Technical note: Simultaneous fully dynamic characterization of multiple input–output relationships in climate models

DOE PAGES

Kravitz, Ben; MacMartin, Douglas G.; Rasch, Philip J.; ...

2017-02-17

We introduce system identification techniques to climate science wherein multiple dynamic input–output relationships can be simultaneously characterized in a single simulation. This method, involving multiple small perturbations (in space and time) of an input field while monitoring output fields to quantify responses, allows for identification of different timescales of climate response to forcing without substantially pushing the climate far away from a steady state. We use this technique to determine the steady-state responses of low cloud fraction and latent heat flux to heating perturbations over 22 regions spanning Earth's oceans. We show that the response characteristics are similar to thosemore » of step-change simulations, but in this new method the responses for 22 regions can be characterized simultaneously. Moreover, we can estimate the timescale over which the steady-state response emerges. The proposed methodology could be useful for a wide variety of purposes in climate science, including characterization of teleconnections and uncertainty quantification to identify the effects of climate model tuning parameters.« less

Assessing NARCCAP climate model effects using spatial confidence regions

PubMed Central

French, Joshua P.; McGinnis, Seth; Schwartzman, Armin

2017-01-01

We assess similarities and differences between model effects for the North American Regional Climate Change Assessment Program (NARCCAP) climate models using varying classes of linear regression models. Specifically, we consider how the average temperature effect differs for the various global and regional climate model combinations, including assessment of possible interaction between the effects of global and regional climate models. We use both pointwise and simultaneous inference procedures to identify regions where global and regional climate model effects differ. We also show conclusively that results from pointwise inference are misleading, and that accounting for multiple comparisons is important for making proper inference. PMID:28936474

Simulated impact of climate change on hydrology of multiple watersheds using traditional and recommended snowmelt runoff model methodology

USDA-ARS?s Scientific Manuscript database

For more than three decades, researchers have utilized the Snowmelt Runoff Model (SRM) to test the impacts of climate change on streamflow of snow-fed systems. In this study, the hydrological effects of climate change are modeled over three sequential years using SRM with both typical and recommende...

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.

Stability of the Martian climate system under the seasonal change condition of solar radiation

NASA Astrophysics Data System (ADS)

Nakamura, Takasumi; Tajika, Eiichi

2002-11-01

Previous studies on stability of the Martian climate system used essentially zero-dimensional energy balance climate models (EBMs) under the condition of annual mean solar radiation income. However, areal extent of polar ice caps should affect the Martian climate through the energy balance and the CO2 budget, and results under the seasonal change condition of solar radiation will be different from those under the annual mean condition. We therefore construct a one-dimensional energy balance climate model with CO2-dependent outgoing radiation, seasonal changes of solar radiation income, changes of areal extent of CO2 ice caps, and adsorption of CO2 by regolith. We have investigated behaviors of the Martian climate system and, in particular, examined the effect of the seasonal changes of solar radiation by comparing the results of previous studies under the condition of annual mean solar radiation. One of the major discrepancies between them is the condition for multiple solutions of the Martian climate system. Although the Martian climate system always has multiple solutions under the annual mean condition, under the seasonal change condition, existence of multiple solutions depends on the present amounts of CO2 in the ice caps and the regolith.

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.

Climate change and watershed mercury export: a multiple projection and model analysis

EPA Science Inventory

Future shifts in climatic conditions may impact watershed mercury (Hg) dynamics and transport. We apply an ensemble of watershed models to simulate and assess the responses of hydrological and total Hg (HgT) fluxes and concentrations to two climate change projections in the US Co...

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

NASA Astrophysics Data System (ADS)

Lee, H.

2016-12-01

Precipitation is one of the most important climate variables that are taken into account in studying regional climate. Nevertheless, how precipitation will respond to a changing climate and even its mean state in the current climate are not well represented in regional climate models (RCMs). Hence, comprehensive and mathematically rigorous methodologies to evaluate precipitation and related variables in multiple RCMs are required. The main objective of the current study is to evaluate the joint variability of climate variables related to model performance in simulating precipitation and condense multiple evaluation metrics into a single summary score. We use multi-objective optimization, a mathematical process that provides a set of optimal tradeoff solutions based on a range of evaluation metrics, to characterize the joint representation of precipitation, cloudiness and insolation in RCMs participating in the North American Regional Climate Change Assessment Program (NARCCAP) and Coordinated Regional Climate Downscaling Experiment-North America (CORDEX-NA). We also leverage ground observations, NASA satellite data and the Regional Climate Model Evaluation System (RCMES). Overall, the quantitative comparison of joint probability density functions between the three variables indicates that performance of each model differs markedly between sub-regions and also shows strong seasonal dependence. Because of the large variability across the models, it is important to evaluate models systematically and make future projections using only models showing relatively good performance. Our results indicate that the optimized multi-model ensemble always shows better performance than the arithmetic ensemble mean and may guide reliable future projections.

