Vulnerability of cattle production to climate change on U.S. rangelands

Treesearch

Matt C. Reeves; Karen E. Bagne

2016-01-01

We examined multiple climate change effects on cattle production for U.S. rangelands to estimate relative change and identify sources of vulnerability among seven regions. Climate change effects to 2100 were projected from published models for four elements: forage quantity, vegetation type trajectory, heat stress, and forage variability. Departure of projections from...

Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species?

PubMed Central

Esperón-Rodríguez, Manuel; Baumgartner, John B.; Beaumont, Linda J.

2017-01-01

Background Shrubs play a key role in biogeochemical cycles, prevent soil and water erosion, provide forage for livestock, and are a source of food, wood and non-wood products. However, despite their ecological and societal importance, the influence of different environmental variables on shrub distributions remains unclear. We evaluated the influence of climate and soil characteristics, and whether including soil variables improved the performance of a species distribution model (SDM), Maxent. Methods This study assessed variation in predictions of environmental suitability for 29 Australian shrub species (representing dominant members of six shrubland classes) due to the use of alternative sets of predictor variables. Models were calibrated with (1) climate variables only, (2) climate and soil variables, and (3) soil variables only. Results The predictive power of SDMs differed substantially across species, but generally models calibrated with both climate and soil data performed better than those calibrated only with climate variables. Models calibrated solely with soil variables were the least accurate. We found regional differences in potential shrub species richness across Australia due to the use of different sets of variables. Conclusions Our study provides evidence that predicted patterns of species richness may be sensitive to the choice of predictor set when multiple, plausible alternatives exist, and demonstrates the importance of considering soil properties when modeling availability of habitat for plants. PMID:28652933

Quantifying the increasing sensitivity of power systems to climate variability

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

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

PubMed

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

2017-11-01

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

Relationship between Climate Variability, Wildfire Risk, and Wildfire Occurrence in Wildland-Urban Interface of the Southwestern United States

NASA Astrophysics Data System (ADS)

Kafatos, M.; Kim, S. H.; Jia, S.; Nghiem, S. V.

2017-12-01

As housing units in or near wildlands have grown, the wildland-urban interface (WUI) contain at present approximately one-third of all housing in the contiguous US. Wildfires are a part of the natural cycle in the Southwestern United States (SWUS) but the increasing trend of WUI has made wildfires a serious high-risk hazard. The expansion of WUI has elevated wildfire risks by increasing the chance of human caused ignitions and past fire suppression in the area. Previous studies on climate variability have shown that the SWUS region is prone to frequent droughts and has suffered from severe wildfires in the recent decade. Therefore, assessing the increased vulnerability to the wildfire in WUI is crucial for proactive adaptation under climate change. Our previous study has shown that a strong correlation between North Atlantic Oscillation (NAO) and temperature was found during March-June in the SWUS. The abnormally warm and dry spring conditions, combined with suppression of winter precipitation, can cause an early start of a fire season and high fire risk throughout the summer and fall. Therefore, it is crucial to investigate the connections between climate variability and wildfire danger characteristics. This study aims to identify climate variability using multiple climate indices such as NAO, El Niño-Southern Oscillation and the Pacific Decadal Oscillation closely related with droughts in the SWUS region. Correlation between the variability and fire frequency and severity in WUI were examined. Also, we investigated climate variability and its relationship on local wildfire potential using both Keetch-Byram Drought Index (KBDI) and Fire Weather Index (FWI) which have been used to assessing wildfire potential in the U.S.A and Canada, respectively. We examined the long-term variability of the fire potential indices and relationships between the indices and historical occurrence in WUI using multi-decadal reanalysis data sets. Following our analysis, we investigated joint impacts of multiple climate indices on droughts and human activities in the WUI for regional wildfire potential.

Team climate at Antarctic research stations 1996-2000: leadership matters.

PubMed

Schmidt, Lacey L; Wood, JoAnna; Lugg, Desmond J

2004-08-01

The popular assumption is that extreme environments induce a climate of hostility, incompatibility, and tension by intensifying differences and disagreements among team members. Team members' perceptions of team climate are likely to change over time in an extreme environment, and thus team climate should be considered as a dynamic outcome variable resulting from multiple factors. In order to explore team climate as a dynamic outcome, we explored whether variables at multiple levels of analysis contributed to team climate over time for teams living and working in Antarctica. Data for this study were collected from volunteers involved in Australian National Antarctic Research Expeditions conducted from 1996 to 2000. Multilevel analysis was used to partition and estimate the variance in team climate and to explore factors explaining variance at the group/team, individual, and weekly levels. Most of the variance in perceptions of team climate was at the individual level (57%). Team climate had less variance at the group level (16%) and at the weekly level (26%). Results indicated that perceived leadership effectiveness was significantly related to team climate. Perceived leadership effectiveness accounted for an estimated 77% of the group level variance, which equated to 14% of the overall variance in team climate. Our results suggest that exploring the characteristics and behaviors that constitute effective leadership would contribute to a more complete and useful picture of team climate, as well as guide selection research.

Multiple causes of nonstationarity in the Weihe annual low-flow series

NASA Astrophysics Data System (ADS)

Xiong, Bin; Xiong, Lihua; Chen, Jie; Xu, Chong-Yu; Li, Lingqi

2018-02-01

Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced various nonstationary components into low-flow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, through a nonstationary frequency analysis framework with the generalized linear model (GLM) to consider time-varying distribution parameters, the multiple explanatory variables were incorporated to explain the variation in low-flow distribution parameters. These variables are comprised of the three indices of human activities (HAs; i.e., population, POP; irrigation area, IAR; and gross domestic product, GDP) and the eight measuring indices of the climate and catchment conditions (i.e., total precipitation P, mean frequency of precipitation events λ, temperature T, potential evapotranspiration (EP), climate aridity index AIEP, base-flow index (BFI), recession constant K and the recession-related aridity index AIK). This framework was applied to model the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China (also known as the Wei He River). The results from stepwise regression for the optimal explanatory variables show that the variables related to irrigation, recession, temperature and precipitation play an important role in modeling. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that the nonstationary distribution model with any one of all explanatory variables is better than the one without explanatory variables, the nonstationary gamma distribution model with four optimal variables is the best model and AIK is of the highest relative importance among these four variables, followed by IAR, BFI and AIEP. We conclude that the incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to analyze future occurrences of low-flow extremes in similar areas.

Meteorological Modes of Variability for Fine Particulate Matter (PM2.5) Air Quality in the United States: Implications for PM2.5 Sensitivity to Climate Change

EPA Science Inventory

We applied a multiple linear regression model to understand the relationships of PM_2.5 with meteorological variables in the contiguous US and from there to infer the sensitivity of PM_2.5 to climate change. We used 2004-2008 PM_2.5 observations fro...

Rapid emergence of climate change in environmental drivers of marine ecosystems.

PubMed

Henson, Stephanie A; Beaulieu, Claudie; Ilyina, Tatiana; John, Jasmin G; Long, Matthew; Séférian, Roland; Tjiputra, Jerry; Sarmiento, Jorge L

2017-03-07

Climate change is expected to modify ecological responses in the ocean, with the potential for important effects on the ecosystem services provided to humankind. Here we address the question of how rapidly multiple drivers of marine ecosystem change develop in the future ocean. By analysing an ensemble of models we find that, within the next 15 years, the climate change-driven trends in multiple ecosystem drivers emerge from the background of natural variability in 55% of the ocean and propagate rapidly to encompass 86% of the ocean by 2050 under a 'business-as-usual' scenario. However, we also demonstrate that the exposure of marine ecosystems to climate change-induced stress can be drastically reduced via climate mitigation measures; with mitigation, the proportion of ocean susceptible to multiple drivers within the next 15 years is reduced to 34%. Mitigation slows the pace at which multiple drivers emerge, allowing an additional 20 years for adaptation in marine ecological and socio-economic systems alike.

Rapid emergence of climate change in environmental drivers of marine ecosystems

PubMed Central

Henson, Stephanie A.; Beaulieu, Claudie; Ilyina, Tatiana; John, Jasmin G.; Long, Matthew; Séférian, Roland; Tjiputra, Jerry; Sarmiento, Jorge L.

2017-01-01

Climate change is expected to modify ecological responses in the ocean, with the potential for important effects on the ecosystem services provided to humankind. Here we address the question of how rapidly multiple drivers of marine ecosystem change develop in the future ocean. By analysing an ensemble of models we find that, within the next 15 years, the climate change-driven trends in multiple ecosystem drivers emerge from the background of natural variability in 55% of the ocean and propagate rapidly to encompass 86% of the ocean by 2050 under a ‘business-as-usual' scenario. However, we also demonstrate that the exposure of marine ecosystems to climate change-induced stress can be drastically reduced via climate mitigation measures; with mitigation, the proportion of ocean susceptible to multiple drivers within the next 15 years is reduced to 34%. Mitigation slows the pace at which multiple drivers emerge, allowing an additional 20 years for adaptation in marine ecological and socio-economic systems alike. PMID:28267144

Rapid emergence of climate change in environmental drivers of marine ecosystems

NASA Astrophysics Data System (ADS)

Henson, Stephanie A.; Beaulieu, Claudie; Ilyina, Tatiana; John, Jasmin G.; Long, Matthew; Séférian, Roland; Tjiputra, Jerry; Sarmiento, Jorge L.

2017-03-01

Climate change is expected to modify ecological responses in the ocean, with the potential for important effects on the ecosystem services provided to humankind. Here we address the question of how rapidly multiple drivers of marine ecosystem change develop in the future ocean. By analysing an ensemble of models we find that, within the next 15 years, the climate change-driven trends in multiple ecosystem drivers emerge from the background of natural variability in 55% of the ocean and propagate rapidly to encompass 86% of the ocean by 2050 under a `business-as-usual' scenario. However, we also demonstrate that the exposure of marine ecosystems to climate change-induced stress can be drastically reduced via climate mitigation measures; with mitigation, the proportion of ocean susceptible to multiple drivers within the next 15 years is reduced to 34%. Mitigation slows the pace at which multiple drivers emerge, allowing an additional 20 years for adaptation in marine ecological and socio-economic systems alike.

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.

Developing an approach to effectively use super ensemble experiments for the projection of hydrological extremes under climate change

NASA Astrophysics Data System (ADS)

Watanabe, S.; Kim, H.; Utsumi, N.

2017-12-01

This study aims to develop a new approach which projects hydrology under climate change using super ensemble experiments. The use of multiple ensemble is essential for the estimation of extreme, which is a major issue in the impact assessment of climate change. Hence, the super ensemble experiments are recently conducted by some research programs. While it is necessary to use multiple ensemble, the multiple calculations of hydrological simulation for each output of ensemble simulations needs considerable calculation costs. To effectively use the super ensemble experiments, we adopt a strategy to use runoff projected by climate models directly. The general approach of hydrological projection is to conduct hydrological model simulations which include land-surface and river routing process using atmospheric boundary conditions projected by climate models as inputs. This study, on the other hand, simulates only river routing model using runoff projected by climate models. In general, the climate model output is systematically biased so that a preprocessing which corrects such bias is necessary for impact assessments. Various bias correction methods have been proposed, but, to the best of our knowledge, no method has proposed for variables other than surface meteorology. Here, we newly propose a method for utilizing the projected future runoff directly. The developed method estimates and corrects the bias based on the pseudo-observation which is a result of retrospective offline simulation. We show an application of this approach to the super ensemble experiments conducted under the program of Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI). More than 400 ensemble experiments from multiple climate models are available. The results of the validation using historical simulations by HAPPI indicates that the output of this approach can effectively reproduce retrospective runoff variability. Likewise, the bias of runoff from super ensemble climate projections is corrected, and the impact of climate change on hydrologic extremes is assessed in a cost-efficient way.

Deglacial variability of Antarctic Intermediate Water penetration into the North Atlantic from authigenic neodymium isotope ratios

NASA Astrophysics Data System (ADS)

Xie, Ruifang C.; Marcantonio, Franco; Schmidt, Matthew W.

2012-09-01

Understanding intermediate water circulation across the last deglacial is critical in assessing the role of oceanic heat transport associated with Atlantic Meridional Overturning Circulation variability across abrupt climate events. However, the links between intermediate water circulation and abrupt climate events such as the Younger Dryas (YD) and Heinrich Event 1 (H1) are still poorly constrained. Here, we reconstruct changes in Antarctic Intermediate Water (AAIW) circulation in the subtropical North Atlantic over the past 25 kyr by measuring authigenic neodymium isotope ratios in sediments from two sites in the Florida Straits. Our authigenic Nd isotope records suggest that there was little to no penetration of AAIW into the subtropical North Atlantic during the YD and H1. Variations in the northward penetration of AAIW into the Florida Straits documented in our authigenic Nd isotope record are synchronous with multiple climatic archives, including the Greenland ice core δ18O record, the Cariaco Basin atmosphere Δ14C reconstruction, the Bermuda Rise sedimentary Pa/Th record, and nutrient and stable isotope data from the tropical North Atlantic. The synchroneity of our Nd records with multiple climatic archives suggests a tight connection between AAIW variability and high-latitude North Atlantic climate change.

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.

Do climate variables and human density affect Achatina fulica (Bowditch) (Gastropoda: Pulmonata) shell length, total weight and condition factor?

PubMed

Albuquerque, F S; Peso-Aguiar, M C; Assunção-Albuquerque, M J T; Gálvez, L

2009-08-01

The length-weight relationship and condition factor have been broadly investigated in snails to obtain the index of physical condition of populations and evaluate habitat quality. Herein, our goal was to describe the best predictors that explain Achatina fulica biometrical parameters and well being in a recently introduced population. From November 2001 to November 2002, monthly snail samples were collected in Lauro de Freitas City, Bahia, Brazil. Shell length and total weight were measured in the laboratory and the potential curve and condition factor were calculated. Five environmental variables were considered: temperature range, mean temperature, humidity, precipitation and human density. Multiple regressions were used to generate models including multiple predictors, via model selection approach, and then ranked with AIC criteria. Partial regressions were used to obtain the separated coefficients of determination of climate and human density models. A total of 1.460 individuals were collected, presenting a shell length range between 4.8 to 102.5 mm (mean: 42.18 mm). The relationship between total length and total weight revealed that Achatina fulica presented a negative allometric growth. Simple regression indicated that humidity has a significant influence on A. fulica total length and weight. Temperature range was the main variable that influenced the condition factor. Multiple regressions showed that climatic and human variables explain a small proportion of the variance in shell length and total weight, but may explain up to 55.7% of the condition factor variance. Consequently, we believe that the well being and biometric parameters of A. fulica can be influenced by climatic and human density factors.

Climate change but not unemployment explains the changing suicidality in Thessaloniki Greece (2000-2012).

PubMed

Fountoulakis, Konstantinos N; Savopoulos, Christos; Zannis, Prodromos; Apostolopoulou, Martha; Fountoukidis, Ilias; Kakaletsis, Nikolaos; Kanellos, Ilias; Dimellis, Dimos; Hyphantis, Thomas; Tsikerdekis, Athanasios; Pompili, Maurizio; Hatzitolios, Apostolos I

2016-03-15

Recently there was a debate concerning the etiology behind attempts and completed suicides. The aim of the current study was to search for possible correlations between the rates of attempted and completed suicide and climate variables and regional unemployment per year in the county of Thessaloniki, Macedonia, northern Greece, for the years 2000-12. The regional rates of suicide and attempted suicide as well as regional unemployment were available from previous publications of the authors. The climate variables were calculated from the daily E-OBS gridded dataset which is based on observational data Only the male suicide rates correlate significantly with high mean annual temperature but not with unemployment. The multiple linear regression analysis results suggest that temperature is the only variable that determines male suicides and explains 51% of their variance. Unemployment fails to contribute significantly to the model. There seems to be a seasonal distribution for attempts with mean rates being higher for the period from May to October and the rates clearly correlate with temperature. The highest mean rates were observed during May and August and the lowest during December and February. Multiple linear regression analysis suggests that temperature also determines the female attempts rate although the explained variable is significant but very low (3-5%) Climate variables and specifically high temperature correlate both with suicide and attempted suicide rates but with a different way between males and females. The climate effect was stronger than the effect of unemployment. Copyright © 2016 Elsevier B.V. All rights reserved.

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

USGS Publications Warehouse

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

2014-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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

Temperature variability is a key component in accurately forecasting the effects of climate change on pest phenology.

PubMed

Merrill, Scott C; Peairs, Frank B

2017-02-01

Models describing the effects of climate change on arthropod pest ecology are needed to help mitigate and adapt to forthcoming changes. Challenges arise because climate data are at resolutions that do not readily synchronize with arthropod biology. Here we explain how multiple sources of climate and weather data can be synthesized to quantify the effects of climate change on pest phenology. Predictions of phenological events differ substantially between models that incorporate scale-appropriate temperature variability and models that do not. As an illustrative example, we predicted adult emergence of a pest of sunflower, the sunflower stem weevil Cylindrocopturus adspersus (LeConte). Predictions of the timing of phenological events differed by an average of 11 days between models with different temperature variability inputs. Moreover, as temperature variability increases, developmental rates accelerate. Our work details a phenological modeling approach intended to help develop tools to plan for and mitigate the effects of climate change. Results show that selection of scale-appropriate temperature data is of more importance than selecting a climate change emission scenario. Predictions derived without appropriate temperature variability inputs will likely result in substantial phenological event miscalculations. Additionally, results suggest that increased temperature instability will lead to accelerated pest development. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Vulnerability of riparian obligate species to the interactive effect of fire, climate and hydrological change

Treesearch

Megan M. Friggens; Rachel Loehman; Lisa Holsinger; Deborah Finch

2014-01-01

Climate change is expected to have multiple direct and indirect impacts on ecosystems in the interior western U.S. (Christensen et al., 2007; IPCC 2013). Global climate predictions for the Southwest include higher temperatures, more variable rainfall, and more drought periods, which will likely exacerbate the ongoing issues relating to wildfire and water allocation in...

Genetic consequences of forest population dynamics influenced by historic climatic variability in the western USA

Treesearch

Robert D. Westfall; Constance I. Millar

2004-01-01

We review recent advances in climate science that show cyclic climatic variation over multiple time scales and give examples of the impacts of this variation on plant populations in the western USA. The paleohistorical reconstructions we review and others indicate that plant specles track these cycles in individualistically complex ways. These dynamic histories suggest...

Revealing, Reducing, and Representing Uncertainties in New Hydrologic Projections for Climate-changed Futures

NASA Astrophysics Data System (ADS)

Arnold, Jeffrey; Clark, Martyn; Gutmann, Ethan; Wood, Andy; Nijssen, Bart; Rasmussen, Roy

2016-04-01

The United States Army Corps of Engineers (USACE) has had primary responsibility for multi-purpose water resource operations on most of the major river systems in the U.S. for more than 200 years. In that time, the USACE projects and programs making up those operations have proved mostly robust against the range of natural climate variability encountered over their operating life spans. However, in some watersheds and for some variables, climate change now is known to be shifting the hydroclimatic baseline around which that natural variability occurs and changing the range of that variability as well. This makes historical stationarity an inappropriate basis for assessing continued project operations under climate-changed futures. That means new hydroclimatic projections are required at multiple scales to inform decisions about specific threats and impacts, and for possible adaptation responses to limit water-resource vulnerabilities and enhance operational resilience. However, projections of possible future hydroclimatologies have myriad complex uncertainties that require explicit guidance for interpreting and using them to inform those decisions about climate vulnerabilities and resilience. Moreover, many of these uncertainties overlap and interact. Recent work, for example, has shown the importance of assessing the uncertainties from multiple sources including: global model structure [Meehl et al., 2005; Knutti and Sedlacek, 2013]; internal climate variability [Deser et al., 2012; Kay et al., 2014]; climate downscaling methods [Gutmann et al., 2012; Mearns et al., 2013]; and hydrologic models [Addor et al., 2014; Vano et al., 2014; Mendoza et al., 2015]. Revealing, reducing, and representing these uncertainties is essential for defining the plausible quantitative climate change narratives required to inform water-resource decision-making. And to be useful, such quantitative narratives, or storylines, of climate change threats and hydrologic impacts must sample from the full range of uncertainties associated with all parts of the simulation chain, from global climate models with simulations of natural climate variability, through regional climate downscaling, and on to modeling of affected hydrologic processes and downstream water resources impacts. This talk will present part of the work underway now both to reveal and reduce some important uncertainties and to develop explicit guidance for future generation of quantitative hydroclimatic storylines. Topics will include: 1- model structural and parameter-set limitations of some methods widely used to quantify climate impacts to hydrologic processes [Gutmann et al., 2014; Newman et al., 2015]; 2- development and evaluation of new, spatially consistent, U.S. national-scale climate downscaling and hydrologic simulation capabilities directly relevant at the multiple scales of water-resource decision-making [Newman et al., 2015; Mizukami et al., 2015; Gutmann et al., 2016]; and 3- development and evaluation of advanced streamflow forecasting methods to reduce and represent integrated uncertainties in a tractable way [Wood et al., 2014; Wood et al., 2015]. A key focus will be areas where climatologic and hydrologic science is currently under-developed to inform decisions - or is perhaps wrongly scaled or misapplied in practice - indicating the need for additional fundamental science and interpretation.

High skill in low-frequency climate response through fluctuation dissipation theorems despite structural instability.

PubMed

Majda, Andrew J; Abramov, Rafail; Gershgorin, Boris

2010-01-12

Climate change science focuses on predicting the coarse-grained, planetary-scale, longtime changes in the climate system due to either changes in external forcing or internal variability, such as the impact of increased carbon dioxide. The predictions of climate change science are carried out through comprehensive, computational atmospheric, and oceanic simulation models, which necessarily parameterize physical features such as clouds, sea ice cover, etc. Recently, it has been suggested that there is irreducible imprecision in such climate models that manifests itself as structural instability in climate statistics and which can significantly hamper the skill of computer models for climate change. A systematic approach to deal with this irreducible imprecision is advocated through algorithms based on the Fluctuation Dissipation Theorem (FDT). There are important practical and computational advantages for climate change science when a skillful FDT algorithm is established. The FDT response operator can be utilized directly for multiple climate change scenarios, multiple changes in forcing, and other parameters, such as damping and inverse modelling directly without the need of running the complex climate model in each individual case. The high skill of FDT in predicting climate change, despite structural instability, is developed in an unambiguous fashion using mathematical theory as guidelines in three different test models: a generic class of analytical models mimicking the dynamical core of the computer climate models, reduced stochastic models for low-frequency variability, and models with a significant new type of irreducible imprecision involving many fast, unstable modes.

Organizational climate configurations: relationships to collective attitudes, customer satisfaction, and financial performance.

PubMed

Schulte, Mathis; Ostroff, Cheri; Shmulyian, Svetlana; Kinicki, Angelo

2009-05-01

Research on organizational climate has tended to focus on independent dimensions of climate rather than studying the total social context as configurations of multiple climate dimensions. The authors examined relationships between configurations of unit-level climate dimensions and organizational outcomes. Three profile characteristics represented climate configurations: (1) elevation, or the mean score across climate dimensions; (2) variability, or the extent to which scores across dimensions vary; and (3) shape, or the pattern of the dimensions. Across 2 studies (1,120 employees in 120 bank branches and 4,317 employees in 86 food distribution stores), results indicated that elevation was related to collective employee attitudes and service perceptions, while shape was related to customer satisfaction and financial performance. With respect to profile variability, results were mixed. The discussion focuses on future directions for taking a configural approach to organizational climate. (c) 2009 APA, all rights reserved.

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

USGS Publications Warehouse

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

2009-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

EXAMINING THE IMPACT OF CLIMATE CHANGE AND VARIABILITY OF REGIONAL AIR QUALITY OVER THE UNITED STATES

EPA Science Inventory

The United States has established a series of standards for criteria and other air pollutants to safeguard air quality to protect human health and the environment. The Climate Impact on Regional Air Quality (CIRAQ) project, a collaborative research effort involving multiple Fede...

The annual cycles of phytoplankton biomass

USGS Publications Warehouse

Winder, M.; Cloern, J.E.

2010-01-01

Terrestrial plants are powerful climate sentinels because their annual cycles of growth, reproduction and senescence are finely tuned to the annual climate cycle having a period of one year. Consistency in the seasonal phasing of terrestrial plant activity provides a relatively low-noise background from which phenological shifts can be detected and attributed to climate change. Here, we ask whether phytoplankton biomass also fluctuates over a consistent annual cycle in lake, estuarine-coastal and ocean ecosystems and whether there is a characteristic phenology of phytoplankton as a consistent phase and amplitude of variability. We compiled 125 time series of phytoplankton biomass (chloro-phyll a concentration) from temperate and subtropical zones and used wavelet analysis to extract their dominant periods of variability and the recurrence strength at those periods. Fewer than half (48%) of the series had a dominant 12-month period of variability, commonly expressed as the canonical spring-bloom pattern. About 20 per cent had a dominant six-month period of variability, commonly expressed as the spring and autumn or winter and summer blooms of temperate lakes and oceans. These annual patterns varied in recurrence strength across sites, and did not persist over the full series duration at some sites. About a third of the series had no component of variability at either the six-or 12-month period, reflecting a series of irregular pulses of biomass. These findings show that there is high variability of annual phytoplankton cycles across ecosystems, and that climate-driven annual cycles can be obscured by other drivers of population variability, including human disturbance, aperiodic weather events and strong trophic coupling between phytoplankton and their consumers. Regulation of phytoplankton biomass by multiple processes operating at multiple time scales adds complexity to the challenge of detecting climate-driven trends in aquatic ecosystems where the noise to signal ratio is high. ?? 2010 The Royal Society.

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.

Climatological Modeling of Monthly Air Temperature and Precipitation in Egypt through GIS Techniques

NASA Astrophysics Data System (ADS)

El Kenawy, A.

2009-09-01

This paper describes a method for modeling and mapping four climatic variables (maximum temperature, minimum temperature, mean temperature and total precipitation) in Egypt using a multiple regression approach implemented in a GIS environment. In this model, a set of variables including latitude, longitude, elevation within a distance of 5, 10 and 15 km, slope, aspect, distance to the Mediterranean Sea, distance to the Red Sea, distance to the Nile, ratio between land and water masses within a radius of 5, 10, 15 km, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), the Normalized Difference Temperature Index (NDTI) and reflectance are included as independent variables. These variables were integrated as raster layers in MiraMon software at a spatial resolution of 1 km. Climatic variables were considered as dependent variables and averaged from quality controlled and homogenized 39 series distributing across the entire country during the period of (1957-2006). For each climatic variable, digital and objective maps were finally obtained using the multiple regression coefficients at monthly, seasonal and annual timescale. The accuracy of these maps were assessed through cross-validation between predicted and observed values using a set of statistics including coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean bias Error (MBE) and D Willmott statistic. These maps are valuable in the sense of spatial resolution as well as the number of observatories involved in the current analysis.

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

The Climate Variability & Predictability (CVP) Program supports research aimed at providing process-level understanding of the climate system through observation, modeling, analysis, and field studies. This vital knowledge is needed to improve climate models and predictions so that scientists can better anticipate the impacts of future climate variability and change. To achieve its mission, the CVP Program supports research carried out at NOAA and other federal laboratories, NOAA Cooperative Institutes, and academic institutions. The Program also coordinates its sponsored projects with major national and international scientific bodies including the World Climate Research Programme (WCRP), the International and U.S. Climate Variability and Predictability (CLIVAR/US CLIVAR) Program, and the U.S. Global Change Research Program (USGCRP). The CVP program sits within NOAA's Climate Program Office (http://cpo.noaa.gov/CVP). The CVP Program currently supports multiple projects in areas that are aimed at improved representation of physical processes in global models. Some of the topics that are currently funded include: i) Improved Understanding of Intraseasonal Tropical Variability - DYNAMO field campaign and post -field projects, and the new climate model improvement teams focused on MJO processes; ii) Climate Process Teams (CPTs, co-funded with NSF) with projects focused on Cloud macrophysical parameterization and its application to aerosol indirect effects, and Internal-Wave Driven Mixing in Global Ocean Models; iii) Improved Understanding of Tropical Pacific Processes, Biases, and Climatology; iv) Understanding Arctic Sea Ice Mechanism and Predictability;v) AMOC Mechanisms and Decadal Predictability Recent results from CVP-funded projects will be summarized. Additional information can be found at http://cpo.noaa.gov/CVP.

Marine biodiversity–ecosystem functions under uncertain environmental futures

PubMed Central

Bulling, Mark T.; Hicks, Natalie; Murray, Leigh; Paterson, David M.; Raffaelli, Dave; White, Piran C. L.; Solan, Martin

2010-01-01

Anthropogenic activity is currently leading to dramatic transformations of ecosystems and losses of biodiversity. The recognition that these ecosystems provide services that are essential for human well-being has led to a major interest in the forms of the biodiversity–ecosystem functioning relationship. However, there is a lack of studies examining the impact of climate change on these relationships and it remains unclear how multiple climatic drivers may affect levels of ecosystem functioning. Here, we examine the roles of two important climate change variables, temperature and concentration of atmospheric carbon dioxide, on the relationship between invertebrate species richness and nutrient release in a model benthic estuarine system. We found a positive relationship between invertebrate species richness and the levels of release of NH4-N into the water column, but no effect of species richness on the release of PO4-P. Higher temperatures and greater concentrations of atmospheric carbon dioxide had a negative impact on nutrient release. Importantly, we found significant interactions between the climate variables, indicating that reliably predicting the effects of future climate change will not be straightforward as multiple drivers are unlikely to have purely additive effects, resulting in increased levels of uncertainty. PMID:20513718

Marine biodiversity-ecosystem functions under uncertain environmental futures.

PubMed

Bulling, Mark T; Hicks, Natalie; Murray, Leigh; Paterson, David M; Raffaelli, Dave; White, Piran C L; Solan, Martin

2010-07-12

Anthropogenic activity is currently leading to dramatic transformations of ecosystems and losses of biodiversity. The recognition that these ecosystems provide services that are essential for human well-being has led to a major interest in the forms of the biodiversity-ecosystem functioning relationship. However, there is a lack of studies examining the impact of climate change on these relationships and it remains unclear how multiple climatic drivers may affect levels of ecosystem functioning. Here, we examine the roles of two important climate change variables, temperature and concentration of atmospheric carbon dioxide, on the relationship between invertebrate species richness and nutrient release in a model benthic estuarine system. We found a positive relationship between invertebrate species richness and the levels of release of NH(4)-N into the water column, but no effect of species richness on the release of PO(4)-P. Higher temperatures and greater concentrations of atmospheric carbon dioxide had a negative impact on nutrient release. Importantly, we found significant interactions between the climate variables, indicating that reliably predicting the effects of future climate change will not be straightforward as multiple drivers are unlikely to have purely additive effects, resulting in increased levels of uncertainty.

Combined influence of multiple climatic factors on the incidence of bacterial foodborne diseases.

PubMed

Park, Myoung Su; Park, Ki Hwan; Bahk, Gyung Jin

2018-01-01

Information regarding the relationship between the incidence of foodborne diseases (FBD) and climatic factors is useful in designing preventive strategies for FBD based on anticipated future climate change. To better predict the effect of climate change on foodborne pathogens, the present study investigated the combined influence of multiple climatic factors on bacterial FBD incidence in South Korea. During 2011-2015, the relationships between 8 climatic factors and the incidences of 13 bacterial FBD, were determined based on inpatient stays, on a monthly basis using the Pearson correlation analyses, multicollinearity tests, principal component analysis (PCA), and the seasonal autoregressive integrated moving average (SARIMA) modeling. Of the 8 climatic variables, the combination of temperature, relative humidity, precipitation, insolation, and cloudiness was significantly associated with salmonellosis (P<0.01), vibriosis (P<0.05), and enterohemorrhagic Escherichia coli O157:H7 infection (P<0.01). The combined effects of snowfall, wind speed, duration of sunshine, and cloudiness were not significant for these 3 FBD. Other FBD, including campylobacteriosis, were not significantly associated with any combination of climatic factors. These findings indicate that the relationships between multiple climatic factors and bacterial FBD incidence can be valuable for the development of prediction models for future patterns of diseases in response to changes in climate. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Using Probabilistic Methods in Water Scarcity Assessments: A First Step Towards a Water Scarcity Risk Assessment Framework

NASA Technical Reports Server (NTRS)

Veldkamp, Ted; Wada, Yoshihide; Aerts, Jeroen; Ward, Phillip

2016-01-01

Water scarcity -driven by climate change, climate variability, and socioeconomic developments- is recognized as one of the most important global risks, both in terms of likelihood and impact. Whilst a wide range of studies have assessed the role of long term climate change and socioeconomic trends on global water scarcity, the impact of variability is less well understood. Moreover, the interactions between different forcing mechanisms, and their combined effect on changes in water scarcity conditions, are often neglected. Therefore, we provide a first step towards a framework for global water scarcity risk assessments, applying probabilistic methods to estimate water scarcity risks for different return periods under current and future conditions while using multiple climate and socioeconomic scenarios.

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.

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.

Multiple stressors, nonlinear effects and the implications of climate change impacts on marine coastal ecosystems.

PubMed

Hewitt, Judi E; Ellis, Joanne I; Thrush, Simon F

2016-08-01

Global climate change will undoubtedly be a pressure on coastal marine ecosystems, affecting not only species distributions and physiology but also ecosystem functioning. In the coastal zone, the environmental variables that may drive ecological responses to climate change include temperature, wave energy, upwelling events and freshwater inputs, and all act and interact at a variety of spatial and temporal scales. To date, we have a poor understanding of how climate-related environmental changes may affect coastal marine ecosystems or which environmental variables are likely to produce priority effects. Here we use time series data (17 years) of coastal benthic macrofauna to investigate responses to a range of climate-influenced variables including sea-surface temperature, southern oscillation indices (SOI, Z4), wind-wave exposure, freshwater inputs and rainfall. We investigate responses from the abundances of individual species to abundances of functional traits and test whether species that are near the edge of their tolerance to another stressor (in this case sedimentation) may exhibit stronger responses. The responses we observed were all nonlinear and some exhibited thresholds. While temperature was most frequently an important predictor, wave exposure and ENSO-related variables were also frequently important and most ecological variables responded to interactions between environmental variables. There were also indications that species sensitive to another stressor responded more strongly to weaker climate-related environmental change at the stressed site than the unstressed site. The observed interactions between climate variables, effects on key species or functional traits, and synergistic effects of additional anthropogenic stressors have important implications for understanding and predicting the ecological consequences of climate change to coastal ecosystems. © 2015 John Wiley & Sons Ltd.

Using non-systematic surveys to investigate effects of regional climate variability on Australasian gannets in the Hauraki Gulf, New Zealand

NASA Astrophysics Data System (ADS)

Srinivasan, Mridula; Dassis, Mariela; Benn, Emily; Stockin, Karen A.; Martinez, Emmanuelle; Machovsky-Capuska, Gabriel E.

2015-05-01

Few studies have investigated regional and natural climate variability on seabird populations using ocean reanalysis datasets (e.g. Simple Ocean Data Assimilation (SODA)) that integrate atmospheric information to supplement ocean observations and provide improved estimates of ocean conditions. Herein we use a non-systematic dataset on Australasian gannets (Morus serrator) from 2001 to 2009 to identify potential connections between Gannet Sightings Per Unit Effort (GSPUE) and climate and oceanographic variability in a region of known importance for breeding seabirds, the Hauraki Gulf (HG), New Zealand. While no statistically significant relationships between GSPUE and global climate indices were determined, there was a significant correlation between GSPUE and regional SST anomaly for HG. Also, there appears to be a strong link between global climate indices and regional climate in the HG. Further, based on cross-correlation function coefficients and lagged multiple regression models, we identified potential leading and lagging climate variables, and climate variables but with limited predictive capacity in forecasting future GSPUE. Despite significant inter-annual variability and marginally cooler SSTs since 2001, gannet sightings appear to be increasing. We hypothesize that at present underlying physical changes in the marine ecosystem may be insufficient to affect supply of preferred gannet main prey (pilchard Sardinops spp.), which tolerate a wide thermal range. Our study showcases the potential scientific value of lengthy non-systematic data streams and when designed properly (i.e., contain abundance, flock size, and spatial data), can yield useful information in climate impact studies on seabirds and other marine fauna. Such information can be invaluable for enhancing conservation measures for protected species in fiscally constrained research environments.

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.

Quantitative Assessment of Antarctic Climate Variability and Change

NASA Astrophysics Data System (ADS)

Ordonez, A.; Schneider, D. P.

2013-12-01

The Antarctic climate is both extreme and highly variable, but there are indications it may be changing. As the climate in Antarctica can affect global sea level and ocean circulation, it is important to understand and monitor its behavior. Observational and model data have been used to study climate change in Antarctica and the Southern Ocean, though observational data is sparse and models have difficulty reproducing many observed climate features. For example, a leading hypothesis that ozone depletion has been responsible for sea ice trends is struggling with the inability of ozone-forced models to reproduce the observed sea ice increase. The extent to which this data-model disagreement represents inadequate observations versus model biases is unknown. This research assessed a variety of climate change indicators to present an overview of Antarctic climate that will allow scientists to easily access this data and compare indicators with other observational data and model output. Indicators were obtained from observational and reanalysis data for variables such as temperature, sea ice area, and zonal wind stress. Multiple datasets were used for key variables. Monthly and annual anomaly data from Antarctica and the Southern Ocean as well as tropical indices were plotted as time series on common axes for comparison. Trends and correlations were also computed. Zonal wind, surface temperature, and austral springtime sea ice had strong relationships and were further discussed in terms of how they may relate to climate variability and change in the Antarctic. This analysis will enable hypothesized mechanisms of Antarctic climate change to be critically evaluated.

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.

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

USGS Publications Warehouse

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

2014-01-01

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

Stoichiometry of hydrological C, N, and P losses across climate and geology: An environmental matrix approach across New Zealand primary forests

NASA Astrophysics Data System (ADS)

McGroddy, M. E.; Baisden, W. T.; Hedin, L. O.

2008-03-01

Hydrologic losses can play a key role in regulating ecosystem nutrient balances, particularly in regions where baseline nutrient cycles are not augmented by industrial deposition. We used first-order streams to integrate hydrologic losses at the watershed scale across unpolluted old-growth forests in New Zealand. We employed a matrix approach to resolve how stream water concentrations of dissolved organic carbon (DOC), organic and inorganic nitrogen (DON and DIN), and organic and inorganic phosphorus (DOP and DIP) varied as a function of landscape differences in climate and geology. We found stream water total dissolved nitrogen (TDN) to be dominated by organic forms (medians for DON, 81.3%, nitrate-N, 12.6%, and ammonium-N, 3.9%). The median stream water DOC:TDN:TDP molar ratio of 1050:21:1 favored C slightly over N and P when compared to typical temperate forest foliage ratios. Using the full set of variables in a multiple regression approach explained approximately half of the variability in DON, DOC, and TDP concentrations. Building on this approach we combined a simplified set of variables with a simple water balance model in a regression designed to predict DON export at larger spatial scales. Incorporating the effects of climate and geologic variables on nutrient exports will greatly aid the development of integrated Earth-climate biogeochemical models which are able to take into account multiple element dynamics and complex natural landscapes.

Spatiotemporal patterns of evapotranspiration along the North American east coast as influenced by multiple environmental changes

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

Yang, Qichun; Tian, Hanqin; Li, Xia

The North American east coast has experienced significant land-use and climate changes since the beginning of the 20th century. In this study, using the Dynamic Land Ecosystem Model 2.0 driven by time-series input data of land use, climate and atmospheric CO 2, we examined how these driving forces have affected the spatiotemporal trends and variability of evapotranspiration (ET) in this region during 1901–2008. Annual ET in the North American east coast during this period was 648.3 ± 38.6 mm/year and demonstrated an increasing trend. Factorial model simulations indicated that climate variability explained 76% of the inter-annual ET variability. Although land-usemore » change only explained 16% of the ET temporal variability, afforestation induced the upward trend of ET and increased annual ET by 12.8 mm/year. Elevated atmospheric CO 2 reduced annual ET by 0.84 mm, and its potential impacts under future atmospheric CO 2 levels could be much larger than estimates for the historical 1901–2008 period. Climate change determined the spatial pattern of ET changes across the entire study area, whereas land-use changes dramatically affected ET in watersheds with significant land conversions. In spite of the multiple benefits from afforestation, its impacts on water resources should be considered in future land-use policy making. As a result, elevated ET may also affect fresh water availability for the increasing social and economic water demands.« less

Spatiotemporal patterns of evapotranspiration along the North American east coast as influenced by multiple environmental changes

DOE PAGES

Yang, Qichun; Tian, Hanqin; Li, Xia; ...

2014-08-08

The North American east coast has experienced significant land-use and climate changes since the beginning of the 20th century. In this study, using the Dynamic Land Ecosystem Model 2.0 driven by time-series input data of land use, climate and atmospheric CO 2, we examined how these driving forces have affected the spatiotemporal trends and variability of evapotranspiration (ET) in this region during 1901–2008. Annual ET in the North American east coast during this period was 648.3 ± 38.6 mm/year and demonstrated an increasing trend. Factorial model simulations indicated that climate variability explained 76% of the inter-annual ET variability. Although land-usemore » change only explained 16% of the ET temporal variability, afforestation induced the upward trend of ET and increased annual ET by 12.8 mm/year. Elevated atmospheric CO 2 reduced annual ET by 0.84 mm, and its potential impacts under future atmospheric CO 2 levels could be much larger than estimates for the historical 1901–2008 period. Climate change determined the spatial pattern of ET changes across the entire study area, whereas land-use changes dramatically affected ET in watersheds with significant land conversions. In spite of the multiple benefits from afforestation, its impacts on water resources should be considered in future land-use policy making. As a result, elevated ET may also affect fresh water availability for the increasing social and economic water demands.« less

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Climate change and dead zones.

PubMed

Altieri, Andrew H; Gedan, Keryn B

2015-04-01

Estuaries and coastal seas provide valuable ecosystem services but are particularly vulnerable to the co-occurring threats of climate change and oxygen-depleted dead zones. We analyzed the severity of climate change predicted for existing dead zones, and found that 94% of dead zones are in regions that will experience at least a 2 °C temperature increase by the end of the century. We then reviewed how climate change will exacerbate hypoxic conditions through oceanographic, ecological, and physiological processes. We found evidence that suggests numerous climate variables including temperature, ocean acidification, sea-level rise, precipitation, wind, and storm patterns will affect dead zones, and that each of those factors has the potential to act through multiple pathways on both oxygen availability and ecological responses to hypoxia. Given the variety and strength of the mechanisms by which climate change exacerbates hypoxia, and the rates at which climate is changing, we posit that climate change variables are contributing to the dead zone epidemic by acting synergistically with one another and with recognized anthropogenic triggers of hypoxia including eutrophication. This suggests that a multidisciplinary, integrated approach that considers the full range of climate variables is needed to track and potentially reverse the spread of dead zones. © 2014 John Wiley & Sons Ltd.

Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

NASA Astrophysics Data System (ADS)

Soares dos Santos, T.; Mendes, D.; Rodrigues Torres, R.

2016-01-01

Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANNs) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon; northeastern Brazil; and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model output and observed monthly precipitation. We used general circulation model (GCM) experiments for the 20th century (RCP historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANNs significantly outperform the MLR downscaling of monthly precipitation variability.

Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

NASA Astrophysics Data System (ADS)

dos Santos, T. S.; Mendes, D.; Torres, R. R.

2015-08-01

Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANN) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon, Northeastern Brazil and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model out- put and observed monthly precipitation. We used GCMs experiments for the 20th century (RCP Historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANN significantly outperforms the MLR downscaling of monthly precipitation variability.

Grassland agriculture

USDA-ARS?s Scientific Manuscript database

Agriculture in grassland environments is facing multiple stresses from: shifting demographics, declining and fragmented agricultural landscapes, declining environmental quality, variable and changing climate, volatile and increasing energy costs, marginal economic returns, and globalization. Degrad...

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

USGS Publications Warehouse

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

2015-01-01

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

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

PubMed

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

2015-11-01

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

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.

Describing rainfall in northern Australia using multiple climate indices

NASA Astrophysics Data System (ADS)

Wilks Rogers, Cassandra Denise; Beringer, Jason

2017-02-01

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

Climate and atmosphere simulator for experiments on ecological systems in changing environments.

PubMed

Verdier, Bruno; Jouanneau, Isabelle; Simonnet, Benoit; Rabin, Christian; Van Dooren, Tom J M; Delpierre, Nicolas; Clobert, Jean; Abbadie, Luc; Ferrière, Régis; Le Galliard, Jean-François

2014-01-01

Grand challenges in global change research and environmental science raise the need for replicated experiments on ecosystems subjected to controlled changes in multiple environmental factors. We designed and developed the Ecolab as a variable climate and atmosphere simulator for multifactor experimentation on natural or artificial ecosystems. The Ecolab integrates atmosphere conditioning technology optimized for accuracy and reliability. The centerpiece is a highly contained, 13-m(3) chamber to host communities of aquatic and terrestrial species and control climate (temperature, humidity, rainfall, irradiance) and atmosphere conditions (O2 and CO2 concentrations). Temperature in the atmosphere and in the water or soil column can be controlled independently of each other. All climatic and atmospheric variables can be programmed to follow dynamical trajectories and simulate gradual as well as step changes. We demonstrate the Ecolab's capacity to simulate a broad range of atmospheric and climatic conditions, their diurnal and seasonal variations, and to support the growth of a model terrestrial plant in two contrasting climate scenarios. The adaptability of the Ecolab design makes it possible to study interactions between variable climate-atmosphere factors and biotic disturbances. Developed as an open-access, multichamber platform, this equipment is available to the international scientific community for exploring interactions and feedbacks between ecological and climate systems.

Air Pollutants, Climate, and the Prevalence of Pediatric Asthma in Urban Areas of China

PubMed Central

Zhang, Juanjuan; Yan, Li; Fu, Wenlong; Yi, Jing; Chen, Yuzhi; Liu, Chuanhe; Xu, Dongqun; Wang, Qiang

2016-01-01

Background. Prevalence of childhood asthma varies significantly among regions, while its reasons are not clear yet with only a few studies reporting relevant causes for this variation. Objective. To investigate the potential role of city-average levels of air pollutants and climatic factors in order to distinguish differences in asthma prevalence in China and explain their reasons. Methods. Data pertaining to 10,777 asthmatic patients were obtained from the third nationwide survey of childhood asthma in China's urban areas. Annual mean concentrations of air pollutants and other climatic factors were obtained for the same period from several government departments. Data analysis was implemented with descriptive statistics, Pearson correlation coefficient, and multiple regression analysis. Results. Pearson correlation analysis showed that the situation of childhood asthma was strongly linked with SO2, relative humidity, and hours of sunshine (p < 0.05). Multiple regression analysis indicated that, among the predictor variables in the final step, SO2 was found to be the most powerful predictor variable amongst all (β = −19.572, p < 0.05). Furthermore, results had shown that hours of sunshine (β = −0.014, p < 0.05) was a significant component summary predictor variable. Conclusion. The findings of this study do not suggest that air pollutants or climate, at least in terms of children, plays a major role in explaining regional differences in asthma prevalence in China. PMID:27556031

The impact of justice climate and justice orientation on work outcomes: a cross-level multifoci framework.

PubMed

Liao, Hui; Rupp, Deborah E

2005-03-01

In this article, which takes a person-situation approach, the authors propose and test a cross-level multifoci model of workplace justice. They crossed 3 types of justice (procedural, informational, and interpersonal) with 2 foci (organization and supervisor) and aggregated to the group level to create 6 distinct justice climate variables. They then tested for the effects of these variables on either organization-directed or supervisor-directed commitment, satisfaction, and citizenship behavior. The authors also tested justice orientation as a moderator of these relationships. The results, based on 231 employees constituting 44 work groups representing multiple organizations and occupations, revealed that 4 forms of justice climate (organization-focused procedural and informational justice climate and supervisor-focused procedural and interpersonal justice climate) were significantly related to various work outcomes after controlling for corresponding individual-level justice perceptions. In addition, some moderation effects were found. Implications for organizations and future research are discussed.

Impacts of Model Bias on the Climate Change Signal and Effects of Weighted Ensembles of Regional Climate Model Simulations: A Case Study over Southern Québec, Canada

DOE PAGES

Eum, Hyung-Il; Gachon, Philippe; Laprise, René

2016-01-01

This study examined the impact of model biases on climate change signals for daily precipitation and for minimum and maximum temperatures. Through the use of multiple climate scenarios from 12 regional climate model simulations, the ensemble mean, and three synthetic simulations generated by a weighting procedure, we investigated intermodel seasonal climate change signals between current and future periods, for both median and extreme precipitation/temperature values. A significant dependence of seasonal climate change signals on the model biases over southern Québec in Canada was detected for temperatures, but not for precipitation. This suggests that the regional temperature change signal is affectedmore » by local processes. Seasonally, model bias affects future mean and extreme values in winter and summer. In addition, potentially large increases in future extremes of temperature and precipitation values were projected. For three synthetic scenarios, systematically less bias and a narrow range of mean change for all variables were projected compared to those of climate model simulations. In addition, synthetic scenarios were found to better capture the spatial variability of extreme cold temperatures than the ensemble mean scenario. Finally, these results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture the spatial variability of extreme climate events.« less

Impacts of Model Bias on the Climate Change Signal and Effects of Weighted Ensembles of Regional Climate Model Simulations: A Case Study over Southern Québec, Canada

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

Eum, Hyung-Il; Gachon, Philippe; Laprise, René

This study examined the impact of model biases on climate change signals for daily precipitation and for minimum and maximum temperatures. Through the use of multiple climate scenarios from 12 regional climate model simulations, the ensemble mean, and three synthetic simulations generated by a weighting procedure, we investigated intermodel seasonal climate change signals between current and future periods, for both median and extreme precipitation/temperature values. A significant dependence of seasonal climate change signals on the model biases over southern Québec in Canada was detected for temperatures, but not for precipitation. This suggests that the regional temperature change signal is affectedmore » by local processes. Seasonally, model bias affects future mean and extreme values in winter and summer. In addition, potentially large increases in future extremes of temperature and precipitation values were projected. For three synthetic scenarios, systematically less bias and a narrow range of mean change for all variables were projected compared to those of climate model simulations. In addition, synthetic scenarios were found to better capture the spatial variability of extreme cold temperatures than the ensemble mean scenario. Finally, these results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture the spatial variability of extreme climate events.« less

Modelling Spatial Dependence Structures Between Climate Variables by Combining Mixture Models with Copula Models

NASA Astrophysics Data System (ADS)

Khan, F.; Pilz, J.; Spöck, G.

2017-12-01

Spatio-temporal dependence structures play a pivotal role in understanding the meteorological characteristics of a basin or sub-basin. This further affects the hydrological conditions and consequently will provide misleading results if these structures are not taken into account properly. In this study we modeled the spatial dependence structure between climate variables including maximum, minimum temperature and precipitation in the Monsoon dominated region of Pakistan. For temperature, six, and for precipitation four meteorological stations have been considered. For modelling the dependence structure between temperature and precipitation at multiple sites, we utilized C-Vine, D-Vine and Student t-copula models. For temperature, multivariate mixture normal distributions and for precipitation gamma distributions have been used as marginals under the copula models. A comparison was made between C-Vine, D-Vine and Student t-copula by observational and simulated spatial dependence structure to choose an appropriate model for the climate data. The results show that all copula models performed well, however, there are subtle differences in their performances. The copula models captured the patterns of spatial dependence structures between climate variables at multiple meteorological sites, however, the t-copula showed poor performance in reproducing the dependence structure with respect to magnitude. It was observed that important statistics of observed data have been closely approximated except of maximum values for temperature and minimum values for minimum temperature. Probability density functions of simulated data closely follow the probability density functions of observational data for all variables. C and D-Vines are better tools when it comes to modelling the dependence between variables, however, Student t-copulas compete closely for precipitation. Keywords: Copula model, C-Vine, D-Vine, Spatial dependence structure, Monsoon dominated region of Pakistan, Mixture models, EM algorithm.

Integrating plant ecological responses to climate extremes from individual to ecosystem levels.

PubMed

Felton, Andrew J; Smith, Melinda D

2017-06-19

Climate extremes will elicit responses from the individual to the ecosystem level. However, only recently have ecologists begun to synthetically assess responses to climate extremes across multiple levels of ecological organization. We review the literature to examine how plant responses vary and interact across levels of organization, focusing on how individual, population and community responses may inform ecosystem-level responses in herbaceous and forest plant communities. We report a high degree of variability at the individual level, and a consequential inconsistency in the translation of individual or population responses to directional changes in community- or ecosystem-level processes. The scaling of individual or population responses to community or ecosystem responses is often predicated upon the functional identity of the species in the community, in particular, the dominant species. Furthermore, the reported stability in plant community composition and functioning with respect to extremes is often driven by processes that operate at the community level, such as species niche partitioning and compensatory responses during or after the event. Future research efforts would benefit from assessing ecological responses across multiple levels of organization, as this will provide both a holistic and mechanistic understanding of ecosystem responses to increasing climatic variability.This article is part of the themed issue 'Behavioural, ecological and evolutionary responses to extreme climatic events'. © 2017 The Author(s).

Multi-year climate variability in the Southwestern United States within a context of a dynamically downscaled twentieth century reanalysis

NASA Astrophysics Data System (ADS)

Carrillo, Carlos M.; Castro, Christopher L.; Chang, Hsin-I.; Luong, Thang M.

2017-12-01

This investigation evaluates whether there is coherency in warm and cool season precipitation at the low-frequency scale that may be responsible for multi-year droughts in the US Southwest. This low-frequency climate variability at the decadal scale and longer is studied within the context of a twentieth-century reanalysis (20CR) and its dynamically-downscaled version (DD-20CR). A spectral domain matrix methods technique (Multiple-Taper-Method Singular Value Decomposition) is applied to these datasets to identify statistically significant spatiotemporal precipitation patterns for the cool (November-April) and warm (July-August) seasons. The low-frequency variability in the 20CR is evaluated by exploring global to continental-scale spatiotemporal variability in moisture flux convergence (MFC) to the occurrence of multiyear droughts and pluvials in Central America, as this region has a demonstrated anti-phase relationship in low-frequency climate variability with northern Mexico and the southwestern US By using the MFC in lieu of precipitation, this study reveals that the 20CR is able to resolve well the low-frequency, multiyear climate variability. In the context of the DD-20CR, multiyear droughts and pluvials in the southwestern US (in the early twentieth century) are significantly related to this low-frequency climate variability. The precipitation anomalies at these low-frequency timescales are in phase between the cool and warm seasons, consistent with the concept of dual-season drought as has been suggested in tree ring studies.

Annual Proxy Records from Tropical Cloud Forest Trees in the Monteverde Cloud Forest, Costa Rica

NASA Astrophysics Data System (ADS)

Anchukaitis, K. J.; Evans, M. N.; Wheelwright, N. T.; Schrag, D. P.

2005-12-01

The extinction of the Golden Toad (Bufo periglenes) from Costa Rica's Monteverde Cloud Forest prompted research into the causes of ecological change in the montane forests of Costa Rica. Subsequent analysis of meteorological data has suggested that warmer global surface and tropical Pacific sea surface temperatures contribute to an observed decrease in cloud cover at Monteverde. However, while recent studies may have concluded that climate change is already having an effect on cloud forest environments in Costa Rica, without the context provided by long-term climate records, it is difficult to confidently conclude that the observed ecological changes are the result of anthropogenic climate forcing, land clearance in the lowland rainforest, or natural variability in tropical climate. To address this, we develop high-resolution proxy paleoclimate records from trees without annual rings in the Monteverde Cloud Forest in Costa Rica. Calibration of an age model in these trees is a fundamental prerequisite for proxy paleoclimate reconstructions. Our approach exploits the isotopic seasonality in the δ18O of water sources (fog versus rainfall) used by trees over the course of a single year. Ocotea tenera individuals of known age and measured annual growth increments were sampled in long-term monitored plantation sites in order to test this proposed age model. High-resolution (200μm increments) stable isotope measurements on cellulose reveal distinct, coherent δ18O cycles of 6 to 10‰. The calculated growth rates derived from the isotope timeseries match those observed from basal growth increment measurements. Spatial fidelity in the age model and climate signal is examined by using multiple cores from multiple trees and multiple sites. These data support our hypothesis that annual isotope cycles in these trees can be used to provide chronological control in the absence of rings. The ability of trees to record interannual climate variability in local hydrometeorology and remote climate forcing is evaluated using the isotope signal from multiple trees, local meteorological observations, and climate field data for the well-observed 1997-1998 warm El Niño-Southern Oscillation (ENSO) event. The successful calibration of our age model is a necessary step toward the development of long, annually-resolved paleoclimate reconstructions from old trees, even without rings, which will be used to evaluate the cause of recent observed climate change at Monteverde and as proxies for tropical climate field reconstructions.

Empirical analyses of plant-climate relationships for the western United States

Treesearch

Gerald E. Rehfeldt; Nicholas L. Crookston; Marcus V. Warwell; Jeffrey S. Evans

2006-01-01

The Random Forests multiple-regression tree was used to model climate profiles of 25 biotic communities of the western United States and nine of their constituent species. Analyses of the communities were based on a gridded sample of ca. 140,000 points, while those for the species used presence-absence data from ca. 120,000 locations. Independent variables included 35...

Role of Perturbing Ocean Initial Condition in Simulated Regional Sea Level Change

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

Hu, Aixue; Meehl, Gerald; Stammer, Detlef

Multiple lines of observational evidence indicate that the global climate has been getting warmer since the early 20th century. This warmer climate has led to a global mean sea level rise of about 18 cm during the 20th century, and over 6 cm for the first 15 years of the 21st century. Regionally the sea level rise is not uniform due in large part to internal climate variability. To better serve the community, the uncertainties of predicting/projecting regional sea level changes associated with internal climate variability need to be quantified. Previous research on this topic has used single-model large ensemblesmore » with perturbed atmospheric initial conditions (ICs). Here we compare uncertainties associated with perturbing ICs in just the atmosphere and just the ocean using a state-of-the-art coupled climate model. We find that by perturbing the oceanic ICs, the uncertainties in regional sea level changes increase compared to those with perturbed atmospheric ICs. In order for us to better assess the full spectrum of the impacts of such internal climate variability on regional and global sea level rise, approaches that involve perturbing both atmospheric and oceanic initial conditions are thus necessary.« less

Role of Perturbing Ocean Initial Condition in Simulated Regional Sea Level Change

DOE PAGES

Hu, Aixue; Meehl, Gerald; Stammer, Detlef; ...

2017-06-05

Multiple lines of observational evidence indicate that the global climate has been getting warmer since the early 20th century. This warmer climate has led to a global mean sea level rise of about 18 cm during the 20th century, and over 6 cm for the first 15 years of the 21st century. Regionally the sea level rise is not uniform due in large part to internal climate variability. To better serve the community, the uncertainties of predicting/projecting regional sea level changes associated with internal climate variability need to be quantified. Previous research on this topic has used single-model large ensemblesmore » with perturbed atmospheric initial conditions (ICs). Here we compare uncertainties associated with perturbing ICs in just the atmosphere and just the ocean using a state-of-the-art coupled climate model. We find that by perturbing the oceanic ICs, the uncertainties in regional sea level changes increase compared to those with perturbed atmospheric ICs. In order for us to better assess the full spectrum of the impacts of such internal climate variability on regional and global sea level rise, approaches that involve perturbing both atmospheric and oceanic initial conditions are thus necessary.« less

A Climate Data Record (CDR) for the global terrestrial water budget: 1984–2010

DOE PAGES

Zhang, Yu; Pan, Ming; Sheffield, Justin; ...

2018-01-12

Closing the terrestrial water budget is necessary to provide consistent estimates of budget components for understanding water resources and changes over time. Given the lack of in situ observations of budget components at anything but local scale, merging information from multiple data sources (e.g., in situ observation, satellite remote sensing, land surface model, and reanalysis) through data assimilation techniques that optimize the estimation of fluxes is a promising approach. Conditioned on the current limited data availability, a systematic method is developed to optimally combine multiple available data sources for precipitation ( P), evapotranspiration (ET), runoff ( R), and the totalmore » water storage change (TWSC) at 0.5° spatial resolution globally and to obtain water budget closure (i.e., to enforce P-ET- R-TWSC = 0) through a constrained Kalman filter (CKF) data assimilation technique under the assumption that the deviation from the ensemble mean of all data sources for the same budget variable is used as a proxy of the uncertainty in individual water budget variables. The resulting long-term (1984–2010), monthly 0.5° resolution global terrestrial water cycle Climate Data Record (CDR) data set is developed under the auspices of the National Aeronautics and Space Administration (NASA) Earth System Data Records (ESDRs) program. This data set serves to bridge the gap between sparsely gauged regions and the regions with sufficient in situ observations in investigating the temporal and spatial variability in the terrestrial hydrology at multiple scales. The CDR created in this study is validated against in situ measurements like river discharge from the Global Runoff Data Centre (GRDC) and the United States Geological Survey (USGS), and ET from FLUXNET. The data set is shown to be reliable and can serve the scientific community in understanding historical climate variability in water cycle fluxes and stores, benchmarking the current climate, and validating models.« less

A Climate Data Record (CDR) for the global terrestrial water budget: 1984–2010

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

Zhang, Yu; Pan, Ming; Sheffield, Justin

Closing the terrestrial water budget is necessary to provide consistent estimates of budget components for understanding water resources and changes over time. Given the lack of in situ observations of budget components at anything but local scale, merging information from multiple data sources (e.g., in situ observation, satellite remote sensing, land surface model, and reanalysis) through data assimilation techniques that optimize the estimation of fluxes is a promising approach. Conditioned on the current limited data availability, a systematic method is developed to optimally combine multiple available data sources for precipitation ( P), evapotranspiration (ET), runoff ( R), and the totalmore » water storage change (TWSC) at 0.5° spatial resolution globally and to obtain water budget closure (i.e., to enforce P-ET- R-TWSC = 0) through a constrained Kalman filter (CKF) data assimilation technique under the assumption that the deviation from the ensemble mean of all data sources for the same budget variable is used as a proxy of the uncertainty in individual water budget variables. The resulting long-term (1984–2010), monthly 0.5° resolution global terrestrial water cycle Climate Data Record (CDR) data set is developed under the auspices of the National Aeronautics and Space Administration (NASA) Earth System Data Records (ESDRs) program. This data set serves to bridge the gap between sparsely gauged regions and the regions with sufficient in situ observations in investigating the temporal and spatial variability in the terrestrial hydrology at multiple scales. The CDR created in this study is validated against in situ measurements like river discharge from the Global Runoff Data Centre (GRDC) and the United States Geological Survey (USGS), and ET from FLUXNET. The data set is shown to be reliable and can serve the scientific community in understanding historical climate variability in water cycle fluxes and stores, benchmarking the current climate, and validating models.« less

A Climate Data Record (CDR) for the global terrestrial water budget: 1984-2010

NASA Astrophysics Data System (ADS)

Zhang, Yu; Pan, Ming; Sheffield, Justin; Siemann, Amanda L.; Fisher, Colby K.; Liang, Miaoling; Beck, Hylke E.; Wanders, Niko; MacCracken, Rosalyn F.; Houser, Paul R.; Zhou, Tian; Lettenmaier, Dennis P.; Pinker, Rachel T.; Bytheway, Janice; Kummerow, Christian D.; Wood, Eric F.

2018-01-01

Closing the terrestrial water budget is necessary to provide consistent estimates of budget components for understanding water resources and changes over time. Given the lack of in situ observations of budget components at anything but local scale, merging information from multiple data sources (e.g., in situ observation, satellite remote sensing, land surface model, and reanalysis) through data assimilation techniques that optimize the estimation of fluxes is a promising approach. Conditioned on the current limited data availability, a systematic method is developed to optimally combine multiple available data sources for precipitation (P), evapotranspiration (ET), runoff (R), and the total water storage change (TWSC) at 0.5° spatial resolution globally and to obtain water budget closure (i.e., to enforce P - ET - R - TWSC = 0) through a constrained Kalman filter (CKF) data assimilation technique under the assumption that the deviation from the ensemble mean of all data sources for the same budget variable is used as a proxy of the uncertainty in individual water budget variables. The resulting long-term (1984-2010), monthly 0.5° resolution global terrestrial water cycle Climate Data Record (CDR) data set is developed under the auspices of the National Aeronautics and Space Administration (NASA) Earth System Data Records (ESDRs) program. This data set serves to bridge the gap between sparsely gauged regions and the regions with sufficient in situ observations in investigating the temporal and spatial variability in the terrestrial hydrology at multiple scales. The CDR created in this study is validated against in situ measurements like river discharge from the Global Runoff Data Centre (GRDC) and the United States Geological Survey (USGS), and ET from FLUXNET. The data set is shown to be reliable and can serve the scientific community in understanding historical climate variability in water cycle fluxes and stores, benchmarking the current climate, and validating models.

Regional Climate Simulation and Data Assimilation with Variable-Resolution GCMs

NASA Technical Reports Server (NTRS)

Fox-Rabinovitz, Michael S.

2002-01-01

Variable resolution GCMs using a global stretched grid (SG) with enhanced regional resolution over one or multiple areas of interest represents a viable new approach to regional climateklimate change and data assimilation studies and applications. The multiple areas of interest, at least one within each global quadrant, include the major global mountains and major global monsoonal circulations over North America, South America, India-China, and Australia. They also can include the polar domains, and the European and African regions. The SG-approach provides an efficient regional downscaling to mesoscales, and it is an ideal tool for representing consistent interactions of globaYlarge- and regionallmeso- scales while preserving the high quality of global circulation. Basically, the SG-GCM simulations are no different from those of the traditional uniform-grid GCM simulations besides using a variable-resolution grid. Several existing SG-GCMs developed by major centers and groups are briefly described. The major discussion is based on the GEOS (Goddard Earth Observing System) SG-GCM regional climate simulations.

Workplace support, discrimination, and person-organization fit: tests of the theory of work adjustment with LGB individuals.

PubMed

Velez, Brandon L; Moradi, Bonnie

2012-07-01

The present study explored the links of 2 workplace contextual variables--perceptions of workplace heterosexist discrimination and lesbian, gay, and bisexual (LGB)-supportive climates--with job satisfaction and turnover intentions in a sample of LGB employees. An extension of the theory of work adjustment (TWA) was used as the conceptual framework for the study; as such, perceived person-organization (P-O) fit was tested as a mediator of the relations between the workplace contextual variables and job outcomes. Data were analyzed from 326 LGB employees. Zero-order correlations indicated that perceptions of workplace heterosexist discrimination and LGB-supportive climates were correlated in expected directions with P-O fit, job satisfaction, and turnover intentions. Structural equation modeling (SEM) was used to compare multiple alternative measurement models evaluating the discriminant validity of the 2 workplace contextual variables relative to one another, and the 3 TWA job variables relative to one another; SEM was also used to test the hypothesized mediation model. Comparisons of multiple alternative measurement models supported the construct distinctiveness of the variables of interest. The test of the hypothesized structural model revealed that only LGB-supportive climates (and not workplace heterosexist discrimination) had a unique direct positive link with P-O fit and, through the mediating role of P-O fit, had significant indirect positive and negative relations with job satisfaction and turnover intentions, respectively. Moreover, P-O fit had a significant indirect negative link with turnover intentions through job satisfaction.

Sub-daily Statistical Downscaling of Meteorological Variables Using Neural Networks

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

Kumar, Jitendra; Brooks, Bjørn-Gustaf J.; Thornton, Peter E

2012-01-01

A new open source neural network temporal downscaling model is described and tested using CRU-NCEP reanal ysis and CCSM3 climate model output. We downscaled multiple meteorological variables in tandem from monthly to sub-daily time steps while also retaining consistent correlations between variables. We found that our feed forward, error backpropagation approach produced synthetic 6 hourly meteorology with biases no greater than 0.6% across all variables and variance that was accurate within 1% for all variables except atmospheric pressure, wind speed, and precipitation. Correlations between downscaled output and the expected (original) monthly means exceeded 0.99 for all variables, which indicates thatmore » this approach would work well for generating atmospheric forcing data consistent with mass and energy conserved GCM output. Our neural network approach performed well for variables that had correlations to other variables of about 0.3 and better and its skill was increased by downscaling multiple correlated variables together. Poor replication of precipitation intensity however required further post-processing in order to obtain the expected probability distribution. The concurrence of precipitation events with expected changes in sub ordinate variables (e.g., less incident shortwave radiation during precipitation events) were nearly as consistent in the downscaled data as in the training data with probabilities that differed by no more than 6%. Our downscaling approach requires training data at the target time step and relies on a weak assumption that climate variability in the extrapolated data is similar to variability in the training data.« less

Subtropical Climate Variability since the Last Glacial Maximum from Speleothem Precipitation Reconstructions in Florida

NASA Astrophysics Data System (ADS)

Polk, J.; van Beynen, P.; DeLong, K. L.; Asmerom, Y.; Polyak, V. J.

2017-12-01

Teleconnections between the tropical-subtropical regions of the Americas since the Last Glacial Maximum (LGM), particularly the Mid- to Late-Holocene, and high-resolution proxy records refining climate variability over this period continue to receive increasing attention. Here, we present a high-resolution, precisely dated speleothem record spanning multiple periods of time since the LGM ( 30 ka) for the Florida peninsula. The data indicate that the amount effect plays a significant role in determining the isotopic signal of the speleothem calcite. Collectively, the records indicate distinct differences in climate in the region between the LGM, Mid-Holocene, and Late Holocene, including a progressive shift in ocean composition and precipitation isotopic values through the period, suggesting Florida's sensitivity to regional and global climatic shifts. Comparisons between speleothem δ18O values and Gulf of Mexico marine records reveal a strong connection between the Gulf region and the terrestrial subtropical climate in the Late Holocene, while the North Atlantic's influence is clear in the earlier portions of the record. Warmer sea surface temperatures correspond to enhanced evaporation, leading to more intense atmospheric convection in Florida, and thereby modulating the isotopic composition of rainfall above the cave. These regional signals in climate extend from the subtropics to the tropics, with a clear covariance between the speleothem signal and other proxy records from around the region, as well as global agreement during the LGM period with other records. These latter connections appear to be driven by changes in the mean position of the Intertropical Convergence Zone and time series analysis of the δ18O values reveals significant multidecadal periodicities in the record, which are evidenced by agreement with the AMV and other multidecadal influences (NAO and PDO) likely having varying influence throughout the period of record. The climate variability recorded in our record suggests complex responses to major and abrupt shifts during these periods, likely due to Florida's subtropical location and the influence of multiple climate forcing mechanisms in the region.

Region-Specific Sensitivity of Anemophilous Pollen Deposition to Temperature and Precipitation

PubMed Central

Donders, Timme H.; Hagemans, Kimberley; Dekker, Stefan C.; de Weger, Letty A.; de Klerk, Pim; Wagner-Cremer, Friederike

2014-01-01

Understanding relations between climate and pollen production is important for several societal and ecological challenges, importantly pollen forecasting for pollinosis treatment, forensic studies, global change biology, and high-resolution palaeoecological studies of past vegetation and climate fluctuations. For these purposes, we investigate the role of climate variables on annual-scale variations in pollen influx, test the regional consistency of observed patterns, and evaluate the potential to reconstruct high-frequency signals from sediment archives. A 43-year pollen-trap record from the Netherlands is used to investigate relations between annual pollen influx, climate variables (monthly and seasonal temperature and precipitation values), and the North Atlantic Oscillation climate index. Spearman rank correlation analysis shows that specifically in Alnus, Betula, Corylus, Fraxinus, Quercus and Plantago both temperature in the year prior to (T-1), as well as in the growing season (T), are highly significant factors (TApril rs between 0.30 [P<0.05[ and 0.58 [P<0.0001]; TJuli-1 rs between 0.32 [P<0.05[ and 0.56 [P<0.0001]) in the annual pollen influx of wind-pollinated plants. Total annual pollen prediction models based on multiple climate variables yield R2 between 0.38 and 0.62 (P<0.0001). The effect of precipitation is minimal. A second trapping station in the SE Netherlands, shows consistent trends and annual variability, suggesting the climate factors are regionally relevant. Summer temperature is thought to influence the formation of reproductive structures, while temperature during the flowering season influences pollen release. This study provides a first predictive model for seasonal pollen forecasting, and also aides forensic studies. Furthermore, variations in pollen accumulation rates from a sub-fossil peat deposit are comparable with the pollen trap data. This suggests that high frequency variability pollen records from natural archives reflect annual past climate variability, and can be used in palaeoecological and -climatological studies to bridge between population- and species-scale responses to climate forcing. PMID:25133631

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

NASA Astrophysics Data System (ADS)

Murari, K. K.; Jayaraman, T.

2014-12-01

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

Modeling hydrologic responses to deforestation/forestation and climate change at multiple scales in the Southern US and China

Treesearch

Ge Sun; Steven McNulty; Jianbiao Lu; James Vose; Devendra Amayta; Guoyi Zhou; Zhiqiang Zhang

2006-01-01

Watershed management and restoration practices require a clear understanding of the basic eco-hydrologic processes and ecosystem responses to disturbances at multiple scales (Bruijnzeel, 2004; Scott et al., 2005). Worldwide century-long forest hydrologic research has documented that deforestation and forestation (i.e. reforestation and afforestation) can have variable...

Combining landscape variables and species traits can improve the utility of climate change vulnerability assessments

USGS Publications Warehouse

Nadeau, Christopher P.; Fuller, Angela K.

2016-01-01

Conservation organizations worldwide are investing in climate change vulnerability assessments. Most vulnerability assessment methods focus on either landscape features or species traits that can affect a species vulnerability to climate change. However, landscape features and species traits likely interact to affect vulnerability. We compare a landscape-based assessment, a trait-based assessment, and an assessment that combines landscape variables and species traits for 113 species of birds, herpetofauna, and mammals in the northeastern United States. Our aim is to better understand which species traits and landscape variables have the largest influence on assessment results and which types of vulnerability assessments are most useful for different objectives. Species traits were most important for determining which species will be most vulnerable to climate change. The sensitivity of species to dispersal barriers and the species average natal dispersal distance were the most important traits. Landscape features were most important for determining where species will be most vulnerable because species were most vulnerable in areas where multiple landscape features combined to increase vulnerability, regardless of species traits. The interaction between landscape variables and species traits was important when determining how to reduce climate change vulnerability. For example, an assessment that combines information on landscape connectivity, climate change velocity, and natal dispersal distance suggests that increasing landscape connectivity may not reduce the vulnerability of many species. Assessments that include landscape features and species traits will likely be most useful in guiding conservation under climate change.

Farming with Grass: Achieving Sustainable Mixed Agricultural Landscapes

USDA-ARS?s Scientific Manuscript database

Agriculture in grassland environments is facing multiple stresses from shifting demographics, declining and fragmented agricultural landscapes, declining environmental quality, variable and changing climate, volatile and increasing energy costs, marginal economic returns, and globalization. Grassla...

