotic environmental factors. The association of environmental and spatial variables with trophic, life-history, and thermal traits at two spatial scales (regional basin-scale, local site-scale) was quantified using multivariate statistics and variation partitioning. We defined these two scales (regional, local) on which to measure variation and then applied this measurement framework identically in all 5 study years. With this framework, we found that there was no change in the spatial scales of fish community traits over the course of the study, although there were small inter-annual shifts in the importance of regional basin- and local site-scale variables in determining community trait composition (e.g., life-history, trophic, and thermal). The overriding effects of regional-scale variables may be related to inter-annual variation in average summer temperature. Additionally, drivers of fish community traits were highly variable among study years, with some years dominated by environmental variation and others dominated by spatially structured variation. The influence of spatial factors on trait composition was dynamic, which suggests that spatial patterns in fish communities over large landscapes are transient. Air temperature and vegetation were significant variables in most years, underscoring the importance of future climate change and shoreline development as drivers of fish community structure. Overall, a trait-based hierarchical framework may be a useful conservation tool, as it highlights the multi-scaled interactive effect of variables over a large landscape.

High-resolution, regional-scale crop yield simulations for the Southwestern United States

NASA Astrophysics Data System (ADS)

Stack, D. H.; Kafatos, M.; Medvigy, D.; El-Askary, H. M.; Hatzopoulos, N.; Kim, J.; Kim, S.; Prasad, A. K.; Tremback, C.; Walko, R. L.; Asrar, G. R.

2012-12-01

Over the past few decades, there have been many process-based crop models developed with the goal of better understanding the impacts of climate, soils, and management decisions on crop yields. These models simulate the growth and development of crops in response to environmental drivers. Traditionally, process-based crop models have been run at the individual farm level for yield optimization and management scenario testing. Few previous studies have used these models over broader geographic regions, largely due to the lack of gridded high-resolution meteorological and soil datasets required as inputs for these data intensive process-based models. In particular, assessment of regional-scale yield variability due to climate change requires high-resolution, regional-scale, climate projections, and such projections have been unavailable until recently. The goal of this study was to create a framework for extending the Agricultural Production Systems sIMulator (APSIM) crop model for use at regional scales and analyze spatial and temporal yield changes in the Southwestern United States (CA, AZ, and NV). Using the scripting language Python, an automated pipeline was developed to link Regional Climate Model (RCM) output with the APSIM crop model, thus creating a one-way nested modeling framework. This framework was used to combine climate, soil, land use, and agricultural management datasets in order to better understand the relationship between climate variability and crop yield at the regional-scale. Three different RCMs were used to drive APSIM: OLAM, RAMS, and WRF. Preliminary results suggest that, depending on the model inputs, there is some variability between simulated RCM driven maize yields and historical yields obtained from the United States Department of Agriculture (USDA). Furthermore, these simulations showed strong non-linear correlations between yield and meteorological drivers, with critical threshold values for some of the inputs (e.g. minimum and maximum temperature), beyond which the yields were negatively affected. These results are now being used for further regional-scale yield analysis as the aforementioned framework is adaptable to multiple geographic regions and crop types.

Statistical downscaling of mean temperature, maximum temperature, and minimum temperature on the Loess Plateau, China

NASA Astrophysics Data System (ADS)

Jiang, L.

2017-12-01

Climate change is considered to be one of the greatest environmental threats. Global climate models (GCMs) are the primary tool used for studying climate change. However, GCMs are limited because of their coarse spatial resolution and inability to resolve important sub-grid scale features such as terrain and clouds. Statistical downscaling methods can be used to downscale large-scale variables to local-scale. In this study, we assess the applicability of the Statistical Downscaling Model (SDSM) in downscaling the outputs from Beijing Normal University Earth System Model (BNU-ESM). The study focus on the the Loess Plateau, China, and the variables for downscaling include daily mean temperature (TMEAN), maximum temperature (TMAX) and minimum temperature (TMIN). The results show that SDSM performs well for these three climatic variables on the Loess Plateau. After downscaling, the root mean square errors for TMEAN, TMAX, TMIN for BNU-ESM were reduced by 70.9%, 75.1%, and 67.2%, respectively. All the rates of change in TMEAN, TMAX and TMIN during the 21st century decreased after SDSM downscaling. We also show that SDSM can effectively reduce uncertainty, compared with the raw model outputs. TMEAN uncertainty was reduced by 27.1%, 26.8%, and 16.3% for the future scenarios of RCP 2.6, RCP 4.5 and RCP 8.5, respectively. The corresponding reductions in uncertainty were 23.6%, 30.7%, and 18.7% for TMAX; 37.6%, 31.8%, and 23.2% for TMIN.

Santa Ana Winds of Southern California: Their Climatology and Variability Spanning 6.5 Decades from Regional Dynamical Modelling

NASA Astrophysics Data System (ADS)

Guzman-Morales, J.; Gershunov, A.

2015-12-01

Santa Ana Winds (SAWs) are an integral feature of the regional climate of Southern California/Northern Baja California region. In spite of their tremendous episodic impacts on the health, economy and mood of the region, climate-scale behavior of SAW is poorly understood. In the present work, we identify SAWs in mesoscale dynamical downscaling of a global reanalysis product and construct an hourly SAW catalogue spanning 65 years. We describe the long-term SAW climatology at relevant time-space resolutions, i.e, we developed local and regional SAW indices and analyse their variability on hourly, daily, annual, and multi-decadal timescales. Local and regional SAW indices are validated with available anemometer observations. Characteristic behaviors are revealed, e.g. the SAW intensity-duration relationship. At interdecadal time scales, we find that seasonal SAW activity is sensitive to prominent large-scale low-frequency modes of climate variability rooted in the tropical and north Pacific ocean-atmosphere system that are also known to affect the hydroclimate of this region. Lastly, we do not find any long-term trend in SAW frequency and intensity as previously reported. Instead, we identify a significant long-term trend in SAW behavior whereby contribution of extreme SAW events to total seasonal SAW activity has been increasing at the expense of moderate events. These findings motivate further investigation on SAW evolution in future climate and its impact on wildfires.

Do GCM's predict the climate.... Or the low frequency weather?

NASA Astrophysics Data System (ADS)

Lovejoy, S.; Schertzer, D.; Varon, D.

2012-04-01

Over twenty-five years ago, a three-regime scaling model was proposed describing the statistical variability of the atmosphere over time scales ranging from weather scales out to ≈ 100 kyrs. Using modern in situ data reanalyses, monthly surface series (at 5ox5o), 8 "multiproxy" (yearly) series of the Northern hemisphere from 1500 - 1980, and GRIP and Vostok paleotemperatures at 5.2 and ≈ 100 year resolutions (over the past 91-420 kyrs), we refine the model and show how it can be understood with the help of new developments in nonlinear dynamics, especially multifractals and cascades. In a scaling range, mean fluctuations in state variables such as temperature ΔT vary in power law manners ≈ Δt**H the where Δt is the duration. At small (weather) scales the fluctuation exponents are generally H>0; they grow with scale (Δt). At longer scales Δt >τw (≈ 10 days) H changes sign, the fluctuations decrease with scale; this is the low variability, "low frequency weather" regime. In this regime, the spectrum is a relatively flat "plateau", it's variability is low, stable, corresponding to our usual idea of "long term weather statistics". Finally for longer times, Δt>τc ≈ 10 - 100 years, once again H>0, so that the variability increases with scale: the true climate regime. These scaling regimes allow us to objectively define the weather as fluctuations over periods <τw, to define "climate states" as fluctuations at scale τc and then "climate change" as the fluctuations at longer periods (Δt>τc). We show that the intermediate low frequency weather regime is the result of the weather regime undergoing a "dimensional transition": at temporal scales longer than the typical lifetime of planetary structures (τw), the spatial degrees of freedom are rapidly quenched so that only the temporal degrees of freedom are important. This low frequency weather regime has statistical properties well reproduced not only by stochastic cascade models of weather, but also by control runs (i.e. without climate forcing) of GCM based climate forecasting systems including those of the Institut Pierre Simon Laplace (Paris) and the Earth Forecasting System (Hamburg). In order for these systems to go beyond simply predicting low frequency weather i.e. in order for them to predict the climate, they need appropriate climate forcings and/ or new internal mechanisms of variability. Using statistical scaling techniques we examine the scale dependence of fluctuations from forced and unforced GCM outputs, including from the ECHO-G and EFS simulations in the Millenium climate reconstruction project and compare this with data, multiproxies and paleo data. Our general conclusion is that the models systematically underestimate the multidecadal, multicentennial scale variability.

Spatiotemporal variability of temperature and precipitation in Gansu Province (Northwest China) during 1951-2015

NASA Astrophysics Data System (ADS)

Wen, Xiaohu; Wu, Xiaoqing; Gao, Meng

2017-11-01

Climate change is potentially challenging the sustainable development in many parts of the world, especially the semi-arid and arid regions on the earth. Northwest China (NWC) is one of the most arid areas in East Asia, and Gansu Province is located at the important climate transition zone in NWC. Spatiotemporal variability of both temperature and precipitation were analyzed based on the daily observation dataset at 29 meteorological stations over Gansu during 1951-2015. The Mann-Kendall trend test was utilized to detect monotonic trends in extreme climate indices, mean temperature, and total precipitation. The results revealed that the warming trends were statistically significant at most stations in Gansu, especially at the high altitude stations; however, the change trends in annual and seasonal precipitation over Gansu were not significant as expected. Furthermore, the 29 stations were spatially grouped using hierarchical clustering method. The regional-averaged temperature anomalies also showed a significant warming trend beginning at the end of 1970s. Spatial variations were also observed in the annual and seasonal precipitation over Gansu. In general, precipitation increased in the western part of Gansu while decreased in the eastern part. Additionally, the wavelet analyses revealed that the teleconnection between large scale circulation and summer precipitation varied not only from region to region, but also was different at different time scale and different time periods. Analysis of large-scale atmospheric circulation changes showed that a strengthening anticyclonic circulation, increasing geopotential height and rapid warming over the Eurasian continent were considered to be attributable to climate change in Gansu and even in NWC.

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.

Alexander Polonsky Global warming hiatus, ocean variability and regional climate change

NASA Astrophysics Data System (ADS)

Polonsky, A.

2016-02-01

This presentation generalizes the results concerning ocean variability, large-scale interdecadal ocean-atmosphere interaction in the Atlantic and Pacific Oceans and their impact on global and regional climate change carried out by the author and his colleagues for about 20 years. It is demonstrated once more that Atlantic Multidecadal Oscillation (AMO, which was early referred by the author as "interdecadal mode of North Atlantic Oscillation") is the crucial natural interdecadal climatic signal for the Atlantic-European and Mediterranean regions. It is characterized by amplitude which is the same order as human-induced centennial climate change and exceeds trend-like anthropogenic change at the decadal scale. Fast increasing of the global and Northern Hemisphere air temperature in the last 30 yrs of XX century (especially pronounced in the North Atlantic region and surrounded areas) is due to coincidence of human-induced positive trend and transition from the negative to the positive phase of AMO. AMO accounts for about 50% (60%) of the global (Northern Hemisphere) temperature trend in that period. Recent global warming hiatus is mostly the result of switch off the AMO phase. Typical AMO temporal scale is dictated by meridional overturning variability in the Atlantic Ocean and associated magnitude of meridional heat transport. Pacific Decadal Oscillation (PDO) is the other natural interdecadal signal which significantly impacts the global and regional climate variability. The rate of the ocean warming for different periods assessed separately for the upper mixed layer and deeper layers using data of oceanic re-analysis since 1959 confirms the principal role of the natural interdecadal oceanic modes (AMO and PDO) in observing climate change. At the same time a lack of deep-ocean long-term observing system restricts the accuracy of assessment of the heat redistribution in the World Ocean. I thanks to Pavel Sukhonos for help in the presentation preparing.

Contrasting scaling properties of interglacial and glacial climates

NASA Astrophysics Data System (ADS)

Ditlevsen, Peter; Shao, Zhi-Gang

2017-04-01

Understanding natural climate variability is essential for assessments of climate change. This is reflected in the scaling properties of climate records. The scaling exponents of the interglacial and the glacial climates are fundamentally different. The Holocene record is monofractal, with a scaling exponent H˜0.7. On the contrary, the glacial record is multifractal, with a significantly higher scaling exponent H˜1.2, indicating a longer persistence time and stronger nonlinearities in the glacial climate. The glacial climate is dominated by the strong multi-millennial Dansgaard-Oeschger (DO) events influencing the long-time correlation. However, by separately analysing the last glacial maximum lacking DO events, here we find the same scaling for that period as for the full glacial period. The unbroken scaling thus indicates that the DO events are part of the natural variability and not externally triggered. At glacial time scales, there is a scale break to a trivial scaling, contrasting the DO events from the similarly saw-tooth-shaped glacial cycles. Ref: Zhi-Gang Shao and Peter Ditlevsen, Nature Comm. 7, 10951, 2016

Solar influence on climate during the past millennium: Results from transient simulations with the NCAR Climate System Model

PubMed Central

Ammann, Caspar M.; Joos, Fortunat; Schimel, David S.; Otto-Bliesner, Bette L.; Tomas, Robert A.

2007-01-01

The potential role of solar variations in modulating recent climate has been debated for many decades and recent papers suggest that solar forcing may be less than previously believed. Because solar variability before the satellite period must be scaled from proxy data, large uncertainty exists about phase and magnitude of the forcing. We used a coupled climate system model to determine whether proxy-based irradiance series are capable of inducing climatic variations that resemble variations found in climate reconstructions, and if part of the previously estimated large range of past solar irradiance changes could be excluded. Transient simulations, covering the published range of solar irradiance estimates, were integrated from 850 AD to the present. Solar forcing as well as volcanic and anthropogenic forcing are detectable in the model results despite internal variability. The resulting climates are generally consistent with temperature reconstructions. Smaller, rather than larger, long-term trends in solar irradiance appear more plausible and produced modeled climates in better agreement with the range of Northern Hemisphere temperature proxy records both with respect to phase and magnitude. Despite the direct response of the model to solar forcing, even large solar irradiance change combined with realistic volcanic forcing over past centuries could not explain the late 20th century warming without inclusion of greenhouse gas forcing. Although solar and volcanic effects appear to dominate most of the slow climate variations within the past thousand years, the impacts of greenhouse gases have dominated since the second half of the last century. PMID:17360418

How are streamflow responses to the El Nino Southern Oscillation affected by watershed characteristics?

NASA Astrophysics Data System (ADS)

Rice, Joshua S.; Emanuel, Ryan E.

2017-05-01

Understanding the factors that influence how global climate phenomena, such as the El-Nino Southern Oscillation (ENSO), affect streamflow behavior is an important area of research in the hydrologic sciences. While large-scale patterns in ENSO-streamflow relationships have been thoroughly studied, and are relatively well-understood, information is scarce concerning factors that affect variation in ENSO responses from one watershed to another. To this end, we examined relationships between variability in ENSO activity and streamflow for 2731 watersheds across the conterminous U.S. from 1970 to 2014 using a novel approach to account for the intermediary role of precipitation. We applied an ensemble of regression techniques to describe relationships between variability in ENSO activity and streamflow as a function of watershed characteristics including: hydroclimate, topography, geomorphology, geographic location, land cover, soil characteristics, bedrock geology, and anthropogenic influences. We found that variability in watershed scale ENSO-streamflow relationships was strongly related to factors including: precipitation timing and phase, forest cover, and interactions between watershed topography and geomorphology. These, and other influential factors, share in common the ability to affect the partitioning and movement of water within watersheds. Our results demonstrate that the conceptualization of watersheds as signal filters for hydroclimate inputs, commonly applied to short-term rainfall-runoff responses, also applies to long-term hydrologic responses to sources of recurrent climate variability. These results also show that watershed processes, which are typically studied at relatively fine spatial scales, are also critical for understanding continental scale hydrologic responses to global climate.

Local-scale models reveal ecological niche variability in amphibian and reptile communities from two contrasting biogeographic regions

PubMed Central

Santos, Xavier; Felicísimo, Ángel M.

2016-01-01

Ecological Niche Models (ENMs) are widely used to describe how environmental factors influence species distribution. Modelling at a local scale, compared to a large scale within a high environmental gradient, can improve our understanding of ecological species niches. The main goal of this study is to assess and compare the contribution of environmental variables to amphibian and reptile ENMs in two Spanish national parks located in contrasting biogeographic regions, i.e., the Mediterranean and the Atlantic area. The ENMs were built with maximum entropy modelling using 11 environmental variables in each territory. The contributions of these variables to the models were analysed and classified using various statistical procedures (Mann–Whitney U tests, Principal Components Analysis and General Linear Models). Distance to the hydrological network was consistently the most relevant variable for both parks and taxonomic classes. Topographic variables (i.e., slope and altitude) were the second most predictive variables, followed by climatic variables. Differences in variable contribution were observed between parks and taxonomic classes. Variables related to water availability had the larger contribution to the models in the Mediterranean park, while topography variables were decisive in the Atlantic park. Specific response curves to environmental variables were in accordance with the biogeographic affinity of species (Mediterranean and non-Mediterranean species) and taxonomy (amphibians and reptiles). Interestingly, these results were observed for species located in both parks, particularly those situated at their range limits. Our findings show that ecological niche models built at local scale reveal differences in habitat preferences within a wide environmental gradient. Therefore, modelling at local scales rather than assuming large-scale models could be preferable for the establishment of conservation strategies for herptile species in natural parks. PMID:27761304

Regional climate model simulations indicate limited climatic impacts by operational and planned European wind farms.

PubMed

Vautard, Robert; Thais, Françoise; Tobin, Isabelle; Bréon, François-Marie; Devezeaux de Lavergne, Jean-Guy; Colette, Augustin; Yiou, Pascal; Ruti, Paolo Michele

2014-01-01

The rapid development of wind energy has raised concerns about environmental impacts. Temperature changes are found in the vicinity of wind farms and previous simulations have suggested that large-scale wind farms could alter regional climate. However, assessments of the effects of realistic wind power development scenarios at the scale of a continent are missing. Here we simulate the impacts of current and near-future wind energy production according to European Union energy and climate policies. We use a regional climate model describing the interactions between turbines and the atmosphere, and find limited impacts. A statistically significant signal is only found in winter, with changes within ±0.3 °C and within 0-5% for precipitation. It results from the combination of local wind farm effects and changes due to a weak, but robust, anticyclonic-induced circulation over Europe. However, the impacts remain much weaker than the natural climate interannual variability and changes expected from greenhouse gas emissions.

Cross-scale assessment of potential habitat shifts in a rapidly changing climate

USGS Publications Warehouse

Jarnevich, Catherine S.; Holcombe, Tracy R.; Bella, Elizabeth S.; Carlson, Matthew L.; Graziano, Gino; Lamb, Melinda; Seefeldt, Steven S.; Morisette, Jeffrey T.

2014-01-01

We assessed the ability of climatic, environmental, and anthropogenic variables to predict areas of high-risk for plant invasion and consider the relative importance and contribution of these predictor variables by considering two spatial scales in a region of rapidly changing climate. We created predictive distribution models, using Maxent, for three highly invasive plant species (Canada thistle, white sweetclover, and reed canarygrass) in Alaska at both a regional scale and a local scale. Regional scale models encompassed southern coastal Alaska and were developed from topographic and climatic data at a 2 km (1.2 mi) spatial resolution. Models were applied to future climate (2030). Local scale models were spatially nested within the regional area; these models incorporated physiographic and anthropogenic variables at a 30 m (98.4 ft) resolution. Regional and local models performed well (AUC values > 0.7), with the exception of one species at each spatial scale. Regional models predict an increase in area of suitable habitat for all species by 2030 with a general shift to higher elevation areas; however, the distribution of each species was driven by different climate and topographical variables. In contrast local models indicate that distance to right-of-ways and elevation are associated with habitat suitability for all three species at this spatial level. Combining results from regional models, capturing long-term distribution, and local models, capturing near-term establishment and distribution, offers a new and effective tool for highlighting at-risk areas and provides insight on how variables acting at different scales contribute to suitability predictions. The combinations also provides easy comparison, highlighting agreement between the two scales, where long-term distribution factors predict suitability while near-term do not and vice versa.

River chemistry as a monitor of Yosemite Park mountain hydroclimates

USGS Publications Warehouse

Peterson, David; Smith, Richard; Hager, Stephen; Hicke, Jeffrey A.; Dettinger, Michael; Huber, King

2005-01-01

Climate is the major source of variability in U.S. and global water resources. For example, large-scale variations in the global atmosphere and the Pacific Ocean are responsible for much of the variability in river discharge in Hawaii, Alaska, the U.S. Pacific Northwest, and the U.S. Southwest [Cayan and Peterson, 1989], and thus are closely linked to water and energy resources of the western United States [Cayan et al., 2003].

A Hierarchical Analysis of Tree Growth and Environmental Drivers Across Eastern US Temperate Forests

NASA Astrophysics Data System (ADS)

Mantooth, J.; Dietze, M.

2014-12-01

Improving predictions of how forests in the eastern United States will respond to future global change requires a better understanding of the drivers of variability in tree growth rates. Current inventory data lack the temporal resolution to characterize interannual variability, while existing growth records lack the extent required to assess spatial scales of variability. Therefore, we established a network of forest inventory plots across ten sites across the eastern US, and measured growth in adult trees using increment cores. Sites were chosen to maximize climate space explored, while within sites, plots were spread across primary environmental gradients to explore landscape-level variability in growth. Using the annual growth record available from tree cores, we explored the responses of trees to multiple environmental covariates over multiple spatial and temporal scales. We hypothesized that within and across sites growth rates vary among species, and that intraspecific growth rates increase with temperature along a species' range. We also hypothesized that trees show synchrony in growth responses to landscape-scale climatic changes. Initial analyses of growth increments indicate that across sites, trees with intermediate shade tolerance, e.g. Red Oak (Quercus rubra), tend to have the highest growth rates. At the site level, there is evidence for synchrony in response to large-scale climatic events (e.g. prolonged drought and above average temperatures). However, growth responses to climate at the landscape scale have yet to be detected. Our current analysis utilizes hierarchical Bayesian state-space modeling to focus on growth responses of adult trees to environmental covariates at multiple spatial and temporal scales. This predictive model of tree growth currently incorporates observed effects at the individual, plot, site, and landscape scale. Current analysis using this model shows a potential slowing of growth in the past decade for two sites in the northeastern US (Harvard Forest and Bartlett Experimental Forest), however more work is required to determine the robustness of this trend. Finally, these observations are being incorporated into ecosystem models using the Brown Dog informatics tools and the Predictive Ecosystem Analyzer (PEcAn) data assimilation workflow.

Reconstruction of regional climate and climate change in past decades

NASA Astrophysics Data System (ADS)

von Storch, H.; Feser, F.; Weisse, R.; Zahn, M.

2009-12-01

Regional climate models, which are constrained by large scale information (spectral nudging) provided by re-analyses, allow for the construction of a mostly homogeneous description of regional weather statistics since about 1950. The potential of this approach has been demonstrated for Northern Europe. That data set, named CoastDat, does not only contain hourly data on atmospheric variables, in particular wind, but also on marine weather, i.e., short term water level, current and sea state variations. Another example is the multi-decadal variability of Polar Lows in the subarctic waters. The utility of such data sets is broad, from risk assessments related to coastal wind and wave conditions, assessment of determining the causes for regional climate change, a-posteriori analysis of the efficiency of environmental legislation (example: lead). In the paper, the methodology is outlined, examples are provided and the utility of the product discussed.

Trend and variability in western and central Africa streamflow, and their relation to climate variability between 1950 and 2010

NASA Astrophysics Data System (ADS)

Sidibe, Moussa; Dieppois, Bastien; Mahé, Gil; Paturel, Jean-Emmanuel; Rouché, Nathalie; Amoussou, Ernest; Anifowose, Babatunde; Lawler, Damian

2017-04-01

Unprecedented drought episodes that struck western and central Africa between the late 1960s and 1980s. This triggered many studies investigating rainfall variability and its impacts on food production systems. However, most studies were focused at the catchment scale. In this study, we examine how rainfall variability has impacted on river flow at the subcontinental scale between 1950 and 2010, as well as the key large-scale controls on this relationship. For the first time, we establish a complete, gap-filled, monthly streamflow data set, which extends from 1950 to 2010, over the western and central African region. To achieve this, we used linear regression modelling across and between 600 flow gauging stations (see initial database information at http://www.hydrosciences.fr/sierem/index_en.htm). Streamflow trend and variability are then seasonally assessed at this subcontinental scale and compared to those observed in three different rainfall data sets (i.e. CRU TS3.24, GPCC V7, IRD-HSM). Long-term trends and variability in streamflow are mainly consistent with trends in rainfall. However, these relationships may have been moderated by: i) changes in land use; and ii) contributions from groundwater resources. In particular, we note that the recent post 1990s partial recovery in Sahel rainfall could have, at least partially, positively impacted river flows (e.g. the Senegal and Niger rivers). Using multi-temporal trend and continuous wavelet analysis, the time-evolution of western and central African river flows are analysed, and are all characterized by very strong decadal fluctuations, which can be interpreted as modulations in the baseflow. These decadal fluctuations, which are also significantly detected in rainfall, are likely related to large-scale sea-surface temperature (SST) anomaly patterns, such as the tropical Atlantic SST variability, the Atlantic Multidecadal Oscillation, the Interdecadal Pacific Oscillation and/or the Pacific Decadal Oscillation. Furthermore, hitherto-poorly understood hydroclimatic processes related to these teleconnections at decadal timescales will be examined in this study. Influences of the catchment properties (e.g. size, shape, vegetation and landuse cover, soil type, ground-water level, direction of stream flow across climate zones) on these decadal fluctuations in river flows will also be assessed. This study therefore aims to improve the ability of current regional and global climate models to simulate such ranges of variability, to significantly improve regional hydroclimate understanding, as a means for improving the development of future scenarios for water resources in western and central Africa.

Cryptic biodiversity loss linked to global climate change

NASA Astrophysics Data System (ADS)

Bálint, M.; Domisch, S.; Engelhardt, C. H. M.; Haase, P.; Lehrian, S.; Sauer, J.; Theissinger, K.; Pauls, S. U.; Nowak, C.

2011-09-01

Global climate change (GCC) significantly affects distributional patterns of organisms, and considerable impacts on biodiversity are predicted for the next decades. Inferred effects include large-scale range shifts towards higher altitudes and latitudes, facilitation of biological invasions and species extinctions. Alterations of biotic patterns caused by GCC have usually been predicted on the scale of taxonomically recognized morphospecies. However, the effects of climate change at the most fundamental level of biodiversity--intraspecific genetic diversity--remain elusive. Here we show that the use of morphospecies-based assessments of GCC effects will result in underestimations of the true scale of biodiversity loss. Species distribution modelling and assessments of mitochondrial DNA variability in nine montane aquatic insect species in Europe indicate that future range contractions will be accompanied by severe losses of cryptic evolutionary lineages and genetic diversity within these lineages. These losses greatly exceed those at the scale of morphospecies. We also document that the extent of range reduction may be a useful proxy when predicting losses of genetic diversity. Our results demonstrate that intraspecific patterns of genetic diversity should be considered when estimating the effects of climate change on biodiversity.

Evaluation of a Mesoscale Convective System in Variable-Resolution CESM

NASA Astrophysics Data System (ADS)

Payne, A. E.; Jablonowski, C.

2017-12-01

Warm season precipitation over the Southern Great Plains (SGP) follows a well observed diurnal pattern of variability, peaking at night-time, due to the eastward propagation of mesoscale convection systems that develop over the eastern slopes of the Rockies in the late afternoon. While most climate models are unable to adequately capture the organization of convection and characteristic pattern of precipitation over this region, models with high enough resolution to explicitly resolve convection show improvement. However, high resolution simulations are computationally expensive and, in the case of regional climate models, are subject to boundary conditions. Newly developed variable resolution global climate models strike a balance between the benefits of high-resolution regional climate models and the large-scale dynamics of global climate models and low computational cost. Recently developed parameterizations that are insensitive to the model grid scale provide a way to improve model performance. Here, we present an evaluation of the newly available Cloud Layers Unified by Binormals (CLUBB) parameterization scheme in a suite of variable-resolution CESM simulations with resolutions ranging from 110 km to 7 km within a regionally refined region centered over the SGP Atmospheric Radiation Measurement (ARM) site. Simulations utilize the hindcast approach developed by the Department of Energy's Cloud-Associated Parameterizations Testbed (CAPT) for the assessment of climate models. We limit our evaluation to a single mesoscale convective system that passed over the region on May 24, 2008. The effects of grid-resolution on the timing and intensity of precipitation, as well as, on the transition from shallow to deep convection are assessed against ground-based observations from the SGP ARM site, satellite observations and ERA-Interim reanalysis.

Smoothing of millennial scale climate variability in European Loess (and other records)

NASA Astrophysics Data System (ADS)

Zeeden, Christian; Obreht, Igor; Hambach, Ulrich; Veres, Daniel; Marković, Slobodan B.; Lehmkuhl, Frank

2017-04-01

Millennial scale climate variability is seen in various records of the northern hemisphere in the last glacial cycle, and their expression represents a correlation tool beyond the resolution of e.g. luminescence dating. Highest (correlative) dating accuracy is a prerequisite of comparing different geoarchives, especially when related to archaeological findings. Here we attempt to constrain the timing of loess geoarchives representing the environmental context of early humans in south-eastern Europe, and discuss the challenge of dealing with smoothed records. In this contribution, we present rock magnetic and grain size data from the Rasova loess record in the Lower Danube basin (Romania), showing millennial scale climate variability. Additionally, we summarize similar data from the Lower and Middle Danube Basins. A comparison of these loess data and reference records from Greenland ice cores and the Mediterranean-Black Sea region indicates a rather unusual expression of millennial scale climate variability recorded in loess. To explain the observed patterns, we experiment with low-pass filters of reference records to simulate a signal smoothing by natural processes such as e.g. bioturbation and pervasive diagenesis. Low-pass filters avoid high frequency oscillations and focus on the longer period (lower frequency) variability, here using cut-off periods from 1-15 kyr. In our opinion low-pass filters represent simple models for the expression of millennial scale climate variability in low sedimentation environments, and in sediments where signals are smoothed by e.g. bioturbation and/or diagenesis. Using different low-pass filter thresholds allows us to (a) explain observed patterns and their relation to millennial scale climate variability, (b) propose these filtered/smoothed signals as correlation targets for records lacking millennial scale recording, but showing smoothed climate variability on supra-millennial scales, and (c) determine which time resolution specific (loess) records can reproduce. Comparing smoothed records to reference data may be a step forward especially for last glacial stratigraphies, where millennial scale patterns are certainly present but not directly recorded in some geoarchives. Interestingly, smoothed datasets from Greenland and the Black Sea-Mediterranean region are most similar in the last ca. 15 ka and again from ca. 30-50 ka. During the cold phase from ca. 30-15 ka records show dissimilarities, challenging robust correlative time scales in this age range. A potential explanation may be related to the expansion of Northern European and Alpine ice sheets influencing atmospheric systems in the North Atlantic and Eurasian regions and thus leading to regionally and temporally differentiated climatic responses.

Can Regional Climate Models Improve Warm Season Forecasts in the North American Monsoon Region?

NASA Astrophysics Data System (ADS)

Dominguez, F.; Castro, C. L.

2009-12-01

The goal of this work is to improve warm season forecasts in the North American Monsoon Region. To do this, we are dynamically downscaling warm season CFS (Climate Forecast System) reforecasts from 1982-2005 for the contiguous U.S. using the Weather Research and Forecasting (WRF) regional climate model. CFS is the global coupled ocean-atmosphere model used by the Climate Prediction Center (CPC), a branch of the National Center for Environmental Prediction (NCEP), to provide official U.S. seasonal climate forecasts. Recently, NCEP has produced a comprehensive long-term retrospective ensemble CFS reforecasts for the years 1980-2005. These reforecasts show that CFS model 1) has an ability to forecast tropical Pacific SSTs and large-scale teleconnection patterns, at least as evaluated for the winter season; 2) has greater skill in forecasting winter than summer climate; and 3) demonstrates an increase in skill when a greater number of ensembles members are used. The decrease in CFS skill during the warm season is due to the fact that the physical mechanisms of rainfall at this time are more related to mesoscale processes, such as the diurnal cycle of convection, low-level moisture transport, propagation and organization of convection, and surface moisture recycling. In general, these are poorly represented in global atmospheric models. Preliminary simulations for years with extreme summer climate conditions in the western and central U.S. (specifically 1988 and 1993) show that CFS-WRF simulations can provide a more realistic representation of convective rainfall processes. Thus a RCM can potentially add significant value in climate forecasting of the warm season provided the downscaling methodology incorporates the following: 1) spectral nudging to preserve the variability in the large scale circulation while still permitting the development of smaller-scale variability in the RCM; and 2) use of realistic soil moisture initial condition, in this case provided by the North American Regional Reanalysis. With these conditions, downscaled CFS-WRF reforecast simulations can produce realistic continental-scale patterns of warm season precipitation. This includes a reasonable representation of the North American monsoon in the southwest U.S. and northwest Mexico, which is notoriously difficult to represent in a global atmospheric model. We anticipate that this research will help lead the way toward substantially improved real time operational forecasts of North American summer climate with a RCM.

Climate change impact assessment on food security in Indonesia

NASA Astrophysics Data System (ADS)

Ettema, Janneke; Aldrian, Edvin; de Bie, Kees; Jetten, Victor; Mannaerts, Chris

2013-04-01

As Indonesia is the world's fourth most populous country, food security is a persistent challenge. The potential impact of future climate change on the agricultural sector needs to be addressed in order to allow early implementation of mitigation strategies. The complex island topography and local sea-land-air interactions cannot adequately be represented in large scale General Climate Models (GCMs) nor visualized by TRMM. Downscaling is needed. Using meteorological observations and a simple statistical downscaling tool, local future projections are derived from state-of-the-art, large-scale GCM scenarios, provided by the CMIP5 project. To support the agriculture sector, providing information on especially rainfall and temperature variability is essential. Agricultural production forecast is influenced by several rain and temperature factors, such as rainy and dry season onset, offset and length, but also by daily and monthly minimum and maximum temperatures and its rainfall amount. A simple and advanced crop model will be used to address the sensitivity of different crops to temperature and rainfall variability, present-day and future. As case study area, Java Island is chosen as it is fourth largest island in Indonesia but contains more than half of the nation's population and dominates it politically and economically. The objective is to identify regions at agricultural risk due to changing patterns in precipitation and temperature.