The agricultural model intercomparison and improvement project (AgMIP): Protocols and pilot studies

USDA-ARS?s Scientific Manuscript database

The Agricultural Model Intercomparison and Improvement Project (AgMIP) is a distributed climate-scenario simulation research activity for historical period model intercomparison and future climate change conditions with participation of multiple crop and agricultural economic model groups around the...

Incremental dynamical downscaling for probabilistic analysis based on multiple GCM projections

NASA Astrophysics Data System (ADS)

Wakazuki, Y.

2015-12-01

A dynamical downscaling method for probabilistic regional scale climate change projections was developed to cover an uncertainty of multiple general circulation model (GCM) climate simulations. The climatological increments (future minus present climate states) estimated by GCM simulation results were statistically analyzed using the singular vector decomposition. Both positive and negative perturbations from the ensemble mean with the magnitudes of their standard deviations were extracted and were added to the ensemble mean of the climatological increments. The analyzed multiple modal increments were utilized to create multiple modal lateral boundary conditions for the future climate regional climate model (RCM) simulations by adding to an objective analysis data. This data handling is regarded to be an advanced method of the pseudo-global-warming (PGW) method previously developed by Kimura and Kitoh (2007). The incremental handling for GCM simulations realized approximated probabilistic climate change projections with the smaller number of RCM simulations. Three values of a climatological variable simulated by RCMs for a mode were used to estimate the response to the perturbation of the mode. For the probabilistic analysis, climatological variables of RCMs were assumed to show linear response to the multiple modal perturbations, although the non-linearity was seen for local scale rainfall. Probability of temperature was able to be estimated within two modes perturbation simulations, where the number of RCM simulations for the future climate is five. On the other hand, local scale rainfalls needed four modes simulations, where the number of the RCM simulations is nine. The probabilistic method is expected to be used for regional scale climate change impact assessment in the future.

Determing Credibility of Regional Simulations of Future Climate

NASA Astrophysics Data System (ADS)

Mearns, L. O.

2009-12-01

Climate models have been evaluated or validated ever since they were first developed. Establishing that a climate model can reproduce (some) aspects of the current climate of the earth on various spatial and temporal scales has long been a standard procedure for providing confidence in the model's ability to simulate future climate. However, direct links between the successes and failures of models in reproducing the current climate with regard to what future climates the models simulate has been largely lacking. This is to say that the model evaluation process has been largely divorced from the projections of future climate that the models produce. This is evidenced in the separation in the Intergovernmental Panel on Climate Change (IPCC) WG1 report of the chapter on evaluation of models from the chapter on future climate projections. There has also been the assumption of 'one model, one vote, that is, that each model projection is given equal weight in any multi-model ensemble presentation of the projections of future climate. There have been various attempts at determing measures of credibility that would avoid the 'ultrademocratic' assumption of the IPCC. Simple distinctions between models were made by research such as in Giorgi and Mearns (2002), Tebaldi et al., (2005), and Greene et al., (2006). But the metrics used were rather simplistic. More ambitous means of discriminating among the quality of model simulations have been made through the production of complex multivariate metrics, but insufficent work has been produced to verify that the metrics successfully discriminate in meaningful ways. Indeed it has been suggested that we really don't know what a model must successfully model to establish confidence in its regional-scale projections (Gleckler et al., 2008). Perhaps a more process oriented regional expert judgment approach is needed to understand which errors in climate models really matter for the model's response to future forcing. Such an approach is being attempted in the North American Climate Change Assessment Program (NARCCAP) whereby multiple global models are used to drive multiple regional models for the current period and the mid-21st century over the continent. Progress in this endeavor will be reported.

Climate change and watershed mercury export: a multiple projection and model analysis

USGS Publications Warehouse

Golden, Heather E.; Knightes, Christopher D.; Conrads, Paul; Feaster, Toby D.; Davis, Gary M.; Benedict, Stephen T.; Bradley, Paul M.