Forest processes and global environmental change: predicting the effects of individual and multiple stressors

Treesearch

John Aber; Ronald P. Neilson; Steve McNulty; James M. Lenihan; Dominque Bachelet; Raymond J. Drapek

2001-01-01

The purpose of this article is to review the state of prediction of forest ecosystem response to envisioned changes in the physical and chemical climate. These results are offered as one part of the forest sector analysis of the National Assessment of the Potential Consequences of Climate Variability and Change. This article has three sections. The first offers a very...

Inter-comparison of multiple statistically downscaled climate datasets for the Pacific Northwest, USA

PubMed Central

Jiang, Yueyang; Kim, John B.; Still, Christopher J.; Kerns, Becky K.; Kline, Jeffrey D.; Cunningham, Patrick G.

2018-01-01

Statistically downscaled climate data have been widely used to explore possible impacts of climate change in various fields of study. Although many studies have focused on characterizing differences in the downscaling methods, few studies have evaluated actual downscaled datasets being distributed publicly. Spatially focusing on the Pacific Northwest, we compare five statistically downscaled climate datasets distributed publicly in the US: ClimateNA, NASA NEX-DCP30, MACAv2-METDATA, MACAv2-LIVNEH and WorldClim. We compare the downscaled projections of climate change, and the associated observational data used as training data for downscaling. We map and quantify the variability among the datasets and characterize the spatio-temporal patterns of agreement and disagreement among the datasets. Pair-wise comparisons of datasets identify the coast and high-elevation areas as areas of disagreement for temperature. For precipitation, high-elevation areas, rainshadows and the dry, eastern portion of the study area have high dissimilarity among the datasets. By spatially aggregating the variability measures into watersheds, we develop guidance for selecting datasets within the Pacific Northwest climate change impact studies. PMID:29461513

Inter-comparison of multiple statistically downscaled climate datasets for the Pacific Northwest, USA.

PubMed

Jiang, Yueyang; Kim, John B; Still, Christopher J; Kerns, Becky K; Kline, Jeffrey D; Cunningham, Patrick G

2018-02-20

Statistically downscaled climate data have been widely used to explore possible impacts of climate change in various fields of study. Although many studies have focused on characterizing differences in the downscaling methods, few studies have evaluated actual downscaled datasets being distributed publicly. Spatially focusing on the Pacific Northwest, we compare five statistically downscaled climate datasets distributed publicly in the US: ClimateNA, NASA NEX-DCP30, MACAv2-METDATA, MACAv2-LIVNEH and WorldClim. We compare the downscaled projections of climate change, and the associated observational data used as training data for downscaling. We map and quantify the variability among the datasets and characterize the spatio-temporal patterns of agreement and disagreement among the datasets. Pair-wise comparisons of datasets identify the coast and high-elevation areas as areas of disagreement for temperature. For precipitation, high-elevation areas, rainshadows and the dry, eastern portion of the study area have high dissimilarity among the datasets. By spatially aggregating the variability measures into watersheds, we develop guidance for selecting datasets within the Pacific Northwest climate change impact studies.

Translating climate data for business decisions

NASA Astrophysics Data System (ADS)

Steinberg, N.

2015-12-01

Businesses are bound to play an integral role in global and local climate change adaptation efforts, and integrating climate science into business decision-making can help protect companies' bottom-line and the communities which they depend upon. Yet many companies do not have good means to measure and manage climate risks. There are inherent limiting factors to incorporating climate data into existing operations and sourcing strategies. Spatial and temporal incongruities between climate and business models can make integration cumbersome. Even when such incongruities are resolved, raw climate data must undergo multiple transformations until the data is deemed actionable or otherwise translatable in dollar terms. However, the predictability of future impacts is advancing along with the use of second-order variables such as Cooling Degree Days and Water-Limited Crop productivity, helping business managers make better decisions about future energy and water demand requirements under the prospect of rising temperatures and more variable rainfall. This presentation will discuss the methods and opportunities for transforming raw climate data into business metrics. Results for the 2015 Corporate Adaptation Survey, led by Four Twenty Seven and in partnership with Notre Dame Global Adaptation Index, will also be presented to illustrate existing gaps between climate science and its application in the business context.

Climate change and health modeling: horses for courses.

PubMed

Ebi, Kristie L; Rocklöv, Joacim

2014-01-01

Mathematical and statistical models are needed to understand the extent to which weather, climate variability, and climate change are affecting current and may affect future health burdens in the context of other risk factors and a range of possible development pathways, and the temporal and spatial patterns of any changes. Such understanding is needed to guide the design and the implementation of adaptation and mitigation measures. Because each model projection captures only a narrow range of possible futures, and because models serve different purposes, multiple models are needed for each health outcome ('horses for courses'). Multiple modeling results can be used to bracket the ranges of when, where, and with what intensity negative health consequences could arise. This commentary explores some climate change and health modeling issues, particularly modeling exposure-response relationships, developing early warning systems, projecting health risks over coming decades, and modeling to inform decision-making. Research needs are also suggested.

[Factors associated with incidence of dengue in Costa Rica].

PubMed

Mena, Nelson; Troyo, Adriana; Bonilla-Carrión, Roger; Calderón-Arguedas, Olger

2011-04-01

Determine the extent to which socioeconomic, demographic, geographic, and climate variables affected the incidence of dengue and dengue hemorrhagic fever (D/DH) in Costa Rica during the period 1999-2007. A correlational epidemiologic study was conducted that analyzed the cumulative incidence of D/DH from 1999 to 2007 and its association with different variables in the country's 81 cantons. Information was obtained from secondary sources, and the independent variables used for the analysis were selected on the basis of their representativeness in terms of sociodemographic, environmental, and health coverage factors that affect the epidemiology of D/DH. These variables were divided into four groups of indicators: demographic, socioeconomic, housing, and climate and geographical. The data were analyzed by means of simple and multiple Poisson regressions. The Costa Rican cantons with a higher incidence of D/DH were located primarily near the coast, coinciding with some of the variables studied. Temperature, altitude, and the human poverty index were the most relevant variables in explaining the incidence of D/DH, while temperature was the most significant variable in the multiple analyses. The analyses made it possible to correlate a higher incidence of D/DH with lower-altitude cantons, higher temperature, and a high human poverty index ranking. This information is relevant as a first step toward prioritizing and optimizing actions for the prevention and control of this disease.

The role of ENSO in understanding changes in Colombia's annual malaria burden by region, 1960–2006

PubMed Central

Mantilla, Gilma; Oliveros, Hugo; Barnston, Anthony G

2009-01-01

Background Malaria remains a serious problem in Colombia. The number of malaria cases is governed by multiple climatic and non-climatic factors. Malaria control policies, and climate controls such as rainfall and temperature variations associated with the El Niño/Southern Oscillation (ENSO), have been associated with malaria case numbers. Using historical climate data and annual malaria case number data from 1960 to 2006, statistical models are developed to isolate the effects of climate in each of Colombia's five contrasting geographical regions. Methods Because year to year climate variability associated with ENSO causes interannual variability in malaria case numbers, while changes in population and institutional control policy result in more gradual trends, the chosen predictors in the models are annual indices of the ENSO state (sea surface temperature [SST] in the tropical Pacific Ocean) and time reference indices keyed to two major malaria trends during the study period. Two models were used: a Poisson and a Negative Binomial regression model. Two ENSO indices, two time reference indices, and one dummy variable are chosen as candidate predictors. The analysis was conducted using the five geographical regions to match the similar aggregation used by the National Institute of Health for its official reports. Results The Negative Binomial regression model is found better suited to the malaria cases in Colombia. Both the trend variables and the ENSO measures are significant predictors of malaria case numbers in Colombia as a whole, and in two of the five regions. A one degree Celsius change in SST (indicating a weak to moderate ENSO event) is seen to translate to an approximate 20% increase in malaria cases, holding other variables constant. Conclusion Regional differentiation in the role of ENSO in understanding changes in Colombia's annual malaria burden during 1960–2006 was found, constituting a new approach to use ENSO as a significant predictor of the malaria cases in Colombia. These results naturally point to additional needed work: (1) refining the regional and seasonal dependence of climate on the ENSO state, and of malaria on the climate variables; (2) incorporating ENSO-related climate variability into dynamic malaria models. PMID:19133152

Multi-decadal trend and space-time variability of sea level over the Indian Ocean since the 1950s: impact of decadal climate modes

NASA Astrophysics Data System (ADS)

Han, W.; Stammer, D.; Meehl, G. A.; Hu, A.; Sienz, F.

2016-12-01

Sea level varies on decadal and multi-decadal timescales over the Indian Ocean. The variations are not spatially uniform, and can deviate considerably from the global mean sea level rise (SLR) due to various geophysical processes. One of these processes is the change of ocean circulation, which can be partly attributed to natural internal modes of climate variability. Over the Indian Ocean, the most influential climate modes on decadal and multi-decadal timescales are the Interdecadal Pacific Oscillation (IPO) and decadal variability of the Indian Ocean dipole (IOD). Here, we first analyze observational datasets to investigate the impacts of IPO and IOD on spatial patterns of decadal and interdecadal (hereafter decal) sea level variability & multi-decadal trend over the Indian Ocean since the 1950s, using a new statistical approach of Bayesian Dynamical Linear regression Model (DLM). The Bayesian DLM overcomes the limitation of "time-constant (static)" regression coefficients in conventional multiple linear regression model, by allowing the coefficients to vary with time and therefore measuring "time-evolving (dynamical)" relationship between climate modes and sea level. For the multi-decadal sea level trend since the 1950s, our results show that climate modes and non-climate modes (the part that cannot be explained by climate modes) have comparable contributions in magnitudes but with different spatial patterns, with each dominating different regions of the Indian Ocean. For decadal variability, climate modes are the major contributors for sea level variations over most region of the tropical Indian Ocean. The relative importance of IPO and decadal variability of IOD, however, varies spatially. For example, while IOD decadal variability dominates IPO in the eastern equatorial basin (85E-100E, 5S-5N), IPO dominates IOD in causing sea level variations in the tropical southwest Indian Ocean (45E-65E, 12S-2S). To help decipher the possible contribution of external forcing to the multi-decadal sea level trend and decadal variability, we also analyze the model outputs from NCAR's Community Earth System Model (CESM) Large Ensemble Experiments, and compare the results with our observational analyses.

Climate science and famine early warning

USGS Publications Warehouse

Verdin, James P.; Funk, Chris; Senay, Gabriel B.; Choularton, R.

2005-01-01

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.

Climate science and famine early warning.

PubMed

Verdin, James; Funk, Chris; Senay, Gabriel; Choularton, Richard

2005-11-29

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.

Climate science and famine early warning

PubMed Central

Verdin, James; Funk, Chris; Senay, Gabriel; Choularton, Richard

2005-01-01

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised. PMID:16433101

Multi-Site and Multi-Variables Statistical Downscaling Technique in the Monsoon Dominated Region of Pakistan

NASA Astrophysics Data System (ADS)

Khan, Firdos; Pilz, Jürgen

2016-04-01

South Asia is under the severe impacts of changing climate and global warming. The last two decades showed that climate change or global warming is happening and the first decade of 21st century is considered as the warmest decade over Pakistan ever in history where temperature reached 53 0C in 2010. Consequently, the spatio-temporal distribution and intensity of precipitation is badly effected and causes floods, cyclones and hurricanes in the region which further have impacts on agriculture, water, health etc. To cope with the situation, it is important to conduct impact assessment studies and take adaptation and mitigation remedies. For impact assessment studies, we need climate variables at higher resolution. Downscaling techniques are used to produce climate variables at higher resolution; these techniques are broadly divided into two types, statistical downscaling and dynamical downscaling. The target location of this study is the monsoon dominated region of Pakistan. One reason for choosing this area is because the contribution of monsoon rains in this area is more than 80 % of the total rainfall. This study evaluates a statistical downscaling technique which can be then used for downscaling climatic variables. Two statistical techniques i.e. quantile regression and copula modeling are combined in order to produce realistic results for climate variables in the area under-study. To reduce the dimension of input data and deal with multicollinearity problems, empirical orthogonal functions will be used. Advantages of this new method are: (1) it is more robust to outliers as compared to ordinary least squares estimates and other estimation methods based on central tendency and dispersion measures; (2) it preserves the dependence among variables and among sites and (3) it can be used to combine different types of distributions. This is important in our case because we are dealing with climatic variables having different distributions over different meteorological stations. The proposed model will be validated by using the (National Centers for Environmental Prediction / National Center for Atmospheric Research) NCEP/NCAR predictors for the period of 1960-1990 and validated for 1990-2000. To investigate the efficiency of the proposed model, it will be compared with the multivariate multiple regression model and with dynamical downscaling climate models by using different climate indices that describe the frequency, intensity and duration of the variables of interest. KEY WORDS: Climate change, Copula, Monsoon, Quantile regression, Spatio-temporal distribution.

Mars dust storms - Interannual variability and chaos

NASA Technical Reports Server (NTRS)

Ingersoll, Andrew P.; Lyons, James R.

1993-01-01

The hypothesis is that the global climate system, consisting of atmospheric dust interacting with the circulation, produces its own interannual variability when forced at the annual frequency. The model has two time-dependent variables representing the amount of atmospheric dust in the northern and southern hemispheres, respectively. Absorption of sunlight by the dust drives a cross-equatorial Hadley cell that brings more dust into the heated hemisphere. The circulation decays when the dust storm covers the globe. Interannual variability manifests itself either as a periodic solution in which the period is a multiple of the Martian year, or as an aperiodic (chaotic) solution that never repeats. Both kinds of solution are found in the model, lending support to the idea that interannual variability is an intrinsic property of the global climate system. The next step is to develop a hierarchy of dust-circulation models capable of being integrated for many years.

A transient stochastic weather generator incorporating climate model uncertainty

NASA Astrophysics Data System (ADS)

Glenis, Vassilis; Pinamonti, Valentina; Hall, Jim W.; Kilsby, Chris G.

2015-11-01

Stochastic weather generators (WGs), which provide long synthetic time series of weather variables such as rainfall and potential evapotranspiration (PET), have found widespread use in water resources modelling. When conditioned upon the changes in climatic statistics (change factors, CFs) predicted by climate models, WGs provide a useful tool for climate impacts assessment and adaption planning. The latest climate modelling exercises have involved large numbers of global and regional climate models integrations, designed to explore the implications of uncertainties in the climate model formulation and parameter settings: so called 'perturbed physics ensembles' (PPEs). In this paper we show how these climate model uncertainties can be propagated through to impact studies by testing multiple vectors of CFs, each vector derived from a different sample from a PPE. We combine this with a new methodology to parameterise the projected time-evolution of CFs. We demonstrate how, when conditioned upon these time-dependent CFs, an existing, well validated and widely used WG can be used to generate non-stationary simulations of future climate that are consistent with probabilistic outputs from the Met Office Hadley Centre's Perturbed Physics Ensemble. The WG enables extensive sampling of natural variability and climate model uncertainty, providing the basis for development of robust water resources management strategies in the context of a non-stationary climate.

Hierarchical, parallel computing strategies using component object model for process modelling responses of forest plantations to interacting multiple stresses

Treesearch

J. G. Isebrands; G. E. Host; K. Lenz; G. Wu; H. W. Stech

2000-01-01

Process models are powerful research tools for assessing the effects of multiple environmental stresses on forest plantations. These models are driven by interacting environmental variables and often include genetic factors necessary for assessing forest plantation growth over a range of different site, climate, and silvicultural conditions. However, process models are...

Disentangling synergistic climate drivers on the evolution of two species of planktonic foraminifera on regional and global scales

NASA Astrophysics Data System (ADS)

Brombacher, A.; Wilson, P. A.; Bailey, I.; Ezard, T. H. G.

2016-02-01

Evolution is driven by a combination of biotic and abiotic factors. When quantifying the effects of abiotic drivers, evolutionary change is generally described as a response to a single environmental parameter assumed to represent global climate. However, climate is a complex system of many interacting factors and characterized by high regional variability. Therefore, to understand the role of climate in evolutionary change, we need to consider multiple environmental parameters, across local, regional and global scales, as well as their interactions. The deep-sea microfossil record is sufficiently complete that sufficiently continuous multivariate climatic and multivariate trait data can be obtained from the same samples. Here we present morphological records of the planktonic foraminifera species Globoconella puncticulata and Truncorotalia crassaformis over a 500,000-year interval directly preceding the extinction of G. puncticulata (2.41 Ma). Material was collected from five North Atlantic sites (ODP Sites 659 [18° N], 925 [3° N] and 981 [55° N], IODP Site U1313 [41° N] and DSDP Site 606 [37° N]). Test size and shape of over 35,000 individuals were measured and compared to site-specific records of sea surface temperature, primary productivity and marine aeolian dust deposition, as well as to global records of ice volume, ocean circulation and atmospheric CO2, and all two-way interactions. Morphological parameters respond weakly to individual climate parameters. Once interactions among all studied climate parameters were incorporated, abiotic change explained around 35% of the evolutionary variance. Observed covariances between environmental parameters vary strongly with glacial-interglacial cyclicity, implying that the relationships among climate variables and their relative influences on evolutionary change varied through time. This time dependence cautions against unfettered use of dimension reduction techniques, such as principal components analysis, to extract a single, supposedly dominant, proxy. Furthermore species' responses differed between geographic locations, impressing the need to test how interactions among multiple climate variables at different regional settings shape the biotic microevolutionary response to local and global abiotic change.

Projecting future precipitation and temperature at sites with diverse climate through multiple statistical downscaling schemes

NASA Astrophysics Data System (ADS)

Vallam, P.; Qin, X. S.

2017-10-01

Anthropogenic-driven climate change would affect the global ecosystem and is becoming a world-wide concern. Numerous studies have been undertaken to determine the future trends of meteorological variables at different scales. Despite these studies, there remains significant uncertainty in the prediction of future climates. To examine the uncertainty arising from using different schemes to downscale the meteorological variables for the future horizons, projections from different statistical downscaling schemes were examined. These schemes included statistical downscaling method (SDSM), change factor incorporated with LARS-WG, and bias corrected disaggregation (BCD) method. Global circulation models (GCMs) based on CMIP3 (HadCM3) and CMIP5 (CanESM2) were utilized to perturb the changes in the future climate. Five study sites (i.e., Alice Springs, Edmonton, Frankfurt, Miami, and Singapore) with diverse climatic conditions were chosen for examining the spatial variability of applying various statistical downscaling schemes. The study results indicated that the regions experiencing heavy precipitation intensities were most likely to demonstrate the divergence between the predictions from various statistical downscaling methods. Also, the variance computed in projecting the weather extremes indicated the uncertainty derived from selection of downscaling tools and climate models. This study could help gain an improved understanding about the features of different downscaling approaches and the overall downscaling uncertainty.

Tolerance to multiple climate stressors: A case study of Douglas-fir drought and cold hardiness

USGS Publications Warehouse

Bansal, Sheel; Harrington, Constance A; St. Clair, John Bradley

2016-01-01

Summary: 1. Drought and freeze events are two of the most common forms of climate extremes which result in tree damage or death, and the frequency and intensity of both stressors may increase with climate change. Few studies have examined natural covariation in stress tolerance traits to cope with multiple stressors among wild plant populations. 2. We assessed the capacity of coastal Douglas-fir (Pseudotsuga menziesii var. menziesii), an ecologically and economically important species in the northwestern USA, to tolerate both drought and cold stress on 35 populations grown in common gardens. We used principal components analysis to combine drought and cold hardiness trait data into generalized stress hardiness traits to model geographic variation in hardiness as a function of climate across the Douglas-fir range. 3. Drought and cold hardiness converged among populations along winter temperature gradients and diverged along summer precipitation gradients. Populations originating in regions with cold winters had relatively high tolerance to both drought and cold stress, which is likely due to overlapping adaptations for coping with winter desiccation. Populations from regions with dry summers had increased drought hardiness but reduced cold hardiness, suggesting a trade-off in tolerance mechanisms. 4. Our findings highlight the necessity to look beyond bivariate trait–climate relationships and instead consider multiple traits and climate variables to effectively model and manage for the impacts of climate change on widespread species.

Influences of climate, fire, and topography on contemporary age structure patterns of Douglas-fir at 205 old forest sites in western Oregon

Treesearch

Nathan J. Poage; Peter J. Weisberg; Peter C. Impara; John C. Tappeiner; Thomas S. Sensenig

2009-01-01

Knowledge of forest development is basic to understanding the ecology, dynamics, and management of forest ecosystems. We hypothesized that the age structure patterns of Douglas-fir at 205 old forest sites in western Oregon are extremely variable with long and (or) multiple establishment periods common, and that these patterns reflect variation in regional-scale climate...

Assessing potential climate change pressures across the conterminous United States: mapping plant hardiness zones, heat zones, growing degree days, and cumulative drought severity throughout this century

Treesearch

Stephen N. Matthews; Louis R. Iverson; Matthew P. Peters; Anantha M. Prasad

2018-01-01

The maps and tables presented here represent potential variability of projected climate change across the conterminous United States during three 30-year periods in this century and emphasizes the importance of evaluating multiple signals of change across large spatial domains. Maps of growing degree days, plant hardiness zones, heat zones, and cumulative drought...

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.

The Nevada NSF EPSCoR infrastructure for climate change science, education, and outreach project: highlights and progress on investigations of ecological change and water resources along elevational gradients

NASA Astrophysics Data System (ADS)

Saito, L.; Biondi, F.; Fenstermaker, L. F.; Arnone, J.; Devitt, D.; Riddle, B.; Young, M.

2010-12-01

In 2008, the Nevada System of Higher Education received a 5-year, $15 million grant from the National Science Foundation’s (NSF) Experimental Program to Stimulate Competitive Research (EPSCoR). The mission of the project is to create a statewide interdisciplinary program to stimulate transformative research, education, and outreach about the effects of regional climate change on ecosystem services (especially water resources), and support use of this knowledge by policy makers and stakeholders. The overarching question that this effort will address is: how will climate change affect water resources, disturbance regimes and linked ecosystem and human services? While the overall project includes cyberinfrastructure, policy, education and climate modeling, this presentation will focus on the ecological change and water resources components. The goals of these two components are: 1) improving understanding of processes controlling local- and basin-wide impacts of climate on species dynamics, disturbance regimes, and water recharge rates; 2) evaluating interactions between landscape-level processes and biophysical indicators; 3) evaluating interactions between surface and groundwater systems; 4) predicting changes in wildfire regime, primary productivity, and biodiversity (including invasive species); and 5) assessing how interactions between water and ecology will differ under climate change and/or climate variability scenarios. To achieve these goals, the two components will quantify present-day climate variability at multiple temporal and spatial scales, including at multiple elevations within Nevada’s Basin and Range ecosystem continuum. This presentation will discuss key elements for achieving these goals, including the establishment of instrumented transects spanning a range of elevations and vegetation zones in eastern and southern Nevada.

Estimating switchgrass productivity in the Great Plains using satellite vegetation index and site environmental variables

USGS Publications Warehouse

Gu, Yingxin; Wylie, Bruce K.; Howard, Daniel M.

2015-01-01

Switchgrass is being evaluated as a potential feedstock source for cellulosic biofuels and is being cultivated in several regions of the United States. The recent availability of switchgrass land cover maps derived from the National Agricultural Statistics Service cropland data layer for the conterminous United States provides an opportunity to assess the environmental conditions of switchgrass over large areas and across different geographic locations. The main goal of this study is to develop a data-driven multiple regression switchgrass productivity model and identify the optimal climate and environment conditions for the highly productive switchgrass in the Great Plains (GP). Environmental and climate variables used in the study include elevation, soil organic carbon, available water capacity, climate, and seasonal weather. Satellite-derived growing season averaged Normalized Difference Vegetation Index (GSN) was used as a proxy for switchgrass productivity. Multiple regression analyses indicate that there are strong correlations between site environmental variables and switchgrass productivity (r = 0.95). Sufficient precipitation and suitable temperature during the growing season (i.e., not too hot or too cold) are favorable for switchgrass growth. Elevation and soil characteristics (e.g., soil available water capacity) are also an important factor impacting switchgrass productivity. An anticipated switchgrass biomass productivity map for the entire GP based on site environmental and climate conditions and switchgrass productivity model was generated. Highly productive switchgrass areas are mainly located in the eastern part of the GP. Results from this study can help land managers and biofuel plant investors better understand the general environmental and climate conditions influencing switchgrass growth and make optimal land use decisions regarding switchgrass development in the GP.

Reconstructing Deep Ocean Circulation in the North Atlantic from Bermuda Rise, and Beyond

NASA Astrophysics Data System (ADS)

McManus, J. F.

2016-12-01

The large-scale subsurface circulation of the ocean is an important component of the Earth's climate system, and contributes to the global and regional transport of heat and mass. Assessing how this system has changed in the past is thus a priority for understanding natural climate variability. A long-coring campaign on Bermuda Rise has provided additional abundant high-quality sediments from this site of rapid accumulation in the deep western basin, situated beneath the subtropical gyre of the North Atlantic Ocean. These sediments allow the high-resolution reconstruction of deepwater chemistry and export from this key location throughout the last 150,000 years, covering the entire last glacial cycle in a continuous section of 35 meters in core KNR191-CDH19. The suite of proxy indicators analyzed includes uranium-series disequilibria, neodymium isotopes, and benthic stable isotopes. Combined with multiple previous studies of nearby cores on Bermuda Rise, the published and new proxy data from CDH19 confirm the variability of the deep circulation in the Atlantic Ocean in association with past climate changes. The multiple indicators, along with complementary data from other locations, display coherent evidence for contrasts between deep circulation during glacial and interglacial intervals, with persistent strong, deep ventilation only within the peak interglacial of marine isotope stage 5e (MIS 5e) and the Holocene. In contrast, repeated, dramatic variability in deep ocean circulation accompanied the millennial climate changes of the last glaciation and deglaciation. The largest magnitude circulation shifts occurred at the transitions into stadials associated with the Hudson strait iceberg discharges and between them and the ensuing northern interstadial warmings, significantly exceeding that of the overall glacial-interglacial difference, highlighting the potential oceanographic and climatic importance of short-term perturbations to the deep ocean circulation.

North Atlantic Jet Variability in PMIP3 LGM Simulations

NASA Astrophysics Data System (ADS)

Hezel, P.; Li, C.

2017-12-01

North Atlantic jet variability in glacial climates has been shown inmodelling studies to be strongly influenced by upstream ice sheettopography. We analyze the results of 8 models from the PMIP3simulations, forced with a hybrid Laurentide Ice Sheet topography, andcompare them to the PMIP2 simulations which were forced with theICE-5G topography, to develop a general understanding of the NorthAtlantic jet and jet variability. The strengthening of the jet andreduced spatial variability is a robust feature of the last glacialmaximum (LGM) simulations compared to the pre-industrial state.However, the canonical picture of the LGM North Atlantic jet as beingmore zonal and elongated compared to pre-industrial climate states isnot a robust result across models, and may have arisen in theliterature as a function of multiple studies performed with the samemodel.

Potential impacts of climate variability on dengue hemorrhagic fever in Honduras, 2010.

PubMed

Zambrano, L I; Sevilla, C; Reyes-García, S Z; Sierra, M; Kafati, R; Rodriguez-Morales, A J; Mattar, S

2012-12-01

Climate change and variability are affecting human health and disease direct or indirectly through many mechanisms. Dengue is one of those diseases that is strongly influenced by climate variability; however its study in Central America has been poorly approached. In this study, we assessed potential associations between macroclimatic and microclimatic variation and dengue hemorrhagic fever (DHF) cases in the main hospital of Honduras during 2010. In this year, 3,353 cases of DHF were reported in the Hospital Escuela, Tegucigalpa. Climatic periods marked a difference of 158% in the mean incidence of cases, from El Niño weeks (-99% of cases below the mean incidence) to La Niña months (+59% of cases above it) (p<0.01). Linear regression showed significantly higher dengue incidence with lower values of Oceanic Niño Index (p=0.0097), higher rain probability (p=0.0149), accumulated rain (p=0.0443) and higher relative humidity (p=0.0292). At a multiple linear regression model using those variables, ONI values shown to be the most important and significant factor found to be associated with the monthly occurrence of DHF cases (r²=0.649; βstandardized=-0.836; p=0.01). As has been shown herein, climate variability is an important element influencing the dengue epidemiology in Honduras. However, it is necessary to extend these studies in this and other countries in the Central America region, because these models can be applied for surveillance as well as for prediction of dengue.

The Borderlands and climate change: Chapter 10 in United States-Mexican Borderlands: Facing tomorrow's challenges through USGS science

USGS Publications Warehouse

Fitzpatrick, Joan; Gray, Floyd; Dubiel, Russell; Langman, Jeff; Moring, J. Bruce; Norman, Laura M.; Page, William R.; Parcher, Jean W.

2013-01-01

The prediction of global climate change in response to both natural forces and human activity is one of the defining issues of our times. The unprecedented observational capacity of modern earth-orbiting satellites coupled with the development of robust computational representations (models) of the Earth’s weather and climate systems afford us the opportunity to observe and investigate how these systems work now, how they have worked in the past, and how they will work in the future when forced in specific ways. In the most recent report on global climate change by the Intergovernmental Panel on Climate Change (IPCC; Solomon and others, 2007), analyses using multiple climate models support recent observations that the Earth’s climate is changing in response to a combination of natural and human-induced causes. These changes will be significant in the United States–Mexican border region, where the process of climate change affects all of the Borderlands challenge themes discussed in the preceding chapters. The dual possibilities of both significantly-changed climate and increasing variability in climate make it challenging to take full measure of the potential effects because the Borderlands already experience a high degree of interannual variability and climatological extremes.

Job satisfaction and associated variables among nurse assistants working in residential care.

PubMed

Wallin, Anneli Orrung; Jakobsson, Ulf; Edberg, Anna-Karin

2012-12-01

While the work situation for nurse assistants in residential care is strenuous, they themselves often state that they are satisfied with their job. More knowledge is clearly needed of the interrelationship of variables associated with job satisfaction. This study aims to investigate job satisfaction and explore associated variables among nurse assistants working in residential care. A total of 225 respondents completed a questionnaire measuring general job satisfaction, satisfaction with nursing-care provision and measures concerning person-centered care, work climate, leadership, and health complaints. Job satisfaction was the outcome measure and comparisons were made among those reporting low, moderate, and high levels of job satisfaction; multiple regression analyses were used to explore associated variables. The caring climate and personalized care provision were associated with general job satisfaction. High levels of satisfaction with nursing-care provision were also associated with the general work climate, organizational and environmental support, and leadership. Low job satisfaction was mainly associated with health complaints. Nurse assistants working in a positive work climate, caring climate, with a positive attitude to their leaders, who receive organizational and environmental support, provide person-centered care and experience a higher degree of job satisfaction. It seems essential, however, to include both general and context-specific measures when investigating job satisfaction in this field as they reveal different aspects of the nurse assistant's work situation.

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.

Comparative Analysis of Upper Ocean Heat Content Variability from Ensemble Operational Ocean Analyses

NASA Technical Reports Server (NTRS)

Xue, Yan; Balmaseda, Magdalena A.; Boyer, Tim; Ferry, Nicolas; Good, Simon; Ishikawa, Ichiro; Rienecker, Michele; Rosati, Tony; Yin, Yonghong; Kumar, Arun

2012-01-01

Upper ocean heat content (HC) is one of the key indicators of climate variability on many time-scales extending from seasonal to interannual to long-term climate trends. For example, HC in the tropical Pacific provides information on thermocline anomalies that is critical for the longlead forecast skill of ENSO. Since HC variability is also associated with SST variability, a better understanding and monitoring of HC variability can help us understand and forecast SST variability associated with ENSO and other modes such as Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO), Tropical Atlantic Variability (TAV) and Atlantic Multidecadal Oscillation (AMO). An accurate ocean initialization of HC anomalies in coupled climate models could also contribute to skill in decadal climate prediction. Errors, and/or uncertainties, in the estimation of HC variability can be affected by many factors including uncertainties in surface forcings, ocean model biases, and deficiencies in data assimilation schemes. Changes in observing systems can also leave an imprint on the estimated variability. The availability of multiple operational ocean analyses (ORA) that are routinely produced by operational and research centers around the world provides an opportunity to assess uncertainties in HC analyses, to help identify gaps in observing systems as they impact the quality of ORAs and therefore climate model forecasts. A comparison of ORAs also gives an opportunity to identify deficiencies in data assimilation schemes, and can be used as a basis for development of real-time multi-model ensemble HC monitoring products. The OceanObs09 Conference called for an intercomparison of ORAs and use of ORAs for global ocean monitoring. As a follow up, we intercompared HC variations from ten ORAs -- two objective analyses based on in-situ data only and eight model analyses based on ocean data assimilation systems. The mean, annual cycle, interannual variability and longterm trend of HC have been analyzed

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.

Predicting Trophic Interactions and Habitat Utilization in the California Current Ecosystem

DTIC Science & Technology

2015-09-30

spatial and temporal distribution of key marine organisms over multiple trophic levels, and (2) natural and anthropogenic variability in ecosystem...areas of climate modeling in upwelling regions (E. Curchitser), physical-biological modeling in the CCLME (J. Fiechter and C. Edwards), data...optimal growth conditions). By comparing interannual changes in fat depot against EOF modes for environmental variability (i.e., SST) and prey

Ecosystem response to climatic variables - air temperature and precipitation: How can these variables alter plant productions in C4-grass dominant ecosystem?

NASA Astrophysics Data System (ADS)

Jung, C. G.; Jiang, L.; Luo, Y.

2017-12-01

Understanding net primary production (NPP) response to the key climatic variables, temperature and precipitation, is essential since the response could be represented by one of future consequences from ecosystem responses. Under future climatic warming, fluctuating precipitation is expected. In addition, NPP solely could not explain whole ecosystem response; therefore, not only NPP, but also above- and below-ground NPP (ANPP and BNPP, respectively) need to be examined. This examination needs to include how the plant productions response along temperature and precipitation gradients. Several studies have examined the response of NPP against each of single climatic variable, but understanding the response of ANPP and BNPP to the multiple variables is notably poor. In this study, we used the plant productions data (NPP, ANPP, and BNPP) with climatic variables, i.e., air temperature and precipitation, from 1999 to 2015 under warming and clipping treatments (mimicking hay-harvesting) in C4-grass dominant ecosystem located in central Oklahoma, United States. Firstly, we examined the nonlinear relationships with the climatic variables for NPP, ANPP and BNPP; and then predicted possible responses in the temperature - precipitation space by using a linear mixed effect model. Nonlinearities of NPP, ANPP and BNPP to the climatic variables have been found to show unimodal curves, and nonlinear models have better goodness of fit as shown lower Akaike information criterion (AIC) than linear models. Optimum condition for NPP is represented at high temperature and precipitation level whereas BNPP is maximized at moderate precipitation levels while ANPP has same range of NPP's optimum condition. Clipping significantly reduced ANPP while there was no clipping effect on NPP and BNPP. Furthermore, inclining NPP and ANPP have shown in a range from moderate to high precipitation level with increasing temperature while inclining pattern for BNPP was observed in moderate precipitation level. Overall, the C4-grass dominant ecosystem has a potential for considerable increases in NPP in hotter and wetter conditions as shown a range from moderate to high temperature and precipitation levels; ANPP has peaked at the high temperature and precipitation level, but maximum BNPP needs moderate precipitation level and high temperature.

Variable climatic conditions dominate recent phytoplankton dynamics in Chesapeake Bay

NASA Astrophysics Data System (ADS)

Harding, Lawrence W., Jr.; Mallonee, Michael E.; Perry, Elgin S.; Miller, W. David; Adolf, Jason E.; Gallegos, Charles L.; Paerl, Hans W.

2016-03-01

Variable climatic conditions strongly influence phytoplankton dynamics in estuaries globally. Our study area is Chesapeake Bay, a highly productive ecosystem providing natural resources, transportation, and recreation for nearly 16 million people inhabiting a 165,000-km2 watershed. Since World War II, nutrient over-enrichment has led to multiple ecosystem impairments caused by increased phytoplankton biomass as chlorophyll-a (chl-a). Doubled nitrogen (N) loadings from 1945-1980 led to increased chl-a, reduced water clarity, and low dissolved oxygen (DO), while decreased N loadings from 1981-2012 suggest modest improvement. The recent 30+ years are characterized by high inter-annual variability of chl-a, coinciding with irregular dry and wet periods, complicating the detection of long-term trends. Here, we synthesize time-series data for historical and recent N loadings (TN, NO2 + NO3), chl-a, floral composition, and net primary productivity (NPP) to distinguish secular changes caused by nutrient over-enrichment from spatio-temporal variability imposed by climatic conditions. Wet years showed higher chl-a, higher diatom abundance, and increased NPP, while dry years showed lower chl-a, lower diatom abundance, and decreased NPP. Our findings support a conceptual model wherein variable climatic conditions dominate recent phytoplankton dynamics against a backdrop of nutrient over-enrichment, emphasizing the need to separate these effects to gauge progress toward improving water quality in estuaries.

Variable climatic conditions dominate recent phytoplankton dynamics in Chesapeake Bay.

PubMed

Harding, Lawrence W; Mallonee, Michael E; Perry, Elgin S; Miller, W David; Adolf, Jason E; Gallegos, Charles L; Paerl, Hans W

2016-03-30

Variable climatic conditions strongly influence phytoplankton dynamics in estuaries globally. Our study area is Chesapeake Bay, a highly productive ecosystem providing natural resources, transportation, and recreation for nearly 16 million people inhabiting a 165,000-km(2) watershed. Since World War II, nutrient over-enrichment has led to multiple ecosystem impairments caused by increased phytoplankton biomass as chlorophyll-a (chl-a). Doubled nitrogen (N) loadings from 1945-1980 led to increased chl-a, reduced water clarity, and low dissolved oxygen (DO), while decreased N loadings from 1981-2012 suggest modest improvement. The recent 30+ years are characterized by high inter-annual variability of chl-a, coinciding with irregular dry and wet periods, complicating the detection of long-term trends. Here, we synthesize time-series data for historical and recent N loadings (TN, NO2 + NO3), chl-a, floral composition, and net primary productivity (NPP) to distinguish secular changes caused by nutrient over-enrichment from spatio-temporal variability imposed by climatic conditions. Wet years showed higher chl-a, higher diatom abundance, and increased NPP, while dry years showed lower chl-a, lower diatom abundance, and decreased NPP. Our findings support a conceptual model wherein variable climatic conditions dominate recent phytoplankton dynamics against a backdrop of nutrient over-enrichment, emphasizing the need to separate these effects to gauge progress toward improving water quality in estuaries.

Variable climatic conditions dominate recent phytoplankton dynamics in Chesapeake Bay

PubMed Central

Harding, Jr., Lawrence W.; Mallonee, Michael E.; Perry, Elgin S.; Miller, W. David; Adolf, Jason E.; Gallegos, Charles L.; Paerl, Hans W.

2016-01-01

Variable climatic conditions strongly influence phytoplankton dynamics in estuaries globally. Our study area is Chesapeake Bay, a highly productive ecosystem providing natural resources, transportation, and recreation for nearly 16 million people inhabiting a 165,000-km2 watershed. Since World War II, nutrient over-enrichment has led to multiple ecosystem impairments caused by increased phytoplankton biomass as chlorophyll-a (chl-a). Doubled nitrogen (N) loadings from 1945–1980 led to increased chl-a, reduced water clarity, and low dissolved oxygen (DO), while decreased N loadings from 1981–2012 suggest modest improvement. The recent 30+ years are characterized by high inter-annual variability of chl-a, coinciding with irregular dry and wet periods, complicating the detection of long-term trends. Here, we synthesize time-series data for historical and recent N loadings (TN, NO2 + NO3), chl-a, floral composition, and net primary productivity (NPP) to distinguish secular changes caused by nutrient over-enrichment from spatio-temporal variability imposed by climatic conditions. Wet years showed higher chl-a, higher diatom abundance, and increased NPP, while dry years showed lower chl-a, lower diatom abundance, and decreased NPP. Our findings support a conceptual model wherein variable climatic conditions dominate recent phytoplankton dynamics against a backdrop of nutrient over-enrichment, emphasizing the need to separate these effects to gauge progress toward improving water quality in estuaries. PMID:27026279

ANALYSIS OF BIOTIC AND ABIOTIC FACTORS INFLUENCING THE OCCURRENCE OF WEST NILE VIRUS INFECTION IN TUNISIA.

PubMed

Ben Hassine, Th; Calistri, P; Ippoliti, C; Conte, A; Danzetta, M L; Bruno, R; Lelli, R; Bejaoui, M; Hammami, S

2014-01-01

Eco-climatic conditions are often associated with the occurrence of West Nile Disease (WND) cases. Among the complex set of biotic and abiotic factors influencing the emergence and spread of this vector-borne disease, two main variables have been considered to have a great influence on the probability of West Nile Virus (WNV) introduction and circulation in Tunisia: the presence of susceptible bird populations and the existence of geographical areas where the environmental and climatic conditions are more favourable to mosquito multiplications. The aim of this study was to identify and classify the climatic and environmental variables possibly associated with the occurrence of WNVhuman cases in Tunisia. The following environmental and climatic variables have been considered: wetlands and humid areas, Normalised Difference Vegetation Index (NDVI), temperatures and elevation. A preliminary analysis for the characterization of main variables associated with areas with a history of WNV human cases in Tunisia between 1997 and 2011 has been made. This preliminary analysis clearly indicates the closeness to marshes ecosystem, where migratory bird populations are located, as an important risk factor for WNV infection. On the contrary the temperature absolute seems to be not a significant factor in Tunisian epidemiological situation. In relation to NDVI values, more complex considerations should be made.

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.

Assessing Climate Change in Early Warm Season and Impacts on Wildfire Potential in the Southwestern United States

NASA Astrophysics Data System (ADS)

Kafatos, M.; Kim, S. H.; Kim, J.; Nghiem, S. V.; Fujioka, F.; Myoung, B.

2016-12-01

Wildfires are an important concern in the Southwestern United States (SWUS) where the prevalent semi-arid to arid climate, vegetation types and hot and dry warm seasons challenge strategic fire management. Although they are part of the natural cycle related to the region's climate, significant growth of urban areas and expansion of the wildland-urban interface, have made wildfires a serious high-risk hazard. Previous studies also showed that the SWUS region is prone to frequent droughts due to large variations in wet season rainfall and has suffered from a number of severe wildfires in the recent decades. Despite the increasing trend in large wildfires, future wildfire risk assessment studies at regional scales for proactive adaptations are lacking. Our previous study revealed strong correlations between the North Atlantic Oscillation (NAO) and temperatures during March-June in SWUS. The abnormally warm and dry conditions in an NAO-positive spring, combined with reduced winter precipitation, can cause an early start of a fire season and extend it for several seasons, from late spring to fall. A strong interannual variation of the Keetch-Byram Drought Index (KBDI) during the early warm season was also found in the 35 year period 1979 - 2013 of the North American Regional Reanalysis (NARR) dataset. Thus, it is crucial to investigate the climate change impact that early warm season temperatures have on future wildfire danger potential. Our study reported here examines fine-resolution fire-weather variables for 2041-2070 projected in the North American Regional Climate Change Assessment Program (NARCCAP). The high-resolution climate data were obtained from multiple regional climate models (RCM) driven by multiple climate scenarios projected from multiple global climate models (GCMs) in conjunction with multiple greenhouse gas concentration pathways. The local wildfire potential in future climate is investigated using both the Keetch-Byram Drought Index (KBDI) and the Canadian Fire Weather Index (FWI) which have been widely used for assessing wildfire potential in the U.S.A and Canada, respectively.

Comparison of climate envelope models developed using expert-selected variables versus statistical selection

USGS Publications Warehouse

Brandt, Laura A.; Benscoter, Allison; Harvey, Rebecca G.; Speroterra, Carolina; Bucklin, David N.; Romañach, Stephanie; Watling, James I.; Mazzotti, Frank J.

2017-01-01

Climate envelope models are widely used to describe potential future distribution of species under different climate change scenarios. It is broadly recognized that there are both strengths and limitations to using climate envelope models and that outcomes are sensitive to initial assumptions, inputs, and modeling methods Selection of predictor variables, a central step in modeling, is one of the areas where different techniques can yield varying results. Selection of climate variables to use as predictors is often done using statistical approaches that develop correlations between occurrences and climate data. These approaches have received criticism in that they rely on the statistical properties of the data rather than directly incorporating biological information about species responses to temperature and precipitation. We evaluated and compared models and prediction maps for 15 threatened or endangered species in Florida based on two variable selection techniques: expert opinion and a statistical method. We compared model performance between these two approaches for contemporary predictions, and the spatial correlation, spatial overlap and area predicted for contemporary and future climate predictions. In general, experts identified more variables as being important than the statistical method and there was low overlap in the variable sets (<40%) between the two methods Despite these differences in variable sets (expert versus statistical), models had high performance metrics (>0.9 for area under the curve (AUC) and >0.7 for true skill statistic (TSS). Spatial overlap, which compares the spatial configuration between maps constructed using the different variable selection techniques, was only moderate overall (about 60%), with a great deal of variability across species. Difference in spatial overlap was even greater under future climate projections, indicating additional divergence of model outputs from different variable selection techniques. Our work is in agreement with other studies which have found that for broad-scale species distribution modeling, using statistical methods of variable selection is a useful first step, especially when there is a need to model a large number of species or expert knowledge of the species is limited. Expert input can then be used to refine models that seem unrealistic or for species that experts believe are particularly sensitive to change. It also emphasizes the importance of using multiple models to reduce uncertainty and improve map outputs for conservation planning. Where outputs overlap or show the same direction of change there is greater certainty in the predictions. Areas of disagreement can be used for learning by asking why the models do not agree, and may highlight areas where additional on-the-ground data collection could improve the models.

Multi-objective optimization for generating a weighted multi-model ensemble

NASA Astrophysics Data System (ADS)

Lee, H.

2017-12-01

Many studies have demonstrated that multi-model ensembles generally show better skill than each ensemble member. When generating weighted multi-model ensembles, the first step is measuring the performance of individual model simulations using observations. There is a consensus on the assignment of weighting factors based on a single evaluation metric. When considering only one evaluation metric, the weighting factor for each model is proportional to a performance score or inversely proportional to an error for the model. While this conventional approach can provide appropriate combinations of multiple models, the approach confronts a big challenge when there are multiple metrics under consideration. When considering multiple evaluation metrics, it is obvious that a simple averaging of multiple performance scores or model ranks does not address the trade-off problem between conflicting metrics. So far, there seems to be no best method to generate weighted multi-model ensembles based on multiple performance metrics. The current study applies the multi-objective optimization, a mathematical process that provides a set of optimal trade-off solutions based on a range of evaluation metrics, to combining multiple performance metrics for the global climate models and their dynamically downscaled regional climate simulations over North America and generating a weighted multi-model ensemble. NASA satellite data and the Regional Climate Model Evaluation System (RCMES) software toolkit are used for assessment of the climate simulations. Overall, the performance of each model differs markedly with strong seasonal dependence. Because of the considerable variability across the climate simulations, it is important to evaluate models systematically and make future projections by assigning optimized weighting factors to the models with relatively good performance. Our results indicate that the optimally weighted multi-model ensemble always shows better performance than an arithmetic ensemble mean and may provide reliable future projections.

Temperate Mountain Forest Biodiversity under Climate Change: Compensating Negative Effects by Increasing Structural Complexity

PubMed Central

Braunisch, Veronika; Coppes, Joy; Arlettaz, Raphaël; Suchant, Rudi; Zellweger, Florian; Bollmann, Kurt

2014-01-01

Species adapted to cold-climatic mountain environments are expected to face a high risk of range contractions, if not local extinctions under climate change. Yet, the populations of many endothermic species may not be primarily affected by physiological constraints, but indirectly by climate-induced changes of habitat characteristics. In mountain forests, where vertebrate species largely depend on vegetation composition and structure, deteriorating habitat suitability may thus be mitigated or even compensated by habitat management aiming at compositional and structural enhancement. We tested this possibility using four cold-adapted bird species with complementary habitat requirements as model organisms. Based on species data and environmental information collected in 300 1-km2 grid cells distributed across four mountain ranges in central Europe, we investigated (1) how species’ occurrence is explained by climate, landscape, and vegetation, (2) to what extent climate change and climate-induced vegetation changes will affect habitat suitability, and (3) whether these changes could be compensated by adaptive habitat management. Species presence was modelled as a function of climate, landscape and vegetation variables under current climate; moreover, vegetation-climate relationships were assessed. The models were extrapolated to the climatic conditions of 2050, assuming the moderate IPCC-scenario A1B, and changes in species’ occurrence probability were quantified. Finally, we assessed the maximum increase in occurrence probability that could be achieved by modifying one or multiple vegetation variables under altered climate conditions. Climate variables contributed significantly to explaining species occurrence, and expected climatic changes, as well as climate-induced vegetation trends, decreased the occurrence probability of all four species, particularly at the low-altitudinal margins of their distribution. These effects could be partly compensated by modifying single vegetation factors, but full compensation would only be achieved if several factors were changed in concert. The results illustrate the possibilities and limitations of adaptive species conservation management under climate change. PMID:24823495

Temperate mountain forest biodiversity under climate change: compensating negative effects by increasing structural complexity.

PubMed

Braunisch, Veronika; Coppes, Joy; Arlettaz, Raphaël; Suchant, Rudi; Zellweger, Florian; Bollmann, Kurt

2014-01-01

Species adapted to cold-climatic mountain environments are expected to face a high risk of range contractions, if not local extinctions under climate change. Yet, the populations of many endothermic species may not be primarily affected by physiological constraints, but indirectly by climate-induced changes of habitat characteristics. In mountain forests, where vertebrate species largely depend on vegetation composition and structure, deteriorating habitat suitability may thus be mitigated or even compensated by habitat management aiming at compositional and structural enhancement. We tested this possibility using four cold-adapted bird species with complementary habitat requirements as model organisms. Based on species data and environmental information collected in 300 1-km2 grid cells distributed across four mountain ranges in central Europe, we investigated (1) how species' occurrence is explained by climate, landscape, and vegetation, (2) to what extent climate change and climate-induced vegetation changes will affect habitat suitability, and (3) whether these changes could be compensated by adaptive habitat management. Species presence was modelled as a function of climate, landscape and vegetation variables under current climate; moreover, vegetation-climate relationships were assessed. The models were extrapolated to the climatic conditions of 2050, assuming the moderate IPCC-scenario A1B, and changes in species' occurrence probability were quantified. Finally, we assessed the maximum increase in occurrence probability that could be achieved by modifying one or multiple vegetation variables under altered climate conditions. Climate variables contributed significantly to explaining species occurrence, and expected climatic changes, as well as climate-induced vegetation trends, decreased the occurrence probability of all four species, particularly at the low-altitudinal margins of their distribution. These effects could be partly compensated by modifying single vegetation factors, but full compensation would only be achieved if several factors were changed in concert. The results illustrate the possibilities and limitations of adaptive species conservation management under climate change.

Linkages Between Multiscale Global Sea Surface Temperature Change and Precipitation Variabilities in the US

NASA Technical Reports Server (NTRS)

Lau, K. M.; Weng, Heng-Yi

1999-01-01

A growing number of evidence indicates that there are coherent patterns of variability in sea surface temperature (SST) anomaly not only at interannual timescales, but also at decadal-to-inter-decadal timescale and beyond. The multi-scale variabilities of SST anomaly have shown great impacts on climate. In this work, we analyze multiple timescales contained in the globally averaged SST anomaly with and their possible relationship with the summer and winter rainfall in the United States over the past four decades.

Putting climate impact estimates to work: the empirical approach of the American Climate Prospectus

NASA Astrophysics Data System (ADS)

Jina, A.; Hsiang, S. M.; Kopp, R. E., III; Rasmussen, D.; Rising, J.

2014-12-01

The American Climate Prospectus (ACP), the technical analysis underlying the Risky Business project, quantitatively assesses climate risks posed to the United States' economy in a number of sectors [1]. Four of these - crop yield, crime, labor productivity, and mortality - draw upon research which identifies social impacts using contemporary variability in climate. We first identify a group of rigorous studies that use climate variability to identify responses to temperature and precipitation, while controlling for unobserved differences between locations. To incorporate multiple studies from a single sector, we employ a meta-analytical approach that draws on Bayesian methods commonly used in medical research and previously implemented in [2]. We generate a series of aggregate response functions for each sector using this meta-analytical method. We combine response functions with downscaled physical climate projections to estimate climate impacts out to the end of the century, incorporating uncertainty from statistical estimates, weather, climate models, and different emissions scenarios. Incorporating multiple studies in a single estimation framework allows us to directly compare impacts across the economy. We find that increased mortality has the largest effect on the US economy, followed by costs associated with decreased labor productivity. Agricultural losses and increases in crime contribute lesser but nonetheless substantial costs, and agriculture, notably, shows many areas benefitting from projected climate changes. The ACP also presents results throughout the 21stcentury. The dynamics of each of the impact categories differs, with, for example, mortality showing little change until the end of the century, but crime showing a monotonic increase from the present day. The ACP approach can expand to include new findings in current sectors, new sectors, and new geographical areas of interest. It represents an analytical framework that can incorporate empirical studies into a broad characterization of climate impacts across an economy, ensuring that each individual study can contribute to guiding policy priorities on climate change. References: [1] T. Houser et al. (2014), American Climate Prospectus, www.climateprospectus.org. [2] Hsiang, Burke, and Miguel (2013), Science.

Bias and robustness of uncertainty components estimates in transient climate projections

NASA Astrophysics Data System (ADS)

Hingray, Benoit; Blanchet, Juliette; Jean-Philippe, Vidal

2016-04-01

A critical issue in climate change studies is the estimation of uncertainties in projections along with the contribution of the different uncertainty sources, including scenario uncertainty, the different components of model uncertainty and internal variability. Quantifying the different uncertainty sources faces actually different problems. For instance and for the sake of simplicity, an estimate of model uncertainty is classically obtained from the empirical variance of the climate responses obtained for the different modeling chains. These estimates are however biased. Another difficulty arises from the limited number of members that are classically available for most modeling chains. In this case, the climate response of one given chain and the effect of its internal variability may be actually difficult if not impossible to separate. The estimate of scenario uncertainty, model uncertainty and internal variability components are thus likely to be not really robust. We explore the importance of the bias and the robustness of the estimates for two classical Analysis of Variance (ANOVA) approaches: a Single Time approach (STANOVA), based on the only data available for the considered projection lead time and a time series based approach (QEANOVA), which assumes quasi-ergodicity of climate outputs over the whole available climate simulation period (Hingray and Saïd, 2014). We explore both issues for a simple but classical configuration where uncertainties in projections are composed of two single sources: model uncertainty and internal climate variability. The bias in model uncertainty estimates is explored from theoretical expressions of unbiased estimators developed for both ANOVA approaches. The robustness of uncertainty estimates is explored for multiple synthetic ensembles of time series projections generated with MonteCarlo simulations. For both ANOVA approaches, when the empirical variance of climate responses is used to estimate model uncertainty, the bias is always positive. It can be especially high with STANOVA. In the most critical configurations, when the number of members available for each modeling chain is small (< 3) and when internal variability explains most of total uncertainty variance (75% or more), the overestimation is higher than 100% of the true model uncertainty variance. The bias can be considerably reduced with a time series ANOVA approach, owing to the multiple time steps accounted for. The longer the transient time period used for the analysis, the larger the reduction. When a quasi-ergodic ANOVA approach is applied to decadal data for the whole 1980-2100 period, the bias is reduced by a factor 2.5 to 20 depending on the projection lead time. In all cases, the bias is likely to be not negligible for a large number of climate impact studies resulting in a likely large overestimation of the contribution of model uncertainty to total variance. For both approaches, the robustness of all uncertainty estimates is higher when more members are available, when internal variability is smaller and/or the response-to-uncertainty ratio is higher. QEANOVA estimates are much more robust than STANOVA ones: QEANOVA simulated confidence intervals are roughly 3 to 5 times smaller than STANOVA ones. Excepted for STANOVA when less than 3 members is available, the robustness is rather high for total uncertainty and moderate for internal variability estimates. For model uncertainty or response-to-uncertainty ratio estimates, the robustness is conversely low for QEANOVA to very low for STANOVA. In the most critical configurations (small number of member, large internal variability), large over- or underestimation of uncertainty components is very thus likely. To propose relevant uncertainty analyses and avoid misleading interpretations, estimates of uncertainty components should be therefore bias corrected and ideally come with estimates of their robustness. This work is part of the COMPLEX Project (European Collaborative Project FP7-ENV-2012 number: 308601; http://www.complex.ac.uk/). Hingray, B., Saïd, M., 2014. Partitioning internal variability and model uncertainty components in a multimodel multireplicate ensemble of climate projections. J.Climate. doi:10.1175/JCLI-D-13-00629.1 Hingray, B., Blanchet, J. (revision) Unbiased estimators for uncertainty components in transient climate projections. J. Climate Hingray, B., Blanchet, J., Vidal, J.P. (revision) Robustness of uncertainty components estimates in climate projections. J.Climate

Association of Climatic Variability, Vector Population and Malarial Disease in District of Visakhapatnam, India: A Modeling and Prediction Analysis.

PubMed

Srimath-Tirumula-Peddinti, Ravi Chandra Pavan Kumar; Neelapu, Nageswara Rao Reddy; Sidagam, Naresh

2015-01-01

Malarial incidence, severity, dynamics and distribution of malaria are strongly determined by climatic factors, i.e., temperature, precipitation, and relative humidity. The objectives of the current study were to analyse and model the relationships among climate, vector and malaria disease in district of Visakhapatnam, India to understand malaria transmission mechanism (MTM). Epidemiological, vector and climate data were analysed for the years 2005 to 2011 in Visakhapatnam to understand the magnitude, trends and seasonal patterns of the malarial disease. Statistical software MINITAB ver. 14 was used for performing correlation, linear and multiple regression analysis. Perennial malaria disease incidence and mosquito population was observed in the district of Visakhapatnam with peaks in seasons. All the climatic variables have a significant influence on disease incidence as well as on mosquito populations. Correlation coefficient analysis, seasonal index and seasonal analysis demonstrated significant relationships among climatic factors, mosquito population and malaria disease incidence in the district of Visakhapatnam, India. Multiple regression and ARIMA (I) models are best suited models for modeling and prediction of disease incidences and mosquito population. Predicted values of average temperature, mosquito population and malarial cases increased along with the year. Developed MTM algorithm observed a major MTM cycle following the June to August rains and occurring between June to September and minor MTM cycles following March to April rains and occurring between March to April in the district of Visakhapatnam. Fluctuations in climatic factors favored an increase in mosquito populations and thereby increasing the number of malarial cases. Rainfall, temperatures (20°C to 33°C) and humidity (66% to 81%) maintained a warmer, wetter climate for mosquito growth, parasite development and malaria transmission. Changes in climatic factors influence malaria directly by modifying the behaviour and geographical distribution of vectors and by changing the length of the life cycle of the parasite.

Association of Climatic Variability, Vector Population and Malarial Disease in District of Visakhapatnam, India: A Modeling and Prediction Analysis

PubMed Central

Srimath-Tirumula-Peddinti, Ravi Chandra Pavan Kumar; Neelapu, Nageswara Rao Reddy; Sidagam, Naresh

2015-01-01

Background Malarial incidence, severity, dynamics and distribution of malaria are strongly determined by climatic factors, i.e., temperature, precipitation, and relative humidity. The objectives of the current study were to analyse and model the relationships among climate, vector and malaria disease in district of Visakhapatnam, India to understand malaria transmission mechanism (MTM). Methodology Epidemiological, vector and climate data were analysed for the years 2005 to 2011 in Visakhapatnam to understand the magnitude, trends and seasonal patterns of the malarial disease. Statistical software MINITAB ver. 14 was used for performing correlation, linear and multiple regression analysis. Results/Findings Perennial malaria disease incidence and mosquito population was observed in the district of Visakhapatnam with peaks in seasons. All the climatic variables have a significant influence on disease incidence as well as on mosquito populations. Correlation coefficient analysis, seasonal index and seasonal analysis demonstrated significant relationships among climatic factors, mosquito population and malaria disease incidence in the district of Visakhapatnam, India. Multiple regression and ARIMA (I) models are best suited models for modeling and prediction of disease incidences and mosquito population. Predicted values of average temperature, mosquito population and malarial cases increased along with the year. Developed MTM algorithm observed a major MTM cycle following the June to August rains and occurring between June to September and minor MTM cycles following March to April rains and occurring between March to April in the district of Visakhapatnam. Fluctuations in climatic factors favored an increase in mosquito populations and thereby increasing the number of malarial cases. Rainfall, temperatures (20°C to 33°C) and humidity (66% to 81%) maintained a warmer, wetter climate for mosquito growth, parasite development and malaria transmission. Conclusions/Significance Changes in climatic factors influence malaria directly by modifying the behaviour and geographical distribution of vectors and by changing the length of the life cycle of the parasite. PMID:26110279

Influences of climate on aflatoxin producing fungi and aflatoxin contamination.

PubMed

Cotty, Peter J; Jaime-Garcia, Ramon

2007-10-20

Aflatoxins are potent mycotoxins that cause developmental and immune system suppression, cancer, and death. As a result of regulations intended to reduce human exposure, crop contamination with aflatoxins causes significant economic loss for producers, marketers, and processors of diverse susceptible crops. Aflatoxin contamination occurs when specific fungi in the genus Aspergillus infect crops. Many industries frequently affected by aflatoxin contamination know from experience and anecdote that fluctuations in climate impact the extent of contamination. Climate influences contamination, in part, by direct effects on the causative fungi. As climate shifts, so do the complex communities of aflatoxin-producing fungi. This includes changes in the quantity of aflatoxin-producers in the environment and alterations to fungal community structure. Fluctuations in climate also influence predisposition of hosts to contamination by altering crop development and by affecting insects that create wounds on which aflatoxin-producers proliferate. Aflatoxin contamination is prevalent both in warm humid climates and in irrigated hot deserts. In temperate regions, contamination may be severe during drought. The contamination process is frequently broken down into two phases with the first phase occurring on the developing crop and the second phase affecting the crop after maturation. Rain and temperature influence the phases differently with dry, hot conditions favoring the first and warm, wet conditions favoring the second. Contamination varies with climate both temporally and spatially. Geostatistics and multiple regression analyses have shed light on influences of weather on contamination. Geostatistical analyses have been used to identify recurrent contamination patterns and to match these with environmental variables. In the process environmental conditions with the greatest impact on contamination are identified. Likewise, multiple regression analyses allow ranking of environmental variables based on relative influence on contamination. Understanding the impact of climate may allow development of improved management procedures, better allocation of monitoring efforts, and adjustment of agronomic practices in anticipation of global climate change.

Global methane and nitrous oxide emissions from terrestrial ecosystems due to multiple environmental changes

DOE PAGES

Tian, Hanqin; Chen, Guangsheng; Lu, Chaoqun; ...

2015-03-16

Greenhouse gas (GHG)-induced climate change is among the most pressing sustainability challenges facing humanity today, posing serious risks for ecosystem health. Methane (CH 4) and nitrous oxide (N 2O) are the two most important GHGs after carbon dioxide (CO 2), but their regional and global budgets are not well known. In this paper, we applied a process-based coupled biogeochemical model to concurrently estimate the magnitude and spatial and temporal patterns of CH 4 and N 2O fluxes as driven by multiple environmental changes, including climate variability, rising atmospheric CO 2, increasing nitrogen deposition, tropospheric ozone pollution, land use change, andmore » nitrogen fertilizer use.« less

Elevated temperature is more effective than elevated [CO2 ] in exposing genotypic variation in Telopea speciosissima growth plasticity: implications for woody plant populations under climate change.

PubMed

Huang, Guomin; Rymer, Paul D; Duan, Honglang; Smith, Renee A; Tissue, David T

2015-10-01

Intraspecific variation in phenotypic plasticity is a critical determinant of plant species capacity to cope with climate change. A long-standing hypothesis states that greater levels of environmental variability will select for genotypes with greater phenotypic plasticity. However, few studies have examined how genotypes of woody species originating from contrasting environments respond to multiple climate change factors. Here, we investigated the main and interactive effects of elevated [CO2 ] (CE ) and elevated temperature (TE ) on growth and physiology of Coastal (warmer, less variable temperature environment) and Upland (cooler, more variable temperature environment) genotypes of an Australian woody species Telopea speciosissima. Both genotypes were positively responsive to CE (35% and 29% increase in whole-plant dry mass and leaf area, respectively), but only the Coastal genotype exhibited positive growth responses to TE . We found that the Coastal genotype exhibited greater growth response to TE (47% and 85% increase in whole-plant dry mass and leaf area, respectively) when compared with the Upland genotype (no change in dry mass or leaf area). No intraspecific variation in physiological plasticity was detected under CE or TE , and the interactive effects of CE and TE on intraspecific variation in phenotypic plasticity were also largely absent. Overall, TE was a more effective climate factor than CE in exposing genotypic variation in our woody species. Our results contradict the paradigm that genotypes from more variable climates will exhibit greater phenotypic plasticity in future climate regimes. © 2015 John Wiley & Sons Ltd.

Spatiotemporal Patterns of Evapotranspiration in Response to Multiple Environmental Factors Simulated by the Community Land Model

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

Shi, Xiaoying; Mao, Jiafu; Thornton, P.

Spatiotemporal patterns of evapotranspiration (ET) over the period from 1982 to 2008 are investigated and attributed to multiple environmental factors using the Community Land Model version 4 (CLM4). Our results show that CLM4 captures the spatial distribution and interannual variability of ET well when compared to observation-based estimates. We find that climate dominates the predicted variability in ET. Elevated atmospheric CO2 concentration also plays an important role in modulating the trend of predicted ET over most land areas, and replaces climate to function as the dominant factor controlling ET changes over the North America, South America and Asia regions. Comparedmore » to the effect of climate and CO2 concentration, the roles of other factors such as nitrogen deposition, land use change and aerosol deposition are less pronounced and regionally dependent. The aerosol deposition contribution is the third most important factor for trends of ET over Europe, while it has the smallest impact over other regions. As ET is a dominant component of the terrestrial water cycle, our results suggest that environmental factors like elevated CO2, nitrogen and aerosol depositions, and land use change, in addition to climate, could have significant impact on future projections of water resources and water cycle dynamics at global and regional scales.« less

Crop Yield Simulations Using Multiple Regional Climate Models in the Southwestern United States

NASA Astrophysics Data System (ADS)

Stack, D.; Kafatos, M.; Kim, S.; Kim, J.; Walko, R. L.

2013-12-01

Agricultural productivity (described by crop yield) is strongly dependent on climate conditions determined by meteorological parameters (e.g., temperature, rainfall, and solar radiation). California is the largest producer of agricultural products in the United States, but crops in associated arid and semi-arid regions live near their physiological limits (e.g., in hot summer conditions with little precipitation). Thus, accurate climate data are essential in assessing the impact of climate variability on agricultural productivity in the Southwestern United States and other arid regions. To address this issue, we produced simulated climate datasets and used them as input for the crop production model. For climate data, we employed two different regional climate models (WRF and OLAM) using a fine-resolution (8km) grid. Performances of the two different models are evaluated in a fine-resolution regional climate hindcast experiment for 10 years from 2001 to 2010 by comparing them to the North American Regional Reanalysis (NARR) dataset. Based on this comparison, multi-model ensembles with variable weighting are used to alleviate model bias and improve the accuracy of crop model productivity over large geographic regions (county and state). Finally, by using a specific crop-yield simulation model (APSIM) in conjunction with meteorological forcings from the multi-regional climate model ensemble, we demonstrate the degree to which maize yields are sensitive to the regional climate in the Southwestern United States.

Mid-Piacensian mean annual sea surface temperature: an analysis for data-model comparisons

USGS Publications Warehouse

Dowsett, Harry J.; Robinson, Marci M.; Foley, Kevin M.; Stoll, Danielle K.

2010-01-01

Numerical models of the global climate system are the primary tools used to understand and project climate disruptions in the form of future global warming. The Pliocene has been identified as the closest, albeit imperfect, analog to climate conditions expected for the end of this century, making an independent data set of Pliocene conditions necessary for ground truthing model results. Because most climate model output is produced in the form ofmean annual conditions, we present a derivative of the USGS PRISM3 Global Climate Reconstruction which integrates multiple proxies of sea surface temperature (SST) into single surface temperature anomalies. We analyze temperature estimates from faunal and floral assemblage data,Mg/Ca values and alkenone unsaturation indices to arrive at a single mean annual SST anomaly (Pliocene minus modern) best describing each PRISM site, understanding that multiple proxies should not necessarily show concordance. The power of themultiple proxy approach lies within its diversity, as no two proxies measure the same environmental variable. This data set can be used to verify climate model output, to serve as a starting point for model inter-comparisons, and for quantifying uncertainty in Pliocene model prediction in perturbed physics ensembles.

Evaluating within-population variability in behavior and demography for the adaptive potential of a dispersal-limited species to climate change

USGS Publications Warehouse

Muñoz, David J.; Miller Hesed, Kyle; Grant, Evan H. Campbell; Miller, David A.W.

2016-01-01

Multiple pathways exist for species to respond to changing climates. However, responses of dispersal-limited species will be more strongly tied to ability to adapt within existing populations as rates of environmental change will likely exceed movement rates. Here, we assess adaptive capacity in Plethodon cinereus, a dispersal-limited woodland salamander. We quantify plasticity in behavior and variation in demography to observed variation in environmental variables over a 5-year period. We found strong evidence that temperature and rainfall influence P. cinereus surface presence, indicating changes in climate are likely to affect seasonal activity patterns. We also found that warmer summer temperatures reduced individual growth rates into the autumn, which is likely to have negative demographic consequences. Reduced growth rates may delay reproductive maturity and lead to reductions in size-specific fecundity, potentially reducing population-level persistence. To better understand within-population variability in responses, we examined differences between two common color morphs. Previous evidence suggests that the color polymorphism may be linked to physiological differences in heat and moisture tolerance. We found only moderate support for morph-specific differences for the relationship between individual growth and temperature. Measuring environmental sensitivity to climatic variability is the first step in predicting species' responses to climate change. Our results suggest phenological shifts and changes in growth rates are likely responses under scenarios where further warming occurs, and we discuss possible adaptive strategies for resulting selective pressures.

Mid-latitude shrub steppe plant communities: Climate change consequences for soil water resources

USGS Publications Warehouse

Palmquist, Kyle A.; Schlaepfer, Daniel R.; Bradford, John B.; Lauenroth, Willliam K.

2016-01-01

In the coming century, climate change is projected to impact precipitation and temperature regimes worldwide, with especially large effects in drylands. We use big sagebrush ecosystems as a model dryland ecosystem to explore the impacts of altered climate on ecohydrology and the implications of those changes for big sagebrush plant communities using output from 10 Global Circulation Models (GCMs) for two representative concentration pathways (RCPs). We ask: 1) What is the magnitude of variability in future temperature and precipitation regimes among GCMs and RCPs for big sagebrush ecosystems and 2) How will altered climate and uncertainty in climate forecasts influence key aspects of big sagebrush water balance? We explored these questions across 1980-2010, 2030-2060, and 2070-2100 to determine how changes in water balance might develop through the 21st century. We assessed ecohydrological variables at 898 sagebrush sites across the western US using a process-based soil water model, SOILWAT to model all components of daily water balance using site-specific vegetation parameters and site-specific soil properties for multiple soil layers. Our modeling approach allowed for changes in vegetation based on climate. Temperature increased across all GCMs and RCPs, while changes in precipitation were more variable across GCMs. Winter and spring precipitation was predicted to increase in the future (7% by 2030-2060, 12% by 2070-2100), resulting in slight increases in soil water potential (SWP) in winter. Despite wetter winter soil conditions, SWP decreased in late spring and summer due to increased evapotranspiration (6% by 2030-2060, 10% by 2070-2100) and groundwater recharge (26% and 30% increase by 2030-2060 and 2070-2100). Thus, despite increased precipitation in the cold season, soils may dry out earlier in the year, resulting in potentially longer drier summer conditions. If winter precipitation cannot offset drier summer conditions in the future, we expect big sagebrush regeneration and survival will be negatively impacted, potentially resulting in shifts in the relative abundance of big sagebrush plant functional groups. Our results also highlight the importance of assessing multiple GCMs to understand the range of climate change outcomes on ecohydrology, which was contingent on the GCM chosen.

Simulation of an ensemble of future climate time series with an hourly weather generator

NASA Astrophysics Data System (ADS)

Caporali, E.; Fatichi, S.; Ivanov, V. Y.; Kim, J.

2010-12-01

There is evidence that climate change is occurring in many regions of the world. The necessity of climate change predictions at the local scale and fine temporal resolution is thus warranted for hydrological, ecological, geomorphological, and agricultural applications that can provide thematic insights into the corresponding impacts. Numerous downscaling techniques have been proposed to bridge the gap between the spatial scales adopted in General Circulation Models (GCM) and regional analyses. Nevertheless, the time and spatial resolutions obtained as well as the type of meteorological variables may not be sufficient for detailed studies of climate change effects at the local scales. In this context, this study presents a stochastic downscaling technique that makes use of an hourly weather generator to simulate time series of predicted future climate. Using a Bayesian approach, the downscaling procedure derives distributions of factors of change for several climate statistics from a multi-model ensemble of GCMs. Factors of change are sampled from their distributions using a Monte Carlo technique to entirely account for the probabilistic information obtained with the Bayesian multi-model ensemble. Factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. The weather generator can reproduce a wide set of climate variables and statistics over a range of temporal scales, from extremes, to the low-frequency inter-annual variability. The final result of such a procedure is the generation of an ensemble of hourly time series of meteorological variables that can be considered as representative of future climate, as inferred from GCMs. The generated ensemble of scenarios also accounts for the uncertainty derived from multiple GCMs used in downscaling. Applications of the procedure in reproducing present and future climates are presented for different locations world-wide: Tucson (AZ), Detroit (MI), and Firenze (Italy). The stochastic downscaling is carried out with eight GCMs from the CMIP3 multi-model dataset (IPCC 4AR, A1B scenario).

Reconstructing Tropical Southwest Pacific Climate Variability and Mean State Changes at Vanuatu during the Medieval Climate Anomaly using Geochemical Proxies from Corals

NASA Astrophysics Data System (ADS)

Lawman, A. E.; Quinn, T. M.; Partin, J. W.; Taylor, F. W.; Thirumalai, K.; WU, C. C.; Shen, C. C.