Interannual variation of carbon fluxes from three contrasting evergreen forests: the role of forest dynamics and climate.

PubMed

Sierra, Carlos A; Loescher, Henry W; Harmon, Mark E; Richardson, Andrew D; Hollinger, David Y; Perakis, Steven S

2009-10-01

Interannual variation of carbon fluxes can be attributed to a number of biotic and abiotic controls that operate at different spatial and temporal scales. Type and frequency of disturbance, forest dynamics, and climate regimes are important sources of variability. Assessing the variability of carbon fluxes from these specific sources can enhance the interpretation of past and current observations. Being able to separate the variability caused by forest dynamics from that induced by climate will also give us the ability to determine if the current observed carbon fluxes are within an expected range or whether the ecosystem is undergoing unexpected change. Sources of interannual variation in ecosystem carbon fluxes from three evergreen ecosystems, a tropical, a temperate coniferous, and a boreal forest, were explored using the simulation model STANDCARB. We identified key processes that introduced variation in annual fluxes, but their relative importance differed among the ecosystems studied. In the tropical site, intrinsic forest dynamics contributed approximately 30% of the total variation in annual carbon fluxes. In the temperate and boreal sites, where many forest processes occur over longer temporal scales than those at the tropical site, climate controlled more of the variation among annual fluxes. These results suggest that climate-related variability affects the rates of carbon exchange differently among sites. Simulations in which temperature, precipitation, and radiation varied from year to year (based on historical records of climate variation) had less net carbon stores than simulations in which these variables were held constant (based on historical records of monthly average climate), a result caused by the functional relationship between temperature and respiration. This suggests that, under a more variable temperature regime, large respiratory pulses may become more frequent and high enough to cause a reduction in ecosystem carbon stores. Our results also show that the variation of annual carbon fluxes poses an important challenge in our ability to determine whether an ecosystem is a source, a sink, or is neutral in regard to CO2 at longer timescales. In simulations where climate change negatively affected ecosystem carbon stores, there was a 20% chance of committing Type II error, even with 20 years of sequential data.

Influence of Climate on PM2.5 Concentrations over Europe : a Meteorological Analysis using a 9-year Model Simulation

NASA Astrophysics Data System (ADS)

Lecoeur, À.; Seigneur, C.; Terray, L.; Pagé, C.

2012-04-01

In the early 1970s, it has been demonstrated that a large number of deaths and health problems are associated with particulate pollution. As a consequence, several governments have set health-based air quality standards to protect public health. Particulate matter with an aerodynamical diameter of 2.5 μg.m-3 or less (PM2.5) is particularly concerned by these measures. As PM2.5 concentrations are strongly dependent on meteorological conditions, it is important to investigate the relationships between PM2.5 and meteorological parameters. This will help to understand the processes at play and anticipate the effects of climate change on PM2.5 air quality. Most of the previous work agree that temperature, wind speed, humidity, rain rate and mixing height are the meteorological variables that impact PM2.5 concentrations the most. A large number of those studies used Global Circulation Models (GCM) and Chemical Transport Models (CTM) and focus on the USA. They typically predict a diminution of PM2.5 concentrations in the future, with some geographical and/or temporal discrepancies, when only the climate evolution is considered. When considering changes in emissions along with climate, no consensus has yet been found. Furthermore, the correlations between PM2.5 concentrations and meteorological variables are often low, which prevents a straightforward analysis of their relationships. In this work, we consider that PM2.5 concentrations depend on both large-scale atmospheric circulation and local meteorological variables. We thus investigate the influence of present climate on PM2.5 concentrations over Europe by representing it using a weather regimes/types approach. We start by exploring the relationships between classical weather regimes, meteorological variables and PM2.5 concentrations over five stations in Europe, using the EMEP air quality database. The pressure at sea level is used in the classification as it effectively describes the atmospheric circulation. We experimentally verify some intuitive results: weather regimes associated with weak (resp. high) precipitation, wind and low (resp. high) temperatures correspond to higher (resp. lower) PM2.5 concentrations. We also observe that rain rate is the variable that impacts PM2.5 concentrations the most. Next, we search for better relationships by adding this second variable to the classification: we therefore build new weather regimes, called weather types. Because of the low number of the EMEP observations, we compute PM2.5 concentrations with the Polyphemus/Polair3D CTM for years between 2000 and 2008 in order to obtain a spatially and temporally complete dataset of PM2.5 concentrations and chemical components, which can be used to relate PM2.5 concentrations to meteorological regimes and specific variables. By classifying both a large-scale variable and a local variable that influence the PM2.5 concentrations and using gridded data of the modeled concentrations of PM2.5, we obtain a more robust analysis. The results of this work will provide the basis to predict the effects of climate change (via the evolution of weather regimes/types frequencies) on PM2.5 chemical composition and concentrations.

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.

Assessing the Dynamic Effects of Climate on Individual Tree Growth Across Time and Space

NASA Astrophysics Data System (ADS)

Itter, M.; Finley, A. O.; D'Amato, A. W.; Foster, J. R.; Bradford, J. B.

2015-12-01

The relationship between climate variability and an ecosystem process, such as forest growth, is frequently not fixed over time, but changes due to complex interactions between unobserved ecological factors and the process of interest. Climate data and forecasts are frequently spatially and temporally misaligned with ecological observations making inference regarding the effects of climate on ecosystem processes particularly challenging. Here we develop a Bayesian dynamic hierarchical model for annual tree growth increment that allows the effects of climate to evolve over time, applies climate data at a spatial-temporal scale consistent with observations, and controls for individual-level variability commonly encountered in ecological datasets. The model is applied to individual tree data from northern Minnesota using a modified Thornthwaite-type water balance model to transform PRISM temperature and precipitation estimates to physiologically relevant values of actual and potential evapotranspiration (AET, PET), and climatic water deficit. Model results indicate that mean tree growth is most sensitive to AET during the growing season and PET and minimum temperature in the spring prior to growth. The effects of these variables on tree growth, however, are not stationary with significant effects observed in only a subset of years during the 111-year study period. Importantly, significant effects of climate do not result from anomalous climate observations, but follow from large growth deviations unexplained by tree age and size, and time since forest disturbance. Results differ markedly from alternative models that assume the effects of climate are stationary over time or apply climate estimates at the individual scale. Forecasts of future tree growth as a function of climate follow directly from the dynamic hierarchical model allowing for assessment of forest change. Current work is focused on extending the model framework to include regional climate and ecosystem effects for application to a larger tree growth dataset spanning a latitudinal gradient within the US from Maine to Florida.

Comparing large-scale hydrological model predictions with observed streamflow in the Pacific Northwest: effects of climate and groundwater

Treesearch

Mohammad Safeeq; Guillaume S. Mauger; Gordon E. Grant; Ivan Arismendi; Alan F. Hamlet; Se-Yeun Lee

2014-01-01

Assessing uncertainties in hydrologic models can improve accuracy in predicting future streamflow. Here, simulated streamflows using the Variable Infiltration Capacity (VIC) model at coarse (1/16°) and fine (1/120°) spatial resolutions were evaluated against observed streamflows from 217 watersheds. In...

Spread in the magnitude of climate model interdecadal global temperature variability traced to disagreements over high-latitude oceans

NASA Astrophysics Data System (ADS)

Brown, Patrick T.; Li, Wenhong; Jiang, Jonathan H.; Su, Hui

2016-12-01

Unforced variability in global mean surface air temperature can obscure or exaggerate global warming on interdecadal time scales; thus, understanding both the magnitude and generating mechanisms of such variability is of critical importance for both attribution studies as well as decadal climate prediction. Coupled atmosphere-ocean general circulation models (climate models) simulate a wide range of magnitudes of unforced interdecadal variability in global mean surface air temperature (UITglobal), hampering efforts to quantify the influence of UITglobal on contemporary global temperature trends. Recently, a preliminary consensus has emerged that unforced interdecadal variability in local surface temperatures (UITlocal) over the tropical Pacific Ocean is particularly influential on UITglobal. Therefore, a reasonable hypothesis might be that the large spread in the magnitude of UITglobal across climate models can be explained by the spread in the magnitude of simulated tropical Pacific UITlocal. Here we show that this hypothesis is mostly false. Instead, the spread in the magnitude of UITglobal is linked much more strongly to the spread in the magnitude of UITlocal over high-latitude regions characterized by significant variability in oceanic convection, sea ice concentration, and energy flux at both the surface and the top of the atmosphere. Thus, efforts to constrain the climate model produced range of UITglobal magnitude would be best served by focusing on the simulation of air-sea interaction at high latitudes.

Modeling climate change impacts on groundwater resources using transient stochastic climatic scenarios

NASA Astrophysics Data System (ADS)

Goderniaux, Pascal; BrouyèRe, Serge; Blenkinsop, Stephen; Burton, Aidan; Fowler, Hayley J.; Orban, Philippe; Dassargues, Alain

2011-12-01

Several studies have highlighted the potential negative impact of climate change on groundwater reserves, but additional work is required to help water managers plan for future changes. In particular, existing studies provide projections for a stationary climate representative of the end of the century, although information is demanded for the near future. Such time-slice experiments fail to account for the transient nature of climatic changes over the century. Moreover, uncertainty linked to natural climate variability is not explicitly considered in previous studies. In this study we substantially improve upon the state-of-the-art by using a sophisticated transient weather generator in combination with an integrated surface-subsurface hydrological model (Geer basin, Belgium) developed with the finite element modeling software "HydroGeoSphere." This version of the weather generator enables the stochastic generation of large numbers of equiprobable climatic time series, representing transient climate change, and used to assess impacts in a probabilistic way. For the Geer basin, 30 equiprobable climate change scenarios from 2010 to 2085 have been generated for each of six different regional climate models (RCMs). Results show that although the 95% confidence intervals calculated around projected groundwater levels remain large, the climate change signal becomes stronger than that of natural climate variability by 2085. Additionally, the weather generator's ability to simulate transient climate change enabled the assessment of the likely time scale and associated uncertainty of a specific impact, providing managers with additional information when planning further investment. This methodology constitutes a real improvement in the field of groundwater projections under climate change conditions.

Spectral Kernel Approach to Study Radiative Response of Climate Variables and Interannual Variability of Reflected Solar Spectrum

NASA Technical Reports Server (NTRS)

Jin, Zhonghai; Wielicki, Bruce A.; Loukachine, Constantin; Charlock, Thomas P.; Young, David; Noeel, Stefan

2011-01-01

The radiative kernel approach provides a simple way to separate the radiative response to different climate parameters and to decompose the feedback into radiative and climate response components. Using CERES/MODIS/Geostationary data, we calculated and analyzed the solar spectral reflectance kernels for various climate parameters on zonal, regional, and global spatial scales. The kernel linearity is tested. Errors in the kernel due to nonlinearity can vary strongly depending on climate parameter, wavelength, surface, and solar elevation; they are large in some absorption bands for some parameters but are negligible in most conditions. The spectral kernels are used to calculate the radiative responses to different climate parameter changes in different latitudes. The results show that the radiative response in high latitudes is sensitive to the coverage of snow and sea ice. The radiative response in low latitudes is contributed mainly by cloud property changes, especially cloud fraction and optical depth. The large cloud height effect is confined to absorption bands, while the cloud particle size effect is found mainly in the near infrared. The kernel approach, which is based on calculations using CERES retrievals, is then tested by direct comparison with spectral measurements from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) (a different instrument on a different spacecraft). The monthly mean interannual variability of spectral reflectance based on the kernel technique is consistent with satellite observations over the ocean, but not over land, where both model and data have large uncertainty. RMS errors in kernel ]derived monthly global mean reflectance over the ocean compared to observations are about 0.001, and the sampling error is likely a major component.

Millennial- to century-scale variability in Gulf of Mexico Holocene climate records

USGS Publications Warehouse

Poore, R.Z.; Dowsett, H.J.; Verardo, S.; Quinn, T.M.

2003-01-01

Proxy records from two piston cores in the Gulf of Mexico (GOM) provide a detailed (50-100 year resolution) record of climate variability over the last 14,000 years. Long-term (millennial-scale) trends and changes are related to the transition from glacial to interglacial conditions and movement of the average position of the Intertropical Convergence Zone (ITCZ) related to orbital forcing. The ??18O of the surface-dwelling planktic foraminifer Globigerinoides ruber show negative excursions between 14 and 10.2 ka (radiocarbon years) that reflect influx of meltwater into the western GOM during melting of the Laurentide Ice Sheet. The relative abundance of the planktic foraminifer Globigerinoides sacculifer is related to transport of Caribbean water into the GOM. Maximum transport of Caribbean surface waters and moisture into the GOM associated with a northward migration of the average position of the ITCZ occurs between about 6.5 and 4.5 ka. In addition, abundance variations of G. sacculifer show century-scale variability throughout most of the Holocene. The GOM record is consistent with records from other areas, suggesting that century-scale variability is a pervasive feature of Holocene climate. The frequency of several cycles in the climate records is similar to cycles identified in proxy records of solar variability, indicating that at least some of the century-scale climate variability during the Holocene is due to external (solar) forcing.

Climate Drivers of Spatiotemporal Variability of Precipitation in the Source Region of Yangtze River

NASA Astrophysics Data System (ADS)

Du, Y.; Berndtsson, R.; An, D.; Yuan, F.

2017-12-01

Variability of precipitation regime has significant influence on the environment sustainability in the source region of Yangtze River, especially when the vegetation degradation and biodiversity reduction have already occurred. Understanding the linkage between variability of local precipitation and global teleconnection patterns is essential for water resources management. Based on physical reasoning, indices of the climate drivers can provide a practical way of predicting precipitation. Due to high seasonal variability of precipitation, climate drivers of the seasonal precipitation also varies. However, few reports have gone through the teleconnections between large scale patterns with seasonal precipitation in the source region of Yangtze River. The objectives of this study are therefore (1) assessment of temporal trend and spatial variability of precipitation in the source region of Yangtze River; (2) identification of climate indices with strong influence on seasonal precipitation anomalies; (3) prediction of seasonal precipitation based on revealed climate indices. Principal component analysis and Spearman rank correlation were used to detect significant relationships. A feed-forward artificial neural network(ANN) was developed to predict seasonal precipitation using significant correlated climate indices. Different influencing climate indices were revealed for precipitation in each season, with significant level and lag times. Significant influencing factors were selected to be the predictors for ANN model. With correlation coefficients between observed and simulated precipitation over 0.5, the results were eligible to predict the precipitation of spring, summer and winter using teleconnections, which can improve integrated water resources management in the source region of Yangtze River.

The influence of large-scale wind power on global climate.

PubMed

Keith, David W; Decarolis, Joseph F; Denkenberger, David C; Lenschow, Donald H; Malyshev, Sergey L; Pacala, Stephen; Rasch, Philip J

2004-11-16

Large-scale use of wind power can alter local and global climate by extracting kinetic energy and altering turbulent transport in the atmospheric boundary layer. We report climate-model simulations that address the possible climatic impacts of wind power at regional to global scales by using two general circulation models and several parameterizations of the interaction of wind turbines with the boundary layer. We find that very large amounts of wind power can produce nonnegligible climatic change at continental scales. Although large-scale effects are observed, wind power has a negligible effect on global-mean surface temperature, and it would deliver enormous global benefits by reducing emissions of CO(2) and air pollutants. Our results may enable a comparison between the climate impacts due to wind power and the reduction in climatic impacts achieved by the substitution of wind for fossil fuels.

Antarctic Meteorology and Climatology

NASA Astrophysics Data System (ADS)

King, J. C.; Turner, J.

1997-07-01

This book is a comprehensive survey of the climatology and meteorology of Antarctica. The first section of the book reviews the methods by which we can observe the Antarctic atmosphere and presents a synthesis of climatological measurements. In the second section, the authors consider the processes that maintain the observed climate, from large-scale atmospheric circulation to small-scale processes. The final section reviews our current knowledge of the variability of Antarctic climate and the possible effects of "greenhouse" warming. The authors stress links among the Antarctic atmosphere, other elements of the Antarctic climate system (oceans, sea ice and ice sheets), and the global climate system. This volume will be of greatest interest to meteorologists and climatologists with a specialized interest in Antarctica, but it will also appeal to researchers in Antarctic glaciology, oceanography and biology. Graduates and undergraduates studying physical geography, and the earth, atmospheric and environmental sciences will find much useful background material in the book.

The Teleconnection Pattern between Hawaii Rainfall and Large-Scale Circulation in Present-Day Conditions and during the Holocene

NASA Astrophysics Data System (ADS)

Li, S.; Elison Timm, O.

2016-12-01

Kona lows (KLs) are a type of seasonal cut-off cyclones in the North Pacific around the Hawaiian Islands during the cold season month (Oct.-Apr.). KL are important for the annual rainfall budget of the Hawaiian Islands. In this study, the relationship between KLs and North Pacific climate variability was analyzed in order to understand the interannual variations in the number of KLs. The main objectives were (1) to identify mechanisms that control the activity of KL during the recent decades, and (2) to test the stability of the statistical relationships with respect to decadal-scale variability and/or climate shifts. Our study is based on the new data set of KL counts from 1979-2014 (Kaiser, 2014, Univ. Hawaii). For the analysis of large-scale climate pattern, ERA-interim 6-hourly data from 1979-2014 were used including sea level pressure, geopotential heights of the 500hPa level, potential vorticity and zonal wind at 250hPa. We derived cold season averages for the mean and 8-16 day synoptic variability. Standard climate indices for tropical-extratropical climate variability included NINO3.4 and PDO index, and NPO index. The results from the linear correlation analysis show that local 8-16day PV250 variability north of Hawaii can represent Kona Low activity between 1979-2014. KL activity and PV250 variability had both a negative correlation with NINO3.4 index. However, the correlation with NINO3.4 index has diminished after 1995. This shift in correlation coincides with transition of the PDO index from a positive value to negative value during 1990's. Our results suggest that a negative PDO can reduce the ENSO influence on Kona Low activity by modulating the upper zonal jet response. We investigated further the relationship between NPO and Kona Low activity. A strong correlation with the NPO index was found. Two factors that control the time-dependent impacts of ENSO on the KL activity. After the mid-1990s the central tropical Pacific SST were more closely tied to the extratropical NPO mode. In order to test if the results are applicable in the context of paleoclimate applications, where the stability of tropical-extratropical teleconnection mechanisms is crucial for the interpretation of paleoclimate proxy data, we tested the robustness of our results in the TraCE-21ka simulation during the Holocene.

Cross-scale impact of climate temporal variability on ecosystem water and carbon fluxes

DOE PAGES

Paschalis, Athanasios; Fatichi, Simone; Katul, Gabriel G.; ...

2015-08-07

While the importance of ecosystem functioning is undisputed in the context of climate change and Earth system modeling, the role of short-scale temporal variability of hydrometeorological forcing (~1 h) on the related ecosystem processes remains to be fully understood. Additionally, various impacts of meteorological forcing variability on water and carbon fluxes across a range of scales are explored here using numerical simulations. Synthetic meteorological drivers that highlight dynamic features of the short temporal scale in series of precipitation, temperature, and radiation are constructed. These drivers force a mechanistic ecohydrological model that propagates information content into the dynamics of water andmore » carbon fluxes for an ensemble of representative ecosystems. The focus of the analysis is on a cross-scale effect of the short-scale forcing variability on the modeled evapotranspiration and ecosystem carbon assimilation. Interannual variability of water and carbon fluxes is emphasized in the analysis. The main study inferences are summarized as follows: (a) short-scale variability of meteorological input does affect water and carbon fluxes across a wide range of time scales, spanning from the hourly to the annual and longer scales; (b) different ecosystems respond to the various characteristics of the short-scale variability of the climate forcing in various ways, depending on dominant factors limiting system productivity; (c) whenever short-scale variability of meteorological forcing influences primarily fast processes such as photosynthesis, its impact on the slow-scale variability of water and carbon fluxes is small; and (d) whenever short-scale variability of the meteorological forcing impacts slow processes such as movement and storage of water in the soil, the effects of the variability can propagate to annual and longer time scales.« less

Cross-scale impact of climate temporal variability on ecosystem water and carbon fluxes

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

Paschalis, Athanasios; Fatichi, Simone; Katul, Gabriel G.

While the importance of ecosystem functioning is undisputed in the context of climate change and Earth system modeling, the role of short-scale temporal variability of hydrometeorological forcing (~1 h) on the related ecosystem processes remains to be fully understood. Additionally, various impacts of meteorological forcing variability on water and carbon fluxes across a range of scales are explored here using numerical simulations. Synthetic meteorological drivers that highlight dynamic features of the short temporal scale in series of precipitation, temperature, and radiation are constructed. These drivers force a mechanistic ecohydrological model that propagates information content into the dynamics of water andmore » carbon fluxes for an ensemble of representative ecosystems. The focus of the analysis is on a cross-scale effect of the short-scale forcing variability on the modeled evapotranspiration and ecosystem carbon assimilation. Interannual variability of water and carbon fluxes is emphasized in the analysis. The main study inferences are summarized as follows: (a) short-scale variability of meteorological input does affect water and carbon fluxes across a wide range of time scales, spanning from the hourly to the annual and longer scales; (b) different ecosystems respond to the various characteristics of the short-scale variability of the climate forcing in various ways, depending on dominant factors limiting system productivity; (c) whenever short-scale variability of meteorological forcing influences primarily fast processes such as photosynthesis, its impact on the slow-scale variability of water and carbon fluxes is small; and (d) whenever short-scale variability of the meteorological forcing impacts slow processes such as movement and storage of water in the soil, the effects of the variability can propagate to annual and longer time scales.« less

Extreme climatic events drive mammal irruptions: regression analysis of 100-year trends in desert rainfall and temperature

PubMed Central

Greenville, Aaron C; Wardle, Glenda M; Dickman, Chris R

2012-01-01

Extreme climatic events, such as flooding rains, extended decadal droughts and heat waves have been identified increasingly as important regulators of natural populations. Climate models predict that global warming will drive changes in rainfall and increase the frequency and severity of extreme events. Consequently, to anticipate how organisms will respond we need to document how changes in extremes of temperature and rainfall compare to trends in the mean values of these variables and over what spatial scales the patterns are consistent. Using the longest historical weather records available for central Australia – 100 years – and quantile regression methods, we investigate if extreme climate events have changed at similar rates to median events, if annual rainfall has increased in variability, and if the frequency of large rainfall events has increased over this period. Specifically, we compared local (individual weather stations) and regional (Simpson Desert) spatial scales, and quantified trends in median (50th quantile) and extreme weather values (5th, 10th, 90th, and 95th quantiles). We found that median and extreme annual minimum and maximum temperatures have increased at both spatial scales over the past century. Rainfall changes have been inconsistent across the Simpson Desert; individual weather stations showed increases in annual rainfall, increased frequency of large rainfall events or more prolonged droughts, depending on the location. In contrast to our prediction, we found no evidence that intra-annual rainfall had become more variable over time. Using long-term live-trapping records (22 years) of desert small mammals as a case study, we demonstrate that irruptive events are driven by extreme rainfalls (>95th quantile) and that increases in the magnitude and frequency of extreme rainfall events are likely to drive changes in the populations of these species through direct and indirect changes in predation pressure and wildfires. PMID:23170202

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.

Introduction

NASA Astrophysics Data System (ADS)

Sun, De-Zheng; Bryan, Frank

Largely following the order in which the lectures were given in the graduate class on climate dynamics at the University of Colorado, the book starts with the topic of moist convection in the tropics. Summarizing decades-long research into a succinct article, Moncrieff [this volume] reviews the state of the art of understanding of organized precipitating convective systems with an eye to improving the representation of such systems in global weather and climate models. Moncrieff also addresses in this chapter the multi-scale convective organization in the Madden-Julian Oscillation, a major source of intraseasonal variability in the tropics. The second chapter proceeds to a prominent phenomenon on the seasonal time scale: monsoons. In covering this topic, Li [this volume] focuses his analysis on the Asian monsoon and dissects the physical processes that are responsible for its intraseasonal and interannual variability. All three subcomponents of the Asian monsoon are covered here: the Indian monsoon, the East Asian monsoon, and the Western North Pacific monsoon.

Water quality in the Schuylkill River, Pennsylvania: the potential for long-lead forecasts

NASA Astrophysics Data System (ADS)

Block, P. J.; Peralez, J.

2012-12-01

Prior analysis of pathogen levels in the Schuylkill River has led to a categorical daily forecast of water quality (denoted as red, yellow, or green flag days.) The forecast, available to the public online through the Philadelphia Water Department, is predominantly based on the local precipitation forecast. In this study, we explore the feasibility of extending the forecast to the seasonal scale by associating large-scale climate drivers with local precipitation and water quality parameter levels. This advance information is relevant for recreational activities, ecosystem health, and water treatment (energy, chemicals), as the Schuylkill provides 40% of Philadelphia's water supply. Preliminary results indicate skillful prediction of average summertime water quality parameters and characteristics, including chloride, coliform, turbidity, alkalinity, and others, using season-ahead oceanic and atmospheric variables, predominantly from the North Atlantic. Water quality parameter trends, including historic land use changes along the river, association with climatic variables, and prediction models will be presented.

Assessing and managing water scarcity within the Nile River Transboundary Basin

NASA Astrophysics Data System (ADS)

Butts, M. B.; Wendi, D.; Jessen, O. Z.; Riegels, N. D.

2012-04-01

The Nile Basin is the main source of water in the North Eastern Region of Africa and is perhaps one of the most critical river basins in Africa as the riparian countries constitute 40% of the population on the continent but only 10% of the area. This resource is under considerable stress with rising levels of water scarcity, high population growth, watershed degradation, and loss of environmental services. The potential impacts of climate change may significantly exacerbate this situation as the water resources in the Nile Basin are critically sensitive to climate change (Conway, Hanson, Doherty, & Persechino, 2007). The motivation for this study is an assessment of climate change impacts and adaptation potential for floods and droughts within the UNEP project "Adapting to climate change induced water stress in the Nile River Basin", supported by SIDA. This project is being carried out as collaboration between DHI, the UK Met Office, and the Nile Basin Initiative (NBI). The Nile Basin exhibits highly diverse climatological and hydrological characteristics. Thus climate change impacts and adaptive capacity must be addressed at both regional and sub-basin scales. While the main focus of the project is the regional scale, sub-basin scale modelling is required to reflect variability within the basin. One of the major challenges in addressing this variability is the scarcity of data. This paper presents an initial screening modelling study of the water balance of the Nile Basin along with estimates of expected future impacts of climate change on the water balance. This initial study is focussed on the Ethiopian Highlands and the Lake Victoria regions, where the impact of climate change on rainfall is important. A robust sub-basin based monthly water balance model is developed and applied to selected sub-basins. The models were developed and calibrated using publicly available data. One of the major challenges in addressing this variability within the basin is the scarcity of spatial data and the results for the Kagera sub-basin show that it is important to represent the spatial distribution of the hydro-geographic characteristics such as rainfall, soil type, etc., in order to develop a reasonable representation of the water balance. These initial results show that the changes in the water balance and flow regime under climate change exhibit large uncertainty. From an examination the flow duration curves, however, there seems to be a consensus, based on an ensemble of climate projections, that flows will increase slightly the short term (2011-2030) and decrease significantly in the long term 2080-2099. The large uncertainties together with the natural variability in the Nile suggest that there is a strong need to maximise adaptive capacity with the region.

Canary in the coal mine: Historical oxygen decline in the Gulf of St. Lawrence due to large scale climate changes

NASA Astrophysics Data System (ADS)

Claret, M.; Galbraith, E. D.; Palter, J. B.; Gilbert, D.; Bianchi, D.; Dunne, J. P.

2016-02-01

The regional signature of anthropogenic climate change on the atmosphere and upper ocean is often difficult to discern from observational timeseries, dominated as they are by decadal climate variability. Here we argue that a long-term decline of dissolved oxygen concentrations observed in the Gulf of S. Lawrence (GoSL) is consistent with anthropogenic climate change. Oxygen concentrations in the GoSL have declined markedly since 1930 due primarily to an increase of oxygen-poor North Atlantic Central Waters relative to Labrador Current Waters (Gilbert et al. 2005). We compare these observations to a climate warming simulation using a very high-resolution global coupled ocean-atmospheric climate model. The numerical model (CM2.6), developed by the Geophysical Fluid Dynamics Laboratory, is strongly eddying and includes a biogeochemical module with dissolved oxygen. The warming scenario shows that oxygen in the GoSL decreases and it is associated to changes in western boundary currents and wind patterns in the North Atlantic. We speculate that the large-scale changes behind the simulated decrease in GoSL oxygen have also been at play in the real world over the past century, although they are difficult to resolve in noisy atmospheric data.

Statistical downscaling of mean temperature, maximum temperature, and minimum temperature on the Loess Plateau, China

NASA Astrophysics Data System (ADS)

Lin, Jiang; Miao, Chiyuan

2017-04-01

Climate change is considered to be one of the greatest environmental threats. This has urged scientific communities to focus on the hot topic. Global climate models (GCMs) are the primary tool used for studying climate change. However, GCMs are limited because of their coarse spatial resolution and inability to resolve important sub-grid scale features such as terrain and clouds. Statistical downscaling methods can be used to downscale large-scale variables to local-scale. In this study, we assess the applicability of the widely used Statistical Downscaling Model (SDSM) for the Loess Plateau, China. The observed variables included daily mean temperature (TMEAN), maximum temperature (TMAX) and minimum temperature (TMIN) from 1961 to 2005. The and the daily atmospheric data were taken from reanalysis data from 1961 to 2005, and global climate model outputs from Beijing Normal University Earth System Model (BNU-ESM) from 1961 to 2099 and from observations . The results show that SDSM performs well for these three climatic variables on the Loess Plateau. After downscaling, the root mean square errors for TMEAN, TMAX, TMIN for BNU-ESM were reduced by 70.9%, 75.1%, and 67.2%, respectively. All the rates of change in TMEAN, TMAX and TMIN during the 21st century decreased after SDSM downscaling. We also show that SDSM can effectively reduce uncertainty, compared with the raw model outputs. TMEAN uncertainty was reduced by 27.1%, 26.8%, and 16.3% for the future scenarios of RCP 2.6, RCP 4.5 and RCP 8.5, respectively. The corresponding reductions in uncertainty were 23.6%, 30.7%, and 18.7% for TMAX, ; and 37.6%, 31.8%, and 23.2% for TMIN.

Multi-scale enhancement of climate prediction over land by improving the model sensitivity to vegetation variability

NASA Astrophysics Data System (ADS)

Alessandri, A.; Catalano, F.; De Felice, M.; Hurk, B. V. D.; Doblas-Reyes, F. J.; Boussetta, S.; Balsamo, G.; Miller, P. A.

2017-12-01

Here we demonstrate, for the first time, that the implementation of a realistic representation of vegetation in Earth System Models (ESMs) can significantly improve climate simulation and prediction across multiple time-scales. The effective sub-grid vegetation fractional coverage vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the surface resistance to evapotranspiration, albedo, roughness lenght, and soil field capacity. To adequately represent this effect in the EC-Earth ESM, we included an exponential dependence of the vegetation cover on the Leaf Area Index.By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (20th Century) simulations and retrospective predictions to the decadal (5-years), seasonal (2-4 months) and weather (4 days) time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation-cover consistently correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste Brazil and South East Asia, mainly related to improved performance in the surface evapotranspiration.Above results are discussed in a peer-review paper just being accepted for publication on Climate Dynamics (Alessandri et al., 2017; doi:10.1007/s00382-017-3766-y).

Can We Use Single-Column Models for Understanding the Boundary Layer Cloud-Climate Feedback?

NASA Astrophysics Data System (ADS)

Dal Gesso, S.; Neggers, R. A. J.

2018-02-01

This study explores how to drive Single-Column Models (SCMs) with existing data sets of General Circulation Model (GCM) outputs, with the aim of studying the boundary layer cloud response to climate change in the marine subtropical trade wind regime. The EC-EARTH SCM is driven with the large-scale tendencies and boundary conditions as derived from two different data sets, consisting of high-frequency outputs of GCM simulations. SCM simulations are performed near Barbados Cloud Observatory in the dry season (January-April), when fair-weather cumulus is the dominant low-cloud regime. This climate regime is characterized by a near equilibrium in the free troposphere between the long-wave radiative cooling and the large-scale advection of warm air. In the SCM, this equilibrium is ensured by scaling the monthly mean dynamical tendency of temperature and humidity such that it balances that of the model physics in the free troposphere. In this setup, the high-frequency variability in the forcing is maintained, and the boundary layer physics acts freely. This technique yields representative cloud amount and structure in the SCM for the current climate. Furthermore, the cloud response to a sea surface warming of 4 K as produced by the SCM is consistent with that of the forcing GCM.

Identifying Decadal to Multi-decadal Variability in the Pacific by Empirical Mode Decomposition

NASA Astrophysics Data System (ADS)

Sommers, L. A.; Hamlington, B.; Cheon, S. H.

2016-12-01

Large scale climate variability in the Pacific Ocean like that associated with ENSO and the Pacific Decadal Oscillation (PDO) has been shown to have a significant impact on climate and sea level across a range of timescales. The changes related to these climate signals have worldwide impacts on fisheries, weather, and precipitation patterns among others. Understanding these inter-annual to multi-decadal oscillations is imperative to longer term climate forecasts and understanding how climate will behave, and its effect on changes in sea level. With a 110-year reconstruction of sea level, we examine decadal to multi-decadal variability seen in the sea level fluctuations in the Pacific Ocean. Using empirical mode decomposition (EMD), we break down regional sea level into a series of intrinsic mode functions (IMFs) and attempt attribution of these IMFs to specific climate modes of variability. In particular, and not unexpectedly, we identify IMFs associated with the PDO, finding correlations between the PDO Index and IMFs in the Pacific Ocean upwards of 0.6-0.8 over the 110-year reconstructed record. Perhaps more significantly, we also find evidence of a longer multi-decadal signal ( 50-60 years) in the higher order IMFs. This lower frequency variability has been suggested in previous literature as influencing GMSL, but here we find a regional pattern associated with this multi-decadal signal. By identifying and separating these periodic climate signals, we can gain a better understanding of how the sea level variability associated with these modes can impact sea level on short timescales and serve to exacerbate the effects of long-term sea level change.