2013-01-01

Future shifts in climatic conditions may impact watershed mercury (Hg) dynamics and transport. An ensemble of watershed models was applied in the present study to simulate and evaluate the responses of hydrological and total Hg (THg) fluxes from the landscape to the watershed outlet and in-stream THg concentrations to contrasting climate change projections for a watershed in the southeastern coastal plain of the United States. Simulations were conducted under stationary atmospheric deposition and land cover conditions to explicitly evaluate the effect of projected precipitation and temperature on watershed Hg export (i.e., the flux of Hg at the watershed outlet). Based on downscaled inputs from 2 global circulation models that capture extremes of projected wet (Community Climate System Model, Ver 3 [CCSM3]) and dry (ECHAM4/HOPE-G [ECHO]) conditions for this region, watershed model simulation results suggest a decrease of approximately 19% in ensemble-averaged mean annual watershed THg fluxes using the ECHO climate-change model and an increase of approximately 5% in THg fluxes with the CCSM3 model. Ensemble-averaged mean annual ECHO in-stream THg concentrations increased 20%, while those of CCSM3 decreased by 9% between the baseline and projected simulation periods. Watershed model simulation results using both climate change models suggest that monthly watershed THg fluxes increase during the summer, when projected flow is higher than baseline conditions. The present study's multiple watershed model approach underscores the uncertainty associated with climate change response projections and their use in climate change management decisions. Thus, single-model predictions can be misleading, particularly in developmental stages of watershed Hg modeling.

Quantifying Hydro-biogeochemical Model Sensitivity in Assessment of Climate Change Effect on Hyporheic Zone Processes

NASA Astrophysics Data System (ADS)

Song, X.; Chen, X.; Dai, H.; Hammond, G. E.; Song, H. S.; Stegen, J.

2016-12-01

The hyporheic zone is an active region for biogeochemical processes such as carbon and nitrogen cycling, where the groundwater and surface water mix and interact with each other with distinct biogeochemical and thermal properties. The biogeochemical dynamics within the hyporheic zone are driven by both river water and groundwater hydraulic dynamics, which are directly affected by climate change scenarios. Besides that, the hydraulic and thermal properties of local sediments and microbial and chemical processes also play important roles in biogeochemical dynamics. Thus for a comprehensive understanding of the biogeochemical processes in the hyporheic zone, a coupled thermo-hydro-biogeochemical model is needed. As multiple uncertainty sources are involved in the integrated model, it is important to identify its key modules/parameters through sensitivity analysis. In this study, we develop a 2D cross-section model in the hyporheic zone at the DOE Hanford site adjacent to Columbia River and use this model to quantify module and parametric sensitivity on assessment of climate change. To achieve this purpose, We 1) develop a facies-based groundwater flow and heat transfer model that incorporates facies geometry and heterogeneity characterized from a field data set, 2) derive multiple reaction networks/pathways from batch experiments with in-situ samples and integrate temperate dependent reactive transport modules to the flow model, 3) assign multiple climate change scenarios to the coupled model by analyzing historical river stage data, 4) apply a variance-based global sensitivity analysis to quantify scenario/module/parameter uncertainty in hierarchy level. The objectives of the research include: 1) identifing the key control factors of the coupled thermo-hydro-biogeochemical model in the assessment of climate change, and 2) quantify the carbon consumption in different climate change scenarios in the hyporheic zone.

Evaluating the Sensitivity of Agricultural Model Performance to Different Climate Inputs: Supplemental Material

NASA Technical Reports Server (NTRS)

Glotter, Michael J.; Ruane, Alex C.; Moyer, Elisabeth J.; Elliott, Joshua W.

2015-01-01

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled and observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources reanalysis, reanalysis that is bias corrected with observed climate, and a control dataset and compared with observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by non-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. Some issues persist for all choices of climate inputs: crop yields appear to be oversensitive to precipitation fluctuations but under sensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves.

Evaluating the sensitivity of agricultural model performance to different climate inputs

PubMed Central

Glotter, Michael J.; Moyer, Elisabeth J.; Ruane, Alex C.; Elliott, Joshua W.

2017-01-01

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled to observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections, but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely-used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources – reanalysis, reanalysis bias-corrected with observed climate, and a control dataset – and compared to observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by un-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. However, some issues persist for all choices of climate inputs: crop yields appear oversensitive to precipitation fluctuations but undersensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves. PMID:29097985

Data-based discharge extrapolation: estimating annual discharge for a partially gauged large river basin from its small sub-basins

NASA Astrophysics Data System (ADS)

Gong, L.