2017-12-01

The Medieval Climate Anomaly (MCA: 950-1250 CE) is identified as a period during the last 2 millennia with Northern Hemisphere surface temperatures similar to the present. However, our understanding of tropical climate variability during the MCA is poorly constrained due to a lack of sub-annually resolved proxy records. We investigate seasonal and interannual variability during the MCA using geochemical records developed from two well preserved Porites lutea fossilized corals from the tropical southwest Pacific (Tasmaloum, Vanuatu; 15.6°S, 166.9°E). Absolute U/Th dates of 1127.1 ± 2.7 CE and 1105.1 ± 3.0 CE indicate that the selected fossil corals lived during the MCA. We use paired coral Sr/Ca and δ18O measurements to reconstruct sea surface temperature (SST) and the δ18O of seawater (a proxy for salinity). To provide context for the fossil coral records and test whether the mean state and climate variability at Vanuatu during the MCA is similar to the modern climate, our analysis also incorporates two modern coral records from Sabine Bank (15.9°S, 166.0°E) and Malo Channel (15.7°S, 167.2°E), Vanuatu for comparison. We quantify the uncertainty in our modern and fossil coral SST estimates via replication with multiple, overlapping coral records. Both the modern and fossil corals reproduce their respective mean SST value over their common period of overlap, which is 25 years in both cases. Based on over 100 years of monthly Sr/Ca data from each time period, we find that SSTs at Vanuatu during the MCA are 1.3 ± 0.7°C cooler relative to the modern. We also find that the median amplitude of the annual cycle is 0.8 ± 0.3°C larger during the MCA relative to the modern. Multiple data analysis techniques, including the standard deviation and the difference between the 95th and 5th percentiles of the annual SST cycle estimates, also show that the MCA has greater annual SST variability relative to the modern. Stable isotope data acquisition is ongoing, and when complete we will have a suite of records of paired coral Sr/Ca and δ18O measurements. We will apply similar statistical techniques developed for the Sr/Ca-SST record to also investigate variability in the δ18O of seawater (salinity). Modern salinity variability at Vanuatu arises due to hydrological anomalies associated with the El Niño-Southern Oscillation in the tropical Pacific.

An Integrated Multivariable Visualization Tool for Marine Sanctuary Climate Assessments

NASA Astrophysics Data System (ADS)

Shein, K. A.; Johnston, S.; Stachniewicz, J.; Duncan, B.; Cecil, D.; Ansari, S.; Urzen, M.

2012-12-01

The comprehensive development and use of ecological climate impact assessments by ecosystem managers can be limited by data access and visualization methods that require a priori knowledge about the various large and complex climate data products necessary to those impact assessments. In addition, it can be difficult to geographically and temporally integrate climate and ecological data to fully characterize climate-driven ecological impacts. To address these considerations, we have enhanced and extended the functionality of the NOAA National Climatic Data Center's Weather and Climate Toolkit (WCT). The WCT is a freely available Java-based tool designed to access and display NCDC's georeferenced climate data products (e.g., satellite, radar, and reanalysis gridded data). However, the WCT requires users already know how to obtain the data products, which products are preferred for a given variable, and which products are most relevant to their needs. Developed in cooperation with research and management customers at the Gulf of the Farallones National Marine Sanctuary, the Integrated Marine Protected Area Climate Tools (IMPACT) modification to the WCT simplifies or eliminates these requirements, while simultaneously adding core analytical functionality to the tool. Designed for use by marine ecosystem managers, WCT-IMPACT accesses a suite of data products that have been identified as relevant to marine ecosystem climate impact assessments, such as NOAA's Climate Data Records. WCT-IMPACT regularly crops these products to the geographic boundaries of each included marine protected area (MPA), and those clipped regions are processed to produce MPA-specific analytics. The tool retrieves the most appropriate data files based on the user selection of MPA, environmental variable(s), and time frame. Once the data are loaded, they may be visualized, explored, analyzed, and exported to other formats (e.g., Google KML). Multiple variables may be simultaneously visualized using a 4-panel display and compared via a variety of statistics such as difference, probability, or correlation maps.; NCDC's Weather and Climate Toolkit image of NARR-A non-convective cloud cover (%) over the Pacific Coast on June 17, 2012 at 09:00 GMT.

Collaborative Research: Process-resolving Decomposition of the Global Temperature Response to Modes of Low Frequency Variability in a Changing Climate

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

Cai, Ming; Deng, Yi

2015-02-06

El Niño-Southern Oscillation (ENSO) and Annular Modes (AMs) represent respectively the most important modes of low frequency variability in the tropical and extratropical circulations. The future projection of the ENSO and AM variability, however, remains highly uncertain with the state-of-the-art coupled general circulation models. A comprehensive understanding of the factors responsible for the inter-model discrepancies in projecting future changes in the ENSO and AM variability, in terms of multiple feedback processes involved, has yet to be achieved. The proposed research aims to identify sources of such uncertainty and establish a set of process-resolving quantitative evaluations of the existing predictions ofmore » the future ENSO and AM variability. The proposed process-resolving evaluations are based on a feedback analysis method formulated in Lu and Cai (2009), which is capable of partitioning 3D temperature anomalies/perturbations into components linked to 1) radiation-related thermodynamic processes such as cloud and water vapor feedbacks, 2) local dynamical processes including convection and turbulent/diffusive energy transfer and 3) non-local dynamical processes such as the horizontal energy transport in the oceans and atmosphere. Taking advantage of the high-resolution, multi-model ensemble products from the Coupled Model Intercomparison Project Phase 5 (CMIP5) soon to be available at the Lawrence Livermore National Lab, we will conduct a process-resolving decomposition of the global three-dimensional (3D) temperature (including SST) response to the ENSO and AM variability in the preindustrial, historical and future climate simulated by these models. Specific research tasks include 1) identifying the model-observation discrepancies in the global temperature response to ENSO and AM variability and attributing such discrepancies to specific feedback processes, 2) delineating the influence of anthropogenic radiative forcing on the key feedback processes operating on ENSO and AM variability and quantifying their relative contributions to the changes in the temperature anomalies associated with different phases of ENSO and AMs, and 3) investigating the linkages between model feedback processes that lead to inter-model differences in time-mean temperature projection and model feedback processes that cause inter-model differences in the simulated ENSO and AM temperature response. Through a thorough model-observation and inter-model comparison of the multiple energetic processes associated with ENSO and AM variability, the proposed research serves to identify key uncertainties in model representation of ENSO and AM variability, and investigate how the model uncertainty in predicting time-mean response is related to the uncertainty in predicting response of the low-frequency modes. The proposal is thus a direct response to the first topical area of the solicitation: Interaction of Climate Change and Low Frequency Modes of Natural Climate Variability. It ultimately supports the accomplishment of the BER climate science activity Long Term Measure (LTM): "Deliver improved scientific data and models about the potential response of the Earth's climate and terrestrial biosphere to increased greenhouse gas levels for policy makers to determine safe levels of greenhouse gases in the atmosphere."« less

A multiscale, hierarchical model of pulse dynamics in arid-land ecosystems

USGS Publications Warehouse

Collins, Scott L.; Belnap, Jayne; Grimm, N. B.; Rudgers, J. A.; Dahm, Clifford N.; D'Odorico, P.; Litvak, M.; Natvig, D. O.; Peters, Douglas C.; Pockman, W. T.; Sinsabaugh, R. L.; Wolf, B. O.

2014-01-01

Ecological processes in arid lands are often described by the pulse-reserve paradigm, in which rain events drive biological activity until moisture is depleted, leaving a reserve. This paradigm is frequently applied to processes stimulated by one or a few precipitation events within a growing season. Here we expand the original framework in time and space and include other pulses that interact with rainfall. This new hierarchical pulse-dynamics framework integrates space and time through pulse-driven exchanges, interactions, transitions, and transfers that occur across individual to multiple pulses extending from micro to watershed scales. Climate change will likely alter the size, frequency, and intensity of precipitation pulses in the future, and arid-land ecosystems are known to be highly sensitive to climate variability. Thus, a more comprehensive understanding of arid-land pulse dynamics is needed to determine how these ecosystems will respond to, and be shaped by, increased climate variability.

Reduced interdecadal variability of Atlantic Meridional Overturning Circulation under global warming.

PubMed

Cheng, Jun; Liu, Zhengyu; Zhang, Shaoqing; Liu, Wei; Dong, Lina; Liu, Peng; Li, Hongli

2016-03-22

Interdecadal variability of the Atlantic Meridional Overturning Circulation (AMOC-IV) plays an important role in climate variation and has significant societal impacts. Past climate reconstruction indicates that AMOC-IV has likely undergone significant changes. Despite some previous studies, responses of AMOC-IV to global warming remain unclear, in particular regarding its amplitude and time scale. In this study, we analyze the responses of AMOC-IV under various scenarios of future global warming in multiple models and find that AMOC-IV becomes weaker and shorter with enhanced global warming. From the present climate condition to the strongest future warming scenario, on average, the major period of AMOC-IV is shortened from ∼50 y to ∼20 y, and the amplitude is reduced by ∼60%. These reductions in period and amplitude of AMOC-IV are suggested to be associated with increased oceanic stratification under global warming and, in turn, the speedup of oceanic baroclinic Rossby waves.

Assessments of Maize Yield Potential in the Korean Peninsula Using Multiple Crop Models

NASA Astrophysics Data System (ADS)

Kim, S. H.; Myoung, B.; Lim, C. H.; Lee, S. G.; Lee, W. K.; Kafatos, M.

2015-12-01

The Korean Peninsular has unique agricultural environments due to the differences in the political and socio-economical systems between the Republic of Korea (SK, hereafter) and the Democratic Peoples' Republic of Korea (NK, hereafter). NK has been suffering from the lack of food supplies caused by natural disasters, land degradation and failed political system. The neighboring developed country SK has a better agricultural system but very low food self-sufficiency rate (around 1% of maize). Maize is an important crop in both countries since it is staple food for NK and SK is No. 2 maize importing country in the world after Japan. Therefore evaluating maize yield potential (Yp) in the two distinct regions is essential to assess food security under climate change and variability. In this study, we have utilized multiple process-based crop models capable of regional-scale assessments to evaluate maize Yp over the Korean Peninsula - the GIS version of EPIC model (GEPIC) and APSIM model that can be expanded to regional scales (APSIM regions). First we evaluated model performance and skill for 20 years from 1991 to 2010 using reanalysis data (Local Data Assimilation and Prediction System (LDAPS); 1.5km resolution) and observed data. Each model's performances were compared over different regions within the Korean Peninsula of different regional climate characteristics. To quantify the major influence of individual climate variables, we also conducted a sensitivity test using 20 years of climatology. Lastly, a multi-model ensemble analysis was performed to reduce crop model uncertainties. The results will provide valuable information for estimating the climate change or variability impacts on Yp over the Korean Peninsula.

Simulating and Predicting Cereal Crop Yields in Ethiopia: Model Calibration and Verification

NASA Astrophysics Data System (ADS)

Yang, M.; Wang, G.; Ahmed, K. F.; Eggen, M.; Adugna, B.; Anagnostou, E. N.

2017-12-01

Agriculture in developing countries are extremely vulnerable to climate variability and changes. In East Africa, most people live in the rural areas with outdated agriculture techniques and infrastructure. Smallholder agriculture continues to play a key role in this area, and the rate of irrigation is among the lowest of the world. As a result, seasonal and inter-annual weather patterns play an important role in the spatiotemporal variability of crop yields. This study investigates how various climate variables (e.g., temperature, precipitation, sunshine) and agricultural practice (e.g., fertilization, irrigation, planting date) influence cereal crop yields using a process-based model (DSSAT) and statistical analysis, and focuses on the Blue Nile Basin of Ethiopia. The DSSAT model is driven with meteorological forcing from the ECMWF's latest reanalysis product that cover the past 35 years; the statistical model will be developed by linking the same meteorological reanalysis data with harvest data at the woreda level from the Ethiopian national dataset. Results from this study will set the stage for the development of a seasonal prediction system for weather and crop yields in Ethiopia, which will serve multiple sectors in coping with the agricultural impact of climate variability.

Impacts of air pollution and climate change on forest ecosystems - Multiple stressors and ecosystem services

EPA Science Inventory

The nature of scientific investigation involving hypothesis testing dictates the need to conduct controlled experiments, limiting the number of independent variables in order to identify cause and effect relationships. Single or two-factor studies are useful to identify potentia...

Plant developmental responses to climate change.

PubMed

Gray, Sharon B; Brady, Siobhan M

2016-11-01

Climate change is multi-faceted, and includes changing concentrations of greenhouse gases in the atmosphere, rising temperatures, changes in precipitation patterns, and increasing frequency of extreme weather events. Here, we focus on the effects of rising atmospheric CO 2 concentrations, rising temperature, and drought stress and their interaction on plant developmental processes in leaves, roots, and in reproductive structures. While in some cases these responses are conserved across species, such as decreased root elongation, perturbation of root growth angle and reduced seed yield in response to drought, or an increase in root biomass in shallow soil in response to elevated CO 2 , most responses are variable within and between species and are dependent on developmental stage. These variable responses include species-specific thresholds that arrest development of reproductive structures, reduce root growth rate and the rate of leaf initiation and expansion in response to elevated temperature. Leaf developmental responses to elevated CO 2 vary by cell type and by species. Variability also exists between C 3 and C 4 species in response to elevated CO 2 , especially in terms of growth and seed yield stimulation. At the molecular level, significantly less is understood regarding conservation and variability in molecular mechanisms underlying these traits. Abscisic acid-mediated changes in cell wall expansion likely underlie reductions in growth rate in response to drought, and changes in known regulators of flowering time likely underlie altered reproductive transitions in response to elevated temperature and CO 2 . Genes that underlie most other organ or tissue-level responses have largely only been identified in a single species in response to a single stress and their level of conservation is unknown. We conclude that there is a need for further research regarding the molecular mechanisms of plant developmental responses to climate change factors in general, and that this lack of data is particularly prevalent in the case of interactive effects of multiple climate change factors. As future growing conditions will likely expose plants to multiple climate change factors simultaneously, with a sum negative influence on global agriculture, further research in this area is critical. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Demands, skill discretion, decision authority and social climate at work as determinants of major depression in a 3-year follow-up study.

PubMed

Fandiño-Losada, Andrés; Forsell, Yvonne; Lundberg, Ingvar

2013-07-01

The psychosocial work environment may be a determinant of the development and course of depressive disorders, but the literature shows inconsistent findings. Thus, the aim of this study is to determine longitudinal effects of the job demands-control-support model (JDCSM) variables on the occurrence of major depression among working men and women from the general population. The sample comprised 4,710 working women and men living in Stockholm, who answered the same questionnaire twice, 3 years apart, who were not depressed during the first wave and had the same job in both waves. The questionnaire included JDCSM variables (demands, skill discretion, decision authority and social climate) and other co-variables (income, education, occupational group, social support, help and small children at home, living with an adult and depressive symptoms at time 1; and negative life events at time 2). Multiple logistic regressions were run to calculate odds ratios of having major depression at time 2, after adjustment for other JDCSM variables and co-variables. Among women, inadequate work social climate was the only significant risk indicator for major depression. Surprisingly, among men, high job demands and low skill discretion appeared as protective factors against major depression. The results showed a strong relationship between inadequate social climate and major depression among women, while there were no certain effects for the remaining exposure variables. Among men, few cases of major depression hampered well-founded conclusions regarding our findings of low job demands and high skill discretion as related to major depression.

Weak climatic control of stand-scale fire history during the late holocene.

PubMed

Gavin, Daniel G; Hu, Feng Sheng; Lertzman, Kenneth; Corbett, Peter

2006-07-01

Forest fire occurrence is affected by multiple controls that operate at local to regional scales. At the spatial scale of forest stands, regional climatic controls may be obscured by local controls (e.g., stochastic ignitions, topography, and fuel loads), but the long-term role of such local controls is poorly understood. We report here stand-scale (<100 ha) fire histories of the past 5000 years based on the analysis of sediment charcoal at two lakes 11 km apart in southeastern British Columbia. The two lakes are today located in similar subalpine forests, and they likely have experienced the same late-Holocene climatic changes because of their close proximity. We evaluated two independent properties of fire history: (1) fire-interval distribution, a measure of the overall incidence of fire, and (2) fire synchroneity, a measure of the co-occurrence of fire (here, assessed at centennial to millennial time scales due to the resolution of sediment records). Fire-interval distributions differed between the sites prior to, but not after, 2500 yr before present. When the entire 5000-yr period is considered, no statistical synchrony between fire-episode dates existed between the two sites at any temporal scale, but for the last 2500 yr marginal levels of synchrony occurred at centennial scales. Each individual fire record exhibited little coherency with regional climate changes. In contrast, variations in the composite record (average of both sites) matched variations in climate evidenced by late-Holocene glacial advances. This was probably due to the increased sample size and spatial extent represented by the composite record (up to 200 ha) plus increased regional climatic variability over the last several millennia, which may have partially overridden local, non-climatic controls. We conclude that (1) over past millennia, neighboring stands with similar modern conditions may have experienced different fire intervals and asynchronous patterns in fire episodes, likely because local controls outweighed the synchronizing effect of climate; (2) the influence of climate on fire occurrence is more strongly expressed when climatic variability is relatively great; and (3) multiple records from a region are essential if climate-fire relations are to be reliably described.

Changing flood frequencies under opposing late Pleistocene eastern Mediterranean climates.

PubMed

Ben Dor, Yoav; Armon, Moshe; Ahlborn, Marieke; Morin, Efrat; Erel, Yigal; Brauer, Achim; Schwab, Markus Julius; Tjallingii, Rik; Enzel, Yehouda

2018-05-31

Floods comprise a dominant hydroclimatic phenomenon in aridlands with significant implications for humans, infrastructure, and landscape evolution worldwide. The study of short-term hydroclimatic variability, such as floods, and its forecasting for episodes of changing climate therefore poses a dominant challenge for the scientific community, and predominantly relies on modeling. Testing the capabilities of climate models to properly describe past and forecast future short-term hydroclimatic phenomena such as floods requires verification against suitable geological archives. However, determining flood frequency during changing climate is rarely achieved, because modern and paleoflood records, especially in arid regions, are often too short or discontinuous. Thus, coeval independent climate reconstructions and paleoflood records are required to further understand the impact of climate change on flood generation. Dead Sea lake levels reflect the mean centennial-millennial hydrological budget in the eastern Mediterranean. In contrast, floods in the large watersheds draining directly into the Dead Sea, are linked to short-term synoptic circulation patterns reflecting hydroclimatic variability. These two very different records are combined in this study to resolve flood frequency during opposing mean climates. Two 700-year-long, seasonally-resolved flood time series constructed from late Pleistocene Dead Sea varved sediments, coeval with significant Dead Sea lake level variations are reported. These series demonstrate that episodes of rising lake levels are characterized by higher frequency of floods, shorter intervals between years of multiple floods, and asignificantly larger number of years that experienced multiple floods. In addition, floods cluster into intervals of intense flooding, characterized by 75% and 20% increased frequency above their respective background frequencies during rising and falling lake-levels, respectively. Mean centennial precipitation in the eastern Mediterranean is therefore coupled with drastic changes in flood frequencies. These drastic changes in flood frequencies are linked to changes in the track, depth, and frequency of mid-latitude eastern Mediterranean cyclones, determining mean climatology resulting in wetter and drier regional climatic episodes.

Spatiotemporal patterns of evapotranspiration in response to multiple environmental factors simulated by the Community Land Model

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

Shi, Xiaoying; Mao, Jiafu; Thornton, Peter E

In this study, spatial and temporal patterns of evapotranspiration (ET) over the period of 1982-2008 are investigated and attributed to multiple environmental factors using the Community Land Model version 4 (CLM4). Our results show that CLM4 captures the spatial distribution and interannual variability of ET well when compared to observation-based estimates derived from the FLUXNET network of eddy covariance towers using the model tree ensembles (MTE) approach. We find that climate trends and variability dominate predicted variability in ET. Elevated atmospheric CO2 concentration also plays an important role in modulating the trend of predicted ET over most land areas, andmore » functions as the dominant factor controlling ET changes over North America, South America and Asia regions. Compared to the effect of climate change and CO2 concentration, the roles of other factors such as nitrogen deposition, land use change and aerosol deposition are less pronounced and regionally dependent. For example, the aerosol deposition contribution is the third-most important factor for trends of ET over Europe, while it has the smallest impact on ET trend over other regions. As ET is a dominant component of the terrestrial water cycle, our results suggest that environmental factors like elevated CO2, nitrogen and aerosol depositions, and land use and land cover change, in addition to climate, could have significant impact on future projections of water resources and water cycle dynamics at global and regional scales.« less

Climates of U.S. cities in the 21st century

NASA Astrophysics Data System (ADS)

Krayenhoff, E. S.; Georgescu, M.; Moustaoui, M.

2017-12-01

Urban climates are projected to warm over the 21st century due to global climate change and urban development. To assess this projected warming, Weather Research and Forecasting (WRF) model simulations are performed at 20 km resolution over the contiguous U.S. for three 10-year periods: contemporary (2000-2009), mid-century (2050-2059), and end-of-century (2090-2099). Urban land use projections are derived from the EPA's ICLUS data set, and future climate projections are based on two global climate models and two greenhouse gas emissions scenarios. The potential for design implementations such as `green' roofs and high albedo roofs to offset the projected warming is considered. Effects of urban expansion, urban densification and infrastructure adaptation on urban climate are compared over the century. Assessment considers impacts at both seasonal and diurnal scales, isolates fair weather impacts, and considers multiple climate variables: air temperature, precipitation, humidity, wind speed, and surface energy budget partitioning.

Attribution of declining Western U.S. Snowpack to human effects

USGS Publications Warehouse

Pierce, D.W.; Barnett, T.P.; Hidalgo, H.G.; Das, T.; Bonfils, Celine; Santer, B.D.; Bala, G.; Dettinger, M.D.; Cayan, D.R.; Mirin, A.; Wood, A.W.; Nozawa, T.

2008-01-01

Observations show snowpack has declined across much of the western United States over the period 1950-99. This reduction has important social and economic implications, as water retained in the snowpack from winter storms forms an important part of the hydrological cycle and water supply in the region. A formal model-based detection and attribution (D-A) study of these reductions is performed. The detection variable is the ratio of 1 April snow water equivalent (SWE) to water-year-to-date precipitation (P), chosen to reduce the effect of P variability on the results. Estimates of natural internal climate variability are obtained from 1600 years of two control simulations performed with fully coupled ocean-atmosphere climate models. Estimates of the SWE/P response to anthropogenic greenhouse gases, ozone, and some aerosols are taken from multiple-member ensembles of perturbation experiments run with two models. The D-A shows the observations and anthropogenically forced models have greater SWE/P reductions than can be explained by natural internal climate variability alone. Model-estimated effects of changes in solar and volcanic forcing likewise do not explain the SWE/P reductions. The mean model estimate is that about half of the SWE/P reductions observed in the west from 1950 to 1999 are the result of climate changes forced by anthropogenic greenhouse gases, ozone, and aerosols. ?? 2008 American Meteorological Society.

Community shifts under climate change: mechanisms at multiple scales.

PubMed

Gornish, Elise S; Tylianakis, Jason M

2013-07-01

Processes that drive ecological dynamics differ across spatial scales. Therefore, the pathways through which plant communities and plant-insect relationships respond to changing environmental conditions are also expected to be scale-dependent. Furthermore, the processes that affect individual species or interactions at single sites may differ from those affecting communities across multiple sites. We reviewed and synthesized peer-reviewed literature to identify patterns in biotic or abiotic pathways underpinning changes in the composition and diversity of plant communities under three components of climate change (increasing temperature, CO2, and changes in precipitation) and how these differ across spatial scales. We also explored how these changes to plants affect plant-insect interactions. The relative frequency of biotic vs. abiotic pathways of climate effects at larger spatial scales often differ from those at smaller scales. Local-scale studies show variable responses to climate drivers, often driven by biotic factors. However, larger scale studies identify changes to species composition and/or reduced diversity as a result of abiotic factors. Differing pathways of climate effects can result from different responses of multiple species, habitat effects, and differing effects of invasions at local vs. regional to global scales. Plant community changes can affect higher trophic levels as a result of spatial or phenological mismatch, foliar quality changes, and plant abundance changes, though studies on plant-insect interactions at larger scales are rare. Climate-induced changes to plant communities will have considerable effects on community-scale trophic exchanges, which may differ from the responses of individual species or pairwise interactions.

Flood characteristics of Alaskan streams

USGS Publications Warehouse

Lamke, R.D.

1979-01-01

Peak discharge data for Alaskan streams are summarized and analyzed. Multiple-regression equations relating peak discharge magnitude and frequency to climatic and physical characteristics of 260 gaged basins were determined in order to estimate average recurrence interval of floods at ungaged sites. These equations are for 1.25-, 2-, 5-, 10-, 25-, and 50-year average recurrence intervals. In this report, Alaska was divided into two regions, one having a maritime climate with fall and winter rains and floods, the other having spring and summer floods of a variety or combinations of causes. Average standard errors of the six multiple-regression equations for these two regions were 48 and 74 percent, respectively. Maximum recorded floods at more than 400 sites throughout Alaska are tabulated. Maps showing lines of equal intensity of the principal climatic variables found to be significant (mean annual precipitation and mean minimum January temperature), and location of the 260 sites used in the multiple-regression analyses are included. Little flood data have been collected in western and arctic Alaska, and the predictive equations are therefore less reliable for those areas. (Woodard-USGS)

Dynamic hydro-climatic networks in pristine and regulated rivers

NASA Astrophysics Data System (ADS)

Botter, G.; Basso, S.; Lazzaro, G.; Doulatyari, B.; Biswal, B.; Schirmer, M.; Rinaldo, A.

2014-12-01

Flow patterns observed at-a-station are the dynamical byproduct of a cascade of processes involving different compartments of the hydro-climatic network (e.g., climate, rainfall, soil, vegetation) that regulates the transformation of rainfall into streamflows. In complex branching rivers, flow regimes result from the heterogeneous arrangement around the stream network of multiple hydrologic cascades that simultaneously occur within distinct contributing areas. As such, flow regimes are seen as the integrated output of a complex "network of networks", which can be properly characterized by its degree of temporal variability and spatial heterogeneity. Hydrologic networks that generate river flow regimes are dynamic in nature. In pristine rivers, the time-variance naturally emerges at multiple timescales from climate variability (namely, seasonality and inter-annual fluctuations), implying that the magnitude (and the features) of the water flow between two nodes may be highly variable across different seasons and years. Conversely, the spatial distribution of river flow regimes within pristine rivers involves scale-dependent transport features, as well as regional climatic and soil use gradients, which in small and meso-scale catchments (A < 103 km2) are usually mild enough to guarantee quite uniform flow regimes and high spatial correlations. Human-impacted rivers, instead, constitute hybrid networks where observed spatio-temporal patterns are dominated by anthropogenic shifts, such as landscape alterations and river regulation. In regulated rivers, the magnitude and the features of water flows from node to node may change significantly through time due to damming and withdrawals. However, regulation may impact river regimes in a spatially heterogeneous manner (e.g. in localized river reaches), with a significant decrease of spatial correlations and network connectivity. Provided that the spatial and temporal dynamics of flow regimes in complex rivers may strongly impact important biotic processes involved in the river food web (e.g. biofilm and riparian vegetation dynamics), the study of rivers as dynamic networks provides important clues to water management strategies and freshwater ecosystem studies.

Latest Holocene Climate Variability revealed by a high-resolution multiple Proxy Record off Lisbon (Portugal)

NASA Astrophysics Data System (ADS)

Abrantes, F.; Lebreiro, S.; Ferreira, A.; Gil, I.; Jonsdottir, H.; Rodrigues, T.; Kissel, C.; Grimalt, J.

2003-04-01

The North Atlantic Oscillation (NAO) is known to have a major influence on the wintertime climate of the Atlantic basin and surrounding countries, determining precipitation and wind conditions at mid-latitudes. A comparison of Hurrel's NAO index to the mean winter (January-March) discharge of the Iberian Tagus River reveals a good negative correlation to negative NAO, while the years of largest upwelling anomalies, as referred in the literature, appear to be in good agreement with positive NAO. On this basis, a better understanding of the long-term variability of the NAO and Atlantic climate variability can be gained from high-resolution climate records from the Lisbon area. Climate variability of the last 2,000 years is assessed through a multiple proxy study of sedimentary sequences recovered from the Tagus prodelta deposition center, off Lisbon (Western Iberia). Physical properties, XRF and magnetic properties from core logging, grain size, δ18O, TOC, CaCO3, total alkenones, n-alkanes, alkenone SST, diatoms, benthic and planktonic foraminiferal assemblage compositions and fluxes are the proxies employed. The age model for site D13902 is based on AMS C-14 dates from mollusc and planktonic foraminifera shells, the reservoir correction for which was obtained by dating 3 pre-bomb, mollusc shells from the study area. Preliminary results indicate a Little Ice Age (LIA - 1300 - 1600 AD) alkenone derived SSTs around 15 degC followed by a sharp and rapid increase towards 19 degC. In spite the strong variability observed for most records, this low temperature interval is marked by a general increase in organic carbon, total alkenone concentration, diatom and foraminiferal abundances, as well as an increase in the sediment fine fraction and XRF determined Fe content, pointing to important river input and higher productivity. The Medieval Warm Period (1080 - 1300 AD) is characterized by 17-18 degC SSTs, increased mean grain size, but lower magnetic susceptibility and Fe contents, also accompanied by low values for total alkenone, n-alkanes and organic carbon concentration as well as low diatom abundance which may reflect decreased runoff and productivity. Major peaks in magnetic susceptibility and grain size occur at both periods and are interpreted as the record of flood-like events that are likely to reflect times of primarily negative NAO.

Climate, streamflow, and legacy effects on growth of riparian Populus angustifolia in the arid San Luis Valley, Colorado

USGS Publications Warehouse

Andersen, Douglas

2016-01-01

Knowledge of the factors affecting the vigor of desert riparian trees is important for their conservation and management. I used multiple regression to assess effects of streamflow and climate (12–14 years of data) or climate alone (up to 60 years of data) on radial growth of clonal narrowleaf cottonwood (Populus angustifolia), a foundation species in the arid, Closed Basin portion of the San Luis Valley, Colorado. I collected increment cores from trees (14–90 cm DBH) at four sites along each of Sand and Deadman creeks (total N = 85), including both perennial and ephemeral reaches. Analyses on trees <110 m from the stream channel explained 33–64% of the variation in standardized growth index (SGI) over the period having discharge measurements. Only 3 of 7 models included a streamflow variable; inclusion of prior-year conditions was common. Models for trees farther from the channel or over a deep water table explained 23–71% of SGI variability, and 4 of 5 contained a streamflow variable. Analyses using solely climate variables over longer time periods explained 17–85% of SGI variability, and 10 of 12 included a variable indexing summer precipitation. Three large, abrupt shifts in recent decades from wet to dry conditions (indexed by a seasonal Palmer Drought Severity Index) coincided with dramatically reduced radial growth. Each shift was presumably associated with branch dieback that produced a legacy effect apparent in many SGI series: uncharacteristically low SGI in the year following the shift. My results suggest trees in locations distant from the active channel rely on the regional shallow unconfined aquifer, summer rainfall, or both to meet water demands. The landscape-level differences in the water supplies sustaining these trees imply variable effects from shifts in winter-versus monsoon-related precipitation, and from climate change versus streamflow or groundwater management.

Climatic variability in the Gulf of California associated with the Medieval Warm Period and the Little Ice Age

NASA Astrophysics Data System (ADS)

Flores-Castillo, O. D. L. A.; Martínez-López, A.; Perez-Cruz, L. L.

2017-12-01

Marine ecosystems close to the coasts are highly susceptible to be affected both by the variability due to natural processes of the climate system as well as by anthropogenic activities. The Gulf of California, located near the tropical Pacific region, whose influence on the long-term global climate has already been demonstrated, represents a great opportunity to assess the regional response to these effects. This study reconstructs some of the oceanographic and climatic conditions that occurred simultaneously with the Medieval Warm Period (MWP) and the Little Ice Age (LIA) climatic periods in the southern region of the gulf. This reconstruction was based on the use of multiple indirect indicators or proxies of paleoproduction and geochemistry (determined by isotope-ratios mass spectrometer interfaced with an elemental analyzer and inductively coupled plasma mass spectrometry) preserved in a high-resolution laminated sedimentary sequence collected in the slope of southeastern coast of the Gulf of California (24.2822 ° N and 108.3037 ° W). The main effects of these periods were higher precipitation conditions that generated a greater fluvial contribution during the MWP besides a bigger oxygenation of the water mass near the bottom. These conditions were followed by an increase in exported production, decrease in the oxygen content of the water near the bottom and an increase in the denitrification during the transition to the LIA. The results confirm the existence of oceanographic and climatic variability on a secular scale in the Gulf of California associated with both global climatic periods.

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.

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 ...

Adaptation of rainfed agriculture to climatic variability in the Mixteca Alta Region of Oaxaca, Mexico

NASA Astrophysics Data System (ADS)

Rogé, P.; Friedman, A. R.; Astier, M.; Altieri, M.

2015-12-01

The traditional management systems of the Mixteca Alta Region of Oaxaca, Mexico offer historical lessons about resilience to climatic variability. We interviewed small farmers to inquire about the dynamics of abandonment and persistence of a traditional management systems. We interpret farmers' narratives from a perspective of general agroecological resilience. In addition, we facilitated workshops in small farmers described their adaptation to past climate challenges and identified 14 indicators that they subsequently used to evaluate the condition of their agroecosystems. The most recent years presented increasingly extreme climatic and socioeconomic hardships: increased temperatures, delayed rainy seasons, reduced capacity of soils to retain soil moisture, changing cultural norms, and reduced rural labor. Farmers reported that their cropping systems were changing for multiple reasons: more drought, later rainfall onset, decreased rural labor, and introduced labor-saving technologies. Examination of climate data found that farmers' climate narratives were largely consistent with the observational record. There have been increases in temperature and rainfall intensity, and an increase in rainfall seasonality that may be perceived as later rainfall onset. Farmers ranked landscape-scale indicators as more marginal than farmer management or soil quality indicators. From this analysis, farmers proposed strategies to improve the ability of their agroecosystems to cope with climatic variability. Notably, they recognized that social organizing and education are required for landscape-level indicators to be improved. Transformative change is required to develop novel cropping systems and complementary activities to agriculture that will allow for farming to be sustained in the face of these challenges. Climate change adaptation by small farmers involves much more than just a set of farming practices, but also community action to tackle collective problems.

Multi-timescale data assimilation for atmosphere–ocean state estimates

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

Steiger, Nathan; Hakim, Gregory

2016-06-24

Paleoclimate proxy data span seasonal to millennial timescales, and Earth's climate system has both high- and low-frequency components. Yet it is currently unclear how best to incorporate multiple timescales of proxy data into a single reconstruction framework and to also capture both high- and low-frequency components of reconstructed variables. Here we present a data assimilation approach that can explicitly incorporate proxy data at arbitrary timescales. The principal advantage of using such an approach is that it allows much more proxy data to inform a climate reconstruction, though there can be additional benefits. Through a series of offline data-assimilation-based pseudoproxy experiments,more » we find that atmosphere–ocean states are most skillfully reconstructed by incorporating proxies across multiple timescales compared to using proxies at short (annual) or long (~ decadal) timescales alone. Additionally, reconstructions that incorporate long-timescale pseudoproxies improve the low-frequency components of the reconstructions relative to using only high-resolution pseudoproxies. We argue that this is because time averaging high-resolution observations improves their covariance relationship with the slowly varying components of the coupled-climate system, which the data assimilation algorithm can exploit. These results are consistent across the climate models considered, despite the model variables having very different spectral characteristics. Furthermore, our results also suggest that it may be possible to reconstruct features of the oceanic meridional overturning circulation based on atmospheric surface temperature proxies, though here we find such reconstructions lack spectral power over a broad range of frequencies.« less

Multi-proxy reconstructions and the power of integration across marine, terrestrial, and freshwater ecosystems. (Invited)

NASA Astrophysics Data System (ADS)

Black, B.

2013-12-01

Over the past decade, dendrochronology (tree-ring analysis) techniques have been increasingly applied to growth increments of various bivalve, fish, and coral species. In particular, the use of crossdating ensures that all increments in a dataset have assigned the correct calendar year of formation and that the resulting chronology is exactly placed in time. Such temporal alignment facilitates direct comparisons among chronologies that span diverse taxa and ecosystems, illustrating the pervasive, synchronizing influence of climate from alpine forests to the continental slope. Such an approach can be particularly beneficial to reconstructions in that each species captures climate signals from its unique 'perspective' of life history and habitat. For example, combinations of tree-ring data and chronologies for the long-lived bivalve Pacific geoduck (Panopea generosa) capture substantially more variance in regional sea surface temperatures than either proxy could explain alone. Just as importantly, networks of chronologies spanning multiple trophic levels can help identify climate variables critical to ecosystem functioning, which can then be targeted to generate most biologically relevant reconstructions possible. Along the west coast of North America, fish and bivalve chronologies in combination with records of seabird reproductive success indicate that winter sea-level pressure is closely associated with California Current productivity, which can be hind-cast over the past six centuries using coastal tree-ring chronologies. Thus, multiple proxies not only increase reconstruction skill, but also help isolate climate variables most closely linked to ecosystem structure and functioning.

Multi-proxy reconstructions and the power of integration across marine, terrestrial, and freshwater ecosystems. (Invited)

NASA Astrophysics Data System (ADS)

Barrett, P. J.

2011-12-01

Over the past decade, dendrochronology (tree-ring analysis) techniques have been increasingly applied to growth increments of various bivalve, fish, and coral species. In particular, the use of crossdating ensures that all increments in a dataset have assigned the correct calendar year of formation and that the resulting chronology is exactly placed in time. Such temporal alignment facilitates direct comparisons among chronologies that span diverse taxa and ecosystems, illustrating the pervasive, synchronizing influence of climate from alpine forests to the continental slope. Such an approach can be particularly beneficial to reconstructions in that each species captures climate signals from its unique 'perspective' of life history and habitat. For example, combinations of tree-ring data and chronologies for the long-lived bivalve Pacific geoduck (Panopea generosa) capture substantially more variance in regional sea surface temperatures than either proxy could explain alone. Just as importantly, networks of chronologies spanning multiple trophic levels can help identify climate variables critical to ecosystem functioning, which can then be targeted to generate most biologically relevant reconstructions possible. Along the west coast of North America, fish and bivalve chronologies in combination with records of seabird reproductive success indicate that winter sea-level pressure is closely associated with California Current productivity, which can be hind-cast over the past six centuries using coastal tree-ring chronologies. Thus, multiple proxies not only increase reconstruction skill, but also help isolate climate variables most closely linked to ecosystem structure and functioning.

Temporal dynamics of direct N2O fluxes from agro-ecosystems in cold climates: importance of year-round measurements in multiple cropping systems

NASA Astrophysics Data System (ADS)

Wagner-Riddle, C.; Tenuta, M.

2014-12-01

Soil N2O fluxes (direct emissions) are highly variable in time and space due to soil, weather and management drivers. In cold climates, freeze/thaw cycles and short growing seasons can enhance soil N2O production contributing to the temporal variability of fluxes. Year-round measurements of N2O fluxes in multiple cropping systems are needed to decrease the uncertainty of annual emission estimates and to devise mitigation practices for emission reduction in cold climates. We have deployed a micrometeorological flux-gradient approach coupled to a tunable diode laser absorption spectroscopy system at two long-term sites in Canada: Elora, Ontario (2000-2014) and Glenlea, Manitoba (2006-2014). Quasi-simultaneous half-hourly flux measurements on four 4-ha fields within a level and aerodynamically homogeneous landscape were obtained allowing for comparison of crop type and/or management practices within and between years. Annual crops such as corn, soybeans, wheat, and barley received typical inorganic fertilizer and/or manure applications, and best management practices such as timing of application and reduced tillage were studied. Perennial grass-alfalfa hayfields were compared to annual crops during selected time periods. Here we synthesize the long-term datasets from these two sites, and quantify the overall contribution of non-growing season (mainly freeze/thaw cycles) and growing season (mainly nitrogen application) to annual emission totals. Uncertainties of regional estimates for cold-climates will be assessed using these long-term datasets.

Effects of climate on numbers of northern prairie wetlands

USGS Publications Warehouse

Larson, Diane L.

1995-01-01

The amount of water held in individual wetland basins depends not only on local climate patterns but also on groundwater flow regime, soil permeability, and basin size. Most wetland basins in the northern prairies hold water in some years and are dry in others. To assess the potential effect of climate change on the number of wetland basins holding water in a given year, one must first determine how much of the variability in number of wet basins is accounted for by climatic variables. I used multiple linear regression to examine the relationship between climate variables and percentage of wet basins throughout the Prairie Pothole Region of Canada and the United States. The region was divided into three areas: parkland, Canadian grassland, and United States grassland (i.e., North Dakota and South Dakota). The models - which included variables for spring and fall temperature, yearly precipitation, the previous year's count of wet basins, and for grassland areas, the previous fall precipitation - accounted for 63 to 65% of the variation in the number of wet basins. I then explored the sensitivities of the models to changes in temperature and precipitation, as might be associated with increased greenhouse gas concentrations. Parkland wetlands are shown to be much more vulnerable to increased temperatures than are wetlands in either Canadian or United States grasslands. Sensitivity to increased precipitation did not vary geographically. These results have implications for waterfowl and other wildlife populations that depend on availability of wetlands in the parklands for breeding or during periods of drought in the southern grasslands.

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.

Selecting climate change scenarios for regional hydrologic impact studies based on climate extremes indices

NASA Astrophysics Data System (ADS)

Seo, Seung Beom; Kim, Young-Oh; Kim, Youngil; Eum, Hyung-Il

2018-04-01

When selecting a subset of climate change scenarios (GCM models), the priority is to ensure that the subset reflects the comprehensive range of possible model results for all variables concerned. Though many studies have attempted to improve the scenario selection, there is a lack of studies that discuss methods to ensure that the results from a subset of climate models contain the same range of uncertainty in hydrologic variables as when all models are considered. We applied the Katsavounidis-Kuo-Zhang (KKZ) algorithm to select a subset of climate change scenarios and demonstrated its ability to reduce the number of GCM models in an ensemble, while the ranges of multiple climate extremes indices were preserved. First, we analyzed the role of 27 ETCCDI climate extremes indices for scenario selection and selected the representative climate extreme indices. Before the selection of a subset, we excluded a few deficient GCM models that could not represent the observed climate regime. Subsequently, we discovered that a subset of GCM models selected by the KKZ algorithm with the representative climate extreme indices could not capture the full potential range of changes in hydrologic extremes (e.g., 3-day peak flow and 7-day low flow) in some regional case studies. However, the application of the KKZ algorithm with a different set of climate indices, which are correlated to the hydrologic extremes, enabled the overcoming of this limitation. Key climate indices, dependent on the hydrologic extremes to be projected, must therefore be determined prior to the selection of a subset of GCM models.

Effect of climate, intra and inter-annual variability, on nutrients emission (C,N, P) in stream water: lessons from an agricultural long term observatory of the temperate zone

NASA Astrophysics Data System (ADS)

Gascuel-Odoux, Chantal; Remi, Dupas; Patrick, Durand; Ophélie, Fovet; Gerard, Gruau; Anne, Jaffrezic; Guillaume, Humbert; Philippe, Merot; Gu, Sen

2016-04-01

Agriculture greatly contributes to modify C, N and P cycles, particularly in animal breeding regions due to high inputs. Climatic conditions, intra and inter-annual variabilities, modify nutrient stream water emissions, acting in time on transfer and transformation, accumulation and mobilization processes, connecting and disconnecting in time different compartments (soil, riparian areas, groundwater). In agricultural catchments, nutrient perturbations are dominated by agricultural land use, and decoupling human activities and climate effects is far from easy. Climate change generally appears as a secondary driver compared to land use. If studied, generally only one nutrient is considered. Only long term, high frequency and multiple element data series can decouple these two drivers. The Kervidy-Naizin watershed belongs to the AgrHyS environmental research observatory (http://www6.inra.fr/ore_agrhys_eng), itself included in RBV (French catchment network of the CZO). On this catchment, 6 years of daily data on DOC, NO3, SRP, TP concentrations allow us to analyze the effect of seasonal and inter-annual climatic variabilities on water quality (C, N, P). Different papers have been published on the effect of climate on nitrate (Molenat et al, 2008), SRP and TP (Dupas et al, 2015) and DOC (Humbert et al, 2015). We will present first results comparing the effect of climate on these three major solute forms of C, N and P. While C and P dynamics are very close and controlled by fluctuation of water table downslope, i.e. in riparian areas, mobilizing C and P in time, nitrate dynamics is controlled by GW dynamics upslope acting as the major N reservoir. As example, the dryness conditions in summer appears a key factor of the C and P emissions in autumn. All the three solute forms interact when anoxic conditions are observed in riparian zones. These basic processes explain how climatic variability can influence and explain interactions between C, N and P emissions in stream water. These results underline three major lack in most of our observatories: high frequency data as flood event are important for C and P emissions; multiple element approach, as very few observatories have currently C, N and P, their solute and particulate forms; climate but also soil wetness, GW fluctuations explaining biotransformation and connection between reservoirs on catchments, so that linking hydrological and biogeochimical condition is necessary to explain export. These lacks of observations is a barrier to develop process based models assessing and predicting the effect of climate on water quality. References Dupas R., Gruau G., Sen Gu, Humbert G., Jaffrezic A., Gascuel-Odoux C., 2015. Groundwater control of biogeochemical processes causing phosphorus release from riparian wetlands. Water Research 84, 307-314 Humbert G., Jaffrezic A., Fovet O., Gruau G., Durand P., 2015. Dry-season length and runoff control annual variability in stream DOC dynamics in a small, shallow groundwater-dominated agricultural watershed. Water Resources Research. Molenat J., Gascuel-Odoux C., Ruiz L., Gruau G., 2008. Role of water table dynamics on stream nitrate export and concentration in agricultural headwater. Journal of Hydrology 348, 363- 378.

The local and global climate forcings induced inhomogeneity of Indian rainfall.

PubMed

Nair, P J; Chakraborty, A; Varikoden, H; Francis, P A; Kuttippurath, J

2018-04-16

India is home for more than a billion people and its economy is largely based on agrarian society. Therefore, rainfall received not only decides its livelihood, but also influences its water security and economy. This situation warrants continuous surveillance and analysis of Indian rainfall. These kinds of studies would also help forecasters to better tune their models for accurate weather prediction. Here, we introduce a new method for estimating variability and trends in rainfall over different climate regions of India. The method based on multiple linear regression helps to assess contributions of different remote and local climate forcings to seasonal and regional inhomogeneity in rainfall. We show that the Indian Summer Monsoon Rainfall (ISMR) variability is governed by Eastern and Central Pacific El Niño Southern Oscillation, equatorial zonal winds, Atlantic zonal mode and surface temperatures of the Arabian Sea and Bay of Bengal, and the North East Monsoon Rainfall variability is controlled by the sea surface temperature of the North Atlantic and extratropial oceans. Also, our analyses reveal significant positive trends (0.43 mm/day/dec) in the North West for ISMR in the 1979-2017 period. This study cautions against the significant changes in Indian rainfall in a perspective of global climate change.

From global change to a butterfly flapping: biophysics and behaviour affect tropical climate change impacts.

PubMed

Bonebrake, Timothy C; Boggs, Carol L; Stamberger, Jeannie A; Deutsch, Curtis A; Ehrlich, Paul R

2014-10-22

Difficulty in characterizing the relationship between climatic variability and climate change vulnerability arises when we consider the multiple scales at which this variation occurs, be it temporal (from minute to annual) or spatial (from centimetres to kilometres). We studied populations of a single widely distributed butterfly species, Chlosyne lacinia, to examine the physiological, morphological, thermoregulatory and biophysical underpinnings of adaptation to tropical and temperate climates. Microclimatic and morphological data along with a biophysical model documented the importance of solar radiation in predicting butterfly body temperature. We also integrated the biophysics with a physiologically based insect fitness model to quantify the influence of solar radiation, morphology and behaviour on warming impact projections. While warming is projected to have some detrimental impacts on tropical ectotherms, fitness impacts in this study are not as negative as models that assume body and air temperature equivalence would suggest. We additionally show that behavioural thermoregulation can diminish direct warming impacts, though indirect thermoregulatory consequences could further complicate predictions. With these results, at multiple spatial and temporal scales, we show the importance of biophysics and behaviour for studying biodiversity consequences of global climate change, and stress that tropical climate change impacts are likely to be context-dependent. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

From global change to a butterfly flapping: biophysics and behaviour affect tropical climate change impacts

PubMed Central

Bonebrake, Timothy C.; Boggs, Carol L.; Stamberger, Jeannie A.; Deutsch, Curtis A.; Ehrlich, Paul R.

2014-01-01

Difficulty in characterizing the relationship between climatic variability and climate change vulnerability arises when we consider the multiple scales at which this variation occurs, be it temporal (from minute to annual) or spatial (from centimetres to kilometres). We studied populations of a single widely distributed butterfly species, Chlosyne lacinia, to examine the physiological, morphological, thermoregulatory and biophysical underpinnings of adaptation to tropical and temperate climates. Microclimatic and morphological data along with a biophysical model documented the importance of solar radiation in predicting butterfly body temperature. We also integrated the biophysics with a physiologically based insect fitness model to quantify the influence of solar radiation, morphology and behaviour on warming impact projections. While warming is projected to have some detrimental impacts on tropical ectotherms, fitness impacts in this study are not as negative as models that assume body and air temperature equivalence would suggest. We additionally show that behavioural thermoregulation can diminish direct warming impacts, though indirect thermoregulatory consequences could further complicate predictions. With these results, at multiple spatial and temporal scales, we show the importance of biophysics and behaviour for studying biodiversity consequences of global climate change, and stress that tropical climate change impacts are likely to be context-dependent. PMID:25165769

Satellite-based trends of solar radiation and cloud parameters in Europe

NASA Astrophysics Data System (ADS)

Pfeifroth, Uwe; Bojanowski, Jedrzej S.; Clerbaux, Nicolas; Manara, Veronica; Sanchez-Lorenzo, Arturo; Trentmann, Jörg; Walawender, Jakub P.; Hollmann, Rainer

2018-04-01

Solar radiation is the main driver of the Earth's climate. Measuring solar radiation and analysing its interaction with clouds are essential for the understanding of the climate system. The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) generates satellite-based, high-quality climate data records, with a focus on the energy balance and water cycle. Here, multiple of these data records are analyzed in a common framework to assess the consistency in trends and spatio-temporal variability of surface solar radiation, top-of-atmosphere reflected solar radiation and cloud fraction. This multi-parameter analysis focuses on Europe and covers the time period from 1992 to 2015. A high correlation between these three variables has been found over Europe. An overall consistency of the climate data records reveals an increase of surface solar radiation and a decrease in top-of-atmosphere reflected radiation. In addition, those trends are confirmed by negative trends in cloud cover. This consistency documents the high quality and stability of the CM SAF climate data records, which are mostly derived independently from each other. The results of this study indicate that one of the main reasons for the positive trend in surface solar radiation since the 1990's is a decrease in cloud coverage even if an aerosol contribution cannot be completely ruled out.

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.

Development of the Wintertime Sr/Ca-SST Record from Red Sea Corals as a Proxy for the North Atlantic Oscillation

NASA Astrophysics Data System (ADS)

Bernstein, W. N.; Hughen, K. A.

2009-12-01

The North Atlantic Oscillation (NAO) is one of the most pronounced and influential patterns in winter atmospheric circulation variability. This meridional redistribution of atmospheric mass across the Atlantic Ocean produces large changes in the intensity, number and direction of storms generated within the basin, and the regional climate of surrounding continents. The NAO exerts a significant impact on society, through influences on agriculture, fisheries, water management, energy generation and coastal development. NAO effects on climate extend from eastern North America across Europe to the eastern Mediterranean and Middle East. Changes in NAO behavior during the late 20th century have been linked to global warming; yet despite its importance, the causes and long-term patterns of NAO variability in the past remain poorly understood. In order to better predict the influence of the NAO on climate in the future, it is critical to examine multi-century NAO variability. The Red Sea is an excellent location from which to generate long NAO records for two reasons. First, patterns of wintertime sea surface temperature (SST) and salinity (SSS) in the Red Sea are highly correlated with NAO variability (Visbeck et al. 2001; Hurrell et al. 2003). Second, the tropical/subtropical Red Sea region contains fast growing long-lived massive Porites spp. corals with annually banded skeletons. These corals are ideal for generating well-dated high-resolution paleoclimatic records that extend well beyond the instrumental period. Here we present a study of winter SST and NAO variability in the Red sea region based on coral Sr/Ca data. In 2008, we collected multiple drill cores ranging in length from 1 to 4.1 meters from Porites corals at six sites spanning a large SST gradient. Sr/Ca measurements from multiple corals will be regressed against 23 years of satellite SST data, expanding the SST range over which we calibrate. A sampling resolution of 0.5mm will yield greater than bi-weekly temporal resolution for downcore SST reconstructions over the past 140 years, which will be used to evaluate the ability of the coral proxies to capture instrumental NAO variability. We expect that this winter Sr/Ca record will exhibit coherence with the NAO similar to that evident between Red Sea instrumental SST and the NAO index. Future work will involve construction of an NAO record back ~400 years, using the multi-core Sr/Ca-SST calibration applied to a combination of new records from modern and fossil coral material. This record will be examined to identify changes in NAO behavior as a function of frequency, and to compare frequency-dependent NAO variability between periods of relatively warm and cold hemispheric climate. This analysis will allow us to test the hypothesized link between NAO behavior and mean climate conditions, and if confirmed, improve predictions regarding the role of the NAO in impending climate change. References Hurrell, J. et al., 2003, in The North Atlantic Oscillation: Climatic Significance and Environmental Impact, 1-36 (A.G.U., Washington, D.C.). Visbeck, M. et al., 2001, Proc. Nat. Acad. Sci. 98, 12876-12877.

Evaluation of Stochastic Rainfall Models in Capturing Climate Variability for Future Drought and Flood Risk Assessment

NASA Astrophysics Data System (ADS)

Chowdhury, A. F. M. K.; Lockart, N.; Willgoose, G. R.; Kuczera, G. A.; Kiem, A.; Nadeeka, P. M.

2016-12-01

One of the key objectives of stochastic rainfall modelling is to capture the full variability of climate system for future drought and flood risk assessment. However, it is not clear how well these models can capture the future climate variability when they are calibrated to Global/Regional Climate Model data (GCM/RCM) as these datasets are usually available for very short future period/s (e.g. 20 years). This study has assessed the ability of two stochastic daily rainfall models to capture climate variability by calibrating them to a dynamically downscaled RCM dataset in an east Australian catchment for 1990-2010, 2020-2040, and 2060-2080 epochs. The two stochastic models are: (1) a hierarchical Markov Chain (MC) model, which we developed in a previous study and (2) a semi-parametric MC model developed by Mehrotra and Sharma (2007). Our hierarchical model uses stochastic parameters of MC and Gamma distribution, while the semi-parametric model uses a modified MC process with memory of past periods and kernel density estimation. This study has generated multiple realizations of rainfall series by using parameters of each model calibrated to the RCM dataset for each epoch. The generated rainfall series are used to generate synthetic streamflow by using a SimHyd hydrology model. Assessing the synthetic rainfall and streamflow series, this study has found that both stochastic models can incorporate a range of variability in rainfall as well as streamflow generation for both current and future periods. However, the hierarchical model tends to overestimate the multiyear variability of wet spell lengths (therefore, is less likely to simulate long periods of drought and flood), while the semi-parametric model tends to overestimate the mean annual rainfall depths and streamflow volumes (hence, simulated droughts are likely to be less severe). Sensitivity of these limitations of both stochastic models in terms of future drought and flood risk assessment will be discussed.

Adaptation to Interannual and Interdecadal Climate Variability in Agricultural Production Systems of the Argentine Pampas

NASA Astrophysics Data System (ADS)

Podestá, G. P.; Bert, F.; Weber, E.; Laciana, C.; Rajagopalan, B.; Letson, D.

2007-05-01

Agricultural ecosystems play a central role in world food production and food security, and involve one of the most climate-sensitive sectors of society-agriculture. We focus on crop production in the Argentine Pampas, one of the world's major agricultural regions. Climate of the Pampas shows marked variability at both interannual and decadal time scales. We explored the scope for adaptive management in response to climate information on interannual scales. We show that different assumptions about what decision makers are trying to achieve (i.e., their objective functions) may change what actions are considered as "optimal" for a given climate context. Optimal actions also were used to estimate the economic value of forecasts of an ENSO phase. Decision constraints (e.g., crop rotations) have critical influence on value of the forecasting system. Gaps in knowledge or misconceptions about climate variability were identified in open-ended "mental model" interviews. Results were used to design educational interventions. A marked increase in precipitation since the 1970s, together with new production technologies, led to major changes in land use patterns in the Pampas. Continuous cropping has widely replaced agriculture-pasture rotations. Nevertheless, production systems that evolved partly in response to increased rainfall may not be viable if climate reverts to a drier epoch. We use historical data to define a range of plausible climate trajectories 20-30 years hence. Regional scenarios are downscaled using semi-parametric weather generators to produce multiple realizations of daily weather consistent with decadal scenarios. Finally, we use the synthetic climate, crop growth models, and realistic models of decision-making under risk to compute risk metrics (e.g., probability of yields or profits being below a threshold). Climatically optimal and marginal locations show differential responses: probabilities of negative economic results are much higher in currently marginal areas if precipitations decrease.

Simulation of Plant Physiological Process Using Fuzzy Variables

Treesearch

Daniel L. Schmoldt

1991-01-01

Qualitative modelling can help us understand and project effects of multiple stresses on trees. It is not practical to collect and correlate empirical data for all combinations of plant/environments and human/climate stresses, especially for mature trees in natural settings. Therefore, a mechanistic model was developed to describe ecophysiological processes. This model...

Growth and demography of Pinaleno high elevation forests

Treesearch

Christopher O' Connor; Donald A. Falk; Ann M. Lynch; Craig P. Wilcox; Thomas W. Swetnam; Tyson L. Swetnam

2010-01-01

The project goal is to understand how multiple disturbance events including fire, insect outbreaks, and climate variability interact in space and time, and how they combine to influence forest species composition, spatial structure, and tree population dynamics in high elevation forests of the Pinaleno Mountains. Information from each of these components is needed in...

Riparian Areas of the Southwest: Learning from Repeat Photographs

ERIC Educational Resources Information Center

Zaimes, George N.; Crimmins, Michael A.

2010-01-01

Spatial and temporal variability of riparian areas, as well as potential impacts from climate change, are concepts that land and water managers and stakeholders need to understand to effectively manage and protect riparian areas. Rapid population growth in the southwestern United States, and multiple-use designation of most riparian areas, makes…

Development of the Long-Term Agro-ecosystem Research (LTAR) Network: Current status and future trends

USDA-ARS?s Scientific Manuscript database

Long-term research conducted at multiple scales is critical to assessing the effects of key long term drivers (e.g., global population growth; land-use change; increased competition for natural resources; climate variability and change) on our ability to sustain or enhance agricultural production to...

A synthesis of sedimentary records of Australian environmental change during the last 2000 years

NASA Astrophysics Data System (ADS)

Tyler, J. J.; Karoly, D. J.; Gell, P.; Goodwin, I. D.

2013-12-01

Our understanding of Southern Hemispheric climate variability on multidecadal to multicentennial timescales is limited by a scarcity of quantitative, highly resolved climate records, a problem which is particularly manifest in Australia. To date there are no quantitative, annually resolved records from within continental Australia which extend further back in time than the most recent c. 300 years [Neukom and Gergis, 2012; PAGES 2k Consortium, 2013]. By contrast, a number of marine, lake, peat and speleothem sedimentary records exist, some of which span multiple millennia at sub-decadal resolution. Here we report a database of existing sedimentary records of environmental change in Australia [Freeman et al., 2011], of which 25 have sample resolutions < 100 years/sample and which span > 500 years in duration. The majority of these records are located in southeastern Australia, providing an invaluable resource with which to examine regional scale climate and environmental change. Although most of the records can not be quantitatively related to climate variability, Empirical Orthogonal Functions coupled with Monte Carlo iterative age modelling, demonstrate coherent patterns of environmental and ecological change. This coherency, as well as comparisons with a limited number of quantitative records, suggests that regional hydroclimatic changes were responsible for the observed patterns. Here, we discuss the implications of these findings with respect to Southern Hemisphere climate during the last 2000 years. In addition, we review the progress and potential of ongoing research in the region. References: Freeman, R., I. D. Goodwin, and T. Donovan (2011), Paleoclimate data synthesis and data base for the reconstruction of climate variability and impacts in NSW over the past 2000 years., Climate Futures Technical Report, 1/2011, 50 pages. Neukom, R., and J. Gergis (2012), Southern Hemisphere high-resolution palaeoclimate records of the last 2000 years, Holocene, 22(5), 501-524, doi:10.1177/0959683611427335. PAGES 2k Consortium (2013), Continental-scale temperature variability during the past two millennia, Nature Geoscience, 6, 339-346.

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

NASA Technical Reports Server (NTRS)

Makowski, David; Asseng, Senthold; Ewert, Frank; Bassu, Simona; Durand, Jean-Louis; Martre, Pierre; Adam, Myriam; Aggarwal, Pramod K.; Angulo, Carlos; Baron, Chritian;

Remote sensing of land surface phenology

USGS Publications Warehouse

Meier, G.A.; Brown, Jesslyn F.

2014-01-01

Remote sensing of land-surface phenology is an important method for studying the patterns of plant and animal growth cycles. Phenological events are sensitive to climate variation; therefore phenology data provide important baseline information documenting trends in ecology and detecting the impacts of climate change on multiple scales. The USGS Remote sensing of land surface phenology program produces annually, nine phenology indicator variables at 250 m and 1,000 m resolution for the contiguous U.S. The 12 year archive is available at http://phenology.cr.usgs.gov/index.php.

An advanced stochastic weather generator for simulating 2-D high-resolution climate variables

NASA Astrophysics Data System (ADS)

Peleg, Nadav; Fatichi, Simone; Paschalis, Athanasios; Molnar, Peter; Burlando, Paolo

2017-07-01

A new stochastic weather generator, Advanced WEather GENerator for a two-dimensional grid (AWE-GEN-2d) is presented. The model combines physical and stochastic approaches to simulate key meteorological variables at high spatial and temporal resolution: 2 km × 2 km and 5 min for precipitation and cloud cover and 100 m × 100 m and 1 h for near-surface air temperature, solar radiation, vapor pressure, atmospheric pressure, and near-surface wind. The model requires spatially distributed data for the calibration process, which can nowadays be obtained by remote sensing devices (weather radar and satellites), reanalysis data sets and ground stations. AWE-GEN-2d is parsimonious in terms of computational demand and therefore is particularly suitable for studies where exploring internal climatic variability at multiple spatial and temporal scales is fundamental. Applications of the model include models of environmental systems, such as hydrological and geomorphological models, where high-resolution spatial and temporal meteorological forcing is crucial. The weather generator was calibrated and validated for the Engelberg region, an area with complex topography in the Swiss Alps. Model test shows that the climate variables are generated by AWE-GEN-2d with a level of accuracy that is sufficient for many practical applications.

Seasonality in cholera dynamics: A rainfall-driven model explains the wide range of patterns in endemic areas

NASA Astrophysics Data System (ADS)

Baracchini, Theo; King, Aaron A.; Bouma, Menno J.; Rodó, Xavier; Bertuzzo, Enrico; Pascual, Mercedes

2017-10-01

Seasonal patterns in cholera dynamics exhibit pronounced variability across geographical regions, showing single or multiple peaks at different times of the year. Although multiple hypotheses related to local climate variables have been proposed, an understanding of this seasonal variation remains incomplete. The historical Bengal region, which encompasses the full range of cholera's seasonality observed worldwide, provides a unique opportunity to gain insights on underlying environmental drivers. Here, we propose a mechanistic, rainfall-temperature driven, stochastic epidemiological model which explicitly accounts for the fluctuations of the aquatic reservoir, and analyze with this model the historical dataset of cholera mortality in the Bengal region. Parameters are inferred with a recently developed sequential Monte Carlo method for likelihood maximization in partially observed Markov processes. Results indicate that the hydrological regime is a major driver of the seasonal dynamics of cholera. Rainfall tends to buffer the propagation of the disease in wet regions due to the longer residence times of water in the environment and an associated dilution effect, whereas it enhances cholera resurgence in dry regions. Moreover, the dynamics of the environmental water reservoir determine whether the seasonality is unimodal or bimodal, as well as its phase relative to the monsoon. Thus, the full range of seasonal patterns can be explained based solely on the local variation of rainfall and temperature. Given the close connection between cholera seasonality and environmental conditions, a deeper understanding of the underlying mechanisms would allow the better management and planning of public health policies with respect to climate variability and climate change.

North American Megadroughts in the Common Era: Reconstructions and Simulations

NASA Technical Reports Server (NTRS)

Cook, Benjamin I.; Cook, Edward R.; Smerdon, Jason E.; Seager, Richard; Williams, A. Park; Coats, Sloan; Stahle, David W.; Villanueva Diaz, Jose

2016-01-01

During the Medieval Climate Anomaly (MCA), Western North America experienced episodes of intense aridity that persisted for multiple decades or longer. These megadroughts are well documented in many proxy records, but the causal mechanisms are poorly understood. General circulation models (GCMs) simulate megadroughts, but do not reproduce the temporal clustering of events during the MCA, suggesting they are not caused by the time history of volcanic or solar forcing. Instead, GCMs generate megadroughts through (1) internal atmospheric variability, (2) sea-surface temperatures, and (3) land surface and dust aerosol feedbacks. While no hypothesis has been definitively rejected, and no GCM has accurately reproduced all features (e.g., timing, duration, and extent) of any specific megadrought, their persistence suggests a role for processes that impart memory to the climate system (land surface and ocean dynamics). Over the 21st century, GCMs project an increase in the risk of megadrought occurrence through greenhouse gas forced reductions in precipitation and increases in evaporative demand. This drying is robust across models and multiple drought indicators, but major uncertainties still need to be resolved. These include the potential moderation of vegetation evaporative losses at higher atmospheric [CO2], variations in land surface model complexity, and decadal to multidecadal modes of natural climate variability that could delay or advance onset of aridification over the the next several decades. Because future droughts will arise from both natural variability and greenhouse gas forced trends in hydroclimate, improving our understanding of the natural drivers of persistent multidecadal megadroughts should be a major research priority.

Mid-latitude shrub steppe plant communities: climate change consequences for soil water resources.

PubMed

Palmquist, Kyle A; Schlaepfer, Daniel R; Bradford, John B; Lauenroth, William K

2016-09-01

In the coming century, climate change is projected to impact precipitation and temperature regimes worldwide, with especially large effects in drylands. We use big sagebrush ecosystems as a model dryland ecosystem to explore the impacts of altered climate on ecohydrology and the implications of those changes for big sagebrush plant communities using output from 10 Global Circulation Models (GCMs) for two representative concentration pathways (RCPs). We ask: (1) What is the magnitude of variability in future temperature and precipitation regimes among GCMs and RCPs for big sagebrush ecosystems, and (2) How will altered climate and uncertainty in climate forecasts influence key aspects of big sagebrush water balance? We explored these questions across 1980-2010, 2030-2060, and 2070-2100 to determine how changes in water balance might develop through the 21st century. We assessed ecohydrological variables at 898 sagebrush sites across the western US using a process-based soil water model, SOILWAT, to model all components of daily water balance using site-specific vegetation parameters and site-specific soil properties for multiple soil layers. Our modeling approach allowed for changes in vegetation based on climate. Temperature increased across all GCMs and RCPs, whereas changes in precipitation were more variable across GCMs. Winter and spring precipitation was predicted to increase in the future (7% by 2030-2060, 12% by 2070-2100), resulting in slight increases in soil water potential (SWP) in winter. Despite wetter winter soil conditions, SWP decreased in late spring and summer due to increased evapotranspiration (6% by 2030-2060, 10% by 2070-2100) and groundwater recharge (26% and 30% increase by 2030-2060 and 2070-2100). Thus, despite increased precipitation in the cold season, soils may dry out earlier in the year, resulting in potentially longer, drier summer conditions. If winter precipitation cannot offset drier summer conditions in the future, we expect big sagebrush regeneration and survival will be negatively impacted, potentially resulting in shifts in the relative abundance of big sagebrush plant functional groups. Our results also highlight the importance of assessing multiple GCMs to understand the range of climate change outcomes on ecohydrology, which was contingent on the GCM chosen. © 2016 by the Ecological Society of America.

Climate change projections of heat stress in Europe: From meteorological variables to impacts on productivity

NASA Astrophysics Data System (ADS)

Casanueva, Ana; Kotlarski, Sven; Liniger, Mark A.

2017-04-01

Future climate change is likely to have important impacts in many socio-economic sectors. In particular, higher summer temperatures or more prolonged heat waves may be responsible for health problems and productivity losses related to heat stress, especially affecting people exposed to such situations (e.g. working under outside settings or in non-acclimatized workplaces). Heat stress on the body under work load and consequently their productivity loss can be described through heat stress indices that are based on multiple meteorological parameters such as temperature, humidity, wind and radiation. Exploring the changes of these variables under a warmer climate is of prime importance for the Impacts, Adaptation and Vulnerability communities. In particular, the H2020 project HEAT-SHIELD aims at analyzing the impact of climate change on heat stress in strategic industries in Europe (manufacturing, construction, transportation, tourism and agriculture) within an inter-sectoral framework (climate scientists, biometeorologists, physiologists and stakeholders). In the present work we explore present and future heat stress over Europe using an ensemble of the state-of-the-art RCMs from the EURO-CORDEX initiative. Since RCMs cannot be directly used in impact studies due to their partly substantial biases, a standard bias correction method (empirical quantile mapping) is applied to correct the individual variables that are then used to derive heat stress indices. The objectives of this study are twofold, 1) to test the ability of the separately bias corrected variables to reproduce the main characteristics of heat stress indices in present climate conditions and 2) to explore climate change projections of heat stress indices. We use the wet bulb globe temperature (WBGT) as primary heat stress index, considering two different versions for indoor (or in the shade, based on temperature and humidity conditions) and outdoor settings (including also wind and radiation). The WBGT is the most widely used heat stress index for working people and can be easily interpreted by means of ISO standards. Within the HEAT-SHIELD project, climate change projections of the WBGT will be used to assess the impact of climate change on workers' health and productivity.

Disentangling the relative role of climate change on tree growth in an extreme Mediterranean environment.

PubMed

Madrigal-González, Jaime; Andivia, Enrique; Zavala, Miguel A; Stoffel, Markus; Calatayud, Joaquín; Sánchez-Salguero, Raúl; Ballesteros-Cánovas, Juan

2018-06-14

Climate change can impair ecosystem functions and services in extensive dry forests worldwide. However, attribution of climate change impacts on tree growth and forest productivity is challenging due to multiple inter-annual patterns of climatic variability associated with atmospheric and oceanic circulations. Moreover, growth responses to rising atmospheric CO 2 , namely carbon fertilization, as well as size ontogenetic changes can obscure the climate change signature as well. Here we apply Structural Equation Models (SEM) to investigate the relative role of climate change on tree growth in an extreme Mediterranean environment (i.e., extreme in terms of the combination of sandy-unconsolidated soils and climatic aridity). Specifically, we analyzed potential direct and indirect pathways by which different sources of climatic variability (i.e. warming and precipitation trends, the North Atlantic Oscillation, [NAO]; the Mediterranean Oscillation, [MOI]; the Atlantic Mediterranean Oscillation, [AMO]) affect aridity through their control on local climate (in terms of mean annual temperature and total annual precipitation), and subsequently tree productivity, in terms of basal area increments (BAI). Our results support the predominant role of Diameter at Breast Height (DHB) as the main growth driver. In terms of climate, NAO and AMO are the most important drivers of tree growth through their control of aridity (via effects of precipitation and temperature, respectively). Furthermore and contrary to current expectations, our findings also support a net positive role of climate warming on growth over the last 50 years and suggest that impacts of climate warming should be evaluated considering multi-annual and multi-decadal periods of local climate defined by atmospheric and oceanic circulation in the North Atlantic. Copyright © 2018 Elsevier B.V. All rights reserved.

Projecting the Global Distribution of the Emerging Amphibian Fungal Pathogen, Batrachochytrium dendrobatidis, Based on IPCC Climate Futures.

PubMed

Xie, Gisselle Yang; Olson, Deanna H; Blaustein, Andrew R

2016-01-01

Projected changes in climate conditions are emerging as significant risk factors to numerous species, affecting habitat conditions and community interactions. Projections suggest species range shifts in response to climate change modifying environmental suitability and is supported by observational evidence. Both pathogens and their hosts can shift ranges with climate change. We consider how climate change may influence the distribution of the emerging infectious amphibian chytrid fungus, Batrachochytrium dendrobatidis (Bd), a pathogen associated with worldwide amphibian population losses. Using an expanded global Bd database and a novel modeling approach, we examined a broad set of climate metrics to model the Bd-climate niche globally and regionally, then project how climate change may influence Bd distributions. Previous research showed that Bd distribution is dependent on climatic variables, in particular temperature. We trained a machine-learning model (random forest) with the most comprehensive global compilation of Bd sampling records (~5,000 site-level records, mid-2014 summary), including 13 climatic variables. We projected future Bd environmental suitability under IPCC scenarios. The learning model was trained with combined worldwide data (non-region specific) and also separately per region (region-specific). One goal of our study was to estimate of how Bd spatial risks may change under climate change based on the best available data. Our models supported differences in Bd-climate relationships among geographic regions. We projected that Bd ranges will shift into higher latitudes and altitudes due to increased environmental suitability in those regions under predicted climate change. Specifically, our model showed a broad expansion of areas environmentally suitable for establishment of Bd on amphibian hosts in the temperate zones of the Northern Hemisphere. Our projections are useful for the development of monitoring designs in these areas, especially for sensitive species and those vulnerable to multiple threats.

Climate Variability and Dengue Fever in Warm and Humid Mexico

PubMed Central

Colón-González, Felipe J.; Lake, Iain R.; Bentham, Graham

2011-01-01

Multiple linear regression models were fitted to look for associations between changes in the incidence rate of dengue fever and climate variability in the warm and humid region of Mexico. Data were collected for 12 Mexican provinces over a 23-year period (January 1985 to December 2007). Our results show that the incidence rate or risk of infection is higher during El Niño events and in the warm and wet season. We provide evidence to show that dengue fever incidence was positively associated with the strength of El Niño and the minimum temperature, especially during the cool and dry season. Our study complements the understanding of dengue fever dynamics in the region and may be useful for the development of early warning systems. PMID:21540386

Climate variability and dengue fever in warm and humid Mexico.

PubMed

Colón-González, Felipe J; Lake, Iain R; Bentham, Graham

2011-05-01

Multiple linear regression models were fitted to look for associations between changes in the incidence rate of dengue fever and climate variability in the warm and humid region of Mexico. Data were collected for 12 Mexican provinces over a 23-year period (January 1985 to December 2007). Our results show that the incidence rate or risk of infection is higher during El Niño events and in the warm and wet season. We provide evidence to show that dengue fever incidence was positively associated with the strength of El Niño and the minimum temperature, especially during the cool and dry season. Our study complements the understanding of dengue fever dynamics in the region and may be useful for the development of early warning systems.