A climate-based multivariate extreme emulator of met-ocean-hydrological events for coastal flooding

NASA Astrophysics Data System (ADS)

Camus, Paula; Rueda, Ana; Mendez, Fernando J.; Tomas, Antonio; Del Jesus, Manuel; Losada, Iñigo J.

2015-04-01

Atmosphere-ocean general circulation models (AOGCMs) are useful to analyze large-scale climate variability (long-term historical periods, future climate projections). However, applications such as coastal flood modeling require climate information at finer scale. Besides, flooding events depend on multiple climate conditions: waves, surge levels from the open-ocean and river discharge caused by precipitation. Therefore, a multivariate statistical downscaling approach is adopted to reproduce relationships between variables and due to its low computational cost. The proposed method can be considered as a hybrid approach which combines a probabilistic weather type downscaling model with a stochastic weather generator component. Predictand distributions are reproduced modeling the relationship with AOGCM predictors based on a physical division in weather types (Camus et al., 2012). The multivariate dependence structure of the predictand (extreme events) is introduced linking the independent marginal distributions of the variables by a probabilistic copula regression (Ben Ayala et al., 2014). This hybrid approach is applied for the downscaling of AOGCM data to daily precipitation and maximum significant wave height and storm-surge in different locations along the Spanish coast. Reanalysis data is used to assess the proposed method. A commonly predictor for the three variables involved is classified using a regression-guided clustering algorithm. The most appropriate statistical model (general extreme value distribution, pareto distribution) for daily conditions is fitted. Stochastic simulation of the present climate is performed obtaining the set of hydraulic boundary conditions needed for high resolution coastal flood modeling. References: Camus, P., Menéndez, M., Méndez, F.J., Izaguirre, C., Espejo, A., Cánovas, V., Pérez, J., Rueda, A., Losada, I.J., Medina, R. (2014b). A weather-type statistical downscaling framework for ocean wave climate. Journal of Geophysical Research, doi: 10.1002/2014JC010141. Ben Ayala, M.A., Chebana, F., Ouarda, T.B.M.J. (2014). Probabilistic Gaussian Copula Regression Model for Multisite and Multivariable Downscaling, Journal of Climate, 27, 3331-3347.

Stability of ENSO and Its Tropical Pacific Teleconnections over the Last Millennium

NASA Technical Reports Server (NTRS)

Lewis, Sophie; Legrande, A. N.

2015-01-01

Determining past changes in the amplitude, frequency and teleconnections of the El Nio Southern Oscillation (ENSO) is important for understanding its potential sensitivity to future anthropogenic climate change. Palaeo-reconstructions from proxy records provide long-term information of ENSO interactions with the background climatic state through time. However, it remains unclear how ENSO characteristics have changed through time, and precisely which signals proxies record. Proxy interpretations are underpinned by the assumption of stationarity in relationships between local and remote climates, and often utilise archives from single locations located in the Pacific Ocean to reconstruct ENSO histories. Here, we investigate the stationarity of ENSO teleconnections using the Last Millennium experiment of CMIP5 (Coupled Model Intercomparison Project phase 5) (Taylor et al., 2012). We show that modelled ENSO characteristics vary on decadal- to centennial-scales, resulting from internal variability and external forcings, such as tropical volcanic eruptions. Furthermore, the relationship between ENSO conditions and local climates across the Pacific basin varies throughout the Last Millennium. Results show the stability of teleconnections is regionally dependent and proxies may reveal complex changes in teleconnected patterns, rather than large-scale changes in base ENSO characteristics. As such, proxy insights into ENSO likely require evidence to be synthesised over large spatial areas in order to deconvolve changes occurring in the NINO3.4 region from those pertaining to proxy-relevant local climatic variables. To obtain robust histories of the ENSO and its remote impacts, we recommend interpretations of proxy records should be considered in conjunction with palaeo-reconstructions from within the Central Pacific

Integrated approaches to climate-crop modelling: needs and challenges.

PubMed

Betts, Richard A

2005-11-29

This paper discusses the need for a more integrated approach to modelling changes in climate and crops, and some of the challenges posed by this. While changes in atmospheric composition are expected to exert an increasing radiative forcing of climate change leading to further warming of global mean temperatures and shifts in precipitation patterns, these are not the only climatic processes which may influence crop production. Changes in the physical characteristics of the land cover may also affect climate; these may arise directly from land use activities and may also result from the large-scale responses of crops to seasonal, interannual and decadal changes in the atmospheric state. Climate models used to drive crop models may, therefore, need to consider changes in the land surface, either as imposed boundary conditions or as feedbacks from an interactive climate-vegetation model. Crops may also respond directly to changes in atmospheric composition, such as the concentrations of carbon dioxide (CO2), ozone (03) and compounds of sulphur and nitrogen, so crop models should consider these processes as well as climate change. Changes in these, and the responses of the crops, may be intimately linked with meteorological processes so crop and climate models should consider synergies between climate and atmospheric chemistry. Some crop responses may occur at scales too small to significantly influence meteorology, so may not need to be included as feedbacks within climate models. However, the volume of data required to drive the appropriate crop models may be very large, especially if short-time-scale variability is important. Implementation of crop models within climate models would minimize the need to transfer large quantities of data between separate modelling systems. It should also be noted that crop responses to climate change may interact with other impacts of climate change, such as hydrological changes. For example, the availability of water for irrigation may be affected by changes in runoff as a direct consequence of climate change, and may also be affected by climate-related changes in demand for water for other uses. It is, therefore, necessary to consider the interactions between the responses of several impacts sectors to climate change. Overall, there is a strong case for a much closer coupling between models of climate, crops and hydrology, but this in itself poses challenges arising from issues of scale and errors in the models. A strategy is proposed whereby the pursuit of a fully coupled climate-chemistry-crop-hydrology model is paralleled by continued use of separate climate and land surface models but with a focus on consistency between the models.

Climatic variation and the distribution of an amphibian polyploid complex

USGS Publications Warehouse

Otto, C.R.V.; Snodgrass, J.W.; Forester, D.C.; Mitchell, J.C.; Miller, R.W.

2007-01-01

1. The establishment of polyploid populations involves the persistence and growth of the polyploid in the presence of the progenitor species. Although there have been a number of animal polyploid species documented, relatively few inquiries have been made into the large-scale mechanisms of polyploid establishment in animal groups. Herein we investigate the influence of regional climatic conditions on the distributional patterns of a diploid-tetraploid species pair of gray treefrogs, Hyla chrysoscelis and H. versicolor (Anura: Hylidae) in the mid-Atlantic region of eastern North America. 2. Calling surveys at breeding sites were used to document the distribution of each species. Twelve climatic models and one elevation model were generated to predict climatic and elevation values for gray treefrog breeding sites. A canonical analysis of discriminants was used to describe relationships between climatic variables, elevation and the distribution of H. chrysoscelis and H. versicolor. 3. There was a strong correlation between several climatic variables, elevation and the distribution of the gray treefrog complex. Specifically, the tetraploid species almost exclusively occupied areas of higher elevation, where climatic conditions were relatively severe (colder, drier, greater annual variation). In contrast, the diploid species was restricted to lower elevations, where climatic conditions were warmer, wetter and exhibited less annual variation. 4. Clusters of syntopic sites were associated with areas of high variation in annual temperature and precipitation during the breeding season. 5. Our data suggest that large-scale climatic conditions have played a role in the establishment of the polyploid H. versicolor in at least some portions of its range. The occurrence of the polyploid and absence of the progenitor in colder, drier and more varied environments suggests the polyploid may posses a tolerance of severe environmental conditions that is not possessed by the diploid progenitor. 6. Our findings support the hypothesis that increased tolerance to severe environmental conditions is a plausible mechanism of polyploid establishment.

Inter-decadal variability of phytoplankton biomass along the coastal West Antarctic Peninsula.

PubMed

Kim, Hyewon; Ducklow, Hugh W; Abele, Doris; Ruiz Barlett, Eduardo M; Buma, Anita G J; Meredith, Michael P; Rozema, Patrick D; Schofield, Oscar M; Venables, Hugh J; Schloss, Irene R

2018-06-28

The West Antarctic Peninsula (WAP) is a climatically sensitive region where periods of strong warming have caused significant changes in the marine ecosystem and food-web processes. Tight coupling between phytoplankton and higher trophic levels implies that the coastal WAP is a bottom-up controlled system, where changes in phytoplankton dynamics may largely impact other food-web components. Here, we analysed the inter-decadal time series of year-round chlorophyll- a (Chl) collected from three stations along the coastal WAP: Carlini Station at Potter Cove (PC) on King George Island, Palmer Station on Anvers Island and Rothera Station on Adelaide Island. There were trends towards increased phytoplankton biomass at Carlini Station (PC) and Palmer Station, while phytoplankton biomass declined significantly at Rothera Station over the studied period. The impacts of two relevant climate modes to the WAP, the El Niño-Southern Oscillation and the Southern Annular Mode, on winter and spring phytoplankton biomass appear to be different among the three sampling stations, suggesting an important role of local-scale forcing than large-scale forcing on phytoplankton dynamics at each station. The inter-annual variability of seasonal bloom progression derived from considering all three stations together captured ecologically meaningful, seasonally co-occurring bloom patterns which were primarily constrained by water-column stability strength. Our findings highlight a coupled link between phytoplankton and physical and climate dynamics along the coastal WAP, which may improve our understanding of overall WAP food-web responses to climate change and variability.This article is part of the theme issue 'The marine system of the West Antarctic Peninsula: status and strategy for progress in a region of rapid change'. © 2018 The Author(s).

A probabilistic method for streamflow projection and associated uncertainty analysis in a data sparse alpine region

NASA Astrophysics Data System (ADS)

Ren, Weiwei; Yang, Tao; Shi, Pengfei; Xu, Chong-yu; Zhang, Ke; Zhou, Xudong; Shao, Quanxi; Ciais, Philippe

2018-06-01

Climate change imposes profound influence on regional hydrological cycle and water security in many alpine regions worldwide. Investigating regional climate impacts using watershed scale hydrological models requires a large number of input data such as topography, meteorological and hydrological data. However, data scarcity in alpine regions seriously restricts evaluation of climate change impacts on water cycle using conventional approaches based on global or regional climate models, statistical downscaling methods and hydrological models. Therefore, this study is dedicated to development of a probabilistic model to replace the conventional approaches for streamflow projection. The probabilistic model was built upon an advanced Bayesian Neural Network (BNN) approach directly fed by the large-scale climate predictor variables and tested in a typical data sparse alpine region, the Kaidu River basin in Central Asia. Results show that BNN model performs better than the general methods across a number of statistical measures. The BNN method with flexible model structures by active indicator functions, which reduce the dependence on the initial specification for the input variables and the number of hidden units, can work well in a data limited region. Moreover, it can provide more reliable streamflow projections with a robust generalization ability. Forced by the latest bias-corrected GCM scenarios, streamflow projections for the 21st century under three RCP emission pathways were constructed and analyzed. Briefly, the proposed probabilistic projection approach could improve runoff predictive ability over conventional methods and provide better support to water resources planning and management under data limited conditions as well as enable a facilitated climate change impact analysis on runoff and water resources in alpine regions worldwide.

Climate of the Arctic marine environment.

PubMed

Walsh, John E

2008-03-01

The climate of the Arctic marine environment is characterized by strong seasonality in the incoming solar radiation and by tremendous spatial variations arising from a variety of surface types, including open ocean, sea ice, large islands, and proximity to major landmasses. Interannual and decadal-scale variations are prominent features of Arctic climate, complicating the distinction between natural and anthropogenically driven variations. Nevertheless, climate models consistently indicate that the Arctic is the most climatically sensitive region of the Northern Hemisphere, especially near the sea ice margins. The Arctic marine environment has shown changes over the past several decades, and these changes are part of a broader global warming that exceeds the range of natural variability over the past 1000 years. Record minima of sea ice coverage during the past few summers and increased melt from Greenland have important implications for the hydrographic regime of the Arctic marine environment. The recent changes in the atmosphere (temperature, precipitation, pressure), sea ice, and ocean appear to be a coordinated response to systematic variations of the large-scale atmospheric circulation, superimposed on a general warming that is likely associated with increasing greenhouse gases. The changes have been sufficiently large in some sectors (e.g., the Bering/Chukchi Seas) that consequences for marine ecosystems appear to be underway. Global climate models indicate an additional warming of several degrees Celsius in much of the Arctic marine environment by 2050. However, the warming is seasonal (largest in autumn and winter), spatially variable, and closely associated with further retreat of sea ice. Additional changes predicted for 2050 are a general decrease of sea level pressure (largest in the Bering sector) and an increase of precipitation. While predictions of changes in storminess cannot be made with confidence, the predicted reduction of sea ice cover will almost certainly lead to increased oceanic mixing, ocean wave generation, and coastal flooding.

Use of NARCCAP data to characterize regional climate uncertainty in the impact of global climate change on large river fish population: Missouri River sturgeon example

NASA Astrophysics Data System (ADS)

Anderson, C. J.; Wildhaber, M. L.; Wikle, C. K.; Moran, E. H.; Franz, K. J.; Dey, R.

2012-12-01

Climate change operates over a broad range of spatial and temporal scales. Understanding the effects of change on ecosystems requires accounting for the propagation of information and uncertainty across these scales. For example, to understand potential climate change effects on fish populations in riverine ecosystems, climate conditions predicted by course-resolution atmosphere-ocean global climate models must first be translated to the regional climate scale. In turn, this regional information is used to force watershed models, which are used to force river condition models, which impact the population response. A critical challenge in such a multiscale modeling environment is to quantify sources of uncertainty given the highly nonlinear nature of interactions between climate variables and the individual organism. We use a hierarchical modeling approach for accommodating uncertainty in multiscale ecological impact studies. This framework allows for uncertainty due to system models, model parameter settings, and stochastic parameterizations. This approach is a hybrid between physical (deterministic) downscaling and statistical downscaling, recognizing that there is uncertainty in both. We use NARCCAP data to determine confidence the capability of climate models to simulate relevant processes and to quantify regional climate variability within the context of the hierarchical model of uncertainty quantification. By confidence, we mean the ability of the regional climate model to replicate observed mechanisms. We use the NCEP-driven simulations for this analysis. This provides a base from which regional change can be categorized as either a modification of previously observed mechanisms or emergence of new processes. The management implications for these categories of change are significantly different in that procedures to address impacts from existing processes may already be known and need adjustment; whereas, an emergent processes may require new management strategies. The results from hierarchical analysis of uncertainty are used to study the relative change in weights of the endangered Missouri River pallid sturgeon (Scaphirhynchus albus) under a 21st century climate scenario.

Circumpolar spatio-temporal patterns and contributing climatic factors of wildfire activity in the Arctic tundra from 2001-2015

NASA Astrophysics Data System (ADS)

Masrur, Arif; Petrov, Andrey N.; DeGroote, John

2018-01-01

Recent years have seen an increased frequency of wildfire events in different parts of Arctic tundra ecosystems. Contemporary studies have largely attributed these wildfire events to the Arctic’s rapidly changing climate and increased atmospheric disturbances (i.e. thunderstorms). However, existing research has primarily examined the wildfire-climate dynamics of individual large wildfire events. No studies have investigated wildfire activity, including climatic drivers, for the entire tundra biome across multiple years, i.e. at the planetary scale. To address this limitation, this paper provides a planetary/circumpolar scale analyses of space-time patterns of tundra wildfire occurrence and climatic association in the Arctic over a 15 year period (2001-2015). In doing so, we have leveraged and analyzed NASA Terra’s MODIS active fire and MERRA climate reanalysis products at multiple temporal scales (decadal, seasonal and monthly). Our exploratory spatial data analysis found that tundra wildfire occurrence was spatially clustered and fire intensity was spatially autocorrelated across the Arctic regions. Most of the wildfire events occurred in the peak summer months (June-August). Our multi-temporal (decadal, seasonal and monthly) scale analyses provide further support to the link between climate variability and wildfire activity. Specifically, we found that warm and dry conditions in the late spring to mid-summer influenced tundra wildfire occurrence, spatio-temporal distribution, and fire intensity. Additionally, reduced average surface precipitation and soil moisture levels in the winter-spring period were associated with increased fire intensity in the following summer. These findings enrich contemporary knowledge on tundra wildfire’s spatial and seasonal patterns, and shed new light on tundra wildfire-climate relationships in the circumpolar context. Furthermore, this first pan-Arctic analysis provides a strong incentive and direction for future studies which integrate multiple datasets (i.e. climate, fuels, topography, and ignition sources) to accurately estimate carbon emission from tundra burning and its global climate feedbacks in coming decades.

Downscaling large-scale circulation to local winter climate using neural network techniques

NASA Astrophysics Data System (ADS)

Cavazos Perez, Maria Tereza

1998-12-01

The severe impacts of climate variability on society reveal the increasing need for improving regional-scale climate diagnosis. A new downscaling approach for climate diagnosis is developed here. It is based on neural network techniques that derive transfer functions from the large-scale atmospheric controls to the local winter climate in northeastern Mexico and southeastern Texas during the 1985-93 period. A first neural network (NN) model employs time-lagged component scores from a rotated principal component analysis of SLP, 500-hPa heights, and 1000-500 hPa thickness as predictors of daily precipitation. The model is able to reproduce the phase and, to some decree, the amplitude of large rainfall events, reflecting the influence of the large-scale circulation. Large errors are found over the Sierra Madre, over the Gulf of Mexico, and during El Nino events, suggesting an increase in the importance of meso-scale rainfall processes. However, errors are also due to the lack of randomization of the input data and the absence of local atmospheric predictors such as moisture. Thus, a second NN model uses time-lagged specific humidity at the Earth's surface and at the 700 hPa level, SLP tendency, and 700-500 hPa thickness as input to a self-organizing map (SOM) that pre-classifies the atmospheric fields into different patterns. The results from the SOM classification document that negative (positive) anomalies of winter precipitation over the region are associated with: (1) weaker (stronger) Aleutian low; (2) stronger (weaker) North Pacific high; (3) negative (positive) phase of the Pacific North American pattern; and (4) La Nina (El Nino) events. The SOM atmospheric patterns are then used as input to a feed-forward NN that captures over 60% of the daily rainfall variance and 94% of the daily minimum temperature variance over the region. This demonstrates the ability of artificial neural network models to simulate realistic relationships on daily time scales. The results of this research also reveal that the SOM pre-classification of days with similar atmospheric conditions succeeded in emphasizing the differences of the atmospheric variance conducive to extreme events. This resulted in a downscaling NN model that is highly sensitive to local-scale weather anomalies associated with El Nino and extreme cold events.

A note on Bjerkne's hypothesis for North Atlantic variability

NASA Astrophysics Data System (ADS)

Bryan, Kirk; Stouffer, Ron

1991-01-01

On decadal time-scales the historical surface temperature record over land in the Northern Hemisphere is dominated by polar amplified variations. These variations are coherent with SST anomalies concentrated in the Northwest Atlantic, but extending with lesser amplitude into the North Pacific as well. Bierknes suggested that multi-year SST anomalies in the subpolar North Atlantic were due to irregular changes in the intensity of the thermohaline circulation. In support of the Bjerknes hypothesis there is evidence that winter overturning in the Labrador Sea was suppressed for a brief period from 1967-1969 by a cap of relative fresh water at the surface. Cause and effect are unclear, but this event was associated with a marked cooling of the entire Northern Hemisphere. The difference in SST averaged over the Northern Hemisphere oceans and SST averaged over the Southern Hemisphere oceans from the equator to 40°S is coherent with Sahel summer rainfall on decadal time scales. Empirical evidence is supported by numerical experiments with the British Meteorological Office atmospheric climate model which simulate augmented monsoonal rainfall in the Sahel region of Africa in response to realistic warm SST anomalies in the Northwest Atlantic. A coupled ocean-atmosphere global model exhibits two equilibrium climate states. One has an active thermohaline circulation in the North Atlantic and the other does not. The two climate states provide an extreme example which illustrates the type of large scale air sea interaction Bjerknes visualized as a mechanism for North Atlantic climate variability on decadal time-scales.

Watershed-scale response of groundwater recharge to inter-annual and inter-decadal variability in precipitation (Alberta, Canada)

NASA Astrophysics Data System (ADS)

Hayashi, Masaki; Farrow, Christopher R.

2014-12-01

Groundwater recharge sets a constraint on aquifer water balance in the context of water management. Historical data on groundwater and other relevant hydrological processes can be used to understand the effects of climatic variability on recharge, but such data sets are rare. The climate of the Canadian prairies is characterized by large inter-annual and inter-decadal variability in precipitation, which provides opportunities to examine the response of groundwater recharge to changes in meteorological conditions. A decadal study was conducted in a small (250 km2) prairie watershed in Alberta, Canada. Relative magnitude of annual recharge, indicated by water-level rise, was significantly correlated with a combination of growing-season precipitation and snowmelt runoff, which drives depression-focussed infiltration of meltwater. Annual precipitation was greater than vapour flux at an experimental site in some years and smaller in other years. On average precipitation minus vapour flux was 10 mm y-1, which was comparable to the magnitude of watershed-scale groundwater recharge estimated from creek baseflow. Average baseflow showed a distinct shift from a low value (4 mm y-1) in 1982-1995 to a high value (15 mm y-1) in 2003-2013, indicating the sensitivity of groundwater recharge to a decadal-scale variability of meteorological conditions.

Using Climate Regionalization to Understand Climate Forecast System Version 2 (CFSv2) Precipitation Performance for the Conterminous United States (CONUS)

NASA Technical Reports Server (NTRS)

Regonda, Satish K.; Zaitchik, Benjamin F.; Badr, Hamada S.; Rodell, Matthew

2016-01-01

Dynamically based seasonal forecasts are prone to systematic spatial biases due to imperfections in the underlying global climate model (GCM). This can result in low-forecast skill when the GCM misplaces teleconnections or fails to resolve geographic barriers, even if the prediction of large-scale dynamics is accurate. To characterize and address this issue, this study applies objective climate regionalization to identify discrepancies between the Climate Forecast SystemVersion 2 (CFSv2) and precipitation observations across the Contiguous United States (CONUS). Regionalization shows that CFSv2 1 month forecasts capture the general spatial character of warm season precipitation variability but that forecast regions systematically differ from observation in some transition zones. CFSv2 predictive skill for these misclassified areas is systematically reduced relative to correctly regionalized areas and CONUS as a whole. In these incorrectly regionalized areas, higher skill can be obtained by using a regional-scale forecast in place of the local grid cell prediction.

Hydrologic Impacts of Climate Change: Quantification of Uncertainties (Alexander von Humboldt Medal Lecture)

NASA Astrophysics Data System (ADS)

Mujumdar, Pradeep P.

2014-05-01

Climate change results in regional hydrologic change. The three prominent signals of global climate change, viz., increase in global average temperatures, rise in sea levels and change in precipitation patterns convert into signals of regional hydrologic change in terms of modifications in water availability, evaporative water demand, hydrologic extremes of floods and droughts, water quality, salinity intrusion in coastal aquifers, groundwater recharge and other related phenomena. A major research focus in hydrologic sciences in recent years has been assessment of impacts of climate change at regional scales. An important research issue addressed in this context deals with responses of water fluxes on a catchment scale to the global climatic change. A commonly adopted methodology for assessing the regional hydrologic impacts of climate change is to use the climate projections provided by the General Circulation Models (GCMs) for specified emission scenarios in conjunction with the process-based hydrologic models to generate the corresponding hydrologic projections. The scaling problem arising because of the large spatial scales at which the GCMs operate compared to those required in distributed hydrologic models, and their inability to satisfactorily simulate the variables of interest to hydrology are addressed by downscaling the GCM simulations to hydrologic scales. Projections obtained with this procedure are burdened with a large uncertainty introduced by the choice of GCMs and emission scenarios, small samples of historical data against which the models are calibrated, downscaling methods used and other sources. Development of methodologies to quantify and reduce such uncertainties is a current area of research in hydrology. In this presentation, an overview of recent research carried out by the author's group on assessment of hydrologic impacts of climate change addressing scale issues and quantification of uncertainties is provided. Methodologies developed with conditional random fields, Dempster-Shafer theory, possibility theory, imprecise probabilities and non-stationary extreme value theory are discussed. Specific applications on uncertainty quantification in impacts on streamflows, evaporative water demands, river water quality and urban flooding are presented. A brief discussion on detection and attribution of hydrologic change at river basin scales, contribution of landuse change and likely alterations in return levels of hydrologic extremes is also provided.

Trends and Natural Variability of Spring Onset in the Coterminous United States as Evaluated by a New Gridded Dataset of Spring Indices

NASA Astrophysics Data System (ADS)

Ault, T.; Schwartz, M. D.; Zurita-Milla, R.; Weltzin, J. F.; Betancourt, J. L.

2015-12-01

Climate change is expected to modify the timing of seasonal transitions this century, impacting wildlife migrations, ecosystem function, and agricultural activity. Tracking seasonal transitions in a consistent manner across space and through time requires indices that can be used for monitoring and managing biophysical and ecological systems during the coming decades. Here a new gridded dataset of spring indices is described and used to understand interannual, decadal, and secular trends across the coterminous US. This dataset is derived from daily interpolated meteorological data, and results are compared with historical station data to ensure the trends and variations are robust. Regional trends in the first leaf index range from -0.8 to -1.6 days per decade, while first bloom index trends are between -0.4 and -1.2 for most regions. However, these trends are modulated by interannual to multidecadal variations, which are substantial throughout the regions considered here. These findings emphasize the important role large-scale climate modes of variability play in modulating spring onset on interannual to multidecadal timescales. Finally, there is some potential for successful sub-seasonal forecasts of spring onset, as indices from most regions are significantly correlated with antecedent large-scale modes of variability.

Climate variability rather than overstocking causes recent large scale cover changes of Tibetan pastures

NASA Astrophysics Data System (ADS)

Lehnert, Lukas; Wesche, Karsten; Trachte, Katja; Reudenbach, Christoph; Miehe, Georg; Bendix, Jörg

2016-04-01

The Tibetan Plateau has been entitled "Third-Pole-Environment" because of its outstanding importance for the climate and the hydrology in East and South-east Asia. Its climatological and hydrological influences are strongly affected by the local grassland vegetation which is supposed to be subject to ongoing degradation. On a local scale, numerous studies focused on grassland degradation of the Tibetan pastures. However, because methods and scales substantially differed among previous studies, the overall pattern of the degradation in the Tibetan Plateau is unknown. Consequently, a satellite based approach was selected to cope with the spatial limitations. Therefore, a MODIS-based vegetation cover product was developed which is fully validated against 600 in situ measurements covering a wide extent of the Tibetan Plateau. The vegetation cover as a proxy for grassland degradation is modelled with low error rates using support vector machine regressions. To identify the changes in the vegetation cover, the trends seen in the new vegetation cover product since the beginning of the new millennium were analysed. The drivers of the vegetation changes were identified by the analysis of trends of climatic variables (precipitation and 2 m air temperature) and land-use (livestock numbers) over the same time. The results reveal that - in contrast to the prevailing opinion - pasture degradation on the Tibetan Plateau is not a generally proceeding process because areas of positive and negative changes are almost equal in extent. The positive and negative vegetation changes have regionally different triggers: While, from 2000 on, the vegetation cover has increased in the north-eastern part of the Tibetan Plateau due to increasing precipitation, it has declined in the central and western parts due to rising air temperature and declining precipitation. Increasing livestock numbers as a result of land use changes exacerbated the negative trends but, contrarily to the assumptions of former studies, were not their exclusive driver. Thus, it can be concluded that climate variability instead of overgrazing has been the primary cause for large scale vegetation cover changes on the Tibetan Plateau since the new millennium.

Statistical downscaling of precipitation using long short-term memory recurrent neural networks

NASA Astrophysics Data System (ADS)

Misra, Saptarshi; Sarkar, Sudeshna; Mitra, Pabitra

2017-11-01

Hydrological impacts of global climate change on regional scale are generally assessed by downscaling large-scale climatic variables, simulated by General Circulation Models (GCMs), to regional, small-scale hydrometeorological variables like precipitation, temperature, etc. In this study, we propose a new statistical downscaling model based on Recurrent Neural Network with Long Short-Term Memory which captures the spatio-temporal dependencies in local rainfall. The previous studies have used several other methods such as linear regression, quantile regression, kernel regression, beta regression, and artificial neural networks. Deep neural networks and recurrent neural networks have been shown to be highly promising in modeling complex and highly non-linear relationships between input and output variables in different domains and hence we investigated their performance in the task of statistical downscaling. We have tested this model on two datasets—one on precipitation in Mahanadi basin in India and the second on precipitation in Campbell River basin in Canada. Our autoencoder coupled long short-term memory recurrent neural network model performs the best compared to other existing methods on both the datasets with respect to temporal cross-correlation, mean squared error, and capturing the extremes.

Improving seasonal forecasts of hydroclimatic variables through the state of multiple large-scale climate signals

NASA Astrophysics Data System (ADS)

Castelletti, A.; Giuliani, M.; Block, P. J.

2017-12-01

Increasingly uncertain hydrologic regimes combined with more frequent and intense extreme events are challenging water systems management worldwide, emphasizing the need of accurate medium- to long-term predictions to timely prompt anticipatory operations. Despite modern forecasts are skillful over short lead time (from hours to days), predictability generally tends to decrease on longer lead times. Global climate teleconnection, such as El Niño Southern Oscillation (ENSO), may contribute in extending forecast lead times. However, ENSO teleconnection is well defined in some locations, such as Western USA and Australia, while there is no consensus on how it can be detected and used in other regions, particularly in Europe, Africa, and Asia. In this work, we generalize the Niño Index Phase Analysis (NIPA) framework by contributing the Multi Variate Niño Index Phase Analysis (MV-NIPA), which allows capturing the state of multiple large-scale climate signals (i.e. ENSO, North Atlantic Oscillation, Pacific Decadal Oscillation, Atlantic Multi-decadal Oscillation, Indian Ocean Dipole) to forecast hydroclimatic variables on a seasonal time scale. Specifically, our approach distinguishes the different phases of the considered climate signals and, for each phase, identifies relevant anomalies in Sea Surface Temperature (SST) that influence the local hydrologic conditions. The potential of the MV-NIPA framework is demonstrated through an application to the Lake Como system, a regulated lake in northern Italy which is mainly operated for flood control and irrigation supply. Numerical results show high correlations between seasonal SST values and one season-ahead precipitation in the Lake Como basin. The skill of the resulting MV-NIPA forecast outperforms the one of ECMWF products. This information represents a valuable contribution to partially anticipate the summer water availability, especially during drought events, ultimately supporting the improvement of the Lake Como operations.

Variability, trends, and teleconnections of stream flows with large-scale climate signals in the Omo-Ghibe River Basin, Ethiopia.

PubMed

Degefu, Mekonnen Adnew; Bewket, Woldeamlak

2017-04-01

This study assesses variability, trends, and teleconnections of stream flow with large-scale climate signals (global sea surface temperatures (SSTs)) for the Omo-Ghibe River Basin of Ethiopia. Fourteen hydrological indices of variability and extremes were defined from daily stream flow data series and analyzed for two common periods, which are 1972-2006 for 5 stations and 1982-2006 for 15 stations. The Mann-Kendall's test was used to detect trends at 0.05 significance level, and simple correlation analysis was applied to evaluate associations between the selected stream flow indices and SSTs. We found weak and mixed (upward and downward) trend signals for annual and wet (Kiremt) season flows. Indices generated for high-flow (flood) magnitudes showed the same weak trend signals. However, trend tests for flood frequencies and low-flow magnitudes showed little evidences of increasing change. It was also found that El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) are the major anomalies affecting stream flow variability in the Omo-Ghibe Basin. The strongest associations are observed between ENSO/Niño3.4 and the stream flow in August and September, mean Kiremt flow (July-September), and flood frequency (peak over threshold on average three peaks per year (POT3_Fre)). The findings of this study provide a general overview on the long-term stream flow variability and predictability of stream flows for the Omo-Ghibe River Basin.

The Baltic Sea natural long-term variability of salinity

NASA Astrophysics Data System (ADS)

Schimanke, Semjon; Markus Meier, H. E.

2015-04-01

The Baltic Sea is one of the largest brackish sea areas of the world. The sensitive state of the Baltic Sea is sustained by a fresh-water surplus by river discharge and precipitation on one hand as well as inflows of highly saline and oxygen-rich water masses from the North Sea on the other. Major inflows which are crucial for the renewal of the deep water occur very intermittent with a mean frequency of approximately one per year. Stagnation periods (periods without major inflows) lead for instance to a reduction of oxygen concentration in the deep Baltic Sea spreading hypoxic conditions. Depending on the amount of salt water inflow and fresh-water supply the deep water salinity of the Baltic Sea varies between 11 to 14 PSU on the decadal scale. The goal of this study is to understand the contribution of different driving factors for the decadal to multi-decadal variability of salinity in the Baltic Sea. Continuous measurement series of salinity exist from the 1950 but are not sufficiently long for the investigation of long-term fluctuations. Therefore, a climate simulation of more than 800 years has been carried out with the Rossby Center Ocean model (RCO). RCO is a biogeochemical regional climate model which covers the entire Baltic Sea. It is driven with atmospheric data dynamical downscaled from a GCM mimicking natural climate variability. The analysis focus on the role of variations in river discharge and precipitation, changes in wind speed and direction, fluctuations in temperature and shifts in large scale pressure patterns (e.g. NAO). Hereby, the length of the simulation will allow to identify mechanisms working on decadal to multi-decadal time scales. Moreover, it will be discussed how likely long stagnation periods are under natural climate variability and if the observed exceptional long stagnation period between 1983-1993 might be related to beginning climate change.

Global Scale Remote Sensing Monitoring of Endorheic Lake Systems

NASA Astrophysics Data System (ADS)

Scuderi, L. A.