Climate patterns as predictors of amphibians species richness and indicators of potential stress

USGS Publications Warehouse

Battaglin, W.; Hay, L.; McCabe, G.; Nanjappa, P.; Gallant, Alisa L.

2005-01-01

Amphibians occupy a range of habitats throughout the world, but species richness is greatest in regions with moist, warm climates. We modeled the statistical relations of anuran and urodele species richness with mean annual climate for the conterminous United States, and compared the strength of these relations at national and regional levels. Model variables were calculated for county and subcounty mapping units, and included 40-year (1960-1999) annual mean and mean annual climate statistics, mapping unit average elevation, mapping unit land area, and estimates of anuran and urodele species richness. Climate data were derived from more than 7,500 first-order and cooperative meteorological stations and were interpolated to the mapping units using multiple linear regression models. Anuran and urodele species richness were calculated from the United States Geological Survey's Amphibian Research and Monitoring Initiative (ARMI) National Atlas for Amphibian Distributions. The national multivariate linear regression (MLR) model of anuran species richness had an adjusted coefficient of determination (R2) value of 0.64 and the national MLR model for urodele species richness had an R2 value of 0.45. Stratifying the United States by coarse-resolution ecological regions provided models for anUrans that ranged in R2 values from 0.15 to 0.78. Regional models for urodeles had R2 values. ranging from 0.27 to 0.74. In general, regional models for anurans were more strongly influenced by temperature variables, whereas precipitation variables had a larger influence on urodele models.

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).

Reduced interdecadal variability of Atlantic Meridional Overturning Circulation under global warming

PubMed Central

Cheng, Jun; Liu, Zhengyu; Zhang, Shaoqing; Liu, Wei; Dong, Lina; Liu, Peng; Li, Hongli

2016-01-01

Interdecadal variability of the Atlantic Meridional Overturning Circulation (AMOC-IV) plays an important role in climate variation and has significant societal impacts. Past climate reconstruction indicates that AMOC-IV has likely undergone significant changes. Despite some previous studies, responses of AMOC-IV to global warming remain unclear, in particular regarding its amplitude and time scale. In this study, we analyze the responses of AMOC-IV under various scenarios of future global warming in multiple models and find that AMOC-IV becomes weaker and shorter with enhanced global warming. From the present climate condition to the strongest future warming scenario, on average, the major period of AMOC-IV is shortened from ∼50 y to ∼20 y, and the amplitude is reduced by ∼60%. These reductions in period and amplitude of AMOC-IV are suggested to be associated with increased oceanic stratification under global warming and, in turn, the speedup of oceanic baroclinic Rossby waves. PMID:26951654

Multi-Objective Control Optimization for Greenhouse Environment Using Evolutionary Algorithms

PubMed Central

Hu, Haigen; Xu, Lihong; Wei, Ruihua; Zhu, Bingkun

2011-01-01

This paper investigates the issue of tuning the Proportional Integral and Derivative (PID) controller parameters for a greenhouse climate control system using an Evolutionary Algorithm (EA) based on multiple performance measures such as good static-dynamic performance specifications and the smooth process of control. A model of nonlinear thermodynamic laws between numerous system variables affecting the greenhouse climate is formulated. The proposed tuning scheme is tested for greenhouse climate control by minimizing the integrated time square error (ITSE) and the control increment or rate in a simulation experiment. The results show that by tuning the gain parameters the controllers can achieve good control performance through step responses such as small overshoot, fast settling time, and less rise time and steady state error. Besides, it can be applied to tuning the system with different properties, such as strong interactions among variables, nonlinearities and conflicting performance criteria. The results implicate that it is a quite effective and promising tuning method using multi-objective optimization algorithms in the complex greenhouse production. PMID:22163927

Communication, social capital and workplace health management as determinants of the innovative climate in German banks.

PubMed

Köhler, Thorsten; Janssen, Christian; Plath, Sven-Christoph; Reese, Jens Peter; Lay, Jann; Steinhausen, Simone; Gloede, Tristan; Kowalski, Christoph; Schulz-Nieswandt, Frank; Pfaff, Holger

2010-12-01

The present study aims to measure the determinants of the innovative climate in German banks with a focus on workplace health management (WHM). We analyze the determinants of innovative climate with multiple regressions using a dataset based on standardized telephone interviews conducted with health promotion experts from 198 randomly selected German banks. The regression analysis provided a good explanation of the variance in the dependent variable (R² = 55%). Communication climate (β = 0.55; p < 0.001), social capital (β = 0.21; p < 0.01), the establishment of a WHM program (β = 0.13; p < 0.05) as well as company size (β = 0.15; p < 0.01) were found to have a significant impact on an organization's innovative climate. In order to foster an innovation-friendly climate, organizations should establish shared values. An active step in this direction involves strengthening the organizations' social capital and communication climate through trustworthy management decisions such as the implementation of a WHM program.

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.

Wind and rain are the primary climate factors driving changing phenology of an aerial insectivore.

PubMed

Irons, Rachel D; Harding Scurr, April; Rose, Alexandra P; Hagelin, Julie C; Blake, Tricia; Doak, Daniel F

2017-04-26

While the ecological effects of climate change have been widely observed, most efforts to document these impacts in terrestrial systems have concentrated on the impacts of temperature. We used tree swallow ( Tachycineta bicolor ) nest observations from two widely separated sites in central Alaska to examine the aspects of climate affecting breeding phenology at the northern extent of this species' range. We found that two measures of breeding phenology, annual lay and hatch dates, are more strongly predicted by windiness and precipitation than by temperature. At our longest-monitored site, breeding phenology has advanced at nearly twice the rate seen in more southern populations, and these changes correspond to long-term declines in windiness. Overall, adverse spring climate conditions known to negatively impact foraging success of swallows (wet, windy weather) appear to influence breeding phenology more than variation in temperature. Separate analyses show that short windy periods significantly delay initiation of individual clutches within years. While past reviews have emphasized that increasing variability in climate conditions may create physiological and ecological challenges for natural populations, we find that long-term reductions in inclement weather corresponded to earlier reproduction in one of our study populations. To better predict climate change impacts, ecologists need to more carefully test effects of multiple climate variables, including some, like windiness, that may be of paramount importance to some species, but have rarely been considered as strong drivers of ecological responses to climate alteration. © 2017 The Author(s).

Wind and rain are the primary climate factors driving changing phenology of an aerial insectivore

PubMed Central

Irons, Rachel D.; Harding Scurr, April; Rose, Alexandra P.; Hagelin, Julie C.; Blake, Tricia

2017-01-01

While the ecological effects of climate change have been widely observed, most efforts to document these impacts in terrestrial systems have concentrated on the impacts of temperature. We used tree swallow (Tachycineta bicolor) nest observations from two widely separated sites in central Alaska to examine the aspects of climate affecting breeding phenology at the northern extent of this species' range. We found that two measures of breeding phenology, annual lay and hatch dates, are more strongly predicted by windiness and precipitation than by temperature. At our longest-monitored site, breeding phenology has advanced at nearly twice the rate seen in more southern populations, and these changes correspond to long-term declines in windiness. Overall, adverse spring climate conditions known to negatively impact foraging success of swallows (wet, windy weather) appear to influence breeding phenology more than variation in temperature. Separate analyses show that short windy periods significantly delay initiation of individual clutches within years. While past reviews have emphasized that increasing variability in climate conditions may create physiological and ecological challenges for natural populations, we find that long-term reductions in inclement weather corresponded to earlier reproduction in one of our study populations. To better predict climate change impacts, ecologists need to more carefully test effects of multiple climate variables, including some, like windiness, that may be of paramount importance to some species, but have rarely been considered as strong drivers of ecological responses to climate alteration. PMID:28446701

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.

A two-fold increase of carbon cycle sensitivity to tropical temperature variations.

PubMed

Wang, Xuhui; Piao, Shilong; Ciais, Philippe; Friedlingstein, Pierre; Myneni, Ranga B; Cox, Peter; Heimann, Martin; Miller, John; Peng, Shushi; Wang, Tao; Yang, Hui; Chen, Anping

2014-02-13

Earth system models project that the tropical land carbon sink will decrease in size in response to an increase in warming and drought during this century, probably causing a positive climate feedback. But available data are too limited at present to test the predicted changes in the tropical carbon balance in response to climate change. Long-term atmospheric carbon dioxide data provide a global record that integrates the interannual variability of the global carbon balance. Multiple lines of evidence demonstrate that most of this variability originates in the terrestrial biosphere. In particular, the year-to-year variations in the atmospheric carbon dioxide growth rate (CGR) are thought to be the result of fluctuations in the carbon fluxes of tropical land areas. Recently, the response of CGR to tropical climate interannual variability was used to put a constraint on the sensitivity of tropical land carbon to climate change. Here we use the long-term CGR record from Mauna Loa and the South Pole to show that the sensitivity of CGR to tropical temperature interannual variability has increased by a factor of 1.9 ± 0.3 in the past five decades. We find that this sensitivity was greater when tropical land regions experienced drier conditions. This suggests that the sensitivity of CGR to interannual temperature variations is regulated by moisture conditions, even though the direct correlation between CGR and tropical precipitation is weak. We also find that present terrestrial carbon cycle models do not capture the observed enhancement in CGR sensitivity in the past five decades. More realistic model predictions of future carbon cycle and climate feedbacks require a better understanding of the processes driving the response of tropical ecosystems to drought and warming.

Evidence for Holocene centennial variability in sea ice cover based on IP25 biomarker reconstruction in the southern Kara Sea (Arctic Ocean)

NASA Astrophysics Data System (ADS)

Hörner, Tanja; Stein, Rüdiger; Fahl, Kirsten

2017-10-01

The Holocene is characterized by the late Holocene cooling trend as well as by internal short-term centennial fluctuations. Because Arctic sea ice acts as a significant component (amplifier) within the climate system, investigating its past long- and short-term variability and controlling processes is beneficial for future climate predictions. This study presents the first biomarker-based (IP25 and PIP25) sea ice reconstruction from the Kara Sea (core BP00-07/7), covering the last 8 ka. These biomarker proxies reflect conspicuous short-term sea ice variability during the last 6.5 ka that is identified unprecedentedly in the source region of Arctic sea ice by means of a direct sea ice indicator. Prominent peaks of extensive sea ice cover occurred at 3, 2, 1.3 and 0.3 ka. Spectral analysis of the IP25 record revealed 400- and 950-year cycles. These periodicities may be related to the Arctic/North Atlantic Oscillation, but probably also to internal climate system fluctuations. This demonstrates that sea ice belongs to a complex system that more likely depends on multiple internal forcing.

Untangling the causes a decadal-scale drought: a case study in southeast Australia.

NASA Astrophysics Data System (ADS)

Lewis, Sophie; Gallant, Ailie

2017-04-01

Prolonged droughts on the order of multiple years to a decade have recently afflicted many parts of highly populated regions around the globe, for example, the southwest United States and southeast Australia. However, the causes of these droughts remain unclear. A significant contribution from natural decadal-scale climate variability is likely, but there is also conflicting evidence of any contribution from anthropogenic climate change. This work aims to untangle the causes of a 13-year drought in southeast Australia spanning 1997-2009. A suite of historical and control simulations from fully coupled GCMs contained in the CMIP5 archive are employed, and the potential contributions of random climate variability, SST forcing and anthropogenic forcing to the drought are examined. It is likely that random, decadal-scale variability played a significant role in producing the prolonged rainfall deficits across southeast Australia. These were reinforced by several years with El Niño-like conditions, which commonly induce drought in the region, and a lack of La Niña conditions, which are more likely to bring rain. Evidence of contribution of anthropogenic forcing to the drought is limited

Variability in seeds: biological, ecological, and agricultural implications.

PubMed

Mitchell, Jack; Johnston, Iain G; Bassel, George W

2017-02-01

Variability is observed in biology across multiple scales, ranging from populations, individuals, and cells to the molecular components within cells. This review explores the sources and roles of this variability across these scales, focusing on seeds. From a biological perspective, the role and the impact this variability has on seed behaviour and adaptation to the environment is discussed. The consequences of seed variability on agricultural production systems, which demand uniformity, are also examined. We suggest that by understanding the basis and underlying mechanisms of variability in seeds, strategies to increase seed population uniformity can be developed, leading to enhanced agricultural production across variable climatic conditions. © The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Early Holocene hydroclimate of Baffin Bay: Understanding the interplay between abrupt climate change events and ice sheet fluctuations

NASA Astrophysics Data System (ADS)

Corcoran, M. C.; Thomas, E. K.; Castañeda, I. S.; Briner, J. P.

2017-12-01

Understanding the causes of ice sheet fluctuations resulting in sea level rise is essential in today's warming climate. In high-latitude ice-sheet-proximal environments such as Baffin Bay, studying both the cause and the rate of ice sheet variability during past abrupt climate change events aids in predictions. Past climate reconstructions are used to understand ice sheet responses to changes in temperature and precipitation. The 9,300 and 8,200 yr BP events are examples of abrupt climate change events in the Baffin Bay region during which there were multiple re-advances of the Greenland and Laurentide ice sheets. High-resolution (decadal-scale) hydroclimate variability near the ice sheet margins during these abrupt climate change events is still unknown. We will generate a decadal-scale record of early Holocene temperature and precipitation using leaf wax hydrogen isotopes, δ2Hwax, from a lake sediment archive on Baffin Island, western Baffin Bay, to better understand abrupt climate change in this region. Shifts in temperature and moisture source result in changes in environmental water δ2H, which in turn is reflected in δ2Hwax, allowing for past hydroclimate to be determined from these compound-specific isotopes. The combination of terrestrial and aquatic δ2Hwax is used to determine soil evaporation and is ultimately used to reconstruct moisture variability. We will compare our results with a previous analysis of δ2Hwax and branched glycerol dialkyl glycerol tetraethers, a temperature and pH proxy, in lake sediment from western Greenland, eastern Baffin Bay, which indicates that cool and dry climate occurred in response to freshwater forcing events in the Labrador Sea. Reconstructing and comparing records on both the western and eastern sides of Baffin Bay during the early Holocene will allow for a spatial understanding of temperature and moisture balance changes during abrupt climate events, aiding in ice sheet modeling and predictions of future sea level rise.

Variability and change of sea level and its components in the Indo-Pacific region during the altimetry era

NASA Astrophysics Data System (ADS)

Wu, Quran; Zhang, Xuebin; Church, John A.; Hu, Jianyu

2017-03-01

Previous studies have shown that regional sea level exhibits interannual and decadal variations associated with the modes of climate variability. A better understanding of those low-frequency sea level variations benefits the detection and attribution of climate change signals. Nonetheless, the contributions of thermosteric, halosteric, and mass sea level components to sea level variability and trend patterns remain unclear. By focusing on signals associated with dominant climate modes in the Indo-Pacific region, we estimate the interannual and decadal fingerprints and trend of each sea level component utilizing a multivariate linear regression of two adjoint-based ocean reanalyses. Sea level interannual, decadal, and trend patterns primarily come from thermosteric sea level (TSSL). Halosteric sea level (HSSL) is of regional importance in the Pacific Ocean on decadal time scale and dominates sea level trends in the northeast subtropical Pacific. The compensation between TSSL and HSSL is identified in their decadal variability and trends. The interannual and decadal variability of temperature generally peak at subsurface around 100 m but that of salinity tend to be surface-intensified. Decadal temperature and salinity signals extend deeper into the ocean in some regions than their interannual equivalents. Mass sea level (MassSL) is critical for the interannual and decadal variability of sea level over shelf seas. Inconsistencies exist in MassSL trend patterns among various estimates. This study highlights regions where multiple processes work together to control sea level variability and change. Further work is required to better understand the interaction of different processes in those regions.

Key ecological responses to nitrogen are altered by climate ...

EPA Pesticide Factsheets

Here we review the effects of nitrogen and climate (e.g. temperature and precipitation) on four aspects of ecosystem structure and function including hydrologic-coupled nitrogen cycling, carbon cycling, acidification and biodiversity. Ecosystems are simultaneously exposed to multiple stressors; two dominant drivers threatening ecosystems are anthropogenic nitrogen loading and climate change. Evaluating the cumulative effects of these stressors provides a holistic view of ecosystem vulnerability, which would better inform policy decisions aimed to protect the sustainability of ecosystems. Our current knowledge of the cumulative effects of these stressors is growing, but limited. The goal of this paper is to synthesize the state of scientific knowledge on how ecosystems are affected by the interactions of meteorlogic/climatic factors (e.g., temperature and precipitation) and nitrogen addition. Understanding the interactions of meteorlogic/climatic factors and nitrogen will help to inform how current and projected variability may affect ecosystem response.

Using Multiple Metrics to Analyze Trends and Sensitivity of Climate Variability in New York City

NASA Astrophysics Data System (ADS)

Huang, J.; Towey, K.; Booth, J. F.; Baez, S. D.

2017-12-01

As the overall temperature of Earth continues to warm, changes in the Earth's climate are being observed through extreme weather events, such as heavy precipitation events and heat waves. This study examines the daily precipitation and temperature record of the greater New York City region during the 1979-2014 period. Daily station observations from three greater New York City airports: John F. Kennedy (JFK), LaGuardia (LGA) and Newark (EWR), are used in this study. Multiple statistical metrics are used in this study to analyze trends and variability in temperature and precipitation in the greater New York City region. The temperature climatology reveals a distinct seasonal cycle, while the precipitation climatology exhibits greater annual variability. Two types of thresholds are used to examine the variability of extreme events: extreme threshold and daily anomaly threshold. The extreme threshold indicates how the strength of the overall maximum is changing whereas the daily anomaly threshold indicates if the strength of the daily maximum is changing over time. We observed an increase in the frequency of anomalous daily precipitation events over the last 36 years, with the greatest frequency occurring in 2011. The most extreme precipitation events occur during the months of late summer through early fall, with approximately four expected extreme events occurring per year during the summer and fall. For temperature, the greatest frequency and variation in temperature anomalies occur during winter and spring. In addition, temperature variance is also analyzed to determine if there is greater day-to-day temperature variability today than in the past.

Stochastic sensitivity analysis of nitrogen pollution to climate change in a river basin with complex pollution sources.

PubMed

Yang, Xiaoying; Tan, Lit; He, Ruimin; Fu, Guangtao; Ye, Jinyin; Liu, Qun; Wang, Guoqing

2017-12-01

It is increasingly recognized that climate change could impose both direct and indirect impacts on the quality of the water environment. Previous studies have mostly concentrated on evaluating the impacts of climate change on non-point source pollution in agricultural watersheds. Few studies have assessed the impacts of climate change on the water quality of river basins with complex point and non-point pollution sources. In view of the gap, this paper aims to establish a framework for stochastic assessment of the sensitivity of water quality to future climate change in a river basin with complex pollution sources. A sub-daily soil and water assessment tool (SWAT) model was developed to simulate the discharge, transport, and transformation of nitrogen from multiple point and non-point pollution sources in the upper Huai River basin of China. A weather generator was used to produce 50 years of synthetic daily weather data series for all 25 combinations of precipitation (changes by - 10, 0, 10, 20, and 30%) and temperature change (increases by 0, 1, 2, 3, and 4 °C) scenarios. The generated daily rainfall series was disaggregated into the hourly scale and then used to drive the sub-daily SWAT model to simulate the nitrogen cycle under different climate change scenarios. Our results in the study region have indicated that (1) both total nitrogen (TN) loads and concentrations are insensitive to temperature change; (2) TN loads are highly sensitive to precipitation change, while TN concentrations are moderately sensitive; (3) the impacts of climate change on TN concentrations are more spatiotemporally variable than its impacts on TN loads; and (4) wide distributions of TN loads and TN concentrations under individual climate change scenario illustrate the important role of climatic variability in affecting water quality conditions. In summary, the large variability in SWAT simulation results within and between each climate change scenario highlights the uncertainty of the impacts of climate change and the need to incorporate extreme conditions in managing water environment and developing climate change adaptation and mitigation strategies.

Populations of concern: Chapter 9

USGS Publications Warehouse

Gamble, Janet; Balbus, John; Berger, Martha; Bouye, Karen; Campbell, Vince; Chief, Karletta; Conlon, K.; Crimmins, Allison; Flanagan, Barry; Gonzalez-Maddux, C.; Hallisey, E.; Hutchins, S.; Jantarasami, L.; Khoury, S.; Kiefer, M.; Kolling, J.; Lynn, K.; Manangan, A.; McDonald, M.; Morello-Frosch, R.; Hiza, Margaret; Sheffield, P.; Thigpen Tart, K.; Watson, J.; Whyte, K.P.; Wolkin, A.F.

2016-01-01

Climate change is already causing, and is expected to continue to cause, a range of health impacts that vary across different population groups in the United States. The vulnerability of any given group is a function of its sensitivity to climate change related health risks, its exposure to those risks, and its capacity for responding to or coping with climate variability and change. Vulnerable groups of people, described here as populations of concern, include those with low income, some communities of color, immigrant groups (including those with limited English proficiency), Indigenous peoples, children and pregnant women, older adults, vulnerable occupational groups, persons with disabilities, and persons with preexisting or chronic medical conditions. Planners and public health officials, politicians and physicians, scientists and social service providers are tasked with understanding and responding to the health impacts of climate change. Collectively, their characterization of vulnerability should consider how populations of concern experience disproportionate, multiple, and complex risks to their health and well-being in response to climate change. Some groups face a number of stressors related to both climate and non-climate factors. For example, people living in impoverished urban or isolated rural areas, floodplains, coastlines, and other at-risk locations are more vulnerable not only to extreme weather and persistent climate change but also to social and economic stressors. Many of these stressors can occur simultaneously or consecutively. Over time, this “accumulation” of multiple, complex stressors is expected to become more evident1 as climate impacts interact with stressors associated with existing mental and physical health conditions and with other socioeconomic and demographic factors.

Evaluation and prediction of shrub cover in coastal Oregon forests (USA)

Treesearch

Becky K. Kerns; Janet L. Ohmann

2004-01-01

We used data from regional forest inventories and research programs, coupled with mapped climatic and topographic information, to explore relationships and develop multiple linear regression (MLR) and regression tree models for total and deciduous shrub cover in the Oregon coastal province. Results from both types of models indicate that forest structure variables were...

Shifts in biomass and productivity for a subtropical dry forest in response to simulated elevated hurricane disturbances

Treesearch

Jennifer A Holm; Skip J Van Bloem; Guy R Larocque; Herman H Shugart

2017-01-01

Caribbean tropical forests are subject to hurricane disturbances of great variability. In addition to natural storm incongruity, climate change can alter storm formation, duration, frequency, and intensity. This model-based investigation assessed the impacts of multiple storms of different intensities and occurrence frequencies on the long-term dynamics of subtropical...

Detection of bifurcations in noisy coupled systems from multiple time series

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

Williamson, Mark S., E-mail: m.s.williamson@exeter.ac.uk; Lenton, Timothy M.

We generalize a method of detecting an approaching bifurcation in a time series of a noisy system from the special case of one dynamical variable to multiple dynamical variables. For a system described by a stochastic differential equation consisting of an autonomous deterministic part with one dynamical variable and an additive white noise term, small perturbations away from the system's fixed point will decay slower the closer the system is to a bifurcation. This phenomenon is known as critical slowing down and all such systems exhibit this decay-type behaviour. However, when the deterministic part has multiple coupled dynamical variables, themore » possible dynamics can be much richer, exhibiting oscillatory and chaotic behaviour. In our generalization to the multi-variable case, we find additional indicators to decay rate, such as frequency of oscillation. In the case of approaching a homoclinic bifurcation, there is no change in decay rate but there is a decrease in frequency of oscillations. The expanded method therefore adds extra tools to help detect and classify approaching bifurcations given multiple time series, where the underlying dynamics are not fully known. Our generalisation also allows bifurcation detection to be applied spatially if one treats each spatial location as a new dynamical variable. One may then determine the unstable spatial mode(s). This is also something that has not been possible with the single variable method. The method is applicable to any set of time series regardless of its origin, but may be particularly useful when anticipating abrupt changes in the multi-dimensional climate system.« less

Detection of bifurcations in noisy coupled systems from multiple time series

NASA Astrophysics Data System (ADS)

Williamson, Mark S.; Lenton, Timothy M.

2015-03-01

We generalize a method of detecting an approaching bifurcation in a time series of a noisy system from the special case of one dynamical variable to multiple dynamical variables. For a system described by a stochastic differential equation consisting of an autonomous deterministic part with one dynamical variable and an additive white noise term, small perturbations away from the system's fixed point will decay slower the closer the system is to a bifurcation. This phenomenon is known as critical slowing down and all such systems exhibit this decay-type behaviour. However, when the deterministic part has multiple coupled dynamical variables, the possible dynamics can be much richer, exhibiting oscillatory and chaotic behaviour. In our generalization to the multi-variable case, we find additional indicators to decay rate, such as frequency of oscillation. In the case of approaching a homoclinic bifurcation, there is no change in decay rate but there is a decrease in frequency of oscillations. The expanded method therefore adds extra tools to help detect and classify approaching bifurcations given multiple time series, where the underlying dynamics are not fully known. Our generalisation also allows bifurcation detection to be applied spatially if one treats each spatial location as a new dynamical variable. One may then determine the unstable spatial mode(s). This is also something that has not been possible with the single variable method. The method is applicable to any set of time series regardless of its origin, but may be particularly useful when anticipating abrupt changes in the multi-dimensional climate system.

Local adaptation in migrated interior Douglas-fir seedlings is mediated by ectomycorrhizas and other soil factors.

PubMed

Pickles, Brian J; Twieg, Brendan D; O'Neill, Gregory A; Mohn, William W; Simard, Suzanne W

2015-08-01

Separating edaphic impacts on tree distributions from those of climate and geography is notoriously difficult. Aboveground and belowground factors play important roles, and determining their relative contribution to tree success will greatly assist in refining predictive models and forestry strategies in a changing climate. In a common glasshouse, seedlings of interior Douglas-fir (Pseudotsuga menziesii var. glauca) from multiple populations were grown in multiple forest soils. Fungicide was applied to half of the seedlings to separate soil fungal and nonfungal impacts on seedling performance. Soils of varying geographic and climatic distance from seed origin were compared, using a transfer function approach. Seedling height and biomass were optimized following seed transfer into drier soils, whereas survival was optimized when elevation transfer was minimised. Fungicide application reduced ectomycorrhizal root colonization by c. 50%, with treated seedlings exhibiting greater survival but reduced biomass. Local adaptation of Douglas-fir populations to soils was mediated by soil fungi to some extent in 56% of soil origin by response variable combinations. Mediation by edaphic factors in general occurred in 81% of combinations. Soil biota, hitherto unaccounted for in climate models, interacts with biogeography to influence plant ranges in a changing climate. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Sensitivity of the regional climate in the Middle East and North Africa to volcanic perturbations

NASA Astrophysics Data System (ADS)

Dogar, Muhammad Mubashar; Stenchikov, Georgiy; Osipov, Sergey; Wyman, Bruce; Zhao, Ming

2017-08-01

The Middle East and North Africa (MENA) regional climate appears to be extremely sensitive to volcanic eruptions. Winter cooling after the 1991 Pinatubo eruption far exceeded the mean hemispheric temperature anomaly, even causing snowfall in Israel. To better understand MENA climate variability, the climate responses to the El Chichón and Pinatubo volcanic eruptions are analyzed using observations, NOAA/National Centers for Environmental Prediction Climate Forecast System Reanalysis, and output from the Geophysical Fluid Dynamics Laboratory's High-Resolution Atmospheric Model. A multiple regression analysis both for the observations and the model output is performed on seasonal summer and winter composites to separate out the contributions from climate trends, El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian summer monsoon, and volcanic aerosols. Strong regional temperature and precipitation responses over the MENA region are found in both winter and summer. The model and the observations both show that a positive NAO amplifies the MENA volcanic winter cooling. In boreal summer, the patterns of changing temperature and precipitation suggest a weakening and southward shift of the Intertropical Convergence Zone, caused by volcanic surface cooling and weakening of the Indian and West African monsoons. The model captures the main features of the climate response; however, it underestimates the total cooling, especially in winter, and exhibits a different spatial pattern of the NAO climate response in MENA compared to the observations. The conducted analysis sheds light on the internal mechanisms of MENA climate variability and helps to selectively diagnose the model deficiencies.

Demographic Responses To Climate Manipulations Across a Species Range

NASA Astrophysics Data System (ADS)

Oldfather, M. F.

2016-12-01

Species biogeographic responses to climate change will occur through the local extinction and establishment of populations. The overall performance of populations across a species range is shaped by the idiosyncratic sensitivities of demographic rates to the changing climate conditions. Heterogeneous topography partially decouples temperature and soil moisture presenting an opportunity to disentangle demographic sensitivity to multiple local climate variables and refine range shift predictions in response to complex climate change. Since 2013, I have monitored 16 populations of a long-lived alpine plant, Ivesia lycopodioides var. scandularis (Rosaceae) across the entirety of its altitudinal range in the arid White Mountains, CA (3350 - 4420m). I quantified microclimatic soil moisture and temperature, and the demographic rates of over 4,000 individuals. Demographic rates exhibited sensitivity to accumulated degree-days (ex. reproduction), soil volumetric water content (ex. germination), or the interaction between these climate variables (ex. survival). These observations motivated an experimental test of the relationship between demography and local climate with manipulations of increased summertime temperature and precipitation in nine populations. All demographic rates were sensitive to the climate manipulations and the magnitude of the demographic response depended on the population's location within the range. However, the modeled population growth rate was only minimally affected by the manipulations in most populations. The inverse responses of many of the demographic rates may allow populations to demographically buffer against the climate manipulations. However, in one low elevation edge population the negative effect of heating on survival overwhelmed the positive effect on germination, indicating that the capacity of populations to demographically buffer may have a limit.

Application of multi-scale wavelet entropy and multi-resolution Volterra models for climatic downscaling

NASA Astrophysics Data System (ADS)

Sehgal, V.; Lakhanpal, A.; Maheswaran, R.; Khosa, R.; Sridhar, Venkataramana

2018-01-01

This study proposes a wavelet-based multi-resolution modeling approach for statistical downscaling of GCM variables to mean monthly precipitation for five locations at Krishna Basin, India. Climatic dataset from NCEP is used for training the proposed models (Jan.'69 to Dec.'94) and are applied to corresponding CanCM4 GCM variables to simulate precipitation for the validation (Jan.'95-Dec.'05) and forecast (Jan.'06-Dec.'35) periods. The observed precipitation data is obtained from the India Meteorological Department (IMD) gridded precipitation product at 0.25 degree spatial resolution. This paper proposes a novel Multi-Scale Wavelet Entropy (MWE) based approach for clustering climatic variables into suitable clusters using k-means methodology. Principal Component Analysis (PCA) is used to obtain the representative Principal Components (PC) explaining 90-95% variance for each cluster. A multi-resolution non-linear approach combining Discrete Wavelet Transform (DWT) and Second Order Volterra (SoV) is used to model the representative PCs to obtain the downscaled precipitation for each downscaling location (W-P-SoV model). The results establish that wavelet-based multi-resolution SoV models perform significantly better compared to the traditional Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) based frameworks. It is observed that the proposed MWE-based clustering and subsequent PCA, helps reduce the dimensionality of the input climatic variables, while capturing more variability compared to stand-alone k-means (no MWE). The proposed models perform better in estimating the number of precipitation events during the non-monsoon periods whereas the models with clustering without MWE over-estimate the rainfall during the dry season.

Sex-specific differences of craniofacial traits in Croatia: the impact of environment in a small geographic area.

PubMed

Buretic-Tomljanovic, Alena; Giacometti, Jasminka; Ostojic, Sasa; Kapovic, Miljenko

2007-01-01

Craniometric variation in humans reflects different genetic and environmental influences. Long-term climatic adaptation is less likely to show an impact on size and shape variation in a small local area than at the global level. The aim of this work was to assess the contribution of the particular environmental factors to body height and craniofacial variability in a small geographic area of Croatia. A total of 632 subjects, aged 18-21, participated in the survey. Body height, head length, head breadth, head height, head circumference, cephalic index, morphological face height, face breadth, and facial index were analysed regarding geographic, climatic and dietary conditions in different regions of the country, and correlated with the specific climatic variables (cumulative multiyear sunshine duration, cumulative multiyear average precipitation, multiyear average air temperatures) and calcium concentrations in drinking water. Significant differences between groups classified according to geographic, climatic or dietary affiliation, and the impact of the environmental predictors on the variation in the investigated traits were assessed using multiple forward stepwise regression analyses. Higher body height measures in both sexes were significantly correlated with Mediterranean diet type. Mediterranean diet type also contributed to higher head length and head circumference measures in females. Cephalic index values correlated to geographic regions in both sexes, showing an increase from southern to eastern Croatia. In the same direction, head length significantly decreased in males and head breadth increased in females. Mediterranean climate was associated with higher and narrower faces in females. The analysis of the particular climatic variables did not reveal a significant influence on body height in either sex. Concurrently, climatic features influenced all craniofacial traits in females and only head length and facial index in males. Mediterranean climate, characterized by higher average sunshine duration, higher average precipitation and higher average air temperatures, was associated with longer, higher and narrower skulls, higher head circumference, lower cephalic index, and higher and narrower faces (lower facial index). Calcium concentrations in drinking water did not correlate significantly with any dependent variable. A significant effect of environmental factors on body height and craniofacial variability was found in Croatian young adult population. This effect was more pronounced in females, revealing sex-specific craniofacial differentiation. However, the impact of environment was low and may explain only 1.0-7.32% variation of the investigated traits.

Drought and immunity determine the intensity of West Nile virus epidemics and climate change impacts

DOE PAGES

Paull, Sara H.; Horton, Daniel E.; Ashfaq, Moetasim; ...

2017-02-08

The effect of global climate change on infectious disease remains hotly debated because multiple extrinsic and intrinsic drivers interact to influence transmission dynamics in nonlinear ways. The dominant drivers of widespread pathogens, like West Nile virus, can be challenging to identify due to regional variability in vector and host ecology, with past studies producing disparate findings. Here, we used analyses at national and state scales to examine a suite of climatic and intrinsic drivers of continental-scale West Nile virus epidemics, including an empirically derived mechanistic relationship between temperature and transmission potential that accounts for spatial variability in vectors. We foundmore » that drought was the primary climatic driver of increased West Nile virus epidemics, rather than within-season or winter temperatures, or precipitation independently. Local-scale data from one region suggested drought increased epidemics via changes in mosquito infection prevalence rather than mosquito abundance. In addition, human acquired immunity following regional epidemics limited subsequent transmission in many states. We show that over the next 30 years, increased drought severity from climate change could triple West Nile virus cases, but only in regions with low human immunity. Lastly, these results illustrate how changes in drought severity can alter the transmission dynamics of vector-borne diseases.« less

Drought and immunity determine the intensity of West Nile virus epidemics and climate change impacts

PubMed Central

Horton, Daniel E.; Ashfaq, Moetasim; Rastogi, Deeksha; Kramer, Laura D.; Diffenbaugh, Noah S.

2017-01-01

The effect of global climate change on infectious disease remains hotly debated because multiple extrinsic and intrinsic drivers interact to influence transmission dynamics in nonlinear ways. The dominant drivers of widespread pathogens, like West Nile virus, can be challenging to identify due to regional variability in vector and host ecology, with past studies producing disparate findings. Here, we used analyses at national and state scales to examine a suite of climatic and intrinsic drivers of continental-scale West Nile virus epidemics, including an empirically derived mechanistic relationship between temperature and transmission potential that accounts for spatial variability in vectors. We found that drought was the primary climatic driver of increased West Nile virus epidemics, rather than within-season or winter temperatures, or precipitation independently. Local-scale data from one region suggested drought increased epidemics via changes in mosquito infection prevalence rather than mosquito abundance. In addition, human acquired immunity following regional epidemics limited subsequent transmission in many states. We show that over the next 30 years, increased drought severity from climate change could triple West Nile virus cases, but only in regions with low human immunity. These results illustrate how changes in drought severity can alter the transmission dynamics of vector-borne diseases. PMID:28179512

Drought and immunity determine the intensity of West Nile virus epidemics and climate change impacts

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

Paull, Sara H.; Horton, Daniel E.; Ashfaq, Moetasim

The effect of global climate change on infectious disease remains hotly debated because multiple extrinsic and intrinsic drivers interact to influence transmission dynamics in nonlinear ways. The dominant drivers of widespread pathogens, like West Nile virus, can be challenging to identify due to regional variability in vector and host ecology, with past studies producing disparate findings. Here, we used analyses at national and state scales to examine a suite of climatic and intrinsic drivers of continental-scale West Nile virus epidemics, including an empirically derived mechanistic relationship between temperature and transmission potential that accounts for spatial variability in vectors. We foundmore » that drought was the primary climatic driver of increased West Nile virus epidemics, rather than within-season or winter temperatures, or precipitation independently. Local-scale data from one region suggested drought increased epidemics via changes in mosquito infection prevalence rather than mosquito abundance. In addition, human acquired immunity following regional epidemics limited subsequent transmission in many states. We show that over the next 30 years, increased drought severity from climate change could triple West Nile virus cases, but only in regions with low human immunity. Lastly, these results illustrate how changes in drought severity can alter the transmission dynamics of vector-borne diseases.« less

Realized niche width of a brackish water submerged aquatic vegetation under current environmental conditions and projected influences of climate change.

PubMed

Kotta, Jonne; Möller, Tiia; Orav-Kotta, Helen; Pärnoja, Merli

2014-12-01

Little is known about how organisms might respond to multiple climate stressors and this lack of knowledge limits our ability to manage coastal ecosystems under contemporary climate change. Ecological models provide managers and decision makers with greater certainty that the systems affected by their decisions are accurately represented. In this study Boosted Regression Trees modelling was used to relate the cover of submerged aquatic vegetation to the abiotic environment in the brackish Baltic Sea. The analyses showed that the majority of the studied submerged aquatic species are most sensitive to changes in water temperature, current velocity and winter ice scour. Surprisingly, water salinity, turbidity and eutrophication have little impact on the distributional pattern of the studied biota. Both small and large scale environmental variability contributes to the variability of submerged aquatic vegetation. When modelling species distribution under the projected influences of climate change, all of the studied submerged aquatic species appear to be very resilient to a broad range of environmental perturbation and biomass gains are expected when seawater temperature increases. This is mainly because vegetation develops faster in spring and has a longer growing season under the projected climate change scenario. Copyright © 2014 Elsevier Ltd. All rights reserved.

Upgrades to the REA method for producing probabilistic climate change projections

NASA Astrophysics Data System (ADS)

Xu, Ying; Gao, Xuejie; Giorgi, Filippo

2010-05-01

We present an augmented version of the Reliability Ensemble Averaging (REA) method designed to generate probabilistic climate change information from ensembles of climate model simulations. Compared to the original version, the augmented one includes consideration of multiple variables and statistics in the calculation of the performance-based weights. In addition, the model convergence criterion previously employed is removed. The method is applied to the calculation of changes in mean and variability for temperature and precipitation over different sub-regions of East Asia based on the recently completed CMIP3 multi-model ensemble. Comparison of the new and old REA methods, along with the simple averaging procedure, and the use of different combinations of performance metrics shows that at fine sub-regional scales the choice of weighting is relevant. This is mostly because the models show a substantial spread in performance for the simulation of precipitation statistics, a result that supports the use of model weighting as a useful option to account for wide ranges of quality of models. The REA method, and in particular the upgraded one, provides a simple and flexible framework for assessing the uncertainty related to the aggregation of results from ensembles of models in order to produce climate change information at the regional scale. KEY WORDS: REA method, Climate change, CMIP3

Drought and immunity determine the intensity of West Nile virus epidemics and climate change impacts.

PubMed

Paull, Sara H; Horton, Daniel E; Ashfaq, Moetasim; Rastogi, Deeksha; Kramer, Laura D; Diffenbaugh, Noah S; Kilpatrick, A Marm

2017-02-08

The effect of global climate change on infectious disease remains hotly debated because multiple extrinsic and intrinsic drivers interact to influence transmission dynamics in nonlinear ways. The dominant drivers of widespread pathogens, like West Nile virus, can be challenging to identify due to regional variability in vector and host ecology, with past studies producing disparate findings. Here, we used analyses at national and state scales to examine a suite of climatic and intrinsic drivers of continental-scale West Nile virus epidemics, including an empirically derived mechanistic relationship between temperature and transmission potential that accounts for spatial variability in vectors. We found that drought was the primary climatic driver of increased West Nile virus epidemics, rather than within-season or winter temperatures, or precipitation independently. Local-scale data from one region suggested drought increased epidemics via changes in mosquito infection prevalence rather than mosquito abundance. In addition, human acquired immunity following regional epidemics limited subsequent transmission in many states. We show that over the next 30 years, increased drought severity from climate change could triple West Nile virus cases, but only in regions with low human immunity. These results illustrate how changes in drought severity can alter the transmission dynamics of vector-borne diseases. © 2017 The Author(s).

Assessing Portuguese Guadiana Basin water management impacts under climate change and paleoclimate variability

NASA Astrophysics Data System (ADS)

Maia, Rodrigo; Oliveira, Bruno; Ramos, Vanessa; Brekke, Levi

2014-05-01

The water balance in each reservoir and the subsequent, related, water resource management decisions are, presently, highly information dependent and are therefore often limited to a reactive response (even if aimed towards preventing future issues regarding the water system). Taking advantage of the availability of scenarios for climate projections, it is now possible to estimate the likely future evolution of climate which represents an important stepping stone towards proactive, adaptative, water resource management. The purpose of the present study was to assess the potential effects of climate change in terms of temperature, precipitation, runoff and water availability/scarcity for application in water resource management decisions. The analysis here presented was applied to the Portuguese portion of the Guadiana River Basin, using a combination of observed climate and runoff data and the results of the Global Climate Models. The Guadiana River Basin was represented by its reservoirs on the Portuguese portion of the basin and, for the future period, an estimated value of the inflows originating in the Spanish part of the Basin. The change in climate was determined in terms of relative and absolute variations of climate (precipitation and temperature) and hydrology (runoff and water balance related information). Apart from the previously referred data, an hydrological model and a water management model were applied so as to obtain an extended range of data regarding runoff generation (calibrated to observed data) and water balance in the reservoirs (considering the climate change impacts in the inflows, outflows and water consumption). The water management model was defined in order to represent the reservoirs interaction including upstream to downstream discharges and water transfers. Under the present climate change context, decision-makers and stakeholders are ever more vulnerable to the uncertainties of climate. Projected climate in the Guadiana basin indicates an increase in temperatures and a reduction of the precipitation values which go well beyond the observed values and, therefore, must be forcefully included in any realistic proactive water resource management decision. Using the results of this study it is possible to estimate future water availability and consumption satisfaction allowing for the elaboration of informed management decisions. In this study, the CMIP 3 Global Climate Models were considered for the definition of the effects of climate change, using the median and extreme tendencies based on the range of variation of the multiple climate projection scenarios. The observed climate variability, along with these model-derived tendencies, were used to inform the hydrology and water management models for the historical and future periods, respectively. Additionally, for a more comprehensive analysis on climate variability, a stochastic model was implemented based on the paleoclimate variability obtained from tree-ring records.

Climate variation explains a third of global crop yield variability

PubMed Central

Ray, Deepak K.; Gerber, James S.; MacDonald, Graham K.; West, Paul C.

2015-01-01

Many studies have examined the role of mean climate change in agriculture, but an understanding of the influence of inter-annual climate variations on crop yields in different regions remains elusive. We use detailed crop statistics time series for ~13,500 political units to examine how recent climate variability led to variations in maize, rice, wheat and soybean crop yields worldwide. While some areas show no significant influence of climate variability, in substantial areas of the global breadbaskets, >60% of the yield variability can be explained by climate variability. Globally, climate variability accounts for roughly a third (~32–39%) of the observed yield variability. Our study uniquely illustrates spatial patterns in the relationship between climate variability and crop yield variability, highlighting where variations in temperature, precipitation or their interaction explain yield variability. We discuss key drivers for the observed variations to target further research and policy interventions geared towards buffering future crop production from climate variability. PMID:25609225

a New Framework for Characterising Simulated Droughts for Future Climates

NASA Astrophysics Data System (ADS)

Sharma, A.; Rashid, M.; Johnson, F.

2017-12-01

Significant attention has been focussed on metrics for quantifying drought. Lesser attention has been given to the unsuitability of current metrics in quantifying drought in a changing climate due to the clear non-stationarity in potential and actual evapotranspiration well into the future (Asadi-Zarch et al, 2015). This talk presents a new basis for simulating drought designed specifically for use with climate model simulations. Given the known uncertainty of climate model rainfall simulations, along with their inability to represent low-frequency variability attributes, the approach here adopts a predictive model for drought using selected atmospheric indicators. This model is based on a wavelet decomposition of relevant atmospheric predictors to filter out less relevant frequencies and formulate a better characterisation of the drought metric chosen as response. Once ascertained using observed precipication and associated atmospheric variables, these can be formulated from GCM simulations using a multivariate bias correction tool (Mehrotra and Sharma, 2016) that accounts for low-frequency variability, and a regression tool that accounts for nonlinear dependence (Sharma and Mehrotra, 2014). Use of only the relevant frequencies, as well as the corrected representation of cross-variable dependence, allows greater accuracy in characterising observed drought, from GCM simulations. Using simulations from a range of GCMs across Australia, we show here that this new method offers considerable advantages in representing drought compared to traditionally followed alternatives that rely on modelled rainfall instead. Reference:Asadi Zarch, M. A., B. Sivakumar, and A. Sharma (2015), Droughts in a warming climate: A global assessment of Standardized precipitation index (SPI) and Reconnaissance drought index (RDI), Journal of Hydrology, 526, 183-195. Mehrotra, R., and A. Sharma (2016), A Multivariate Quantile-Matching Bias Correction Approach with Auto- and Cross-Dependence across Multiple Time Scales: Implications for Downscaling, Journal of Climate, 29(10), 3519-3539. Sharma, A., and R. Mehrotra (2014), An information theoretic alternative to model a natural system using observational information alone, Water Resources Research, 50, 650-660, doi:10.1002/2013WR013845.

Anthropogenic nitrogen deposition alters growth responses of European beech (Fagus sylvativa L.) to climate change.

PubMed

Hess, Carsten; Niemeyer, Thomas; Fichtner, Andreas; Jansen, Kirstin; Kunz, Matthias; Maneke, Moritz; von Wehrden, Henrik; Quante, Markus; Walmsley, David; von Oheimb, Goddert; Härdtle, Werner

2018-02-01

Global change affects the functioning of forest ecosystems and the services they provide, but little is known about the interactive effects of co-occurring global change drivers on important functions such as tree growth and vitality. In the present study we quantified the interactive (i.e. synergistic or antagonistic) effects of atmospheric nitrogen (N) deposition and climatic variables (temperature, precipitation) on tree growth (in terms of tree-ring width, TRW), taking forest ecosystems with European beech (Fagus sylvatica L.) as an example. We hypothesised that (i) N deposition and climatic variables can evoke non-additive responses of the radial increment of beech trees, and (ii) N loads have the potential to strengthen the trees' sensitivity to climate change. In young stands, we found a synergistic positive effect of N deposition and annual mean temperature on TRW, possibly linked to the alleviation of an N shortage in young stands. In mature stands, however, high N deposition significantly increased the trees' sensitivity to increasing annual mean temperatures (antagonistic effect on TRW), possibly due to increased fine root dieback, decreasing mycorrhizal colonization or shifts in biomass allocation patterns (aboveground vs. belowground). Accordingly, N deposition and climatic variables caused both synergistic and antagonistic effects on the radial increment of beech trees, depending on tree age and stand characteristics. Hence, the nature of interactions could mediate the long-term effects of global change drivers (including N deposition) on forest carbon sequestration. In conclusion, our findings illustrate that interaction processes between climatic variables and N deposition are complex and have the potential to impair growth and performance of European beech. This in turn emphasises the importance of multiple-factor studies to foster an integrated understanding and models aiming at improved projections of tree growth responses to co-occurring drivers of global change. Copyright © 2017 Elsevier Ltd. All rights reserved.

Suspended sediment flux modeling with artificial neural network: An example of the Longchuanjiang River in the Upper Yangtze Catchment, China

NASA Astrophysics Data System (ADS)

Zhu, Yun-Mei; Lu, X. X.; Zhou, Yue

2007-02-01

Artificial neural network (ANN) was used to model the monthly suspended sediment flux in the Longchuanjiang River, the Upper Yangtze Catchment, China. The suspended sediment flux was related to the average rainfall, temperature, rainfall intensity and water discharge. It is demonstrated that ANN is capable of modeling the monthly suspended sediment flux with fairly good accuracy when proper variables and their lag effect on the suspended sediment flux are used as inputs. Compared with multiple linear regression and power relation models, ANN can generate a better fit under the same data requirement. In addition, ANN can provide more reasonable predictions for extremely high or low values, because of the distributed information processing system and the nonlinear transformation involved. Compared with the ANNs that use the values of the dependent variable at previous time steps as inputs, the ANNs established in this research with only climate variables have an advantage because it can be used to assess hydrological responses to climate change.

Contribution of Anthropogenic Warming to California Drought During 2012-2014

NASA Technical Reports Server (NTRS)

Williams, A. Park; Seager, Richard; Abatzoglou, John T.; Cook, Benjamin I.; Smerdon, Jason E.; Cook, Edward R.

2015-01-01

A suite of climate data sets and multiple representations of atmospheric moisture demand are used to calculate many estimates of the self-calibrated Palmer Drought Severity Index, a proxy for near-surface soil moisture, across California from 1901 to 2014 at high spatial resolution. Based on the ensemble of calculations, California drought conditions were record breaking in 2014, but probably not record breaking in 2012-2014, contrary to prior findings. Regionally, the 2012-2014 drought was record breaking in the agriculturally important southern Central Valley and highly populated coastal areas. Contributions of individual climate variables to recent drought are also examined, including the temperature component associated with anthropogenic warming. Precipitation is the primary driver of drought variability but anthropogenic warming is estimated to have accounted for 8-27 percent of the observed drought anomaly in 2012-2014 and 5-18 percent in 2014. Although natural variability dominates, anthropogenic warming has substantially increased the overall likelihood of extreme California droughts.

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.

Potential impacts of climate variability on respiratory morbidity in children, infants, and adults.

PubMed

Souza, Amaury de; Fernandes, Widinei Alves; Pavão, Hamilton Germano; Lastoria, Giancarlo; Albrez, Edilce do Amaral

2012-01-01

To determine whether climate variability influences the number of hospitalizations for respiratory diseases in infants, children, and adults in the city of Campo Grande, Brazil. We used daily data on admissions for respiratory diseases, precipitation, air temperature, humidity, and wind speed for the 2004-2008 period. We calculated the thermal comfort index, effective temperature, and effective temperature with wind speed (wind-chill or heat index) using the meteorological data obtained. Generalized linear models, with Poisson multiple regression, were used in order to predict hospitalizations for respiratory disease. The variables studied were (collectively) found to show relatively high correlation coefficients in relation to hospital admission for pneumonia in children (R² = 68.4%), infants (R² = 71.8%), and adults (R² = 81.8%). Our results indicate a quantitative risk for an increase in the number of hospitalizations of children, infants, and adults, according to the increase or decrease in temperature, humidity, precipitation, wind speed, and thermal comfort index in the city under study.

Climate change impacts on human exposures to air pollution ...

EPA Pesticide Factsheets

This is an abstract for a presentations at the Annual Conference of the International Society on Exposure Science and Environmental Epidemiology. This presentation will serve as an introduction to the symposium. As we consider the potential health impacts of a warming planet, the relationships between climate change and air pollutants become increasingly important to understand. These relationships are complex and highly variable, causing a variety of environmental impacts at local, regional and global scales. Human exposures and health impacts for air pollutants have the potential to be altered by changes in climate through multiple factors that drive population exposures to these pollutants. Research on this topic will provide both state and local governments with the tools and scientific knowledge base to undertake any necessary adaptation of the air pollution regulations and/or public health management systems in the face of climate change.

Undergraduate research internships: veterinary students' experiences and the relation with internship quality.

PubMed

Jaarsma, Debbie A D C; Muijtjens, Arno M M; Dolmans, Diana H J M; Schuurmans, Eva M; Van Beukelen, Peter; Scherpbier, Albert J J A

2009-05-01

The learning environment of undergraduate research internships has received little attention, compared to postgraduate research training. This study investigates students' experiences with research internships, particularly the quality of supervision, development of research skills, the intellectual and social climate, infrastructure support, and the clarity of goals and the relationship between the experiences and the quality of students' research reports and their overall satisfaction with internships. A questionnaire (23 items, a 5-point Likert scale) was administered to 101 Year five veterinary students after completion of a research internship. Multiple linear regression analyses were conducted with quality of supervision, development of research skills, climate, infrastructure and clarity of goals as independent variables and the quality of students' research reports and students' overall satisfaction as dependent variables. The response rate was 79.2%. Students' experiences are generally positive. Students' experiences with the intellectual and social climate are significantly correlated with the quality of research reports whilst the quality of supervision is significantly correlated with both the quality of research reports and students' overall satisfaction with the internship. Both the quality of supervision and the climate are found to be crucial factors in students' research learning and satisfaction with the internship.

Factors associated with the patient safety climate at a teaching hospital1

PubMed Central

Luiz, Raíssa Bianca; Simões, Ana Lúcia de Assis; Barichello, Elizabeth; Barbosa, Maria Helena

2015-01-01

Objectives: to investigate the association between the scores of the patient safety climate and socio-demographic and professional variables. Methods: an observational, sectional and quantitative study, conducted at a large public teaching hospital. The Safety Attitudes Questionnaire was used, translated and validated for Brazil. Data analysis used the software Statistical Package for Social Sciences. In the bivariate analysis, we used Student's t-test, analysis of variance and Spearman's correlation of (α=0.05). To identify predictors for the safety climate scores, multiple linear regression was used, having the safety climate domain as the main outcome (α=0.01). Results: most participants were women, nursing staff, who worked in direct care to adult patients in critical areas, without a graduate degree and without any other employment. The average and median total score of the instrument corresponded to 61.8 (SD=13.7) and 63.3, respectively. The variable professional performance was found as a factor associated with the safety environment for the domain perception of service management and hospital management (p=0.01). Conclusion: the identification of factors associated with the safety environment permits the construction of strategies for safe practices in the hospitals. PMID:26487138

Bayesian hierarchical models for regional climate reconstructions of the last glacial maximum

NASA Astrophysics Data System (ADS)

Weitzel, Nils; Hense, Andreas; Ohlwein, Christian

2017-04-01

Spatio-temporal reconstructions of past climate are important for the understanding of the long term behavior of the climate system and the sensitivity to forcing changes. Unfortunately, they are subject to large uncertainties, have to deal with a complex proxy-climate structure, and a physically reasonable interpolation between the sparse proxy observations is difficult. Bayesian Hierarchical Models (BHMs) are a class of statistical models that is well suited for spatio-temporal reconstructions of past climate because they permit the inclusion of multiple sources of information (e.g. records from different proxy types, uncertain age information, output from climate simulations) and quantify uncertainties in a statistically rigorous way. BHMs in paleoclimatology typically consist of three stages which are modeled individually and are combined using Bayesian inference techniques. The data stage models the proxy-climate relation (often named transfer function), the process stage models the spatio-temporal distribution of the climate variables of interest, and the prior stage consists of prior distributions of the model parameters. For our BHMs, we translate well-known proxy-climate transfer functions for pollen to a Bayesian framework. In addition, we can include Gaussian distributed local climate information from preprocessed proxy records. The process stage combines physically reasonable spatial structures from prior distributions with proxy records which leads to a multivariate posterior probability distribution for the reconstructed climate variables. The prior distributions that constrain the possible spatial structure of the climate variables are calculated from climate simulation output. We present results from pseudoproxy tests as well as new regional reconstructions of temperatures for the last glacial maximum (LGM, ˜ 21,000 years BP). These reconstructions combine proxy data syntheses with information from climate simulations for the LGM that were performed in the PMIP3 project. The proxy data syntheses consist either of raw pollen data or of normally distributed climate data from preprocessed proxy records. Future extensions of our method contain the inclusion of other proxy types (transfer functions), the implementation of other spatial interpolation techniques, the use of age uncertainties, and the extension to spatio-temporal reconstructions of the last deglaciation. Our work is part of the PalMod project funded by the German Federal Ministry of Education and Science (BMBF).

Molecules in the mud: Combining ancient DNA and lipid biomarkers to reconstruct vegetation response to climate variability during the Last Interglacial and the Holocene on Baffin Island, Arctic Canada

NASA Astrophysics Data System (ADS)

Crump, S. E.; Sepúlveda, J.; Bunce, M.; Miller, G. H.

2017-12-01

Modern ecological studies are revealing that the "greening" of the Arctic, resulting from a poleward shift in woody vegetation ranges, is already underway. The increasing abundance of shrubs in tundra ecosystems plays an important role in the global climate system through multiple positive feedbacks, yet uncertainty in future predictions of terrestrial vegetation means that climate models are likely not capturing these feedbacks accurately. Recently developed molecular techniques for reconstructing past vegetation and climate allow for a closer look at the paleo-record in order to improve our understanding of tundra community responses to climate variability; our current research focus is to apply these tools to both Last Interglacial and Holocene warm times. Here we present initial results from a small lake on southern Baffin Island spanning the last 7.2 ka. We reconstruct climate with both bulk geochemical and biomarker proxies, primarily using biogenic silica and branched glycerol dialkyl glycerol tetraethers (brGDGTs) as temperature indicators. We assess shifts in plant community using multivariate analysis of sedimentary ancient DNA (sedaDNA) metabarcoding data. This combination of approaches reveals that the vegetation community has responded sensitively to early Holocene warmth, Neoglacial cooling, and possibly modern anthropogenic warming. To our knowledge, this represents the first combination of a quantitative, biomarker-based climate reconstruction with a sedaDNA-based paleoecological reconstruction, and offers a glimpse at the potential of these molecular techniques used in tandem.

Environmental controls on alpine cirque size

NASA Astrophysics Data System (ADS)

Delmas, Magali; Gunnell, Yanni; Calvet, Marc

2014-02-01

Pleistocene alpine cirques are emblematic landforms of mountain scenery, yet their deceptively simple template conceals complex controlling variables. This comparative study presents a new database of 1071 cirques, the largest of its kind, located in the French eastern Pyrenees. It is embedded in a review of previous work on cirque morphometry and thus provides a perspective on a global scale. First-order cirque attributes of length, width, and amplitude were measured; and their power as predictors of climatic and lithological variables and as proxies for the duration of glacier activity was tested using ANOVA, simple and multiple linear regression, and their various post-hoc tests. Conventional variables such as cirque aspect, floor elevation, and exposure with respect to regional precipitation-bearing weather systems are shown to present some consistency in spatial patterns determined by solar radiation, the morning-afternoon effect, and wind-blown snow accumulation in the lee of ridgetops. This confirms in greater detail the previously encountered links between landforms and climate. A special focus on the influence of bedrock lithology, a previously neglected nonclimatic variable, highlights the potential for spurious relations in the use of cirque size as a proxy of past environmental conditions. Cirques are showcased as complex landforms resulting from the combination of many climatic and nonclimatic variables that remain difficult to rank by order of importance. Apart from a few statistically weak trends, several combinations of different factors in different proportions are shown to produce similar morphometric outcomes, suggesting a case of equifinality in landform development.

Coherence among the Northern Hemisphere land, cryosphere, and ocean responses to natural variability and anthropogenic forcing during the satellite era

NASA Astrophysics Data System (ADS)

Gonsamo, Alemu; Chen, Jing M.; Shindell, Drew T.; Asner, Gregory P.

2016-08-01

A lack of long-term measurements across Earth's biological and physical systems has made observation-based detection and attribution of climate change impacts to anthropogenic forcing and natural variability difficult. Here we explore coherence among land, cryosphere and ocean responses to recent climate change using 3 decades (1980-2012) of observational satellite and field data throughout the Northern Hemisphere. Our results show coherent interannual variability among snow cover, spring phenology, solar radiation, Scandinavian Pattern, and North Atlantic Oscillation. The interannual variability of the atmospheric peak-to-trough CO2 amplitude is mostly impacted by temperature-mediated effects of El Niño/Southern Oscillation (ENSO) and Pacific/North American Pattern (PNA), whereas CO2 concentration is affected by Polar Pattern control on sea ice extent dynamics. This is assuming the trend in anthropogenic CO2 emission remains constant, or the interannual changes in the trends are negligible. Our analysis suggests that sea ice decline-related CO2 release may outweigh increased CO2 uptake through longer growing seasons and higher temperatures. The direct effects of variation in solar radiation and leading teleconnections, at least in part via their impacts on temperature, dominate the interannual variability of land, cryosphere and ocean indicators. Our results reveal a coherent long-term changes in multiple physical and biological systems that are consistent with anthropogenic forcing of Earth's climate and inconsistent with natural drivers.

Building Quantitative Hydrologic Storylines from Process-based Models for Managing Water Resources in the U.S. Under Climate-changed Futures

NASA Astrophysics Data System (ADS)

Arnold, J.; Gutmann, E. D.; Clark, M. P.; Nijssen, B.; Vano, J. A.; Addor, N.; Wood, A.; Newman, A. J.; Mizukami, N.; Brekke, L. D.; Rasmussen, R.; Mendoza, P. A.

2016-12-01

Climate change narratives for water-resource applications must represent the change signals contextualized by hydroclimatic process variability and uncertainty at multiple scales. Building narratives of plausible change includes assessing uncertainties across GCM structure, internal climate variability, climate downscaling methods, and hydrologic models. Work with this linked modeling chain has dealt mostly with GCM sampling directed separately to either model fidelity (does the model correctly reproduce the physical processes in the world?) or sensitivity (of different model responses to CO2 forcings) or diversity (of model type, structure, and complexity). This leaves unaddressed any interactions among those measures and with other components in the modeling chain used to identify water-resource vulnerabilities to specific climate threats. However, time-sensitive, real-world vulnerability studies typically cannot accommodate a full uncertainty ensemble across the whole modeling chain, so a gap has opened between current scientific knowledge and most routine applications for climate-changed hydrology. To close that gap, the US Army Corps of Engineers, the Bureau of Reclamation, and the National Center for Atmospheric Research are working on techniques to subsample uncertainties objectively across modeling chain components and to integrate results into quantitative hydrologic storylines of climate-changed futures. Importantly, these quantitative storylines are not drawn from a small sample of models or components. Rather, they stem from the more comprehensive characterization of the full uncertainty space for each component. Equally important from the perspective of water-resource practitioners, these quantitative hydrologic storylines are anchored in actual design and operations decisions potentially affected by climate change. This talk will describe part of our work characterizing variability and uncertainty across modeling chain components and their interactions using newly developed observational data, models and model outputs, and post-processing tools for making the resulting quantitative storylines most useful in practical hydrology applications.

Motivation and exercise dependence: a study based on self-determination theory.

PubMed

González-Cutre, David; Sicilia, Alvaro

2012-06-01

The objective of this study was to use self-determination theory to analyze the relationships of several motivational variables with exercise dependence. The study involved 531 exercisers, ranging in age from 16 to 60 years old, who responded to differentquestionnaires assessing perception of motivational climate, satisfaction of basic psychological needs, motivation types, and exercise dependence. The results of multiple mediation analysis revealed that ego-involving climate and perceived competence positively predicted exercise dependence in a directed and mediated manner through introjected and external regulation. Gender and age did not moderate the analyzed relationships. These results allow us to better understand the motivational process explaining exercise dependence, demonstrating the negative influence of the ego-involving climate in the context of exercise.

Differential responses of carbon and water vapor fluxes to climate among evergreen needleleaf forests in the USA

DOE PAGES

Wagle, Pradeep; Xiao, Xiangming; Kolb, Thomas E.; ...

2016-05-31

Here, understanding the differences in carbon and water vapor fluxes of spatially distributed evergreen needleleaf forests (ENFs) is crucial for accurately estimating regional or global carbon and water budgets and when predicting the responses of ENFs to current and future climate. We compared the fluxes of ten AmeriFlux ENF sites to investigate cross-site variability in net ecosystem exchange of carbon (NEE), gross primary production (GPP), and evapotranspiration (ET). We used wavelet cross-correlation analysis to examine responses of NEE and ET to common climatic drivers over multiple timescales and also determined optimum values of air temperature (T a) and vapor pressuremore » deficit (VPD) for NEE and ET.« less

Differential responses of carbon and water vapor fluxes to climate among evergreen needleleaf forests in the USA

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

Wagle, Pradeep; Xiao, Xiangming; Kolb, Thomas E.

Here, understanding the differences in carbon and water vapor fluxes of spatially distributed evergreen needleleaf forests (ENFs) is crucial for accurately estimating regional or global carbon and water budgets and when predicting the responses of ENFs to current and future climate. We compared the fluxes of ten AmeriFlux ENF sites to investigate cross-site variability in net ecosystem exchange of carbon (NEE), gross primary production (GPP), and evapotranspiration (ET). We used wavelet cross-correlation analysis to examine responses of NEE and ET to common climatic drivers over multiple timescales and also determined optimum values of air temperature (T a) and vapor pressuremore » deficit (VPD) for NEE and ET.« less

Farmers' Perceptions of Climate Variability and Factors Influencing Adaptation: Evidence from Anhui and Jiangsu, China

NASA Astrophysics Data System (ADS)

Kibue, Grace Wanjiru; Liu, Xiaoyu; Zheng, Jufeng; zhang, Xuhui; Pan, Genxing; Li, Lianqing; Han, Xiaojun

2016-05-01

Impacts of climate variability and climate change are on the rise in China posing great threat to agriculture and rural livelihoods. Consequently, China is undertaking research to find solutions of confronting climate change and variability. However, most studies of climate change and variability in China largely fail to address farmers' perceptions of climate variability and adaptation. Yet, without an understanding of farmers' perceptions, strategies are unlikely to be effective. We conducted questionnaire surveys of farmers in two farming regions, Yifeng, Jiangsu and Qinxi, Anhui achieving 280 and 293 responses, respectively. Additionally, we used climatological data to corroborate the farmers' perceptions of climate variability. We found that farmers' were aware of climate variability such that were consistent with climate records. However, perceived impacts of climate variability differed between the two regions and were influenced by farmers' characteristics. In addition, the vast majorities of farmers were yet to make adjustments in their farming practices as a result of numerous challenges. These challenges included socioeconomic and socio-cultural barriers. Results of logit modeling showed that farmers are more likely to adapt to climate variability if contact with extension services, frequency of seeking information, household heads' education, and climate variability perceptions are improved. These results suggest the need for policy makers to understand farmers' perceptions of climate variability and change in order to formulate policies that foster adaptation, and ultimately protect China's agricultural assets.

Farmers' Perceptions of Climate Variability and Factors Influencing Adaptation: Evidence from Anhui and Jiangsu, China.

PubMed

Kibue, Grace Wanjiru; Liu, Xiaoyu; Zheng, Jufeng; Zhang, Xuhui; Pan, Genxing; Li, Lianqing; Han, Xiaojun

2016-05-01

Impacts of climate variability and climate change are on the rise in China posing great threat to agriculture and rural livelihoods. Consequently, China is undertaking research to find solutions of confronting climate change and variability. However, most studies of climate change and variability in China largely fail to address farmers' perceptions of climate variability and adaptation. Yet, without an understanding of farmers' perceptions, strategies are unlikely to be effective. We conducted questionnaire surveys of farmers in two farming regions, Yifeng, Jiangsu and Qinxi, Anhui achieving 280 and 293 responses, respectively. Additionally, we used climatological data to corroborate the farmers' perceptions of climate variability. We found that farmers' were aware of climate variability such that were consistent with climate records. However, perceived impacts of climate variability differed between the two regions and were influenced by farmers' characteristics. In addition, the vast majorities of farmers were yet to make adjustments in their farming practices as a result of numerous challenges. These challenges included socioeconomic and socio-cultural barriers. Results of logit modeling showed that farmers are more likely to adapt to climate variability if contact with extension services, frequency of seeking information, household heads' education, and climate variability perceptions are improved. These results suggest the need for policy makers to understand farmers' perceptions of climate variability and change in order to formulate policies that foster adaptation, and ultimately protect China's agricultural assets.

Spatial patterns of recent Antarctic surface temperature trends and the importance of natural variability: lessons from multiple reconstructions and the CMIP5 models

NASA Astrophysics Data System (ADS)

Smith, Karen L.; Polvani, Lorenzo M.

2017-04-01

The recent annually averaged warming of the Antarctic Peninsula, and of West Antarctica, stands in stark contrast to very small trends over East Antarctica. This asymmetry arises primarily from a highly significant warming of West Antarctica in austral spring and a cooling of East Antarctica in austral autumn. Here we examine whether this East-West asymmetry is a response to anthropogenic climate forcings or a manifestation of natural climate variability. We compare the observed Antarctic surface air temperature trends over two distinct time periods (1960-2005 and 1979-2005), and with those simulated by 40 models participating in Phase 5 of the Coupled Model Intercomparison Project (CMIP5). We find that the observed East-West asymmetry differs substantially between the two periods and, furthermore, that it is completely absent from the forced response seen in the CMIP5 multi-model mean, from which all natural variability is eliminated by the averaging. We also examine the relationship between the Southern Annular mode (SAM) and Antarctic temperature trends, in both models and reanalyses, and again conclude that there is little evidence of anthropogenic SAM-induced driving of the recent temperature trends. These results offer new, compelling evidence pointing to natural climate variability as a key contributor to the recent warming of West Antarctica and of the Peninsula.

Temporal variability in the importance of hydrologic, biotic, and climatic descriptors of dissolved oxygen dynamics in a shallow tidal-marsh creek

NASA Astrophysics Data System (ADS)

Nelson, N.; Munoz-Carpena, R.; Neale, P.; Tzortziou, M.; Megonigal, P.

2017-12-01

Due to strong abiotic forcing, dissolved oxygen (DO) in shallow tidal creeks often disobeys the conventional explanation of general aquatic DO cycling as biologically-regulated. In the present work, we seek to quantify the relative importance of abiotic (hydrologic and climatic), and biotic (primary productivity as represented by chlorophyll-a) descriptors of tidal creek DO. By fitting multiple linear regression models of DO to hourly chlorophyll-a, water quality, hydrology, and weather data collected in a tidal creek of a Chesapeake Bay marsh (Maryland, USA), temporal shifts (summer - early winter) in the relative importance of tidal creek DO descriptors were uncovered. Moreover, this analysis identified an alternative approach to evaluating tidal stage as a driver of DO by dividing stage into two DO-relevant variables: stage above and below bankfull depth. Within the hydrologic variable class, stage below bankfull depth dominated as an important descriptor, thus highlighting the role of pore water drainage and mixing as influential processes forcing tidal creek DO. Study findings suggest that tidal creek DO dynamics are explained by a balance of hydrologic, climatic, and biotic descriptors during warmer seasons due to many of these variables (i.e., chlorophyll-a, water temperature) acting as tracers of estuarine-marsh water mixing; conversely, in early winter months when estuarine and marsh waters differ less distinctly, hydrologic variables increase in relative importance as descriptors of tidal creek DO. These findings underline important distinctions in the underlying mechanisms dictating DO variability in shallow tidal marsh-creek environments relative to open water estuarine systems.

Temporal variability in the importance of hydrologic, biotic, and climatic descriptors of dissolved oxygen dynamics in a shallow tidal-marsh creek

NASA Astrophysics Data System (ADS)

Nelson, Natalie G.; Muñoz-Carpena, Rafael; Neale, Patrick J.; Tzortziou, Maria; Megonigal, J. Patrick

2017-08-01

Due to strong abiotic forcing, dissolved oxygen (DO) in shallow tidal creeks often disobeys the conventional explanation of general aquatic DO cycling as biologically regulated. In the present work, we seek to quantify the relative importance of abiotic (hydrologic and climatic), and biotic (primary productivity as represented by chlorophyll-a) descriptors of tidal creek DO. By fitting multiple linear regression models of DO to hourly chlorophyll-a, water quality, hydrology, and weather data collected in a tidal creek of a Chesapeake Bay marsh (Maryland, USA), temporal shifts (summer-early winter) in the relative importance of tidal creek DO descriptors were uncovered. Moreover, this analysis identified an alternative approach to evaluating tidal stage as a driver of DO by dividing stage into two DO-relevant variables: stage above and below bankfull depth. Within the hydrologic variable class, stage below bankfull depth dominated as an important descriptor, thus highlighting the role of pore water drainage and mixing as influential processes forcing tidal creek DO. Study findings suggest that tidal creek DO dynamics are explained by a balance of hydrologic, climatic, and biotic descriptors during warmer seasons due to many of these variables (i.e., chlorophyll-a, water temperature) acting as tracers of estuarine-marsh water mixing; conversely, in early winter months when estuarine and marsh waters differ less distinctly, hydrologic variables increase in relative importance as descriptors of tidal creek DO. These findings underline important distinctions in the underlying mechanisms dictating DO variability in shallow tidal marsh-creek environments relative to open water estuarine systems.

Food Price Volatility and Decadal Climate Variability

NASA Astrophysics Data System (ADS)

Brown, M. E.

2013-12-01

The agriculture system is under pressure to increase production every year as global population expands and more people move from a diet mostly made up of grains, to one with more meat, dairy and processed foods. Weather shocks and large changes in international commodity prices in the last decade have increased pressure on local food prices. This paper will review several studies that link climate variability as measured with satellite remote sensing to food price dynamics in 36 developing countries where local monthly food price data is available. The focus of the research is to understand how weather and climate, as measured by variations in the growing season using satellite remote sensing, has affected agricultural production, food prices and access to food in agricultural societies. Economies are vulnerable to extreme weather at multiple levels. Subsistence small holders who hold livestock and consume much of the food they produce are vulnerable to food production variability. The broader society, however, is also vulnerable to extreme weather because of the secondary effects on market functioning, resource availability, and large-scale impacts on employment in trading, trucking and wage labor that are caused by weather-related shocks. Food price variability captures many of these broad impacts and can be used to diagnose weather-related vulnerability across multiple sectors. The paper will trace these connections using market-level data and analysis. The context of the analysis is the humanitarian aid community, using the guidance of the USAID Famine Early Warning Systems Network and the United Nation's World Food Program in their response to food security crises. These organizations have worked over the past three decades to provide baseline information on food production through satellite remote sensing data and agricultural yield models, as well as assessments of food access through a food price database. Econometric models and spatial analysis are used to describe the connection between shocks and food prices, and to demonstrate the importance of these metrics in overall outcomes in food-insecure communities.

Increasing Megadrought Risk at the Intersection of Decadal to Centennial Variability and Climate Change

NASA Astrophysics Data System (ADS)

Overpeck, J. T.; Parsons, L. A.; Loope, G. R.; Ault, T.; Cole, J. E.; Otto-Bliesner, B. L.; Buckle, N.; Stevenson, S.; Fasullo, J.

2016-12-01

Even more than the 1930's U.S. Dust Bowl Drought, the 20th century Sahel drought stands out as the most unprecedented drought of the instrumental era, in part because it extended over multiple decades. Paleoclimatic evidence makes it clear that this Sahel drought was nonetheless not really unprecedented - droughts many decades long have occurred in sub-Saharan Africa regularly over the last several thousand years, and these constitute what is now increasingly referred to as "megadrought." Paleoclimatic evidence also makes it clear that all drought-prone semi-arid and arid regions of the globe, including southwestern North America, southeastern Australia, and the Mediterranean/Middle Eastern region likely experienced multiple such multidecadal megadroughts in recent pre-Anthropocene Earth history. In other regions of the globe, including parts of South Asia and Amazonia, short but devastating droughts of the last 50-150 years, were also eclipsed in recent Earth history by much more serious megadrought, although these megadroughts were shorter than the multidecadal droughts of Africa or SW North America. In the past, megadroughts have occurred for reasons that are increasingly well understood in terms of ocean-atmosphere dynamics that led to unusually persistent precipitation deficits. Many of these same dynamics are well simulated in state-of-the-art Earth System Models, and yet comparisons between simulated and observed paleohydroclimatic variability suggests the models generally underestimate the risk of megadrought. Paleohydroclimatic records in some cases overestimate drought persistence, but there appear to be other issues at play that need to be better understood and simulated: positive land-atmosphere feedbacks, overly energetic interannual (i.e., ENSO) modes of variability, and insufficient internal multidecadal to centennial coupled climate system variability. Taking these issues and the impact of anthropogenic climate change into account means that the risk of megadrought is increasing significantly in many regions of the globe as the planet warms - tools, including critical paleoclimatic data, are being developed to help anticipate and adapt to this growing challenge.

Climate and the complexity of migratory phenology: sexes, migratory distance, and arrival distributions

NASA Astrophysics Data System (ADS)

Macmynowski, Dena P.; Root, Terry L.

2007-05-01

The intra- and inter-season complexity of bird migration has received limited attention in climatic change research. Our phenological analysis of 22 species collected in Chicago, USA, (1979 2002) evaluates the relationship between multi-scalar climate variables and differences (1) in arrival timing between sexes, (2) in arrival distributions among species, and (3) between spring and fall migration. The early migratory period for earliest arriving species (i.e., short-distance migrants) and earliest arriving individuals of a species (i.e., males) most frequently correlate with climate variables. Compared to long-distance migrant species, four times as many short-distance migrants correlate with spring temperature, while 8 of 11 (73%) of long-distance migrant species’ arrival is correlated with the North Atlantic Oscillation (NAO). While migratory phenology has been correlated with NAO in Europe, we believe that this is the first documentation of a significant association in North America. Geographically proximate conditions apparently influence migratory timing for short-distance migrants while continental-scale climate (e.g., NAO) seemingly influences the phenology of Neotropical migrants. The preponderance of climate correlations is with the early migratory period, not the median of arrival, suggesting that early spring conditions constrain the onset or rate of migration for some species. The seasonal arrival distribution provides considerable information about migratory passage beyond what is apparent from statistical analyses of phenology. A relationship between climate and fall phenology is not detected at this location. Analysis of the within-season complexity of migration, including multiple metrics of arrival, is essential to detect species’ responses to changing climate as well as evaluate the underlying biological mechanisms.

HIST-EU - a dataset of European relevance, a database to enable long-term climate variability studies on regional scale

NASA Astrophysics Data System (ADS)

Auer, I.; Böhm, R.; Ganekind, M.; Schöner, W.; Nemec, J.; Chimani, B.

2010-09-01

Instrumental time series of different climate elements are an important requisite for climate and climate impact studies. Long-term time series can improve our understanding of climate change during the instrumental period. During recent decades a number of national and international initiatives in European countries have significantly increased the number of existing long-term instrumental series; however a publically available data base covering Europe has not been created so far. For the "Greater Alpine Region" (4-19 deg E, 43-49 deg N, 0-3500m asl) the HISTALP data base has been established consisting of monthly homogenised temperature, pressure, precipitation, sunshine and cloudiness records. The data set may be described as follows: Long-term (fully exploiting the potential of systematically measured data). dense (network density adequate in respect to the spatial coherence of the given climate element) quality improved (outliers removed, gaps filled) homogenised (earlier sections adjusted to the recent state of the measuring site) multiple (covering more than one climate element) user friendly (well described and kept in different modes for different applications) HIST-EU is inteded to be a data set of European relevance allowing studying climate variability on regional scale. It focuses on data collection, data recovery and rescue, and homogenizing. HIST-EU will use the infrastructure of HISTALP (www.zamg.ac.at/histalp) and will allow free or restricted data access due to the regulations of data providers. HIST-EU will be carried out under the umbrella of ECSN/EUMETNET.

Using sensitive montane amphibian species as indicators of hydroclimatic change in meadow ecosystems of the Sierra Nevada, California

NASA Astrophysics Data System (ADS)

Peek, R.; Viers, J.; Yarnell, S. M.

2012-12-01

Climate change can affect sensitive species and ecosystems in many ways, yet sparse data and the inability to apply various climate models at functional spatial scales often prevents relevant research from being utilized in conservation management plans. Climate change has been linked to declines and disturbances in a multitude of species and habitats, and in California, one of the greatest climatic concerns is the predicted reduction in mountain snowpack and associated snowmelt. These decreases in natural storage of water as snow in mountain regions can affect the timing and variability of critical snowmelt runoff periods—important seasonal signals that species in montane ecosystems have evolved life history strategies around—leading to greater intra-annual variability and diminished summer and fall stream flows. Although many species distribution models exist, few provide ways to integrate continually updated and revised Global Climate Models (GCMs), hydrologic data unique to a watershed, and ecological responses that can be incorporated into conservation strategies. This study documents a novel and applicable method of combining boosted regression tree (BRT) modeling and species distributions with hydroclimatic data as a potential management tool for conservation. Boosted regression trees are suitable for ecological distribution modeling because they can reduce both bias and variance, as well as handle sharp discontinuities common in sparsely sampled species or large study areas. This approach was used to quantify the effects of hydroclimatic changes on the distribution of key riparian-associated amphibian species in montane meadow habitats in the Sierra Nevada at the sub-watershed level. Based on modeling using current species range maps in conjunction with three climate scenarios (near, mid, and far), extreme range contractions were observed for all sensitive species (southern long-toed salamander, mountain yellow-legged frog, Yosemite toad) by the year 2100. Among many environmental and hydroclimatic variables used in the model, snowpack and snowmelt (runoff) variables were consistently among the most informative in predicting species occupancy. Few sub-watersheds contained greater than 50% probability of species occupancy throughout the modeled time period; however several core areas were identified as more resilient to climate change for each species. There was overlap among species in areas that were predicted to remain hydroclimatically stable, particularly in sub-watersheds that contain high meadow density. Quantifying these areas of habitat stability, or "resiliency", may ultimately be the most useful outcome of BRT modeling, with the flexibility to utilize multiple GCMs at varying scales. Ultimately managers need to consider both short term and long term conservation goals by identifying and protecting suitable habitat areas most resilient to climate change to give multiple species the best chance to persist. This approach provides a unique tool for conservation management which can be easily applied to a variety of data and species, and provides useful knowledge at both near and long term time scales.

A plant’s perspective of extremes: Terrestrial plant responses to changing climatic variability

PubMed Central

Reyer, C.; Leuzinger, S.; Rammig, A.; Wolf, A.; Bartholomeus, R. P.; Bonfante, A.; de Lorenzi, F.; Dury, M.; Gloning, P.; Abou Jaoudé, R.; Klein, T.; Kuster, T. M.; Martins, M.; Niedrist, G.; Riccardi, M.; Wohlfahrt, G.; de Angelis, P.; de Dato, G.; François, L.; Menzel, A.; Pereira, M.

2013-01-01

We review observational, experimental and model results on how plants respond to extreme climatic conditions induced by changing climatic variability. Distinguishing between impacts of changing mean climatic conditions and changing climatic variability on terrestrial ecosystems is generally underrated in current studies. The goals of our review are thus (1) to identify plant processes that are vulnerable to changes in the variability of climatic variables rather than to changes in their mean, and (2) to depict/evaluate available study designs to quantify responses of plants to changing climatic variability. We find that phenology is largely affected by changing mean climate but also that impacts of climatic variability are much less studied but potentially damaging. We note that plant water relations seem to be very vulnerable to extremes driven by changes in temperature and precipitation and that heatwaves and flooding have stronger impacts on physiological processes than changing mean climate. Moreover, interacting phenological and physiological processes are likely to further complicate plant responses to changing climatic variability. Phenological and physiological processes and their interactions culminate in even more sophisticated responses to changing mean climate and climatic variability at the species and community level. Generally, observational studies are well suited to study plant responses to changing mean climate, but less suitable to gain a mechanistic understanding of plant responses to climatic variability. Experiments seem best suited to simulate extreme events. In models, temporal resolution and model structure are crucial to capture plant responses to changing climatic variability. We highlight that a combination of experimental, observational and /or modeling studies have the potential to overcome important caveats of the respective individual approaches. PMID:23504722

Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales

NASA Technical Reports Server (NTRS)

Zeng, Fanwei; Collatz, George James; Pinzon, Jorge E.; Ivanoff, Alvaro

2013-01-01

Satellite observations of surface reflected solar radiation contain informationabout variability in the absorption of solar radiation by vegetation. Understanding thecauses of variability is important for models that use these data to drive land surface fluxesor for benchmarking prognostic vegetation models. Here we evaluated the interannualvariability in the new 30.5-year long global satellite-derived surface reflectance index data,Global Inventory Modeling and Mapping Studies normalized difference vegetation index(GIMMS NDVI3g). Pearsons correlation and multiple linear stepwise regression analyseswere applied to quantify the NDVI interannual variability driven by climate anomalies, andto evaluate the effects of potential interference (snow, aerosols and clouds) on the NDVIsignal. We found ecologically plausible strong controls on NDVI variability by antecedent precipitation and current monthly temperature with distinct spatial patterns. Precipitation correlations were strongest for temperate to tropical water limited herbaceous systemswhere in some regions and seasons 40 of the NDVI variance could be explained byprecipitation anomalies. Temperature correlations were strongest in northern mid- to-high-latitudes in the spring and early summer where up to 70 of the NDVI variance was explained by temperature anomalies. We find that, in western and central North America,winter-spring precipitation determines early summer growth while more recent precipitation controls NDVI variability in late summer. In contrast, current or prior wetseason precipitation anomalies were correlated with all months of NDVI in sub-tropical herbaceous vegetation. Snow, aerosols and clouds as well as unexplained phenomena still account for part of the NDVI variance despite corrections. Nevertheless, this study demonstrates that GIMMS NDVI3g represents real responses of vegetation to climate variability that are useful for global models.

Reconstructing Past Seasonal to Multicentennial-Scale Variability in the NE Atlantic Ocean Using the Long-Lived Marine Bivalve Mollusk Glycymeris glycymeris

NASA Astrophysics Data System (ADS)

Reynolds, D. J.; Hall, I. R.; Slater, S. M.; Scourse, J. D.; Halloran, P. R.; Sayer, M. D. J.

2017-11-01

The lack of long-term, highly resolved (annual to subannual) and absolutely dated baseline records of marine variability extending beyond the instrumental period (last 50-100 years) hinders our ability to develop a comprehensive understanding of the role the ocean plays in the climate system. Specifically, without such records, it remains difficult to fully quantify the range of natural climate variability mediated by the ocean and to robustly attribute recent changes to anthropogenic or natural drivers. Here we present a 211 year (1799-2010 C.E.; all dates hereafter are Common Era) seawater temperature (SWT) reconstruction from the northeast Atlantic Ocean derived from absolutely dated, annually resolved, oxygen isotope ratios recorded in the shell carbonate (δ18Oshell) of the long-lived marine bivalve mollusk Glycymeris glycymeris. The annual record was calibrated using subannually resolved δ18Oshell values drilled from multiple shells covering the instrumental period. Calibration verification statistics and spatial correlation analyses indicate that the δ18Oshell record contains significant skill at reconstructing Northeast Atlantic Ocean mean summer SWT variability associated with changes in subpolar gyre dynamics and the North Atlantic Current. Reconciling differences between the δ18Oshell data and corresponding growth increment width chronology demonstrates that 68% of the variability in G. glycymeris shell growth can be explained by the combined influence of biological productivity and SWT variability. These data suggest that G. glycymeris can provide seasonal to multicentennial absolutely dated baseline records of past marine variability that will lead to the development of a quantitative understanding of the role the marine environment plays in the global climate system.

Climate and weather influences on spatial temporal patterns of mountain pine beetle populations in Washington and Oregon.

PubMed

Preisler, Haiganoush K; Hicke, Jeffrey A; Ager, Alan A; Hayes, Jane L

2012-11-01

Widespread outbreaks of mountain pine beetle in North America have drawn the attention of scientists, forest managers, and the public. There is strong evidence that climate change has contributed to the extent and severity of recent outbreaks. Scientists are interested in quantifying relationships between bark beetle population dynamics and trends in climate. Process models that simulate climate suitability for mountain pine beetle outbreaks have advanced our understanding of beetle population dynamics; however, there are few studies that have assessed their accuracy across multiple outbreaks or at larger spatial scales. This study used the observed number of trees killed by mountain pine beetles per square kilometer in Oregon and Washington, USA, over the past three decades to quantify and assess the influence of climate and weather variables on beetle activity over longer time periods and larger scales than previously studied. Influences of temperature and precipitation in addition to process model output variables were assessed at annual and climatological time scales. The statistical analysis showed that new attacks are more likely to occur at locations with climatological mean August temperatures >15 degrees C. After controlling for beetle pressure, the variables with the largest effect on the odds of an outbreak exceeding a certain size were minimum winter temperature (positive relationship) and drought conditions in current and previous years. Precipitation levels in the year prior to the outbreak had a positive effect, possibly an indication of the influence of this driver on brood size. Two-year cumulative precipitation had a negative effect, a possible indication of the influence of drought on tree stress. Among the process model variables, cold tolerance was the strongest indicator of an outbreak increasing to epidemic size. A weather suitability index developed from the regression analysis indicated a 2.5x increase in the odds of outbreak at locations with highly suitable weather vs. locations with low suitability. The models were useful for estimating expected amounts of damage (total area with outbreaks) and for quantifying the contribution of climate to total damage. Overall, the results confirm the importance of climate and weather on the spatial expansion of bark beetle outbreaks over time.

Meteorological Contribution to Variability in Particulate Matter Concentrations

NASA Astrophysics Data System (ADS)

Woods, H. L.; Spak, S. N.; Holloway, T.

2006-12-01

Local concentrations of fine particulate matter (PM) are driven by a number of processes, including emissions of aerosols and gaseous precursors, atmospheric chemistry, and meteorology at local, regional, and global scales. We apply statistical downscaling methods, typically used for regional climate analysis, to estimate the contribution of regional scale meteorology to PM mass concentration variability at a range of sites in the Upper Midwestern U.S. Multiple years of daily PM10 and PM2.5 data, reported by the U.S. Environmental Protection Agency (EPA), are correlated with large-scale meteorology over the region from the National Centers for Environmental Prediction (NCEP) reanalysis data. We use two statistical downscaling methods (multiple linear regression, MLR, and analog) to identify which processes have the greatest impact on aerosol concentration variability. Empirical Orthogonal Functions of the NCEP meteorological data are correlated with PM timeseries at measurement sites. We examine which meteorological variables exert the greatest influence on PM variability, and which sites exhibit the greatest response to regional meteorology. To evaluate model performance, measurement data are withheld for limited periods, and compared with model results. Preliminary results suggest that regional meteorological processes account over 50% of aerosol concentration variability at study sites.

An Investigation of the Hydroclimate Variability of Eastern Africa

NASA Astrophysics Data System (ADS)

Smith, K. A.; Semazzi, F. H. M.

2015-12-01

The flow of the Victoria Nile, and the productivity of the dams along it, is determined by the level of Lake Victoria, which is primarily dictated by the rainfall and temperature variability over the Lake Victoria Basin. Notwithstanding the indisputable decline of water resources over the lake basin during the Long Rains of March - May, there is a strong indication based on IPCC climate projections that this trend, which has persisted for several decades, will reverse in the next few decades. This phenomenon has come to be known as the Eastern-Central African climate change paradox and could have profound implications on sustainable development for the next few decades in Lake Victoria Basin. The purpose of this study is to investigate the climate variability associated with the East African Climate Change Paradox for the recent decades. This research analyzes observations to understand the sources of variability and potential physical mechanisms related to the decline in precipitation over Eastern Africa. We then investigate the hydrological factors involved in the decline of Lake Victoria levels in the context of the decline in rainfall. While East Africa has been experiencing persistent decline of the Long Rains for multiple decades, this same decline is not seen in annual rainfall. The remaining seasons show an increase in rainfall which is compensating for the decline of the Long Rains. It is possible that the Long Rains season is shifting in such a way that the season starts earlier, in February, and ending sooner. The corresponding annual Lake Victoria levels modeled using observed rainfall do not decline in the recent decades, except when the Long Rains seasonal variability is considered without variability from other seasons. This shift could impact hydroelectric power planning on a monthly or seasonal time scale, and could potentially have a large impact on agriculture, since it would shift the growing season in the region.

Drivers and uncertainties of forecasted range shifts for warm-water fishes under climate and land cover change

USGS Publications Warehouse

Bouska, Kristen; Whitledge, Gregory W.; Lant, Christopher; Schoof, Justin

2018-01-01

Land cover is an important determinant of aquatic habitat and is projected to shift with climate changes, yet climate-driven land cover changes are rarely factored into climate assessments. To quantify impacts and uncertainty of coupled climate and land cover change on warm-water fish species’ distributions, we used an ensemble model approach to project distributions of 14 species. For each species, current range projections were compared to 27 scenario-based projections and aggregated to visualize uncertainty. Multiple regression and model selection techniques were used to identify drivers of range change. Novel, or no-analogue, climates were assessed to evaluate transferability of models. Changes in total probability of occurrence ranged widely across species, from a 63% increase to a 65% decrease. Distributional gains and losses were largely driven by temperature and flow variables and underscore the importance of habitat heterogeneity and connectivity to facilitate adaptation to changing conditions. Finally, novel climate conditions were driven by mean annual maximum temperature, which stresses the importance of understanding the role of temperature on fish physiology and the role of temperature-mitigating management practices.

Observed climate variability over Chad using multiple observational and reanalysis datasets

NASA Astrophysics Data System (ADS)

Maharana, Pyarimohan; Abdel-Lathif, Ahmat Younous; Pattnayak, Kanhu Charan

2018-03-01

Chad is the largest of Africa's landlocked countries and one of the least studied region of the African continent. The major portion of Chad lies in the Sahel region, which is known for its rapid climate change. In this study, multiple observational datasets are analyzed from 1950 to 2014, in order to examine the trend of precipitation and temperature along with their variability over Chad to understand possible impacts of climate change over this region. Trend analysis of the climatic fields has been carried out using Mann-Kendall test. The precipitation over Chad is mostly contributed during summer by West African Monsoon, with maximum northward limit of 18° N. The Atlantic Ocean as well as the Mediterranean Sea are the major source of moisture for the summer rainfall over Chad. Based on the rainfall time series, the entire study period has been divided in to wet (1950 to 1965), dry (1966 to 1990) and recovery period (1991 to 2014). The rainfall has decreased drastically for almost 3 decades during the dry period resulted into various drought years. The temperature increases at a rate of 0.15 °C/decade during the entire period of analysis. The seasonal rainfall as well as temperature plays a major role in the change of land use/cover. The decrease of monsoon rainfall during the dry period reduces the C4 cover drastically; this reduction of C4 grass cover leads to increase of C3 grass cover. The slow revival of rainfall is still not good enough for the increase of shrub cover but it favors the gradual reduction of bare land over Chad.

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.

Testing the generality of a trophic-cascade model for plague

USGS Publications Warehouse

Collinge, S.K.; Johnson, W.C.; Ray, C.; Matchett, R.; Grensten, J.; Cully, J.F.; Gage, K.L.; Kosoy, M.Y.; Loye, J.E.; Martin, A.P.

2005-01-01

Climate may affect the dynamics of infectious diseases by shifting pathogen, vector, or host species abundance, population dynamics, or community interactions. Black-tailed prairie dogs (Cynomys ludovicianus) are highly susceptible to plague, yet little is known about factors that influence the dynamics of plague epizootics in prairie dogs. We investigated temporal patterns of plague occurrence in black-tailed prairie dogs to assess the generality of links between climate and plague occurrence found in previous analyses of human plague cases. We examined long-term data on climate and plague occurrence in prairie dog colonies within two study areas. Multiple regression analyses revealed that plague occurrence in prairie dogs was not associated with climatic variables in our Colorado study area. In contrast, plague occurrence was strongly associated with climatic variables in our Montana study area. The models with most support included a positive association with precipitation in April-July of the previous year, in addition to a positive association with the number of "warm" days and a negative association with the number of "hot" days in the same year as reported plague events. We conclude that the timing and magnitude of precipitation and temperature may affect plague occurrence in some geographic areas. The best climatic predictors of plague occurrence in prairie dogs within our Montana study area are quite similar to the best climatic predictors of human plague cases in the southwestern United States. This correspondence across regions and species suggests support for a (temperature-modulated) trophic-cascade model for plague, including climatic effects on rodent abundance, flea abundance, and pathogen transmission, at least in regions that experience strong climatic signals. ?? 2005 EcoHealth Journal Consortium.

Solar Forced Dansgaard/Oeschger Events?

NASA Technical Reports Server (NTRS)

Muscheler, R.; Beer, J.

2006-01-01

Climate records for the last ice age (which ended 11,500 years ago) show enormous climate fluctuations in the North Atlantic region - the so-called Dansgaard/Oeschger events. During these events air temperatures in Greenland changed on the order of 10 degrees Celsius within a few decades. These changes were attributed to shifts in ocean circulation which influences the warm water supply from lower latitudes to the North Atlantic region. Interestingly, the rapid warmings tend to recur approximately every 1500 years or multiples thereof. This has led researchers to speculate about an external cause for these changes with the variable Sun being one possible candidate. Support for this hypothesis came from climate reconstructions, which suggested that the Sun influenced the climate in the North Atlantic region on these time scales during the last approximately 12,000 years of relatively stable Holocene climate. However, Be-10 measurements in ice cores do not indicate that the Sun caused or triggered the Dansgaard/Oeschger events. Depending on the solar magnetic shielding more or less Be-10 is produced in the Earth's atmosphere. Therefore, 10Be can be used as a proxy for solar activity changes. Since Be-10 can be measured in ice cores, it is possible to compare the variable solar forcing directly with the climate record from the same ice core. This removes any uncertainties in the relative dating, and the solar-climate link can be reliably studied. Notwithstanding that some Dansgaard/Oeschger warmings could be related to increased solar activity, there is no indication that this is the case for all of the Dansgaard/Oeschger events. Therefore, during the last ice age the Be-10 and ice core climate data do not indicate a persistent solar influence on North Atlantic climate.

Quantitative analysis of oyster larval proteome provides new insights into the effects of multiple climate change stressors.

PubMed

Dineshram, Ramadoss; Chandramouli, Kondethimmanahalli; Ko, Ginger Wai Kuen; Zhang, Huoming; Qian, Pei-Yuan; Ravasi, Timothy; Thiyagarajan, Vengatesen

2016-06-01

The metamorphosis of planktonic larvae of the Pacific oyster (Crassostrea gigas) underpins their complex life-history strategy by switching on the molecular machinery required for sessile life and building calcite shells. Metamorphosis becomes a survival bottleneck, which will be pressured by different anthropogenically induced climate change-related variables. Therefore, it is important to understand how metamorphosing larvae interact with emerging climate change stressors. To predict how larvae might be affected in a future ocean, we examined changes in the proteome of metamorphosing larvae under multiple stressors: decreased pH (pH 7.4), increased temperature (30 °C), and reduced salinity (15 psu). Quantitative protein expression profiling using iTRAQ-LC-MS/MS identified more than 1300 proteins. Decreased pH had a negative effect on metamorphosis by down-regulating several proteins involved in energy production, metabolism, and protein synthesis. However, warming switched on these down-regulated pathways at pH 7.4. Under multiple stressors, cell signaling, energy production, growth, and developmental pathways were up-regulated, although metamorphosis was still reduced. Despite the lack of lethal effects, significant physiological responses to both individual and interacting climate change related stressors were observed at proteome level. The metamorphosing larvae of the C. gigas population in the Yellow Sea appear to have adequate phenotypic plasticity at the proteome level to survive in future coastal oceans, but with developmental and physiological costs. © 2016 John Wiley & Sons Ltd.

Projecting the Global Distribution of the Emerging Amphibian Fungal Pathogen, Batrachochytrium dendrobatidis, Based on IPCC Climate Futures

PubMed Central

Olson, Deanna H.; Blaustein, Andrew R.

2016-01-01

Projected changes in climate conditions are emerging as significant risk factors to numerous species, affecting habitat conditions and community interactions. Projections suggest species range shifts in response to climate change modifying environmental suitability and is supported by observational evidence. Both pathogens and their hosts can shift ranges with climate change. We consider how climate change may influence the distribution of the emerging infectious amphibian chytrid fungus, Batrachochytrium dendrobatidis (Bd), a pathogen associated with worldwide amphibian population losses. Using an expanded global Bd database and a novel modeling approach, we examined a broad set of climate metrics to model the Bd-climate niche globally and regionally, then project how climate change may influence Bd distributions. Previous research showed that Bd distribution is dependent on climatic variables, in particular temperature. We trained a machine-learning model (random forest) with the most comprehensive global compilation of Bd sampling records (~5,000 site-level records, mid-2014 summary), including 13 climatic variables. We projected future Bd environmental suitability under IPCC scenarios. The learning model was trained with combined worldwide data (non-region specific) and also separately per region (region-specific). One goal of our study was to estimate of how Bd spatial risks may change under climate change based on the best available data. Our models supported differences in Bd-climate relationships among geographic regions. We projected that Bd ranges will shift into higher latitudes and altitudes due to increased environmental suitability in those regions under predicted climate change. Specifically, our model showed a broad expansion of areas environmentally suitable for establishment of Bd on amphibian hosts in the temperate zones of the Northern Hemisphere. Our projections are useful for the development of monitoring designs in these areas, especially for sensitive species and those vulnerable to multiple threats. PMID:27513565

Spatiotemporal Trends in late-Holocene Fire Regimes in Arctic and Boreal Alaska

NASA Astrophysics Data System (ADS)

Hoecker, T. J.; Higuera, P. E.; Hu, F.; Kelly, R.

2015-12-01

Alaskan arctic and boreal ecosystems are of global importance owing to their sensitivity and feedbacks to directional climate change. Wildfires are a primary driver of boreal carbon balance, and altered fire regimes may significantly impact global climate through the release of stored carbon and changes to surface albedo. Paleoecological records provide a window to how these systems respond to change by revealing climatic and disturbance variability throughout the Holocene. These long-term records highlight the sensitivity of fire regimes to climate and vegetation change, including responses to the relatively warm Medieval Climate Anomaly (MCA), and the relatively cool Little Ice Age (LIA). Over millennial timescales, boreal forests and arctic tundra have been resilient to climate change, but continued directional climate change may result in novel vegetation compositions and fire regimes, with potentially significant implications for global climate. Here we present a spatiotemporal synthesis of 22 published sediment-charcoal records from three Alaskan ecoregions. We add to this network eight records collected in June 2015 from an additional ecoregion. Variability in fire return intervals (FRIs) was quantified within and among ecoregions and climatic periods spanning the past 2 millennia, based on a peak analysis representing local fire events. Preliminary results suggest that fire regimes were responsive to centennial-scale climatic shifts, including the MCA and LIA, but the degree of sensitivity varies by ecoregion. Over the past 2000 years, FRIs were shortest during the MCA, indicating the potential for climate warming to promote high rates of burning. FRIs in tundra regions of northwestern Alaska and in interior boreal forests were 20% shorter during the MCA than during the LIA, and 25% shorter in boreal forest in the south-central Brooks Range. Burning was likely promoted during the warmer, drier MCA through lower fuel moisture. Quantifying fire-regime response to climate forcing across multiple ecoregions helps reveal the mechanisms that connect fire and climate in Alaskan ecosystems.

The role of family planning communications--an agent of reinforcement or change.

PubMed

Chen, E C

1981-12-01

Results are presented of a multiple classification analysis of responses to a 1972 KAP survey in Taiwan of 2013 married women aged 18-34 designed to determine whether family planning communication is primarily a reinforcement agent or a change agent. 2 types of independent variables, social demographic variables including age, number of children, residence, education, employment status, and duration of marriage; and social climate variables including ever receiving family planning information from mass media and ever discussing family planning with others, were used. KAP levels, the dependent variables, were measured by 2 variables each: awareness of effective methods and awareness of government supply of contraceptives for knowledge, wish for additional children and approve of 2-child family for attitude, and never use contraception and neither want children nor use contraception for practice. Social demographic and attitudinal variables were found to be the critical ones, while social climate and knowledge variables had only negligible effects on various stages of family planning adoption, indicating that family planning communications functioned primarily as a reinforcement agent. The effects of social demographic variables were prominent in all stages of contraceptive adoption. Examination of effects of individual variables on various stages of family planning adoption still supported the argument that family planning communications played a reinforcement role. Family planning communications functioned well in diffusing family planning knowledge and accessibility, but social demographic variables and desire for additional children were the most decisive influences on use of contraception.

Plants remember past weather: a study for atmospheric pollen concentrations of Ambrosia, Poaceae and Populus

NASA Astrophysics Data System (ADS)

Matyasovszky, István; Makra, László; Csépe, Zoltán; Sümeghy, Zoltán; Deák, Áron József; Pál-Molnár, Elemér; Tusnády, Gábor

2015-10-01

After extreme dry (wet) summers or years, pollen production of different taxa may decrease (increase) substantially. Accordingly, studying effects of current and past meteorological conditions on current pollen concentrations for different taxa have of major importance. The purpose of this study is separating the weight of current and past weather conditions influencing current pollen productions of three taxa. Two procedures, namely multiple correlations and factor analysis with special transformation are used. The 11-year (1997-2007) data sets include daily pollen counts of Ambrosia (ragweed), Poaceae (grasses) and Populus (poplar), as well as daily values of four climate variables (temperature, relative humidity, global solar flux and precipitation). Multiple correlations of daily pollen counts with simultaneous values of daily meteorological variables do not show annual course for Ambrosia, but do show definite trends for Populus and Poaceae. Results received using the two methods revealed characteristic similarities. For all the three taxa, the continental rainfall peak and additional local showers in the growing season can strengthen the weight of the current meteorological elements. However, due to the precipitation, big amount of water can be stored in the soil contributing to the effect of the past climate elements during dry periods. Higher climate sensitivity (especially water sensitivity) of the herbaceous taxa ( Ambrosia and Poaceae) can be definitely established compared to the arboreal Populus. Separation of the weight of the current and past weather conditions for different taxa involves practical importance both for health care and agricultural production.

A Variable-Resolution Stretched-Grid General Circulation Model and Data Assimilation System with Multiple Areas of Interest: Studying the Anomalous Regional Climate Events of 1998

NASA Technical Reports Server (NTRS)

Fox-Rabinovitz, Michael S.; Takacs, Lawrence; Govindaraju, Ravi C.; Atlas, Robert (Technical Monitor)

2002-01-01

The new stretched-grid design with multiple (four) areas of interest, one at each global quadrant, is implemented into both a stretched-grid GCM (general circulation model) and a stretched-grid data assimilation system (DAS). The four areas of interest include: the U.S./Northern Mexico, the El Nino area/Central South America, India/China, and the Eastern Indian Ocean/Australia. Both the stretched-grid GCM and DAS annual (November 1997 through December 1998) integrations are performed with 50 km regional resolution. The efficient regional down-scaling to mesoscales is obtained for each of the four areas of interest while the consistent interactions between regional and global scales and the high quality of global circulation, are preserved. This is the advantage of the stretched-grid approach. The global variable resolution DAS incorporating the stretched-grid GCM has been developed and tested as an efficient tool for producing regional analyses and diagnostics with enhanced mesoscale resolution. The anomalous regional climate events of 1998 that occurred over the U.S., Mexico, South America, China, India, African Sahel, and Australia are investigated in both simulation and data assimilation modes. Tree assimilated products are also used, along with gauge precipitation data, for validating the simulation results. The obtained results show that the stretched-grid GCM and DAS are capable of producing realistic high quality simulated and assimilated products at mesoscale resolution for regional climate studies and applications.

Developmental phenotypic plasticity helps bridge stochastic weather events associated with climate change.

PubMed

Burggren, Warren

2018-05-10

The slow, inexorable rise in annual average global temperatures and acidification of the oceans are often advanced as consequences of global change. However, many environmental changes, especially those involving weather (as opposed to climate), are often stochastic, variable and extreme, particularly in temperate terrestrial or freshwater habitats. Moreover, few studies of animal and plant phenotypic plasticity employ realistic (i.e. short-term, stochastic) environmental change in their protocols. Here, I posit that the frequently abrupt environmental changes (days, weeks, months) accompanying much longer-term general climate change (e.g. global warming over decades or centuries) require consideration of the true nature of environmental change (as opposed to statistical means) coupled with an expansion of focus to consider developmental phenotypic plasticity. Such plasticity can be in multiple forms - obligatory/facultative, beneficial/deleterious - depending upon the degree and rate of environmental variability at specific points in organismal development. Essentially, adult phenotypic plasticity, as important as it is, will be irrelevant if developing offspring lack sufficient plasticity to create modified phenotypes necessary for survival. © 2018. Published by The Company of Biologists Ltd.

The links between ecosystem multifunctionality and above- and belowground biodiversity are mediated by climate.

PubMed

Jing, Xin; Sanders, Nathan J; Shi, Yu; Chu, Haiyan; Classen, Aimée T; Zhao, Ke; Chen, Litong; Shi, Yue; Jiang, Youxu; He, Jin-Sheng

2015-09-02

Plant biodiversity is often correlated with ecosystem functioning in terrestrial ecosystems. However, we know little about the relative and combined effects of above- and belowground biodiversity on multiple ecosystem functions (for example, ecosystem multifunctionality, EMF) or how climate might mediate those relationships. Here we tease apart the effects of biotic and abiotic factors, both above- and belowground, on EMF on the Tibetan Plateau, China. We found that a suite of biotic and abiotic variables account for up to 86% of the variation in EMF, with the combined effects of above- and belowground biodiversity accounting for 45% of the variation in EMF. Our results have two important implications: first, including belowground biodiversity in models can improve the ability to explain and predict EMF. Second, regional-scale variation in climate, and perhaps climate change, can determine, or at least modify, the effects of biodiversity on EMF in natural ecosystems.

Nurse practitioners: leadership behaviors and organizational climate.

PubMed

Jones, L C; Guberski, T D; Soeken, K L

1990-01-01

The purpose of this article is to examine the relationships of individual nurse practitioners' perceptions of the leadership climate in their organizations and self-reported formal and informal leadership behaviors. The nine climate dimensions (Structure, Responsibility, Reward, Perceived Support of Risk Taking, Warmth, Support, Standard Setting, Conflict, and Identity) identified by Litwin and Stringer in 1968 were used to predict five leadership dimensions (Meeting Organizational Needs, Managing Resources, Leadership Competence, Task Accomplishment, and Communications). Demographic variables of age, educational level, and percent of time spent performing administrative functions were forced as a first step in each multiple regression analysis and used to explain a significant amount of variance in all but one analysis. All leadership dimensions were predicted by at least one organizational climate dimension: (1) Meeting Organizational Needs by Risk and Reward; (2) Managing Resources by Risk and Structure; (3) Leadership Competence by Risk and Standards; (4) Task Accomplishment by Structure, Risk, and Standards; and (5) Communication by Rewards.

Assessing Inter-Sectoral Climate Change Risks: The Role of ISIMIP

NASA Technical Reports Server (NTRS)

Rosenzweig, Cynthia; Arnell, Nigel W.; Ebi, Kristie L.; Lotze-Campen, Hermann; Raes, Frank; Rapley, Chris; Smith, Mark Stafford; Cramer, Wolfgang; Frieler, Katja; Reyer, Christopher P. O.;

The links between ecosystem multifunctionality and above- and belowground biodiversity are mediated by climate

DOE PAGES

Jing, Xin; Sanders, Nathan J.; Shi, Yu; ...

2015-09-02

Plant biodiversity is often correlated with ecosystem functioning in terrestrial ecosystems. However, we know little about the relative and combined effects of above- and belowground biodiversity on multiple ecosystem functions (for example, ecosystem multifunctionality, EMF) or how climate might mediate those relationships. Here we tease apart the effects of biotic and abiotic factors, both above- and belowground, on EMF on the Tibetan Plateau, China. We found that a suite of biotic and abiotic variables account for up to 86% of the variation in EMF, with the combined effects of above- and belowground biodiversity accounting for 45% of the variation inmore » EMF. Our results have two important implications: first, including belowground biodiversity in models can improve the ability to explain and predict EMF. Second, regional-scale variation in climate, and perhaps climate change, can determine, or at least modify, the effects of biodiversity on EMF in natural ecosystems.« less

The links between ecosystem multifunctionality and above- and belowground biodiversity are mediated by climate

PubMed Central

Jing, Xin; Sanders, Nathan J.; Shi, Yu; Chu, Haiyan; Classen, Aimée T.; Zhao, Ke; Chen, Litong; Shi, Yue; Jiang, Youxu; He, Jin-Sheng

2015-01-01

Plant biodiversity is often correlated with ecosystem functioning in terrestrial ecosystems. However, we know little about the relative and combined effects of above- and belowground biodiversity on multiple ecosystem functions (for example, ecosystem multifunctionality, EMF) or how climate might mediate those relationships. Here we tease apart the effects of biotic and abiotic factors, both above- and belowground, on EMF on the Tibetan Plateau, China. We found that a suite of biotic and abiotic variables account for up to 86% of the variation in EMF, with the combined effects of above- and belowground biodiversity accounting for 45% of the variation in EMF. Our results have two important implications: first, including belowground biodiversity in models can improve the ability to explain and predict EMF. Second, regional-scale variation in climate, and perhaps climate change, can determine, or at least modify, the effects of biodiversity on EMF in natural ecosystems. PMID:26328906

The links between ecosystem multifunctionality and above- and belowground biodiversity are mediated by climate

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

Jing, Xin; Sanders, Nathan J.; Shi, Yu

Plant biodiversity is often correlated with ecosystem functioning in terrestrial ecosystems. However, we know little about the relative and combined effects of above- and belowground biodiversity on multiple ecosystem functions (for example, ecosystem multifunctionality, EMF) or how climate might mediate those relationships. Here we tease apart the effects of biotic and abiotic factors, both above- and belowground, on EMF on the Tibetan Plateau, China. We found that a suite of biotic and abiotic variables account for up to 86% of the variation in EMF, with the combined effects of above- and belowground biodiversity accounting for 45% of the variation inmore » EMF. Our results have two important implications: first, including belowground biodiversity in models can improve the ability to explain and predict EMF. Second, regional-scale variation in climate, and perhaps climate change, can determine, or at least modify, the effects of biodiversity on EMF in natural ecosystems.« less

A multiple-proxy approach to understanding rapid Holocene climate change in Southeast Greenland

NASA Astrophysics Data System (ADS)

Davin, S. H.; Bradley, R. S.; Balascio, N. L.; de Wet, G.

2012-12-01

The susceptibility of the Arctic to climate change has made it an excellent workshop for paleoclimatological research. Although there have been previous studies concerning climate variability carried out in the Arctic, there remains a critical dearth of knowledge due the limited number of high-resolution Holocene climate-proxy records available from this region. This gap skews our understanding of observed and predicted climate change, and fuels uncertainty both in the realms of science and policy. This study takes a comprehensive approach to tracking Holocene climate variability in the vicinity of Tasiilaq, Southeast Greenland using a ~5.6 m sediment core from Lower Sermilik Lake. An age-depth model for the core has been established using 8 radiocarbon dates, the oldest of which was taken at 4 m down core and has been been dated to approximately 6.2 kyr BP. The bottom meter of the core below the final radiocarbon date contains a transition from cobbles and coarse sand to organic-rich laminations, indicating the termination of direct glacial influence and therefore likely marking the end of the last glacial period in this region. The remainder of the core is similarly organic-rich, with light-to-dark brown laminations ranging from 0.5 -1 cm in thickness and riddled with turbidites. Using this core in tandem with findings from an on-site assessment of the geomorphic history of the locale we attempt to assess and infer the rapid climatic shifts associated with the Holocene on a sub-centennial scale. Such changes include the termination of the last glacial period, the Mid-Holocene Climatic Optimum, the Neoglacial Period, the Medieval Climatic Optimum, and the Little Ice Age. A multiple proxy approach including magnetic susceptibility, bulk organic geochemistry, elemental profiles acquired by XRF scanning, grain-size, and spectral data will be used to characterize the sediment and infer paleoclimate conditions. Additionally, percent biogenic silica by weight has been quantified via diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), and validated by a traditional wet leaching method. The use of the emerging DRIFTS technology to obtain inferred biogenic silica concentrations has not been widely applied to arctic lacustrine sediments and will help to contribute to the presently limited pool of literature on the topic. Preliminary results of the data reveal high frequency fluctuations between laminations superimposed on long-term trends, which has revealed already some correlation with Holocene climatic events. The data provided by this barrage of proxies is to be presented and will contribute to the understanding of Holocene Arctic climate change at a sub-centennial scale.

Variable effects of climate on forest growth in relation to climate extremes, disturbance, and forest dynamics.

PubMed

Itter, Malcolm S; Finley, Andrew O; D'Amato, Anthony W; Foster, Jane R; Bradford, John B

2017-06-01

Changes in the frequency, duration, and severity of climate extremes are forecast to occur under global climate change. The impacts of climate extremes on forest productivity and health remain difficult to predict due to potential interactions with disturbance events and forest dynamics-changes in forest stand composition, density, size and age structure over time. Such interactions may lead to non-linear forest growth responses to climate involving thresholds and lag effects. Understanding how forest dynamics influence growth responses to climate is particularly important given stand structure and composition can be modified through management to increase forest resistance and resilience to climate change. To inform such adaptive management, we develop a hierarchical Bayesian state space model in which climate effects on tree growth are allowed to vary over time and in relation to past climate extremes, disturbance events, and forest dynamics. The model is an important step toward integrating disturbance and forest dynamics into predictions of forest growth responses to climate extremes. We apply the model to a dendrochronology data set from forest stands of varying composition, structure, and development stage in northeastern Minnesota that have experienced extreme climate years and forest tent caterpillar defoliation events. Mean forest growth was most sensitive to water balance variables representing climatic water deficit. Forest growth responses to water deficit were partitioned into responses driven by climatic threshold exceedances and interactions with insect defoliation. Forest growth was both resistant and resilient to climate extremes with the majority of forest growth responses occurring after multiple climatic threshold exceedances across seasons and years. Interactions between climate and disturbance were observed in a subset of years with insect defoliation increasing forest growth sensitivity to water availability. Forest growth was particularly sensitive to climate extremes during periods of high stem density following major regeneration events when average inter-tree competition was high. Results suggest the resistance and resilience of forest growth to climate extremes can be increased through management steps such as thinning to reduce competition during early stages of stand development and small-group selection harvests to maintain forest structures characteristic of older, mature stands. © 2017 by the Ecological Society of America.

Variable effects of climate on forest growth in relation to climate extremes, disturbance, and forest dynamics

USGS Publications Warehouse

Itter, Malcolm S.; Finley, Andrew O.; D'Amato, Anthony W.; Foster, Jane R.; Bradford, John B.

2017-01-01

Changes in the frequency, duration, and severity of climate extremes are forecast to occur under global climate change. The impacts of climate extremes on forest productivity and health remain difficult to predict due to potential interactions with disturbance events and forest dynamics—changes in forest stand composition, density, size and age structure over time. Such interactions may lead to non-linear forest growth responses to climate involving thresholds and lag effects. Understanding how forest dynamics influence growth responses to climate is particularly important given stand structure and composition can be modified through management to increase forest resistance and resilience to climate change. To inform such adaptive management, we develop a hierarchical Bayesian state space model in which climate effects on tree growth are allowed to vary over time and in relation to past climate extremes, disturbance events, and forest dynamics. The model is an important step toward integrating disturbance and forest dynamics into predictions of forest growth responses to climate extremes. We apply the model to a dendrochronology data set from forest stands of varying composition, structure, and development stage in northeastern Minnesota that have experienced extreme climate years and forest tent caterpillar defoliation events. Mean forest growth was most sensitive to water balance variables representing climatic water deficit. Forest growth responses to water deficit were partitioned into responses driven by climatic threshold exceedances and interactions with insect defoliation. Forest growth was both resistant and resilient to climate extremes with the majority of forest growth responses occurring after multiple climatic threshold exceedances across seasons and years. Interactions between climate and disturbance were observed in a subset of years with insect defoliation increasing forest growth sensitivity to water availability. Forest growth was particularly sensitive to climate extremes during periods of high stem density following major regeneration events when average inter-tree competition was high. Results suggest the resistance and resilience of forest growth to climate extremes can be increased through management steps such as thinning to reduce competition during early stages of stand development and small-group selection harvests to maintain forest structures characteristic of older, mature stands.

An improved Multimodel Approach for Global Sea Surface Temperature Forecasts

NASA Astrophysics Data System (ADS)

Khan, M. Z. K.; Mehrotra, R.; Sharma, A.

2014-12-01

The concept of ensemble combinations for formulating improved climate forecasts has gained popularity in recent years. However, many climate models share similar physics or modeling processes, which may lead to similar (or strongly correlated) forecasts. Recent approaches for combining forecasts that take into consideration differences in model accuracy over space and time have either ignored the similarity of forecast among the models or followed a pairwise dynamic combination approach. Here we present a basis for combining model predictions, illustrating the improvements that can be achieved if procedures for factoring in inter-model dependence are utilised. The utility of the approach is demonstrated by combining sea surface temperature (SST) forecasts from five climate models over a period of 1960-2005. The variable of interest, the monthly global sea surface temperature anomalies (SSTA) at a 50´50 latitude-longitude grid, is predicted three months in advance to demonstrate the utility of the proposed algorithm. Results indicate that the proposed approach offers consistent and significant improvements for majority of grid points compared to the case where the dependence among the models is ignored. Therefore, the proposed approach of combining multiple models by taking into account the existing interdependence, provides an attractive alternative to obtain improved climate forecast. In addition, an approach to combine seasonal forecasts from multiple climate models with varying periods of availability is also demonstrated.

Spatial climate patterns explain negligible variation in strength of compensatory density feedbacks in birds and mammals.

PubMed

Herrando-Pérez, Salvador; Delean, Steven; Brook, Barry W; Cassey, Phillip; Bradshaw, Corey J A

2014-01-01

The use of long-term population data to separate the demographic role of climate from density-modified demographic processes has become a major topic of ecological investigation over the last two decades. Although the ecological and evolutionary mechanisms that determine the strength of density feedbacks are now well understood, the degree to which climate gradients shape those processes across taxa and broad spatial scales remains unclear. Intuitively, harsh or highly variable environmental conditions should weaken compensatory density feedbacks because populations are hypothetically unable to achieve or maintain densities at which social and trophic interactions (e.g., competition, parasitism, predation, disease) might systematically reduce population growth. Here we investigate variation in the strength of compensatory density feedback, from long-term time series of abundance over 146 species of birds and mammals, in response to spatial gradients of broad-scale temperature precipitation variables covering 97 localities in 28 countries. We use information-theoretic metrics to rank phylogenetic generalized least-squares regression models that control for sample size (time-series length) and phylogenetic non-independence. Climatic factors explained < 1% of the remaining variation in density-feedback strength across species, with the highest non-control, model-averaged effect sizes related to extreme precipitation variables. We could not link our results directly to other published studies, because ecologists use contrasting responses, predictors and statistical approaches to correlate density feedback and climate--at the expense of comparability in a macroecological context. Censuses of multiple populations within a given species, and a priori knowledge of the spatial scales at which density feedbacks interact with climate, seem to be necessary to determine cross-taxa variation in this phenomenon. Despite the availability of robust modelling tools, the appropriate data have not yet been gathered for most species, meaning that we cannot yet make any robust generalisations about how demographic feedbacks interact with climate.

Spatial Climate Patterns Explain Negligible Variation in Strength of Compensatory Density Feedbacks in Birds and Mammals

PubMed Central

Herrando-Pérez, Salvador; Delean, Steven; Brook, Barry W.; Cassey, Phillip; Bradshaw, Corey J. A.

2014-01-01

The use of long-term population data to separate the demographic role of climate from density-modified demographic processes has become a major topic of ecological investigation over the last two decades. Although the ecological and evolutionary mechanisms that determine the strength of density feedbacks are now well understood, the degree to which climate gradients shape those processes across taxa and broad spatial scales remains unclear. Intuitively, harsh or highly variable environmental conditions should weaken compensatory density feedbacks because populations are hypothetically unable to achieve or maintain densities at which social and trophic interactions (e.g., competition, parasitism, predation, disease) might systematically reduce population growth. Here we investigate variation in the strength of compensatory density feedback, from long-term time series of abundance over 146 species of birds and mammals, in response to spatial gradients of broad-scale temperature precipitation variables covering 97 localities in 28 countries. We use information-theoretic metrics to rank phylogenetic generalized least-squares regression models that control for sample size (time-series length) and phylogenetic non-independence. Climatic factors explained < 1% of the remaining variation in density-feedback strength across species, with the highest non-control, model-averaged effect sizes related to extreme precipitation variables. We could not link our results directly to other published studies, because ecologists use contrasting responses, predictors and statistical approaches to correlate density feedback and climate – at the expense of comparability in a macroecological context. Censuses of multiple populations within a given species, and a priori knowledge of the spatial scales at which density feedbacks interact with climate, seem to be necessary to determine cross-taxa variation in this phenomenon. Despite the availability of robust modelling tools, the appropriate data have not yet been gathered for most species, meaning that we cannot yet make any robust generalisations about how demographic feedbacks interact with climate. PMID:24618822

Impacts of weather versus climate and driver uncertainty on multi-centennial ecosystem model simulations

NASA Astrophysics Data System (ADS)

Rollinson, C.; Simkins, J.; Fer, I.; Desai, A. R.; Dietze, M.

2017-12-01

Simulations of ecosystem dynamics and comparisons with empirical data require accurate, continuous, and often sub-daily meteorology records that are spatially aligned to the scale of the empirical data. A wealth of meteorology data for the past, present, and future is available through site-specific observations, modern reanalysis products, and gridded GCM simulations. However, these products are mismatched in spatial and temporal resolution, often with both different means and seasonal patterns. We have designed and implemented a two-step meteorological downscaling and ensemble generation method that combines multiple meteorology data products through debiasing and temporal downscaling protocols. Our methodology is designed to preserve the covariance among seven meteorological variables for use as drivers in ecosystem model simulations: temperature, precipitation, short- and longwave radiation, surface pressure, humidity, and wind. Furthermore, our method propagates uncertainty through the downscaling process and results in ensembles of meteorology that can be compared to paleoclimate reconstructions and used to analyze the effects of both high- and low-frequency climate anomalies on ecosystem dynamics. Using a multiple linear regression approach, we have combined hourly, 0.125-degree gridded data from the NLDAS (1980-present) with CRUNCEP (1901-2010) and CMIP5 historical (1850-2005), past millennium (850-1849), and future (1950-2100) GCM simulations. This has resulted in an ensemble of continuous, hourly-resolved meteorology from from the paleo era into the future with variability in weather events as well as low-frequency climatic changes. We investigate the influence of extreme sub-daily weather phenomena versus long-term climatic changes in an ensemble of ecosystem models that range in atmospheric and biological complexity. Through data assimilation with paleoclimate reconstructions of past climate, we can improve data-model comparisons using observations of vegetation change from the past 1200 years. Accounting for driver uncertainty in model evaluation can help determine the relative influence of structural versus parameterization errors in ecosystem modelings.

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).

Marine assemblages respond rapidly to winter climate variability.

PubMed

Morley, James W; Batt, Ryan D; Pinsky, Malin L

2017-07-01

Even species within the same assemblage have varied responses to climate change, and there is a poor understanding for why some taxa are more sensitive to climate than others. In addition, multiple mechanisms can drive species' responses, and responses may be specific to certain life stages or times of year. To test how marine species respond to climate variability, we analyzed 73 diverse taxa off the southeast US coast in 26 years of scientific trawl survey data and determined how changes in distribution and biomass relate to temperature. We found that winter temperatures were particularly useful for explaining interannual variation in species' distribution and biomass, although the direction and magnitude of the response varied among species from strongly negative, to little response, to strongly positive. Across species, the response to winter temperature varied greatly, with much of this variation being explained by thermal preference. A separate analysis of annual commercial fishery landings revealed that winter temperatures may also impact several important fisheries in the southeast United States. Based on the life stages of the species surveyed, winter temperature appears to act through overwinter mortality of juveniles or as a cue for migration timing. We predict that this assemblage will be responsive to projected increases in temperature and that winter temperature may be broadly important for species relationships with climate on a global scale. © The Authors Global Change Biology Published by John Wiley & Sons Ltd.

Influence and predictive capacity of climate anomalies on daily to decadal extremes in canopy photosynthesis.

PubMed

Desai, Ankur R

2014-02-01

Significant advances have been made over the past decades in capabilities to simulate diurnal and seasonal variation of leaf-level and canopy-scale photosynthesis in temperate and boreal forests. However, long-term prediction of future forest productivity in a changing climate may be more dependent on how climate and biological anomalies influence extremes in interannual to decadal variability of canopy ecosystem carbon exchanges. These exchanges can differ markedly from leaf level responses, especially owing to the prevalence of long lags in nutrient and water cycling. Until recently, multiple long-term (10+ year) high temporal frequency (daily) observations of canopy exchange were not available to reliably assess this claim. An analysis of one of the longest running North American eddy covariance flux towers reveals that single climate variables do not adequately explain carbon exchange anomalies beyond the seasonal timescale. Daily to weekly lagged anomalies of photosynthesis positively autocorrelate with daily photosynthesis. This effect suggests a negative feedback in photosynthetic response to climate extremes, such as anomalies in evapotranspiration and maximum temperature. Moisture stress in the prior season did inhibit photosynthesis, but mechanisms are difficult to assess. A complex interplay of integrated and lagged productivity and moisture-limiting factors indicate a critical role of seasonal thresholds that limit growing season length and peak productivity. These results lead toward a new conceptual framework for improving earth system models with long-term flux tower observations.

Regional Climate Variability Under Model Simulations of Solar Geoengineering

NASA Astrophysics Data System (ADS)

Dagon, Katherine; Schrag, Daniel P.

2017-11-01

Solar geoengineering has been shown in modeling studies to successfully mitigate global mean surface temperature changes from greenhouse warming. Changes in land surface hydrology are complicated by the direct effect of carbon dioxide (CO2) on vegetation, which alters the flux of water from the land surface to the atmosphere. Here we investigate changes in boreal summer climate variability under solar geoengineering using multiple ensembles of model simulations. We find that spatially uniform solar geoengineering creates a strong meridional gradient in the Northern Hemisphere temperature response, with less consistent patterns in precipitation, evapotranspiration, and soil moisture. Using regional summertime temperature and precipitation results across 31-member ensembles, we show a decrease in the frequency of heat waves and consecutive dry days under solar geoengineering relative to a high-CO2 world. However in some regions solar geoengineering of this amount does not completely reduce summer heat extremes relative to present day climate. In western Russia and Siberia, an increase in heat waves is connected to a decrease in surface soil moisture that favors persistent high temperatures. Heat waves decrease in the central United States and the Sahel, while the hydrologic response increases terrestrial water storage. Regional changes in soil moisture exhibit trends over time as the model adjusts to solar geoengineering, particularly in Siberia and the Sahel, leading to robust shifts in climate variance. These results suggest potential benefits and complications of large-scale uniform climate intervention schemes.

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.

Assessing Climate Change Risks Using a Multi-Model Approach

NASA Astrophysics Data System (ADS)

Knorr, W.; Scholze, M.; Prentice, C.

2007-12-01

We quantify the risks of climate-induced changes in key ecosystem processes during the 21st century by forcing a dynamic global vegetation model with multiple scenarios from the IPCC AR4 data archive using 16 climate models and mapping the proportions of model runs showing exceedance of natural variability in wildfire frequency and freshwater supply or shifts in vegetation cover. Our analysis does not assign probabilities to scenarios. Instead, we consider the distribution of outcomes within three sets of model runs grouped according to the amount of global warming they simulate: < 2 degree C (including committed climate change simulations), 2-3 degree C, and >3 degree C. Here, we are contrasting two different methods for calculating the risks: first we use an equal weighting approach giving every model within one of the three sets the same weight, and second, we weight the models according to their ability to model ENSO. The differences are underpinning the need for the development of more robust performance metrics for global climate models.

Importance of Preserving Cross-correlation in developing Statistically Downscaled Climate Forcings and in estimating Land-surface Fluxes and States

NASA Astrophysics Data System (ADS)

Das Bhowmik, R.; Arumugam, S.

2015-12-01

Multivariate downscaling techniques exhibited superiority over univariate regression schemes in terms of preserving cross-correlations between multiple variables- precipitation and temperature - from GCMs. This study focuses on two aspects: (a) develop an analytical solutions on estimating biases in cross-correlations from univariate downscaling approaches and (b) quantify the uncertainty in land-surface states and fluxes due to biases in cross-correlations in downscaled climate forcings. Both these aspects are evaluated using climate forcings available from both historical climate simulations and CMIP5 hindcasts over the entire US. The analytical solution basically relates the univariate regression parameters, co-efficient of determination of regression and the co-variance ratio between GCM and downscaled values. The analytical solutions are compared with the downscaled univariate forcings by choosing the desired p-value (Type-1 error) in preserving the observed cross-correlation. . For quantifying the impacts of biases on cross-correlation on estimating streamflow and groundwater, we corrupt the downscaled climate forcings with different cross-correlation structure.

Eco-hydrological Modeling in the Framework of Climate Change

NASA Astrophysics Data System (ADS)

Fatichi, Simone; Ivanov, Valeriy Y.; Caporali, Enrica

2010-05-01

A blueprint methodology for studying climate change impacts, as inferred from climate models, on eco-hydrological dynamics at the plot and small catchment scale is presented. Input hydro-meteorological variables for hydrological and eco-hydrological models for present and future climates are reproduced using a stochastic downscaling technique and a weather generator, "AWE-GEN". The generated time series of meteorological variables for the present climate and an ensemble of possible future climates serve as input to a newly developed physically-based eco-hydrological model "Tethys-Chloris". An application of the proposed methodology is realized reproducing the current (1961-2000) and multiple future (2081-2100) climates for the location of Tucson (Arizona). A general reduction of precipitation and a significant increase of air temperature are inferred. The eco-hydrological model is successively applied to detect changes in water recharge and vegetation dynamics for a desert shrub ecosystem, typical of the semi-arid climate of south Arizona. Results for the future climate account for uncertainties in the downscaling and are produced in terms of probability density functions. A comparison of control and future scenarios is discussed in terms of changes in the hydrological balance components, energy fluxes, and indices of vegetation productivity. An appreciable effect of climate change can be observed in metrics of vegetation performance. The negative impact on vegetation due to amplification of water stress in a warmer and dryer climate is offset by a positive effect of carbon dioxide augment. This implies a positive shift in plant capabilities to exploit water. Consequently, the plant water use efficiency and rain use efficiency are expected to increase. Interesting differences in the long-term vegetation productivity are also observed for the ensemble of future climates. The reduction of precipitation and the substantial maintenance of vegetation cover ultimately leads to the depletion of soil moisture and recharge to deeper layers. Such an outcome can affect the long-tem water availability in semi-arid systems and expose plants to more severe and frequent periods of stress.

Using historical and projected future climate model simulations as drivers of agricultural and biological models (Invited)

NASA Astrophysics Data System (ADS)

Stefanova, L. B.

2013-12-01

Climate model evaluation is frequently performed as a first step in analyzing climate change simulations. Atmospheric scientists are accustomed to evaluating climate models through the assessment of model climatology and biases, the models' representation of large-scale modes of variability (such as ENSO, PDO, AMO, etc) and the relationship between these modes and local variability (e.g. the connection between ENSO and the wintertime precipitation in the Southeast US). While these provide valuable information about the fidelity of historical and projected climate model simulations from an atmospheric scientist's point of view, the application of climate model data to fields such as agriculture, ecology and biology may require additional analyses focused on the particular application's requirements and sensitivities. Typically, historical climate simulations are used to determine a mapping between the model and observed climate, either through a simple (additive for temperature or multiplicative for precipitation) or a more sophisticated (such as quantile matching) bias correction on a monthly or seasonal time scale. Plants, animals and humans however are not directly affected by monthly or seasonal means. To assess the impact of projected climate change on living organisms and related industries (e.g. agriculture, forestry, conservation, utilities, etc.), derivative measures such as the heating degree-days (HDD), cooling degree-days (CDD), growing degree-days (GDD), accumulated chill hours (ACH), wet season onset (WSO) and duration (WSD), among others, are frequently useful. We will present a comparison of the projected changes in such derivative measures calculated by applying: (a) the traditional temperature/precipitation bias correction described above versus (b) a bias correction based on the mapping between the historical model and observed derivative measures themselves. In addition, we will present and discuss examples of various application-based climate model evaluations, such as: (a) agricultural crop yield estimates and (b) species population viability estimates modeled using observed climate data vs. historical climate simulations.

Predicting the Impacts of Climate Change on Central American Agriculture

NASA Astrophysics Data System (ADS)

Winter, J. M.; Ruane, A. C.; Rosenzweig, C.

2011-12-01

Agriculture is a vital component of Central America's economy. Poor crop yields and harvest reliability can produce food insecurity, malnutrition, and conflict. Regional climate models (RCMs) and agricultural models have the potential to greatly enhance the efficiency of Central American agriculture and water resources management under both current and future climates. A series of numerical experiments was conducted using Regional Climate Model Version 3 (RegCM3) and the Weather Research and Forecasting Model (WRF) to evaluate the ability of RCMs to reproduce the current climate of Central America and assess changes in temperature and precipitation under multiple future climate scenarios. Control simulations were thoroughly compared to a variety of observational datasets, including local weather station data, gridded meteorological data, and high-resolution satellite-based precipitation products. Future climate simulations were analyzed for both mean shifts in climate and changes in climate variability, including extreme events (droughts, heat waves, floods). To explore the impacts of changing climate on maize, bean, and rice yields in Central America, RCM output was used to force the Decision Support System for Agrotechnology Transfer Model (DSSAT). These results were synthesized to create climate change impacts predictions for Central American agriculture that explicitly account for evolving distributions of precipitation and temperature extremes.

A synthesis of the theories and concepts of early human evolution.

PubMed

Maslin, Mark A; Shultz, Susanne; Trauth, Martin H

2015-03-05

Current evidence suggests that many of the major events in hominin evolution occurred in East Africa. Hence, over the past two decades, there has been intensive work undertaken to understand African palaeoclimate and tectonics in order to put together a coherent picture of how the environment of Africa has varied over the past 10 Myr. A new consensus is emerging that suggests the unusual geology and climate of East Africa created a complex, environmentally very variable setting. This new understanding of East African climate has led to the pulsed climate variability hypothesis that suggests the long-term drying trend in East Africa was punctuated by episodes of short alternating periods of extreme humidity and aridity which may have driven hominin speciation, encephalization and dispersals out of Africa. This hypothesis is unique as it provides a conceptual framework within which other evolutionary theories can be examined: first, at macro-scale comparing phylogenetic gradualism and punctuated equilibrium; second, at a more focused level of human evolution comparing allopatric speciation, aridity hypothesis, turnover pulse hypothesis, variability selection hypothesis, Red Queen hypothesis and sympatric speciation based on sexual selection. It is proposed that each one of these mechanisms may have been acting on hominins during these short periods of climate variability, which then produce a range of different traits that led to the emergence of new species. In the case of Homo erectus (sensu lato), it is not just brain size that changes but life history (shortened inter-birth intervals, delayed development), body size and dimorphism, shoulder morphology to allow thrown projectiles, adaptation to long-distance running, ecological flexibility and social behaviour. The future of evolutionary research should be to create evidence-based meta-narratives, which encompass multiple mechanisms that select for different traits leading ultimately to speciation.

A synthesis of the theories and concepts of early human evolution

PubMed Central

Maslin, Mark A.; Shultz, Susanne; Trauth, Martin H.

2015-01-01

Current evidence suggests that many of the major events in hominin evolution occurred in East Africa. Hence, over the past two decades, there has been intensive work undertaken to understand African palaeoclimate and tectonics in order to put together a coherent picture of how the environment of Africa has varied over the past 10 Myr. A new consensus is emerging that suggests the unusual geology and climate of East Africa created a complex, environmentally very variable setting. This new understanding of East African climate has led to the pulsed climate variability hypothesis that suggests the long-term drying trend in East Africa was punctuated by episodes of short alternating periods of extreme humidity and aridity which may have driven hominin speciation, encephalization and dispersals out of Africa. This hypothesis is unique as it provides a conceptual framework within which other evolutionary theories can be examined: first, at macro-scale comparing phylogenetic gradualism and punctuated equilibrium; second, at a more focused level of human evolution comparing allopatric speciation, aridity hypothesis, turnover pulse hypothesis, variability selection hypothesis, Red Queen hypothesis and sympatric speciation based on sexual selection. It is proposed that each one of these mechanisms may have been acting on hominins during these short periods of climate variability, which then produce a range of different traits that led to the emergence of new species. In the case of Homo erectus (sensu lato), it is not just brain size that changes but life history (shortened inter-birth intervals, delayed development), body size and dimorphism, shoulder morphology to allow thrown projectiles, adaptation to long-distance running, ecological flexibility and social behaviour. The future of evolutionary research should be to create evidence-based meta-narratives, which encompass multiple mechanisms that select for different traits leading ultimately to speciation. PMID:25602068

a System Dynamics Approach for Looking at the Human and Environmental Interactions of Community-Based Irrigation Systems in New Mexico

NASA Astrophysics Data System (ADS)

Ochoa, C. G.; Tidwell, V. C.

2012-12-01

In the arid southwestern United States community water management systems have adapted to cope with climate variability and with socio-cultural and economic changes that have occurred since the establishment of these systems more than 300 years ago. In New Mexico, the community-based irrigation systems were established by Spanish settlers and have endured climate variability in the form of low levels of precipitation and have prevailed over important socio-political changes including the transfer of territory between Spain and Mexico, and between Mexico and the United States. Because of their inherent nature of integrating land and water use with society involvement these community-based systems have multiple and complex economic, ecological, and cultural interactions. Current urban population growth and more variable climate conditions are adding pressure to the survival of these systems. We are conducting a multi-disciplinary research project that focuses on characterizing these intrinsically complex human and natural interactions in three community-based irrigation systems in northern New Mexico. We are using a system dynamics approach to integrate different hydrological, ecological, socio-cultural and economic aspects of these three irrigation systems. Coupled with intensive field data collection, we are building a system dynamics model that will enable us to simulate important linkages and interactions between environmental and human elements occurring in each of these water management systems. We will test different climate variability and population growth scenarios and the expectation is that we will be able to identify critical tipping points of these systems. Results from this model can be used to inform policy recommendations relevant to the environment and to urban and agricultural land use planning in the arid southwestern United States.

A New Formulation for Fresh Snow Density over Antarctica for the regional climate model Modèle Atmosphérique Régionale (MAR).

NASA Astrophysics Data System (ADS)

Tedesco, M.; Datta, R.; Fettweis, X.; Agosta, C.

2015-12-01

Surface-layer snow density is important to processes contributing to surface mass balance, but is highly variable over Antarctica due to a wide range of near-surface climate conditions over the continent. Formulations for fresh snow density have typically either used fixed values or been modeled empirically using field data that is limited to specific seasons or regions. There is also currently limited work exploring how the sensitivity to fresh snow density in regional climate models varies with resolution. Here, we present a new formulation compiled from (a) over 1600 distinct density profiles from multiple sources across Antarctica and (b) near-surface variables from the regional climate model Modèle Atmosphérique Régionale (MAR). Observed values represent coastal areas as well as the plateau, in both West and East Antarctica (although East Antarctica is dominant). However, no measurements are included from the Antarctic Peninsula, which is both highly topographically variable and extends to lower latitudes than the remainder of the continent. In order to assess the applicability of this fresh snow density formulation to the Antarctic Peninsula at high resolutions, a version of MAR is run for several years both at low-resolution at the continental scale and at a high resolution for the Antarctic Peninsula alone. This setup is run both with and without the new fresh density formulation to quantify the sensitivity of the energy balance and SMB components to fresh snow density. Outputs are compared with near-surface atmospheric variables available from AWS stations (provided by the University of Wisconsin Madison) as well as net accumulation values from the SAMBA database (provided from the Laboratoire de Glaciologie et Géophysique de l'Environnement).

Modeling contemporary climate profiles of whitebark pine (Pinus albicaulis) and predicting responses to global warming

Treesearch

Marcus V. Warwell; Gerald E. Rehfeldt; Nicholas L. Crookston

2006-01-01

The Random Forests multiple regression tree was used to develop an empirically-based bioclimate model for the distribution of Pinus albicaulis (whitebark pine) in western North America, latitudes 31° to 51° N and longitudes 102° to 125° W. Independent variables included 35 simple expressions of temperature and precipitation and their interactions....

Hydraulic redistribution of soil water in two old-growth coniferous forests: quantifying patterns and controls.

Treesearch

J.M. Warren; F.C. Meinzer; J.R. Brooks; J.-C. Domec; R. Coulombe

2006-01-01

We incorporated soil/plant biophysical properties into a simple model to predict seasonal trajectories of hydraulic redistribution (HR). We measured soil water content, water potential root conductivity, and climate across multiple years in two old-growth coniferous forests. The HR variability within sites (0 to 0.5 mm/d) was linked to spatial patterns of roots, soil...

The NASA Reanalysis Ensemble Service - Advanced Capabilities for Integrated Reanalysis Access and Intercomparison

NASA Astrophysics Data System (ADS)

Tamkin, G.; Schnase, J. L.; Duffy, D.; Li, J.; Strong, S.; Thompson, J. H.

2017-12-01

NASA's efforts to advance climate analytics-as-a-service are making new capabilities available to the research community: (1) A full-featured Reanalysis Ensemble Service (RES) comprising monthly means data from multiple reanalysis data sets, accessible through an enhanced set of extraction, analytic, arithmetic, and intercomparison operations. The operations are made accessible through NASA's climate data analytics Web services and our client-side Climate Data Services Python library, CDSlib; (2) A cloud-based, high-performance Virtual Real-Time Analytics Testbed supporting a select set of climate variables. This near real-time capability enables advanced technologies like Spark and Hadoop-based MapReduce analytics over native NetCDF files; and (3) A WPS-compliant Web service interface to our climate data analytics service that will enable greater interoperability with next-generation systems such as ESGF. The Reanalysis Ensemble Service includes the following: - New API that supports full temporal, spatial, and grid-based resolution services with sample queries - A Docker-ready RES application to deploy across platforms - Extended capabilities that enable single- and multiple reanalysis area average, vertical average, re-gridding, standard deviation, and ensemble averages - Convenient, one-stop shopping for commonly used data products from multiple reanalyses including basic sub-setting and arithmetic operations (e.g., avg, sum, max, min, var, count, anomaly) - Full support for the MERRA-2 reanalysis dataset in addition to, ECMWF ERA-Interim, NCEP CFSR, JMA JRA-55 and NOAA/ESRL 20CR… - A Jupyter notebook-based distribution mechanism designed for client use cases that combines CDSlib documentation with interactive scenarios and personalized project management - Supporting analytic services for NASA GMAO Forward Processing datasets - Basic uncertainty quantification services that combine heterogeneous ensemble products with comparative observational products (e.g., reanalysis, observational, visualization) - The ability to compute and visualize multiple reanalysis for ease of inter-comparisons - Automated tools to retrieve and prepare data collections for analytic processing

Understanding climate impacts on vegetation using a spatiotemporal non-linear Granger causality framework

NASA Astrophysics Data System (ADS)

Papagiannopoulou, Christina; Decubber, Stijn; Miralles, Diego; Demuzere, Matthias; Dorigo, Wouter; Verhoest, Niko; Waegeman, Willem

2017-04-01

Satellite data provide an abundance of information about crucial climatic and environmental variables. These data - consisting of global records, spanning up to 35 years and having the form of multivariate time series with different spatial and temporal resolutions - enable the study of key climate-vegetation interactions. Although methods which are based on correlations and linear models are typically used for this purpose, their assumptions for linearity about the climate-vegetation relationships are too simplistic. Therefore, we adopt a recently proposed non-linear Granger causality analysis [1], in which we incorporate spatial information, concatenating data from neighboring pixels and training a joint model on the combined data. Experimental results based on global data sets show that considering non-linear relationships leads to a higher explained variance of past vegetation dynamics, compared to simple linear models. Our approach consists of several steps. First, we compile an extensive database [1], which includes multiple data sets for land surface temperature, near-surface air temperature, surface radiation, precipitation, snow water equivalents and surface soil moisture. Based on this database, high-level features are constructed and considered as predictors in our machine-learning framework. These high-level features include (de-trended) seasonal anomalies, lagged variables, past cumulative variables, and extreme indices, all calculated based on the raw climatic data. Second, we apply a spatiotemporal non-linear Granger causality framework - in which the linear predictive model is substituted for a non-linear machine learning algorithm - in order to assess which of these predictor variables Granger-cause vegetation dynamics at each 1° pixel. We use the de-trended anomalies of Normalized Difference Vegetation Index (NDVI) to characterize vegetation, being the target variable of our framework. Experimental results indicate that climate strongly (Granger-)causes vegetation dynamics in most regions globally. More specifically, water availability is the most dominant vegetation driver, being the dominant vegetation driver in 54% of the vegetated surface. Furthermore, our results show that precipitation and soil moisture have prolonged impacts on vegetation in semiarid regions, with up to 10% of additional explained variance on the vegetation dynamics occurring three months later. Finally, hydro-climatic extremes seem to have a remarkable impact on vegetation, since they also explain up to 10% of additional variance of vegetation in certain regions despite their infrequent occurrence. References [1] Papagiannopoulou, C., Miralles, D. G., Verhoest, N. E. C., Dorigo, W. A., and Waegeman, W.: A non-linear Granger causality framework to investigate climate-vegetation dynamics, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-266, in review, 2016.

Influence of internal variability on population exposure to hydroclimatic changes

NASA Astrophysics Data System (ADS)

Mankin, Justin S.; Viviroli, Daniel; Mekonnen, Mesfin M.; Hoekstra, Arjen Y.; Horton, Radley M.; E Smerdon, Jason; Diffenbaugh, Noah S.