2010-12-01

Semi-arid regions of the world contain thousands of endorheic lakes in large shallow basins. Due to their generally remote locations few are continuously monitored. Documentation of recent variability is essential to assessing how endorheic lakes respond to short-term meteorological conditions and longer-term decadal-scale climatic variability and is critical in determining future disturbance of hydrological regimes with respect to predicted warming and drying in the mid-latitudes. Short- and long-term departures from climatic averages, rapid environmental shifts and increased population pressures may result in significant fluctuations in the hydrologic budgets of these lakes and adversely impact endorheic lake/basin ecosystems. Information on flooding variability is also critical in estimating changes in P/E balances and on the production of exposed and easily deflated surfaces that may impact dust loading locally and regionally. In order to provide information on how these lakes respond we need to understand how entire systems respond hydrologically to different climatic inputs. This requires monitoring and analysis of regional to continental-scale systems. To date, this level of monitoring has not been achieved in an operational system. In order to assess the possibility of creating a global-scale lake inundation database we analyzed two contrasting lake systems in western North America (Mexico and New Mexico, USA) and China (Inner Mongolia). We asked two major questions: 1) is it possible to quickly and accurately quantify current lake inundation events in near real time using remote sensing? and, 2) is it possible to differentiate variable meteorological sources and resultant lake inundation responses using this type of database? With respect to these results we outline an automated lake monitoring approach using MODIS data and real-time processing systems that may provide future global monitoring capabilities.

Development and application of downscaled hydroclimatic predictor variables for use in climate vulnerability and assessment studies

USGS Publications Warehouse

Thorne, James; Boynton, Ryan; Flint, Lorraine; Flint, Alan; N'goc Le, Thuy

2012-01-01

This paper outlines the production of 270-meter grid-scale maps for 14 climate and derivative hydrologic variables for a region that encompasses the State of California and all the streams that flow into it. The paper describes the Basin Characterization Model (BCM), a map-based, mechanistic model used to process the hydrological variables. Three historic and three future time periods of 30 years (1911–1940, 1941–1970, 1971–2000, 2010–2039, 2040–2069, and 2070–2099) were developed that summarize 180 years of monthly historic and future climate values. These comprise a standardized set of fine-scale climate data that were shared with 14 research groups, including the U.S. National Park Service and several University of California groups as part of this project. We present three analyses done with the outputs from the Basin Characterization Model: trends in hydrologic variables over baseline, the most recent 30-year period; a calibration and validation effort that uses measured discharge values from 139 streamgages and compares those to Basin Characterization Model-derived projections of discharge for the same basins; and an assessment of the trends of specific hydrological variables that links historical trend to projected future change under four future climate projections. Overall, increases in potential evapotranspiration dominate other influences in future hydrologic cycles. Increased potential evapotranspiration drives decreasing runoff even under forecasts with increased precipitation, and drives increased climatic water deficit, which may lead to conversion of dominant vegetation types across large parts of the study region as well as have implications for rain-fed agriculture. The potential evapotranspiration is driven by air temperatures, and the Basin Characterization Model permits it to be integrated with a water balance model that can be derived for landscapes and summarized by watershed. These results show the utility of using a process-based model with modules representing different hydrological pathways that can be inter-linked.

Assessing the Effects of Climate on Global Fluvial Discharge Variability

NASA Astrophysics Data System (ADS)

Hansford, M. R.; Plink-Bjorklund, P.

2017-12-01

Plink-Bjorklund (2015) established the link between precipitation seasonality and river discharge variability in the monsoon domain and subtropical rivers (see also Leier et al, 2005; Fielding et al., 2009), resulting in distinct morphodynamic processes and a sedimentary record distinct from perennial precipitation zone in tropical rainforest zone and mid latitudes. This study further develops our understanding of discharge variability using a modern global river database created with data from the Global Runoff Data Centre (GRDC). The database consists of daily discharge for 595 river stations and examines them using a series of discharge variability indexes (DVI) on different temporal scales to examine how discharge variability occurs in river systems around the globe. These indexes examine discharge of individual days and monthly averages that allows for comparison of river systems against each other, regardless of size of the river. Comparing river discharge patterns in seven climate zones (arid, cold, humid subtropics, monsoonal, polar, rainforest, and temperate) based off the Koppen-Geiger climate classifications reveals a first order climatic control on discharge patterns and correspondingly sediment transport. Four groupings of discharge patterns emerge when coming climate zones and DVI: persistent, moderate, seasonal, and erratic. This dataset has incredible predictive power about the nature of discharge in fluvial systems around the world. These seasonal effects on surface water supply affects river morphodynamics and sedimentation on a wide timeframe, ranging from large single events to an inter-annual or even decadal timeframe. The resulting sedimentary deposits lead to differences in fluvial architecture on a range of depositional scales from sedimentary structures and bedforms to channel complex systems. These differences are important to accurately model for several reasons, ranging from stratigraphic and paleoenviromental reconstructions to more economic reasons, such as predicting reservoir presence, distribution, and connectivity in continental basins. The ultimate objective of this research is to develop differentiated fluvial facies and architecture based on the observed discharge patterns in the different climate zones.

Paleoecology and high-resolution paleohydrology of a kettle peatland in upper Michigan

NASA Astrophysics Data System (ADS)

Booth, Robert K.; Jackson, Stephen T.; Gray, Catherine E. D.

2004-01-01

We investigated the developmental and hydrological history of a Sphagnum-dominated, kettle peatland in Upper Michigan using testate amoebae, plant macrofossils, and pollen. Our primary objective was to determine if the paleohydrological record of the peatland represents a record of past climate variability at subcentennial to millennial time scales. To assess the role of millennial-scale climate variability on peatland paleohydrology, we compared the timing of peatland and upland vegetation changes. To investigate the role of higher-frequency climate variability on peatland paleohydrology, we used testate amoebae to reconstruct a high-resolution, hydrologic history of the peatland for the past 5100 years, and compared this record to other regional records of paleoclimate and vegetation. Comparisons revealed coherent patterns of hydrological, vegetational, and climatic changes, suggesting that peatland paleohydrology responded to climate variability at millennial to sub-centennial time scales. Although ombrotrophic peatlands have been the focus of most high-resolution peatland paleoclimate research, paleohydrological records from Sphagnum-dominated, closed-basin peatlands record high-frequency and low-magnitude climatic changes and thus represent a significant source of unexplored paleoclimate data.

Creating Near-Term Climate Scenarios for AgMIP

NASA Astrophysics Data System (ADS)

Goddard, L.; Greene, A. M.; Baethgen, W.

2012-12-01

For the next assessment report of the IPCC (AR5), attention is being given to development of climate information that is appropriate for adaptation, such as decadal-scale and near-term predictions intended to capture the combined effects of natural climate variability and the emerging climate change signal. While the science and practice evolve for the production and use of dynamic decadal prediction, information relevant to agricultural decision-makers can be gained from analysis of past decadal-scale trends and variability. Statistical approaches that mimic the characteristics of observed year-to-year variability can indicate the range of possibilities and their likelihood. In this talk we present work towards development of near-term climate scenarios, which are needed to engage decision-makers and stakeholders in the regions in current decision-making. The work includes analyses of decadal-scale variability and trends in the AgMIP regions, and statistical approaches that capture year-to-year variability and the associated persistence of wet and dry years. We will outline the general methodology and some of the specific considerations in the regional application of the methodology for different AgMIP regions, such those for Western Africa versus southern Africa. We will also show some examples of quality checks and informational summaries of the generated data, including (1) metrics of information quality such as probabilistic reliability for a suite of relevant climate variables and indices important for agriculture; (2) quality checks relative to the use of this climate data in crop models; and, (3) summary statistics (e.g., for 5-10-year periods or across given spatial scales).

Volcanic Eruptions and Climate

NASA Technical Reports Server (NTRS)

LeGrande, Allegra N.; Anchukaitis, Kevin J.

2015-01-01

Volcanic eruptions represent some of the most climatically important and societally disruptive short-term events in human history. Large eruptions inject ash, dust, sulfurous gases (e.g. SO2, H2S), halogens (e.g. Hcl and Hbr), and water vapor into the Earth's atmosphere. Sulfurous emissions principally interact with the climate by converting into sulfate aerosols that reduce incoming solar radiation, warming the stratosphere and altering ozone creation, reducing global mean surface temperature, and suppressing the hydrological cycle. In this issue, we focus on the history, processes, and consequences of these large eruptions that inject enough material into the stratosphere to significantly affect the climate system. In terms of the changes wrought on the energy balance of the Earth System, these transient events can temporarily have a radiative forcing magnitude larger than the range of solar, greenhouse gas, and land use variability over the last millennium. In simulations as well as modern and paleoclimate observations, volcanic eruptions cause large inter-annual to decadal-scale changes in climate. Active debates persist concerning their role in longer-term (multi-decadal to centennial) modification of the Earth System, however.

The Landscape Evolution Observatory: a large-scale controllable infrastructure to study coupled Earth-surface processes

USGS Publications Warehouse

Pangle, Luke A.; DeLong, Stephen B.; Abramson, Nate; Adams, John; Barron-Gafford, Greg A.; Breshears, David D.; Brooks, Paul D.; Chorover, Jon; Dietrich, William E.; Dontsova, Katerina; Durcik, Matej; Espeleta, Javier; Ferré, T.P.A.; Ferriere, Regis; Henderson, Whitney; Hunt, Edward A.; Huxman, Travis E.; Millar, David; Murphy, Brendan; Niu, Guo-Yue; Pavao-Zuckerman, Mitch; Pelletier, Jon D.; Rasmussen, Craig; Ruiz, Joaquin; Saleska, Scott; Schaap, Marcel; Sibayan, Michael; Troch, Peter A.; Tuller, Markus; van Haren, Joost; Zeng, Xubin

2015-01-01

Zero-order drainage basins, and their constituent hillslopes, are the fundamental geomorphic unit comprising much of Earth's uplands. The convergent topography of these landscapes generates spatially variable substrate and moisture content, facilitating biological diversity and influencing how the landscape filters precipitation and sequesters atmospheric carbon dioxide. In light of these significant ecosystem services, refining our understanding of how these functions are affected by landscape evolution, weather variability, and long-term climate change is imperative. In this paper we introduce the Landscape Evolution Observatory (LEO): a large-scale controllable infrastructure consisting of three replicated artificial landscapes (each 330 m2 surface area) within the climate-controlled Biosphere 2 facility in Arizona, USA. At LEO, experimental manipulation of rainfall, air temperature, relative humidity, and wind speed are possible at unprecedented scale. The Landscape Evolution Observatory was designed as a community resource to advance understanding of how topography, physical and chemical properties of soil, and biological communities coevolve, and how this coevolution affects water, carbon, and energy cycles at multiple spatial scales. With well-defined boundary conditions and an extensive network of sensors and samplers, LEO enables an iterative scientific approach that includes numerical model development and virtual experimentation, physical experimentation, data analysis, and model refinement. We plan to engage the broader scientific community through public dissemination of data from LEO, collaborative experimental design, and community-based model development.

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

Curious or spurious correlations within a national-scale forest inventory?

Treesearch

Christopher W. Woodall; James A. Westfall

2012-01-01

Foresters are increasingly required to assess trends not only in traditional forest attributes (e.g., growing-stock volumes), but also across suites of forest health indicators and site/climate variables. Given the tenuous relationship between correlation and causality within extremely large datasets, the goal of this study was to use a nationwide annual forest...

Explaining spatial variability in stream habitats using both natural and management-influenced landscape predictors

Treesearch

K.J. Anlauf; D.W. Jensen; K.M. Burnett; E.A. Steel; K. Christiansen; J.C. Firman; B.E. Feist; D.P. Larsen

2011-01-01

1. The distribution and composition of in-stream habitats are reflections of landscape scale geomorphic and climatic controls. Correspondingly, Pacific salmon (Oncorhynchus spp.) are largely adapted to and constrained by the quality and complexity of those in-stream habitat conditions. The degree to which lands have been fragmented and managed can...

Global linkages between teleconnection patterns and the terrestrial biosphere

NASA Astrophysics Data System (ADS)

Dahlin, Kyla M.; Ault, Toby R.

2018-07-01

Interannual variability in the global carbon cycle is largely due to variations in carbon uptake by terrestrial ecosystems, yet linkages between climate variability and variability in the terrestrial carbon cycle are not well understood at the global scale. Using a 30-year satellite record of semi-monthly leaf area index (LAI), we show that four modes of climate variability - El Niño/Southern Oscillation, the North Atlantic Oscillation, the Atlantic Meridional Mode, and the Indian Ocean Dipole Mode - strongly impact interannual vegetation growth patterns, with 68% of the land surface impacted by at least one of these teleconnection patterns, yet the spatial distribution of these impacts is heterogeneous. Considering the patterns' impacts by biome, none has an exclusively positive or negative relationship with LAI. Our findings imply that future changes in the frequency and/or magnitude of teleconnection patterns will lead to diverse changes to the terrestrial biosphere and the global carbon cycle.

Impacts of variability in cellulosic biomass yields on energy security.

PubMed

Mullins, Kimberley A; Matthews, H Scott; Griffin, W Michael; Anex, Robert

2014-07-01

The practice of modeling biomass yields on the basis of deterministic point values aggregated over space and time obscures important risks associated with large-scale biofuel use, particularly risks related to drought-induced yield reductions that may become increasingly frequent under a changing climate. Using switchgrass as a case study, this work quantifies the variability in expected yields over time and space through switchgrass growth modeling under historical and simulated future weather. The predicted switchgrass yields across the United States range from about 12 to 19 Mg/ha, and the 80% confidence intervals range from 20 to 60% of the mean. Average yields are predicted to decrease with increased temperatures and weather variability induced by climate change. Feedstock yield variability needs to be a central part of modeling to ensure that policy makers acknowledge risks to energy supplies and develop strategies or contingency plans that mitigate those risks.

Hydroclimatic drivers, Water-borne Diseases, and Population Vulnerability in Bengal Delta

NASA Astrophysics Data System (ADS)

Akanda, A. S.; Jutla, A. S.

2012-04-01

Water-borne diarrheal disease outbreaks in the Bengal Delta region, such as cholera, rotavirus, and dysentery, show distinct seasonal peaks and spatial signatures in their origin and progression. However, the mechanisms behind these seasonal phenomena, especially the role of regional climatic and hydrologic processes behind the disease outbreaks, are not fully understood. Overall diarrheal disease prevalence and the population vulnerability to transmission mechanisms thus remain severely underestimated. Recent findings suggest that diarrheal incidence in the spring is strongly associated with scarcity of freshwater flow volumes, while the abundance of water in monsoon show strong positive correlation with autumn diarrheal burden. The role of large-scale ocean-atmospheric processes that tend to modulate meteorological, hydrological, and environmental conditions over large regions and the effects on the ecological states conducive to the vectors and triggers of diarrheal outbreaks over large geographic regions are not well understood. We take a large scale approach to conduct detailed diagnostic analyses of a range of climate, hydrological, and ecosystem variables to investigate their links to outbreaks, occurrence, and transmission of the most prevalent water-borne diarrheal diseases. We employ satellite remote sensing data products to track coastal ecosystems and plankton processes related to cholera outbreaks. In addition, we investigate the effect of large scale hydroclimatic extremes (e.g., droughts and floods, El Nino) to identify how diarrheal transmission and epidemic outbreaks are most likely to respond to shifts in climatic, hydrologic, and ecological changes over coming decades. We argue that controlling diarrheal disease burden will require an integrated predictive surveillance approach - a combination of prediction and prevention - with recent advances in climate-based predictive capabilities and demonstrated successes in primary and tertiary prevention in endemic regions.

Downscaling of global climate change estimates to regional scales: An application to Iberian rainfall in wintertime

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

von Storch, H.; Zorita, E.; Cubasch, U.

A statistical strategy to deduct regional-scale features from climate general circulation model (GCM) simulations has been designed and tested. The main idea is to interrelate the characteristic patterns of observed simultaneous variations of regional climate parameters and of large-scale atmospheric flow using the canonical correlation technique. The large-scale North Atlantic sea level pressure (SLP) is related to the regional, variable, winter (DJF) mean Iberian Peninsula rainfall. The skill of the resulting statistical model is shown by reproducing, to a good approximation, the winter mean Iberian rainfall from 1900 to present from the observed North Atlantic mean SLP distributions. It ismore » shown that this observed relationship between these two variables is not well reproduced in the output of a general circulation model (GCM). The implications for Iberian rainfall changes as the response to increasing atmospheric greenhouse-gas concentrations simulated by two GCM experiments are examined with the proposed statistical model. In an instantaneous [open quotes]2 CO[sub 2][close quotes] doubling experiment, using the simulated change of the mean North Atlantic SLP field to predict Iberian rainfall yields, there is an insignificant increase of area-averaged rainfall of I mm/month, with maximum values of 4 mm/month in the northwest of the peninsula. In contrast, for the four GCM grid points representing the lberian Peninsula, the change is - 10 mm/month, with a minimum of - 19 mm/month in the southwest. In the second experiment, with the IPCC scenario A ([open quotes]business as usual[close quotes]) increase of CO[sub 2], the statistical-model results partially differ from the directly simulated rainfall changes: in the experimental range of 100 years, the area-averaged rainfall decreases by 7 mm/month (statistical model), and by 9 mm/month (GCM); at the same time the amplitude of the interdecadal variability is quite different. 17 refs., 10 figs.« less

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

Evidence for a possible modern and mid-Holocene solar influence on climate from Lake Titicaca, South America

NASA Astrophysics Data System (ADS)

Theissen, K. M.; Dunbar, R. B.

2005-12-01

In tropical regions, there are few paleoclimate archives with the necessary resolution to investigate climate variability at interannual-to-decadal timescales prior to the onset of the instrumental record. Interannual variability associated with the El Niño Southern Oscillation (ENSO) is well documented in the instrumental record and the importance of the precessional forcing of millennial variability has been established in studies of tropical paleoclimate records. In contrast, decade-to-century variability is still poorly understood. Here, we examine interannual to decadal variability in the northern Altiplano of South America using digital image analysis of a floating interval of varved sediments of middle Holocene age (~6160-6310 yr BP) from Lake Titicaca. Multi-taper method (MTM) and wavelet frequency-domain analyses were performed on a time series generated from a gray-scaled digital image of the mm-thick laminations. Our results indicate significant power at a decadal periodicity (10-12 years) associated with the Schwabe cycle of solar activity. Frequency-domain analysis also indicates power at 2-2.5 year periodicities associated with ENSO. Similarly, spectral analysis of a 75 year instrumental record of Titicaca lake level shows significant power at both solar and ENSO periodicities. Although both of the examined records are short, our results imply that during both the mid-Holocene and modern times, solar and ENSO variability may have contributed to high frequency climate fluctuations over the northern Altiplano. We suspect that solar influence on large-scale atmospheric circulation features may account for the decadal variability in the mid-Holocene and present-day water balance of the Altiplano.

Drivers for spatial variability in agricultural soil organic carbon stocks in Germany

NASA Astrophysics Data System (ADS)

Vos, Cora; Don, Axel; Hobley, Eleanor; Prietz, Roland; Heidkamp, Arne; Freibauer, Annette

2017-04-01

Soil organic carbon is one of the largest components of the global carbon cycle. It has recently gained importance in global efforts to mitigate climate change through carbon sequestration. In order to find locations suitable for carbon sequestration, and estimate the sequestration potential, however, it is necessary to understand the factors influencing the high spatial variability of soil organic carbon stocks. Due to numerous interacting factors that influence its dynamics, soil organic carbon stocks are difficult to predict. In the course of the German Agricultural Soil Inventory over 2500 agricultural sites were sampled and their soil organic carbon stocks determined. Data relating to more than 200 potential drivers of SOC stocks were compiled from laboratory measurements, farmer questionnaires and climate stations. The aims of this study were to 1) give an overview of soil organic carbon stocks in Germany's agricultural soils, 2) to quantify and explain the influence of explanatory variables on soil organic carbon stocks. Two different machine learning algorithms were used to identify the most important variables and multiple regression models were used to explore the influence of those variables. Models for predicting carbon stocks in different depth increments between 0-100 cm were developed, explaining up to 62% (validation, 98% calibration) of total variance. Land-use, land-use history, clay content and electrical conductivity were main predictors in the topsoil, while bedrock material, relief and electrical conductivity governed the variability of subsoil carbon stocks. We found 32% of all soils to be deeply anthropogenically transformed. The influence of climate related variables was surprisingly small (≤5% of explained variance), while site variables explained a large share of soil carbon variability (46-100% of explained variance), in particular in the subsoil. Thus, the understanding of SOC dynamics at regional scale requires a thorough description of the variability in soil physical parameters. Agronomic management impact on SOC stocks is important near the soil surface, but is mainly attributable to land-use and not to other management factors on this large regional scale. The importance of historical land-use practices as well as anthropogenic soil transformations to SOC stocks highlights the need for prudent soil management and conservation policies.

Mesoscale to Synoptic Scale Cloud Variability

NASA Technical Reports Server (NTRS)

Rossow, William B.

1998-01-01

The atmospheric circulation and its interaction with the oceanic circulation involve non-linear and non-local exchanges of energy and water over a very large range of space and time scales. These exchanges are revealed, in part, by the related variations of clouds, which occur on a similar range of scales as the atmospheric motions that produce them. Collection of comprehensive measurements of the properties of the atmosphere, clouds and surface allows for diagnosis of some of these exchanges. The use of a multi-satellite-network approach by the International Satellite Cloud Climatology Project (ISCCP) comes closest to providing complete coverage of the relevant range space and time scales over which the clouds, atmosphere and ocean vary. A nearly 15-yr dataset is now available that covers the range from 3 hr and 30 km to decade and planetary. This paper considers three topics: (1) cloud variations at the smallest scales and how they may influence radiation-cloud interactions, and (2) cloud variations at "moderate" scales and how they may cause natural climate variability, and (3) cloud variations at the largest scales and how they affect the climate. The emphasis in this discussion is on the more mature subject of cloud-radiation interactions. There is now a need to begin similar detailed diagnostic studies of water exchange processes.

Understanding the Changes in Global Crop Yields Through Changes in Climate and Technology

NASA Astrophysics Data System (ADS)

Najafi, Ehsan; Devineni, Naresh; Khanbilvardi, Reza M.; Kogan, Felix

2018-03-01

During the last few decades, the global agricultural production has risen and technology enhancement is still contributing to yield growth. However, population growth, water crisis, deforestation, and climate change threaten the global food security. An understanding of the variables that caused past changes in crop yields can help improve future crop prediction models. In this article, we present a comprehensive global analysis of the changes in the crop yields and how they relate to different large-scale and regional climate variables, climate change variables and technology in a unified framework. A new multilevel model for yield prediction at the country level is developed and demonstrated. The structural relationships between average yield and climate attributes as well as trends are estimated simultaneously. All countries are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. El Niño-southern oscillation (ENSO), Palmer drought severity index (PDSI), geopotential height anomalies (GPH), historical carbon dioxide (CO2) concentration and country-based time series of GDP per capita as an approximation of technology measurement are used as predictors to estimate annual agricultural crop yields for each country from 1961 to 2013. Results indicate that these variables can explain the variability in historical crop yields for most of the countries and the model performs well under out-of-sample verifications. While some countries were not generally affected by climatic factors, PDSI and GPH acted both positively and negatively in different regions for crop yields in many countries.

Forecasting climate change impacts to plant community composition in the Sonoran Desert region

USGS Publications Warehouse

Munson, Seth M.; Webb, Robert H.; Belnap, Jayne; Hubbard, J. Andrew; Swann, Don E.; Rutman, Sue

2012-01-01

Hotter and drier conditions projected for the southwestern United States can have a large impact on the abundance and composition of long-lived desert plant species. We used long-term vegetation monitoring results from 39 large plots across four protected sites in the Sonoran Desert region to determine how plant species have responded to past climate variability. This cross-site analysis identified the plant species and functional types susceptible to climate change, the magnitude of their responses, and potential climate thresholds. In the relatively mesic mesquite savanna communities, perennial grasses declined with a decrease in annual precipitation, cacti increased, and there was a reversal of the Prosopis velutina expansion experienced in the 20th century in response to increasing mean annual temperature (MAT). In the more xeric Arizona Upland communities, the dominant leguminous tree, Cercidium microphyllum, declined on hillslopes, and the shrub Fouquieria splendens decreased, especially on south- and west-facing slopes in response to increasing MAT. In the most xeric shrublands, the codominant species Larrea tridentata and its hemiparasite Krameria grayi decreased with a decrease in cool season precipitation and increased aridity, respectively. This regional-scale assessment of plant species response to recent climate variability is critical for forecasting future shifts in plant community composition, structure, and productivity.

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...

Integrated approaches to climate–crop modelling: needs and challenges

PubMed Central

A. Betts, Richard

2005-01-01

This paper discusses the need for a more integrated approach to modelling changes in climate and crops, and some of the challenges posed by this. While changes in atmospheric composition are expected to exert an increasing radiative forcing of climate change leading to further warming of global mean temperatures and shifts in precipitation patterns, these are not the only climatic processes which may influence crop production. Changes in the physical characteristics of the land cover may also affect climate; these may arise directly from land use activities and may also result from the large-scale responses of crops to seasonal, interannual and decadal changes in the atmospheric state. Climate models used to drive crop models may, therefore, need to consider changes in the land surface, either as imposed boundary conditions or as feedbacks from an interactive climate–vegetation model. Crops may also respond directly to changes in atmospheric composition, such as the concentrations of carbon dioxide (CO2), ozone (O3) and compounds of sulphur and nitrogen, so crop models should consider these processes as well as climate change. Changes in these, and the responses of the crops, may be intimately linked with meteorological processes so crop and climate models should consider synergies between climate and atmospheric chemistry. Some crop responses may occur at scales too small to significantly influence meteorology, so may not need to be included as feedbacks within climate models. However, the volume of data required to drive the appropriate crop models may be very large, especially if short-time-scale variability is important. Implementation of crop models within climate models would minimize the need to transfer large quantities of data between separate modelling systems. It should also be noted that crop responses to climate change may interact with other impacts of climate change, such as hydrological changes. For example, the availability of water for irrigation may be affected by changes in runoff as a direct consequence of climate change, and may also be affected by climate-related changes in demand for water for other uses. It is, therefore, necessary to consider the interactions between the responses of several impacts sectors to climate change. Overall, there is a strong case for a much closer coupling between models of climate, crops and hydrology, but this in itself poses challenges arising from issues of scale and errors in the models. A strategy is proposed whereby the pursuit of a fully coupled climate–chemistry–crop–hydrology model is paralleled by continued use of separate climate and land surface models but with a focus on consistency between the models. PMID:16433093

Ecological forecasting in the presence of abrupt regime shifts

NASA Astrophysics Data System (ADS)

Dippner, Joachim W.; Kröncke, Ingrid

2015-10-01

Regime shifts may cause an intrinsic decrease in the potential predictability of marine ecosystems. In such cases, forecasts of biological variables fail. To improve prediction of long-term variability in environmental variables, we constructed a multivariate climate index and applied it to forecast ecological time series. The concept is demonstrated herein using climate and macrozoobenthos data from the southern North Sea. Special emphasis is given to the influence of selection of length of fitting period to the quality of forecast skill especially in the presence of regime shifts. Our results indicate that the performance of multivariate predictors in biological forecasts is much better than that of single large-scale climate indices, especially in the presence of regime shifts. The approach used to develop the index is generally applicable to all geographical regions in the world and to all areas of marine biology, from the species level up to biodiversity. Such forecasts are of vital interest for practical aspects of the sustainable management of marine ecosystems and the conservation of ecosystem goods and services.

Decreasing spatial variability in precipitation extremes in southwestern China and the local/large-scale influencing factors

NASA Astrophysics Data System (ADS)

Liu, Meixian; Xu, Xianli; Sun, Alex

2015-07-01

Climate extremes can cause devastating damage to human society and ecosystems. Recent studies have drawn many conclusions about trends in climate extremes, but few have focused on quantitative analysis of their spatial variability and underlying mechanisms. By using the techniques of overlapping moving windows, the Mann-Kendall trend test, correlation, and stepwise regression, this study examined the spatial-temporal variation of precipitation extremes and investigated the potential key factors influencing this variation in southwestern (SW) China, a globally important biodiversity hot spot and climate-sensitive region. Results showed that the changing trends of precipitation extremes were not spatially uniform, but the spatial variability of these precipitation extremes decreased from 1959 to 2012. Further analysis found that atmospheric circulations rather than local factors (land cover, topographic conditions, etc.) were the main cause of such precipitation extremes. This study suggests that droughts or floods may become more homogenously widespread throughout SW China. Hence, region-wide assessments and coordination are needed to help mitigate the economic and ecological impacts.

European land CO2 sink influenced by NAO and East-Atlantic Pattern coupling

PubMed Central

Bastos, Ana; Janssens, Ivan A.; Gouveia, Célia M.; Trigo, Ricardo M.; Ciais, Philippe; Chevallier, Frédéric; Peñuelas, Josep; Rödenbeck, Christian; Piao, Shilong; Friedlingstein, Pierre; Running, Steven W.

2016-01-01

Large-scale climate patterns control variability in the global carbon sink. In Europe, the North-Atlantic Oscillation (NAO) influences vegetation activity, however the East-Atlantic (EA) pattern is known to modulate NAO strength and location. Using observation-driven and modelled data sets, we show that multi-annual variability patterns of European Net Biome Productivity (NBP) are linked to anomalies in heat and water transport controlled by the NAO–EA interplay. Enhanced NBP occurs when NAO and EA are both in negative phase, associated with cool summers with wet soils which enhance photosynthesis. During anti-phase periods, NBP is reduced through distinct impacts of climate anomalies in photosynthesis and respiration. The predominance of anti-phase years in the early 2000s may explain the European-wide reduction of carbon uptake during this period, reported in previous studies. Results show that improving the capability of simulating atmospheric circulation patterns may better constrain regional carbon sink variability in coupled carbon-climate models. PMID:26777730

European land CO2 sink influenced by NAO and East-Atlantic Pattern coupling.

PubMed

Bastos, Ana; Janssens, Ivan A; Gouveia, Célia M; Trigo, Ricardo M; Ciais, Philippe; Chevallier, Frédéric; Peñuelas, Josep; Rödenbeck, Christian; Piao, Shilong; Friedlingstein, Pierre; Running, Steven W

2016-01-18

Large-scale climate patterns control variability in the global carbon sink. In Europe, the North-Atlantic Oscillation (NAO) influences vegetation activity, however the East-Atlantic (EA) pattern is known to modulate NAO strength and location. Using observation-driven and modelled data sets, we show that multi-annual variability patterns of European Net Biome Productivity (NBP) are linked to anomalies in heat and water transport controlled by the NAO-EA interplay. Enhanced NBP occurs when NAO and EA are both in negative phase, associated with cool summers with wet soils which enhance photosynthesis. During anti-phase periods, NBP is reduced through distinct impacts of climate anomalies in photosynthesis and respiration. The predominance of anti-phase years in the early 2000s may explain the European-wide reduction of carbon uptake during this period, reported in previous studies. Results show that improving the capability of simulating atmospheric circulation patterns may better constrain regional carbon sink variability in coupled carbon-climate models.

Time Scales and Sources of European Temperature Variability

NASA Astrophysics Data System (ADS)

Årthun, Marius; Kolstad, Erik W.; Eldevik, Tor; Keenlyside, Noel S.

2018-04-01

Skillful predictions of continental climate would be of great practical benefit for society and stakeholders. It nevertheless remains fundamentally unresolved to what extent climate is predictable, for what features, at what time scales, and by which mechanisms. Here we identify the dominant time scales and sources of European surface air temperature (SAT) variability during the cold season using a coupled climate reanalysis, and a statistical method that estimates SAT variability due to atmospheric circulation anomalies. We find that eastern Europe is dominated by subdecadal SAT variability associated with the North Atlantic Oscillation, whereas interdecadal and multidecadal SAT variability over northern and southern Europe are thermodynamically driven by ocean temperature anomalies. Our results provide evidence that temperature anomalies in the North Atlantic Ocean are advected over land by the mean westerly winds and, hence, provide a mechanism through which ocean temperature controls the variability and provides predictability of European SAT.

Evaluation and time series analysis of mountain snow from MODIS and VIIRS fractional snow cover products

NASA Astrophysics Data System (ADS)

Bormann, K.; Rittger, K.; Painter, T. H.

2016-12-01

The continuation of large-scale snow cover records into the future is crucial for monitoring the impacts of global pressures such as climate change and weather variability on the cryosphere. With daily MODIS records since 2000 from a now ageing MODIS constellation (Terra & Aqua) and daily VIIRS records since 2012 from the Suomi-NPP platform, the consistency of information between the two optical sensors must be understood. First, we evaluated snow cover maps derived from both MODIS and VIIRS retrievals with coincident cloud-free Landsat 8 OLI maps across a range of locations. We found that both MODIS and VIIRS snow cover maps show similar errors when evaluated with Landsat OLI retrievals. Preliminary results also show a general agreement in regional snowline between the two sensors that is maintained during the spring snowline retreat where the proportion of mixed pixels is increased. The agreement between sensors supports the future use of VIIRS snow cover maps to continue the long-term record beyond the lifetime of MODIS. Second, we use snowline elevation to quantify large scale snow cover variability and to monitor potential changes in the rain/snow transition zone where climate change pressures may be enhanced. Despite the large inter-annual variability that is often observed in snow metrics, we expect that over the 16-year time series we will see a rise in seasonal elevation of the snowline and consequently an increasing rain/snow transition boundary in mountain environments. These results form the basis for global snowline elevation monitoring using optical remote sensing data and highlight regional differences in snowline elevation dynamics. The long-term variability in observed snowline elevation provides a recent climatology of mountain snowpack across several regions that will likely to be of interest to those interested in climate change impacts in mountain environments. This work will also be of interest to existing users of MODSCAG and VIIRSCAG snow cover products and those working in remote sensing of the mountain snowpack.

The summer North Atlantic Oscillation (SNAO) variability on decadal to paleoclimate time scales

NASA Astrophysics Data System (ADS)

Linderholm, H. W.; Folland, C. K.; Zhang, P.; Gunnarson, B. E.; Jeong, J. H.; Ren, H.

2017-12-01

The summer North Atlantic Oscillation (SNAO), strongly related to the latitude of the North Atlantic and European summer storm tracks, exerts a considerable influence on European summer climate variability and extremes. Here we extend the period covered by the SNAO from July and August to June, July and August (JJA). As well as marked interannual variability, the JJA SNAO has shown a large inter-decadal change since the 1970s. Decadally averaged, there has been a change from a very positive to a rather negative SNAO phase. This change in SNAO phase is opposite in sign from that expected by a number of climate models under enhanced greenhouse forcing by the late twenty first century. It has led to noticeably wetter summers in North West Europe in the last decade. On interannual to multidecadal timescales, SNAO variability is linked to variations in North Atlantic sea surface temperature (SST): observations and models indicate an association between the Atlantic Multi-decadal Oscillation (AMO) where the cold (warm) phase of the AMO corresponds a positive (negative) phase of the SNAO. Observations also indicate a link with SST in the Gulf Stream region of the North Atlantic where, particularly on decadal time scales, SST warming may favour a more positive phase of the SNAO. Influences of Arctic climate change on North Atlantic and European atmospheric circulation may also exist, particularly reduced sea ice coverage, perhaps favouring the negative phase of the SNAO. A new tree-ring data based JJA SNAO reconstruction extending over the last millennium, as well as climate model output for the same period, enables us to examine the influence of North Atlantic SST and Arctic sea-ice coverage, as well as SNAO impacts on European summer climate, in a long-term, pre-industrial context.

Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model

NASA Astrophysics Data System (ADS)

Davini, Paolo; von Hardenberg, Jost; Corti, Susanna; Christensen, Hannah M.; Juricke, Stephan; Subramanian, Aneesh; Watson, Peter A. G.; Weisheimer, Antje; Palmer, Tim N.

2017-03-01

The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and future climate to model resolution and stochastic parameterisation. The EC-Earth Earth system model is used to explore the impact of stochastic physics in a large ensemble of 30-year climate integrations at five different atmospheric horizontal resolutions (from 125 up to 16 km). The project includes more than 120 simulations in both a historical scenario (1979-2008) and a climate change projection (2039-2068), together with coupled transient runs (1850-2100). A total of 20.4 million core hours have been used, made available from a single year grant from PRACE (the Partnership for Advanced Computing in Europe), and close to 1.5 PB of output data have been produced on SuperMUC IBM Petascale System at the Leibniz Supercomputing Centre (LRZ) in Garching, Germany. About 140 TB of post-processed data are stored on the CINECA supercomputing centre archives and are freely accessible to the community thanks to an EUDAT data pilot project. This paper presents the technical and scientific set-up of the experiments, including the details on the forcing used for the simulations performed, defining the SPHINX v1.0 protocol. In addition, an overview of preliminary results is given. An improvement in the simulation of Euro-Atlantic atmospheric blocking following resolution increase is observed. It is also shown that including stochastic parameterisation in the low-resolution runs helps to improve some aspects of the tropical climate - specifically the Madden-Julian Oscillation and the tropical rainfall variability. 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).

Responses of runoff to historical and future climate variability over China

NASA Astrophysics Data System (ADS)

Wu, Chuanhao; Hu, Bill X.; Huang, Guoru; Wang, Peng; Xu, Kai

2018-03-01

China has suffered some of the effects of global warming, and one of the potential implications of climate warming is the alteration of the temporal-spatial patterns of water resources. Based on the long-term (1960-2008) water budget data and climate projections from 28 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5), this study investigated the responses of runoff (R) to historical and future climate variability in China at both grid and catchment scales using the Budyko-based elasticity method. Results show that there is a large spatial variation in precipitation (P) elasticity (from 1.1 to 3.2) and potential evaporation (PET) elasticity (from -2.2 to -0.1) across China. The P elasticity is larger in north-eastern and western China than in southern China, while the opposite occurs for PET elasticity. The catchment properties' elasticity of R appears to have a strong non-linear relationship with the mean annual aridity index and tends to be more significant in more arid regions. For the period 1960-2008, the climate contribution to R ranges from -2.4 to 3.6 % yr-1 across China, with the negative contribution in north-eastern China and the positive contribution in western China and some parts of the south-west. The results of climate projections indicate that although there is large uncertainty involved in the 28 GCMs, most project a consistent change in P (or PET) in China at the annual scale. For the period 2071-2100, the mean annual P is projected to increase in most parts of China, especially the western regions, while the mean annual PET is projected to increase in all of China, particularly the southern regions. Furthermore, greater increases are projected for higher emission scenarios. Overall, due to climate change, the arid regions and humid regions of China are projected to become wetter and drier in the period 2071-2100, respectively (relative to the baseline 1971-2000).

Forced and Internal Multi-Decadal Variability in the North Atlantic and their Climate Impacts

NASA Astrophysics Data System (ADS)

Ting, M.

2017-12-01

Atlantic Multidecadal Variability (AMV), a basin-wide North Atlantic sea surface temperature warming or cooling pattern varying on decadal and longer time scales, is one of the most important climate variations in the Atlantic basin. The AMV has shown to be associated with significant climate impacts regionally and globally, from Atlantic hurricane activities, frequency and severity of droughts across North America, as well as rainfall anomalies across the African Sahel and northeast Brazil. Despite the important impacts of the AMV, its mechanisms are not completely understood. In particular, it is not clear how much of the historical Atlantic SST fluctuations were forced by anthropogenic sources such as greenhouse warming and aerosol cooling, versus driven internally by changes in the coupled ocean-atmosphere processes in the Atlantic. Using climate models such as the NCAR large ensemble simulations, we were able to successfully separate the forced and internally generated North Atlantic sea surface temperature anomalies through a signal-to-noise maximizing Empirical Orthogonal Function (S/N EOF) analysis method. Two forced modes were identified with one representing a hemispherical symmetric mode and one asymmetric mode. The symmetric mode largely represents the greenhouse forced component while the asymmetric mode resembles the anthropogenic aerosol forcing. When statistically removing both of the forced modes, the residual multidecadal Atlantic SST variability shows a very similar structure as the AMV in the preindustrial simulation. The distinct climate impacts of each of these modes are also identified and the implications and challenges for decadal climate prediction will be discussed.

Landsat and GRACE observations of arid wetland dynamics in a dryland river system under multi-decadal hydroclimatic extremes

NASA Astrophysics Data System (ADS)

Xie, Zunyi; Huete, Alfredo; Ma, Xuanlong; Restrepo-Coupe, Natalia; Devadas, Rakhesh; Clarke, Kenneth; Lewis, Megan

2016-12-01

Arid wetlands are important for biodiversity conservation, but sensitive and vulnerable to climate variability and hydroclimatic events. Amplification of the water cycle, including the increasing frequency and severity of droughts and wet extremes, is expected to alter spatial and temporal hydrological patterns in arid wetlands globally, with potential threats to ecosystem services and their functioning. Despite these pressing challenges, the ecohydrological interactions and resilience of arid wetlands to highly variable water regimes over long time periods remain largely unknown. Recent broad-scale drought and floods over Australia provide unique opportunities to improve our understanding of arid wetland ecosystem responses to hydroclimatic extremes. Here we analysed the ecohydrological dynamics of the Coongie Lakes arid wetland in central Australia, one of the world's largest Ramsar-designated wetlands, using more than two decades (1988-2011) of vegetation and floodwater extent retrievals derived from Landsat satellite observations. To explore the impacts of large-scale hydrological fluctuations on the arid wetland, we further coupled Landsat measurements with Total Water Storage Anomaly (TWSA) data obtained from the Gravity Recovery and Climate Experiment (GRACE) satellites. Pronounced seasonal and inter-annual variabilities of flood and vegetation activities were observed over the wetland, with variations in vegetation growth extent highly correlated with flood extent (r = 0.64, p < 0.05) that ranged from nearly zero to 3456 km2. We reported the hydrological dynamics and associated ecosystem responses to be largely driven by the two phases (El Niño and La Niña) of the El Nino-Southern Oscillation (ENSO) ocean-atmosphere system. Changes in flood and vegetation extent were better explained by GRACE-TWSA (r = 0.8, lag = 0 month) than rainfall (r = 0.34, lag = 3 months) over the water source area, demonstrating that TWS is a valuable hydrological indicator for complex dryland river systems. The protracted Millennium Drought from 2001 to 2009 resulted in long-term absence of major flood events, which substantially suppressed wetland vegetation growth. However, the 2010-11 La Niña induced flooding events led to an exceptionally large resurgence of vegetation, with a mean vegetation growth extent anomaly exceeding the historical average (1988-2011) by more than 1.5 standard deviations, suggesting a significant resilience of arid wetland ecosystems to climate variability. This study showed the ecological functioning of arid wetlands is particularly sensitive to large-scale hydrological fluctuations and extreme drought conditions, and vulnerable to future altered water regimes due to climate change. The methods developed herein can be applied to arid wetlands located in other dryland river systems across the globe.

Groundwater level response to low-frequency (interannual to multidecadal) climate variability: an overview across Portugal

NASA Astrophysics Data System (ADS)

Neves, M. L.

2017-12-01

The impact of climate variability on groundwater systems is central to the successful management and sustainability of water resources. In Portugal, strong changes in the seasonal distribution of precipitation, with a concentration of rainfall during the winter season and an increase in the frequency and intensity of droughts, in conjunction with warming, are expected to have a profound impact on water resources. Nonetheless, there is still limited knowledge on the impact of climate variability on aquifer systems across the country. The primary goal of this study is to provide a national-scale assessment of the relative contribution of climate to the temporal and spatial variance of groundwater recharge across the four main hydrogeological units in which the country is divided. Monthly hydrological data sets spanning a common 30 year period include groundwater levels from the Portuguese National System for Water Research Information and precipitation data from both meteorological stations and ERA-Interim global atmospheric reanalysis. The links between large-scale climatic patterns, precipitation, and groundwater levels are explored using singular spectral analysis, wavelet coherence and lag correlation methods. Hydrologic time-series sampling diverse geographic regions and aquifer types have common non-stationary oscillatory components, which can be associated with the leading modes of atmospheric circulation in the western north Atlantic, namely the North Atlantic (NAO) and the Eastern Atlantic (EA) oscillations. Maps of the spatial distribution of the relative contribution of each mode of variability to the total variance of the groundwater levels illustrate which atmospheric mode impacts the most a particular aquifer. The results display the links between groundwater recharge and climate teleconnections but also emphasize the distinctive types of modulation of the climate signals among the several hydrogeological units and aquifer systems under consideration. This work is supported by FCT- project UID/GEO/50019/2013 - IDL.

Climate Modeling and Causal Identification for Sea Ice Predictability

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

Hunke, Elizabeth Clare; Urrego Blanco, Jorge Rolando; Urban, Nathan Mark

This project aims to better understand causes of ongoing changes in the Arctic climate system, particularly as decreasing sea ice trends have been observed in recent decades and are expected to continue in the future. As part of the Sea Ice Prediction Network, a multi-agency effort to improve sea ice prediction products on seasonal-to-interannual time scales, our team is studying sensitivity of sea ice to a collection of physical process and feedback mechanism in the coupled climate system. During 2017 we completed a set of climate model simulations using the fully coupled ACME-HiLAT model. The simulations consisted of experiments inmore » which cloud, sea ice, and air-ocean turbulent exchange parameters previously identified as important for driving output uncertainty in climate models were perturbed to account for parameter uncertainty in simulated climate variables. We conducted a sensitivity study to these parameters, which built upon a previous study we made for standalone simulations (Urrego-Blanco et al., 2016, 2017). Using the results from the ensemble of coupled simulations, we are examining robust relationships between climate variables that emerge across the experiments. We are also using causal discovery techniques to identify interaction pathways among climate variables which can help identify physical mechanisms and provide guidance in predictability studies. This work further builds on and leverages the large ensemble of standalone sea ice simulations produced in our previous w14_seaice project.« less

Relative importance of climate changes at different time scales on net primary productivity-a case study of the Karst area of northwest Guangxi, China.

PubMed

Liu, Huiyu; Zhang, Mingyang; Lin, Zhenshan

2017-10-05

Climate changes are considered to significantly impact net primary productivity (NPP). However, there are few studies on how climate changes at multiple time scales impact NPP. With MODIS NPP product and station-based observations of sunshine duration, annual average temperature and annual precipitation, impacts of climate changes at different time scales on annual NPP, have been studied with EEMD (ensemble empirical mode decomposition) method in the Karst area of northwest Guangxi, China, during 2000-2013. Moreover, with partial least squares regression (PLSR) model, the relative importance of climatic variables for annual NPP has been explored. The results show that (1) only at quasi 3-year time scale do sunshine duration and temperature have significantly positive relations with NPP. (2) Annual precipitation has no significant relation to NPP by direct comparison, but significantly positive relation at 5-year time scale, which is because 5-year time scale is not the dominant scale of precipitation; (3) the changes of NPP may be dominated by inter-annual variabilities. (4) Multiple time scales analysis will greatly improve the performance of PLSR model for estimating NPP. The variable importance in projection (VIP) scores of sunshine duration and temperature at quasi 3-year time scale, and precipitation at quasi 5-year time scale are greater than 0.8, indicating important for NPP during 2000-2013. However, sunshine duration and temperature at quasi 3-year time scale are much more important. Our results underscore the importance of multiple time scales analysis for revealing the relations of NPP to changing climate.

Ecological niche models reveal the importance of climate variability for the biogeography of protosteloid amoebae

PubMed Central

Aguilar, María; Lado, Carlos

2012-01-01

Habitat availability and environmental preferences of species are among the most important factors in determining the success of dispersal processes and therefore in shaping the distribution of protists. We explored the differences in fundamental niches and potential distributions of an ecological guild of slime moulds—protosteloid amoebae—in the Iberian Peninsula. A large set of samples collected in a north-east to south-west transect of approximately 1000 km along the peninsula was used to test the hypothesis that, together with the existence of suitable microhabitats, climate conditions may determine the probability of survival of species. Although protosteloid amoebae share similar morphologies and life history strategies, canonical correspondence analyses showed that they have varied ecological optima, and that climate conditions have an important effect in niche differentiation. Maxent environmental niche models provided consistent predictions of the probability of presence of the species based on climate data, and they were used to generate maps of potential distribution in an ‘everything is everywhere' scenario. The most important climatic factors were, in both analyses, variables that measure changes in conditions throughout the year, confirming that the alternation of fruiting bodies, cysts and amoeboid stages in the life cycles of protosteloid amoebae constitutes an advantage for surviving in a changing environment. Microhabitat affinity seems to be influenced by climatic conditions, which suggests that the micro-environment may vary at a local scale and change together with the external climate at a larger scale. PMID:22402402

Ecological niche models reveal the importance of climate variability for the biogeography of protosteloid amoebae.

PubMed

Aguilar, María; Lado, Carlos

2012-08-01

Habitat availability and environmental preferences of species are among the most important factors in determining the success of dispersal processes and therefore in shaping the distribution of protists. We explored the differences in fundamental niches and potential distributions of an ecological guild of slime moulds-protosteloid amoebae-in the Iberian Peninsula. A large set of samples collected in a north-east to south-west transect of approximately 1000 km along the peninsula was used to test the hypothesis that, together with the existence of suitable microhabitats, climate conditions may determine the probability of survival of species. Although protosteloid amoebae share similar morphologies and life history strategies, canonical correspondence analyses showed that they have varied ecological optima, and that climate conditions have an important effect in niche differentiation. Maxent environmental niche models provided consistent predictions of the probability of presence of the species based on climate data, and they were used to generate maps of potential distribution in an 'everything is everywhere' scenario. The most important climatic factors were, in both analyses, variables that measure changes in conditions throughout the year, confirming that the alternation of fruiting bodies, cysts and amoeboid stages in the life cycles of protosteloid amoebae constitutes an advantage for surviving in a changing environment. Microhabitat affinity seems to be influenced by climatic conditions, which suggests that the micro-environment may vary at a local scale and change together with the external climate at a larger scale.

Modeling water scarcity over south Asia: Incorporating crop growth and irrigation models into the Variable Infiltration Capacity (VIC) model

NASA Astrophysics Data System (ADS)

Troy, Tara J.; Ines, Amor V. M.; Lall, Upmanu; Robertson, Andrew W.

2013-04-01

Large-scale hydrologic models, such as the Variable Infiltration Capacity (VIC) model, are used for a variety of studies, from drought monitoring to projecting the potential impact of climate change on the hydrologic cycle decades in advance. The majority of these models simulates the natural hydrological cycle and neglects the effects of human activities such as irrigation, which can result in streamflow withdrawals and increased evapotranspiration. In some parts of the world, these activities do not significantly affect the hydrologic cycle, but this is not the case in south Asia where irrigated agriculture has a large water footprint. To address this gap, we incorporate a crop growth model and irrigation model into the VIC model in order to simulate the impacts of irrigated and rainfed agriculture on the hydrologic cycle over south Asia (Indus, Ganges, and Brahmaputra basin and peninsular India). The crop growth model responds to climate signals, including temperature and water stress, to simulate the growth of maize, wheat, rice, and millet. For the primarily rainfed maize crop, the crop growth model shows good correlation with observed All-India yields (0.7) with lower correlations for the irrigated wheat and rice crops (0.4). The difference in correlation is because irrigation provides a buffer against climate conditions, so that rainfed crop growth is more tied to climate than irrigated crop growth. The irrigation water demands induce hydrologic water stress in significant parts of the region, particularly in the Indus, with the streamflow unable to meet the irrigation demands. Although rainfall can vary significantly in south Asia, we find that water scarcity is largely chronic due to the irrigation demands rather than being intermittent due to climate variability.

Climate warming, marine protected areas and the ocean-scale integrity of coral reef ecosystems.

PubMed

Graham, Nicholas A J; McClanahan, Tim R; MacNeil, M Aaron; Wilson, Shaun K; Polunin, Nicholas V C; Jennings, Simon; Chabanet, Pascale; Clark, Susan; Spalding, Mark D; Letourneur, Yves; Bigot, Lionel; Galzin, René; Ohman, Marcus C; Garpe, Kajsa C; Edwards, Alasdair J; Sheppard, Charles R C

2008-08-27

Coral reefs have emerged as one of the ecosystems most vulnerable to climate variation and change. While the contribution of a warming climate to the loss of live coral cover has been well documented across large spatial and temporal scales, the associated effects on fish have not. Here, we respond to recent and repeated calls to assess the importance of local management in conserving coral reefs in the context of global climate change. Such information is important, as coral reef fish assemblages are the most species dense vertebrate communities on earth, contributing critical ecosystem functions and providing crucial ecosystem services to human societies in tropical countries. Our assessment of the impacts of the 1998 mass bleaching event on coral cover, reef structural complexity, and reef associated fishes spans 7 countries, 66 sites and 26 degrees of latitude in the Indian Ocean. Using Bayesian meta-analysis we show that changes in the size structure, diversity and trophic composition of the reef fish community have followed coral declines. Although the ocean scale integrity of these coral reef ecosystems has been lost, it is positive to see the effects are spatially variable at multiple scales, with impacts and vulnerability affected by geography but not management regime. Existing no-take marine protected areas still support high biomass of fish, however they had no positive affect on the ecosystem response to large-scale disturbance. This suggests a need for future conservation and management efforts to identify and protect regional refugia, which should be integrated into existing management frameworks and combined with policies to improve system-wide resilience to climate variation and change.

A method to encapsulate model structural uncertainty in ensemble projections of future climate: EPIC v1.0

NASA Astrophysics Data System (ADS)

Lewis, Jared; Bodeker, Greg E.; Kremser, Stefanie; Tait, Andrew

2017-12-01

A method, based on climate pattern scaling, has been developed to expand a small number of projections of fields of a selected climate variable (X) into an ensemble that encapsulates a wide range of indicative model structural uncertainties. The method described in this paper is referred to as the Ensemble Projections Incorporating Climate model uncertainty (EPIC) method. Each ensemble member is constructed by adding contributions from (1) a climatology derived from observations that represents the time-invariant part of the signal; (2) a contribution from forced changes in X, where those changes can be statistically related to changes in global mean surface temperature (Tglobal); and (3) a contribution from unforced variability that is generated by a stochastic weather generator. The patterns of unforced variability are also allowed to respond to changes in Tglobal. The statistical relationships between changes in X (and its patterns of variability) and Tglobal are obtained in a training phase. Then, in an implementation phase, 190 simulations of Tglobal are generated using a simple climate model tuned to emulate 19 different global climate models (GCMs) and 10 different carbon cycle models. Using the generated Tglobal time series and the correlation between the forced changes in X and Tglobal, obtained in the training phase, the forced change in the X field can be generated many times using Monte Carlo analysis. A stochastic weather generator is used to generate realistic representations of weather which include spatial coherence. Because GCMs and regional climate models (RCMs) are less likely to correctly represent unforced variability compared to observations, the stochastic weather generator takes as input measures of variability derived from observations, but also responds to forced changes in climate in a way that is consistent with the RCM projections. This approach to generating a large ensemble of projections is many orders of magnitude more computationally efficient than running multiple GCM or RCM simulations. Such a large ensemble of projections permits a description of a probability density function (PDF) of future climate states rather than a small number of individual story lines within that PDF, which may not be representative of the PDF as a whole; the EPIC method largely corrects for such potential sampling biases. The method is useful for providing projections of changes in climate to users wishing to investigate the impacts and implications of climate change in a probabilistic way. A web-based tool, using the EPIC method to provide probabilistic projections of changes in daily maximum and minimum temperatures for New Zealand, has been developed and is described in this paper.

Sources of uncertainty in hydrological climate impact assessment: a cross-scale study

NASA Astrophysics Data System (ADS)

Hattermann, F. F.; Vetter, T.; Breuer, L.; Su, Buda; Daggupati, P.; Donnelly, C.; Fekete, B.; Flörke, F.; Gosling, S. N.; Hoffmann, P.; Liersch, S.; Masaki, Y.; Motovilov, Y.; Müller, C.; Samaniego, L.; Stacke, T.; Wada, Y.; Yang, T.; Krysnaova, V.

2018-01-01

Climate change impacts on water availability and hydrological extremes are major concerns as regards the Sustainable Development Goals. Impacts on hydrology are normally investigated as part of a modelling chain, in which climate projections from multiple climate models are used as inputs to multiple impact models, under different greenhouse gas emissions scenarios, which result in different amounts of global temperature rise. While the goal is generally to investigate the relevance of changes in climate for the water cycle, water resources or hydrological extremes, it is often the case that variations in other components of the model chain obscure the effect of climate scenario variation. This is particularly important when assessing the impacts of relatively lower magnitudes of global warming, such as those associated with the aspirational goals of the Paris Agreement. In our study, we use ANOVA (analyses of variance) to allocate and quantify the main sources of uncertainty in the hydrological impact modelling chain. In turn we determine the statistical significance of different sources of uncertainty. We achieve this by using a set of five climate models and up to 13 hydrological models, for nine large scale river basins across the globe, under four emissions scenarios. The impact variable we consider in our analysis is daily river discharge. We analyze overall water availability and flow regime, including seasonality, high flows and low flows. Scaling effects are investigated by separately looking at discharge generated by global and regional hydrological models respectively. Finally, we compare our results with other recently published studies. We find that small differences in global temperature rise associated with some emissions scenarios have mostly significant impacts on river discharge—however, climate model related uncertainty is so large that it obscures the sensitivity of the hydrological system.

Accounting for observational uncertainties in the evaluation of low latitude turbulent air-sea fluxes simulated in a suite of IPSL model versions

NASA Astrophysics Data System (ADS)

Servonnat, Jerome; Braconnot, Pascale; Gainusa-Bogdan, Alina

2015-04-01

Turbulent momentum and heat (sensible and latent) fluxes at the air-sea interface are key components of the whole energetic of the Earth's climate and their good representation in climate models is of prime importance. In this work, we use the methodology developed by Braconnot & Frankignoul (1993) to perform a Hotelling T2 test on spatio-temporal fields (annual cycles). This statistic provides a quantitative measure accounting for an estimate of the observational uncertainty for the evaluation of low-latitude turbulent air-sea fluxes in a suite of IPSL model versions. The spread within the observational ensemble of turbulent flux data products assembled by Gainusa-Bogdan et al (submitted) is used as an estimate of the observational uncertainty for the different turbulent fluxes. The methodology holds on a selection of a small number of dominating variability patterns (EOFs) that are common to both the model and the observations for the comparison. Consequently it focuses on the large-scale variability patterns and avoids the possibly noisy smaller scales. The results show that different versions of the IPSL couple model share common large scale model biases, but also that there the skill on sea surface temperature is not necessarily directly related to the skill in the representation of the different turbulent fluxes. Despite the large error bars on the observations the test clearly distinguish the different merits of the different model version. The analyses of the common EOF patterns and related time series provide guidance on the major differences with the observations. This work is a first attempt to use such statistic on the evaluation of the spatio-temporal variability of the turbulent fluxes, accounting for an observational uncertainty, and represents an efficient tool for systematic evaluation of simulated air-seafluxes, considering both the fluxes and the related atmospheric variables. References Braconnot, P., and C. Frankignoul (1993), Testing Model Simulations of the Thermocline Depth Variability in the Tropical Atlantic from 1982 through 1984, J. Phys. Oceanogr., 23(4), 626-647 Gainusa-Bogdan A., Braconnot P. and Servonnat J. (submitted), Using an ensemble data set of turbulent air-sea fluxes to evaluate the IPSL climate model in tropical regions, Journal of Geophysical Research Atmosphere, 2014JD022985

Southern Ocean Deep-Convection as a Driver of Centennial-to-Millennial-Scale Climate Variability at Southern High Latitudes

NASA Astrophysics Data System (ADS)

Pedro, J. B.; Martin, T.; Steig, E. J.; Jochum, M.; Park, W.; Rasmussen, S.

2015-12-01

Antarctic Isotope Maxima (AIM) are centennial-to-millennial scale warming events observed in Antarctic ice core records from the last glacial period and deglaciation. Mounting evidence links AIM events to parallel variations in atmospheric CO2, Southern Ocean (SO) sea surface temperatures and Antarctic Bottom Water production. According to the prevailing view, AIM events are forced from the North Atlantic by melt-water discharge from ice sheets suppressing the production of North Atlantic Deep Water and associated northward heat transport in the Atlantic. However observations and model studies increasingly suggest that melt-water fluxes have the wrong timing to be invoked as such a trigger. Here, drawing on results form the Kiel Climate Model, we present an alternative hypothesis in which AIM events are forced via internal oscillations in SO deep-convection. The quasi-periodic timescale of deep-convection events is set by heat (buoyancy) accumulation at SO intermediate depths and stochastic variability in sea ice conditions and freshening at the surface. Massive heat release from the SO convective zone drives Antarctic and large-scale southern hemisphere warming via a two-stage process involving changes in the location of Southern Ocean fronts, in the strength and intensity of the Westerlies and in meridional ocean and atmospheric heat flux anomalies. The potential for AIM events to be driven by internal Southern Ocean processes and the identification of time-lags internal to the southern high latitudes challenges conventional views on the North Atlantic as the pacemaker of millennial-scale climate variability.

Southwest Pacific Ocean Circulation and Climate Experiment (SPICE) scientific advances and future west pacific coordination

NASA Astrophysics Data System (ADS)

Ganachaud, A. S.; Sprintall, J.; Lin, X.; Ando, K.

2016-02-01

The Southwest Pacific Ocean Circulation and Climate Experiment (SPICE) is an international research program under the auspices of CLIVAR (Climate Variability and Predictability). The key objectives are to understand the Southwest Pacific Ocean circulation and Convergence Zone (SPCZ) dynamics, as well as their influence on regional and basin-scale climate patterns. It was designed to measure and monitor the ocean circulation, and to validate and improve numerical models. South Pacific oceanic waters are carried from the subtropical gyre centre in the westward flowing South Equatorial Current (SEC), towards the southwest Pacific-a major circulation pathway that redistributes water from the subtropics to the equator and Southern Ocean. Water transit through the Coral and Solomon Seas is potentially of great importance to tropical climate prediction because changes in either the temperature or the amount of water arriving at the equator have the capability to modulate ENSO and produce basin-scale climate feedbacks. On average, the oceanic circulation is driven by the Trade Winds, and subject to substantial variability, related with the SPCZ position and intensity. The circulation is complex, with the SEC splitting into zonal jets upon encountering island archipelagos, before joining either the East Australian Current or the New Guinea Costal UnderCurrent towards the equator. SPICE included large, coordinated in situ measurement programs and high resolution numerical simulations of the area. After 8 years of substantial in situ oceanic observational and modeling efforts, our understanding of the region has much improved. We have a refined description of the SPCZ behavior, boundary currents, pathways, and water mass transformation, including the previously undocumented Solomon Sea. The transports are large and vary substantially in a counter-intuitive way, with asymmetries and gating effects that depend on time scales. We will review the recent advancements and discuss our current knowledge gaps and important emerging research directions. In particular we will discuss how SPICE, along with the Northwestern Pacific Ocean Circulation and Climate Experiment (NPOCE) and Indonesian ThroughFlow (ITF) programs could evolve toward an integrative observing system under CLIVAR coordination.

A century of variation in the dependence of Greenland iceberg calving on ice sheet surface mass balance and regional climate change.

PubMed

Bigg, G R; Wei, H L; Wilton, D J; Zhao, Y; Billings, S A; Hanna, E; Kadirkamanathan, V

2014-06-08

Iceberg calving is a major component of the total mass balance of the Greenland ice sheet (GrIS). A century-long record of Greenland icebergs comes from the International Ice Patrol's record of icebergs (I48N) passing latitude 48° N, off Newfoundland. I48N exhibits strong interannual variability, with a significant increase in amplitude over recent decades. In this study, we show, through a combination of nonlinear system identification and coupled ocean-iceberg modelling, that I48N's variability is predominantly caused by fluctuation in GrIS calving discharge rather than open ocean iceberg melting. We also demonstrate that the episodic variation in iceberg discharge is strongly linked to a nonlinear combination of recent changes in the surface mass balance (SMB) of the GrIS and regional atmospheric and oceanic climate variability, on the scale of the previous 1-3 years, with the dominant causal mechanism shifting between glaciological (SMB) and climatic (ocean temperature) over time. We suggest that this is a change in whether glacial run-off or under-ice melting is dominant, respectively. We also suggest that GrIS calving discharge is episodic on at least a regional scale and has recently been increasing significantly, largely as a result of west Greenland sources.

GLACIOCLIM-SAMBA: A Terre Adelie / Wilkes Land Antarctic surface mass balance observatory

NASA Astrophysics Data System (ADS)

Genthon, C.; Frezzotti, M.; Le Meur, E.; Magand, O.; Six, D.; Wagnon, P.

2005-12-01

While local measurements at hundreds of sites are now available (although sometimes questionable, e.g. Magand et al., this volume) to verify how large-scale models reproduce the spatial distribution of the surface mass balance (SMB) of Antarctica, few field observations yet make it possible to verify current intra- and inter-annual variability and trends of the SMB in the models, and to evaluate the processes that relate this variability with that of climate. It is a major aim of the GLACIOCLIM-SAMBA observatory (http://lgge.obs.ujf-grenoble.fr/~christo/glacioclim/samba/), initiated in 2004, to provide such observations in the Terre Adelie and Wilkes Land area. Recognizing that the largest absolute changes (and thus contribution to sea-level) of Antarctic SMB are expected where the current mean SMB is largest, that is in the coastal regions, SAMBA is largely focused on ice sheet margin. To sample spatial scales compatible with the scales resolved by models used to predict climate and SMB changes, a 150 km accumulation stakes line is being set up from the coast near the French Dumont d'Urville station, towards to Antarctic plateau in the general direction of the Italy/France Concordia station. Ground penetrating radar survey will provide snap-shot SMB interpolation along the stakes line. A blue ice stretch at the coast is being monitored by a 50-stake ablation network. Three 50-stakes networks are being set up near Concordia station to relate coastal and plateau SMB variability and change. An automatic weather station (AWS, including radiation) deployed at the coast, and the D-10, D-47 and DCII Antarctic Meteorological Research Center (http://amrc.ssec.wisc.edu/) AWSs, provide meteorological information to relate observed SMB and climate. Italian meteorology and radiation programs at Concordia, planned micrometeorology special campaigns at the margin, and precipitation monitoring at both sites, should help decipher the processes that relate SMB and climate variability. As a summary of results on the existing observatory as of Jan; 2005: i) The first year mean SMB along a 50 km stakes line was ~60 cm water equivalent (we), which qualifies 2004 as a very high accumulation year in the Terre Adelie area; ii) Spatial variability along the stakes line is high, ranging from 16 to 125 cm (we), confirming the need for spatial sampling consistent with the scales resolved by climate models; iii) At the coastal blue ice, ablation occurs in summer only while the winter SMB is close to 0. The SAMBA observatory is scheduled to operate for at least 10 years, hopefully more if successful, with main support by the French (IPEV) and Italian (PNRA) Polar Institutes. The French ministry of research and Institut National des Sciences de l'univers (Climate Change and Cryosphere and ORE-GLACIOCLIM programs) also contribute support. All SAMBA observations will be distributed and freely available on the internet as soon as the observatory is fully operational and validated.

Long-term variability of the thunderstorm and hail potential in Europe

NASA Astrophysics Data System (ADS)

Mohr, Susanna; Kunz, Michael; Speidel, Johannes; Piper, David

2016-04-01

Severe thunderstorms and associated hazardous weather events such as hail frequently cause considerable damage to buildings, crops, and automobiles, resulting in large monetary costs in many parts of Europe and the world. To relate single extreme hail events to the historic context and to estimate their return periods and possible trends related to climate change, long-term statistics of hail events are required. Due to the local-scale nature of hail and a lack of suitable observation systems, however, hailstorms are not captured reliably and comprehensively for a long period of time. In view of this fact, different proxies (indirect climate data) obtained from sounding stations and regional climate models can be used to infer the probability and intensity of thunderstorms or hailstorms. In contrast to direct observational data, such proxies are available homogeneously over a long time period. The aim of the study is to investigate the potential for severe thunderstorms and their changes over past decades. Statistical analyses of sounding data show that the convective potential over the past 20 - 30 years has significantly increased over large parts of Central Europe, making severe thunderstorms more likely. A similar picture results from analyses of weather types that are most likely associated with damaging hailstorms. These weather patterns have increased, even if only slightly but nevertheless statistically significantly, in the time period from 1971 to 2000. To improve the diagnostics of hail events in regional climate models, a logistic hail model has been developed by means of a multivariate analysis method. The model is based on a combination of appropriate hail-relevant meteorological parameters. The output of the model is a new index that estimates the potential of the atmosphere for hailstorm development, referred to as potential hail index (PHI). Applied to a high-resolved reanalysis run for Europe driven by NCEP/NCAR1, long-term changes of the PHI for 60 years (1951-2010) show large annual and multiannual variability. The trends are mostly positive in the western parts and negative to the east. However, due to the large temporal variability, the trends are not significant at most of the grid points. Furthermore, it becomes clear that the environmental conditions that favor the formation of hailstorms prevail in larger areas. This finding suggests that, despite the local-scale nature of convective storms, the ambient conditions favoring these events are mainly controlled by large-scale circulation patterns and mechanisms. This result is important to estimate the convective potential of the atmosphere in case of single events.