2017-04-01

Future freshwater supply, human water demand, and people’s exposure to water stress are subject to multiple sources of uncertainty, including unknown future pathways of fossil fuel and water consumption, and ‘irreducible’ uncertainty arising from internal climate system variability. Such internal variability can conceal forced hydroclimatic changes on multi-decadal timescales and near-continental spatial-scales. Using three projections of population growth, a large ensemble from a single Earth system model, and assuming stationary per capita water consumption, we quantify the likelihoods of future population exposure to increased hydroclimatic deficits, which we define as the average duration and magnitude by which evapotranspiration exceeds precipitation in a basin. We calculate that by 2060, ∽31%-35% of the global population will be exposed to >50% probability of hydroclimatic deficit increases that exceed existing hydrological storage, with up to 9% of people exposed to >90% probability. However, internal variability, which is an irreducible uncertainty in climate model predictions that is under-sampled in water resource projections, creates substantial uncertainty in predicted exposure: ∽86%-91% of people will reside where irreducible uncertainty spans the potential for both increases and decreases in sub-annual water deficits. In one population scenario, changes in exposure to large hydroclimate deficits vary from -3% to +6% of global population, a range arising entirely from internal variability. The uncertainty in risk arising from irreducible uncertainty in the precise pattern of hydroclimatic change, which is typically conflated with other uncertainties in projections, is critical for climate risk management that seeks to optimize adaptations that are robust to the full set of potential real-world outcomes.

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

Tian, Hanqin; Chen, Guangsheng; Lu, Chaoqun

Greenhouse gas (GHG)-induced climate change is among the most pressing sustainability challenges facing humanity today, posing serious risks for ecosystem health. Methane (CH 4) and nitrous oxide (N 2O) are the two most important GHGs after carbon dioxide (CO 2), but their regional and global budgets are not well known. In this paper, we applied a process-based coupled biogeochemical model to concurrently estimate the magnitude and spatial and temporal patterns of CH 4 and N 2O fluxes as driven by multiple environmental changes, including climate variability, rising atmospheric CO 2, increasing nitrogen deposition, tropospheric ozone pollution, land use change, andmore » nitrogen fertilizer use.« less

The precision problem in conservation and restoration

USGS Publications Warehouse

Hiers, J. Kevin; Jackson, Stephen T.; Hobbs, Richard J.; Bernhardt, Emily S.; Valentine, Leonie E.

2016-01-01

Within the varied contexts of environmental policy, conservation of imperilled species populations, and restoration of damaged habitats, an emphasis on idealized optimal conditions has led to increasingly specific targets for management. Overly-precise conservation targets can reduce habitat variability at multiple scales, with unintended consequences for future ecological resilience. We describe this dilemma in the context of endangered species management, stream restoration, and climate-change adaptation. Inappropriate application of conservation targets can be expensive, with marginal conservation benefit. Reduced habitat variability can limit options for managers trying to balance competing objectives with limited resources. Conservation policies should embrace habitat variability, expand decision-space appropriately, and support adaptation to local circumstances to increase ecological resilience in a rapidly changing world.

Ecologically-Relevant Maps of Landforms and Physiographic Diversity for Climate Adaptation Planning

PubMed Central

Theobald, David M.; Harrison-Atlas, Dylan; Monahan, William B.; Albano, Christine M.

2015-01-01

Key to understanding the implications of climate and land use change on biodiversity and natural resources is to incorporate the physiographic platform on which changes in ecological systems unfold. Here, we advance a detailed classification and high-resolution map of physiography, built by combining landforms and lithology (soil parent material) at multiple spatial scales. We used only relatively static abiotic variables (i.e., excluded climatic and biotic factors) to prevent confounding current ecological patterns and processes with enduring landscape features, and to make the physiographic classification more interpretable for climate adaptation planning. We generated novel spatial databases for 15 landform and 269 physiographic types across the conterminous United States of America. We examined their potential use by natural resource managers by placing them within a contemporary climate change adaptation framework, and found our physiographic databases could play key roles in four of seven general adaptation strategies. We also calculated correlations with common empirical measures of biodiversity to examine the degree to which the physiographic setting explains various aspects of current biodiversity patterns. Additionally, we evaluated the relationship between landform diversity and measures of climate change to explore how changes may unfold across a geophysical template. We found landform types are particularly sensitive to spatial scale, and so we recommend using high-resolution datasets when possible, as well as generating metrics using multiple neighborhood sizes to both minimize and characterize potential unknown biases. We illustrate how our work can inform current strategies for climate change adaptation. The analytical framework and classification of landforms and parent material are easily extendable to other geographies and may be used to promote climate change adaptation in other settings. PMID:26641818

Climate Change, the Energy-water-food Nexus, and the "New" Colorado River Basin

NASA Astrophysics Data System (ADS)

Middleton, R. S.; Bennett, K. E.; Solander, K.; Hopkins, E.

2017-12-01

Climate change, extremes, and climate-driven disturbances are anticipated to have substantial impacts on regional water resources, particularly in the western and southwestern United States. These unprecedented conditions—a no-analog future—will result in challenges to adaptation, mitigation, and resilience planning for the energy-water-food nexus. We have analyzed the impact of climate change on Colorado River flows for multiple climate and disturbance scenarios: 12 global climate models and two CO2 emission scenarios (RCP 4.5 and RCP 8.5) from the Intergovernmental Panel on Climate Change's Coupled Model Intercomparison Study, version 5, and multiple climate-driven forest disturbance scenarios including temperature-drought vegetation mortality and insect infestations. Results indicate a wide range of potential streamflow projections and the potential emergence of a "new" Colorado River basin. Overall, annual streamflow tends to increase under the majority of modeled scenarios due to projected increases in precipitation across the basin, though a significant number of scenarios indicate moderate and potentially substantial reductions in water availability. However, all scenarios indicate severe changes in seasonality of flows and strong variability across headwater systems. This leads to increased fall and winter streamflow, strong reductions in spring and summer flows, and a shift towards earlier snowmelt timing. These impacts are further exacerbated in headwater systems, which are key to driving Colorado River streamflow and hence water supply for both internal and external basin needs. These results shed a new and important slant on the Colorado River basin, where an emergent streamflow pattern may result in difficulties to adjust to these new regimes, resulting in increased stress to the energy-water-food nexus.

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.

Cold Regime Interannual Variability of Primary and Secondary Producer Community Composition in the Southeastern Bering Sea

PubMed Central

Stauffer, Beth A.; Miksis-Olds, Jennifer; Goes, Joaquim I.

2015-01-01

Variability of hydrographic conditions and primary and secondary productivity between cold and warm climatic regimes in the Bering Sea has been the subject of much study in recent years, while interannual variability within a single regime and across multiple trophic levels has been less well-documented. Measurements from an instrumented mooring on the southeastern shelf of the Bering Sea were analyzed for the spring-to-summer transitions within the cold regime years of 2009–2012 to investigate the interannual variability of hydrographic conditions, primary producer biomass, and acoustically-derived secondary producer and consumer abundance and community structure. Hydrographic conditions in 2012 were significantly different than in 2009, 2010, and 2011, driven largely by increased ice extent and thickness, later ice retreat, and earlier stratification of the water column. Primary producer biomass was more tightly coupled to hydrographic conditions in 2012 than in 2009 or 2011, and shallow and mid-column phytoplankton blooms tended to occur independent of one another. There was a high degree of variability in the relationships between different classes of secondary producers and hydrographic conditions, evidence of significant intra-consumer interactions, and trade-offs between different consumer size classes in each year. Phytoplankton blooms stimulated different populations of secondary producers in each year, and summer consumer populations appeared to determine dominant populations in the subsequent spring. Overall, primary producers and secondary producers were more tightly coupled to each other and to hydrographic conditions in the coldest year compared to the warmer years. The highly variable nature of the interactions between the atmospherically-driven hydrographic environment, primary and secondary producers, and within food webs underscores the need to revisit how climatic regimes within the Bering Sea are defined and predicted to function given changing climate scenarios. PMID:26110822

Cold Regime interannual variability of primary and secondary producer community composition in the southeastern Bering Sea.

PubMed

Stauffer, Beth A; Miksis-Olds, Jennifer; Goes, Joaquim I

2015-01-01

Variability of hydrographic conditions and primary and secondary productivity between cold and warm climatic regimes in the Bering Sea has been the subject of much study in recent years, while interannual variability within a single regime and across multiple trophic levels has been less well-documented. Measurements from an instrumented mooring on the southeastern shelf of the Bering Sea were analyzed for the spring-to-summer transitions within the cold regime years of 2009-2012 to investigate the interannual variability of hydrographic conditions, primary producer biomass, and acoustically-derived secondary producer and consumer abundance and community structure. Hydrographic conditions in 2012 were significantly different than in 2009, 2010, and 2011, driven largely by increased ice extent and thickness, later ice retreat, and earlier stratification of the water column. Primary producer biomass was more tightly coupled to hydrographic conditions in 2012 than in 2009 or 2011, and shallow and mid-column phytoplankton blooms tended to occur independent of one another. There was a high degree of variability in the relationships between different classes of secondary producers and hydrographic conditions, evidence of significant intra-consumer interactions, and trade-offs between different consumer size classes in each year. Phytoplankton blooms stimulated different populations of secondary producers in each year, and summer consumer populations appeared to determine dominant populations in the subsequent spring. Overall, primary producers and secondary producers were more tightly coupled to each other and to hydrographic conditions in the coldest year compared to the warmer years. The highly variable nature of the interactions between the atmospherically-driven hydrographic environment, primary and secondary producers, and within food webs underscores the need to revisit how climatic regimes within the Bering Sea are defined and predicted to function given changing climate scenarios.

Flood Protection Decision Making Within a Coupled Human and Natural System

NASA Astrophysics Data System (ADS)

O'Donnell, Greg; O'Connell, Enda

2013-04-01

Due to the perceived threat from climate change, prediction under changing climatic and hydrological conditions has become a dominant theme of hydrological research. Much of this research has been climate model-centric, in which GCM/RCM climate projections have been used to drive hydrological system models to explore potential impacts that should inform adaptation decision-making. However, adaptation fundamentally involves how humans may respond to increasing flood and drought hazards by changing their strategies, activities and behaviours which are coupled in complex ways to the natural systems within which they live and work. Humans are major agents of change in hydrological systems, and representing human activities and behaviours in coupled human and natural hydrological system models is needed to gain insight into the complex interactions that take place, and to inform adaptation decision-making. Governments and their agencies are under pressure to make proactive investments to protect people living in floodplains from the perceived increasing flood hazard. However, adopting this as a universal strategy everywhere is not affordable, particularly in times of economic stringency and given uncertainty about future climatic conditions. It has been suggested that the assumption of stationarity, which has traditionally been invoked in making hydrological risk assessments, is no longer tenable. However, before the assumption of hydrologic nonstationarity is accepted, the ability to cope with the uncertain impacts of global warming on water management via the operational assumption of hydrologic stationarity should be carefully examined. Much can be learned by focussing on natural climate variability and its inherent changes in assessing alternative adaptation strategies. A stationary stochastic multisite flood hazard model has been developed that can exhibit increasing variability/persistence in annual maximum floods, starting with the traditional assumption of independence. This has been coupled to an agent based model of how various stakeholders interact in determining where and when flood protection investments are made in a hypothetical region with multiple sites at risk from flood hazard. Monte Carlo simulation is used to explore how government agencies with finite resources might best invest in flood protection infrastructure in a highly variable climate with a high degree of future uncertainty. Insight is provided into whether proactive or reactive strategies are to be preferred in an increasingly variable climate.

Distant drivers or local signals: where do mercury trends in western Arctic belugas originate?

PubMed

Loseto, L L; Stern, G A; Macdonald, R W

2015-03-15

Temporal trends of contaminants are monitored in Arctic higher trophic level species to inform us on the fate, transport and risk of contaminants as well as advise on global emissions. However, monitoring mercury (Hg) trends in species such as belugas challenge us, as their tissue concentrations reflect complex interactions among Hg deposition and methylation, whale physiology, dietary exposure and foraging patterns. The Beaufort Sea beluga population showed significant increases in Hg during the 1990 s; since that time an additional 10 years of data have been collected. During this time of data collection, changes in the Arctic have affected many processes that underlie the Hg cycle. Here, we examine Hg in beluga tissues and investigate factors that could contribute to the observed trends after removing the effect of age and size on Hg concentrations and dietary factors. Finally, we examine available indicators of climate variability (Arctic Oscillation (AO), the Pacific Decadal Oscillation (PDO) and sea-ice minimum (SIM) concentration) to evaluate their potential to explain beluga Hg trends. Results reveal a decline in Hg concentrations from 2002 to 2012 in the liver of older whales and the muscle of large whales. The temporal increases in Hg in the 1990 s followed by recent declines do not follow trends in Hg emission, and are not easily explained by diet markers highlighting the complexity of feeding, food web dynamics and Hg uptake. Among the regional-scale climate variables the PDO exhibited the most significant relationship with beluga Hg at an eight year lag time. This distant signal points us to consider beluga winter feeding areas. Given that changes in climate will impact ecosystems; it is plausible that these climate variables are important in explaining beluga Hg trends. Such relationships require further investigation of the multiple connections between climate variables and beluga Hg. Copyright © 2014 Elsevier B.V. All rights reserved.

Iranian speleothems: Investigating Quaternary climate variability in semi-arid Western Asia

NASA Astrophysics Data System (ADS)

Carolin, Stacy; Morgan, Jacob; Peckover, Emily; Walker, Richard; Henderson, Gideon; Rowe, Peter; Andrews, Julian; Ersek, Vasile; Sloan, Alastair; Talebian, Morteza; Fattahi, Morteza; Nezamdoust, Javad

2016-04-01

Rapid population growth and limited water supply has highlighted the need for vigorous water resource management practices in the semi-arid regions of Western Asia. One significant unknown in this discussion is the future change in rainfall amount due to the consequential effects of today's greenhouse gas forcing on the regional climate system. Currently, there is little paleoclimate proxy data in Western Asia to extend climate records beyond the limits of the instrumental period, leaving scant evidence to investigate the system's response to various climate forcings on different timescales. Here we present a synthesis of speleothem climate records across northern Iran, from the wetter climate of the Alborz and Zagros mountain ranges to the dry northeast, in order to investigate the magnitude of past climate variability and the forcings responsible. The stalagmites collected from the west and north-central mountain ranges, areas with ~200-400mm mean annual precipitation mostly falling within the fall-winter-spring months, all demonstrate growth limited to the interglacial periods of the Quaternary. We present overlapping Holocene stable isotope records with a complementary trace element record to assist in interpreting the isotopic variability. One of the records is sampled at <4yr resolution and spans 3.7-5.3 kyBP, a contested period of catastrophic droughts that allegedly eradicated civilizations in areas of the near East. Imposed upon decadal-scale variability, the record reveals a 1,000-yr gradual trend toward enriched stable oxygen isotope values, interpreted as a trend toward drier conditions, which ends with an abrupt 300-yr cessation in growth beginning at 4.3 kyBP, coincident with the so-called 4.2 kyBP drought event. From the northeast Iranian plateau, we present a new stalagmite record that spans the penultimate deglaciation and Stages 5e-5a. This region presently receives limited rain annually (~100-300mm/yr, regularly falling between November and May), and the record presented is one of the first speleothem climate records to span a deglaciation in West Asia. To improve our interpretation of the speleothem climate proxy timeseries, we use multiple decades of Tehran GNIP data, meteorological data, and isotope-equipped climate model outputs to investigate the large-scale mechanisms forcing isotopic variations in rainwater across northern Iran. We also examine possible transformation of water isotopes during the transition through the karst aquifer based on site properties, measured dripwater isotopes, and simple model experiments.

Response of Urban Systems to Climate Change in Europe: Heat Stress Exposure and the Effect on Human Health

NASA Astrophysics Data System (ADS)

Stevens, Catherine; Thomas, Bart; Grommen, Mart

2015-04-01

Climate change is driven by global processes such as the global ocean circulation and its variability over time leading to changing weather patterns on regional scales as well as changes in the severity and occurrence of extreme events such as heavy rain- and windstorms, floods, drought, heat waves, etc. The summer 2003 European heat wave was the hottest summer on record in Europe over the past centuries leading to health crises in several countries like France and caused up to 70.000 excess deaths over four months in Central and Western Europe. The main risks induced by global climate change in urbanised areas are considered to be overheating and resulting health effects, increased exposure to flood events, increased damage losses from extreme weather conditions but also shortages in the provision of life-sustaining services. Moreover, the cities themselves create specific or inherent risks and urban adaptation is often very demanding. As most of Europe's inhabitants live in cities, it is of particular relevance to examine the impact of climate variability on urban areas and their populations. The present study focusses on the identification of heat stress variables related to human health and the extraction of this information by processing daily temperature statistics of local urban climate simulations over multiple timeframes of 20 years and three different European cities based on recent, near future and far future global climate predictions. The analyses have been conducted in the framework of the NACLIM FP7 project funded by the European Commission involving local stakeholders such as the cities of Antwerp (Belgium), Berlin (Germany) and Almada (Portugal) represented by different climate and urban characteristics. Apart from the urban-rural temperature increment (urban heat island effect), additional heat stress parameters such as the average number of heat wave days together with their duration and intensities have been covered during this research. In a subsequent step, the heat stress variables are superposed on relevant socio-economic datasets targeting total population and its distribution per age class as well as vulnerable institutions such as hospitals, schools, rest homes and child/day care facilities in order to generate heat stress exposure maps for each use case city and various climate, urban planning and mitigation scenarios. The specifications and requirements for the various scenarios have been consolidated in close collaboration with the local stakeholders during dedicated end-users workshops. The results of this study will allow urban planners and policy makers facing the challenges of climate change and develop sound strategies for evolving towards sustainable and climate resilient cities.

A High-Resolution Biogenic Silica Record From Lake Titicaca, Peru-Bolivia: South American Millennial-Scale Climate Variability From 18-60 Kya

NASA Astrophysics Data System (ADS)

Ekdahl, E. J.; Fritz, S. C.; Stevens, L. R.; Baker, P. A.; Seltzer, G. O.

2004-12-01

Sediments recovered from a deep basin in Lake Titicaca, Peru-Boliva, were analyzed for biogenic silica (BSi) content by extraction of freeze dried sediments in 1% sodium carbonate. Sediments were dated using an age model developed from multiple 14C dates on bulk sediments. The BSi record shows distinct fluctuations in concentration and accumulation rate from 18 to 60 kya. Multi-taper method spectral analysis reveals a significant millennial-scale component to these fluctuations centered at 1370 years. High BSi accumulation rates correlate with enhanced benthic diatom preservation, suggesting that the BSi record is related to variations in lake water level. Modern-day Lake Titicaca lake level and precipitation are strongly related to northern equatorial Atlantic sea surface temperatures, with cooler SSTs related to wetter conditions. Subsequently, the spectral behavior of the GRIP ice core δ 18O record was investigated in order to estimate coherency and linkages between North Atlantic and tropical South American climate. GRIP data exhibit a significant 1370-year spectral peak which comprises approximately 26% of the total variability in the record. Despite a high degree of coherency between millennial-scale periodicities in Lake Titicaca BSi and GRIP δ 18O records, the Lake Titicaca silica record does not show longer term cooling cycles characteristic of D-O cycles found in the GRIP record. Rather, the Lake Titicaca record is highly periodic and more similar in nature to several Antarctic climate proxy records. These results suggest that while South American tropical climate varies in phase with North Atlantic climate, additional forcing mechanisms are manifest in the region which may include tropical Pacific and Southern Ocean variability.

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.

Collaborative Research: Robust Climate Projections and Stochastic Stability of Dynamical Systems

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

Ilya Zaliapin

This project focused on conceptual exploration of El Nino/Southern Oscillation (ENSO) variability and sensitivity using a Delay Differential Equation developed in the project. We have (i) established the existence and continuous dependence of solutions of the model (ii) explored multiple models solutions, and the distribution of solutions extrema, and (iii) established and explored the phase locking phenomenon and the existence of multiple solutions for the same values of model parameters. In addition, we have applied to our model the concept of pullback attractor, which greatly facilitated predictive understanding of the nonlinear model's behavior.

Evaluation of mean climate in a chemistry-climate model simulation

NASA Astrophysics Data System (ADS)

Hong, S.; Park, H.; Wie, J.; Park, R.; Lee, S.; Moon, B. K.

2017-12-01

Incorporation of the interactive chemistry is essential for understanding chemistry-climate interactions and feedback processes in climate models. Here we assess a newly developed chemistry-climate model (GRIMs-Chem), which is based on the Global/Regional Integrated Model system (GRIMs) including the aerosol direct effect as well as stratospheric linearized ozone chemistry (LINOZ). We conducted GRIMs-Chem with observed sea surface temperature during the period of 1979-2010, and compared the simulation results with observations and also with CMIP models. To measure the relative performance of our model, we define the quantitative performance metric using the Taylor diagram. This metric allow us to assess overall features in simulating multiple variables. Overall, our model better reproduce the zonal mean spatial pattern of temperature, horizontal wind, vertical motion, and relative humidity relative to other models. However, the model did not produce good simulations at upper troposphere (200 hPa). It is currently unclear which model processes are responsible for this. AcknowledgementsThis research was supported by the Korea Ministry of Environment (MOE) as "Climate Change Correspondence Program."

Climate Model Diagnostic Analyzer

NASA Technical Reports Server (NTRS)

Lee, Seungwon; Pan, Lei; Zhai, Chengxing; Tang, Benyang; Kubar, Terry; Zhang, Zia; Wang, Wei

2015-01-01

The comprehensive and innovative evaluation of climate models with newly available global observations is critically needed for the improvement of climate model current-state representation and future-state predictability. A climate model diagnostic evaluation process requires physics-based multi-variable analyses that typically involve large-volume and heterogeneous datasets, making them both computation- and data-intensive. With an exploratory nature of climate data analyses and an explosive growth of datasets and service tools, scientists are struggling to keep track of their datasets, tools, and execution/study history, let alone sharing them with others. In response, we have developed a cloud-enabled, provenance-supported, web-service system called Climate Model Diagnostic Analyzer (CMDA). CMDA enables the physics-based, multivariable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. At the same time, CMDA provides a crowd-sourcing space where scientists can organize their work efficiently and share their work with others. CMDA is empowered by many current state-of-the-art software packages in web service, provenance, and semantic search.

Spatial and Temporal Variability in Biogenic Gas Accumulation and Release in The Greater Everglades at Multiple Scales of Measurement

NASA Astrophysics Data System (ADS)

McClellan, M. D.; Cornett, C.; Schaffer, L.; Comas, X.

2017-12-01

Wetlands play a critical role in the carbon (C) cycle by producing and releasing significant amounts of greenhouse biogenic gasses (CO2, CH4) into the atmosphere. Wetlands in tropical and subtropical climates (such as the Florida Everglades) have become of great interest in the past two decades as they account for more than 20% of the global peatland C stock and are located in climates that favor year-round C emissions. Despite the increase in research involving C emission from these types of wetlands, the spatial and temporal variability involving C production, accumulation and release is still highly uncertain, and is the focus of this research at multiple scales of measurement (i.e. lab, field and landscape). Spatial variability in biogenic gas content, build up and release, at both the lab and field scales, was estimated using a series of ground penetrating radar (GPR) surveys constrained with gas traps fitted with time-lapse cameras. Variability in gas content was estimated at the sub-meter scale (lab scale) within two extracted monoliths from different wetland ecosystems at the Disney wilderness Preserve (DWP) and the Blue Cypress Preserve (BCP) using high frequency GPR (1.2 GHz) transects across the monoliths. At the field scale (> 10m) changes in biogenic gas content were estimated using 160 MHz GPR surveys collected within 4 different emergent wetlands at the DWP. Additionally, biogenic gas content from the extracted monoliths was used to developed a landscape comparison of C accumulation and emissions for each different wetland ecosystem. Changes in gas content over time were estimated at the lab scale at high temporal resolution (i.e. sub-hourly) in monoliths from the BCP and Water Conservation Area 1-A. An autonomous rail system was constructed to estimate biogenic gas content variability within the wetland soil matrix using a series of continuous, uninterrupted 1.2 GHz GPR transects along the samples. Measurements were again constrained with an array of gas traps fitted with time-lapse cameras. This research seeks to better understand the spatial and temporal variability of biogenic gas content within wetlands from the Greater Everglades Watershed. Such understanding may help to identify potential hotspots (both in space and time) and their implication for the flux estimates used as input in climate models.

Modeling climate effects on hip fracture rate by the multivariate GARCH model in Montreal region, Canada.

PubMed

Modarres, Reza; Ouarda, Taha B M J; Vanasse, Alain; Orzanco, Maria Gabriela; Gosselin, Pierre

2014-07-01

Changes in extreme meteorological variables and the demographic shift towards an older population have made it important to investigate the association of climate variables and hip fracture by advanced methods in order to determine the climate variables that most affect hip fracture incidence. The nonlinear autoregressive moving average with exogenous variable-generalized autoregressive conditional heteroscedasticity (ARMAX-GARCH) and multivariate GARCH (MGARCH) time series approaches were applied to investigate the nonlinear association between hip fracture rate in female and male patients aged 40-74 and 75+ years and climate variables in the period of 1993-2004, in Montreal, Canada. The models describe 50-56% of daily variation in hip fracture rate and identify snow depth, air temperature, day length and air pressure as the influencing variables on the time-varying mean and variance of the hip fracture rate. The conditional covariance between climate variables and hip fracture rate is increasing exponentially, showing that the effect of climate variables on hip fracture rate is most acute when rates are high and climate conditions are at their worst. In Montreal, climate variables, particularly snow depth and air temperature, appear to be important predictors of hip fracture incidence. The association of climate variables and hip fracture does not seem to change linearly with time, but increases exponentially under harsh climate conditions. The results of this study can be used to provide an adaptive climate-related public health program and ti guide allocation of services for avoiding hip fracture risk.

Modeling climate effects on hip fracture rate by the multivariate GARCH model in Montreal region, Canada

NASA Astrophysics Data System (ADS)

Modarres, Reza; Ouarda, Taha B. M. J.; Vanasse, Alain; Orzanco, Maria Gabriela; Gosselin, Pierre

2014-07-01

Changes in extreme meteorological variables and the demographic shift towards an older population have made it important to investigate the association of climate variables and hip fracture by advanced methods in order to determine the climate variables that most affect hip fracture incidence. The nonlinear autoregressive moving average with exogenous variable-generalized autoregressive conditional heteroscedasticity (ARMA X-GARCH) and multivariate GARCH (MGARCH) time series approaches were applied to investigate the nonlinear association between hip fracture rate in female and male patients aged 40-74 and 75+ years and climate variables in the period of 1993-2004, in Montreal, Canada. The models describe 50-56 % of daily variation in hip fracture rate and identify snow depth, air temperature, day length and air pressure as the influencing variables on the time-varying mean and variance of the hip fracture rate. The conditional covariance between climate variables and hip fracture rate is increasing exponentially, showing that the effect of climate variables on hip fracture rate is most acute when rates are high and climate conditions are at their worst. In Montreal, climate variables, particularly snow depth and air temperature, appear to be important predictors of hip fracture incidence. The association of climate variables and hip fracture does not seem to change linearly with time, but increases exponentially under harsh climate conditions. The results of this study can be used to provide an adaptive climate-related public health program and ti guide allocation of services for avoiding hip fracture risk.

Assessment of Human Health Vulnerability to Climate Variability and Change in Cuba

PubMed Central

Bultó, Paulo Lázaro Ortíz; Rodríguez, Antonio Pérez; Valencia, Alina Rivero; Vega, Nicolás León; Gonzalez, Manuel Díaz; Carrera, Alina Pérez

2006-01-01

In this study we assessed the potential effects of climate variability and change on population health in Cuba. We describe the climate of Cuba as well as the patterns of climate-sensitive diseases of primary concern, particularly dengue fever. Analyses of the associations between climatic anomalies and disease patterns highlight current vulnerability to climate variability. We describe current adaptations, including the application of climate predictions to prevent disease outbreaks. Finally, we present the potential economic costs associated with future impacts due to climate change. The tools used in this study can be useful in the development of appropriate and effective adaptation options to address the increased climate variability associated with climate change. PMID:17185289

Phylogeographic structure in long-tailed voles (Rodentia: Arvicolinae) belies the complex Pleistocene history of isolation, divergence, and recolonization of Northwest North America's fauna.

PubMed

Sawyer, Yadéeh E; Cook, Joseph A

2016-09-01

Quaternary climate fluctuations restructured biodiversity across North American high latitudes through repeated episodes of range contraction, population isolation and divergence, and subsequent expansion. Identifying how species responded to changing environmental conditions not only allows us to explore the mode and tempo of evolution in northern taxa, but also provides a basis for forecasting future biotic response across the highly variable topography of western North America. Using a multilocus approach under a Bayesian coalescent framework, we investigated the phylogeography of a wide-ranging mammal, the long-tailed vole, Microtus longicaudus . We focused on populations along the North Pacific Coast to refine our understanding of diversification by exploring the potentially compounding roles of multiple glacial refugia and more recent fragmentation of an extensive coastal archipelago. Through a combination of genetic data and species distribution models (SDMs), we found that historical climate variability influenced contemporary genetic structure, with multiple isolated locations of persistence (refugia) producing multiple divergent lineages (Beringian or northern, southeast Alaska or coastal, and southern or continental) during glacial advances. These vole lineages all occur along the North Pacific Coast where the confluence of numerous independent lineages in other species has produced overlapping zones of secondary contact, collectively a suture zone. Finally, we detected high levels of neoendemism due to complex island geography that developed in the last 10,000 years with the rising sea levels of the Holocene.

Novel Flood Detection and Analysis Method Using Recurrence Property

NASA Astrophysics Data System (ADS)

Wendi, Dadiyorto; Merz, Bruno; Marwan, Norbert

2016-04-01

Temporal changes in flood hazard are known to be difficult to detect and attribute due to multiple drivers that include processes that are non-stationary and highly variable. These drivers, such as human-induced climate change, natural climate variability, implementation of flood defence, river training, or land use change, could impact variably on space-time scales and influence or mask each other. Flood time series may show complex behavior that vary at a range of time scales and may cluster in time. This study focuses on the application of recurrence based data analysis techniques (recurrence plot) for understanding and quantifying spatio-temporal changes in flood hazard in Germany. The recurrence plot is known as an effective tool to visualize the dynamics of phase space trajectories i.e. constructed from a time series by using an embedding dimension and a time delay, and it is known to be effective in analyzing non-stationary and non-linear time series. The emphasis will be on the identification of characteristic recurrence properties that could associate typical dynamic behavior to certain flood situations.

Observed and Aogcm Simulated Relationships Between us Wind Speeds and Large Scale Modes of Climate Variability

NASA Astrophysics Data System (ADS)

Schoof, J. T.; Pryor, S. C.; Barthelmie, R. J.

2013-12-01

Previous research has indicated that large-scale modes of climate variability, such as El Niño - Southern Oscillation (ENSO), the Arctic Oscillation (AO) and the Pacific-North American pattern (PNA), influence the inter-annual and intra-annual variability of near-surface and upper-level wind speeds over the United States. For example, we have shown that rawinsonde derived wind speeds indicate that 90th percentile of wind speeds at 700 hPa over the Pacific Northwest and Southwestern USA are significantly higher under the negative phase of the PNA, and the Central Plains experiences higher wind speeds at 850 hPa under positive phase Southern Oscillation index while the Northeast exhibits higher wind speeds at 850 hPa under positive phase NAO. Here, we extend this research by further investigating these relationships using both reanalysis products and output from coupled atmosphere-ocean general circulation models (AOGCMs) developed for the 5th Phase of the Coupled Model Intercomparison Project (CMIP5). The research presented has two specific goals. First, we evaluate the AOGCM simulations in terms of their ability to represent the temporal and spatial representations of ENSO, the AO, and the PNA pattern relative to historical observations. The diagnostics used include calculation of the power spectra (and thus representation of the fundamental frequencies of variability) and Taylor diagrams (for comparative assessment of the spatial patterns and their intensities). Our initial results indicate that most AOGCMs produce modes that are qualitatively similar to those observed, but that differ slightly in terms of the spatial pattern, intensity of specific centers of action, and variance explained. Figure 1 illustrates an example of the analysis of the frequencies of variability of two climate modes for the NCEP-NCAR reanalysis (NNR) and a single AOGCM (BCC CSM1). The results show a high degree of similarity in the power spectra but for this AOGCM the variance of the PNA associated with high frequencies are amplified relative to those in NNR. Second, we quantify the observed and AOGCM-simulated relationships between ENSO, AO, and PNA indices and zonal and meridional wind components at multiple levels for the contiguous United States. The results are presented in form of maps displaying the strength of the relationship at different timescales, from daily to annual, and at multiple atmospheric levels, from 10m to 500 mb. The results of the analysis are used to provide context for regional wind climate projections based on 21st century AOGCM simulations.

Climate variability in China during the last millennium based on reconstructions and simulations

NASA Astrophysics Data System (ADS)

García-Bustamante, E.; Luterbacher, J.; Xoplaki, E.; Werner, J. P.; Jungclaus, J.; Zorita, E.; González-Rouco, J. F.; Fernández-Donado, L.; Hegerl, G.; Ge, Q.; Hao, Z.; Wagner, S.

2012-04-01

Multi-decadal to centennial climate variability in China during the last millennium is analysed. We compare the low frequency temperature and precipitation variations from proxy-based reconstructions and palaeo-simulations from climate models. Focusing on the regional responses to the global climate evolution is of high relevance due to the complexity of the interactions between physical mechanisms at different spatio-temporal scales and the potential severity of the derived multiple socio-economic impacts. China stands out as a particularly interesting region, not only due to its complex climatic features, ranging from the semiarid northwestern Tibetan Plateau to the tropical monsoon southeastern climates, but also because of its wealth of proxy data. However, comprehensive assessments of proxy- and model-based information about palaeo-climatic variations in China are, to our knowledge, still lacking. In addition, existing studies depict a general lack of agreement between reconstructions and model simulations with respect to the amplitude and/or occurrence of warmer/colder and wetter/drier periods during the last millennium and the magnitude of the 20th century warming trend. Furthermore, these works are mainly focused on eastern China regions that show a denser proxy data coverage. We investigate how last millennium palaeo-runs compare to independent evidences from an unusual large number of proxy reconstructions over the study area by employing state-of-the-art palaeo-simulations with multi-member ensembles from the CMIP5/PMIP3 project. This shapes an ideal frame for the evaluation of the uncertainties associated to internal and intermodel model variability. Preliminary results indicate that despite the strong regional and seasonal dependencies, temperature reconstructions in China evidence coherent variations among all regions at centennial scale, especially during the last 500 years. The spatial consistency of low frequency temperature changes is an interesting aspect and of relevance for the assessment of forced climatic responses in China. The comparison between reconstructions and simulations from climate models show that, apart from the 20th century warming trend, the variance of the reconstructed mean China temperature lies in the envelope (uncertainty range) spanned by the temperature simulations. The uncertainty arises from the internal (multi-member ensembles) and the inter-model variability. Centennial variations tend to be broadly synchronous in the reconstructions and the simulations. However, the simulations show a delay of the warm period 1000-1300 AD. This warm medieval period both in the simulations and the reconstructions is followed by cooling till 1800 AD. Based on the simulations, the recent warming is not unprecedented and is comparable to the medieval warming. Further steps of this study will address the individual contribution of anthropogenic and natural forcings on climate variability and change during the last millennium in China. We will make use of of models that provide runs including single forcings (fingerprints) for the attribution of climate variations from decadal to multi-centennial time scales. With this aim, we will implement statistical techniques for the detection of optimal signal-to-noise-ratio between external forcings and internal variability of reconstructed temperatures and precipitation. To apply these approaches the uncertainties associated with both reconstructions and simulations will be estimated. The latter will shed some light into the mechanisms behind current climate evolution and will help to constrain uncertainties in the sensitivity of model simulations to increasing CO2 scenarios of future climate change. This work will also contribute to the overall aims of the PAGES 2k initiative in Asia (http://www.pages.unibe.ch/workinggroups/2k-network)

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...

Impact of natural variability on the perception of climate change for the upcoming decades: Analysis of the CanESM2-LE and CESM-LE large ensembles

NASA Astrophysics Data System (ADS)

Rondeau-Genesse, G.; Braun, M.; Chaumont, D.

2017-12-01

The pace of climate change can have a direct impact on the efforts required to adapt. However, for relatively short time scales, this pace can be masked by natural variability (NV). In some cases, this variability might cause, for a few decades, climate change to exceed what would be expected from the greenhouse gas (GHG) emissions alone or, to the contrary, it might cause slowdowns or even hiatuses. This phenomenon is difficult to explore using ensembles such as CMIP5, which are composed of multiple climatological models and thus combine both NV and inter-model differences. This study analyses CanESM2-LE and CESM-LE, two state-of-the-art large ensembles (LE) comprised of multiple realizations from a single climatological model and a single GHG emission scenario. We explore the relationship between NV and climate change over the next few decades in Canada and the United States. Temperature indices, namely the mean annual temperature and the 3-day maximum and minimum temperatures are assessed. Results indicate that under the RCP8.5, temperatures within most of the individual large ensemble members will increase in a roughly linear manner between 2021 and 2060. Nevertheless, in some regions such as parts of Canada and Alaska, there is a 20 to 35% probability that the temperature increase will slow down between 2021 and 2040. Such a slowdown in warming temperatures would provide some leeway for adaptation projects, but this phenomenon is caused by NV alone and, as such, is only temporary. Indeed, members of the large ensembles where a slowdown of warming is found during the 2021-2040 period are two to five times more likely to experience a period of very fast warming in the following decades. The opposite scenario, where the changes expected by 2050 would occur early because of NV, remains fairly uncommon for the mean annual temperature. For the extreme temperature indices however, this early warming still occurs in 5 to 20% of the large ensemble members. As such, while our results indicate that the dominant pattern in Canada and the United States is a fairly linear warming, the chances for other patterns is non negligible for the upcoming decades. This reinforces the need for constant, uninterrupted efforts towards climate change adaptation.

Characteristics, processes, and causes of the spatio-temporal variabilities of the East Asian monsoon system

NASA Astrophysics Data System (ADS)

Huang, Ronghui; Chen, Jilong; Wang, Lin; Lin, Zhongda

2012-09-01

Recent advances in the study of the characteristics, processes, and causes of spatio-temporal variabilities of the East Asian monsoon (EAM) system are reviewed in this paper. The understanding of the EAM system has improved in many aspects: the basic characteristics of horizontal and vertical structures, the annual cycle of the East Asian summer monsoon (EASM) system and the East Asian winter monsoon (EAWM) system, the characteristics of the spatio-temporal variabilities of the EASM system and the EAWM system, and especially the multiple modes of the EAM system and their spatio-temporal variabilities. Some new results have also been achieved in understanding the atmosphere-ocean interaction and atmosphere-land interaction processes that affect the variability of the EAM system. Based on recent studies, the EAM system can be seen as more than a circulation system, it can be viewed as an atmosphere-ocean-land coupled system, namely, the EAM climate system. In addition, further progress has been made in diagnosing the internal physical mechanisms of EAM climate system variability, especially regarding the characteristics and properties of the East Asia-Pacific (EAP) teleconnection over East Asia and the North Pacific, the "Silk Road" teleconnection along the westerly jet stream in the upper troposphere over the Asian continent, and the dynamical effects of quasi-stationary planetary wave activity on EAM system variability. At the end of the paper, some scientific problems regarding understanding the EAM system variability are proposed for further study.

Impacts of 2000-2050 Climate Change on Fine Particulate Matter (PM2.5) Air Quality in China Based on Statistical Projections Using an Ensemble of Global Climate Models

NASA Astrophysics Data System (ADS)

Leung, D. M.; Tai, A. P. K.; Shen, L.; Moch, J. M.; van Donkelaar, A.; Mickley, L. J.

2017-12-01

Fine particulate matter (PM2.5) air quality is strongly dependent on not only on emissions but also meteorological conditions. Here we examine the dominant synoptic circulation patterns that control day-to-day PM2.5 variability over China. We perform principal component (PC) analysis on 1998-2016 NCEP/NCAR Reanalysis I daily meteorological fields to diagnose distinct synoptic meteorological modes, and perform PC regression on spatially interpolated 2014-2016 daily mean PM2.5 concentrations in China to identify modes dominantly explaining PM2.5 variability. We find that synoptic systems, e.g., cold-frontal passages, maritime inflow and frontal precipitation, can explain up to 40% of the day-to-day PM2.5 variability in major metropolitan regions in China. We further investigate how annually changing frequencies of synoptic systems, as well as changing local meteorology, drive interannual PM2.5 variability. We apply a spectral analysis on the PC time series to obtain the 1998-2016 annual median synoptic frequency, and use a forward-selection multiple linear regression (MLR) model of satellite-derived 1998-2015 annual mean PM2.5 concentrations on local meteorology and synoptic frequency, selecting predictors that explain the highest fraction of interannual PM2.5 variability while guarding against multicollinearity. To estimate the effect of climate change on future PM2.5 air quality, we project a multimodel ensemble of 15 CMIP5 models under the RCP8.5 scenario on the PM2.5-to-meteorology sensitivities derived for the present-day from the MLR model. Our results show that climate change could be responsible for increases in PM2.5 of more than 25 μg m-3 in northwestern China and 10 mg m-3 in northeastern China by the 2050s. Increases in synoptic frequency of cold-frontal passages cause only a modest 1 μg m-3 decrease in PM2.5 in North China Plain. Our analyses show that climate change imposes a significant penalty on air quality over China and poses serious threat on human health under the RCP8.5 future.

High Resolution Spatiotemporal Climate Reconstruction and Variability in East Asia during Little Ice Age

NASA Astrophysics Data System (ADS)

Lin, K. H. E.; Wang, P. K.; Lee, S. Y.; Liao, Y. C.; Fan, I. C.; Liao, H. M.

2017-12-01

The Little ice Age (LIA) is one of the most prominent epochs in paleoclimate reconstruction of the Common Era. While the signals of LIA were generally discovered across hemispheres, wide arrays of regional variability were found, and the reconstructed anomalies were sometimes inconsistent across studies by using various proxy data or historical records. This inconsistency is mainly attributed to limited data coverage at fine resolution that can assist high-resolution climate reconstruction in the continuous spatiotemporal trends. Qing dynasty (1644-1911 CE) of China existed in the coldest period of LIA. Owing to a long-standing tradition that acquired local officials to record odds and social or meteorological events, thousands of local chronicles were left. Zhang eds. (2004) took two decades to compile all these meteorological records in a compendium, for which we then digitized and coded all records into our REACHS database system for reconstructing climate. There were in total 1,435 points (sites) in our database for over 80,000 events in the period of time. After implementing two-rounds coding check for data quality control (accuracy rate 87.2%), multiple indexes were retrieved for reconstructing annually and seasonally resolved temperature and precipitation series for North, Central, and South China. The reconstruction methods include frequency count and grading, with usage of multiple regression models to test sensitivity and to calculate correlations among several reconstructed series. Validation was also conducted through comparison with instrumental data and with other reconstructed series in previous studies. Major research results reveal interannual (3-5 years), decadal (8-12 years), and interdecadal (≈30 years) variabilities with strong regional expressions across East China. Cooling effect was not homogenously distributed in space and time. Flood and drought conditions frequently repeated but the spatiotemporal pattern was variant, indicating likely different climate regimes that can be linked to the dynamism of large atmospheric circulation and East Asian monsoon. Spatiotemporal analysis of extreme events such as typhoons and extreme droughts also indicated similar patterns. More detailed analysis are undertaken to explain the physical mechanisms that can drive these changes.

A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

NASA Astrophysics Data System (ADS)

Forkel, Matthias; Dorigo, Wouter; Lasslop, Gitta; Teubner, Irene; Chuvieco, Emilio; Thonicke, Kirsten

2017-12-01

Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. However, the climatic, environmental, and socioeconomic factors that control global fire activity in vegetation are only poorly understood, and in various complexities and formulations are represented in global process-oriented vegetation-fire models. Data-driven model approaches such as machine learning algorithms have successfully been used to identify and better understand controlling factors for fire activity. However, such machine learning models cannot be easily adapted or even implemented within process-oriented global vegetation-fire models. To overcome this gap between machine learning-based approaches and process-oriented global fire models, we introduce a new flexible data-driven fire modelling approach here (Satellite Observations to predict FIre Activity, SOFIA approach version 1). SOFIA models can use several predictor variables and functional relationships to estimate burned area that can be easily adapted with more complex process-oriented vegetation-fire models. We created an ensemble of SOFIA models to test the importance of several predictor variables. SOFIA models result in the highest performance in predicting burned area if they account for a direct restriction of fire activity under wet conditions and if they include a land cover-dependent restriction or allowance of fire activity by vegetation density and biomass. The use of vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. We further analyse spatial patterns of the sensitivity between anthropogenic, climate, and vegetation predictor variables and burned area. We finally discuss how multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with data-driven modelling and model-data integration approaches can guide the future development of global process-oriented vegetation-fire models.

Parametric vs. non-parametric daily weather generator: validation and comparison

NASA Astrophysics Data System (ADS)

Dubrovsky, Martin

2016-04-01

As the climate models (GCMs and RCMs) fail to satisfactorily reproduce the real-world surface weather regime, various statistical methods are applied to downscale GCM/RCM outputs into site-specific weather series. The stochastic weather generators are among the most favourite downscaling methods capable to produce realistic (observed like) meteorological inputs for agrological, hydrological and other impact models used in assessing sensitivity of various ecosystems to climate change/variability. To name their advantages, the generators may (i) produce arbitrarily long multi-variate synthetic weather series representing both present and changed climates (in the latter case, the generators are commonly modified by GCM/RCM-based climate change scenarios), (ii) be run in various time steps and for multiple weather variables (the generators reproduce the correlations among variables), (iii) be interpolated (and run also for sites where no weather data are available to calibrate the generator). This contribution will compare two stochastic daily weather generators in terms of their ability to reproduce various features of the daily weather series. M&Rfi is a parametric generator: Markov chain model is used to model precipitation occurrence, precipitation amount is modelled by the Gamma distribution, and the 1st order autoregressive model is used to generate non-precipitation surface weather variables. The non-parametric GoMeZ generator is based on the nearest neighbours resampling technique making no assumption on the distribution of the variables being generated. Various settings of both weather generators will be assumed in the present validation tests. The generators will be validated in terms of (a) extreme temperature and precipitation characteristics (annual and 30 years extremes and maxima of duration of hot/cold/dry/wet spells); (b) selected validation statistics developed within the frame of VALUE project. The tests will be based on observational weather series from several European stations available from the ECA&D database.

Global climate simulations with the A1F1 scenario for 2000-2100: Meltwater, temperature and river flow impacts in India

NASA Astrophysics Data System (ADS)

Erickson, D. J.; Branstetter, M. L.; Wilbanks, T. J.; Ganguly, A. R.; Hoffman, F. M.; King, A. W.; Buja, L.; Panwar, T. S.

2008-05-01

Climate simulations based on the assumptions implicit in the SRES A1F1 scenario for the period 2000-2100 using CCSM3 are analyzed. We find temperature increases of 3-9oC over Northern India by the end of this century. We will discuss the implications and resulting alterations of the hydrologic cycle as the climate evolves from 2000-2100. In particular, we will assess the changes in the surface latent and sensible heat energy budget, the Indian regional water budgets including trends in the timing and duration of the Indian monsoon and the resulting impacts on mean river flow and hydroelectric power generation potential. These analyses will also be examined within the context of heat index, droughts, floods and related estimates of societal robustness and resiliency. We will compare our new insights with the existing literature. Climate simulations based on the SRES A2 and B1 scenarios forced with land cover have indicated increased cloud cover and precipitation, resulting in decreased incident radiation and higher latent heat fluxes, in India during June, July and August by 2050 (Feddema et al., 2005). Analyses of historical records in the context of the Indian Monsoon Rainfall (IMR) have suggested an evolving relation of IMR with natural climate variability caused by El Nino events (Krishna Kumar et al., 2006), studied the combined effects of natural climate variability and global warming (Kripalini et al., 2003) on IMR, as well as demonstrated an increasing trend of extreme rain events in a warming environment (Goswami et al., 2006). In addition, the vulnerability of the Indian agriculture sector to climate change was analyzed and mapped at district-levels by combining with multiple global stressors (O'Brien et al., 2004). [[References::: (1) Feddema, J.J., Oleson, K.W., Bonan, G.B., Mearns, L.O., Buja, L.E., Meehl, G.A., and W.M. Washington (2005): The importance of land-cover change in simulating future climates, Science, 310 (5754): 1674-1678, 9 December.... (2) Goswami, B.N., Venugopal, V., Sengupta, D., Madhusoodanan, and P.K. Xavier (2006): Increasing trend of extreme rain events over India in a warming environment, Science, 314 (5804): 1442-1445, 1 December.... (3) Kripalini, R.H., Kulkarni, A., Sabade, S.S., and M.L. Khandekar (2003): Indian monsoon variability in a global warming scenario, Natural Hazards, 29: 189-206.... (4) Krishna Kumar, M., Rajagolapan, B., Hoerling, M., Bates, G., and M. Cane (2006): Unraveling the mystery of Indian Monsoon failure during El Nino, Science, 314 (5796): 115-119, 6 October.... (5) O'Brien, K., Leichenko, R., Kelkar, U., Venema, H., Aandhal, G., Tompkins, H., Javed, A., Bhadwal, S., Barg, S., Nygaard, L., and J. West (2004): Mapping vulnerability to multiple stressors: climate change and globalization in India, Global Environmental Change, 14: 303-313.

Identifying Factors Causing Variability in Greenhouse Gas (GHG) Fluxes in a Polygonal Tundra Landscape

NASA Astrophysics Data System (ADS)

Arora, B.; Wainwright, H. M.; Vaughn, L. S.; Curtis, J. B.; Torn, M. S.; Dafflon, B.; Hubbard, S. S.

2017-12-01

Greenhouse gas (GHG) flux variations in Arctic tundra environments are important to understand because of the vast amount of soil carbon stored in these regions and the potential of these regions to convert from a global carbon sink to a source under warmer conditions. Multiple factors potentially contribute to GHG flux variations observed in these environments, including snowmelt timing, growing season length, active layer thickness, water table variations, and temperature fluctuations. The objectives of this study are to investigate temporal variability in CO2 and CH4 fluxes at Barrow, AK over three successive growing seasons (2012-14) and to determine the factors influencing this variability using a novel entropy-based classification scheme. We analyzed soil, vegetation, and climate parameters as well as GHG fluxes at multiple locations within low-, flat- and high-centered polygons at Barrow, AK as part of the Next Generation Ecosystem Experiment (NGEE) Arctic project. Entropy results indicate that different environmental factors govern variability in GHG fluxes under different spatiotemporal settings. In particular, flat-centered polygons are more likely to become significant sources of CO2 during warm and dry years as opposed to high-centered polygons that contribute considerably to CO2 emissions during cold and wet years. In contrast, the highest CH4 emissions were always associated with low-centered polygons. Temporal variability in CO2 fluxes was primarily associated with factors affecting soil temperature and/or vegetation dynamics during early and late season periods. Temporal variability in CH4 fluxes was primarily associated with changes in vegetation cover and its covariability with primary controls such as seasonal thaw—rather than direct response to changes in soil moisture. Overall, entropy results document which factors became important under different spatiotemporal settings, thus providing clues concerning the manner in which ecosystem properties may be altered regionally in a future climate.

Partitioning the impacts of spatial and climatological rainfall variability in urban drainage modeling

NASA Astrophysics Data System (ADS)

Peleg, Nadav; Blumensaat, Frank; Molnar, Peter; Fatichi, Simone; Burlando, Paolo

2017-03-01

The performance of urban drainage systems is typically examined using hydrological and hydrodynamic models where rainfall input is uniformly distributed, i.e., derived from a single or very few rain gauges. When models are fed with a single uniformly distributed rainfall realization, the response of the urban drainage system to the rainfall variability remains unexplored. The goal of this study was to understand how climate variability and spatial rainfall variability, jointly or individually considered, affect the response of a calibrated hydrodynamic urban drainage model. A stochastic spatially distributed rainfall generator (STREAP - Space-Time Realizations of Areal Precipitation) was used to simulate many realizations of rainfall for a 30-year period, accounting for both climate variability and spatial rainfall variability. The generated rainfall ensemble was used as input into a calibrated hydrodynamic model (EPA SWMM - the US EPA's Storm Water Management Model) to simulate surface runoff and channel flow in a small urban catchment in the city of Lucerne, Switzerland. The variability of peak flows in response to rainfall of different return periods was evaluated at three different locations in the urban drainage network and partitioned among its sources. The main contribution to the total flow variability was found to originate from the natural climate variability (on average over 74 %). In addition, the relative contribution of the spatial rainfall variability to the total flow variability was found to increase with longer return periods. This suggests that while the use of spatially distributed rainfall data can supply valuable information for sewer network design (typically based on rainfall with return periods from 5 to 15 years), there is a more pronounced relevance when conducting flood risk assessments for larger return periods. The results show the importance of using multiple distributed rainfall realizations in urban hydrology studies to capture the total flow variability in the response of the urban drainage systems to heavy rainfall events.

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.

Interactions of Mean Climate Change and Climate Variability on Food Security Extremes

NASA Technical Reports Server (NTRS)

Ruane, Alexander C.; McDermid, Sonali; Mavromatis, Theodoros; Hudson, Nicholas; Morales, Monica; Simmons, John; Prabodha, Agalawatte; Ahmad, Ashfaq; Ahmad, Shakeel; Ahuja, Laj R.

2015-01-01

Recognizing that climate change will affect agricultural systems both through mean changes and through shifts in climate variability and associated extreme events, we present preliminary analyses of climate impacts from a network of 1137 crop modeling sites contributed to the AgMIP Coordinated Climate-Crop Modeling Project (C3MP). At each site sensitivity tests were run according to a common protocol, which enables the fitting of crop model emulators across a range of carbon dioxide, temperature, and water (CTW) changes. C3MP can elucidate several aspects of these changes and quantify crop responses across a wide diversity of farming systems. Here we test the hypothesis that climate change and variability interact in three main ways. First, mean climate changes can affect yields across an entire time period. Second, extreme events (when they do occur) may be more sensitive to climate changes than a year with normal climate. Third, mean climate changes can alter the likelihood of climate extremes, leading to more frequent seasons with anomalies outside of the expected conditions for which management was designed. In this way, shifts in climate variability can result in an increase or reduction of mean yield, as extreme climate events tend to have lower yield than years with normal climate.C3MP maize simulations across 126 farms reveal a clear indication and quantification (as response functions) of mean climate impacts on mean yield and clearly show that mean climate changes will directly affect the variability of yield. Yield reductions from increased climate variability are not as clear as crop models tend to be less sensitive to dangers on the cool and wet extremes of climate variability, likely underestimating losses from water-logging, floods, and frosts.

Lateglacial climate reconstruction on the Bolivian Altiplano inferred from paleoglaciers and paleolakes

NASA Astrophysics Data System (ADS)

Martin, Léo; Blard, Pierre-Henri; Lavé, Jérôme; Prémaillon, Mélody; Jomelli, Vincent; Brunstein, Daniel; Lupker, Maarten; Charreau, Julien; Mariotti, Véronique; Condom, Thomas; Bourles, Didier

2016-04-01

Recent insights shed light on the global mechanisms involved in the abrupt oscillations of the Earth climate for the Late Glacial Maximum (LGM) to Holocene period (Zhang et al., 2014; Banderas et al., 2015). Yet the concomitant patterns of regional climate reorganization on continental areas are for now poorly documented. Particularly, few attempts have been made to propose temporal reconstructions of the regional climate variables in the High Tropical Andes, a region under the influence of multiple global climate forcings (Jomelli et al., 2014). We present new glacial chronologies from four sites of the Bolivian Altiplano: the Wara-Wara valley (17.3°S - 66.1°W), the Zongo valley (16.3°S - 68.1°W), the Cerro Tunupa (19.8°S - 67.6°W) and the Nevado Sajama (18.1°S 68.9°W). These chronologies are based on Cosmic Ray Exposure dating (CRE) from an exceptional suite of recessive moraines. These new data permitted to refine existing chronologies of Smith et al., 2005; Zech et al., 2010 and Blard et al., 2009. In both sites, glaciers recorded stillstand episodes synchronous with cold events such as the Henrich 1 event, the Younger Dryas and the Antarctic Cold Reversal. Since the nearby Altiplano basin registered lake level variations over the same period, we were able to apply a joint modelling of glaciers Equilibrium Line Altitude (ELA) and lake budget. This method permits to derive a temporal evolution of temperature and precipitation for the four sites. These new reconstructions show for all sites that glaciers of the Tropical Andes were influenced by the major climatic events of the Northern and Southern Hemispheres. Furthermore, the temperature variability observed at high latitudes results in these tropical latitudes in major precipitation variability whereas the lateglacial temperature patterns remain globally monotonic. This conversion of global temperature variability into regional precipitation variability support the idea that North Hemisphere cold events are coeval with an important southward deflexion of the Intertropical Convergence Zone (ITCZ) due to the inter-hemispheric temperature gradient (Schneider et al., 2014). Such a southward shift would lead to an increased moist supply of the subequatorial Amazonian basin (Montade et al., 2015) and thus an increased easterly driven moist transport over the Altiplano.

Assessing climate change impacts on water resources in remote mountain regions

NASA Astrophysics Data System (ADS)

Buytaert, Wouter; De Bièvre, Bert

2013-04-01

From a water resources perspective, remote mountain regions are often considered as a basket case. They are often regions where poverty is often interlocked with multiple threats to water supply, data scarcity, and high uncertainties. In these environments, it is paramount to generate locally relevant knowledge about water resources and how they impact local livelihoods. This is often problematic. Existing environmental data collection tends to be geographically biased towards more densely populated regions, and prioritized towards strategic economic activities. Data may also be locked behind institutional and technological barriers. These issues create a "knowledge trap" for data-poor regions, which is especially acute in remote and hard-to-reach mountain regions. We present lessons learned from a decade of water resources research in remote mountain regions of the Andes, Africa and South Asia. We review the entire tool chain of assessing climate change impacts on water resources, including the interrogation and downscaling of global circulation models, translating climate variables in water availability and access, and assessing local vulnerability. In global circulation models, mountain regions often stand out as regions of high uncertainties and lack of agreement of future trends. This is partly a technical artifact because of the different resolution and representation of mountain topography, but it also highlights fundamental uncertainties in climate impacts on mountain climate. This problem also affects downscaling efforts, because regional climate models should be run in very high spatial resolution to resolve local gradients, which is computationally very expensive. At the same time statistical downscaling methods may fail to find significant relations between local climate properties and synoptic processes. Further uncertainties are introduced when downscaled climate variables such as precipitation and temperature are to be translated in hydrologically relevant variables such as streamflow and groundwater recharge. Fundamental limitations in both the understanding of hydrological processes in mountain regions (e.g., glacier melt, wetland attenuation, groundwater flows) and in data availability introduce large uncertainties. Lastly, assessing access to water resources is a major challenge. Topographical gradients and barriers, as well as strong spatiotemporal variations in hydrological processes, makes it particularly difficult to assess which parts of the mountain population is most vulnerable to future perturbations of the water cycle.

Transient Earth system responses to cumulative carbon dioxide emissions: linearities, uncertainties, and probabilities in an observation-constrained model ensemble

NASA Astrophysics Data System (ADS)

Steinacher, M.; Joos, F.

2016-02-01

Information on the relationship between cumulative fossil CO2 emissions and multiple climate targets is essential to design emission mitigation and climate adaptation strategies. In this study, the transient response of a climate or environmental variable per trillion tonnes of CO2 emissions, termed TRE, is quantified for a set of impact-relevant climate variables and from a large set of multi-forcing scenarios extended to year 2300 towards stabilization. An ˜ 1000-member ensemble of the Bern3D-LPJ carbon-climate model is applied and model outcomes are constrained by 26 physical and biogeochemical observational data sets in a Bayesian, Monte Carlo-type framework. Uncertainties in TRE estimates include both scenario uncertainty and model response uncertainty. Cumulative fossil emissions of 1000 Gt C result in a global mean surface air temperature change of 1.9 °C (68 % confidence interval (c.i.): 1.3 to 2.7 °C), a decrease in surface ocean pH of 0.19 (0.18 to 0.22), and a steric sea level rise of 20 cm (13 to 27 cm until 2300). Linearity between cumulative emissions and transient response is high for pH and reasonably high for surface air and sea surface temperatures, but less pronounced for changes in Atlantic meridional overturning, Southern Ocean and tropical surface water saturation with respect to biogenic structures of calcium carbonate, and carbon stocks in soils. The constrained model ensemble is also applied to determine the response to a pulse-like emission and in idealized CO2-only simulations. The transient climate response is constrained, primarily by long-term ocean heat observations, to 1.7 °C (68 % c.i.: 1.3 to 2.2 °C) and the equilibrium climate sensitivity to 2.9 °C (2.0 to 4.2 °C). This is consistent with results by CMIP5 models but inconsistent with recent studies that relied on short-term air temperature data affected by natural climate variability.

Dependence of drivers affects risks associated with compound events

NASA Astrophysics Data System (ADS)

Zscheischler, Jakob; Seneviratne, Sonia I.

2017-04-01

Compound climate extremes are receiving increasing attention because of their disproportionate impacts on humans and ecosystems. Risks assessments, however, generally focus on univariate statistics even when multiple stressors are considered. Concurrent extreme droughts and heatwaves have been observed to cause a suite of extreme impacts on natural and human systems alike. For example, they can substantially affect vegetation health, prompting tree mortality, and thereby facilitating insect outbreaks and fires. In addition, hot droughts have the potential to trigger and intensify fires and can cause severe economical damage. By promoting disease spread, extremely hot and dry conditions also strongly affect human health. We analyse the co-occurrence of dry and hot summers and show that these are strongly correlated for many regions, inducing a much higher frequency of concurrent hot and dry summers than what would be assumed from the independent combination of the univariate statistics. Our results demonstrate how the dependence structure between variables affects the occurrence frequency of multivariate extremes. Assessments based on univariate statistics can thus strongly underestimate risks associated with given extremes, if impacts depend on multiple (dependent) variables. We conclude that a multivariate perspective is necessary in order to appropriately assess changes in climate extremes and their impacts, and to design adaptation strategies.

Microhabitat and Climatic Niche Change Explain Patterns of Diversification among Frog Families.

PubMed

Moen, Daniel S; Wiens, John J

2017-07-01

A major goal of ecology and evolutionary biology is to explain patterns of species richness among clades. Differences in rates of net diversification (speciation minus extinction over time) may often explain these patterns, but the factors that drive variation in diversification rates remain uncertain. Three important candidates are climatic niche position (e.g., whether clades are primarily temperate or tropical), rates of climatic niche change among species within clades, and microhabitat (e.g., aquatic, terrestrial, arboreal). The first two factors have been tested separately in several studies, but the relative importance of all three is largely unknown. Here we explore the correlates of diversification among families of frogs, which collectively represent ∼88% of amphibian species. We assemble and analyze data on phylogeny, climate, and microhabitat for thousands of species. We find that the best-fitting phylogenetic multiple regression model includes all three types of variables: microhabitat, rates of climatic niche change, and climatic niche position. This model explains 67% of the variation in diversification rates among frog families, with arboreal microhabitat explaining ∼31%, niche rates ∼25%, and climatic niche position ∼11%. Surprisingly, we show that microhabitat can have a much stronger influence on diversification than climatic niche position or rates of climatic niche change.

Considering the Differential Impact of Three Facets of Organizational Health Climate on Employees' Well-Being.

PubMed

Zweber, Zandra M; Henning, Robert A; Magley, Vicki J; Faghri, Pouran

2015-01-01

One potential way that healthy organizations can impact employee health is by promoting a climate for health within the organization. Using a definition of health climate that includes support for health from multiple levels within the organization, this study examines whether all three facets of health climate--the workgroup, supervisor, and organization--work together to contribute to employee well-being. Two samples are used in this study to examine health climate at the individual level and group level in order to provide a clearer picture of the impact of the three health climate facets. k-means cluster analysis was used on each sample to determine groups of individuals based on their levels of the three health climate facets. A discriminant function analysis was then run on each sample to determine if clusters differed on a function of employee well-being variables. Results provide evidence that having strength in all three of the facets is the most beneficial in terms of employee well-being at work. Findings from this study suggest that organizations must consider how health is treated within workgroups, how supervisors support employee health, and what the organization does to support employee health when promoting employee health.

Climate Impact of Solar Variability

NASA Technical Reports Server (NTRS)

Schatten, Kenneth H. (Editor); Arking, Albert (Editor)

1990-01-01

The conference on The Climate Impact of Solar Variability, was held at Goddard Space Flight Center from April 24 to 27, 1990. In recent years they developed a renewed interest in the potential effects of increasing greenhouse gases on climate. Carbon dioxide, methane, nitrous oxide, and the chlorofluorocarbons have been increasing at rates that could significantly change climate. There is considerable uncertainty over the magnitude of this anthropogenic change. The climate system is very complex, with feedback processes that are not fully understood. Moreover, there are two sources of natural climate variability (volcanic aerosols and solar variability) added to the anthropogenic changes which may confuse our interpretation of the observed temperature record. Thus, if we could understand the climatic impact of the natural variability, it would aid our interpretation and understanding of man-made climate changes.

Climate variability and vulnerability to climate change: a review

PubMed Central

Thornton, Philip K; Ericksen, Polly J; Herrero, Mario; Challinor, Andrew J

2014-01-01

The focus of the great majority of climate change impact studies is on changes in mean climate. In terms of climate model output, these changes are more robust than changes in climate variability. By concentrating on changes in climate means, the full impacts of climate change on biological and human systems are probably being seriously underestimated. Here, we briefly review the possible impacts of changes in climate variability and the frequency of extreme events on biological and food systems, with a focus on the developing world. We present new analysis that tentatively links increases in climate variability with increasing food insecurity in the future. We consider the ways in which people deal with climate variability and extremes and how they may adapt in the future. Key knowledge and data gaps are highlighted. These include the timing and interactions of different climatic stresses on plant growth and development, particularly at higher temperatures, and the impacts on crops, livestock and farming systems of changes in climate variability and extreme events on pest-weed-disease complexes. We highlight the need to reframe research questions in such a way that they can provide decision makers throughout the food system with actionable answers, and the need for investment in climate and environmental monitoring. Improved understanding of the full range of impacts of climate change on biological and food systems is a critical step in being able to address effectively the effects of climate variability and extreme events on human vulnerability and food security, particularly in agriculturally based developing countries facing the challenge of having to feed rapidly growing populations in the coming decades. PMID:24668802

Assessing climate change impacts, benefits of mitigation, and uncertainties on major global forest regions under multiple socioeconomic and emissions scenarios

DOE PAGES

Kim, John B.; Monier, Erwan; Sohngen, Brent; ...

2017-03-28

We analyze a set of simulations to assess the impact of climate change on global forests where MC2 dynamic global vegetation model (DGVM) was run with climate simulations from the MIT Integrated Global System Model-Community Atmosphere Model (IGSM-CAM) modeling framework. The core study relies on an ensemble of climate simulations under two emissions scenarios: a business-as-usual reference scenario (REF) analogous to the IPCC RCP8.5 scenario, and a greenhouse gas mitigation scenario, called POL3.7, which is in between the IPCC RCP2.6 and RCP4.5 scenarios, and is consistent with a 2 °C global mean warming from pre-industrial by 2100. Evaluating the outcomesmore » of both climate change scenarios in the MC2 model shows that the carbon stocks of most forests around the world increased, with the greatest gains in tropical forest regions. Temperate forest regions are projected to see strong increases in productivity offset by carbon loss to fire. The greatest cost of mitigation in terms of effects on forest carbon stocks are projected to be borne by regions in the southern hemisphere. We compare three sources of uncertainty in climate change impacts on the world’s forests: emissions scenarios, the global system climate response (i.e. climate sensitivity), and natural variability. The role of natural variability on changes in forest carbon and net primary productivity (NPP) is small, but it is substantial for impacts of wildfire. Forest productivity under the REF scenario benefits substantially from the CO 2 fertilization effect and that higher warming alone does not necessarily increase global forest carbon levels. Finally, our analysis underlines why using an ensemble of climate simulations is necessary to derive robust estimates of the benefits of greenhouse gas mitigation. It also demonstrates that constraining estimates of climate sensitivity and advancing our understanding of CO 2 fertilization effects may considerably reduce the range of projections.« less

Stand Competition Determines How Different Tree Species Will Cope with a Warming Climate

PubMed Central

Fernández-de-Uña, Laura; Cañellas, Isabel; Gea-Izquierdo, Guillermo

2015-01-01

Plant-plant interactions influence how forests cope with climate and contribute to modulate species response to future climate scenarios. We analysed the functional relationships between growth, climate and competition for Pinus sylvestris, Quercus pyrenaica and Quercus faginea to investigate how stand competition modifies forest sensitivity to climate and simulated how annual growth rates of these species with different drought tolerance would change throughout the 21st century. Dendroecological data from stands subjected to thinning were modelled using a novel multiplicative nonlinear approach to overcome biases related to the general assumption of a linear relationship between covariates and to better mimic the biological relationships involved. Growth always decreased exponentially with increasing competition, which explained more growth variability than climate in Q. faginea and P. sylvestris. The effect of precipitation was asymptotic in all cases, while the relationship between growth and temperature reached an optimum after which growth declined with warmer temperatures. Our growth projections indicate that the less drought-tolerant P. sylvestris would be more negatively affected by climate change than the studied sub-Mediterranean oaks. Q. faginea and P. sylvestris mean growth would decrease under all the climate change scenarios assessed. However, P. sylvestris growth would decline regardless of the competition level, whereas this decrease would be offset by reduced competition in Q. faginea. Conversely, Q. pyrenaica growth would remain similar to current rates, except for the warmest scenario. Our models shed light on the nature of the species-specific interaction between climate and competition and yield important implications for management. Assuming that individual growth is directly related to tree performance, trees under low competition would better withstand the warmer conditions predicted under climate change scenarios but in a variable manner depending on the species. Thinning following an exponential rule may be desirable to ensure long-term conservation of high-density Mediterranean woodlands, particularly in drought-limited sites. PMID:25826446

Evolution of the Climate Continuum from the Mid-Miocene Climatic Optimum to the Present

NASA Astrophysics Data System (ADS)

Aswasereelert, W.; Meyers, S. R.; Hinnov, L. A.; Kelly, D.

2011-12-01

The recognition of orbital rhythms in paleoclimate data has led to a rich understanding of climate evolution during the Neogene and Quaternary. In contrast, changes in stochastic variability associated with the transition from unipolar to bipolar glaciation have received less attention, although the stochastic component likely preserves key insights about climate. In this study, we seek to evaluate the dominance and character of stochastic climate energy since the Middle Miocene Climatic Optimum (~17 Ma). These analyses extend a previous study that suggested diagnostic stochastic responses associated with Northern Hemisphere ice sheet development during the Plio-Pleistocene (Meyers and Hinnov, 2010). A critical and challenging step necessary to conduct the work is the conversion of depth data to time data. We investigate climate proxy datasets using multiple time scale hypotheses, including depth-derived time scales, sedimentologic/geochemical "tuning", minimal orbital tuning, and comprehensive orbital tuning. To extract the stochastic component of climate, and also explore potential relationships between the orbital parameters and paleoclimate response, a number of approaches rooted in Thomson's (1982) multi-taper spectral method (MTM) are applied. Importantly, the MTM technique is capable of separating the spectral "continuum" - a measure of stochastic variability - from the deterministic periodic orbital signals (spectral "lines") preserved in proxy data. Time series analysis of the proxy records using different chronologic approaches allows us to evaluate the sensitivity of our conclusion about stochastic and deterministic orbital processes during the Middle Miocene to present. Moreover, comparison of individual records permits examination of the spatial dependence of the identified climate responses. Meyers, S.R., and Hinnov, L.A. (2010), Northern Hemisphere glaciation and the evolution of Plio-Pleistocene climate noise: Paleoceanography, 25, PA3207, doi:10.1029/2009PA001834. Thomson, D.J. (1982), Spectrum estimation and harmonic analysis: IEEE Proceedings, v. 70, p. 1055-1096.

Assessing climate change impacts, benefits of mitigation, and uncertainties on major global forest regions under multiple socioeconomic and emissions scenarios

NASA Astrophysics Data System (ADS)

Kim, John B.; Monier, Erwan; Sohngen, Brent; Pitts, G. Stephen; Drapek, Ray; McFarland, James; Ohrel, Sara; Cole, Jefferson

2017-04-01

We analyze a set of simulations to assess the impact of climate change on global forests where MC2 dynamic global vegetation model (DGVM) was run with climate simulations from the MIT Integrated Global System Model-Community Atmosphere Model (IGSM-CAM) modeling framework. The core study relies on an ensemble of climate simulations under two emissions scenarios: a business-as-usual reference scenario (REF) analogous to the IPCC RCP8.5 scenario, and a greenhouse gas mitigation scenario, called POL3.7, which is in between the IPCC RCP2.6 and RCP4.5 scenarios, and is consistent with a 2 °C global mean warming from pre-industrial by 2100. Evaluating the outcomes of both climate change scenarios in the MC2 model shows that the carbon stocks of most forests around the world increased, with the greatest gains in tropical forest regions. Temperate forest regions are projected to see strong increases in productivity offset by carbon loss to fire. The greatest cost of mitigation in terms of effects on forest carbon stocks are projected to be borne by regions in the southern hemisphere. We compare three sources of uncertainty in climate change impacts on the world’s forests: emissions scenarios, the global system climate response (i.e. climate sensitivity), and natural variability. The role of natural variability on changes in forest carbon and net primary productivity (NPP) is small, but it is substantial for impacts of wildfire. Forest productivity under the REF scenario benefits substantially from the CO2 fertilization effect and that higher warming alone does not necessarily increase global forest carbon levels. Our analysis underlines why using an ensemble of climate simulations is necessary to derive robust estimates of the benefits of greenhouse gas mitigation. It also demonstrates that constraining estimates of climate sensitivity and advancing our understanding of CO2 fertilization effects may considerably reduce the range of projections.

Examination of the relationship between management and clinician perception of patient safety climate and patient satisfaction.

PubMed

Mazurenko, Olena; Richter, Jason; Kazley, Abby Swanson; Ford, Eric

2017-04-25

The aim of this study was to explore the relationship between managers and clinicians' agreement on deeming the patient safety climate as high or low and the patients' satisfaction with those organizations. We used two secondary data sets: the Hospital Survey on Patient Safety Culture (2012) and the Hospital Consumer Assessment of Healthcare Providers and Systems (2012). We used ordinary least squares regressions to analyze the relationship between the extent of agreement between managers and clinicians' perceptions of safety climate in relationship to patient satisfaction. The dependent variables were four Hospital Consumer Assessment of Healthcare Providers and Systems patient satisfaction scores: communication with nurses, communication with doctors, communication about medicines, and discharge information. The main independent variables were four groups that were formed based on the extent of managers and clinicians' agreement on four patient safety climate domains: communication openness, feedback and communication about errors, teamwork within units, and teamwork across units. After controlling for hospital and market-level characteristics, we found that patient satisfaction was significantly higher if managers and clinicians reported that patient safety climate is high or if only clinicians perceived the climate as high. Specifically, manager and clinician agreement on high levels of communication openness (β = 2.25, p = .01; β = 2.46, p = .05), feedback and communication about errors (β = 3.0, p = .001; β = 2.89, p = .01), and teamwork across units (β = 2.91, p = .001; β = 3.34, p = .01) was positively and significantly associated with patient satisfaction with discharge information and communication about medication. In addition, more favorable perceptions about patient safety climate by clinicians only yielded similar findings. Organizations should measure and examine patient safety climate from multiple perspectives and be aware that individuals may have varying opinions about safety climate. Hospitals should encourage multidisciplinary collaboration given that staff perceptions about patient safety climate may be associated with patient satisfaction.