Selenium deficiency risk predicted to increase under future climate change.

PubMed

Jones, Gerrad D; Droz, Boris; Greve, Peter; Gottschalk, Pia; Poffet, Deyan; McGrath, Steve P; Seneviratne, Sonia I; Smith, Pete; Winkel, Lenny H E

2017-03-14

Deficiencies of micronutrients, including essential trace elements, affect up to 3 billion people worldwide. The dietary availability of trace elements is determined largely by their soil concentrations. Until now, the mechanisms governing soil concentrations have been evaluated in small-scale studies, which identify soil physicochemical properties as governing variables. However, global concentrations of trace elements and the factors controlling their distributions are virtually unknown. We used 33,241 soil data points to model recent (1980-1999) global distributions of Selenium (Se), an essential trace element that is required for humans. Worldwide, up to one in seven people have been estimated to have low dietary Se intake. Contrary to small-scale studies, soil Se concentrations were dominated by climate-soil interactions. Using moderate climate-change scenarios for 2080-2099, we predicted that changes in climate and soil organic carbon content will lead to overall decreased soil Se concentrations, particularly in agricultural areas; these decreases could increase the prevalence of Se deficiency. The importance of climate-soil interactions to Se distributions suggests that other trace elements with similar retention mechanisms will be similarly affected by climate change.

Correlation Lengths for Estimating the Large-Scale Carbon and Heat Content of the Southern Ocean

NASA Astrophysics Data System (ADS)

Mazloff, M. R.; Cornuelle, B. D.; Gille, S. T.; Verdy, A.

2018-02-01

The spatial correlation scales of oceanic dissolved inorganic carbon, heat content, and carbon and heat exchanges with the atmosphere are estimated from a realistic numerical simulation of the Southern Ocean. Biases in the model are assessed by comparing the simulated sea surface height and temperature scales to those derived from optimally interpolated satellite measurements. While these products do not resolve all ocean scales, they are representative of the climate scale variability we aim to estimate. Results show that constraining the carbon and heat inventory between 35°S and 70°S on time-scales longer than 90 days requires approximately 100 optimally spaced measurement platforms: approximately one platform every 20° longitude by 6° latitude. Carbon flux has slightly longer zonal scales, and requires a coverage of approximately 30° by 6°. Heat flux has much longer scales, and thus a platform distribution of approximately 90° by 10° would be sufficient. Fluxes, however, have significant subseasonal variability. For all fields, and especially fluxes, sustained measurements in time are required to prevent aliasing of the eddy signals into the longer climate scale signals. Our results imply a minimum of 100 biogeochemical-Argo floats are required to monitor the Southern Ocean carbon and heat content and air-sea exchanges on time-scales longer than 90 days. However, an estimate of formal mapping error using the current Argo array implies that in practice even an array of 600 floats (a nominal float density of about 1 every 7° longitude by 3° latitude) will result in nonnegligible uncertainty in estimating climate signals.

Large-scale Agroecosytem's Resiliency to Extreme Hydrometeorological and Climate Extreme Events in the Missouri River Basin

NASA Astrophysics Data System (ADS)

Munoz-Arriola, F.; Smith, K.; Corzo, G.; Chacon, J.; Carrillo-Cruz, C.

2015-12-01

A major challenge for water, energy and food security relies on the capability of agroecosyststems and ecosystems to adapt to a changing climate and land use changes. The interdependency of these forcings, understood through our ability to monitor and model processes across scales, indicate the "depth" of their impact on agroecosystems and ecosystems, and consequently our ability to predict the system's ability to return to a "normal" state. We are particularly interested in explore two questions: (1) how hydrometeorological and climate extreme events (HCEs) affect sub-seasonal to interannual changes in evapotranspiration and soil moisture? And (2) how agroecosystems recover from the effect of such events. To address those questions we use the land surface hydrologic Variable Infiltration Capacity (VIC) model and the Moderate Resolution Imaging Spectrometer-Leaf Area Index (MODIS-LAI) over two time spans (1950-2013 using a seasonal fixed LAI cycle) and 2001-2013 (an 8-day MODIS-LAI). VIC is forced by daily/16th degree resolution precipitation, minimum and maximum temperature, and wind speed. In this large-scale experiment, resiliency is defined by the capacity of a particular agroecosystem, represented by a grid cell's ET, SM, and LAI to return to a historical average. This broad, yet simplistic definition will contribute to identify the possible components and their scales involved in agroecosystems and ecosystems capacity to adapt to the incidence of HCEs and technologies used to intensify agriculture and diversify their use for food and energy production. Preliminary results show that dynamical changes in land use, tracked by MODIS data, require larger time spans to address properly the influence of technologic improvements in crop production as well as the competition for land for biofuel vs. food production. On the other hand, fixed seasonal changes in land use allow us just to identify hydrologic changes mainly due to climate variability.

CLIMATE VARIABILITY, LAND OWNERSHIP AND MIGRATION: EVIDENCE FROM THAILAND ABOUT GENDER IMPACTS

PubMed Central

Curran, Sara R.; Meijer-Irons, Jacqueline

2016-01-01

Scholars point to climate change, often in the form of more frequent and severe drought, as a potential driver of migration in the developing world, particularly for places where populations rely on agriculture for their livelihoods. To date, however, there have been few large-scale, longitudinal studies that explore the relationship between climate change and migration. This study significantly extends current scholarship by evaluating distinctive effects of climatic variation and models these effects on men’s and women’s responsiveness to drought and rainfall. Our study also investigates how land ownership moderates these effects. We find small, but significant, increases in migration above existing migratory levels during periods of prolonged climatic stress, and that these patterns differ both by gender and land tenure. PMID:27547492

ENSO detection and use to inform the operation of large scale water systems

NASA Astrophysics Data System (ADS)

Pham, Vuong; Giuliani, Matteo; Castelletti, Andrea

2016-04-01

El Nino Southern Oscillation (ENSO) is a large-scale, coupled ocean-atmosphere phenomenon occurring in the tropical Pacific Ocean, and is considered one of the most significant factors causing hydro-climatic anomalies throughout the world. Water systems operations could benefit from a better understanding of this global phenomenon, which has the potential for enhancing the accuracy and lead-time of long-range streamflow predictions. In turn, these are key to design interannual water transfers in large scale water systems to contrast increasingly frequent extremes induced by changing climate. Despite the ENSO teleconnection is well defined in some locations such as Western USA and Australia, there is no consensus on how it can be detected and used in other river basins, particularly in Europe, Africa, and Asia. In this work, we contribute a general framework relying on Input Variable Selection techniques for detecting ENSO teleconnection and using this information for improving water reservoir operations. Core of our procedure is the Iterative Input variable Selection (IIS) algorithm, which is employed to find the most relevant determinants of streamflow variability for deriving predictive models based on the selected inputs as well as to find the most valuable information for conditioning operating decisions. Our framework is applied to the multipurpose operations of the Hoa Binh reservoir in the Red River basin (Vietnam), taking into account hydropower production, water supply for irrigation, and flood mitigation during the monsoon season. Numerical results show that our framework is able to quantify the relationship between the ENSO fluctuations and the Red River basin hydrology. Moreover, we demonstrate that such ENSO teleconnection represents valuable information for improving the operations of Hoa Binh reservoir.

20th century trends of drought conditions in the Mediterranean: the influence of large-scale circulation patterns.

NASA Astrophysics Data System (ADS)

Sousa, Pedro; Trigo, Ricardo; Garcia-Herrera, Ricardo

2010-05-01

Here we have used the Self Calibrated PDSI (scPDSI) proposed by Wells et al (2004) as a more appropriate approach to characterize drought conditions in the Mediterranean area. The scPDSI has been shown to perform better (than the original PDSI) when evaluating spatial and temporal drought characteristics for regions outside the USA (Schrier et al, 2005). Seasonal and annual trends for the 1901-2000, 1901-1950 and 1951-2000 periods were computed using the standard Mann-Kendall test for trend significance evaluation. However, statistical significance obtained with this test can be highly misleading because it does not take into account the low variability nature that dominates the seasonal evolution of scPDSI fields. We have now improved these results by employing a modified Mann-Kendall test for auto-correlated series (Hamed and Ramachandra, 1997), such as the scPDSI case. This development allowed for a better definition of the Mediterranean areas characterized by significant changes in the scPDSI, namely the largely negative trends that dominate the Mediterranean basin, with the exceptions of parts of eastern Turkey and northwestern Iberia, since initially these areas were overestimated. The spatio-temporal variability of these indices was evaluated with an EOF analysis, in order to reduce the large dimensionality of the fields under analysis. Spatial representation of the first EOF patterns shows that EOF 1 covers the entire Mediterranean basin (16.4% of EV), while EOF2 is dominated by a W-E dipole (10% EV). The following EOF patterns present smaller scale features, and explain smaller amounts of variance. The EOF patterns have also facilitated the definition of four sub-regions with large socio-economic relevance: 1) Iberia, 2) Italian Peninsula, 3) Balkans and 4) Turkey. Afterwards we perform a comprehensive analysis on the links between the scPDSI and the large-scale atmospheric circulation indices that affect the Mediterranean basin, namely; NAO, EA, and SCAND (Trigo et al., 2006) and where we have also taken into account once again the effect of autocorrelation. Some of these links were obtained with 3 or 6 months lagged relationships, while others were achieved with instantaneous (no lag) links. This analysis was performed for the entire Mediterranean region as a whole, but also for each considered sub-domain. Finally, a stepwise regression model was developed to reproduce summer scPDSI series during the 1951-2002 period, using these large scale indices as predictors in the model. This procedure results in positive Skill Score values against the persistence model. Hamed K.H., Ramachandra A. (1997) "A modified Mann-Kendall trend test for autocorrelated data", Journal of Hidrology, 204, 182-196. Schrier G, Briffa KR, Jones PD, Osborn TJ. (2005). Summer moisture variability across Europe. Journal of Climate 19: 2818-2834. Trigo, R. and 21 authors (2006) Relations between variability in the Mediterranean region and mid-latitude variability. In: P. Lionello, P. Malanotte-Rizzoli & R. Boscolo (Eds), Mediterranean Climate Variability, Amsterdam: Elsevier, pp. 179-226. Wells N, Goddard S and Hayes MJ (2004) A self-calibrating Palmer Drought Severity Index. Journal of Climate 17, 2335-2351.

Usefulness of AIRS-Derived OLR, Temperature, Water Vapor and Cloudiness Anomaly Trends for GCM Validation

NASA Technical Reports Server (NTRS)

Molnar, Gyula I.; Susskind, Joel; Iredell, Lena F.

2010-01-01

Mainly due to their global nature, satellite observations can provide a very useful basis for GCM validations. In particular, satellite sounders such as AIRS provide 3-D spatial information (most useful for GCMs), so the question arises: can we use AIRS datasets for climate variability assessments? We show that the recent (September 2002 February 2010) CERES-observed negative trend in OLR of approx.-0.1 W/sq m/yr averaged over the globe is found in the AIRS OLR data as well. Most importantly, even minute details (down to 1 x 1 degree GCM-scale resolution) of spatial and temporal anomalies and trends of OLR as observed by CERES and computed based on AIRS-retrieved surface and atmospheric geophysical parameters over this time period are essentially the same. The correspondence can be seen even in the very large spatial variations of these trends with local values ranging from -2.6 W/sq m/yr to +3.0 W/sq m/yr in the tropics, for example. This essentially perfect agreement of OLR anomalies and trends derived from observations by two different instruments, in totally independent and different manners, implies that both sets of results must be highly accurate, and indirectly validates the anomalies and trends of other AIRS derived products as well. These products show that global and regional anomalies and trends of OLR, water vapor and cloud cover over the last 7+ years are strongly influenced by EI-Nino-La Nina cycles . We have created climate parameter anomaly datasets using AIRS retrievals which can be compared directly with coupled GCM climate variability assessments. Moreover, interrelationships of these anomalies and trends should also be similar between the observed and GCM-generated datasets, and, in cases of discrepancies, GCM parameterizations could be improved based on the relationships observed in the data. First, we assess spatial "trends" of variability of climatic parameter anomalies [since anomalies relative to the seasonal cycle are good proxies of climate variability] at the common 1x1 degree GCM grid-scale by creating spatial anomaly "trends" based on the first 7+ years of AIRS Version 5 Leve13 data. We suggest that modelers should compare these with their (coupled) GCM's performance covering the same period. We evaluate temporal variability and interrelations of climatic anomalies on global to regional e.g., deep Tropical Hovmoller diagrams, El-Nino-related variability scales, and show the effects of El-Nino-La Nina activity on tropical anomalies and trends of water vapor cloud cover and OLR. For GCMs to be trusted highly for long-term climate change predictions, they should be able to reproduce findings similar to these. In summary, the AIRS-based climate variability analyses provide high quality, informative and physically plausible interrelationships among OLR, temperature, humidity and cloud cover both on the spatial and temporal scales. GCM validations can use these results even directly, e. g., by creating 1x1 degree trendmaps for the same period in coupled climate simulations.

The Global Climate Anomaly in 1940-1942

NASA Astrophysics Data System (ADS)

Brönnimann, S.; Luterbacher, J.; Staehelin, J.; Svendby, T. M.

2003-12-01

An unprecedented climatic anomaly occurred in the tropics and in the Northern Hemisphere in 1940-1942. During a strong and prolonged El Niño [Bigg & Inoué, QJRMS 118 (1992), 125], extremely cold winters were observed in Europe, accompanied by very warm temperatures in Alaska and large parts of the Arctic and a cold North Pacific. The anomalies were strong (comprising the two coldest European winters of the 20th century) and extraordinarily persistent. In addition, exceptionally high values of total ozone are reported [Langlo, Geofys. Publ. 18/6 (1952)], pointing to an anomalous stratospheric circulation. Events of this magnitude have a strong economical and environmental impact; the 1940s anomaly even affected World War II. Studying this anomaly in detail contributes to (1) document the extent of 20th century climate variability, (2) understand large-scale coupling processes between the tropics and the extratropics and between the troposphere and the stratosphere and (3) develop tools to analyze past upper-level climate variability prior to 1948, i.e., the reanalysis period. For this study we have compiled, digitized, and re-evaluated several tens of thousands of temperature and pressure profiles from aircraft and radiosonde ascents up to 50 hPa [Brönnimann, Int. J. Clim. 23 (2003), 769]. The upper-air data were supplemented with data from the Earth's surface and used to statistically reconstruct monthly upper-level fields for the extratropical Northern Hemisphere up to 100 hPa [Brönnimann & Luterbacher, Clim. Dyn., submitted]. Although the quality of the reconstructed stratospheric fields is not comparable to more recent data, it is sufficient to allow a broad characterization of the circulation at 100 hPa during the early 1940s. In addition to upper-air data, several total ozone series from the 1940s were re-evaluated [Brönnimann et al., QJRMS 129 (2003), 2819], providing further information on the stratosphere. In this paper we present an analysis of these new data sets and compare the results to climate model data. It is demonstrated that the climate anomaly at the ground was accompanied in the lower stratosphere by a weak polar vortex and warm temperatures over the polar region, Eurasia, and the North Pacific. The total ozone data show a peak in 1940-1942 in all available records, at sites as far apart as China, North America, central Europe, and the Arctic. The co-occurrence of warm tropical SSTs (due to El Niño), a weak polar vortex and warm lower stratosphere over polar regions, and a total ozone increase is in agreement with findings by van Loon and Labitzke [Mon. Wea. Rev. 115 (1987), 357]. Using the 290-yr control run of the Community Climate System Model CCSM-2.0 provided by UCAR we show that such large-scale coupling events are related to an exceptionally large difference between tropical and northern-extratropical SSTs such as during strong El Niños. The coupling most likely proceeds through a change in planetary wave activity in the northern extratropics that manifests itself in a strong Aleutian low and a weak Icelandic low and in a disturbance of the polar vortex in the stratosphere. The 1940-1942 climate anomaly is not well known among scientists, but it is unprecedented in strength, yet exemplary in character, providing a unique opportunity to study large-scale climate variability.

Young students, satellites aid understanding of climate-biosphere link

NASA Astrophysics Data System (ADS)

White, Michael A.; Schwartz, Mark D.; Running, Steven W.

Data collected by young students from kindergarten through high school are being combined with satellite data to develop a more consistent understanding of the intimate connection between climate dynamics and the terrestrial biosphere. Comparison of the two sets of data involving the onset of budburst among trees and other vegetation has been extremely encouraging.Surface-atmosphere interactions involving exchanges of carbon, water, and energy are strongly affected by interannual variability in the timing and length of the vegetation growing season, and satellite remote sensing has the unique ability to consistently monitor global spatiotemporal variability in growing season dynamics. But without a clear picture of how satellite information (Figure 1) relates to ground conditions, the application of satellite growing season estimates for monitoring of climate-vegetation interactions, calculation of energy budgets, and large-scale ecological modeling is extremely limited.The integrated phenological analysis of field data, satellite observations, and climate advocated by Schwartz [1998], for example, has been primarily limited by the lack of geographically extensive and consistently measured phenology databases.

Development of Spatiotemporal Bias-Correction Techniques for Downscaling GCM Predictions

NASA Astrophysics Data System (ADS)

Hwang, S.; Graham, W. D.; Geurink, J.; Adams, A.; Martinez, C. J.

2010-12-01

Accurately representing the spatial variability of precipitation is an important factor for predicting watershed response to climatic forcing, particularly in small, low-relief watersheds affected by convective storm systems. Although Global Circulation Models (GCMs) generally preserve spatial relationships between large-scale and local-scale mean precipitation trends, most GCM downscaling techniques focus on preserving only observed temporal variability on point by point basis, not spatial patterns of events. Downscaled GCM results (e.g., CMIP3 ensembles) have been widely used to predict hydrologic implications of climate variability and climate change in large snow-dominated river basins in the western United States (Diffenbaugh et al., 2008; Adam et al., 2009). However fewer applications to smaller rain-driven river basins in the southeastern US (where preserving spatial variability of rainfall patterns may be more important) have been reported. In this study a new method was developed to bias-correct GCMs to preserve both the long term temporal mean and variance of the precipitation data, and the spatial structure of daily precipitation fields. Forty-year retrospective simulations (1960-1999) from 16 GCMs were collected (IPCC, 2007; WCRP CMIP3 multi-model database: https://esg.llnl.gov:8443/), and the daily precipitation data at coarse resolution (i.e., 280km) were interpolated to 12km spatial resolution and bias corrected using gridded observations over the state of Florida (Maurer et al., 2002; Wood et al, 2002; Wood et al, 2004). In this method spatial random fields which preserved the observed spatial correlation structure of the historic gridded observations and the spatial mean corresponding to the coarse scale GCM daily rainfall were generated. The spatiotemporal variability of the spatio-temporally bias-corrected GCMs were evaluated against gridded observations, and compared to the original temporally bias-corrected and downscaled CMIP3 data for the central Florida. The hydrologic response of two southwest Florida watersheds to the gridded observation data, the original bias corrected CMIP3 data, and the new spatiotemporally corrected CMIP3 predictions was compared using an integrated surface-subsurface hydrologic model developed by Tampa Bay Water.

Capturing subregional variability in regional-scale climate change vulnerability assessments of natural resources.

PubMed

Buotte, Polly C; Peterson, David L; McKelvey, Kevin S; Hicke, Jeffrey A

2016-03-15

Natural resource vulnerability to climate change can depend on the climatology and ecological conditions at a particular site. Here we present a conceptual framework for incorporating spatial variability in natural resource vulnerability to climate change in a regional-scale assessment. The framework was implemented in the first regional-scale vulnerability assessment conducted by the US Forest Service. During this assessment, five subregional workshops were held to capture variability in vulnerability and to develop adaptation tactics. At each workshop, participants answered a questionnaire to: 1) identify species, resources, or other information missing from the regional assessment, and 2) describe subregional vulnerability to climate change. Workshop participants divided into six resource groups; here we focus on wildlife resources. Participants identified information missing from the regional assessment and multiple instances of subregional variability in climate change vulnerability. We provide recommendations for improving the process of capturing subregional variability in a regional vulnerability assessment. We propose a revised conceptual framework structured around pathways of climate influence, each with separate rankings for exposure, sensitivity, and adaptive capacity. These revisions allow for a quantitative ranking of species, pathways, exposure, sensitivity, and adaptive capacity across subregions. Rankings can be used to direct the development and implementation of future regional research and monitoring programs. The revised conceptual framework is equally applicable as a stand-alone model for assessing climate change vulnerability and as a nested model within a regional assessment for capturing subregional variability in vulnerability. Copyright © 2015 Elsevier Ltd. All rights reserved.

Interactive Correlation Analysis and Visualization of Climate Data

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

Ma, Kwan-Liu

The relationship between our ability to analyze and extract insights from visualization of climate model output and the capability of the available resources to make those visualizations has reached a crisis point. The large volume of data currently produced by climate models is overwhelming the current, decades-old visualization workflow. The traditional methods for visualizing climate output also have not kept pace with changes in the types of grids used, the number of variables involved, and the number of different simulations performed with a climate model or the feature-richness of high-resolution simulations. This project has developed new and faster methods formore » visualization in order to get the most knowledge out of the new generation of high-resolution climate models. While traditional climate images will continue to be useful, there is need for new approaches to visualization and analysis of climate data if we are to gain all the insights available in ultra-large data sets produced by high-resolution model output and ensemble integrations of climate models such as those produced for the Coupled Model Intercomparison Project. Towards that end, we have developed new visualization techniques for performing correlation analysis. We have also introduced highly scalable, parallel rendering methods for visualizing large-scale 3D data. This project was done jointly with climate scientists and visualization researchers at Argonne National Laboratory and NCAR.« less

Assembling Large, Multi-Sensor Climate Datasets Using the SciFlo Grid Workflow System

NASA Astrophysics Data System (ADS)

Wilson, B. D.; Manipon, G.; Xing, Z.; Fetzer, E.

2008-12-01

NASA's Earth Observing System (EOS) is the world's most ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the A-Train platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over periods of years to decades. However, moving from predominantly single-instrument studies to a multi-sensor, measurement-based model for long-duration analysis of important climate variables presents serious challenges for large-scale data mining and data fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another instrument (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the cloud scenes from CloudSat, and repeat the entire analysis over years of AIRS data. To perform such an analysis, one must discover & access multiple datasets from remote sites, find the space/time matchups between instruments swaths and model grids, understand the quality flags and uncertainties for retrieved physical variables, and assemble merged datasets for further scientific and statistical analysis. To meet these large-scale challenges, we are utilizing a Grid computing and dataflow framework, named SciFlo, in which we are deploying a set of versatile and reusable operators for data query, access, subsetting, co-registration, mining, fusion, and advanced statistical analysis. SciFlo is a semantically-enabled ("smart") Grid Workflow system that ties together a peer-to-peer network of computers into an efficient engine for distributed computation. The SciFlo workflow engine enables scientists to do multi-instrument Earth Science by assembling remotely-invokable Web Services (SOAP or http GET URLs), native executables, command-line scripts, and Python codes into a distributed computing flow. A scientist visually authors the graph of operation in the VizFlow GUI, or uses a text editor to modify the simple XML workflow documents. The SciFlo client & server engines optimize the execution of such distributed workflows and allow the user to transparently find and use datasets and operators without worrying about the actual location of the Grid resources. The engine transparently moves data to the operators, and moves operators to the data (on the dozen trusted SciFlo nodes). SciFlo also deploys a variety of Data Grid services to: query datasets in space and time, locate & retrieve on-line data granules, provide on-the-fly variable and spatial subsetting, and perform pairwise instrument matchups for A-Train datasets. These services are combined into efficient workflows to assemble the desired large-scale, merged climate datasets. SciFlo is currently being applied in several large climate studies: comparisons of aerosol optical depth between MODIS, MISR, AERONET ground network, and U. Michigan's IMPACT aerosol transport model; characterization of long-term biases in microwave and infrared instruments (AIRS, MLS) by comparisons to GPS temperature retrievals accurate to 0.1 degrees Kelvin; and construction of a decade-long, multi-sensor water vapor climatology stratified by classified cloud scene by bringing together datasets from AIRS/AMSU, AMSR-E, MLS, MODIS, and CloudSat (NASA MEASUREs grant, Fetzer PI). The presentation will discuss the SciFlo technologies, their application in these distributed workflows, and the many challenges encountered in assembling and analyzing these massive datasets.

Assembling Large, Multi-Sensor Climate Datasets Using the SciFlo Grid Workflow System

NASA Astrophysics Data System (ADS)

Wilson, B.; Manipon, G.; Xing, Z.; Fetzer, E.

2009-04-01

NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over periods of years to decades. However, moving from predominantly single-instrument studies to a multi-sensor, measurement-based model for long-duration analysis of important climate variables presents serious challenges for large-scale data mining and data fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another instrument (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over years of AIRS data. To perform such an analysis, one must discover & access multiple datasets from remote sites, find the space/time "matchups" between instruments swaths and model grids, understand the quality flags and uncertainties for retrieved physical variables, assemble merged datasets, and compute fused products for further scientific and statistical analysis. To meet these large-scale challenges, we are utilizing a Grid computing and dataflow framework, named SciFlo, in which we are deploying a set of versatile and reusable operators for data query, access, subsetting, co-registration, mining, fusion, and advanced statistical analysis. SciFlo is a semantically-enabled ("smart") Grid Workflow system that ties together a peer-to-peer network of computers into an efficient engine for distributed computation. The SciFlo workflow engine enables scientists to do multi-instrument Earth Science by assembling remotely-invokable Web Services (SOAP or http GET URLs), native executables, command-line scripts, and Python codes into a distributed computing flow. A scientist visually authors the graph of operation in the VizFlow GUI, or uses a text editor to modify the simple XML workflow documents. The SciFlo client & server engines optimize the execution of such distributed workflows and allow the user to transparently find and use datasets and operators without worrying about the actual location of the Grid resources. The engine transparently moves data to the operators, and moves operators to the data (on the dozen trusted SciFlo nodes). SciFlo also deploys a variety of Data Grid services to: query datasets in space and time, locate & retrieve on-line data granules, provide on-the-fly variable and spatial subsetting, perform pairwise instrument matchups for A-Train datasets, and compute fused products. These services are combined into efficient workflows to assemble the desired large-scale, merged climate datasets. SciFlo is currently being applied in several large climate studies: comparisons of aerosol optical depth between MODIS, MISR, AERONET ground network, and U. Michigan's IMPACT aerosol transport model; characterization of long-term biases in microwave and infrared instruments (AIRS, MLS) by comparisons to GPS temperature retrievals accurate to 0.1 degrees Kelvin; and construction of a decade-long, multi-sensor water vapor climatology stratified by classified cloud scene by bringing together datasets from AIRS/AMSU, AMSR-E, MLS, MODIS, and CloudSat (NASA MEASUREs grant, Fetzer PI). The presentation will discuss the SciFlo technologies, their application in these distributed workflows, and the many challenges encountered in assembling and analyzing these massive datasets.

An 'Observational Large Ensemble' to compare observed and modeled temperature trend uncertainty due to internal variability.

NASA Astrophysics Data System (ADS)

Poppick, A. N.; McKinnon, K. A.; Dunn-Sigouin, E.; Deser, C.

2017-12-01

Initial condition climate model ensembles suggest that regional temperature trends can be highly variable on decadal timescales due to characteristics of internal climate variability. Accounting for trend uncertainty due to internal variability is therefore necessary to contextualize recent observed temperature changes. However, while the variability of trends in a climate model ensemble can be evaluated directly (as the spread across ensemble members), internal variability simulated by a climate model may be inconsistent with observations. Observation-based methods for assessing the role of internal variability on trend uncertainty are therefore required. Here, we use a statistical resampling approach to assess trend uncertainty due to internal variability in historical 50-year (1966-2015) winter near-surface air temperature trends over North America. We compare this estimate of trend uncertainty to simulated trend variability in the NCAR CESM1 Large Ensemble (LENS), finding that uncertainty in wintertime temperature trends over North America due to internal variability is largely overestimated by CESM1, on average by a factor of 32%. Our observation-based resampling approach is combined with the forced signal from LENS to produce an 'Observational Large Ensemble' (OLENS). The members of OLENS indicate a range of spatially coherent fields of temperature trends resulting from different sequences of internal variability consistent with observations. The smaller trend variability in OLENS suggests that uncertainty in the historical climate change signal in observations due to internal variability is less than suggested by LENS.

Climate forcing for dynamics of dissolved inorganic nutrients at Palmer Station, Antarctica: An interdecadal (1993-2013) analysis

NASA Astrophysics Data System (ADS)

Kim, Hyewon; Doney, Scott C.; Iannuzzi, Richard A.; Meredith, Michael P.; Martinson, Douglas G.; Ducklow, Hugh W.

2016-09-01

We analyzed 20 years (1993-2013) of observations of dissolved inorganic macronutrients (nitrate, N; phosphate, P; and silicate, Si) and chlorophyll a (Chl) at Palmer Station, Antarctica (64.8°S, 64.1°W) to elucidate how large-scale climate and local physical forcing affect the interannual variability in the seasonal phytoplankton bloom and associated drawdown of nutrients. The leading modes of nutrients (N, P, and Si empirical orthogonal functions 1, EOF1) represent overall negative anomalies throughout growing seasons, showing a mixed signal of variability in the initial levels and drawdown thereafter (low-frequency dynamics). The second most common seasonal patterns of nitrate and phosphate (N and P EOF2) capture prolonged drawdown events during December-March, which are correlated to Chl EOF1. Si EOF2 captures a drawdown event during November-December, which is correlated to Chl EOF2. These different drawdown patterns are shaped by different sets of physical and climate forcing mechanisms. N and P drawdown events during December-March are influenced by the winter and spring Southern Annular Mode (SAM) phase, where nutrient utilization is enhanced in a stabilized upper water column as a consequence of SAM-driven winter sea ice and spring wind dynamics. Si drawdown during November-December is influenced by early sea ice retreat, where ice breakup may induce abrupt water column stratification and a subsequent diatom bloom or release of diatom cells from within the sea ice. Our findings underscore that seasonal nutrient dynamics in the coastal WAP are coupled to large-scale climate forcing and related physics, understanding of which may enable improved projections of biogeochemical responses to climate change.

Relationship of a lichen species diversity indicator to environmental factors across the coterminous United States

Treesearch

Susan Will-Wolf; Mark J. Ambrose; Randall S. Morin

2011-01-01

We have investigated relationships between one simple indicator of lichen species diversity and environmental variables in forests across the coterminous United States. We want to know whether this indicator can help quantify the influence that factors such as climate and air quality have on lichen biodiversity at large scales and whether it will be useful in...

Annual water, sediment, nutrient, and organic carbon fluxes in river basins: A global meta-analysis as a function of scale

NASA Astrophysics Data System (ADS)

Mutema, M.; Chaplot, V.; Jewitt, G.; Chivenge, P.; Blöschl, G.

2015-11-01

Process controls on water, sediment, nutrient, and organic carbon exports from the landscape through runoff are not fully understood. This paper provides analyses from 446 sites worldwide to evaluate the impact of environmental factors (MAP and MAT: mean annual precipitation and temperature; CLAY and BD: soil clay content and bulk density; S: slope gradient; LU: land use) on annual exports (RC: runoff coefficients; SL: sediment loads; TOCL: organic carbon losses; TNL: nitrogen losses; TPL: phosphorus losses) from different spatial scales. RC was found to increase, on average, from 18% at local scale (in headwaters), 25% at microcatchment and subcatchment scale (midreaches) to 41% at catchment scale (lower reaches of river basins) in response to multiple factors. SL increased from microplots (468 g m-2 yr-1) to plots (901 g m-2 yr-1), accompanied by decreasing TOCL and TNL. Climate was a major control masking the effects of other factors. For example, RC, SL, TOCL, TNL, and TPL tended to increase with MAP at all spatial scales. These variables, however, decreased with MAT. The impact of CLAY, BD, LU, and S on erosion variables was largely confined to the hillslope scale, where RC, SL, and TOCL decreased with CLAY, while TNL and TPL increased. The results contribute to better understanding of water, nutrient, and carbon cycles in terrestrial ecosystems and should inform river basin modeling and ecosystem management. The important role of spatial climate variability points to a need for comparative research in specific environments at nested spatiotemporal scales.

Interannual variation of carbon fluxes from three contrasting evergreen forests: The role of forest dynamics and climate

USGS Publications Warehouse

Sierra, C.A.; Loescher, H.W.; Harmon, M.E.; Richardson, A.D.; Hollinger, D.Y.; Perakis, S.S.

2009-01-01

Interannual variation of carbon fluxes can be attributed to a number of biotic and abiotic controls that operate at different spatial and temporal scales. Type and frequency of disturbance, forest dynamics, and climate regimes are important sources of variability. Assessing the variability of carbon fluxes from these specific sources can enhance the interpretation of past and current observations. Being able to separate the variability caused by forest dynamics from that induced by climate will also give us the ability to determine if the current observed carbon fluxes are within an expected range or whether the ecosystem is undergoing unexpected change. Sources of interannual variation in ecosystem carbon fluxes from three evergreen ecosystems, a tropical, a temperate coniferous, and a boreal forest, were explored using the simulation model STANDCARB. We identified key processes that introduced variation in annual fluxes, but their relative importance differed among the ecosystems studied. In the tropical site, intrinsic forest dynamics contributed ?? 30% of the total variation in annual carbon fluxes. In the temperate and boreal sites, where many forest processes occur over longer temporal scales than those at the tropical site, climate controlled more of the variation among annual fluxes. These results suggest that climate-related variability affects the rates of carbon exchange differently among sites. Simulations in which temperature, precipitation, and radiation varied from year to year (based on historical records of climate variation) had less net carbon stores than simulations in which these variables were held constant (based on historical records of monthly average climate), a result caused by the functional relationship between temperature and respiration. This suggests that, under a more variable temperature regime, large respiratory pulses may become more frequent and high enough to cause a reduction in ecosystem carbon stores. Our results also show that the variation of annual carbon fluxes poses an important challenge in our ability to determine whether an ecosystem is a source, a sink, or is neutral in regard to CO2 at longer timescales. In simulations where climate change negatively affected ecosystem carbon stores, there was a 20% chance of committing Type II error, even with 20 years of sequential data. ?? 2009 by the Ecological Society of America.

Ecological Assimilation of Land and Climate Observations - the EALCO model

NASA Astrophysics Data System (ADS)

Wang, S.; Zhang, Y.; Trishchenko, A.

2004-05-01

Ecosystems are intrinsically dynamic and interact with climate at a highly integrated level. Climate variables are the main driving factors in controlling the ecosystem physical, physiological, and biogeochemical processes including energy balance, water balance, photosynthesis, respiration, and nutrient cycling. On the other hand, ecosystems function as an integrity and feedback on the climate system through their control on surface radiation balance, energy partitioning, and greenhouse gases exchange. To improve our capability in climate change impact assessment, a comprehensive ecosystem model is required to address the many interactions between climate change and ecosystems. In addition, different ecosystems can have very different responses to the climate change and its variation. To provide more scientific support for ecosystem impact assessment at national scale, it is imperative that ecosystem models have the capability of assimilating the large scale geospatial information including satellite observations, GIS datasets, and climate model outputs or reanalysis. The EALCO model (Ecological Assimilation of Land and Climate Observations) is developed for such purposes. EALCO includes the comprehensive interactions among ecosystem processes and climate, and assimilates a variety of remote sensing products and GIS database. It provides both national and local scale model outputs for ecosystem responses to climate change including radiation and energy balances, water conditions and hydrological cycles, carbon sequestration and greenhouse gas exchange, and nutrient (N) cycling. These results form the foundation for the assessment of climate change impact on ecosystems, their services, and adaptation options. In this poster, the main algorithms for the radiation, energy, water, carbon, and nitrogen simulations were diagrammed. Sample input data layers at Canada national scale were illustrated. Model outputs including the Canada wide spatial distributions of net radiation, evapotranspiration, gross primary production, net primary production, and net ecosystem production were discussed.

Improving the representation of clouds, radiation, and precipitation using spectral nudging in the Weather Research and Forecasting model

NASA Astrophysics Data System (ADS)

Spero, Tanya L.; Otte, Martin J.; Bowden, Jared H.; Nolte, Christopher G.

2014-10-01

Spectral nudging—a scale-selective interior constraint technique—is commonly used in regional climate models to maintain consistency with large-scale forcing while permitting mesoscale features to develop in the downscaled simulations. Several studies have demonstrated that spectral nudging improves the representation of regional climate in reanalysis-forced simulations compared with not using nudging in the interior of the domain. However, in the Weather Research and Forecasting (WRF) model, spectral nudging tends to produce degraded precipitation simulations when compared to analysis nudging—an interior constraint technique that is scale indiscriminate but also operates on moisture fields which until now could not be altered directly by spectral nudging. Since analysis nudging is less desirable for regional climate modeling because it dampens fine-scale variability, changes are proposed to the spectral nudging methodology to capitalize on differences between the nudging techniques and aim to improve the representation of clouds, radiation, and precipitation without compromising other fields. These changes include adding spectral nudging toward moisture, limiting nudging to below the tropopause, and increasing the nudging time scale for potential temperature, all of which collectively improve the representation of mean and extreme precipitation, 2 m temperature, clouds, and radiation, as demonstrated using a model-simulated 20 year historical period. Such improvements to WRF may increase the fidelity of regional climate data used to assess the potential impacts of climate change on human health and the environment and aid in climate change mitigation and adaptation studies.

The use of remotely sensed soil moisture data in large-scale models of the hydrological cycle

NASA Technical Reports Server (NTRS)

Salomonson, V. V.; Gurney, R. J.; Schmugge, T. J.

1985-01-01

Manabe (1982) has reviewed numerical simulations of the atmosphere which provided a framework within which an examination of the dynamics of the hydrological cycle could be conducted. It was found that the climate is sensitive to soil moisture variability in space and time. The challenge arises now to improve the observations of soil moisture so as to provide up-dated boundary condition inputs to large scale models including the hydrological cycle. Attention is given to details regarding the significance of understanding soil moisture variations, soil moisture estimation using remote sensing, and energy and moisture balance modeling.

Influences of large- and regional-scale climate on fish recruitment in the Skagerrak-Kattegat over the last century

NASA Astrophysics Data System (ADS)

Linderholm, Hans W.; Cardinale, Massimiliano; Bartolino, Valerio; Chen, Deliang; Ou, Tinghai; Svedäng, Henrik

2014-06-01

Dynamics of commercial fish stocks are generally associated with fishing pressure and climate variability. Due to short time series, past studies of the relationships between fish stock dynamics and climate have mainly been restricted to the last few decades. Here we analyzed a century-long time series of plaice, cod and haddock from the Skagerrak-Kattegat, to assess the long-term influence of climate on recruitment. Recruitment success (RS) was compared against sea-surface temperature (SST) and atmospheric circulation indices on large (North Atlantic) and regional (Skagerrak-Kattegat) scales. Our results show that the influence of climate on RS was more pronounced on longer, than on shorter timescales. Over the century-long period, a shift from low to high climate sensitivity was seen from the early to the late part for plaice and cod, while the opposite was found for haddock. This shift suggests that the increasing fishing pressure and the climate change in the Skagerrak-Kattegat have resulted in an increased sensitivity of RS to climate for plaice and cod. The diminishing of climate sensitivity in haddock RS, on the other hand, may be linked to the early twentieth century collapse of the stock in the region. While no long-term relationship between RS and the Atlantic Multidecadal Oscillation (AMO) could be found, large RS fluctuations during the positive phase of the AMO (1935-1960), relative to the cold phases, suggests a changed pattern in recruitment during warm periods. On the other hand, this could be due to the increased fishing pressure in the area. Thus, reported correlations between climate and fish may be caused by strong trends in climate in the late-twentieth century, and coincident reduction in fish stocks caused by intense fishing, rather than a stable relationship between climate and fish recruitment per se.

Climate Change and Conservation Planning in California: The San Francisco Bay Area Upland Habitat Goals Approach

NASA Astrophysics Data System (ADS)

Branciforte, R.; Weiss, S. B.; Schaefer, N.

2008-12-01

Climate change threatens California's vast and unique biodiversity. The Bay Area Upland Habitat Goals is a comprehensive regional biodiversity assessment of the 9 counties surrounding San Francisco Bay, and is designing conservation land networks that will serve to protect, manage, and restore that biodiversity. Conservation goals for vegetation, rare plants, mammals, birds, fish, amphibians, reptiles, and invertebrates are set, and those goals are met using the optimization algorithm MARXAN. Climate change issues are being considered in the assessment and network design in several ways. The high spatial variability at mesoclimatic and topoclimatic scales in California creates high local biodiversity, and provides some degree of local resiliency to macroclimatic change. Mesoclimatic variability from 800 m scale PRISM climatic norms is used to assess "mesoclimate spaces" in distinct mountain ranges, so that high mesoclimatic variability, especially local extremes that likely support range limits of species and potential climatic refugia, can be captured in the network. Quantitative measures of network resiliency to climate change include the spatial range of key temperature and precipitation variables within planning units. Topoclimatic variability provides a finer-grained spatial patterning. Downscaling to the topoclimatic scale (10-50 m scale) includes modeling solar radiation across DEMs for predicting maximum temperature differentials, and topographic position indices for modeling minimum temperature differentials. PRISM data are also used to differentiate grasslands into distinct warm and cool types. The overall conservation strategy includes local and regional connectivity so that range shifts can be accommodated.

Oscar: a portable prototype system for the study of climate variability

NASA Astrophysics Data System (ADS)

Madonna, Fabio; Rosoldi, Marco; Amato, Francesco

2015-04-01

The study of the techniques for the exploitation of solar energy implies the knowledge of nature, ecosystem, biological factors and local climate. Clouds, fog, water vapor, and the presence of large concentrations of dust can significantly affect the way to exploit the solar energy. Therefore, a quantitative characterization of the impact of climate variability at the regional scale is needed to increase the efficiency and sustainability of the energy system. OSCAR (Observation System for Climate Application at Regional scale) project, funded in the frame of the PO FESR 2007-2013, aims at the design of a portable prototype system for the study of correlations among the trends of several Essential Climate Variables (ECVs) and the change in the amount of solar irradiance at the ground level. The final goal of this project is to provide a user-friendly low cost solution for the quantification of the impact of regional climate variability on the efficiency of solar cell and concentrators to improve the exploitation of natural sources. The prototype has been designed on the basis of historical measurements performed at CNR-IMAA Atmospheric Observatory (CIAO). Measurements from satellite and data from models have been also considered as ancillary to the study, above all, to fill in the gaps of existing datasets. In this work, the results outcome from the project activities will be presented. The results include: the design and implementation of the prototype system; the development of a methodology for the estimation of the impact of climate variability, mainly due to aerosol, cloud and water vapor, on the solar irradiance using the integration of the observations potentially provided by prototype; the study of correlation between the surface radiation, precipitation and aerosols transport. In particular, a statistical study will be presented to assess the impact of the atmosphere on the solar irradiance at the ground, quantifying the contribution due to aerosol and clouds and separating their effect on the direct and the diffuse components of the solar radiation. This also aims to provide recommendations to the manufacturer of the devices used to exploit solar radiation.

Differential Impacts of Climate Change on Crops and Agricultural Regions in India

NASA Astrophysics Data System (ADS)

Sharma, A. N.

2015-12-01

As India's farmers and policymakers consider potential adaptation strategies to climate change, some questions loom large: - Which climate variables best explain the variability of crop yields? - How does the vulnerability of crop yields to climate vary regionally? - How are these risks likely to change in the future? While process-based crop modelling has started to answer many of these questions, we believe statistical approaches can complement these in improving our understanding of climate vulnerabilities and appropriate responses. We use yield data collected over three decades for more than ten food crops grown in India along with a variety of statistical approaches to answer the above questions. The ability of climate variables to explain yield variation varies greatly by crop and season, which is expected. Equally important, the ability of models to predict crop yields as well as their coefficients varies greatly by district even for districts which are relatively close to each other and similar in their agricultural practices. We believe these results encourage caution and nuance when making projections about climate impacts on crop yields in the future. Most studies about climate impacts on crop yields focus on a handful of major food crops. By extending our analysis to all the crops with long-term district level data in India as well as two growing seasons we gain a more comprehensive picture. Our results indicate that there is a great deal of variability even at relatively small scales, and that this must be taken into account if projections are to be made useful to policymakers.

Learning Across Time Scales: Science, Policy, Management, and Communication

NASA Astrophysics Data System (ADS)

Stewart, M. M.

2002-05-01

This presentation will draw together common themes raised in the session and discuss lessons learned across time scales and their implications for managers and policy makers concerned with both climate change and variability. Session themes will be examined in the context of the upcoming World Summit on Sustainable Development (WSSD) and considered as opportunities for linking climate change policy discussions with lessons learned from the study of adaptation on seasonal to interannual time scales. The presentation will raise questions about future research directions, discuss recommendations for promoting learning across time scales, and explore options for better communicating the links between climate change and variability.

Climate forcings and feedbacks

NASA Technical Reports Server (NTRS)

Hansen, James

1993-01-01

Global temperature has increased significantly during the past century. Understanding the causes of observed global temperature change is impossible in the absence of adequate monitoring of changes in global climate forcings and radiative feedbacks. Climate forcings are changes imposed on the planet's energy balance, such as change of incoming sunlight or a human-induced change of surface properties due to deforestation. Radiative feedbacks are radiative changes induced by climate change, such as alteration of cloud properties or the extent of sea ice. Monitoring of global climate forcings and feedbacks, if sufficiently precise and long-term, can provide a very strong constraint on interpretation of observed temperature change. Such monitoring is essential to eliminate uncertainties about the relative importance of various climate change mechanisms including tropospheric sulfate aerosols from burning of coal and oil smoke from slash and burn agriculture, changes of solar irradiance changes of several greenhouse gases, and many other mechanisms. The considerable variability of observed temperature, together with evidence that a substantial portion of this variability is unforced indicates that observations of climate forcings and feedbacks must be continued for decades. Since the climate system responds to the time integral of the forcing, a further requirement is that the observations be carried out continuously. However, precise observations of forcings and feedbacks will also be able to provide valuable conclusions on shorter time scales. For example, knowledge of the climate forcing by increasing CFC's relative to the forcing by changing ozone is important to policymakers, as is information on the forcing by CO2 relative to the forcing by sulfate aerosols. It will also be possible to obtain valuable tests of climate models on short time scales, if there is precise monitoring of all forcings and feedbacks during and after events such as a large volcanic eruption or an El Nino.

Large-Scale and Global Hydrology. Chapter 92

NASA Technical Reports Server (NTRS)

Rodell, Matthew; Beaudoing, Hiroko Kato; Koster, Randal; Peters-Lidard, Christa D.; Famiglietti, James S.; Lakshmi, Venkat

2016-01-01

Powered by the sun, water moves continuously between and through Earths oceanic, atmospheric, and terrestrial reservoirs. It enables life, shapes Earths surface, and responds to and influences climate change. Scientists measure various features of the water cycle using a combination of ground, airborne, and space-based observations, and seek to characterize it at multiple scales with the aid of numerical models. Over time our understanding of the water cycle and ability to quantify it have improved, owing to advances in observational capabilities, the extension of the data record, and increases in computing power and storage. Here we present some of the most recent estimates of global and continental ocean basin scale water cycle stocks and fluxes and provide examples of modern numerical modeling systems and reanalyses.Further, we discuss prospects for predicting water cycle variability at seasonal and longer scales, which is complicated by a changing climate and direct human impacts related to water management and agriculture. Changes to the water cycle will be among the most obvious and important facets of climate change, thus it is crucial that we continue to invest in our ability to monitor it.

Holocene forest dynamics in central and western Mediterranean: periodicity, spatio-temporal patterns and climate influence.

PubMed

Di Rita, Federico; Fletcher, William J; Aranbarri, Josu; Margaritelli, Giulia; Lirer, Fabrizio; Magri, Donatella

2018-06-12

It is well-known that the Holocene exhibits a millennial-scale climate variability. However, its periodicity, spatio-temporal patterns and underlying processes are not fully deciphered yet. Here we focus on the central and western Mediterranean. We show that recurrent forest declines from the Gulf of Gaeta (central Tyrrhenian Sea) reveal a 1860-yr periodicity, consistent with a ca. 1800-yr climate fluctuation induced by large-scale changes in climate modes, linked to solar activity and/or AMOC intensity. We show that recurrent forest declines and dry events are also recorded in several pollen and palaeohydrological proxy-records in the south-central Mediterranean. We found coeval events also in several palaeohydrological records from the south-western Mediterranean, which however show generally wet climate conditions, indicating a spatio-temporal hydrological pattern opposite to the south-central Mediterranean and suggesting that different expressions of climate modes occurred in the two regions at the same time. We propose that these opposite hydroclimate regimes point to a complex interplay of the prevailing or predominant phases of NAO-like circulation, East Atlantic pattern, and extension and location of the North African anticyclone. At a larger geographical scale, displacements of the ITCZ, modulated by solar activity and/or AMOC intensity, may have also indirectly influenced the observed pattern.

Downscaling Aerosols and the Impact of Neglected Subgrid Processes on Direct Aerosol Radiative Forcing for a Representative Global Climate Model Grid Spacing

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

Gustafson, William I.; Qian, Yun; Fast, Jerome D.

2011-07-13

Recent improvements to many global climate models include detailed, prognostic aerosol calculations intended to better reproduce the observed climate. However, the trace gas and aerosol fields are treated at the grid-cell scale with no attempt to account for sub-grid impacts on the aerosol fields. This paper begins to quantify the error introduced by the neglected sub-grid variability for the shortwave aerosol radiative forcing for a representative climate model grid spacing of 75 km. An analysis of the value added in downscaling aerosol fields is also presented to give context to the WRF-Chem simulations used for the sub-grid analysis. We foundmore » that 1) the impact of neglected sub-grid variability on the aerosol radiative forcing is strongest in regions of complex topography and complicated flow patterns, and 2) scale-induced differences in emissions contribute strongly to the impact of neglected sub-grid processes on the aerosol radiative forcing. The two of these effects together, when simulated at 75 km vs. 3 km in WRF-Chem, result in an average daytime mean bias of over 30% error in top-of-atmosphere shortwave aerosol radiative forcing for a large percentage of central Mexico during the MILAGRO field campaign.« less

Local oceanographic variability influences the performance of juvenile abalone under climate change.

PubMed

Boch, C A; Micheli, F; AlNajjar, M; Monismith, S G; Beers, J M; Bonilla, J C; Espinoza, A M; Vazquez-Vera, L; Woodson, C B

2018-04-03

Climate change is causing warming, deoxygenation, and acidification of the global ocean. However, manifestation of climate change may vary at local scales due to oceanographic conditions. Variation in stressors, such as high temperature and low oxygen, at local scales may lead to variable biological responses and spatial refuges from climate impacts. We conducted outplant experiments at two locations separated by ~2.5 km and two sites at each location separated by ~200 m in the nearshore of Isla Natividad, Mexico to assess how local ocean conditions (warming and hypoxia) may affect juvenile abalone performance. Here, we show that abalone growth and mortality mapped to variability in stress exposure across sites and locations. These insights indicate that management decisions aimed at maintaining and recovering valuable marine species in the face of climate change need to be informed by local variability in environmental conditions.

Using variance structure to quantify responses to perturbation in fish catches

USGS Publications Warehouse

Vidal, Tiffany E.; Irwin, Brian J.; Wagner, Tyler; Rudstam, Lars G.; Jackson, James R.; Bence, James R.

2017-01-01

We present a case study evaluation of gill-net catches of Walleye Sander vitreus to assess potential effects of large-scale changes in Oneida Lake, New York, including the disruption of trophic interactions by double-crested cormorants Phalacrocorax auritus and invasive dreissenid mussels. We used the empirical long-term gill-net time series and a negative binomial linear mixed model to partition the variability in catches into spatial and coherent temporal variance components, hypothesizing that variance partitioning can help quantify spatiotemporal variability and determine whether variance structure differs before and after large-scale perturbations. We found that the mean catch and the total variability of catches decreased following perturbation but that not all sampling locations responded in a consistent manner. There was also evidence of some spatial homogenization concurrent with a restructuring of the relative productivity of individual sites. Specifically, offshore sites generally became more productive following the estimated break point in the gill-net time series. These results provide support for the idea that variance structure is responsive to large-scale perturbations; therefore, variance components have potential utility as statistical indicators of response to a changing environment more broadly. The modeling approach described herein is flexible and would be transferable to other systems and metrics. For example, variance partitioning could be used to examine responses to alternative management regimes, to compare variability across physiographic regions, and to describe differences among climate zones. Understanding how individual variance components respond to perturbation may yield finer-scale insights into ecological shifts than focusing on patterns in the mean responses or total variability alone.

Termites promote resistance of decomposition to spatiotemporal variability in rainfall.

PubMed

Veldhuis, Michiel P; Laso, Francisco J; Olff, Han; Berg, Matty P

2017-02-01

The ecological impact of rapid environmental change will depend on the resistance of key ecosystems processes, which may be promoted by species that exert strong control over local environmental conditions. Recent theoretical work suggests that macrodetritivores increase the resistance of African savanna ecosystems to changing climatic conditions, but experimental evidence is lacking. We examined the effect of large fungus-growing termites and other non-fungus-growing macrodetritivores on decomposition rates empirically with strong spatiotemporal variability in rainfall and temperature. Non-fungus-growing larger macrodetritivores (earthworms, woodlice, millipedes) promoted decomposition rates relative to microbes and small soil fauna (+34%) but both groups reduced their activities with decreasing rainfall. However, fungus-growing termites increased decomposition rates strongest (+123%) under the most water-limited conditions, making overall decomposition rates mostly independent from rainfall. We conclude that fungus-growing termites are of special importance in decoupling decomposition rates from spatiotemporal variability in rainfall due to the buffered environment they create within their extended phenotype (mounds), that allows decomposition to continue when abiotic conditions outside are less favorable. This points at a wider class of possibly important ecological processes, where soil-plant-animal interactions decouple ecosystem processes from large-scale climatic gradients. This may strongly alter predictions from current climate change models. © 2016 by the Ecological Society of America.

Nonlinear detection of large-scale transitions in Plio-Pleistocene African climate

NASA Astrophysics Data System (ADS)

Donges, J. F.; Donner, R. V.; Trauth, M. H.; Marwan, N.; Schellnhuber, H. J.; Kurths, J.

2011-12-01

Potential paleoclimatic driving mechanisms acting on human development present an open problem of cross-disciplinary scientific interest. The analysis of paleoclimate archives encoding the environmental variability in East Africa during the last 5 Ma (million years) has triggered an ongoing debate about possible candidate processes and evolutionary mechanisms. In this work, we apply a novel nonlinear statistical technique, recurrence network analysis, to three distinct marine records of terrigenous dust flux. Our method enables us to identify three epochs with transitions between qualitatively different types of environmental variability in North and East Africa during the (i) Mid-Pliocene (3.35-3.15 Ma BP (before present)), (ii) Early Pleistocene (2.25-1.6 Ma BP), and (iii) Mid-Pleistocene (1.1-0.7 Ma BP). A deeper examination of these transition periods reveals potential climatic drivers, including (i) large-scale changes in ocean currents due to a spatial shift of the Indonesian throughflow in combination with an intensification of Northern Hemisphere glaciation, (ii) a global reorganization of the atmospheric Walker circulation induced in the tropical Pacific and Indian Ocean, and (iii) shifts in the dominating temporal variability pattern of glacial activity during the Mid-Pleistocene, respectively. A statistical reexamination of the available fossil record demonstrates a remarkable coincidence between the detected transition periods and major steps in hominin evolution. This suggests that the observed shifts between more regular and more erratic environmental variability have acted as a trigger for rapid change in the development of humankind in Africa.

Effects of the Pacific Decadal Oscillation and global warming on drought in the US Southwest

NASA Astrophysics Data System (ADS)

Grossmann, I.

2012-12-01

Droughts are among the most expensive weather related disasters in the US. In the semi-arid regions of the US Southwest, where average annual rainfall is already very low, multiyear droughts can have large economic, societal and ecological impacts. The US Southwest relies on annual precipitation maxima during winter and the North American Monsoon (NAM), both of which undergo considerable interannual variability associated with large-scale climate patterns, in particular ENSO, the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO). The region is also part of the subtropical belt projected to become more arid in a warming climate. These impacts have not been combined and compared with projections of long-term variations due to natural climate patterns. This study addresses this need by deriving future projections of rainfall departures for Arizona and New Mexico with the PDO and AMO and combining these with projected global warming impacts. Depending on the precipitation dataset used, the impacts for the ongoing negative PDO phase are projected to be between 1-1.6 times as large as the multi-model means projection of precipitation minus evaporation during 2020-2040 in the IPCC A1B Scenario. The projected precipitation impacts of a combined negative PDO and positive AMO phase are between 1-2 times as large as the A1B Scenario projection. The study also advances earlier work by addressing problems in detecting the effect of the PDO on precipitation. Given the different mechanisms with which the PDO affects precipitation during winter and the NAM season, precipitation impacts are here investigated on a monthly scale. The impacts of the PDO also vary with other climate patterns. This can be partly addressed by investigating precipitation departures in dependence on other patterns. It is further found that the long-term effect of the PDO can be more clearly separated from short-term variability by considering return periods of multi-year drought measures rather than return periods of simple drought measures.

Global Meteorological Drought: A Synthesis of Current Understanding with a Focus on SST Drivers of Precipitation Deficits

NASA Technical Reports Server (NTRS)

Schubert, S.; Stewart, R.; Wang, H.; Barlow, M.; Berbery, H.; Cai, W.; Hoerling, M.; Kanikicharla, K.; Koster, R.; Lyon, B.;

Detection and attribution of climate change at regional scale: case study of Karkheh river basin in the west of Iran

NASA Astrophysics Data System (ADS)

Zohrabi, Narges; Goodarzi, Elahe; Massah Bavani, Alireza; Najafi, Husain

2017-11-01

This research aims at providing a statistical framework for detection and attribution of climate variability and change at regional scale when at least 30 years of observation data are available. While extensive research has been done on detecting significant observed trends in hydroclimate variables and attribution to anthropogenic greenhouse gas emissions in large continents, less attention has been paid for regional scale analysis. The latter is mainly important for adaptation to climate change in different sectors including but not limited to energy, agriculture, and water resources planning and management, and it is still an open discussion in many countries including the West Asian ones. In the absence of regional climate models, an informative framework is suggested providing useful insights for policymakers. It benefits from general flexibility, not being computationally expensive, and applying several trend tests to analyze temporal variations in temperature and precipitation (gradual and step changes). The framework is implemented for a very important river basin in the west of Iran. In general, some increasing and decreasing trends of the interannual precipitation and temperature have been detected. For precipitation annual time series, a reducing step was seen around 1996 compared with the gradual change in most of the stations, which have not experience a dramatical change. The range of natural forcing is found to be ±76 % for precipitation and ±1.4 °C for temperature considering a two-dimensional diagram of precipitation and temperature anomalies from 1000-year control run of global climate model (GCM). Findings out of applying the proposed framework may provide useful insights into how to approach structural and non-structural climate change adaptation strategies from central governments.

Interactions between large-scale modes of climate and their relationship with Australian climate and hydrology

NASA Astrophysics Data System (ADS)

Whan, K. R.; Lindesay, J. A.; Timbal, B.; Raupach, M. R.; Williams, E.

2010-12-01

Australia’s natural environment is adapted to low rainfall availability and high variability but human systems are less able to adapt to variability in the hydrological cycle. Understanding the mechanisms underlying drought persistence and severity is vital to contextualising future climate change. Multiple external forcings mean the mechanisms of drought occurrence in south-eastern Australian are complex. The key influences on SEA climate are El Niño-Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), the Southern Annular Mode (SAM) and the sub-tropical ridge (STR); each of these large-scale climate modes (LSCM) has been studied widely. The need for research into the interactions among the modes has been noted [1], although to date this has received limited attention. Relationships between LSCM and hydrometeorological variability are nonlinear, making linearity assumptions underlying usual statistical techniques (e.g. correlation, principle components analysis) questionable. In the current research a statistical technique that can deal with nonlinear interactions is applied to a new dataset enabling a full examination of the Australian water balance. The Australian Water Availability Project (AWAP) dataset models the Australian water balance on a fine grid [2]. Hydrological parameters (e.g. soil moisture, evaporation, runoff) are modelled from meteorological data, allowing the complete Australian water balance (climate and hydrology) to be examined and the mechanisms of drought to be studied holistically. Classification and regression trees (CART) are a powerful regression-based technique that is capable of accounting for nonlinear effects. Although it has limited previous application in climate research [3] this methodology is particularly informative in cases with multiple predictors and nonlinear relationships such as climate variability. Statistical relationships between variables are the basis for the decision rules in CART that are used to split the data into increasingly homogeneous groups. CART is applied to the AWAP dataset to identify the hydroclimatic regimes associated with various combinations of LSCM and the importance of each mode in producing the regime. Analysis of the LSCM is conducted on a range of hydroclimatic variables to assess the relative and combined influences of these LSCM on the Australian water balance. This gives information about interactions between LSCM that are vital for specific hydroclimatic states (e.g. drought) and about which combinations of LSCM result in specific regimes. The dominant LSCM in different seasons and the relationships among the climate drivers have been identified. 1. Ummenhofer, C., et al., What causes southeast Australia's worst droughts? Geophysical Research Letters, 2009. 36: p. L04706. 2. Raupach, M., et al., Australian Water Availability Project (AWAP). CSIRO Marine and Atmospheric Research Component: Final Report for Phase 3. 2008. 3. Burrows, W., et al., CART Decision-Tree Statistical Analysis and Prediction of Summer Season Maximum Surface Ozone for the Vancouver, Montreal and Atlantic Regions of Canada. Journal of Applied Meteorology, 1995. 34: p. 1848-1862.

Sub-Milankovitch millennial-scale climate variability in Middle Eocene deep-marine sediments

NASA Astrophysics Data System (ADS)

Scotchman, J. I.; Pickering, K. T.; Robinson, S. A.

2009-12-01

Sub-Milankovitch millennial scale climate variability appears ubiquitous throughout the Quaternary and Pleistocene palaeoenvironmental records (e.g. McManus et al., 1999) yet the driving mechanism remains elusive. Possible mechanisms are generally linked to Quaternary-specific oceanic and cryospheric conditions (e.g. Maslin et al., 2001). An alternative external control, such as solar forcing, however, remains a compelling alternative hypothesis (e.g. Bond et al., 2001). This would imply that millennial-scale cycles are an intrinsic part of the Earth’s climatic system and not restricted to any specific period of time. Determining which of these hypotheses is correct impacts on our understanding of the climate system and its role as a driver of cyclic sedimentation during both icehouse and greenhouse climates. Here we show that Middle Eocene, laminated deep-marine sediments deposited in the Ainsa Basin, Spanish Pyrenees, contain 1,565-year (469 mm) cycles modulated by a 7,141-year (2157 mm) period. Climatic oscillations of 1,565-years recorded by element/Al ratios, are interpreted as representing climatically driven variation in sediment supply (terrigenous run-off) to the Ainsa basin. Climatic oscillations with this period are comparable to Quaternary Bond (~1,500-year), Dansgaard-Oeschger (~1,470-year) and Heinrich (~7,200-year) climatic events. Recognition of similar millennial-scale oscillations in the greenhouse climate of the Middle Eocene would appear inconsistent with an origin dependent upon Quaternary-specific conditions. Our observations lend support for pervasive millennial-scale climatic variability present throughout geologic time likely driven by an external forcing mechanism such as solar forcing. References Bond, G., Kromer, B., Beer, J., Muscheler, R., Evans, M.N., Showers, W., Hoffmann, S., Lotti-Bond, R., Hajdas, I., Bonani, G. 2001. Persistent Solar Influence on North Atlantic Climate During the Holocene. Science, 294, 2130-2136 Maslin, M., Seidov, D., Lowe, J. 2001. Synthesis of the nature and causes of rapid climate transitions during the Quaternary. In: The Oceans and rapid climate change: Past, present and future, (Seidov, D., Haupt, B. J. & Maslin, M., Eds.), AGU, Washington, D. C. McManus, J.F., Oppo, D.W. & Cullen, J.L. 1999. A 0.5-Million-Year Record of Millennial-Scale Climate Variability in the North Atlantic. Science, 283, 971-975

Solar Variability in the Context of Other Climate Forcing Mechanisms

NASA Technical Reports Server (NTRS)

Hansen, James E.

1999-01-01

I compare and contrast climate forcings due to solar variability with climate forcings due to other mechanisms of climate change, interpretation of the role of the sun in climate change depends upon climate sensitivity and upon the net forcing by other climate change mechanisms. Among the potential indirect climate forcings due to solar variability, only that due to solar cycle induced ozone changes has been well quantified. There is evidence that the sun has been a significant player in past climate change on decadal to century time scales, and that it has the potential to contribute to climate change in the 21st century.

Reactivity continuum modeling of leaf, root, and wood decomposition across biomes

NASA Astrophysics Data System (ADS)

Koehler, Birgit; Tranvik, Lars J.

2015-07-01

Large carbon dioxide amounts are released to the atmosphere during organic matter decomposition. Yet the large-scale and long-term regulation of this critical process in global carbon cycling by litter chemistry and climate remains poorly understood. We used reactivity continuum (RC) modeling to analyze the decadal data set of the "Long-term Intersite Decomposition Experiment," in which fine litter and wood decomposition was studied in eight biome types (224 time series). In 32 and 46% of all sites the litter content of the acid-unhydrolyzable residue (AUR, formerly referred to as lignin) and the AUR/nitrogen ratio, respectively, retarded initial decomposition rates. This initial rate-retarding effect generally disappeared within the first year of decomposition, and rate-stimulating effects of nutrients and a rate-retarding effect of the carbon/nitrogen ratio became more prevalent. For needles and leaves/grasses, the influence of climate on decomposition decreased over time. For fine roots, the climatic influence was initially smaller but increased toward later-stage decomposition. The climate decomposition index was the strongest climatic predictor of decomposition. The similar variability in initial decomposition rates across litter categories as across biome types suggested that future changes in decomposition may be dominated by warming-induced changes in plant community composition. In general, the RC model parameters successfully predicted independent decomposition data for the different litter-biome combinations (196 time series). We argue that parameterization of large-scale decomposition models with RC model parameters, as opposed to the currently common discrete multiexponential models, could significantly improve their mechanistic foundation and predictive accuracy across climate zones and litter categories.

A collaborative characterization of North American grasslands and rangelands: climate, ecohydrology and carbon sink-source dynamics

NASA Astrophysics Data System (ADS)

Petrie, M. D.; Brunsell, N. A.; Vargas, R.; Collins, S. L.

2013-12-01

Grassland and rangeland ecoregions extend across the North American continent and exhibit diversity in climate, ecosystem services, and biophysical processes. In many grasslands and rangelands, the potential for reductions in ecosystem services and for large-scale ecosystem state change may increase under future climate scenarios. Climate change projections for North America vary, however, and the way changing climate will influence specific ecoregions is largely unknown. To better understand the regional effects of climate change on grasslands and rangelands, it is important to better understand the biophysical characteristics of these systems locally, and to identify the sensitivity of these characteristics to observed climate variation. In our study, we propose to use eddy covariance, soil moisture and precipitation data to identify how the grasslands and rangelands of North America differ in their responses to climate variability through time, with specific focus on the active growing season. Our primary goal is to determine the sensitivity of ecosystem Net Primary Productivity [NPP] to variation in temperature and precipitation patterns, and classify North American grasslands and rangelands by these sensitivities in addition to more standard climate and productivity variables. Our preliminary analyses in mesic, semiarid and arid grasslands in Kansas, Colorado and New Mexico show significant (P < 0.05) differences in climate, carbon sink strength and growing season length, and suggest that patterns of seasonal productivity and precipitation sensitivity may elucidate important grassland and rangeland responses to changing climate. Using change in Gross Primary Productivity (GPP) as an indicator of the onset of photosynthesis in spring and of senescense in the fall, grassland and rangeland ecosystems in Kansas (top and bottom left panels) and New Mexico (bottom right panel) display differing patterns of activity throughout the year.

Complex seasonal patterns of primary producers at the land-sea interface

USGS Publications Warehouse

Cloern, J.E.; Jassby, A.D.