Assessing climate change impacts, benefits of mitigation, and uncertainties on major global forest regions under multiple socioeconomic and emissions scenarios

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

Kim, John B.; Monier, Erwan; Sohngen, Brent

We analyze a set of simulations to assess the impact of climate change on global forests where MC2 dynamic global vegetation model (DGVM) was run with climate simulations from the MIT Integrated Global System Model-Community Atmosphere Model (IGSM-CAM) modeling framework. The core study relies on an ensemble of climate simulations under two emissions scenarios: a business-as-usual reference scenario (REF) analogous to the IPCC RCP8.5 scenario, and a greenhouse gas mitigation scenario, called POL3.7, which is in between the IPCC RCP2.6 and RCP4.5 scenarios, and is consistent with a 2 °C global mean warming from pre-industrial by 2100. Evaluating the outcomesmore » of both climate change scenarios in the MC2 model shows that the carbon stocks of most forests around the world increased, with the greatest gains in tropical forest regions. Temperate forest regions are projected to see strong increases in productivity offset by carbon loss to fire. The greatest cost of mitigation in terms of effects on forest carbon stocks are projected to be borne by regions in the southern hemisphere. We compare three sources of uncertainty in climate change impacts on the world’s forests: emissions scenarios, the global system climate response (i.e. climate sensitivity), and natural variability. The role of natural variability on changes in forest carbon and net primary productivity (NPP) is small, but it is substantial for impacts of wildfire. Forest productivity under the REF scenario benefits substantially from the CO 2 fertilization effect and that higher warming alone does not necessarily increase global forest carbon levels. Finally, our analysis underlines why using an ensemble of climate simulations is necessary to derive robust estimates of the benefits of greenhouse gas mitigation. It also demonstrates that constraining estimates of climate sensitivity and advancing our understanding of CO 2 fertilization effects may considerably reduce the range of projections.« less

Spatial variability and trends in Younger Dryas equilibrium line altitudes across the European Alps using a hypsometrically based ELA model: results and implications

NASA Astrophysics Data System (ADS)

Keeler, D. G.; Rupper, S.; Schaefer, J. M.; Finkel, R. C.; Maurer, J. M.

2016-12-01

Alpine glaciers constitute an important component of terrestrial paleoclimate records due to, among other characteristics, their high sensitivity to climate change, near global extent, and their integration of myriad climate variables into a single, easily detected signal. Because the glacier equilibrium line altitude (ELA) provides a more explicit representation of climate than many other glacier properties, ELA methods allow for more direct comparisons of multiple glaciers within or between regions. Such comparisons allow for more complete investigations of the ultimate causes of mountain glaciation during specific events. Many studies however tend to focus on a limited number of sites, and employ a large variety of different techniques for ELA reconstruction between studies, making wider climate implications more tenuous. Methods of ELA reconstruction that can be rapidly and consistently applied to an arbitrary number of paleo-glaciers would provide a more accurate portrayal of the changes in climate across a given region. Here we present ELA reconstructions from Egesen Stadial moraines across the European Alps using an ELA model accounting for differences in glacier width, glacier shape, bed topography, ice thickness, and glacier length, including several glaciers constrained to the Younger Dryas using surface exposure dating techniques. We compare reconstructed Younger Dryas ELA values to modern ELA values using the same model, or using end of summer snowline estimates where no glacier is currently present. We further provide uncertainty estimates on the ΔELA using bootstrapped Monte Carlo simulations for the various input parameters. Preliminary results compare favorably to previous glacier studies of the European Younger Dryas, but provide greater context from many glaciers across the region as a whole. Such results allow for a more thorough investigation of the spatial variability and trends in climate during the Younger Dryas across the European Alps, and comparisons of other regions in the future.

Atmospheric Circulation and West Greenland Precipitation

NASA Astrophysics Data System (ADS)

Auger, J.; Birkel, S. D.; Maasch, K. A.; Schuenemann, K. C.; Mayewski, P. A.; Osterberg, E. C.; Hawley, R. L.; Marshall, H. P.

2016-12-01

The surface mass balance of the Greenland Ice Sheet has declined substantially in recent decades across West Greenland with important implications for global sea level and freshwater resources. Here, we investigate changes in heat and moisture delivery to West Greenland through changes in atmospheric circulation in order to gain insight into possible future climate. Particular focus is placed on the role of known climate variability, including the North Atlantic Oscillation (NAO) and Atlantic Multidecadal Oscillation (AMO), in influencing the intensity, frequency, and track of cyclones across the North Atlantic. This study utilizes multiple daily climate reanalysis models (CFSR, ERA-Interim, JRA-55) in addition to observational data. Preliminary results indicate a primary influence from the NAO, with a secondary influence from the low frequency oscillation connected to the AMO. Work is ongoing, and a complete synthesis will be presented at the fall meeting.

Uncertainty quantification and propagation in a complex human-environment system driven by fire and climate

NASA Astrophysics Data System (ADS)

Terando, A. J.; Reich, B. J.; Pacifici, K.

2013-12-01

Fire is an important disturbance process in many coupled natural-human systems. Changes in the frequency and severity of fires due to anthropogenic climate change could have significant costs to society and the plant and animal communities that are adapted to a particular fire regime Planning for these changes requires a robust model of the relationship between climate and fire that accounts for multiple sources of uncertainty that are present when simulating ecological and climatological processes. Here we model how anthropogenic climate change could affect the wildfire regime for a region in the Southeast US whose natural ecosystems are dependent on frequent, low-intensity fires while humans are at risk from large catastrophic fires. We develop a modeling framework that incorporates three major sources of uncertainty: (1) uncertainty in the ecological drivers of expected monthly area burned, (2) uncertainty in the environmental drivers influencing the probability of an extreme fire event, and (3) structural uncertainty in different downscaled climate models. In addition we use two policy-relevant emission scenarios (climate stabilization and 'business-as-usual') to characterize the uncertainty in future greenhouse gas forcings. We use a Bayesian framework to incorporate different sources of uncertainty including simulation of predictive errors and Stochastic Search Variable Selection. Our results suggest that although the mean process remains stationary, the probability of extreme fires declines through time, owing to the persistence of high atmospheric moisture content during the peak fire season that dampens the effect of increasing temperatures. Including multiple sources of uncertainty leads to wide prediction intervals, but is potentially more useful for decision-makers that will require adaptation strategies that are robust to rapid but uncertain climate and ecological change.

Climate Scenarios for the NASA / USAID SERVIR Project: Challenges for Multiple Planning Horizons

NASA Technical Reports Server (NTRS)

Robertson, Franklin R.; Roberts, J. B.; Lyon, B.; Funk, C.; Bosilovich, M. G.

2014-01-01

SERVIR, an acronym meaning "to serve" in Spanish, is a joint venture between NASA and the U.S. Agency for International Development (USAID) which provides satellite-based Earth observation data, modeling, and science applications to help developing nations in Central America, East Africa and the Himalayas improve environmental decision making. Anticipating climate variability / climate change impacts has now become an important component of the SERVIR efforts to build capacity in these regions. Uncertainty in hydrometeorological components of climate variations and exposure to extreme events across scales from weather to climate are of particular concern. We report here on work to construct scenarios or outlooks that are being developed as input drivers for decision support systems (DSSs) in a variety of settings. These DSSs are being developed jointly by a broad array NASA Applied Science Team (AST) Investigations and user communities in the three SERVIR Hub Regions, Central America, East Africa and the Himalayas. Issues span hydrologic / water resources modeling, agricultural productivity, and forest carbon reserves. The scenarios needed for these efforts encompass seasonal forecasts, interannual outlooks, and likely decadal / multi-decadal trends. Providing these scenarios across the different AST efforts enables some level of integration in considering regional responses to climate events. We will discuss a number of challenges in developing this continuum of scenarios including the identification and "mining" of predictability, addressing multiple continental regions, issues of downscaling global model integrations to regional / local applications (i.e. hydrologic and crop modeling). We compare / contrast the role of the U.S. National Multi- Model Experiment initiative in seasonal forecasts and the CMIP-5 climate model experiments in supporting these efforts. Examples of these scenarios, their use, and an assessment of their utility as well as limitations will be presented.

Solar Influences on El Nino/Southern Oscillation Dynamics Over the Last Millennium

NASA Astrophysics Data System (ADS)

Stevenson, S.; Capotondi, A.; Fasullo, J.; Otto-Bliesner, B. L.

2017-12-01

The El Niño/Southern Oscillation (ENSO) exhibits considerable differences between the evolution of individual El Nino and La Nina events (`ENSO diversity'), with significant implications for impacts studies. However, the degree to which external forcing may affect ENSO diversity is not well understood, due to both internal variability and potentially compensatory contributions from multiple forcings. The Community Earth System Model Last Millennium Ensemble (CESM LME) provides an ideal testbed for studying the sensitivity of twentieth century ENSO to forced climate changes, as it contains many realizations of the 850-2005 period with differing combinations of forcings. Metrics of ENSO amplitude and diversity are compared across LME simulations, and although forced changes to ENSO amplitude are generally small, forced changes to diversity are often detectable. Anthropogenic changes to greenhouse gas and ozone/aerosol emissions modify the persistence of Eastern and Central Pacific El Nino events, through shifts in the upwelling and zonal advective feedbacks; these influences generally cancel one another over the twentieth century. Natural forcings are generally small over the 20th century, but when epochs of high/low solar irradiance are compared, distinct shifts in the development and termination of El Nino events can be observed. This indicates that solar variability can indeed have a significant role to play in setting the characteristics of tropical Pacific climate variability. Implications for configuring and evaluating projections of future climate change will be discussed.

Nano- and Macroscale Responses of the Deep Pink Sea Urchin, Strongylocentrotus fragilis, to Multiple Stressors Associated with the Oxygen Minimum Zone

NASA Astrophysics Data System (ADS)

Sato, K.; Jung, J. Y.; Levin, L. A.

2016-02-01

The rapid pace of deoxygenation and ocean acidification associated with anthropogenic climate change on upwelling margins will have differing effects on marine species from the population level down to the nanoscale. Driven by the understudied effects of climate change in the deep sea, we address the question, how will dominant echinoid urchins respond to future changes in multiple stressors (i.e. ocean acidification, deoxygenation, and shoaling of hypoxic water and calcium carbonate saturation horizons) on the southern California continental slope? Samples of the sea urchin, Strongylocentrotus fragilis, were collected along gradients of multiple hydrographic variables and analyzed for phenotypic variation with respect to multiple climate change stressors (oxygen, pH, and temperature). We compare fitness traits of S. fragilis collected along the continental slope and through the Oxygen Minimum Zone (OMZ), which include growth rate, morphology, and reproductive output, in addition to nanoscale structural and biomechanical test properties. Our results indicate that growth rate of S. fragilis is directly correlated with dissolved oxygen and pH, but not depth or temperature. Reproductive output, as measured by a standard gonad index, was found to be sensitive at the OMZ core (pH 7.40; O2 0.25 mL/L), which suggests a nonlinear response to chemical stressors. Preliminary analysis of mineral density in test pieces imaged using micro- and nano- computed tomography indicates exposure to conditions in the OMZ reduces calcification. This improved understanding of how continental margin urchins differ along natural physicochemical gradients will provide modern-day insight into the threshold tolerances of species to multiple stressors and will help guide future manipulation experiments as well as fisheries and spatial management.

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.

Geomorphology Drives Amphibian Beta Diversity in Atlantic Forest Lowlands of Southeastern Brazil

PubMed Central

Luiz, Amom Mendes; Leão-Pires, Thiago Augusto; Sawaya, Ricardo J.

2016-01-01

Beta diversity patterns are the outcome of multiple processes operating at different scales. Amphibian assemblages seem to be affected by contemporary climate and dispersal-based processes. However, historical processes involved in present patterns of beta diversity remain poorly understood. We assess and disentangle geomorphological, climatic and spatial drivers of amphibian beta diversity in coastal lowlands of the Atlantic Forest, southeastern Brazil. We tested the hypothesis that geomorphological factors are more important in structuring anuran beta diversity than climatic and spatial factors. We obtained species composition via field survey (N = 766 individuals), museum specimens (N = 9,730) and literature records (N = 4,763). Sampling area was divided in four spatially explicit geomorphological units, representing historical predictors. Climatic descriptors were represented by the first two axis of a Principal Component Analysis. Spatial predictors in different spatial scales were described by Moran Eigenvector Maps. Redundancy Analysis was implemented to partition the explained variation of species composition by geomorphological, climatic and spatial predictors. Moreover, spatial autocorrelation analyses were used to test neutral theory predictions. Beta diversity was spatially structured in broader scales. Shared fraction between climatic and geomorphological variables was an important predictor of species composition (13%), as well as broad scale spatial predictors (13%). However, geomorphological variables alone were the most important predictor of beta diversity (42%). Historical factors related to geomorphology must have played a crucial role in structuring amphibian beta diversity. The complex relationships between geomorphological history and climatic gradients generated by the Serra do Mar Precambrian basements were also important. We highlight the importance of combining spatially explicit historical and contemporary predictors for understanding and disentangling major drivers of beta diversity patterns. PMID:27171522

Geomorphology Drives Amphibian Beta Diversity in Atlantic Forest Lowlands of Southeastern Brazil.

PubMed

Luiz, Amom Mendes; Leão-Pires, Thiago Augusto; Sawaya, Ricardo J

2016-01-01

Beta diversity patterns are the outcome of multiple processes operating at different scales. Amphibian assemblages seem to be affected by contemporary climate and dispersal-based processes. However, historical processes involved in present patterns of beta diversity remain poorly understood. We assess and disentangle geomorphological, climatic and spatial drivers of amphibian beta diversity in coastal lowlands of the Atlantic Forest, southeastern Brazil. We tested the hypothesis that geomorphological factors are more important in structuring anuran beta diversity than climatic and spatial factors. We obtained species composition via field survey (N = 766 individuals), museum specimens (N = 9,730) and literature records (N = 4,763). Sampling area was divided in four spatially explicit geomorphological units, representing historical predictors. Climatic descriptors were represented by the first two axis of a Principal Component Analysis. Spatial predictors in different spatial scales were described by Moran Eigenvector Maps. Redundancy Analysis was implemented to partition the explained variation of species composition by geomorphological, climatic and spatial predictors. Moreover, spatial autocorrelation analyses were used to test neutral theory predictions. Beta diversity was spatially structured in broader scales. Shared fraction between climatic and geomorphological variables was an important predictor of species composition (13%), as well as broad scale spatial predictors (13%). However, geomorphological variables alone were the most important predictor of beta diversity (42%). Historical factors related to geomorphology must have played a crucial role in structuring amphibian beta diversity. The complex relationships between geomorphological history and climatic gradients generated by the Serra do Mar Precambrian basements were also important. We highlight the importance of combining spatially explicit historical and contemporary predictors for understanding and disentangling major drivers of beta diversity patterns.

Earth system responses to cumulative carbon emissions

NASA Astrophysics Data System (ADS)

Steinacher, M.; Joos, F.

2015-07-01

Information on the relationship between cumulative fossil carbon emissions and multiple climate targets are essential to design emission mitigation and climate adaptation strategies. In this study, the transient responses in different climate variables are quantified for a large set of multi-forcing scenarios extended to year 2300 towards stabilization and in idealized experiments using the Bern3D-LPJ carbon-climate model. The model outcomes are constrained by 26 physical and biogeochemical observational data sets in a Bayesian, Monte-Carlo type framework. Cumulative fossil emissions of 1000 Gt C result in a global mean surface air temperature change of 1.88 °C (68 % confidence interval (c.i.): 1.28 to 2.69 °C), a decrease in surface ocean pH of 0.19 (0.18 to 0.22), and in steric sea level rise of 20 cm (13 to 27 cm until 2300). Linearity between cumulative emissions and transient response is high for pH and reasonably high for surface air and sea surface temperatures, but less pronounced for changes in Atlantic Meridional Overturning, Southern Ocean and tropical surface water saturation with respect to biogenic structures of calcium carbonate, and carbon stocks in soils. The slopes of the relationships change when CO2 is stabilized. The Transient Climate Response is constrained, primarily by long-term ocean heat observations, to 1.7 °C (68 % c.i.: 1.3 to 2.2 °C) and the Equilibrium Climate Sensitivity to 2.9 °C (2.0 to 4.2 °C). This is consistent with results by CMIP5 models, but inconsistent with recent studies that relied on short-term air temperature data affected by natural climate variability.

Climate Model Diagnostic Analyzer Web Service System

NASA Astrophysics Data System (ADS)

Lee, S.; Pan, L.; Zhai, C.; Tang, B.; Jiang, J. H.

2013-12-01

The latest Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report stressed the need for the comprehensive and innovative evaluation of climate models with newly available global observations. The traditional approach to climate model evaluation, which compares a single parameter at a time, identifies symptomatic model biases and errors but fails to diagnose the model problems. The model diagnosis process requires physics-based multi-variable comparisons that typically involve large-volume and heterogeneous datasets, making them both computationally- and data-intensive. To address these challenges, we are developing a parallel, distributed web-service system that enables the physics-based multi-variable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. We have developed a methodology to transform an existing science application code into a web service using a Python wrapper interface and Python web service frameworks (i.e., Flask, Gunicorn, and Tornado). The web-service system, called Climate Model Diagnostic Analyzer (CMDA), currently supports (1) all the datasets from Obs4MIPs and a few ocean datasets from NOAA and Argo, which can serve as observation-based reference data for model evaluation and (2) many of CMIP5 model outputs covering a broad range of atmosphere, ocean, and land variables from the CMIP5 specific historical runs and AMIP runs. Analysis capabilities currently supported by CMDA are (1) the calculation of annual and seasonal means of physical variables, (2) the calculation of time evolution of the means in any specified geographical region, (3) the calculation of correlation between two variables, and (4) the calculation of difference between two variables. A web user interface is chosen for CMDA because it not only lowers the learning curve and removes the adoption barrier of the tool but also enables instantaneous use, avoiding the hassle of local software installation and environment incompatibility. CMDA is planned to be used as an educational tool for the summer school organized by JPL's Center for Climate Science in 2014. The requirements of the educational tool are defined with the interaction with the school organizers, and CMDA is customized to meet the requirements accordingly. The tool needs to be production quality for 30+ simultaneous users. The summer school will thus serve as a valuable testbed for the tool development, preparing CMDA to serve the Earth-science modeling and model-analysis community at the end of the project. This work was funded by the NASA Earth Science Program called Computational Modeling Algorithms and Cyberinfrastructure (CMAC).

Potential of satellite-derived ecosystem functional attributes to anticipate species range shifts

NASA Astrophysics Data System (ADS)

Alcaraz-Segura, Domingo; Lomba, Angela; Sousa-Silva, Rita; Nieto-Lugilde, Diego; Alves, Paulo; Georges, Damien; Vicente, Joana R.; Honrado, João P.

2017-05-01

In a world facing rapid environmental changes, anticipating their impacts on biodiversity is of utmost relevance. Remotely-sensed Ecosystem Functional Attributes (EFAs) are promising predictors for Species Distribution Models (SDMs) by offering an early and integrative response of vegetation performance to environmental drivers. Species of high conservation concern would benefit the most from a better ability to anticipate changes in habitat suitability. Here we illustrate how yearly projections from SDMs based on EFAs could reveal short-term changes in potential habitat suitability, anticipating mid-term shifts predicted by climate-change-scenario models. We fitted two sets of SDMs for 41 plant species of conservation concern in the Iberian Peninsula: one calibrated with climate variables for baseline conditions and projected under two climate-change-scenarios (future conditions); and the other calibrated with EFAs for 2001 and projected annually from 2001 to 2013. Range shifts predicted by climate-based models for future conditions were compared to the 2001-2013 trends from EFAs-based models. Projections of EFAs-based models estimated changes (mostly contractions) in habitat suitability that anticipated, for the majority (up to 64%) of species, the mid-term shifts projected by traditional climate-change-scenario forecasting, and showed greater agreement with the business-as-usual scenario than with the sustainable-development one. This study shows how satellite-derived EFAs can be used as meaningful essential biodiversity variables in SDMs to provide early-warnings of range shifts and predictions of short-term fluctuations in suitable conditions for multiple species.

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.

The Precision Problem in Conservation and Restoration.

PubMed

Hiers, J Kevin; Jackson, Stephen T; Hobbs, Richard J; Bernhardt, Emily S; Valentine, Leonie E

2016-11-01

Within the varied contexts of environmental policy, conservation of imperilled species populations, and restoration of damaged habitats, an emphasis on idealized optimal conditions has led to increasingly specific targets for management. Overly-precise conservation targets can reduce habitat variability at multiple scales, with unintended consequences for future ecological resilience. We describe this dilemma in the context of endangered species management, stream restoration, and climate-change adaptation. Inappropriate application of conservation targets can be expensive, with marginal conservation benefit. Reduced habitat variability can limit options for managers trying to balance competing objectives with limited resources. Conservation policies should embrace habitat variability, expand decision-space appropriately, and support adaptation to local circumstances to increase ecological resilience in a rapidly changing world. Copyright © 2016 Elsevier Ltd. All rights reserved.

Foraminifera Models to Interrogate Ostensible Proxy-Model Discrepancies During Late Pliocene

NASA Astrophysics Data System (ADS)

Jacobs, P.; Dowsett, H. J.; de Mutsert, K.

2017-12-01

Planktic foraminifera faunal assemblages have been used in the reconstruction of past oceanic states (e.g. the Last Glacial Maximum, the mid-Piacenzian Warm Period). However these reconstruction efforts have typically relied on inverse modeling using transfer functions or the modern analog technique, which by design seek to translate foraminifera into one or two target oceanic variables, primarily sea surface temperature (SST). These reconstructed SST data have then been used to test the performance of climate models, and discrepancies have been attributed to shortcomings in climate model processes and/or boundary conditions. More recently forward proxy models or proxy system models have been used to leverage the multivariate nature of proxy relationships to their environment, and to "bring models into proxy space". Here we construct ecological models of key planktic foraminifera taxa, calibrated and validated with World Ocean Atlas (WO13) oceanographic data. Multiple modeling methods (e.g. multilayer perceptron neural networks, Mahalanobis distance, logistic regression, and maximum entropy) are investigated to ensure robust results. The resulting models are then driven by a Late Pliocene climate model simulation with biogeochemical as well as temperature variables. Similarities and differences with previous model-proxy comparisons (e.g. PlioMIP) are discussed.

Optimal crop selection and water allocation under limited water supply in irrigation

NASA Astrophysics Data System (ADS)

Stange, Peter; Grießbach, Ulrike; Schütze, Niels

2015-04-01

Due to climate change, extreme weather conditions such as droughts may have an increasing impact on irrigated agriculture. To cope with limited water resources in irrigation systems, a new decision support framework is developed which focuses on an integrated management of both irrigation water supply and demand at the same time. For modeling the regional water demand, local (and site-specific) water demand functions are used which are derived from optimized agronomic response on farms scale. To account for climate variability the agronomic response is represented by stochastic crop water production functions (SCWPF). These functions take into account different soil types, crops and stochastically generated climate scenarios. The SCWPF's are used to compute the water demand considering different conditions, e.g., variable and fixed costs. This generic approach enables the consideration of both multiple crops at farm scale as well as of the aggregated response to water pricing at a regional scale for full and deficit irrigation systems. Within the SAPHIR (SAxonian Platform for High Performance IRrigation) project a prototype of a decision support system is developed which helps to evaluate combined water supply and demand management policies.

Predicting Seagrass Occurrence in a Changing Climate Using Random Forests

NASA Astrophysics Data System (ADS)

Aydin, O.; Butler, K. A.

2017-12-01

Seagrasses are marine plants that can quickly sequester vast amounts of carbon (up to 100 times more and 12 times faster than tropical forests). In this work, we present an integrated GIS and machine learning approach to build a data-driven model of seagrass presence-absence. We outline a random forest approach that avoids the prevalence bias in many ecological presence-absence models. One of our goals is to predict global seagrass occurrence from a spatially limited training sample. In addition, we conduct a sensitivity study which investigates the vulnerability of seagrass to changing climate conditions. We integrate multiple data sources including fine-scale seagrass data from MarineCadastre.gov and the recently available globally extensive publicly available Ecological Marine Units (EMU) dataset. These data are used to train a model for seagrass occurrence along the U.S. coast. In situ oceans data are interpolated using Empirical Bayesian Kriging (EBK) to produce globally extensive prediction variables. A neural network is used to estimate probable future values of prediction variables such as ocean temperature to assess the impact of a warming climate on seagrass occurrence. The proposed workflow can be generalized to many presence-absence models.

Local-scale topoclimate effects on treeline elevations: a country-wide investigation of New Zealand's southern beech treelines.

PubMed

Case, Bradley S; Buckley, Hannah L

2015-01-01

Although treeline elevations are limited globally by growing season temperature, at regional scales treelines frequently deviate below their climatic limit. The cause of these deviations relate to a host of climatic, disturbance, and geomorphic factors that operate at multiple scales. The ability to disentangle the relative effects of these factors is currently hampered by the lack of reliable topoclimatic data, which describe how regional climatic characteristics are modified by topographic effects in mountain areas. In this study we present an analysis of the combined effects of local- and regional-scale factors on southern beech treeline elevation variability at 28 study areas across New Zealand. We apply a mesoscale atmospheric model to generate local-scale (200 m) meteorological data at these treelines and, from these data, we derive a set of topoclimatic indices that reflect possible detrimental and ameliorative influences on tree physiological functioning. Principal components analysis of meteorological data revealed geographic structure in how study areas were situated in multivariate space along gradients of topoclimate. Random forest and conditional inference tree modelling enabled us to tease apart the relative effects of 17 explanatory factors on local-scale treeline elevation variability. Overall, modelling explained about 50% of the variation in treeline elevation variability across the 28 study areas, with local landform and topoclimatic effects generally outweighing those from regional-scale factors across the 28 study areas. Further, the nature of the relationships between treeline elevation variability and the explanatory variables were complex, frequently non-linear, and consistent with the treeline literature. To our knowledge, this is the first study where model-generated meteorological data, and derived topoclimatic indices, have been developed and applied to explain treeline variation. Our results demonstrate the potential of such an approach for ecological research in mountainous environments.

Local-scale topoclimate effects on treeline elevations: a country-wide investigation of New Zealand’s southern beech treelines

PubMed Central

Buckley, Hannah L.

2015-01-01

Although treeline elevations are limited globally by growing season temperature, at regional scales treelines frequently deviate below their climatic limit. The cause of these deviations relate to a host of climatic, disturbance, and geomorphic factors that operate at multiple scales. The ability to disentangle the relative effects of these factors is currently hampered by the lack of reliable topoclimatic data, which describe how regional climatic characteristics are modified by topographic effects in mountain areas. In this study we present an analysis of the combined effects of local- and regional-scale factors on southern beech treeline elevation variability at 28 study areas across New Zealand. We apply a mesoscale atmospheric model to generate local-scale (200 m) meteorological data at these treelines and, from these data, we derive a set of topoclimatic indices that reflect possible detrimental and ameliorative influences on tree physiological functioning. Principal components analysis of meteorological data revealed geographic structure in how study areas were situated in multivariate space along gradients of topoclimate. Random forest and conditional inference tree modelling enabled us to tease apart the relative effects of 17 explanatory factors on local-scale treeline elevation variability. Overall, modelling explained about 50% of the variation in treeline elevation variability across the 28 study areas, with local landform and topoclimatic effects generally outweighing those from regional-scale factors across the 28 study areas. Further, the nature of the relationships between treeline elevation variability and the explanatory variables were complex, frequently non-linear, and consistent with the treeline literature. To our knowledge, this is the first study where model-generated meteorological data, and derived topoclimatic indices, have been developed and applied to explain treeline variation. Our results demonstrate the potential of such an approach for ecological research in mountainous environments. PMID:26528407

THE REGRESSION MODEL OF IRAN LIBRARIES ORGANIZATIONAL CLIMATE

PubMed Central

Jahani, Mohammad Ali; Yaminfirooz, Mousa; Siamian, Hasan

2015-01-01

Background: The purpose of this study was to drawing a regression model of organizational climate of central libraries of Iran’s universities. Methods: This study is an applied research. The statistical population of this study consisted of 96 employees of the central libraries of Iran’s public universities selected among the 117 universities affiliated to the Ministry of Health by Stratified Sampling method (510 people). Climate Qual localized questionnaire was used as research tools. For predicting the organizational climate pattern of the libraries is used from the multivariate linear regression and track diagram. Results: of the 9 variables affecting organizational climate, 5 variables of innovation, teamwork, customer service, psychological safety and deep diversity play a major role in prediction of the organizational climate of Iran’s libraries. The results also indicate that each of these variables with different coefficient have the power to predict organizational climate but the climate score of psychological safety (0.94) plays a very crucial role in predicting the organizational climate. Track diagram showed that five variables of teamwork, customer service, psychological safety, deep diversity and innovation directly effects on the organizational climate variable that contribution of the team work from this influence is more than any other variables. Conclusions: Of the indicator of the organizational climate of climateQual, the contribution of the team work from this influence is more than any other variables that reinforcement of teamwork in academic libraries can be more effective in improving the organizational climate of this type libraries. PMID:26622203

THE REGRESSION MODEL OF IRAN LIBRARIES ORGANIZATIONAL CLIMATE.

PubMed

Jahani, Mohammad Ali; Yaminfirooz, Mousa; Siamian, Hasan

2015-10-01

The purpose of this study was to drawing a regression model of organizational climate of central libraries of Iran's universities. This study is an applied research. The statistical population of this study consisted of 96 employees of the central libraries of Iran's public universities selected among the 117 universities affiliated to the Ministry of Health by Stratified Sampling method (510 people). Climate Qual localized questionnaire was used as research tools. For predicting the organizational climate pattern of the libraries is used from the multivariate linear regression and track diagram. of the 9 variables affecting organizational climate, 5 variables of innovation, teamwork, customer service, psychological safety and deep diversity play a major role in prediction of the organizational climate of Iran's libraries. The results also indicate that each of these variables with different coefficient have the power to predict organizational climate but the climate score of psychological safety (0.94) plays a very crucial role in predicting the organizational climate. Track diagram showed that five variables of teamwork, customer service, psychological safety, deep diversity and innovation directly effects on the organizational climate variable that contribution of the team work from this influence is more than any other variables. Of the indicator of the organizational climate of climateQual, the contribution of the team work from this influence is more than any other variables that reinforcement of teamwork in academic libraries can be more effective in improving the organizational climate of this type libraries.

Potential breeding distributions of U.S. birds predicted with both short-term variability and long-term average climate data.

PubMed

Bateman, Brooke L; Pidgeon, Anna M; Radeloff, Volker C; Flather, Curtis H; VanDerWal, Jeremy; Akçakaya, H Resit; Thogmartin, Wayne E; Albright, Thomas P; Vavrus, Stephen J; Heglund, Patricia J

2016-12-01

Climate conditions, such as temperature or precipitation, averaged over several decades strongly affect species distributions, as evidenced by experimental results and a plethora of models demonstrating statistical relations between species occurrences and long-term climate averages. However, long-term averages can conceal climate changes that have occurred in recent decades and may not capture actual species occurrence well because the distributions of species, especially at the edges of their range, are typically dynamic and may respond strongly to short-term climate variability. Our goal here was to test whether bird occurrence models can be predicted by either covariates based on short-term climate variability or on long-term climate averages. We parameterized species distribution models (SDMs) based on either short-term variability or long-term average climate covariates for 320 bird species in the conterminous USA and tested whether any life-history trait-based guilds were particularly sensitive to short-term conditions. Models including short-term climate variability performed well based on their cross-validated area-under-the-curve AUC score (0.85), as did models based on long-term climate averages (0.84). Similarly, both models performed well compared to independent presence/absence data from the North American Breeding Bird Survey (independent AUC of 0.89 and 0.90, respectively). However, models based on short-term variability covariates more accurately classified true absences for most species (73% of true absences classified within the lowest quarter of environmental suitability vs. 68%). In addition, they have the advantage that they can reveal the dynamic relationship between species and their environment because they capture the spatial fluctuations of species potential breeding distributions. With this information, we can identify which species and guilds are sensitive to climate variability, identify sites of high conservation value where climate variability is low, and assess how species' potential distributions may have already shifted due recent climate change. However, long-term climate averages require less data and processing time and may be more readily available for some areas of interest. Where data on short-term climate variability are not available, long-term climate information is a sufficient predictor of species distributions in many cases. However, short-term climate variability data may provide information not captured with long-term climate data for use in SDMs. © 2016 by the Ecological Society of America.

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.

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

PubMed

Kukal, Meetpal S; Irmak, Suat

2018-02-22

Climate variability and trends affect global crop yields and are characterized as highly dependent on location, crop type, and irrigation. U.S. Great Plains, due to its significance in national food production, evident climate variability, and extensive irrigation is an ideal region of investigation for climate impacts on food production. This paper evaluates climate impacts on maize, sorghum, and soybean yields and effect of irrigation for individual counties in this region by employing extensive crop yield and climate datasets from 1968-2013. Variability in crop yields was a quarter of the regional average yields, with a quarter of this variability explained by climate variability, and temperature and precipitation explained these in singularity or combination at different locations. Observed temperature trend was beneficial for maize yields, but detrimental for sorghum and soybean yields, whereas observed precipitation trend was beneficial for all three crops. Irrigated yields demonstrated increased robustness and an effective mitigation strategy against climate impacts than their non-irrigated counterparts by a considerable fraction. The information, data, and maps provided can serve as an assessment guide for planners, managers, and policy- and decision makers to prioritize agricultural resilience efforts and resource allocation or re-allocation in the regions that exhibit risk from climate variability.

Climate change and pastoralism: impacts, consequences and adaptation.

PubMed

Herrero, M; Addison, J; Bedelian, C; Carabine, E; Havlík, P; Henderson, B; Van De Steeg, J; Thornton, P K

2016-11-01

The authors discuss the main climate change impacts on pastoralist societies, including those on rangelands, livestock and other natural resources, and their extended repercussions on food security, incomes and vulnerability. The impacts of climate change on the rangelands of the globe and on the vulnerability of the people who inhabit them will be severe and diverse, and will require multiple, simultaneous responses. In higher latitudes, the removal of temperature constraints might increase pasture production and livestock productivity, but in tropical arid lands, the impacts are highly location specific, but mostly negative. The authors outline several adaptation options, ranging from implementing new technical practices and diversifying income sources to finding institutional support and introducing new market mechanisms, all of which are pivotal for enhancing the capacity of pastoralists to adapt to climate variability and change. Due to the dynamism of all the changes affecting pastoral societies, strategies that lock pastoral societies into specified development pathways could be maladaptive. Flexible and evolving combinations of practices and policies are the key to successful pastoral adaptation.

Integrated modeling for assessment of energy-water system resilience under changing climate

NASA Astrophysics Data System (ADS)

Yan, E.; Veselka, T.; Zhou, Z.; Koritarov, V.; Mahalik, M.; Qiu, F.; Mahat, V.; Betrie, G.; Clark, C.

2016-12-01

Energy and water systems are intrinsically interconnected. Due to an increase in climate variability and extreme weather events, interdependency between these two systems has been recently intensified resulting significant impacts on both systems and energy output. To address this challenge, an Integrated Water-Energy Systems Assessment Framework (IWESAF) is being developed to integrate multiple existing or developed models from various sectors. The IWESAF currently includes an extreme climate event generator to predict future extreme weather events, hydrologic and reservoir models, riverine temperature model, power plant water use simulator, and power grid operation and cost optimization model. The IWESAF can facilitate the interaction among the modeling systems and provide insights of the sustainability and resilience of the energy-water system under extreme climate events and economic consequence. The regional case demonstration in the Midwest region will be presented. The detailed information on some of individual modeling components will also be presented in several other abstracts submitted to AGU this year.

1,500 year quantitative reconstruction of winter precipitation in the Pacific Northwest

PubMed Central

Steinman, Byron A.; Abbott, Mark B.; Mann, Michael E.; Stansell, Nathan D.; Finney, Bruce P.

2012-01-01

Multiple paleoclimate proxies are required for robust assessment of past hydroclimatic conditions. Currently, estimates of drought variability over the past several thousand years are based largely on tree-ring records. We produced a 1,500-y record of winter precipitation in the Pacific Northwest using a physical model-based analysis of lake sediment oxygen isotope data. Our results indicate that during the Medieval Climate Anomaly (MCA) (900–1300 AD) the Pacific Northwest experienced exceptional wetness in winter and that during the Little Ice Age (LIA) (1450–1850 AD) conditions were drier, contrasting with hydroclimatic anomalies in the desert Southwest and consistent with climate dynamics related to the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO). These findings are somewhat discordant with drought records from tree rings, suggesting that differences in seasonal sensitivity between the two proxies allow a more compete understanding of the climate system and likely explain disparities in inferred climate trends over centennial timescales. PMID:22753510

On climate prediction: how much can we expect from climate memory?

NASA Astrophysics Data System (ADS)

Yuan, Naiming; Huang, Yan; Duan, Jianping; Zhu, Congwen; Xoplaki, Elena; Luterbacher, Jürg

2018-03-01

Slowing variability in climate system is an important source of climate predictability. However, it is still challenging for current dynamical models to fully capture the variability as well as its impacts on future climate. In this study, instead of simulating the internal multi-scale oscillations in dynamical models, we discussed the effects of internal variability in terms of climate memory. By decomposing climate state x(t) at a certain time point t into memory part M(t) and non-memory part ɛ (t) , climate memory effects from the past 30 years on climate prediction are quantified. For variables with strong climate memory, high variance (over 20% ) in x(t) is explained by the memory part M(t), and the effects of climate memory are non-negligible for most climate variables, but the precipitation. Regarding of multi-steps climate prediction, a power law decay of the explained variance was found, indicating long-lasting climate memory effects. The explained variances by climate memory can remain to be higher than 10% for more than 10 time steps. Accordingly, past climate conditions can affect both short (monthly) and long-term (interannual, decadal, or even multidecadal) climate predictions. With the memory part M(t) precisely calculated from Fractional Integral Statistical Model, one only needs to focus on the non-memory part ɛ (t) , which is an important quantity that determines climate predictive skills.

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.

Plastic and evolutionary responses to climate change in fish

PubMed Central

Crozier, Lisa G; Hutchings, Jeffrey A

2014-01-01

The physical and ecological ‘fingerprints’ of anthropogenic climate change over the past century are now well documented in many environments and taxa. We reviewed the evidence for phenotypic responses to recent climate change in fish. Changes in the timing of migration and reproduction, age at maturity, age at juvenile migration, growth, survival and fecundity were associated primarily with changes in temperature. Although these traits can evolve rapidly, only two studies attributed phenotypic changes formally to evolutionary mechanisms. The correlation-based methods most frequently employed point largely to ‘fine-grained’ population responses to environmental variability (i.e. rapid phenotypic changes relative to generation time), consistent with plastic mechanisms. Ultimately, many species will likely adapt to long-term warming trends overlaid on natural climate oscillations. Considering the strong plasticity in all traits studied, we recommend development and expanded use of methods capable of detecting evolutionary change, such as the long term study of selection coefficients and temporal shifts in reaction norms, and increased attention to forecasting adaptive change in response to the synergistic interactions of the multiple selection pressures likely to be associated with climate change. PMID:24454549

Plastic and evolutionary responses to climate change in fish.

PubMed

Crozier, Lisa G; Hutchings, Jeffrey A

2014-01-01

The physical and ecological 'fingerprints' of anthropogenic climate change over the past century are now well documented in many environments and taxa. We reviewed the evidence for phenotypic responses to recent climate change in fish. Changes in the timing of migration and reproduction, age at maturity, age at juvenile migration, growth, survival and fecundity were associated primarily with changes in temperature. Although these traits can evolve rapidly, only two studies attributed phenotypic changes formally to evolutionary mechanisms. The correlation-based methods most frequently employed point largely to 'fine-grained' population responses to environmental variability (i.e. rapid phenotypic changes relative to generation time), consistent with plastic mechanisms. Ultimately, many species will likely adapt to long-term warming trends overlaid on natural climate oscillations. Considering the strong plasticity in all traits studied, we recommend development and expanded use of methods capable of detecting evolutionary change, such as the long term study of selection coefficients and temporal shifts in reaction norms, and increased attention to forecasting adaptive change in response to the synergistic interactions of the multiple selection pressures likely to be associated with climate change.

Addressing Spatial Dependence Bias in Climate Model Simulations—An Independent Component Analysis Approach

NASA Astrophysics Data System (ADS)

Nahar, Jannatun; Johnson, Fiona; Sharma, Ashish

2018-02-01

Conventional bias correction is usually applied on a grid-by-grid basis, meaning that the resulting corrections cannot address biases in the spatial distribution of climate variables. To solve this problem, a two-step bias correction method is proposed here to correct time series at multiple locations conjointly. The first step transforms the data to a set of statistically independent univariate time series, using a technique known as independent component analysis (ICA). The mutually independent signals can then be bias corrected as univariate time series and back-transformed to improve the representation of spatial dependence in the data. The spatially corrected data are then bias corrected at the grid scale in the second step. The method has been applied to two CMIP5 General Circulation Model simulations for six different climate regions of Australia for two climate variables—temperature and precipitation. The results demonstrate that the ICA-based technique leads to considerable improvements in temperature simulations with more modest improvements in precipitation. Overall, the method results in current climate simulations that have greater equivalency in space and time with observational data.

Assessing climate impacts

PubMed Central

Wohl, Ellen E.; Pulwarty, Roger S.; Zhang, Jian Yun

2000-01-01

Assessing climate impacts involves identifying sources and characteristics of climate variability, and mitigating potential negative impacts of that variability. Associated research focuses on climate driving mechanisms, biosphere–hydrosphere responses and mediation, and human responses. Examples of climate impacts come from 1998 flooding in the Yangtze River Basin and hurricanes in the Caribbean and Central America. Although we have limited understanding of the fundamental driving-response interactions associated with climate variability, increasingly powerful measurement and modeling techniques make assessing climate impacts a rapidly developing frontier of science. PMID:11027321

Accounting for interannual variability: A comparison of options for water resources climate change impact assessments

NASA Astrophysics Data System (ADS)

Johnson, Fiona; Sharma, Ashish

2011-04-01

Empirical scaling approaches for constructing rainfall scenarios from general circulation model (GCM) simulations are commonly used in water resources climate change impact assessments. However, these approaches have a number of limitations, not the least of which is that they cannot account for changes in variability or persistence at annual and longer time scales. Bias correction of GCM rainfall projections offers an attractive alternative to scaling methods as it has similar advantages to scaling in that it is computationally simple, can consider multiple GCM outputs, and can be easily applied to different regions or climatic regimes. In addition, it also allows for interannual variability to evolve according to the GCM simulations, which provides additional scenarios for risk assessments. This paper compares two scaling and four bias correction approaches for estimating changes in future rainfall over Australia and for a case study for water supply from the Warragamba catchment, located near Sydney, Australia. A validation of the various rainfall estimation procedures is conducted on the basis of the latter half of the observational rainfall record. It was found that the method leading to the lowest prediction errors varies depending on the rainfall statistic of interest. The flexibility of bias correction approaches in matching rainfall parameters at different frequencies is demonstrated. The results also indicate that for Australia, the scaling approaches lead to smaller estimates of uncertainty associated with changes to interannual variability for the period 2070-2099 compared to the bias correction approaches. These changes are also highlighted using the case study for the Warragamba Dam catchment.

The hydrologic and biogeochemical response of undisturbed mountain ecosystems in the Western United States to multiple stressors: Interactions between climate variability and atmospheric deposition of contaminants

NASA Astrophysics Data System (ADS)

Campbell, D. H.; Mast, M. A.; Clow, D. W.; Ingersoll, G. P.; Nanus, L.

2004-12-01

Wilderness areas and national parks of the West are largely protected from acute changes in land use such as urbanization and natural resource development. However, the ecosystems in these areas are sensitive to both climate variability and atmospheric deposition of acids, nitrogen (N), and toxic contaminants, and these stressors interact in ways that we are just beginning to understand. Here we examine some examples of the interactions between climate variability and nitrogen and mercury cycling in high elevation watersheds. During the recent drought, which began in 2000, streamwater nitrate concentrations nearly doubled in the Loch Vale watershed in Rocky Mountain National Park, exceeding 60 μ M during early snowmelt. Much of the elevated nitrate resulted from an increased percentage contribution to streamwater of nitrate-rich shallow groundwater. In a nearby pond used for breeding by a threatened amphibian species, nitrate concentrations were negligible but ammonium concentrations were extremely high (850 μ M) during the drought. In this case, organic N in pond sediments was likely mineralized and released during cycles of drying and rewetting of pond sediments. Even after 2 years of near-average precipitation, water levels remained below normal and ammonium concentrations remained elevated, indicating that the hydrologic response of this small system has a timescale of many years. Mercury (Hg) deposition at high elevations of the Rocky Mountains is comparable to that of the Midwest and Northeast, but the processes that control Hg cycling in alpine/subalpine ecosystems are not well understood. Methylation and bioaccumulation of Hg must occur before Hg reaches levels harmful to the ecosystem or human health, and both climate and nutrient cycling affect these processes. Fluctuating water levels caused by climate variability can mobilize Hg from lake and pond sediments, increasing reactivity and bioavailability of Hg in the ecosystem. Increased nutrient release from the terrestrial ecosystem (eg. from N saturation) may increase productivity and accumulation of organic matter, altering Hg cycling in the aquatic system. Long durations of ice cover and thick snowpacks are likely to cause elevated methyl Hg in aquatic ecosystems. Snow and ice cover on lakes promotes hypoxia in lake water, favoring production and accumulation of methyl Hg- the percentage of methyl-Hg in lake water under snow and ice was as much as 6 times greater than the percentage measured during late summer in a northwestern Colorado lake. Analysis of long-term trends indicates that climate variability is increasing in the Mountain West. Climatic extremes appear to exacerbate adverse impacts of atmospheric deposition, as well as stressing ecosystems directly. A better understanding of these interactions is needed in order to predict the response of mountain ecosystems to future changes in climate and atmospheric deposition.

Testing a growth efficiency hypothesis with continental-scale phenological variations of common and cloned plants.

PubMed

Liang, Liang; Schwartz, Mark D

2014-10-01

Variation in the timing of plant phenology caused by phenotypic plasticity is a sensitive measure of how organisms respond to weather and climate variability. Although continental-scale gradients in climate and consequential patterns in plant phenology are well recognized, the contribution of underlying genotypic difference to the geography of phenology is less well understood. We hypothesize that different temperate plant genotypes require varying amount of heat energy for resuming annual growth and reproduction as a result of adaptation and other ecological and evolutionary processes along climatic gradients. In particular, at least for some species, the growing degree days (GDD) needed to trigger the same spring phenology events (e.g., budburst and flower bloom) may be less for individuals originated from colder climates than those from warmer climates. This variable intrinsic heat energy requirement in plants can be characterized by the term growth efficiency and is quantitatively reflected in the timing of phenophases-earlier timing indicates higher efficiency (i.e., less heat energy needed to trigger phenophase transitions) and vice versa compared to a standard reference (i.e., either a uniform climate or a uniform genotype). In this study, we tested our hypothesis by comparing variations of budburst and bloom timing of two widely documented plants from the USA National Phenology Network (i.e., red maple-Acer rubrum and forsythia-Forsythia spp.) with cloned indicator plants (lilac-Syringa x chinensis 'Red Rothomagensis') at multiple eastern US sites. Our results indicate that across the accumulated temperature gradient, the two non-clonal plants showed significantly more gradual changes than the cloned plants, manifested by earlier phenology in colder climates and later phenology in warmer climates relative to the baseline clone phenological response. This finding provides initial evidence supporting the growth efficiency hypothesis, and suggests more work is warranted. More studies investigating genotype-determined phenological variations will be useful for better understanding and prediction of the continental-scale patterns of biospheric responses to climate change.

Winter Season Mortality: Will Climate Warming Bring Benefits?

PubMed

Kinney, Patrick L; Schwartz, Joel; Pascal, Mathilde; Petkova, Elisaveta; Tertre, Alain Le; Medina, Sylvia; Vautard, Robert

2015-06-01

Extreme heat events are associated with spikes in mortality, yet death rates are on average highest during the coldest months of the year. Under the assumption that most winter excess mortality is due to cold temperature, many previous studies have concluded that winter mortality will substantially decline in a warming climate. We analyzed whether and to what extent cold temperatures are associated with excess winter mortality across multiple cities and over multiple years within individual cities, using daily temperature and mortality data from 36 US cities (1985-2006) and 3 French cities (1971-2007). Comparing across cities, we found that excess winter mortality did not depend on seasonal temperature range, and was no lower in warmer vs. colder cities, suggesting that temperature is not a key driver of winter excess mortality. Using regression models within monthly strata, we found that variability in daily mortality within cities was not strongly influenced by winter temperature. Finally we found that inadequate control for seasonality in analyses of the effects of cold temperatures led to spuriously large assumed cold effects, and erroneous attribution of winter mortality to cold temperatures. Our findings suggest that reductions in cold-related mortality under warming climate may be much smaller than some have assumed. This should be of interest to researchers and policy makers concerned with projecting future health effects of climate change and developing relevant adaptation strategies.

Western North Pacific Tropical Cyclone Model Tracks in Present and Future Climates

NASA Astrophysics Data System (ADS)

Nakamura, Jennifer; Camargo, Suzana J.; Sobel, Adam H.; Henderson, Naomi; Emanuel, Kerry A.; Kumar, Arun; LaRow, Timothy E.; Murakami, Hiroyuki; Roberts, Malcolm J.; Scoccimarro, Enrico; Vidale, Pier Luigi; Wang, Hui; Wehner, Michael F.; Zhao, Ming

2017-09-01

Western North Pacific tropical cyclone (TC) model tracks are analyzed in two large multimodel ensembles, spanning a large variety of models and multiple future climate scenarios. Two methodologies are used to synthesize the properties of TC tracks in this large data set: cluster analysis and mass moment ellipses. First, the models' TC tracks are compared to observed TC tracks' characteristics, and a subset of the models is chosen for analysis, based on the tracks' similarity to observations and sample size. Potential changes in track types in a warming climate are identified by comparing the kernel smoothed probability distributions of various track variables in historical and future scenarios using a Kolmogorov-Smirnov significance test. Two track changes are identified. The first is a statistically significant increase in the north-south expansion, which can also be viewed as a poleward shift, as TC tracks are prevented from expanding equatorward due to the weak Coriolis force near the equator. The second change is an eastward shift in the storm tracks that occur near the central Pacific in one of the multimodel ensembles, indicating a possible increase in the occurrence of storms near Hawaii in a warming climate. The dependence of the results on which model and future scenario are considered emphasizes the necessity of including multiple models and scenarios when considering future changes in TC characteristics.

Winter season mortality: will climate warming bring benefits?

NASA Astrophysics Data System (ADS)

Kinney, Patrick L.; Schwartz, Joel; Pascal, Mathilde; Petkova, Elisaveta; Le Tertre, Alain; Medina, Sylvia; Vautard, Robert

2015-06-01

Extreme heat events are associated with spikes in mortality, yet death rates are on average highest during the coldest months of the year. Under the assumption that most winter excess mortality is due to cold temperature, many previous studies have concluded that winter mortality will substantially decline in a warming climate. We analyzed whether and to what extent cold temperatures are associated with excess winter mortality across multiple cities and over multiple years within individual cities, using daily temperature and mortality data from 36 US cities (1985-2006) and 3 French cities (1971-2007). Comparing across cities, we found that excess winter mortality did not depend on seasonal temperature range, and was no lower in warmer vs. colder cities, suggesting that temperature is not a key driver of winter excess mortality. Using regression models within monthly strata, we found that variability in daily mortality within cities was not strongly influenced by winter temperature. Finally we found that inadequate control for seasonality in analyses of the effects of cold temperatures led to spuriously large assumed cold effects, and erroneous attribution of winter mortality to cold temperatures. Our findings suggest that reductions in cold-related mortality under warming climate may be much smaller than some have assumed. This should be of interest to researchers and policy makers concerned with projecting future health effects of climate change and developing relevant adaptation strategies.

The Impact of Changing Snowmelt Timing on Non-Irrigated Crop Yield in Idaho

NASA Astrophysics Data System (ADS)

Murray, E. M.; Cobourn, K.; Flores, A. N.; Pierce, J. L.; Kunkel, M. L.

2013-12-01

The impacts of climate change on water resources have implications for both agricultural production and grower welfare. Many mountainous regions in the western U.S. rely on snowmelt as the dominant surface water source, and in Idaho, reconstructions of spring snowmelt timing have demonstrated a trend toward earlier, more variable snowmelt dates within the past 20 years. This earlier date and increased variability in snowmelt timing have serious implications for agriculture, but there is considerable uncertainty about how agricultural impacts vary by region, crop-type, and practices like irrigation vs. dryland farming. Establishing the relationship between snowmelt timing and agricultural yield is important for understanding how changes in large-scale climatic indices (like snowmelt date) may be associated with changes in agricultural yield. This is particularly important where local practitioner behavior is influenced by historically observed relationships between these climate indices and yield. In addition, a better understanding of the influence of changes in snowmelt on non-irrigated crop yield may be extrapolated to better understand how climate change may alter biomass production in non-managed ecosystems. To investigate the impact of snowmelt date on non-irrigated crop yield, we developed a multiple linear regression model to predict historical wheat and barley yield in several Idaho counties as a function of snowmelt date, climate variables (precipitation and growing degree-days), and spatial differences between counties. The relationship between snowmelt timing and non-irrigated crop yield at the county level is strong in many of the models, but differs in magnitude and direction for the two different crops. Results show interesting spatial patterns of variability in the correlation between snowmelt timing and crop yield. In four southern counties that border the Snake River Plain and one county bordering Oregon, non-irrigated wheat and/or barley yield are significantly lower in years with early snowmelt timing, on average (P < 0.10). In contrast, in northern Idaho, barley yield is significantly higher in years with early snowmelt timing. Overall, this statistical modeling exercise indicates that the trend toward earlier snowmelt date may positively impact non-irrigated crop yield in some regions of Idaho, while negatively impacting yield in other areas. Additional research is necessary to identify spatial controls on the variable relationship between snowmelt timing and yield. Regional variability in the response of crops to changes in snowmelt timing may indicate that external factors (e.g. higher amounts of summer rain in northern vs. southern Idaho) may play an important role in crop yield. This study indicates that targeted regional analysis is necessary to determine the influence of climate change on agriculture, as local variability can cause the same forcing to produce opposite results.

Arctic Change Detection: Multiple Observations and Recent Explanations

NASA Astrophysics Data System (ADS)

Soreide, N. N.; Overland, J. E.; Calder, J.

2004-12-01

The recently released Arctic Climate Impact Assessment (ACIA) Report documents Arctic-wide changes and impacts; it provides a long-term perspective for peoples, governments and scientists in coping with these changes. Further, investigation of the last three decades of multivariate biophysical data sets(climate, land and marine ecosystems, cryosphere) and century-long weather records, show two main types of Arctic variability. These are: 1) long-term trends as represented by loss of sea-ice and tundra area and their biological response, and 2) decadal variability in atmospheric forcing and its direct impacts. Three main conclusions are possible: * Temperature anomalies in the last 15 years are unique in the Arctic instrumental record (1880-2003). Historically, there were regional/decadal warm events during winter and spring in the 1930s to 1950s, but meteorological analysis shows that these surface air temperature anomalies are the result of intrinsic variability in regional flow patterns, as contrasted with the Arctic-wide Arctic Oscillation (AO) influence of the 1990s. * These changes are primarily driven by changes in atmospheric circulation, and thus are subject to north/south gradients in hemispheric radiative forcing from volcanic aerosols, insolation cycles and CO2 increase. These north/south differences drive temperature advection in the trough-ridge structure of the AO. This conclusion is based primarily on model results and impacts from volcanos. * Change is likely to be irreversible over at least the next decade. In the previous five years, many ecosystems, such as the Bering Sea and east Greenland, are showing more year-to-year persistence, despite considerable variability in the AO and other climate indices. We hypothesize that the changes occurring in the Arctic are beginning to be significant enough to make the Arctic less sensitive to cold swings in atmospheric variability, although direct mechanisms are unclear. A next step in the post-ACIA period is a comprehensive Arctic Change Detection product which builds upon the ACIA report with regularly updated information. Credibility is based on multiple lines of evidence and cooperation of scientists. The Arctic Change Detection project provides a near-realtime suite of indicators, their potential impacts, recent events, news items, and scientific publications, in an understandable format at www.arctic.noaa.gov. This website makes information about the current status of the Arctic available to a wide audience.

Data and code for the exploratory data analysis of the electrical energy demand in the time domain in Greece.

PubMed

Tyralis, Hristos; Karakatsanis, Georgios; Tzouka, Katerina; Mamassis, Nikos

2017-08-01

We present data and code for visualizing the electrical energy data and weather-, climate-related and socioeconomic variables in the time domain in Greece. The electrical energy data include hourly demand, weekly-ahead forecasted values of the demand provided by the Greek Independent Power Transmission Operator and pricing values in Greece. We also present the daily temperature in Athens and the Gross Domestic Product of Greece. The code combines the data to a single report, which includes all visualizations with combinations of all variables in multiple time scales. The data and code were used in Tyralis et al. (2017) [1].

Ecosystem functioning is enveloped by hydrometeorological variability.

PubMed

Pappas, Christoforos; Mahecha, Miguel D; Frank, David C; Babst, Flurin; Koutsoyiannis, Demetris

2017-09-01

Terrestrial ecosystem processes, and the associated vegetation carbon dynamics, respond differently to hydrometeorological variability across timescales, and so does our scientific understanding of the underlying mechanisms. Long-term variability of the terrestrial carbon cycle is not yet well constrained and the resulting climate-biosphere feedbacks are highly uncertain. Here we present a comprehensive overview of hydrometeorological and ecosystem variability from hourly to decadal timescales integrating multiple in situ and remote-sensing datasets characterizing extra-tropical forest sites. We find that ecosystem variability at all sites is confined within a hydrometeorological envelope across sites and timescales. Furthermore, ecosystem variability demonstrates long-term persistence, highlighting ecological memory and slow ecosystem recovery rates after disturbances. However, simulation results with state-of-the-art process-based models do not reflect this long-term persistent behaviour in ecosystem functioning. Accordingly, we develop a cross-time-scale stochastic framework that captures hydrometeorological and ecosystem variability. Our analysis offers a perspective for terrestrial ecosystem modelling and paves the way for new model-data integration opportunities in Earth system sciences.

The water balance of the urban Salt Lake Valley: a multiple-box model validated by observations

NASA Astrophysics Data System (ADS)

Stwertka, C.; Strong, C.

2012-12-01

A main focus of the recently awarded National Science Foundation (NSF) EPSCoR Track-1 research project "innovative Urban Transitions and Arid-region Hydro-sustainability (iUTAH)" is to quantify the primary components of the water balance for the Wasatch region, and to evaluate their sensitivity to climate change and projected urban development. Building on the multiple-box model that we developed and validated for carbon dioxide (Strong et al 2011), mass balance equations for water in the atmosphere and surface are incorporated into the modeling framework. The model is used to determine how surface fluxes, ground-water transport, biological fluxes, and meteorological processes regulate water cycling within and around the urban Salt Lake Valley. The model is used to evaluate the hypotheses that increased water demand associated with urban growth in Salt Lake Valley will (1) elevate sensitivity to projected climate variability and (2) motivate more attentive management of urban water use and evaporative fluxes.

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.

Water Stress on U.S. Power Production at Decadal Time Horizons

NASA Astrophysics Data System (ADS)

Ganguli, P.; Kumar, D.; Yun, J.; Short, G.; Klausner, J.; Ganguly, A. R.

2014-12-01

Thermoelectric power production at risk, owing to current and projected water scarcity and rising stream temperatures, is assessed for the continental United States (US) at decadal scales. Regional water scarcity is driven by climate variability and change, as well as by multi-sector water demand. While a planning horizon of zero to about thirty years is occasionally prescribed by stakeholders, the challenges to risk assessment at these scales include the difficulty in delineating decadal climate trends from intrinsic natural or multiple model variability. Current generation global climate or earth system models are not credible at the spatial resolutions of power plants, especially for surface water quantity and stream temperatures, which further exacerbates the assessment challenge. Population changes, which are anyway difficult to project, cannot serve as adequate proxies for changes in the water demand across sectors. The hypothesis that robust assessments of power production at risks are possible, despite the uncertainties, has been examined as a proof of concept. An approach is presented for delineating water scarcity and temperature from climate models, observations and population storylines, as well as for assessing power production at risk by examining geospatial correlations of power plant locations within regions where the usable water supply for energy production happens to be scarcer and warmer. Acknowledgment: Funding provided by US DOE's ARPA-E through Award DE-AR0000374.

Seasonal modulation of the Asian summer monsoon between the Medieval Warm Period and Little Ice Age: a multi model study

NASA Astrophysics Data System (ADS)

Kamae, Youichi; Kawana, Toshi; Oshiro, Megumi; Ueda, Hiroaki

2017-12-01

Instrumental and proxy records indicate remarkable global climate variability over the last millennium, influenced by solar irradiance, Earth's orbital parameters, volcanic eruptions and human activities. Numerical model simulations and proxy data suggest an enhanced Asian summer monsoon during the Medieval Warm Period (MWP) compared to the Little Ice Age (LIA). Using multiple climate model simulations, we show that anomalous seasonal insolation over the Northern Hemisphere due to a long cycle of orbital parameters results in a modulation of the Asian summer monsoon transition between the MWP and LIA. Ten climate model simulations prescribing historical radiative forcing that includes orbital parameters consistently reproduce an enhanced MWP Asian monsoon in late summer and a weakened monsoon in early summer. Weakened, then enhanced Northern Hemisphere insolation before and after June leads to a seasonally asymmetric temperature response over the Eurasian continent, resulting in a seasonal reversal of the signs of MWP-LIA anomalies in land-sea thermal contrast, atmospheric circulation, and rainfall from early to late summer. This seasonal asymmetry in monsoon response is consistently found among the different climate models and is reproduced by an idealized model simulation forced solely by orbital parameters. The results of this study indicate that slow variation in the Earth's orbital parameters contributes to centennial variability in the Asian monsoon transition.[Figure not available: see fulltext.

Pioneer farming in southeast Europe during the early sixth millennium BC: Climate-related adaptations in the exploitation of plants and animals

PubMed Central

De Cupere, Bea; Ethier, Jonathan; Marinova, Elena

2018-01-01

The Old World farming system arose in the semi-arid Mediterranean environments of southwest Asia. Pioneer farmers settling the interior of the Balkans by the early sixth millennium BC were among the first to introduce southwest Asian-style cultivation and herding into areas with increasingly continental temperate conditions. Previous research has shown that the bioarchaeological assemblages from early farming sites in southeast Europe vary in their proportions of plant and animal taxa, but the relationship between taxonomic variation and climate has remained poorly understood. To uncover associations between multiple species and environmental factors simultaneously, we explored a dataset including altitude, five bioclimatic and 30 bioarchaeological variables (plant and animal taxa) for 57 of the earliest farming sites in southeast Europe using Canonical Correspondence Analysis (CCA). An extension of correspondence analysis, CCA is widely used in applied ecology to answer similar questions of species-environment relationships, but has not been previously applied in prehistoric archaeology to explore taxonomic and climatic variables in conjunction. The analyses reveal that the changes in plant and animal exploitation which occurred with the northward dispersal of farmers, crops and livestock correlate with south-north climate gradients, and emphasize the importance of adaptations in the animal domain for the initial establishment of farming beyond the Mediterranean areas. PMID:29775469

Uncertainty in future agro-climate projections in the United States and benefits of greenhouse gas mitigation

DOE PAGES

Monier, Erwan; Xu, Liyi; Snyder, Richard

2016-04-26

Scientific challenges exist on how to extract information from the wide range of projected impacts simulated by crop models driven by climate ensembles. A stronger focus is required to understand and identify the mechanisms and drivers of projected changes in crop yield. In this study, we investigate the robustness of future projections of five metrics relevant to agriculture stakeholders (accumulated frost days, dry days, growing season length, plant heat stress and start of field operations). We use a large ensemble of climate simulations by the MIT IGSM-CAM integrated assessment model that accounts for the uncertainty associated with different emissions scenarios,more » climate sensitivities, and representations of natural variability. By the end of the century, the US is projected to experience fewer frosts, a longer growing season, more heat stress and an earlier start of field operations-although the magnitude and even the sign of these changes vary greatly by regions. Projected changes in dry days are shown not to be robust. We highlight the important role of natural variability, in particular for changes in dry days (a precipitation-related index) and heat stress (a threshold index). The wide range of our projections compares well the CMIP5 multi-model ensemble, especially for temperature-related indices. This suggests that using a single climate model that accounts for key sources of uncertainty can provide an efficient and complementary framework to the more common approach of multi-model ensembles. We also show that greenhouse gas mitigation has the potential to significantly reduce adverse effects (heat stress, risks of pest and disease) of climate change on agriculture, while also curtailing potentially beneficial impacts (earlier planting, possibility for multiple cropping). A major benefit of climate mitigation is potentially preventing changes in several indices to emerge from the noise of natural variability, even by 2100. This has major implications considering that any significant climate change impacts on crop yield would result in nation-wide changes in the agriculture sector. Lastly, we argue that the analysis of agro-climate indices should more often complement crop model projections, as they can provide valuable information to better understand the drivers of changes in crop yield and production and thus better inform adaptation decisions.« less

Uncertainty in future agro-climate projections in the United States and benefits of greenhouse gas mitigation

NASA Astrophysics Data System (ADS)

Monier, Erwan; Xu, Liyi; Snyder, Richard

2016-05-01

Scientific challenges exist on how to extract information from the wide range of projected impacts simulated by crop models driven by climate ensembles. A stronger focus is required to understand and identify the mechanisms and drivers of projected changes in crop yield. In this study, we investigate the robustness of future projections of five metrics relevant to agriculture stakeholders (accumulated frost days, dry days, growing season length, plant heat stress and start of field operations). We use a large ensemble of climate simulations by the MIT IGSM-CAM integrated assessment model that accounts for the uncertainty associated with different emissions scenarios, climate sensitivities, and representations of natural variability. By the end of the century, the US is projected to experience fewer frosts, a longer growing season, more heat stress and an earlier start of field operations—although the magnitude and even the sign of these changes vary greatly by regions. Projected changes in dry days are shown not to be robust. We highlight the important role of natural variability, in particular for changes in dry days (a precipitation-related index) and heat stress (a threshold index). The wide range of our projections compares well the CMIP5 multi-model ensemble, especially for temperature-related indices. This suggests that using a single climate model that accounts for key sources of uncertainty can provide an efficient and complementary framework to the more common approach of multi-model ensembles. We also show that greenhouse gas mitigation has the potential to significantly reduce adverse effects (heat stress, risks of pest and disease) of climate change on agriculture, while also curtailing potentially beneficial impacts (earlier planting, possibility for multiple cropping). A major benefit of climate mitigation is potentially preventing changes in several indices to emerge from the noise of natural variability, even by 2100. This has major implications considering that any significant climate change impacts on crop yield would result in nation-wide changes in the agriculture sector. Finally, we argue that the analysis of agro-climate indices should more often complement crop model projections, as they can provide valuable information to better understand the drivers of changes in crop yield and production and thus better inform adaptation decisions.

Uncertainty in future agro-climate projections in the United States and benefits of greenhouse gas mitigation

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

Monier, Erwan; Xu, Liyi; Snyder, Richard

Scientific challenges exist on how to extract information from the wide range of projected impacts simulated by crop models driven by climate ensembles. A stronger focus is required to understand and identify the mechanisms and drivers of projected changes in crop yield. In this study, we investigate the robustness of future projections of five metrics relevant to agriculture stakeholders (accumulated frost days, dry days, growing season length, plant heat stress and start of field operations). We use a large ensemble of climate simulations by the MIT IGSM-CAM integrated assessment model that accounts for the uncertainty associated with different emissions scenarios,more » climate sensitivities, and representations of natural variability. By the end of the century, the US is projected to experience fewer frosts, a longer growing season, more heat stress and an earlier start of field operations-although the magnitude and even the sign of these changes vary greatly by regions. Projected changes in dry days are shown not to be robust. We highlight the important role of natural variability, in particular for changes in dry days (a precipitation-related index) and heat stress (a threshold index). The wide range of our projections compares well the CMIP5 multi-model ensemble, especially for temperature-related indices. This suggests that using a single climate model that accounts for key sources of uncertainty can provide an efficient and complementary framework to the more common approach of multi-model ensembles. We also show that greenhouse gas mitigation has the potential to significantly reduce adverse effects (heat stress, risks of pest and disease) of climate change on agriculture, while also curtailing potentially beneficial impacts (earlier planting, possibility for multiple cropping). A major benefit of climate mitigation is potentially preventing changes in several indices to emerge from the noise of natural variability, even by 2100. This has major implications considering that any significant climate change impacts on crop yield would result in nation-wide changes in the agriculture sector. Lastly, we argue that the analysis of agro-climate indices should more often complement crop model projections, as they can provide valuable information to better understand the drivers of changes in crop yield and production and thus better inform adaptation decisions.« less

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

USDA-ARS?s Scientific Manuscript database

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

Influence of weather and climate on subjective symptom intensity in atopic eczema

NASA Astrophysics Data System (ADS)

Vocks, E.; Busch, R.; Fröhlich, C.; Borelli, S.; Mayer, H.; Ring, J.

The frequent clinical observation that the course of atopic eczema, a skin disease involving a disturbed cutaneous barrier function, is influenced by climate and weather motivated us to analyse these relationships biometrically. In the Swiss high-mountain area of Davos the intensity of itching experienced by patients with atopic eczema was evaluated and compared to 15 single meteorological variables recorded daily during an entire 7-year observation period. By means of univariate analyses and multiple regressions, itch intensity was found to be correlated with some meteorological variables. A clear-cut inverse correlation exists with air temperature (coefficient of correlation: -0.235, P<0.001), but the effects of water vapour pressure, air pressure and hours of sunshine are less pronounced. The results show that itching in atopic eczema is significantly dependent on meteorological conditions. The data suggest that, in patients with atopic eczema, a certain range of thermo-hygric atmospheric conditions with a balance of heat and water loss on the skin surface is essential for the skin to feel comfortable.

Statistical structure of intrinsic climate variability under global warming

NASA Astrophysics Data System (ADS)

Zhu, Xiuhua; Bye, John; Fraedrich, Klaus

2017-04-01

Climate variability is often studied in terms of fluctuations with respect to the mean state, whereas the dependence between the mean and variability is rarely discussed. We propose a new climate metric to measure the relationship between means and standard deviations of annual surface temperature computed over non-overlapping 100-year segments. This metric is analyzed based on equilibrium simulations of the Max Planck Institute-Earth System Model (MPI-ESM): the last millennium climate (800-1799), the future climate projection following the A1B scenario (2100-2199), and the 3100-year unforced control simulation. A linear relationship is globally observed in the control simulation and thus termed intrinsic climate variability, which is most pronounced in the tropical region with negative regression slopes over the Pacific warm pool and positive slopes in the eastern tropical Pacific. It relates to asymmetric changes in temperature extremes and associates fluctuating climate means with increase or decrease in intensity and occurrence of both El Niño and La Niña events. In the future scenario period, the linear regression slopes largely retain their spatial structure with appreciable changes in intensity and geographical locations. Since intrinsic climate variability describes the internal rhythm of the climate system, it may serve as guidance for interpreting climate variability and climate change signals in the past and the future.

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

PubMed

Rohr, Jason R; Raffel, Thomas R

2010-05-04

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

Climate variations and salmonellosis transmission in Adelaide, South Australia: a comparison between regression models

NASA Astrophysics Data System (ADS)

Zhang, Ying; Bi, Peng; Hiller, Janet

2008-01-01

This is the first study to identify appropriate regression models for the association between climate variation and salmonellosis transmission. A comparison between different regression models was conducted using surveillance data in Adelaide, South Australia. By using notified salmonellosis cases and climatic variables from the Adelaide metropolitan area over the period 1990-2003, four regression methods were examined: standard Poisson regression, autoregressive adjusted Poisson regression, multiple linear regression, and a seasonal autoregressive integrated moving average (SARIMA) model. Notified salmonellosis cases in 2004 were used to test the forecasting ability of the four models. Parameter estimation, goodness-of-fit and forecasting ability of the four regression models were compared. Temperatures occurring 2 weeks prior to cases were positively associated with cases of salmonellosis. Rainfall was also inversely related to the number of cases. The comparison of the goodness-of-fit and forecasting ability suggest that the SARIMA model is better than the other three regression models. Temperature and rainfall may be used as climatic predictors of salmonellosis cases in regions with climatic characteristics similar to those of Adelaide. The SARIMA model could, thus, be adopted to quantify the relationship between climate variations and salmonellosis transmission.

Considering the Differential Impact of Three Facets of Organizational Health Climate on Employees' Well-Being

PubMed Central

Zweber, Zandra M.; Henning, Robert A.; Magley, Vicki J.; Faghri, Pouran

2015-01-01

One potential way that healthy organizations can impact employee health is by promoting a climate for health within the organization. Using a definition of health climate that includes support for health from multiple levels within the organization, this study examines whether all three facets of health climate—the workgroup, supervisor, and organization—work together to contribute to employee well-being. Two samples are used in this study to examine health climate at the individual level and group level in order to provide a clearer picture of the impact of the three health climate facets. k-means cluster analysis was used on each sample to determine groups of individuals based on their levels of the three health climate facets. A discriminant function analysis was then run on each sample to determine if clusters differed on a function of employee well-being variables. Results provide evidence that having strength in all three of the facets is the most beneficial in terms of employee well-being at work. Findings from this study suggest that organizations must consider how health is treated within workgroups, how supervisors support employee health, and what the organization does to support employee health when promoting employee health. PMID:26380360

Consistent role of Quaternary climate change in shaping current plant functional diversity patterns across European plant orders.