2008-01-01

Seasonal fluctuations of plant biomass and photosynthesis are key features of the Earth system because they drive variability of atmospheric CO 2, water and nutrient cycling, and food supply to consumers. There is no inventory of phytoplankton seasonal cycles in nearshore coastal ecosystems where forcings from ocean, land and atmosphere intersect. We compiled time series of phytoplankton biomass (chlorophyll a) from 114 estuaries, lagoons, inland seas, bays and shallow coastal waters around the world, and searched for seasonal patterns as common timing and amplitude of monthly variability. The data revealed a broad continuum of seasonal patterns, with large variability across and within ecosystems. This contrasts with annual cycles of terrestrial and oceanic primary producers for which seasonal fluctuations are recurrent and synchronous over large geographic regions. This finding bears on two fundamental ecological questions: (1) how do estuarine and coastal consumers adapt to an irregular and unpredictable food supply, and (2) how can we extract signals of climate change from phytoplankton observations in coastal ecosystems where local-scale processes can mask responses to changing climate? ?? 2008 Blackwell Publishing Ltd/CNRS.

Santa Ana Winds of Southern California: Their climatology, extremes, and behavior spanning six and a half decades

NASA Astrophysics Data System (ADS)

Guzman-Morales, Janin; Gershunov, Alexander; Theiss, Jurgen; Li, Haiqin; Cayan, Daniel

2016-03-01

Santa Ana Winds (SAWs) are an integral feature of the regional climate of Southern California/Northern Baja California region, but their climate-scale behavior is poorly understood. In the present work, we identify SAWs in mesoscale dynamical downscaling of a global reanalysis from 1948 to 2012. Model winds are validated with anemometer observations. SAWs exhibit an organized pattern with strongest easterly winds on westward facing downwind slopes and muted magnitudes at sea and over desert lowlands. We construct hourly local and regional SAW indices and analyze elements of their behavior on daily, annual, and multidecadal timescales. SAWs occurrences peak in winter, but some of the strongest winds have occurred in fall. Finally, we observe that SAW intensity is influenced by prominent large-scale low-frequency modes of climate variability rooted in the tropical and north Pacific ocean-atmosphere system.

Modeling biogeochemical responses of vegetation to ENSO: comparison and analysis on subgrid PFT patches

NASA Astrophysics Data System (ADS)

Xu, M.; Hoffman, F. M.

2016-12-01

The El Niño Southern Oscillation (ENSO) is an important interannual climate variability and has significant consequences and impacts on the global biosphere. The responses of vegetation to ENSO are highly heterogeneous and generally depend on the biophysical and biochemical characteristics associated with model plant functional types (PFTs). The modeled biogeochemical variables from Earth System Models (ESMs) are generally grid averages consisting of several PFTs within a gridcell, which will lead to difficulties in directly comparing them with site observations and large uncertainties in studying their responses to large scale climate variability. In this study, we conducted a transient ENSO simulation for the previoustwo decades from 1995 to 2020 using the DOE ACME v0.3 model. It has a comprehensive terrestrial biogeochemistry model that is fully coupled with a sophisticated atmospheric model with an advanced spectral element dynamical core. The model was driven by the NOAA optimum interpolation sea surface temperature (SST) for contemporary years and CFS v2 nine-month seasonal predicted and reconstructed SST for future years till to 2020. We saved the key biogeochemical variables in the subgrid PFT patches and compared them with site observations directly. Furthermore, we studied the biogeochemical responses of terrestrial vegetation to two largest ENSO events (1997-1998 and 2015-2016) for different PFTs. Our results show that it is useful and meaningful to compare and analyze model simulations in subgrid patches. The comparison and analysis not only gave us the details of responses of terrestrial ecosystem to global climate variability under changing climate, but also the insightful view on the model performance on the PFT level.

Recent Climate Variability in Antarctica from Satellite-derived Temperature Data

NASA Technical Reports Server (NTRS)

Schneider, David P.; Steig, Eric J.; Comiso, Josefino C.

2004-01-01

Recent Antarctic climate variability on month-to-month to interannual time scales is assessed through joint analysis of surface temperatures from satellite thermal infrared observations (T(sub IR)) and passive microwave brightness temperatures (T(sub B)). Although Tw data are limited to clear-sky conditions and T(sub B) data are a product of the temperature and emissivity of the upper approx. 1m of snow, the two data sets share significant covariance. This covariance is largely explained by three empirical modes, which illustrate the spatial and temporal variability of Antarctic surface temperatures. T(sub B) variations are damped compared to TIR variations, as determined by the period of the temperature forcing and the microwave emission depth; however, microwave emissivity does not vary significantly in time. Comparison of the temperature modes with Southern Hemisphere (SH) 500-hPa geopotential height anomalies demonstrates that Antarctic temperature anomalies are predominantly controlled by the principal patterns of SH atmospheric circulation. The leading surface temperature mode strongly correlates with the Southern Annular Mode (SAM) in geopotential height. The second temperature mode reflects the combined influences of the zonal wavenumber-3 and Pacific South American (PSA) patterns in 500-hPa height on month-to-month timescales. ENSO variability projects onto this mode on interannual timescales, but is not by itself a good predictor of Antarctic temperature anomalies. The third temperature mode explains winter warming trends, which may be caused by blocking events, over a large region of the East Antarctic plateau. These results help to place recent climate changes in the context of Antarctica's background climate variability and will aid in the interpretation of ice core paleoclimate records.

Factors influencing teamwork and collaboration within a tertiary medical center

PubMed Central

Chien, Shu Feng; Wan, Thomas TH; Chen, Yu-Chih

2012-01-01

AIM: To understand how work climate and related factors influence teamwork and collaboration in a large medical center. METHODS: A survey of 3462 employees was conducted to generate responses to Sexton’s Safety Attitudes Questionnaire (SAQ) to assess perceptions of work environment via a series of five-point, Likert-scaled questions. Path analysis was performed, using teamwork (TW) and collaboration (CO) as endogenous variables. The exogenous variables are effective communication (EC), safety culture (SC), job satisfaction (JS), work pressure (PR), and work climate (WC). The measurement instruments for the variables or summated subscales are presented. Reliability of each sub-scale are calculated. Alpha Cronbach coefficients are relatively strong: TW (0.81), CO (0.76), EC (0.70), SC (0.83), JS (0.91), WP (0.85), and WC (0.78). Confirmatory factor analysis was performed for each of these constructs. RESULTS: Path analysis enables to identify statistically significant predictors of two endogenous variables, teamwork and intra-organizational collaboration. Significant amounts of variance in perceived teamwork (R2 = 0.59) and in collaboration (R2 = 0.75) are accounted for by the predictor variables. In the initial model, safety culture is the most important predictor of perceived teamwork, with a β weight of 0.51, and work climate is the most significant predictor of collaboration, with a β weight of 0.84. After eliminating statistically insignificant causal paths and allowing correlated predictors1, the revised model shows that work climate is the only predictor positively influencing both teamwork (β = 0.26) and collaboration (β = 0.88). A relatively weak positive (β = 0.14) but statistically significant relationship exists between teamwork and collaboration when the effects of other predictors are simultaneously controlled. CONCLUSION: Hospital executives who are interested in improving collaboration should assess the work climate to ensure that employees are operating in a setting conducive to intra-organizational collaboration. PMID:25237612

The Great Plains low-level jet in 1.5C and 2C HAPPI simulations: Implications for changes in extreme climate events

NASA Astrophysics Data System (ADS)

Weaver, S. J.; Barcikowska, M. J.

2017-12-01

Global temperature targets have become the cornerstone for global climate policy discussions. Given the goal of the Paris Accord to limit the rise in global mean temperature to well below 2.0oC above pre-industrial levels, and pursue efforts toward the more ambitious 1.5oC goal, there is increasing focus in the climate science community on what the relative changes in regional climate extremes may be for these two scenarios. Despite the successes of major climate science modeling efforts, there is still a significant information gap regarding the regional and seasonal changes in some climate extremes over the U.S. as a function of these global mean temperature targets.During the spring and summer, large amounts of heat and moisture are transported northward into the central and eastern U.S. by the Great Plains Low-Level Jet (GPLLJ) - an atmospheric river which dominates the subcontinental scale climate variability during the warm half of the year. Accordingly, the GPLLJ and its vast spatiotemporal variability is highly influential over several types of extreme climate anomalies east of the Rocky Mountains, including, drought and pluvial events, tornadic activity, and the evolution of central U.S warming hole. Changes in the GPLLJ and its variability are probed from the perspective of several hundred climate realizations afforded by the availability of climate model experiments from the Half a degree additional warming, Prognosis, and Projected Impacts (HAPPI) effort - a suite of multi-model ensemble AMIP simulations forced by 1.5oC and 2oC levels of global warming. The multimodel analysis focuses on the variable magnitude of the seasonal changes in the mean GPLLJ and shifts in the extremes of the prominent modes of GPLLJ variability - both of which have implications for the future shifts in extreme climate events over the Great Plains, Midwest, and southeast regions of the U.S.

A global database with parallel measurements to study non-climatic changes

NASA Astrophysics Data System (ADS)

Venema, Victor; Auchman, Renate; Aguilar, Enric

2017-04-01

In this work we introduce the rationale behind the ongoing compilation of a parallel measurements database, in the framework of the International Surface Temperatures Initiative (ISTI) and with the support of the World Meteorological Organization. We intend this database to become instrumental for a better understanding of inhomogeneities affecting the evaluation of long-term changes in daily climate data. Long instrumental climate records are usually affected by non-climatic changes, due to, e.g., (i) station re- locations, (ii) instrument height changes, (iii) instrumentation changes, (iv) observing environment changes, (v) different sampling intervals or data collection procedures, among others. These so-called inhomogeneities distort the climate signal and can hamper the assessment of long-term trends and variability of climate. Thus to study climatic changes we need to accurately distinguish non-climatic and climatic signals. The most direct way to study the influence of non-climatic changes on the distribution and to understand the reasons for these biases is the analysis of parallel measurements representing the old and new situation (in terms of e.g. instruments, location, different radiation shields, etc.). According to the limited number of available studies and our understanding of the causes of inhomogeneity, we expect that they will have a strong impact on the tails of the distribution of air temperatures and most likely of other climate elements. Our abilities to statistically homogenize daily data will be increased by systematically studying different causes of inhomogeneity replicated through parallel measurements. Current studies of non-climatic changes using parallel data are limited to local and regional case studies. However, the effect of specific transitions depends on the local climate and the most interesting climatic questions are about the systematic large-scale biases produced by transitions that occurred in many regions. Important potentially biasing transitions are the adoption of Stevenson screens, relocations (to airports) efforts to reduce undercatchment of precipitation or the move to automatic weather stations. Thus a large global parallel dataset is highly desirable as it allows for the study of systematic biases in the global record. We are interested in data from all climate variables at all time scales; from annual to sub-daily. High-resolution data is important for understanding the physical causes for the differences between the parallel measurements. For the same reason, we are also interested in other climate variables measured at the same station. For example, in case of parallel air temperature measurements, the influencing factors are expected to be global radiation, wind, humidity and cloud cover; in case of parallel precipitation measurements, wind and wet-bulb temperature are potentially important.

The Stochastic predictability limits of GCM internal variability and the Stochastic Seasonal to Interannual Prediction System (StocSIPS)

NASA Astrophysics Data System (ADS)

Del Rio Amador, Lenin; Lovejoy, Shaun

2017-04-01

Over the past ten years, a key advance in our understanding of atmospheric variability is the discovery that between the weather and climate regime lies an intermediate "macroweather" regime, spanning the range of scales from ≈10 days to ≈30 years. Macroweather statistics are characterized by two fundamental symmetries: scaling and the factorization of the joint space-time statistics. In the time domain, the scaling has low intermittency with the additional property that successive fluctuations tend to cancel. In space, on the contrary the scaling has high (multifractal) intermittency corresponding to the existence of different climate zones. These properties have fundamental implications for macroweather forecasting: a) the temporal scaling implies that the system has a long range memory that can be exploited for forecasting; b) the low temporal intermittency implies that mathematically well-established (Gaussian) forecasting techniques can be used; and c), the statistical factorization property implies that although spatial correlations (including teleconnections) may be large, if long enough time series are available, they are not necessarily useful in improving forecasts. Theoretically, these conditions imply the existence of stochastic predictability limits in our talk, we show that these limits apply to GCM's. Based on these statistical implications, we developed the Stochastic Seasonal and Interannual Prediction System (StocSIPS) for the prediction of temperature from regional to global scales and from one month to many years horizons. One of the main components of StocSIPS is the separation and prediction of both the internal and externally forced variabilities. In order to test the theoretical assumptions and consequences for predictability and predictions, we use 41 different CMIP5 model outputs from preindustrial control runs that have fixed external forcings: whose variability is purely internally generated. We first show that these statistical assumptions hold with relatively good accuracy and then we performed hindcasts at global and regional scales from monthly to annual time resolutions using StocSIPS. We obtained excellent agreement between the hindcast Mean Square Skill Score (MSSS) and the theoretical stochastic limits. We also show the application of StocSIPS to the prediction of average global temperature and compare our results with those obtained using multi-model ensemble approaches. StocSIPS has numerous advantages including a) higher MSSS for large time horizons, b) the from convergence to the real - not model - climate, c) much higher computational speed, d) no need for data assimilation, e) no ad hoc post processing and f) no need for downscaling.

Incorporating abundance information and guiding variable selection for climate-based ensemble forecasting of species' distributional shifts.

PubMed

Tanner, Evan P; Papeş, Monica; Elmore, R Dwayne; Fuhlendorf, Samuel D; Davis, Craig A

2017-01-01

Ecological niche models (ENMs) have increasingly been used to estimate the potential effects of climate change on species' distributions worldwide. Recently, predictions of species abundance have also been obtained with such models, though knowledge about the climatic variables affecting species abundance is often lacking. To address this, we used a well-studied guild (temperate North American quail) and the Maxent modeling algorithm to compare model performance of three variable selection approaches: correlation/variable contribution (CVC), biological (i.e., variables known to affect species abundance), and random. We then applied the best approach to forecast potential distributions, under future climatic conditions, and analyze future potential distributions in light of available abundance data and presence-only occurrence data. To estimate species' distributional shifts we generated ensemble forecasts using four global circulation models, four representative concentration pathways, and two time periods (2050 and 2070). Furthermore, we present distributional shifts where 75%, 90%, and 100% of our ensemble models agreed. The CVC variable selection approach outperformed our biological approach for four of the six species. Model projections indicated species-specific effects of climate change on future distributions of temperate North American quail. The Gambel's quail (Callipepla gambelii) was the only species predicted to gain area in climatic suitability across all three scenarios of ensemble model agreement. Conversely, the scaled quail (Callipepla squamata) was the only species predicted to lose area in climatic suitability across all three scenarios of ensemble model agreement. Our models projected future loss of areas for the northern bobwhite (Colinus virginianus) and scaled quail in portions of their distributions which are currently areas of high abundance. Climatic variables that influence local abundance may not always scale up to influence species' distributions. Special attention should be given to selecting variables for ENMs, and tests of model performance should be used to validate the choice of variables.

Assessment of climate change impacts on runoff in China using climate elasticity and multiple CMIP5 GCMs

NASA Astrophysics Data System (ADS)

Wu, C.; Hu, B. X.; Wang, P.; Xu, K.

2017-12-01

The occurrence of climate warming is unequivocal and is expected to alter the temporal-spatial patterns of regional water resources. Based on the long-term (1960-2012) water budget data and climate projections from 28 Global Climate Models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5), this study investigated the responses of runoff (R) to future climate variability in China at both grid and catchment scales using the Budyko-based elasticity method. Results indicate a large spatial variation in precipitation (P) elasticity (from 1.2 to 3.3) and potential evaporation (PET) elasticity (from -2.3 to -0.2) across China. The P elasticity is larger in northeast and western China than in southern China, while the opposite occurs for PET elasticity. Climate projections suggest that there is large uncertainty involved among the GCM simulations, but most project a consistent change in P (or PET) over China at the mean annual scale. During the future period of 2071-2100, the mean annual P will likely increase in most parts of China particularly the western regions, while the mean annual PET will likely increase in the whole China especially the southern regions due to future increases in temperature. Moreover, larger increases are projected for higher emission scenarios. Compared with the baseline 1971-2000, the arid regions and humid regions of China will likely become wetter and drier in the period 2071-2100, respectively.

Understanding multidecadal variability in ENSO amplitude

NASA Astrophysics Data System (ADS)

Russell, A.; Gnanadesikan, A.

2013-12-01

Sea surface temperatures (SSTs) in the tropical Pacific vary as a result of the coupling between the ocean and atmosphere driven largely by the El Niño - Southern Oscillation (ENSO). ENSO has a large impact on the local climate and hydrology of the tropical Pacific, as well as broad-reaching effects on global climate. ENSO amplitude is known to vary on long timescales, which makes it very difficult to quantify its response to climate change and constrain the physical processes that drive it. In order to assess the extent of unforced multidecadal changes in ENSO variability, a linear regression of local SST changes is applied to the GFDL CM2.1 model 4000-yr pre-industrial control run. The resulting regression coefficient strengths, which represent the sensitivity of SST changes to thermocline depth and zonal wind stress, vary by up to a factor of 2 on multi-decadal time scales. This long-term modulation in ocean-atmosphere coupling is highly correlated with ENSO variability, but do not explain the reasons for such variability. Variation in the relationship between SST changes and wind stress points to a role for changing stratification in the central equatorial Pacific in modulating ENSO amplitudes with stronger stratification reducing the response to winds. The main driving mechanism we have identified for higher ENSO variance are changes in the response of zonal winds to SST anomalies. The shifting convection and precipitation patterns associated with the changing state of the atmosphere also contribute to the variability of the regression coefficients. These mechanisms drive much of the variability in ENSO amplitude and hence ocean-atmosphere coupling in the tropical Pacific.

Is the number and size of scales in Liolaemus lizards driven by climate?

PubMed

José Tulli, María; Cruz, Félix B

2018-05-03

Ectothermic vertebrates are sensitive to thermal fluctuations in the environments where they occur. To buffer these fluctuations, ectotherms use different strategies, including the integument, which is a barrier that minimizes temperature exchange between the inner body and the surrounding air. In lizards, this barrier is constituted by keratinized scales of variable size, shape and texture, and its main function is protection, water loss avoidance and thermoregulation. The size of scales in lizards has been proposed to vary in relation to climatic gradients; however, it has also been observed that in some groups of Iguanian lizards could be related to phylogeny. Thus, here, we studied the area and number of scales (dorsal and ventral) of 61 species of Liolaemus lizards distributed in a broad latitudinal and altitudinal gradient to determine the nature of the variation of the scales with climate, and found that the number and size of scales are related to climatic variables, such as temperature and geographical variables as altitude. The evolutionary process that better explained how these morphological variables evolved was the Ornstein-Uhlenbeck model. The number of scales seemed to be related to common ancestry, whereas dorsal and ventral scale areas seemed to vary as a consequence of ecological traits. In fact, the ventral area is less exposed to climate conditions such as ultraviolet radiation or wind and is thus under less pressure to change in response to alterations in external conditions. It is possible that scale ornamentation such as keels and granulosity may bring some more information in this regard. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Age of oil palm plantations causes a strong change in surface biophysical variables

NASA Astrophysics Data System (ADS)

Sabajo, Clifton; le Maire, Guerric; Knohl, Alexander

2016-04-01

Over the last decades, Indonesia has experienced dramatic land transformations with an expansion of oil palm plantations at the expense of tropical forests. As vegetation is a modifier of the climate near the ground these large-scale land transformations are expected to have major impacts on the surface biophysical variables i.e. surface temperature, albedo, and vegetation indices, e.g. the NDVI. Remote sensing data are needed to assess such changes at regional scale. We used 2 Landsat images from Jambi Province in Sumatra/Indonesia covering a chronosequence of oil palm plantations to study the 20 - 25 years life cycle of oil palm plantations and its relation with biophysical variables. Our results show large differences between the surface temperature of young oil palm plantations and forest (up to 9.5 ± 1.5 °C) indicating that the surface temperature is raised substantially after the establishment of oil palm plantations following the removal of forests. During the oil palm plantation lifecycle the surface temperature differences gradually decreases and approaches zero around an oil palm plantation age of 10 years. Similarly, NDVI increases and the albedo decreases approaching typical values of forests. Our results show that in order to assess the full climate effects of oil palm expansion biophysical processes play an important role and the full life cycle of oil palm plantations need to be considered.

Observations of Local Positive Low Cloud Feedback Patterns and Their Role in Internal Variability and Climate Sensitivity

NASA Astrophysics Data System (ADS)

Yuan, Tianle; Oreopoulos, Lazaros; Platnick, Steven E.; Meyer, Kerry

2018-05-01

Modeling studies have shown that cloud feedbacks are sensitive to the spatial pattern of sea surface temperature (SST) anomalies, while cloud feedbacks themselves strongly influence the magnitude of SST anomalies. Observational counterparts to such patterned interactions are still needed. Here we show that distinct large-scale patterns of SST and low-cloud cover (LCC) emerge naturally from objective analyses of observations and demonstrate their close coupling in a positive local SST-LCC feedback loop that may be important for both internal variability and climate change. The two patterns that explain the maximum amount of covariance between SST and LCC correspond to the Interdecadal Pacific Oscillation and the Atlantic Multidecadal Oscillation, leading modes of multidecadal internal variability. Spatial patterns and time series of SST and LCC anomalies associated with both modes point to a strong positive local SST-LCC feedback. In many current climate models, our analyses suggest that SST-LCC feedback strength is too weak compared to observations. Modeled local SST-LCC feedback strength affects simulated internal variability so that stronger feedback produces more intense and more realistic patterns of internal variability. To the extent that the physics of the local positive SST-LCC feedback inferred from observed climate variability applies to future greenhouse warming, we anticipate significant amount of delayed warming because of SST-LCC feedback when anthropogenic SST warming eventually overwhelm the effects of internal variability that may mute anthropogenic warming over parts of the ocean. We postulate that many climate models may be underestimating both future warming and the magnitude of modeled internal variability because of their weak SST-LCC feedback.

Evaluating historical climate and hydrologic trends in the Central Appalachian region of the United States

NASA Astrophysics Data System (ADS)

Gaertner, B. A.; Zegre, N.

2015-12-01

Climate change is surfacing as one of the most important environmental and social issues of the 21st century. Over the last 100 years, observations show increasing trends in global temperatures and intensity and frequency of precipitation events such as flooding, drought, and extreme storms. Global circulation models (GCM) show similar trends for historic and future climate indicators, albeit with geographic and topographic variability at regional and local scale. In order to assess the utility of GCM projections for hydrologic modeling, it is important to quantify how robust GCM outputs are compared to robust historical observations at finer spatial scales. Previous research in the United States has primarily focused on the Western and Northeastern regions due to dominance of snow melt for runoff and aquifer recharge but the impact of climate warming in the mountainous central Appalachian Region is poorly understood. In this research, we assess the performance of GCM-generated historical climate compared to historical observations primarily in the context of forcing data for macro-scale hydrologic modeling. Our results show significant spatial heterogeneity of modeled climate indices when compared to observational trends at the watershed scale. Observational data is showing considerable variability within maximum temperature and precipitation trends, with consistent increases in minimum temperature. The geographic, temperature, and complex topographic gradient throughout the central Appalachian region is likely the contributing factor in temperature and precipitation variability. Variable climate changes are leading to more severe and frequent climate events such as temperature extremes and storm events, which can have significant impacts on our drinking water supply, infrastructure, and health of all downstream communities.

Regional and climate forcing on forage fish and apex predators in the California Current: new insights from a fully coupled ecosystem model.

NASA Astrophysics Data System (ADS)

Fiechter, J.; Rose, K.; Curchitser, E. N.; Huckstadt, L. A.; Costa, D. P.; Hedstrom, K.

2016-12-01

A fully coupled ecosystem model is used to describe the impact of regional and climate variability on changes in abundance and distribution of forage fish and apex predators in the California Current Large Marine Ecosystem. The ecosystem model consists of a biogeochemical submodel (NEMURO) embedded in a regional ocean circulation submodel (ROMS), and both coupled with a multi-species individual-based submodel for two forage fish species (sardine and anchovy) and one apex predator (California sea lion). Sardine and anchovy are specifically included in the model as they exhibit significant interannual and decadal variability in population abundances, and are commonly found in the diet of California sea lions. Output from the model demonstrates how regional-scale (i.e., upwelling intensity) and basin-scale (i.e., PDO and ENSO signals) physical processes control species distributions and predator-prey interactions on interannual time scales. The results also illustrate how variability in environmental conditions leads to the formation of seasonal hotspots where prey and predator spatially overlap. While specifically focused on sardine, anchovy and sea lions, the modeling framework presented here can provide new insights into the physical and biological mechanisms controlling trophic interactions in the California Current, or other regions where similar end-to-end ecosystem models may be implemented.

Habitat availability and gene flow influence diverging local population trajectories under scenarios of climate change: a place-based approach.

PubMed

Schwalm, Donelle; Epps, Clinton W; Rodhouse, Thomas J; Monahan, William B; Castillo, Jessica A; Ray, Chris; Jeffress, Mackenzie R

2016-04-01

Ecological niche theory holds that species distributions are shaped by a large and complex suite of interacting factors. Species distribution models (SDMs) are increasingly used to describe species' niches and predict the effects of future environmental change, including climate change. Currently, SDMs often fail to capture the complexity of species' niches, resulting in predictions that are generally limited to climate-occupancy interactions. Here, we explore the potential impact of climate change on the American pika using a replicated place-based approach that incorporates climate, gene flow, habitat configuration, and microhabitat complexity into SDMs. Using contemporary presence-absence data from occupancy surveys, genetic data to infer connectivity between habitat patches, and 21 environmental niche variables, we built separate SDMs for pika populations inhabiting eight US National Park Service units representing the habitat and climatic breadth of the species across the western United States. We then predicted occurrence probability under current (1981-2010) and three future time periods (out to 2100). Occurrence probabilities and the relative importance of predictor variables varied widely among study areas, revealing important local-scale differences in the realized niche of the American pika. This variation resulted in diverse and - in some cases - highly divergent future potential occupancy patterns for pikas, ranging from complete extirpation in some study areas to stable occupancy patterns in others. Habitat composition and connectivity, which are rarely incorporated in SDM projections, were influential in predicting pika occupancy in all study areas and frequently outranked climate variables. Our findings illustrate the importance of a place-based approach to species distribution modeling that includes fine-scale factors when assessing current and future climate impacts on species' distributions, especially when predictions are intended to manage and conserve species of concern within individual protected areas. © 2015 John Wiley & Sons Ltd.

Progressive Mid-latitude Afforestation: Local and Remote Climate Impacts in the Framework of Two Coupled Earth System Models

NASA Astrophysics Data System (ADS)

Lague, Marysa

Vegetation influences the atmosphere in complex and non-linear ways, such that large-scale changes in vegetation cover can drive changes in climate on both local and global scales. Large-scale land surface changes have been shown to introduce excess energy to one hemisphere, causing a shift in atmospheric circulation on a global scale. However, past work has not quantified how the climate response scales with the area of vegetation. Here, we systematically evaluate the response of climate to linearly increasing the area of forest cover over the northern mid-latitudes. We show that the magnitude of afforestation of the northern mid-latitudes determines the climate response in a non-linear fashion, and identify a threshold in vegetation-induced cloud feedbacks - a concept not previously addressed by large-scale vegetation manipulation experiments. Small increases in tree cover drive compensating cloud feedbacks, while latent heat fluxes reach a threshold after sufficiently large increases in tree cover, causing the troposphere to warm and dry, subsequently reducing cloud cover. Increased absorption of solar radiation at the surface is driven by both surface albedo changes and cloud feedbacks. We identify how vegetation-induced changes in cloud cover further feedback on changes in the global energy balance. We also show how atmospheric cross-equatorial energy transport changes as the area of afforestation is incrementally increased (a relationship which has not previously been demonstrated). This work demonstrates that while some climate effects (such as energy transport) of large scale mid-latitude afforestation scale roughly linearly across a wide range of afforestation areas, others (such as the local partitioning of the surface energy budget) are non-linear, and sensitive to the particular magnitude of mid-latitude forcing. Our results highlight the importance of considering both local and remote climate responses to large-scale vegetation change, and explore the scaling relationship between changes in vegetation cover and the resulting climate impacts.

Long-term wave measurements in a climate change perspective.

NASA Astrophysics Data System (ADS)

Pomaro, Angela; Bertotti, Luciana; Cavaleri, Luigi; Lionello, Piero; Portilla-Yandun, Jesus

2017-04-01

At present multi-decadal time series of wave data needed for climate studies are generally provided by long term model simulations (hindcasts) covering the area of interest. Examples, among many, at different scales are wave hindcasts adopting the wind fields of the ERA-Interim reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF, Reading, U.K.) at the global level and by regional re-analysis as for the Mediterranean Sea (Lionello and Sanna, 2006). Valuable as they are, these estimates are necessarily affected by the approximations involved, the more so because of the problems encountered within modelling processes in small basins using coarse resolution wind fields (Cavaleri and Bertotti, 2004). On the contrary, multi-decadal observed time series are rare. They have the evident advantage of somehow representing the real evolution of the waves, without the shortcomings associated with the limitation of models in reproducing the actual processes and the real variability within the wave fields. Obviously, observed wave time series are not exempt of problems. They represent a very local information, hence their use to describe the wave evolution at large scale is sometimes arguable and, in general, it needs the support of model simulations assessing to which extent the local value is representative of a large scale evolution. Local effects may prevent the identification of trends that are indeed present at large scale. Moreover, a regular maintenance, accurate monitoring and metadata information are crucial issues when considering the reliability of a time series for climate applications. Of course, where available, especially if for several decades, measured data are of great value for a number of reasons and can be valuable clues to delve further into the physics of the processes of interest, especially if considering that waves, as an integrated product of the local climate, if available in an area sensitive to even limited changes of the large scale pattern, can provide related compact and meaningful information. In addition, the availability for the area of interest of a 20-year long dataset of directional spectra (in frequency and direction) offers an independent, but theoretically corresponding and significantly long dataset, allowing to penetrate the wave problem through different perspectives. In particular, we investigate the contribution of the individual wave systems that modulate the variability of waves in the Adriatic Sea. A characterization of wave conditions based on wave spectra in fact brings out a more detailed description of the different wave regimes, their associated meteorological conditions and their variation in time and geographical space.

Centennial-scale links between Atlantic Ocean dynamics and hydroclimate over the last 4400 years: Insights from the northern Gulf of Mexico

NASA Astrophysics Data System (ADS)

Thirumalai, K.; Quinn, T. M.; Okumura, Y.; Richey, J. N.; Partin, J. W.; Poore, R. Z.

2015-12-01

Surface circulation in the Atlantic Ocean is an important mediator of global climate and yet its variability is poorly constrained on centennial timescales. Changes in the Atlantic meridional overturning circulation (AMOC) have been implicated in late Holocene climate variability in the Western Hemisphere, although the relationship between AMOC variability and hydroclimate is uncertain due to the lack of sufficiently highly resolved proxy records. Here we present a replicated reconstruction of sea-surface temperature (SST) and salinity (SSS) from the Garrison Basin in the northern Gulf of Mexico (NGOM) spanning the last 4,400 years to better constrain past sea-surface conditions. We generated time series of paired Mg/Ca (SST proxy) and δ18O (SST and SSS proxy) variations in planktic foraminifer Globigerinoides ruber (white variety) from three multi-cores collected in 2010. Using a Monte Carlo-based technique we produce a stacked record from the three multi-cores and constrain analytical, calibration, chronological, and sampling uncertainties. We apply this technique to existing paired Mg/Ca- δ18O studies in the Gulf of Mexico and Atlantic Ocean to facilitate comparison between time-uncertain proxy reconstructions. The Garrison Basin stack exhibits large centennial-scale variability (σSST~0.6°C; δ18Osw~0.17‰) and indicates a substantially cool (0.9±0.5°C) and fresh (0.26±0.1‰) Little Ice Age (LIA; 1450-1850 A.D.), corroborating extant records from the Gulf of Mexico. Focusing on the last millennium, we analyze a suite of oceanic and terrestrial proxy records to demonstrate a centennial-scale link between salt advection in the Atlantic Ocean, a diagnostic parameter of ocean circulation, and hydroclimate in the adjacent continents. The ensuing multiproxy relationships seem to be consistent with spatial field correlations of limited salinity and rainfall instrumental/reanalysis data, which suggest that NGOM salinity varies with large-scale Atlantic Ocean circulation and continental precipitation. Our results imply significant centennial-scale variability over the late Holocene and are consistent with limited observational analysis indicating a slowdown of AMOC during the LIA.

On the origin of multi-decadal to centennial Greenland temperature anomalies over the past 800 yr

NASA Astrophysics Data System (ADS)

Kobashi, T.; Shindell, D. T.; Kodera, K.; Box, J. E.; Nakaegawa, T.; Kawamura, K.

2012-11-01

The surface temperature of the Greenland ice sheet is among the most important climate variables for assessing how climate change may impact human societies associated with accelerating sea level rise. However, the causes of multi-decadal-to-centennial temperature changes in Greenland are not well understood, largely owing to short observational records. To examine the causes of the Greenland temperature variability, we calculated the Greenland temperature anomalies (GTA(G-NH)) over the past 800 yr by subtracting the standardised NH temperature from the standardised Greenland temperature. It decomposes the Greenland temperature variation into background climate (NH); Polar amplification; and Regional variability (GTA(G-NH)). The Central Greenland polar amplification factor as expressed by the variance ratio = Greenland/NH is 2.6 over the past 161 yr, and 3.3-4.2 over the past 800 yr. The GTA explains 31-35% of the variation of Greenland temperature in the multi-decadal-to-centennial time scale over the past 800 yr. Another orthogonal component of the Greenland and NH temperatures, GTP(G+NH) (Greenland temperature plus = standardized Greenland temperature + standardized NH temperature) exhibited the multi-decadal variations that were likely induced by large volcanic eruptions, increasing greenhouse gasses, and internal variation of climate. We found that the GTA(G-NH) has been influenced by solar-induced changes in atmospheric circulation patterns such as those produced by North Atlantic Oscillation/Arctic Oscillation (NAO/AO). Climate modelling indicates that the anomaly is also likely linked to solar-paced changes in the Atlantic meridional overturning circulation (AMOC) and to associated changes in northward oceanic heat transport.

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).