PubMed

Ordonez, Alejandro; Svenning, Jens-Christian

2017-02-23

Current and historical environmental conditions are known to determine jointly contemporary species distributions and richness patterns. However, whether historical dynamics in species distributions and richness translate to functional diversity patterns remains, for the most part, unknown. The geographic patterns of plant functional space size (richness) and packing (dispersion) for six widely distributed orders of European angiosperms were estimated using atlas distribution data and trait information. Then the relative importance of late-Quaternary glacial-interglacial climate change and contemporary environmental factors (climate, productivity, and topography) as determinants of functional diversity of evaluated orders was assesed. Functional diversity patterns of all evaluated orders exhibited prominent glacial-interglacial climate change imprints, complementing the influence of contemporary environmental conditions. The importance of Quaternary glacial-interglacial climate change factors was comparable to that of contemporary environmental factors across evaluated orders. Therefore, high long-term paleoclimate variability has imposed consistent supplementary constraints on functional diversity of multiple plant groups, a legacy that may permeate to ecosystem functioning and resilience. These findings suggest that strong near-future anthropogenic climate change may elicit long-term functional disequilibria in plant functional diversity.

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Timing of climate variability and grassland productivity

PubMed Central

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

2012-01-01

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

Climate-mediated changes in marine ecosystem regulation during El Niño.

PubMed

Lindegren, Martin; Checkley, David M; Koslow, Julian A; Goericke, Ralf; Ohman, Mark D

2018-02-01

The degree to which ecosystems are regulated through bottom-up, top-down, or direct physical processes represents a long-standing issue in ecology, with important consequences for resource management and conservation. In marine ecosystems, the role of bottom-up and top-down forcing has been shown to vary over spatio-temporal scales, often linked to highly variable and heterogeneously distributed environmental conditions. Ecosystem dynamics in the Northeast Pacific have been suggested to be predominately bottom-up regulated. However, it remains unknown to what extent top-down regulation occurs, or whether the relative importance of bottom-up and top-down forcing may shift in response to climate change. In this study, we investigate the effects and relative importance of bottom-up, top-down, and physical forcing during changing climate conditions on ecosystem regulation in the Southern California Current System (SCCS) using a generalized food web model. This statistical approach is based on nonlinear threshold models and a long-term data set (~60 years) covering multiple trophic levels from phytoplankton to predatory fish. We found bottom-up control to be the primary mode of ecosystem regulation. However, our results also demonstrate an alternative mode of regulation represented by interacting bottom-up and top-down forcing, analogous to wasp-waist dynamics, but occurring across multiple trophic levels and only during periods of reduced bottom-up forcing (i.e., weak upwelling, low nutrient concentrations, and primary production). The shifts in ecosystem regulation are caused by changes in ocean-atmosphere forcing and triggered by highly variable climate conditions associated with El Niño. Furthermore, we show that biota respond differently to major El Niño events during positive or negative phases of the Pacific Decadal Oscillation (PDO), as well as highlight potential concerns for marine and fisheries management by demonstrating increased sensitivity of pelagic fish to exploitation during El Niño. © 2017 John Wiley & Sons Ltd.

Selection of climate change scenario data for impact modelling.

PubMed

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

2012-01-01

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

Multi-scale Drivers of Variations in Atmospheric Evaporative Demand Based on Observations and Physically-based Modeling

NASA Astrophysics Data System (ADS)

Peng, L.; Sheffield, J.; Li, D.

2015-12-01

Evapotranspiration (ET) is a key link between the availability of water resources and climate change and climate variability. Variability of ET has important environmental and socioeconomic implications for managing hydrological hazards, food and energy production. Although there have been many observational and modeling studies of ET, how ET has varied and the drivers of the variations at different temporal scales remain elusive. Much of the uncertainty comes from the atmospheric evaporative demand (AED), which is the combined effect of radiative and aerodynamic controls. The inconsistencies among modeled AED estimates and the limited observational data may originate from multiple sources including the limited time span and uncertainties in the data. To fully investigate and untangle the intertwined drivers of AED, we present a spectrum analysis to identify key controls of AED across multiple temporal scales. We use long-term records of observed pan evaporation for 1961-2006 from 317 weather stations across China and physically-based model estimates of potential evapotranspiration (PET). The model estimates are based on surface meteorology and radiation derived from reanalysis, satellite retrievals and station data. Our analyses show that temperature plays a dominant role in regulating variability of AED at the inter-annual scale. At the monthly and seasonal scales, the primary control of AED shifts from radiation in humid regions to humidity in dry regions. Unlike many studies focusing on the spatial pattern of ET drivers based on a traditional supply and demand framework, this study underlines the importance of temporal scales when discussing controls of ET variations.

Confronting weather and climate models with observational data from soil moisture networks over the United States

PubMed Central

Dirmeyer, Paul A.; Wu, Jiexia; Norton, Holly E.; Dorigo, Wouter A.; Quiring, Steven M.; Ford, Trenton W.; Santanello, Joseph A.; Bosilovich, Michael G.; Ek, Michael B.; Koster, Randal D.; Balsamo, Gianpaolo; Lawrence, David M.

2018-01-01

Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those we find that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation. Statistics for some networks stand out as unlike those of their neighbors, likely due to differences in instrumentation, calibration and maintenance. Buried sensors appear to have less random error than near-field remote sensing techniques, and heat dissipation sensors show less temporal variability than other types. Model soil moistures are evaluated using three metrics: standard deviation in time, temporal correlation (memory) and spatial correlation (length scale). Models do relatively well in capturing large-scale variability of metrics across climate regimes, but poorly reproduce observed patterns at scales of hundreds of kilometers and smaller. Uncoupled land models do no better than coupled model configurations, nor do reanalyses outperform free-running models. Spatial decorrelation scales are found to be difficult to diagnose. Using data for model validation, calibration or data assimilation from multiple soil moisture networks with different types of sensors and measurement techniques requires great caution. Data from models and observations should be put on the same spatial and temporal scales before comparison. PMID:29645013

Confronting Weather and Climate Models with Observational Data from Soil Moisture Networks over the United States

NASA Technical Reports Server (NTRS)

Dirmeyer, Paul A.; Wu, Jiexia; Norton, Holly E.; Dorigo, Wouter A.; Quiring, Steven M.; Ford, Trenton W.; Santanello, Joseph A., Jr.; Bosilovich, Michael G.; Ek, Michael B.; Koster, Randal Dean;

Additive effects of mean temperature, temperature variability, and chlorothalonil to red-eyed treefrog (Agalychnis callidryas) larvae.

PubMed

Alza, Carissa M; Donnelly, Maureen A; Whitfield, Steven M

2016-12-01

Amphibian populations are declining globally, and multiple anthropogenic stressors, including contamination by pesticides and shifting climates, are driving these declines. Climate change may increase average temperatures or increase temperature variability, either of which may affect the susceptibility of nontarget organisms to contaminants. Eight-day ecotoxicological assays were conducted with red-eyed treefrog (Agalychnis callidryas) larvae to test for additive and interactive effects of exposure to the fungicide chlorothalonil, average temperature, and temperature variability on tadpole growth and survival. Egg masses were collected from seasonal ponds at La Selva Biological Station in Costa Rica, and tadpoles were exposed to a series of chlorothalonil concentrations across a range of ecologically relevant mean temperatures (23.4-27.3 °C) and daily temperature fluctuations (1.1-9.9 °C). Survival was measured each day, and tadpole growth was measured at the end of each trial. Concentrations of chlorothalonil ≥60 µg/L reduced survival, although survival was not affected by mean temperature or daily temperature range, and there were no synergistic interactions between chlorothalonil and temperature regime on survival. Chlorothalonil suppressed tadpole growth at relatively low concentrations (∼15 µg/L). There were impacts of both average temperature and daily temperature range on tadpole growth, although there were no synergistic interactions between temperature regimes and chlorothalonil. The results should inform efforts to manage ecosystems impacted by multiple large-scale anthropogenic stressors as well as methods for the design of ecologically appropriate toxicology trials. Environ Toxicol Chem 2016;35:2998-3004. © 2016 SETAC. © 2016 SETAC.

Confronting weather and climate models with observational data from soil moisture networks over the United States.

PubMed

Dirmeyer, Paul A; Wu, Jiexia; Norton, Holly E; Dorigo, Wouter A; Quiring, Steven M; Ford, Trenton W; Santanello, Joseph A; Bosilovich, Michael G; Ek, Michael B; Koster, Randal D; Balsamo, Gianpaolo; Lawrence, David M

2016-04-01

Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those we find that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation. Statistics for some networks stand out as unlike those of their neighbors, likely due to differences in instrumentation, calibration and maintenance. Buried sensors appear to have less random error than near-field remote sensing techniques, and heat dissipation sensors show less temporal variability than other types. Model soil moistures are evaluated using three metrics: standard deviation in time, temporal correlation (memory) and spatial correlation (length scale). Models do relatively well in capturing large-scale variability of metrics across climate regimes, but poorly reproduce observed patterns at scales of hundreds of kilometers and smaller. Uncoupled land models do no better than coupled model configurations, nor do reanalyses outperform free-running models. Spatial decorrelation scales are found to be difficult to diagnose. Using data for model validation, calibration or data assimilation from multiple soil moisture networks with different types of sensors and measurement techniques requires great caution. Data from models and observations should be put on the same spatial and temporal scales before comparison.

SPAGETTA, a Gridded Weather Generator: Calibration, Validation and its Use for Future Climate

NASA Astrophysics Data System (ADS)

Dubrovsky, Martin; Rotach, Mathias W.; Huth, Radan

2017-04-01

Spagetta is a new (started in 2016) stochastic multi-site multi-variate weather generator (WG). It can produce realistic synthetic daily (or monthly, or annual) weather series representing both present and future climate conditions at multiple sites (grids or stations irregularly distributed in space). The generator, whose model is based on the Wilks' (1999) multi-site extension of the parametric (Richardson's type) single site M&Rfi generator, may be run in two modes: In the first mode, it is run as a classical generator, which is calibrated in the first step using weather data from multiple sites, and only then it may produce arbitrarily long synthetic time series mimicking the spatial and temporal structure of the calibration weather data. To generate the weather series representing the future climate, the WG parameters are modified according to the climate change scenario, typically derived from GCM or RCM simulations. In the second mode, the user provides only basic information (not necessarily to be realistic) on the temporal and spatial auto-correlation structure of the surface weather variables and their mean annual cycle; the generator itself derives the parameters of the underlying autoregressive model, which produces the multi-site weather series. In the latter mode of operation, the user is allowed to prescribe the spatially varying trend, which is superimposed to the values produced by the generator; this feature has been implemented for use in developing the methodology for assessing significance of trends in multi-site weather series (for more details see another EGU-2017 contribution: Huth and Dubrovsky, 2017, Evaluating collective significance of climatic trends: A comparison of methods on synthetic data; EGU2017-4993). This contribution will focus on the first (classical) mode. The poster will present (a) model of the generator, (b) results of the validation tests made in terms of the spatial hot/cold/dry/wet spells, and (c) results of the pilot climate change impact experiment, in which (i) the WG parameters representing the spatial and temporal variability are modified using the climate change scenarios and then (ii) the effect on the above spatial validation indices derived from the synthetic series produced by the modified WG is analysed. In this experiment, the generator is calibrated using the E-OBS gridded daily weather data for several European regions, and the climate change scenarios are derived from the selected RCM simulation (taken from the CORDEX database).

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

PubMed

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

2013-06-01

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

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.

Coral-Derived Western Pacific Tropical Sea Surface Temperatures During the Last Millennium

NASA Astrophysics Data System (ADS)

Chen, Tianran; Cobb, Kim M.; Roff, George; Zhao, Jianxin; Yang, Hongqiang; Hu, Minhang; Zhao, Kuan

2018-04-01

Reconstructions of ocean temperatures prior to the industrial era serve to constrain natural climate variability on decadal to centennial timescales, yet relatively few such observations are available from the west Pacific Warm Pool. Here we present multiple coral-based sea surface temperature reconstructions from Yongle Atoll, in the South China Sea over the last 1,250 years (762-2013 Common Era [CE]). Reconstructed coral Sr/Ca-sea surface temperatures indicate that the "Little Ice Age (1711-1817 CE)" period was 0.7°C cooler than the "Medieval Climate Anomaly (913-1132 CE)" and that late 20th century warming of the western Pacific is likely unprecedented over the past millennium. Our findings suggest that the Western Pacific Warm Pool may have expanded (contracted) during the Medieval Climate Anomaly (Little Ice Age), leading to a strengthening (weakening) of the Asian summer monsoon, as recorded in Chinese stalagmites.

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

NASA Astrophysics Data System (ADS)

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

2015-05-01

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

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

NASA Astrophysics Data System (ADS)

Saini, A.

2017-12-01

Climate variability brought various negative results to the environment around us and area under rice crop in Bihar has also faced a lot of negative impacts due to variability in temperature and rainfall. Location of Bihar in Northern Plain of India automatically makes it prime location for agriculture and therefore variability in climatic variables brings highly sensitive results to the agricultural production (especially rice). In this study, rainfall and temperature variables are taken into consideration to investigate the impact on rice cultivated area. Change in climate variable with the passage of time is prevailing since the start of geological time scale, how the variability in climate variables has affected the major crops. Climate index of Pacific Ocean and Indian Ocean influences the seasonal weather in Bihar and therefore role of ENSO and IOD is an interesting point of inquiry. Does there exists direct relation between climate variability and area under agricultural crops? How many important variables directly signals towards the change in area under agriculture production? These entire questions are answered with respect to change in area under rice cultivation of Bihar State of India. Temperature, rainfall and ENSO are a good indicator with respect to rice cultivation in Indian subcontinent. Impact on the area under rice has been signaled through ONI, Niño3 and DMI. Increasing range of temperature in the rice productivity declining years is observed since 1990.

Climate change

USGS Publications Warehouse

Cronin, Thomas M.

2016-01-01

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

Influence of social cognitive and ethnic variables on academic goals of underrepresented students in science and engineering: a multiple-groups analysis.

PubMed

Byars-Winston, Angela; Estrada, Yannine; Howard, Christina; Davis, Dalelia; Zalapa, Juan

2010-04-01

This study investigated the academic interests and goals of 223 African American, Latino/a, Southeast Asian, and Native American undergraduate students in two groups: biological science and engineering (S/E) majors. Using social cognitive career theory (Lent, Brown, & Hackett, 1994), we examined the relationships of social cognitive variables (math/science academic self-efficacy, math/science outcome expectations), along with the influence of ethnic variables (ethnic identity, other-group orientation) and perceptions of campus climate to their math/science interests and goal commitment to earn an S/E degree. Path analysis revealed that the hypothesized model provided good overall fit to the data, revealing significant relationships from outcome expectations to interests and to goals. Paths from academic self-efficacy to S/E goals and from interests to S/E goals varied for students in engineering and biological science. For both groups, other-group orientation was positively related to self-efficacy and support was found for an efficacy-mediated relationship between perceived campus climate and goals. Theoretical and practical implications of the study's findings are considered as well as future research directions.

Influence of Social Cognitive and Ethnic Variables on Academic Goals of Underrepresented Students in Science and Engineering: A Multiple-Groups Analysis

PubMed Central

Byars-Winston, Angela; Estrada, Yannine; Howard, Christina; Davis, Dalelia; Zalapa, Juan

2010-01-01

This study investigated the academic interests and goals of 223 African American, Latino/a, Southeast Asian, and Native American undergraduate students in two groups: biological science and engineering (S/E) majors. Using social cognitive career theory (Lent, Brown, & Hackett, 1994), we examined the relationships of social cognitive variables (math/science academic self-efficacy, math/science outcome expectations), along with the influence of ethnic variables (ethnic identity, other-group orientation) and perceptions of campus climate to their math/science interests and goal commitment to earn an S/E degree. Path analysis revealed that the hypothesized model provided good overall fit to the data, revealing significant relationships from outcome expectations to interests and to goals. Paths from academic self-efficacy to S/E goals and from interests to S/E goals varied for students in engineering and biological science. For both groups, other-group orientation was positively related to self-efficacy and support was found for an efficacy-mediated relationship between perceived campus climate and goals. Theoretical and practical implications of the study’s findings are considered as well as future research directions. PMID:20495610

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

Replumbing of the Biological Pump caused by Millennial Climate Variability

NASA Astrophysics Data System (ADS)

Galbraith, E.; Sarmiento, J.

2008-12-01

It has been hypothesized that millennial-timescale variability in the biological pump was a critical instigator of glacial-interglacial cycles. However, even in the absence of changes in ecosystem function (e.g. due to iron fertilization), determining the mechanisms by which physical climate variability alters the biological pump is not simple. Changes in upper ocean circulation and deep water formation have previously been shown to alter both the downward flux of organic matter and the mass of respired carbon in the ocean interior, often in non- intuitive ways. For example, a reduced upward flux of nutrients at the global scale will decrease the global rate of export production, but it could either increase or decrease the respired carbon content of the ocean interior, depending on where the reduced upward flux of nutrients occurs. Furthermore, viable candidates for physical climate forcing are numerous, including changes in the westerly winds, changes in the depth of the thermocline, and changes in the formation rate of North Atlantic Deep Water, among others. We use a simple, prognostic, light-and temperature-dependent model of biogeochemical cycling within a state-of-the- art global coupled ocean-atmosphere model to examine the response of the biological pump to changes in the coupled Earth system over multiple centuries. The biogeochemical model explicitly distinguishes respired carbon from preformed and saturation carbon, allowing the activity of the biological pump to be clearly quantified. Changes are forced in the model by altering the background climate state, and by manipulating the flux of freshwater to the North Atlantic region. We show how these changes in the physical state of the coupled ocean-atmosphere system impact the distribution and mass of respired carbon in the ocean interior, and the relationship these changes bear to global patterns of export production via the redistribution of nutrients.

Assessment of projected climate change in the Carpathian Region using the Holdridge life zone system

NASA Astrophysics Data System (ADS)

Szelepcsényi, Zoltán; Breuer, Hajnalka; Kis, Anna; Pongrácz, Rita; Sümegi, Pál

2018-01-01

In this paper, expected changes in the spatial and altitudinal distribution patterns of Holdridge life zone (HLZ) types are analysed to assess the possible ecological impacts of future climate change for the Carpathian Region, by using 11 bias-corrected regional climate model simulations of temperature and precipitation. The distribution patterns of HLZ types are characterized by the relative extent, the mean centre and the altitudinal range. According to the applied projections, the following conclusions can be drawn: (a) the altitudinal ranges are likely to expand in the future, (b) the lower and upper altitudinal limits as well as the altitudinal midpoints may move to higher altitudes, (c) a northward shift is expected for most HLZ types and (d) the magnitudes of these shifts can even be multiples of those observed in the last century. Related to the northward shifts, the HLZ types warm temperate thorn steppe and subtropical dry forest can also appear in the southern segment of the target area. However, a large uncertainty in the estimated changes of precipitation patterns was indicated by the following: (a) the expected change in the coverage of the HLZ type cool temperate steppe is extremely uncertain because there is no consensus among the projections even in terms of the sign of the change (high inter-model variability) and (b) a significant trend in the westward/eastward shift is simulated just for some HLZ types (high temporal variability). Finally, it is important to emphasize that the uncertainty of our results is further enhanced by the fact that some important aspects (e.g. seasonality of climate variables, direct CO2 effect, etc.) cannot be considered in the estimating process.

The Potential Financial Costs of Climate Change on Health of Urban and Rural Citizens: A Case Study of Vibrio cholerae Infections at Bukavu Town, South Kivu Province, Eastern of Democratic Republic of Congo.

PubMed

Munyuli, Mb Théodore; Kavuvu, J-M Mbaka; Mulinganya, Guy; Bwinja, G Mulinganya

2013-01-01

Cholera epidemics have a recorded history in eastern Congo dating to 1971. A study was conducted to find out the linkage between climate variability/change and cholera outbreak and to assess the related economic cost in the management of cholera in Congo. This study integrates historical data (20 years) on temperature and rainfall with the burden of disease from cholera in South-Kivu province, eastern Congo. Analyses of precipitation and temperatures characteristics in South-Kivu provinces showed that cholera epidemics are closely associated with climatic factors variability. Peaks in Cholera new cases were in synchrony with peaks in rainfalls. Cholera infection cases declined significantly (P<0.05) with the rise in the average temperature. The monthly number of new Cholera cases oscillated between 5 and 450. For every rise of the average temperature by 0.35 °C to 0.75 °C degree Celsius, and for every change in the rainfall variability by 10-19%, it is likely cholera infection risks will increase by 17 to 25%. The medical cost of treatment of Cholera case infection was found to be of US$50 to 250 per capita. The total costs of Cholera attributable to climate change were found to fall in the range of 4 to 8% of the per capita in annual income in Bukavu town. It is likely that high rainfall favor multiplication of the bacteria and contamination of water sources by the bacteria (Vibrio cholerae). The consumption of polluted water, promiscuity, population density and lack of hygiene are determinants favoring spread and infection of the bacteria among human beings living in over-crowded environments.

Factorial inferential grid grouping and representativeness analysis for a systematic selection of representative grids

NASA Astrophysics Data System (ADS)

Cheng, Guanhui; Huang, Guohe; Dong, Cong; Xu, Ye; Yao, Yao

2017-08-01

A factorial inferential grid grouping and representativeness analysis (FIGGRA) approach is developed to achieve a systematic selection of representative grids in large-scale climate change impact assessment and adaptation (LSCCIAA) studies and other fields of Earth and space sciences. FIGGRA is applied to representative-grid selection for temperature (Tas) and precipitation (Pr) over the Loess Plateau (LP) to verify methodological effectiveness. FIGGRA is effective at and outperforms existing grid-selection approaches (e.g., self-organizing maps) in multiple aspects such as clustering similar grids, differentiating dissimilar grids, and identifying representative grids for both Tas and Pr over LP. In comparison with Pr, the lower spatial heterogeneity and higher spatial discontinuity of Tas over LP lead to higher within-group similarity, lower between-group dissimilarity, lower grid grouping effectiveness, and higher grid representativeness; the lower interannual variability of the spatial distributions of Tas results in lower impacts of the interannual variability on the effectiveness of FIGGRA. For LP, the spatial climatic heterogeneity is the highest in January for Pr and in October for Tas; it decreases from spring, autumn, summer to winter for Tas and from summer, spring, autumn to winter for Pr. Two parameters, i.e., the statistical significance level (α) and the minimum number of grids in every climate zone (Nmin), and their joint effects are significant for the effectiveness of FIGGRA; normalization of a nonnormal climate-variable distribution is helpful for the effectiveness only for Pr. For FIGGRA-based LSCCIAA studies, a low value of Nmin is recommended for both Pr and Tas, and a high and medium value of α for Pr and Tas, respectively.

Prediction of River Flooding using Geospatial and Statistical Analysis in New York, USA and Kent, UK

NASA Astrophysics Data System (ADS)

Marsellos, A.; Tsakiri, K.; Smith, M.

2014-12-01

Flooding in the rivers normally occurs during periods of excessive precipitation (i.e. New York, USA; Kent, UK) or ice jams during the winter period (New York, USA). For the prediction and mapping of the river flooding, it is necessary to evaluate the spatial distribution of the water (volume) in the river as well as study the interaction between the climatic and hydrological variables. Two study areas have been analyzed; one in Mohawk River, New York and one in Kent, United Kingdom (UK). A high resolution Digital Elevation Model (DEM) of the Mohawk River, New York has been used for a GIS flooding simulation to determine the maximum elevation value of the water that cannot continue to be restricted in the trunk stream and as a result flooding in the river may be triggered. The Flooding Trigger Level (FTL) is determined by incremental volumetric and surface calculations from Triangulated Irregular Network (TIN) with the use of GIS software and LiDAR data. The prediction of flooding in the river can also be improved by the statistical analysis of the hydrological and climatic variables in Mohawk River and Kent, UK. A methodology of time series analysis has been applied for the decomposition of the hydrological (water flow and ground water data) and climatic data in both locations. The KZ (Kolmogorov-Zurbenko) filter is used for the decomposition of the time series into the long, seasonal, and short term components. The explanation of the long term component of the water flow using the climatic variables has been improved up to 90% for both locations. Similar analysis has been performed for the prediction of the seasonal and short term component. This methodology can be applied for flooding of the rivers in multiple sites.

SPAGETTA: a Multi-Purpose Gridded Stochastic Weather Generator

NASA Astrophysics Data System (ADS)

Dubrovsky, M.; Huth, R.; Rotach, M. W.; Dabhi, H.

2017-12-01

SPAGETTA is a new multisite/gridded multivariate parametric stochastic weather generator (WG). Site-specific precipitation occurrence and amount are modelled by Markov chain and Gamma distribution, the non-precipitation variables are modelled by an autoregressive (AR) model conditioned on precipitation occurrence, and the spatial coherence of all variables is modelled following the Wilks' (2009) approach. SPAGETTA may be run in two modes. Mode 1: it is run as a classical WG, which is calibrated using weather series from multiple sites, and only then it may produce arbitrarily long synthetic series mimicking the spatial and temporal structure of the calibration data. To generate the weather series representing the future climate, the WG parameters are modified according to the climate change scenario, typically derived from GCM or RCM simulations. Mode 2: the user provides only basic information (not necessarily to be realistic) on the temporal and spatial auto-correlation structure of the weather variables and their mean annual cycle; the generator itself derives the parameters of the underlying AR model, which produces the multi-site weather series. Optionally, the user may add the spatially varying trend, which is superimposed to the synthetic series. The contribution consists of following parts: (a) Model of the WG. (b) Validation of WG in terms of the spatial temperature and precipitation characteristics, including characteristics of spatial hot/cold/dry/wet spells. (c) Results of the climate change impact experiment, in which the WG parameters representing the spatial and temporal variability are modified using the climate change scenarios and the effect on the above spatial validation indices is analysed. In this experiment, the WG is calibrated using the E-OBS gridded daily weather data for several European regions, and the climate change scenarios are derived from the selected RCM simulations (CORDEX database). (d) The second mode of operation will be demonstrated by results obtained while developing the methodology for assessing collective significance of trends in multi-site weather series. The performance of the proposed test statistics is assessed based on large number of realisations of synthetic series produced by WG assuming a given statistical structure and trend of the weather series.

Unexpected weak seasonal climate in the western Mediterranean region during MIS 31, a high-insolation forced interglacial

NASA Astrophysics Data System (ADS)

Oliveira, Dulce; Sánchez Goñi, Maria Fernanda; Naughton, Filipa; Polanco-Martínez, J. M.; Jimenez-Espejo, Francisco J.; Grimalt, Joan O.; Martrat, Belen; Voelker, Antje H. L.; Trigo, Ricardo; Hodell, David; Abrantes, Fátima; Desprat, Stéphanie

2017-04-01

Marine Isotope Stage 31 (MIS 31) is an important analogue for ongoing and projected global warming, yet key questions remain about the regional signature of its extreme orbital forcing and intra-interglacial variability. Based on a new direct land-sea comparison in SW Iberian margin IODP Site U1385 we examine the climatic variability between 1100 and 1050 ka including the ;super interglacial; MIS 31, a period dominated by the 41-ky obliquity periodicity. Pollen and biomarker analyses at centennial-scale-resolution provide new insights into the regional vegetation, precipitation regime and atmospheric and oceanic temperature variability on orbital and suborbital timescales. Our study reveals that atmospheric and SST warmth during MIS 31 was not exceptional in this region highly sensitive to precession. Unexpectedly, this warm stage stands out as a prolonged interval of a temperate and humid climate regime with reduced seasonality, despite the high insolation (precession minima values) forcing. We find that the dominant forcing on the long-term temperate forest development was obliquity, which may have induced a decrease in summer dryness and associated reduction in seasonal precipitation contrast. Moreover, this study provides the first evidence for persistent atmospheric millennial-scale variability during this interval with multiple forest decline events reflecting repeated cooling and drying episodes in SW Iberia. Our direct land-sea comparison shows that the expression of the suborbital cooling events on SW Iberian ecosystems is modulated by the predominance of high or low-latitude forcing depending on the glacial/interglacial baseline climate states. Severe dryness and air-sea cooling is detected under the larger ice volume during glacial MIS 32 and MIS 30. The extreme episodes, which in their climatic imprint are similar to the Heinrich events, are likely related to northern latitude ice-sheet instability and a disruption of the Atlantic Meridional Overturning Circulation (AMOC). In contrast, forest declines during MIS 31 are associated to neither SST cooling nor high-latitude freshwater forcing. Time-series analysis reveals a dominant cyclicity of about 6 ky in the temperate forest record, which points to a potential link with the fourth harmonic of precession and thus low-latitude insolation forcing.

Satellite-based Monotoring of mitiple natural disasters in Mongolian socio-ecological system

NASA Astrophysics Data System (ADS)

Kang, Sinkyu

2016-04-01

In this presentation, a conceptual mechanisms how multiple natural hazards (i.e. drought, dust storm, land degradation, and Dzud) in Mongolia are linked with each other and how satellite earth observation (EO) data can be utilized to analyze cause-and results relations and to predict the natural hazards. Massive loss of livestock and wildlife animal during winter seasons (dzud) is an endemic climatic disaster in the Central Asia grasslands but the mechanisms are not well understood yet. Recent national-wide sever Dzud occurred during 2009-2010 winter in Mongolia. Whereas, high stocking rate of livestock may give negative effects on sustainable use of pastureland. Dzud is a natural mechanism reducing grazing pressure when stocking rate is high enough to cause the negative effect. Both Dzud and land degradation were directly linked with drought phenomena, which is associated with dust storm occurrence because those conditions can cause sparse vegetation and increase of sensible heat generating strong vertical wind. At a lower level of administration (i.e., soum), stepwise multiple regression analysis was conducted to find significant factors of inter-annual livestock change. For a period from 2003 to 2010, various datasets were prepared from national census and satellite data (summer and winter temperature and precipitation, and summer dryness and vegetation index, NDVI). As results, linear regression models were successfully produced at 70% of soums studied. Summer and winter variables appeared equally important in controlling livestock dynamics. Single-factor models were predominant. The primary factor of each soum showed certain regional patterns incident well with climate severity and foraging resource availability (e.g. temperature in north, dryness in south, and NDVI in middle). Our results indicate that Mongolian pastoral livelihood is highly vulnerable to extreme variability of endemic regional climate factors and hence, there are still rooms for enhancing socio-ecological adaptive capacity such as herder's preparedness and governance. We illustrate the seasonal climate-vegetation-livestock interactions with a simplified schematic mechanism model. Our schematic model refined it to give better process-oriented relationships among key variables. Seasonal temperature and precipitation are the primary forcing variables to determine vegetation growth and livestock accessibility to food resources and dryness. Summer standing biomass and winter dry biomass (i.e. residue) were separated and associated with seasonal livestock foraging, respectively. By its mechanistic nature, the schematic model can be applied to test statistical significance of factors associated with annual livestock change or to provide logical grounds on developing a dynamic numerical model in future.

Impact of Stochastic Parameterization Schemes on Coupled and Uncoupled Climate Simulations with the Community Earth System Model

NASA Astrophysics Data System (ADS)

Christensen, H. M.; Berner, J.; Coleman, D.; Palmer, T.

2015-12-01

Stochastic parameterizations have been used for more than a decade in atmospheric models to represent the variability of unresolved sub-grid processes. They have a beneficial effect on the spread and mean state of medium- and extended-range forecasts (Buizza et al. 1999, Palmer et al. 2009). There is also increasing evidence that stochastic parameterization of unresolved processes could be beneficial for the climate of an atmospheric model through noise enhanced variability, noise-induced drift (Berner et al. 2008), and by enabling the climate simulator to explore other flow regimes (Christensen et al. 2015; Dawson and Palmer 2015). We present results showing the impact of including the Stochastically Perturbed Parameterization Tendencies scheme (SPPT) in coupled runs of the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 4 (CAM4) with historical forcing. The SPPT scheme accounts for uncertainty in the CAM physical parameterization schemes, including the convection scheme, by perturbing the parametrised temperature, moisture and wind tendencies with a multiplicative noise term. SPPT results in a large improvement in the variability of the CAM4 modeled climate. In particular, SPPT results in a significant improvement to the representation of the El Nino-Southern Oscillation in CAM4, improving the power spectrum, as well as both the inter- and intra-annual variability of tropical pacific sea surface temperatures. References: Berner, J., Doblas-Reyes, F. J., Palmer, T. N., Shutts, G. J., & Weisheimer, A., 2008. Phil. Trans. R. Soc A, 366, 2559-2577 Buizza, R., Miller, M. and Palmer, T. N., 1999. Q.J.R. Meteorol. Soc., 125, 2887-2908. Christensen, H. M., I. M. Moroz & T. N. Palmer, 2015. Clim. Dynam., doi: 10.1007/s00382-014-2239-9 Dawson, A. and T. N. Palmer, 2015. Clim. Dynam., doi: 10.1007/s00382-014-2238-x Palmer, T.N., R. Buizza, F. Doblas-Reyes, et al., 2009, ECMWF technical memorandum 598.

Time-varying environmental control of phytoplankton in a changing estuarine system.

PubMed

López Abbate, M Celeste; Molinero, Juan Carlos; Guinder, Valeria A; Perillo, Gerardo M E; Freije, R Hugo; Sommer, Ulrich; Spetter, Carla V; Marcovecchio, Jorge E

2017-12-31

Estuaries are among the most valuable aquatic systems by their services to human welfare. However, increasing human activities at the watershed along with the pressure of climate change are fostering the co-occurrence of multiple environmental drivers, and warn of potential negative impacts on estuaries resources. At present, no clear understanding of how coastal ecosystems will respond to the non-stationary effect of multiple drivers. Here we analysed the temporal interaction among multiple environmental drivers and their changing priority on shaping phytoplankton response in the Bahía Blanca Estuary, SW Atlantic Ocean. The interaction among environmental drivers and the number of significant direct and indirect effects on chlorophyll concentration increased over time in concurrence with enhanced anthropogenic stress, changing winter climate and wind patterns. Over the period 1978-1993, proximal variables such as nutrients, water temperature and salinity, showed a dominant effect on chlorophyll, whereas in more recent years (1993-2009) climate signals (SAM and ENSO) boosted indirect effects through its influence on precipitation, wind, water temperature and turbidity. Turbidity emerged as the dominant driver of chlorophyll while in recent years acted synergistically with the concentration of dissolved nitrogen. As a result, chlorophyll concentration showed a significant negative trend and a loss of seasonal peaks reflecting a pronounced reorganisation of the phytoplankton community. We stress the need to account for the changing priority of drivers to understand, and eventually forecast, biological responses under projected scenarios of global anthropogenic change. Copyright © 2017 Elsevier B.V. All rights reserved.

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

NASA Astrophysics Data System (ADS)

Tylmann, Wojciech; Hernández-Almeida, Iván; Grosjean, Martin; José Gómez Navarro, Juan; Larocque-Tobler, Isabelle; Bonk, Alicja; Enters, Dirk; Ustrzycka, Alicja; Piotrowska, Natalia; Przybylak, Rajmund; Wacnik, Agnieszka; Witak, Małgorzata

2016-04-01

Rapid ecosystem transitions and adverse effects on ecosystem services as responses to combined climate and human impacts are of major concern. Yet few quantitative observational data exist, particularly for ecosystems that have a long history of human intervention. Here, we combine quantitative summer and winter climate reconstructions, climate model simulations and proxies for three major environmental pressures (land use, nutrients and erosion) to explore the system dynamics, resilience, and the role of disturbance regimes in varved eutrophic Lake Żabińskie since AD 1000. Comparison between regional and global climate simulations and quantitative climate reconstructions indicate that proxy data capture noticeably natural forced climate variability, while internal variability appears as the dominant source of climate variability in the climate model simulations during most parts of the last millennium. Using different multivariate analyses and change point detection techniques, we identify ecosystem changes through time and shifts between rather stable states and highly variable ones, as expressed by the proxies for land-use, erosion and productivity in the lake. Prior to AD 1600, the lake ecosystem was characterized by a high stability and resilience against considerable observed natural climate variability. In contrast, lake-ecosystem conditions started to fluctuate at high frequency across a broad range of states after AD 1600. The period AD 1748-1868 represents the phase with the strongest human disturbance of the ecosystem. Analyses of the frequency of change points in the multi-proxy dataset suggests that the last 400 years were highly variable and flickering with increasing vulnerability of the ecosystem to the combined effects of climate variability and anthropogenic disturbances. This led to significant rapid ecosystem transformations.

Partitioning sources of variation in vertebrate species richness

USGS Publications Warehouse

Boone, R.B.; Krohn, W.B.

2000-01-01

Aim: To explore biogeographic patterns of terrestrial vertebrates in Maine, USA using techniques that would describe local and spatial correlations with the environment. Location: Maine, USA. Methods: We delineated the ranges within Maine (86,156 km2) of 275 species using literature and expert review. Ranges were combined into species richness maps, and compared to geomorphology, climate, and woody plant distributions. Methods were adapted that compared richness of all vertebrate classes to each environmental correlate, rather than assessing a single explanatory theory. We partitioned variation in species richness into components using tree and multiple linear regression. Methods were used that allowed for useful comparisons between tree and linear regression results. For both methods we partitioned variation into broad-scale (spatially autocorrelated) and fine-scale (spatially uncorrelated) explained and unexplained components. By partitioning variance, and using both tree and linear regression in analyses, we explored the degree of variation in species richness for each vertebrate group that Could be explained by the relative contribution of each environmental variable. Results: In tree regression, climate variation explained richness better (92% of mean deviance explained for all species) than woody plant variation (87%) and geomorphology (86%). Reptiles were highly correlated with environmental variation (93%), followed by mammals, amphibians, and birds (each with 84-82% deviance explained). In multiple linear regression, climate was most closely associated with total vertebrate richness (78%), followed by woody plants (67%) and geomorphology (56%). Again, reptiles were closely correlated with the environment (95%), followed by mammals (73%), amphibians (63%) and birds (57%). Main conclusions: Comparing variation explained using tree and multiple linear regression quantified the importance of nonlinear relationships and local interactions between species richness and environmental variation, identifying the importance of linear relationships between reptiles and the environment, and nonlinear relationships between birds and woody plants, for example. Conservation planners should capture climatic variation in broad-scale designs; temperatures may shift during climate change, but the underlying correlations between the environment and species richness will presumably remain.

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

Lopez, Anthony; Maclaurin, Galen; Roberts, Billy

Long-term variability of solar resource is an important factor in planning a utility-scale photovoltaic (PV) generation plant, and annual generation for a given location can vary significantly from year to year. Based on multiple years of solar irradiance data, an exceedance probability is the amount of energy that could potentially be produced by a power plant in any given year. An exceedance probability accounts for long-term variability and climate cycles (e.g., monsoons or changes in aerosols), which ultimately impact PV energy generation. Study results indicate that a significant bias could be associated with relying solely on typical meteorological year (TMY)more » resource data to capture long-term variability. While the TMY tends to under-predict annual generation overall compared to the P50, there appear to be pockets of over-prediction as well.« less

Assessing climate change impact on complementarity between solar and hydro power in areas affected by glacier shrinkage

NASA Astrophysics Data System (ADS)

Diah Puspitarini, Handriyanti; François, Baptiste; Zoccatelli, Davide; Brown, Casey; Creutin, Jean-Dominique; Zaramella, Mattia; Borga, Marco

2017-04-01

Variable Renewable Energy (VRE) sources such as wind, solar and runoff sources are variable in time and space, following their driving weather variables. In this work we aim to analyse optimal mixes of energy sources, i.e. mixes of sources which minimize the deviation between energy load and generation, for a region in the Upper Adige river basin (Eastern Italian Alps) affected by glacier shrinking. The study focuses on hydropower (run of the river - RoR) and solar energy, and analyses the current situation as well different climate change scenarios. Changes in glacier extent in response to climate warming and/or altered precipitation regimes have the potential to substantially alter the magnitude and timing, as well as the spatial variation of watershed-scale hydrologic fluxes. This may change the complementarity with solar power as well. In this study, we analyse the climate change impact on complementarity between RoR and solar using the Decision Scaling approach (Brown et al. 2012). With this approach, the system vulnerability is separated from the climatic hazard that can come from any set of past or future climate conditions. It departs from conventional top-down impact studies because it explores the sensitivity of the system response to a plausible range of climate variations rather than its sensitivity to the time-varying outcome of individual GCM projections. It mainly relies on the development of Climate Response Functions that bring together i) the sensitivity of some system success and/or failure indicators to key external drivers (i.e. mean features of regional climate) and ii) the future values of these drivers as simulated from climate simulation chains. The main VRE sources used in the study region are solar- and hydro-power (with an important fraction of run-of-the river hydropower). The considered indicator of success is the 'energy penetration' coefficient, defined as the long-run percentage of energy demand naturally met by the VRE on an hourly basis. Climate response functions, developed in a 2D climate change space (change in mean temperature and precipitation), are built from multiple hydro-climatic scenarios obtained by perturbing the observed weather time series with the change factor method, and considering given glacier storage states. Climate experiments are further used for assessing these change factors from different emission scenarios, climate models and future prediction lead times. Their positioning on the Climate Response Function allows discussing the risk/opportunities pertaining to changes in VRE penetration in the future. Results show i) the large impact of glacier shrinkage on the complementarity between solar and RoR energy sources and ii) that the impact is decreasing with time, with the main alterations to be expected in the coming 30 years. Brown, C., Ghile, Y., Laverty, M., Li, K., (2012). Decision scaling: Linking bottom up vulnerability analysis with climate projections in the water sector. Water Resour Res 48. 515 doi:10.1029/2011WR011212

Links between the built environment, climate and population health: interdisciplinary environmental change research in New York City.

PubMed

Rosenthal, Joyce Klein; Sclar, Elliott D; Kinney, Patrick L; Knowlton, Kim; Crauderueff, Robert; Brandt-Rauf, Paul W

2007-10-01

Global climate change is expected to pose increasing challenges for cities in the following decades, placing greater stress and impacts on multiple social and biophysical systems, including population health, coastal development, urban infrastructure, energy demand, and water supplies. Simultaneously, a strong global trend towards urbanisation of poverty exists, with increased challenges for urban populations and local governance to protect and sustain the wellbeing of growing cities. In the context of these 2 overarching trends, interdisciplinary research at the city scale is prioritised for understanding the social impacts of climate change and variability and for the evaluation of strategies in the built environment that might serve as adaptive responses to climate change. This article discusses 2 recent initiatives of The Earth Institute at Columbia University (EI) as examples of research that integrates the methods and objectives of several disciplines, including environmental health science and urban planning, to understand the potential public health impacts of global climate change and mitigative measures for the more localised effects of the urban heat island in the New York City metropolitan region. These efforts embody 2 distinct research approaches. The New York Climate & Health Project created a new integrated modeling system to assess the public health impacts of climate and land use change in the metropolitan region. The Cool City Project aims for more applied policy-oriented research that incorporates the local knowledge of community residents to understand the costs and benefits of interventions in the built environment that might serve to mitigate the harmful impacts of climate change and variability, and protect urban populations from health stressors associated with summertime heat. Both types of research are potentially useful for understanding the impacts of environmental change at the urban scale, the policies needed to address these challenges, and to train scholars capable of collaborative approaches across the social and biophysical sciences.

High-resolution integration of water, energy, and climate models to assess electricity grid vulnerabilities to climate change

NASA Astrophysics Data System (ADS)

Meng, M.; Macknick, J.; Tidwell, V. C.; Zagona, E. A.; Magee, T. M.; Bennett, K.; Middleton, R. S.

2017-12-01

The U.S. electricity sector depends on large amounts of water for hydropower generation and cooling thermoelectric power plants. Variability in water quantity and temperature due to climate change could reduce the performance and reliability of individual power plants and of the electric grid as a system. While studies have modeled water usage in power systems planning, few have linked grid operations with physical water constraints or with climate-induced changes in water resources to capture the role of the energy-water nexus in power systems flexibility and adequacy. In addition, many hydrologic and hydropower models have a limited representation of power sector water demands and grid interaction opportunities of demand response and ancillary services. A multi-model framework was developed to integrate and harmonize electricity, water, and climate models, allowing for high-resolution simulation of the spatial, temporal, and physical dynamics of these interacting systems. The San Juan River basin in the Southwestern U.S., which contains thermoelectric power plants, hydropower facilities, and multiple non-energy water demands, was chosen as a case study. Downscaled data from three global climate models and predicted regional water demand changes were implemented in the simulations. The Variable Infiltration Capacity hydrologic model was used to project inflows, ambient air temperature, and humidity in the San Juan River Basin. Resulting river operations, water deliveries, water shortage sharing agreements, new water demands, and hydroelectricity generation at the basin-scale were estimated with RiverWare. The impacts of water availability and temperature on electric grid dispatch, curtailment, cooling water usage, and electricity generation cost were modeled in PLEXOS. Lack of water availability resulting from climate, new water demands, and shortage sharing agreements will require thermoelectric generators to drastically decrease power production, as much as 50% during intensifying drought scenarios, which can have broader electricity sector system implications. Results relevant to stakeholder and power provider interests highlight the vulnerabilities in grid operations driven by water shortage agreements and changes in the climate.

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.

Capturing temporal and spatial variability in the chemistry of shallow permafrost ponds

NASA Astrophysics Data System (ADS)

Morison, Matthew Q.; Macrae, Merrin L.; Petrone, Richard M.; Fishback, LeeAnn

2017-12-01

Across the circumpolar north, the fate of small freshwater ponds and lakes (< 1 km2) has been the subject of scientific interest due to their ubiquity in the landscape, capacity to exchange carbon and energy with the atmosphere, and their potential to inform researchers about past climates through sediment records. A changing climate has implications for the capacity of ponds and lakes to support organisms and store carbon, which in turn has important feedbacks to climate change. Thus, an improved understanding of pond biogeochemistry is needed. To characterize spatial and temporal patterns in water column chemistry, a suite of tundra ponds were examined to answer the following research questions: (1) does temporal variability exceed spatial variability? (2) If temporal variability exists, do all ponds (or groups of ponds) behave in a similar temporal pattern, linked to seasonal hydrologic drivers or precipitation events? Six shallow ponds located in the Hudson Bay Lowlands region were monitored between May and October 2015 (inclusive, spanning the entire open-water period). The ponds span a range of biophysical conditions including pond area, perimeter, depth, and shoreline development. Water samples were collected regularly, both bimonthly over the ice-free season and intensively during and following a large summer storm event. Samples were analysed for nitrogen speciation (NO3-, NH4+, dissolved organic nitrogen) and major ions (Cl-, SO42-, K+, Ca2+, Mg2+, Na+). Across all ponds, temporal variability (across the season and within a single rain event) exceeded spatial variability (variation among ponds) in concentrations of several major species (Cl-, SO42-, K+, Ca2+, Na+). Evapoconcentration and dilution of pond water with precipitation and runoff inputs were the dominant processes influencing a set of chemical species which are hydrologically driven (Cl-, Na+, K+, Mg2+, dissolved organic nitrogen), whereas the dissolved inorganic nitrogen species were likely mediated by processes within ponds. This work demonstrates the importance of understanding hydrologically driven chemodynamics in permafrost ponds on multiple scales (seasonal and event scale).

Soil carbon cycling proxies: Understanding their critical role in predicting climate change feedbacks

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

Bailey, Vanessa L.; Bond-Lamberty, Ben; DeAngelis, Kristen

The complexity of processes and interactions that drive soil C dynamics necessitate the use of proxy variables to represent soil characteristics that cannot be directly measured (correlative proxies), or that aggregate information about multiple soil characteristics into one variable (integrative proxies). These proxies have proven useful for understanding the soil C cycle, which is highly variable in both space and time, and are now being used to make predictions of the C fate and persistence under future climate scenarios. As these proxies are used at increasingly larger scales, the C pools and processes that proxies represent must be thoughtfully consideredmore » in order to minimize uncertainties in empirical understanding, as well as in model parameters and in model outcomes. The importance of these uncertainties is further amplified by the current need to make predictions of the C cycle for the non steady state environmental conditions resulting from global climate change. To clarify the appropriate uses of proxy variables, we provide specific examples of proxy variables that could improve decision making, adaptation choices, and modeling skill, while not foreclosing on – and also encouraging – continued work on their mechanistic underpinnings. We explore the use of three common soil proxies used to study soil organic matter: metabolic quotient, clay content, and physical fractionation. We also consider emerging data types, specifically genome-sequence data, and how these serve as proxies for microbial community activities. We opine that the demand for increasing mechanistic detail, and the flood of data from new imaging and genetic techniques, does not replace the value of correlative and integrative proxies--variables that are simpler, easier, or cheaper to measure. By closely examining the current knowledge gaps and broad assumptions in soil C cycling with the proxies already in use, we can develop new hypotheses and specify criteria for new and needed proxies.« less

A seasonal hydrologic ensemble prediction system for water resource management

NASA Astrophysics Data System (ADS)

Luo, L.; Wood, E. F.

2006-12-01

A seasonal hydrologic ensemble prediction system, developed for the Ohio River basin, has been improved and expanded to several other regions including the Eastern U.S., Africa and East Asia. The prediction system adopts the traditional Extended Streamflow Prediction (ESP) approach, utilizing the VIC (Variable Infiltration Capacity) hydrological model as the central tool for producing ensemble prediction of soil moisture, snow and streamflow with lead times up to 6-month. VIC is forced by observed meteorology to estimate the hydrological initial condition prior to the forecast, but during the forecast period the atmospheric forcing comes from statistically downscaled, seasonal forecast from dynamic climate models. The seasonal hydrologic ensemble prediction system is currently producing realtime seasonal hydrologic forecast for these regions on a monthly basis. Using hindcasts from a 19-year period (1981-1999), during which seasonal hindcasts from NCEP Climate Forecast System (CFS) and European Union DEMETER project are available, we evaluate the performance of the forecast system over our forecast regions. The evaluation shows that the prediction system using the current forecast approach is able to produce reliable and accurate precipitation, soil moisture and streamflow predictions. The overall skill is much higher then the traditional ESP. In particular, forecasts based on multiple climate model forecast are more skillful than single model-based forecast. This emphasizes the significant need for producing seasonal climate forecast with multiple climate models for hydrologic applications. Forecast from this system is expected to provide very valuable information about future hydrologic states and associated risks for end users, including water resource management and financial sectors.

Simulating the effects of climatic variation on stem carbon accumulation of a ponderosa pine stand: comparison with annual growth increment data.

PubMed

Hunt, E R; Martin, F C; Running, S W

1991-01-01

Simulation models of ecosystem processes may be necessary to separate the long-term effects of climate change on forest productivity from the effects of year-to-year variations in climate. The objective of this study was to compare simulated annual stem growth with measured annual stem growth from 1930 to 1982 for a uniform stand of ponderosa pine (Pinus ponderosa Dougl.) in Montana, USA. The model, FOREST-BGC, was used to simulate growth assuming leaf area index (LAI) was either constant or increasing. The measured stem annual growth increased exponentially over time; the differences between the simulated and measured stem carbon accumulations were not large. Growth trends were removed from both the measured and simulated annual increments of stem carbon to enhance the year-to-year variations in growth resulting from climate. The detrended increments from the increasing LAI simulation fit the detrended increments of the stand data over time with an R(2) of 0.47; the R(2) increased to 0.65 when the previous year's simulated detrended increment was included with the current year's simulated increment to account for autocorrelation. Stepwise multiple linear regression of the detrended increments of the stand data versus monthly meteorological variables had an R(2) of 0.37, and the R(2) increased to 0.47 when the previous year's meteorological data were included to account for autocorrelation. Thus, FOREST-BGC was more sensitive to the effects of year-to-year climate variation on annual stem growth than were multiple linear regression models.

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.

Back to the future: using historical climate variation to project near-term shifts in habitat suitable for coast redwood.

PubMed

Fernández, Miguel; Hamilton, Healy H; Kueppers, Lara M

2015-11-01

Studies that model the effect of climate change on terrestrial ecosystems often use climate projections from downscaled global climate models (GCMs). These simulations are generally too coarse to capture patterns of fine-scale climate variation, such as the sharp coastal energy and moisture gradients associated with wind-driven upwelling of cold water. Coastal upwelling may limit future increases in coastal temperatures, compromising GCMs' ability to provide realistic scenarios of future climate in these coastal ecosystems. Taking advantage of naturally occurring variability in the high-resolution historic climatic record, we developed multiple fine-scale scenarios of California climate that maintain coherent relationships between regional climate and coastal upwelling. We compared these scenarios against coarse resolution GCM projections at a regional scale to evaluate their temporal equivalency. We used these historically based scenarios to estimate potential suitable habitat for coast redwood (Sequoia sempervirens D. Don) under 'normal' combinations of temperature and precipitation, and under anomalous combinations representative of potential future climates. We found that a scenario of warmer temperature with historically normal precipitation is equivalent to climate projected by GCMs for California by 2020-2030 and that under these conditions, climatically suitable habitat for coast redwood significantly contracts at the southern end of its current range. Our results suggest that historical climate data provide a high-resolution alternative to downscaled GCM outputs for near-term ecological forecasts. This method may be particularly useful in other regions where local climate is strongly influenced by ocean-atmosphere dynamics that are not represented by coarse-scale GCMs. © 2015 John Wiley & Sons Ltd.

Principals' instructional management skills and middle school science teacher job satisfaction

NASA Astrophysics Data System (ADS)

Gibbs-Harper, Nzinga A.

The purpose of this research study was to determine if a relationship exists between teachers' perceptions of principals' instructional leadership behaviors and middle school teacher job satisfaction. Additionally, this study sought to assess whether principal's instructional leadership skills were predictors of middle school teachers' satisfaction with work itself. This study drew from 13 middle schools in an urban Mississippi school district. Participants included teachers who taught science. Each teacher was given the Principal Instructional Management Rating Scale (PIMRS; Hallinger, 2011) and the Teacher Job Satisfaction Questionnaire (TJSQ; Lester, 1987) to answer the research questions. The study was guided by two research questions: (a) Is there a relationship between the independent variables Defining the School's Mission, Managing the Instructional Program, and Developing the School Learning Climate Program and the dependent variable Work Itself?; (b) Are Defining the School's Mission, Managing the Instructional Program, and Developing the School Learning Climate Program predictors of Work Itself? The Pearson's correlation and multiple regression analysis were utilized to examine the relationship between the three dimensions of principals' instructional leadership and teacher satisfaction with work itself. The data revealed that there was a strong, positive correlation between all three dimensions of principals' instructional leadership and teacher satisfaction with work itself. However, the multiple regression analysis determined that teachers' perceptions of principals' instructional management skills is a slight predictor of Defining the School's Mission only.

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

PubMed

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

2013-09-01

Dengue fever is a major impact on public health vector-borne disease, and its transmission is influenced by entomological, sociocultural and economic factors. Additionally, climate variability plays an important role in the transmission dynamics. A large scientific consensus has indicated that the strong association between climatic variables and disease could be used to develop models to explain the incidence of the disease. To develop a model that provides a better understanding of dengue transmission dynamics in Medellin and predicts increases in the incidence of the disease. The incidence of dengue fever was used as dependent variable, and weekly climatic factors (maximum, mean and minimum temperature, relative humidity and precipitation) as independent variables. Expert Modeler was used to develop a model to better explain the behavior of the disease. Climatic variables with significant association to the dependent variable were selected through ARIMA models. The model explains 34% of observed variability. Precipitation was the climatic variable showing statistically significant association with the incidence of dengue fever, but with a 20 weeks delay. In Medellin, the transmission of dengue fever was influenced by climate variability, especially precipitation. The strong association dengue fever/precipitation allowed the construction of a model to help understand dengue transmission dynamics. This information will be useful to develop appropriate and timely strategies for dengue control.

Understanding the joint behavior of temperature and precipitation for climate change impact studies

NASA Astrophysics Data System (ADS)

Rana, Arun; Moradkhani, Hamid; Qin, Yueyue

2017-07-01

The multiple downscaled scenario products allow us to assess the uncertainty of the variations of precipitation and temperature in the current and future periods. Probabilistic assessments of both climatic variables help better understand the interdependence of the two and thus, in turn, help in assessing the future with confidence. In the present study, we use ensemble of statistically downscaled precipitation and temperature from various models. The dataset used is multi-model ensemble of 10 global climate models (GCMs) downscaled product from CMIP5 daily dataset using the Bias Correction and Spatial Downscaling (BCSD) technique, generated at Portland State University. The multi-model ensemble of both precipitation and temperature is evaluated for dry and wet periods for 10 sub-basins across Columbia River Basin (CRB). Thereafter, copula is applied to establish the joint distribution of two variables on multi-model ensemble data. The joint distribution is then used to estimate the change in trends of said variables in future, along with estimation of the probabilities of the given change. The joint distribution trends vary, but certainly positive, for dry and wet periods in sub-basins of CRB. Dry season, generally, is indicating a higher positive change in precipitation than temperature (as compared to historical) across sub-basins with wet season inferring otherwise. Probabilities of changes in future, as estimated from the joint distribution, indicate varied degrees and forms during dry season whereas the wet season is rather constant across all the sub-basins.

Climate impact on suicide rates in Finland from 1971 to 2003

NASA Astrophysics Data System (ADS)

Ruuhela, Reija; Hiltunen, Laura; Venäläinen, Ari; Pirinen, Pentti; Partonen, Timo

2009-03-01

Seasonal patterns of death from suicide are well-documented and have been attributed to climatic factors such as solar radiation and ambient temperature. However, studies on the impact of weather and climate on suicide are not consistent, and conflicting data have been reported. In this study, we performed a correlation analysis between nationwide suicide rates and weather variables in Finland during the period 1971-2003. The weather parameters studied were global solar radiation, temperature and precipitation, and a range of time spans from 1 month to 1 year were used in order to elucidate the dose-response relationship, if any, between weather variables and suicide. Single and multiple linear regression models show weak associations using 1-month and 3-month time spans, but robust associations using a 12-month time span. Cumulative global solar radiation had the best explanatory power, while average temperature and cumulative precipitation had only a minor impact on suicide rates. Our results demonstrate that winters with low global radiation may increase the risk of suicide. The best correlation found was for the 5-month period from November to March; the inter-annual variability in the cumulative global radiation for that period explained 40 % of the variation in the male suicide rate and 14 % of the variation in the female suicide rate, both at a statistically significant level. Long-term variations in global radiation may also explain, in part, the observed increasing trend in the suicide rate until 1990 and the decreasing trend since then in Finland.

Bulk canopy resistance: Modeling for the estimation of actual evapotranspiration of maize

NASA Astrophysics Data System (ADS)

Gharsallah, O.; Corbari, C.; Mancini, M.; Rana, G.

2009-04-01

Due to the scarcity of water resources, the correct evaluation of water losses by the crops as evapotranspiration (ET) is very important in irrigation management. This work presents a model for estimating actual evapotranspiration on hourly and daily scales of maize crop grown in well water condition in the Lombardia Region (North Italy). The maize is a difficult crop to model from the soil-canopy-atmosphere point of view, due to its very complex architecture and big height. The present ET model is based on the Penman-Monteith equation using Katerji and Perrier approach for modelling the variable canopy resistance value (rc). In fact rc is a primary factor in the evapotranspiration process and needs to be accurately estimated. Furthermore, ET also has an aerodynamic component, hence it depends on multiple factors such as meteorological variables and crop water condition. The proposed approach appears through a linear model in which rc depends on climate variables and aerodynamic resistance [rc/ra = f(r*/ra)] where ra is the aerodynamic resistance, function of wind speed and crop height, and r* is called "critical" or "climatic" resistance. Here, under humid climate, the model has been applied with good results at both hourly and daily scales. In this study, the reached good accuracy shows that the model worked well and are clearly more accurate than those obtained by using the more diffuse and known standard FAO 56 method for well watered and stressed crops.

Utility of High Temporal Resolution Observations for Heat Health Event Characterization

NASA Astrophysics Data System (ADS)

Palecki, M. A.

2017-12-01

Many heat health watch systems produce a binary on/off warning when conditions are predicted to exceed a given threshold during a day. Days with warnings and their mortality/morbidity statistics are analyzed relative to days not warned to determine the impacts of the event on human health, the effectiveness of warnings, and other statistics. The climate analyses of the heat waves or extreme temperature events are often performed with hourly or daily observations of air temperature, humidity, and other measured or derived variables, especially the maxima and minima of these data. However, since the beginning of the century, 5-minute observations are readily available for many weather and climate stations in the United States. NOAA National Centers for Environmental Information (NCEI) has been collecting 5-minute observations from the NOAA Automated Surface Observing System (ASOS) stations since 2000, and from the U.S. Climate Reference Network (USCRN) stations since 2005. This presentation will demonstrate the efficacy of utilizing 5-minute environmental observations to characterize heat waves by counting the length of time conditions exceed extreme thresholds based on individual and multiple variables and on derived variables such as the heat index. The length and depth of recovery periods between daytime heating periods will also be examined. The length of time under extreme conditions will influence health outcomes for those directly exposed. Longer periods of dangerous conditions also could increase the chances for poor health outcomes for those only exposed intermittently through cumulative impacts.

Reconstruction of Past Mediterranean Climate

NASA Astrophysics Data System (ADS)

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

2007-02-01

First MEDCLIVAR Workshop on Reconstruction of Past Mediterranean Climate; Pablo de Olavide University, Carmona, Spain, 8-11 November 2006; Mediterranean Climate Variability and Predictability (MEDCLIVAR; http://www.medclivar.eu) is a program that coordinates and promotes research on different aspects of Mediterranean climate. The main MEDCLIVAR goals include the reconstruction of past climate, describing patterns and mechanisms characterizing climate space-time variability, extremes at different time and space scales, coupled climate model/empirical reconstruction comparisons, seasonal forecasting, and the identification of the forcings responsible for the observed changes. The program has been endorsed by CLIVAR (Climate Variability and Predictability project) and is funded by the European Science Foundation.

Establishing the common patterns of future tropospheric ozone under diverse climate change scenarios

NASA Astrophysics Data System (ADS)

Jimenez-Guerrero, Pedro; Gómez-Navarro, Juan J.; Jerez, Sonia; Lorente-Plazas, Raquel; Baro, Rocio; Montávez, Juan P.

2013-04-01

The impacts of climate change on air quality may affect long-term air quality planning. However, the policies aimed at improving air quality in the EU directives have not accounted for the variations in the climate. Climate change alone influences future air quality through modifications of gas-phase chemistry, transport, removal, and natural emissions. As such, the aim of this work is to check whether the projected changes in gas-phase air pollution over Europe depends on the scenario driving the regional simulation. For this purpose, two full-transient regional climate change-air quality projections for the first half of the XXI century (1991-2050) have been carried out with MM5+CHIMERE system, including A2 and B2 SRES scenarios. Experiments span the periods 1971-2000, as a reference, and 2071-2100, as future enhanced greenhouse gas and aerosol scenarios (SRES A2 and B2). The atmospheric simulations have a horizontal resolution of 25 km and 23 vertical layers up to 100 mb, and were driven by ECHO-G global climate model outputs. The analysis focuses on the connection between meteorological and air quality variables. Our simulations suggest that the modes of variability for tropospheric ozone and their main precursors hardly change under different SRES scenarios. The effect of changing scenarios has to be sought in the intensity of the changing signal, rather than in the spatial structure of the variation patterns, since the correlation between the spatial patterns of variability in A2 and B2 simulation is r > 0.75 for all gas-phase pollutants included in this study. In both cases, full-transient simulations indicate an enhanced enhanced chemical activity under future scenarios. The causes for tropospheric ozone variations have to be sought in a multiplicity of climate factors, such as increased temperature, different distribution of precipitation patterns across Europe, increased photolysis of primary and secondary pollutants due to lower cloudiness, etc. Nonetheless, according to the results of this work future ozone is conditioned by the dependence of biogenic emissions on the climatological patterns of variability. In this sense, ozone over Europe is mainly driven by the warming-induced increase in biogenic emitting activity (vegetation is kept invariable in the simulations, but estimations of these emissions strongly depends on shortwave radiation and temperature, which are substantially modified in climatic simulations). Moreover, one of the most important drivers for ozone increase is the decrease of cloudiness (related to stronger solar radiation) mostly over southern Europe at the first half of the XXI century. However, given the large uncertainty isoprene sensitivity to climate change and the large uncertainties associated to the cloudiness projections, these results should be carefully considered.

Influence of current climate, historical climate stability and topography on species richness and endemism in Mesoamerican geophyte plants

PubMed Central

2017-01-01

Background A number of biotic and abiotic factors have been proposed as drivers of geographic variation in species richness. As biotic elements, inter-specific interactions are the most widely recognized. Among abiotic factors, in particular for plants, climate and topographic variables as well as their historical variation have been correlated with species richness and endemism. In this study, we determine the extent to which the species richness and endemism of monocot geophyte species in Mesoamerica is predicted by current climate, historical climate stability and topography. Methods Using approximately 2,650 occurrence points representing 507 geophyte taxa, species richness (SR) and weighted endemism (WE) were estimated at a geographic scale using grids of 0.5 × 0.5 decimal degrees resolution using Mexico as the geographic extent. SR and WE were also estimated using species distributions inferred from ecological niche modeling for species with at least five spatially unique occurrence points. Current climate, current to Last Glacial Maximum temperature, precipitation stability and topographic features were used as predictor variables on multiple spatial regression analyses (i.e., spatial autoregressive models, SAR) using the estimates of SR and WE as response variables. The standardized coefficients of the predictor variables that were significant in the regression models were utilized to understand the observed patterns of species richness and endemism. Results Our estimates of SR and WE based on direct occurrence data and distribution modeling generally yielded similar results, though estimates based on ecological niche modeling indicated broader distribution areas for SR and WE than when species richness was directly estimated using georeferenced coordinates. The SR and WE of monocot geophytes were highest along the Trans-Mexican Volcanic Belt, in both cases with higher levels in the central area of this mountain chain. Richness and endemism were also elevated in the southern regions of the Sierra Madre Oriental and Occidental mountain ranges, and in the Tehuacán Valley. Some areas of the Sierra Madre del Sur and Sierra Madre Oriental had high levels of WE, though they are not the areas with the highest SR. The spatial regressions suggest that SR is mostly influenced by current climate, whereas endemism is mainly affected by topography and precipitation stability. Conclusions Both methods (direct occurrence data and ecological niche modeling) used to estimate SR and WE in this study yielded similar results and detected a key area that should be considered in plant conservation strategies: the central region of the Trans-Mexican Volcanic Belt. Our results also corroborated that species richness is more closely correlated with current climate factors while endemism is related to differences in topography and to changes in precipitation levels compared to the LGM climatic conditions. PMID:29062605

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.

Homogenising time series: Beliefs, dogmas and facts

NASA Astrophysics Data System (ADS)

Domonkos, P.

2010-09-01

For obtaining reliable information about climate change and climate variability the use of high quality data series is essentially important, and one basic tool of quality improvements is the statistical homogenisation of observed time series. In the recent decades large number of homogenisation methods has been developed, but the real effects of their application on time series are still not known entirely. The ongoing COST HOME project (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with higher confidence than earlier. As part of the COST activity, a benchmark dataset was built whose characteristics approach well the characteristics of real networks of observed time series. This dataset offers much better opportunity than ever to test the wide variety of homogenisation methods, and analyse the real effects of selected theoretical recommendations. The author believes that several old theoretical rules have to be re-evaluated. Some examples of the hot questions, a) Statistically detected change-points can be accepted only with the confirmation of metadata information? b) Do semi-hierarchic algorithms for detecting multiple change-points in time series function effectively in practise? c) Is it good to limit the spatial comparison of candidate series with up to five other series in the neighbourhood? Empirical results - those from the COST benchmark, and other experiments too - show that real observed time series usually include several inhomogeneities of different sizes. Small inhomogeneities seem like part of the climatic variability, thus the pure application of classic theory that change-points of observed time series can be found and corrected one-by-one is impossible. However, after homogenisation the linear trends, seasonal changes and long-term fluctuations of time series are usually much closer to the reality, than in raw time series. The developers and users of homogenisation methods have to bear in mind that the eventual purpose of homogenisation is not to find change-points, but to have the observed time series with statistical properties those characterise well the climate change and climate variability.

Predicting Low Flow Conditions from Climatic Indices - Indicator-Based Modeling for Climate Change Impact Assessment

NASA Astrophysics Data System (ADS)

Fangmann, Anne; Haberlandt, Uwe

2014-05-01

In the face of climate change, the assessment of future hydrological regimes has become indispensable in the field of water resources management. Investigation of potential change is vital for proper planning, especially with regard to hydrological extremes. Commonly, projection of future streamflow is done applying process-based hydrological models, using climate model data as input, whose complex model structures generally require excessive amounts of time and effort for set-up and computation. This study aims at identifying practical alternatives to the employment of sophisticated models by considering simpler, yet sufficiently accurate methods for modeling rainfall-runoff relations with regard to hydrological extremes. The focus is thereby put on the prediction of low flow periods, which are, in contrast to flood events, characterized by extended durations and spatial dimensions. The models to be established in this study base on indicators, which characterize both meteorological and hydrological conditions within dry periods. This approach makes direct use of the coupling between atmospheric driving forces and streamflow response with the underlying presumption that low-precipitation and high-evaporation periods result in diminished flow, implying that relationships exist between the properties of both meteorological and hydrological events (duration, volume, severity etc.). Eventually, optimal combinations of meteorological indicators are sought that are suitable to predict various low flow characteristics with satisfactory accuracy. Two approaches for model specification are tested: a) multiple linear regression, and b) Fuzzy logic. The data used for this study are daily time series of mean discharge obtained from 294 gauges with variable record length situated in the federal state of Lower Saxony, Germany, as well as interpolated climate variables available for a period from 1951 to 2011. After extraction of a variety of indicators from the available discharge and climate time series on a bi-annual basis, regression and Fuzzy models are fit. Fitting is done in two variations: locally at each of the watersheds in the study area, and regionally, yielding one specific model expression for the entire study area. Models for the individual stations perform well using only the meteorological indicators as predictor variables, while the regional models require the additional input of catchment descriptors to account for the variability of the rainfall-runoff translation processes between the catchments.

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

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.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

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

PubMed Central

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

2006-01-01

How expected increases in climate variability will affect species diversity depends on the role of such variability in regulating the coexistence of competing species. Despite theory linking temporal environmental fluctuations with the maintenance of diversity, the importance of climate variability for stabilizing coexistence remains unknown because of a lack of appropriate long-term observations. Here, we analyze three decades of demographic data from a Kansas prairie to demonstrate that interannual climate variability promotes the coexistence of three common grass species. Specifically, we show that (i) the dynamics of the three species satisfy all requirements of “storage effect” theory based on recruitment variability with overlapping generations, (ii) climate variables are correlated with interannual variation in species performance, and (iii) temporal variability increases low-density growth rates, buffering these species against competitive exclusion. Given that environmental fluctuations are ubiquitous in natural systems, our results suggest that coexistence based on the storage effect may be underappreciated and could provide an important alternative to recent neutral theories of diversity. Field evidence for positive effects of variability on coexistence also emphasizes the need to consider changes in both climate means and variances when forecasting the effects of global change on species diversity. PMID:16908862

Contrasting Responses of the Humboldt Current Ecosystem between the Holocene and MIS5e Interglacials Revealed from Multiple Sediment Records

NASA Astrophysics Data System (ADS)

Salvatteci, R.; Schneider, R. R.; Blanz, T.; Martinez, P.; Crosta, X.

2016-12-01

The Humboldt Current Ecosystem (HCE) off Peru yields about 10% of the global fish catch, producing more fish per unit area than any other region in the world. The high productivity is maintained by the upwelling of cold, nutrient-rich water from the oxygen minimum zone (OMZ), driven by strong trade winds. However, the potential impacts of climate change on upwelling dynamics and oceanographic conditions in the near future are uncertain, threatening local and global economies. Here, we unravel the response of the HCE to contrasting climatic conditions during the last two interglacials (i.e. Holocene and MIS5e) providing an independent insight about the relation between climatic factors and upwelling and productivity dynamics. For this purpose, we used multiple cores to reconstruct past changes in OMZ and upwelling intensity, productivity and fish biomass variability. Chronologies for the Holocene were obtained by multiple 14C ages and laminae correlations among cores, while for the MIS5e they were mainly done by correlation of prominent features in several proxies with other published records. We used a multiproxy approach including alkenones to reconstruct sea surface temperatures, δ15N as a proxy for water column denitrification, redox sensitive metals as proxies for sediment redox conditions, and diatom and fish debris assemblages to reconstruct ecological changes. The results show a very different response of the HCE to climate conditions during the last 2 interglacials, likely driven by changes in Tropical Pacific dynamics. During the Holocene we find that 1) the Late Holocene exhibits higher multi-centennial scale variability compared to the Early Holocene, 2) increased upwelling and a weak OMZ during the mid-Holocene, and 3) long term increase in productivity (diatoms and fishes) from the Early to the Late Holocene. During the MIS5e we find an 1) intense OMZ, 2) strong water column stratification, 3) high siliceous biomass, and 4) low fish biomass compared to the Holocene and a regime shift towards more hemipelagic fishes. Our paleoreconstructions during the globally warm MIS5e are consistent with models indicating that the expected increase in stratification and atmospheric CO2 concentrations may significantly reduce fish capacity in the HCE with heavy ecological and economic consequences.

Tree-ring growth of Scots pine, Common beech and Pedunculate oak under future climate in northeastern Germany

NASA Astrophysics Data System (ADS)

Jurasinski, Gerald; Scharnweber, Tobias; Schröder, Christian; Lennartz, Bernd; Bauwe, Andreas

2017-04-01

Tree growth depends, among other factors, largely on the prevailing climatic conditions. Therefore, tree growth patterns are to be expected under climate change. Here, we analyze the tree-ring growth response of three major European tree species to projected future climate across a climatic (mostly precipitation) gradient in northeastern Germany. We used monthly data for temperature, precipitation, and the standardized precipitation evapotranspiration index (SPEI) over multiple time scales (1, 3, 6, 12, and 24 months) to construct models of tree-ring growth for Scots pine (Pinus syl- vestris L.) at three pure stands, and for Common beech (Fagus sylvatica L.) and Pedunculate oak (Quercus robur L.) at three mature mixed stands. The regression models were derived using a two-step approach based on partial least squares regression (PLSR) to extract potentially well explaining variables followed by ordinary least squares regression (OLSR) to consolidate the models to the least number of variables while retaining high explanatory power. The stability of the models was tested with a comprehensive calibration-verification scheme. All models were successfully verified with R2s ranging from 0.21 for the western pine stand to 0.62 for the beech stand in the east. For growth prediction, climate data forecasted until 2100 by the regional climate model WETTREG2010 based on the A1B Intergovernmental Panel on Climate Change (IPCC) emission scenario was used. For beech and oak, growth rates will likely decrease until the end of the 21st century. For pine, modeled growth trends vary and range from a slight growth increase to a weak decrease in growth rates depending on the position along the climatic gradient. The climatic gradient across the study area will possibly affect the future growth of oak with larger growth reductions towards the drier east. For beech, site-specific adaptations seem to override the influence of the climatic gradient. We conclude that in Northeastern Germany Scots pine has great potential to remain resilient to projected climate change without any greater impairment, whereas Common beech and Pedunculate oak will likely face lesser growth under the expected warmer and dryer climate conditions. The results call for an adaptation of forest management to mitigate the negative effects of climate change for beech and oak in the region.