2013-12-01

Large-scale hydrological models and land surface models are by far the only tools for accessing future water resources in climate change impact studies. Those models estimate discharge with large uncertainties, due to the complex interaction between climate and hydrology, the limited quality and availability of data, as well as model uncertainties. A new purely data-based scale-extrapolation method is proposed, to estimate water resources for a large basin solely from selected small sub-basins, which are typically two-orders-of-magnitude smaller than the large basin. Those small sub-basins contain sufficient information, not only on climate and land surface, but also on hydrological characteristics for the large basin In the Baltic Sea drainage basin, best discharge estimation for the gauged area was achieved with sub-basins that cover 2-4% of the gauged area. There exist multiple sets of sub-basins that resemble the climate and hydrology of the basin equally well. Those multiple sets estimate annual discharge for gauged area consistently well with 5% average error. The scale-extrapolation method is completely data-based; therefore it does not force any modelling error into the prediction. The multiple predictions are expected to bracket the inherent variations and uncertainties of the climate and hydrology of the basin. The method can be applied in both un-gauged basins and un-gauged periods with uncertainty estimation.

Measuring School Climate in High Schools: A Focus on Safety, Engagement, and the Environment

ERIC Educational Resources Information Center

Bradshaw, Catherine P.; Waasdorp, Tracy E.; Debnam, Katrina J.; Johnson, Sarah Lindstrom

2014-01-01

Background: School climate has been linked to multiple student behavioral, academic, health, and social-emotional outcomes. The US Department of Education (USDOE) developed a 3-factor model of school climate comprised of safety, engagement, and environment. This article examines the factor structure and measurement invariance of the USDOE model.…

Model-based scenario planning to develop climate change adaptation strategies for rare plant populations in grassland reserves

Treesearch

Laura Phillips-Mao; Susan M. Galatowitsch; Stephanie A. Snyder; Robert G. Haight

2016-01-01

Incorporating climate change into conservation decision-making at site and population scales is challenging due to uncertainties associated with localized climate change impacts and population responses to multiple interacting impacts and adaptation strategies. We explore the use of spatially explicit population models to facilitate scenario analysis, a conservation...

Effects of cumulus entrainment and multiple cloud types on a January global climate model simulation

NASA Technical Reports Server (NTRS)

Yao, Mao-Sung; Del Genio, Anthony D.

1989-01-01

An improved version of the GISS Model II cumulus parameterization designed for long-term climate integrations is used to study the effects of entrainment and multiple cloud types on the January climate simulation. Instead of prescribing convective mass as a fixed fraction of the cloud base grid-box mass, it is calculated based on the closure assumption that the cumulus convection restores the atmosphere to a neutral moist convective state at cloud base. This change alone significantly improves the distribution of precipitation, convective mass exchanges, and frequencies in the January climate. The vertical structure of the tropical atmosphere exhibits quasi-equilibrium behavior when this closure is used, even though there is no explicit constraint applied above cloud base.

The North American Regional Climate Change Assessment Program (NARCCAP): Status and results

NASA Astrophysics Data System (ADS)

Gutowski, W. J.

2009-12-01

NARCCAP is a multi-institutional program that is investigating systematically the uncertainties in regional scale simulations of contemporary climate and projections of future climate. NARCCAP is supported by multiple federal agencies. NARCCAP is producing an ensemble of high-resolution climate-change scenarios by nesting multiple RCMs in reanalyses and multiple atmosphere-ocean GCM simulations of contemporary and future-scenario climates. The RCM domains cover the contiguous U.S., northern Mexico, and most of Canada. The simulation suite also includes time-slice, high resolution GCMs that use sea-surface temperatures from parent atmosphere-ocean GCMs. The baseline resolution of the RCMs and time-slice GCMs is 50 km. Simulations use three sources of boundary conditions: National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) AMIP-II Reanalysis, GCMs simulating contemporary climate and GCMs using the A2 SRES emission scenario for the twenty-first century. Simulations cover 1979-2004 and 2038-2060, with the first 3 years discarded for spin-up. The resulting RCM and time-slice simulations offer opportunity for extensive analysis of RCM simulations as well as a basis for multiple high-resolution climate scenarios for climate change impacts assessments. Geophysical statisticians are developing measures of uncertainty from the ensemble. To enable very high-resolution simulations of specific regions, both RCM and high-resolution time-slice simulations are saving output needed for further downscaling. All output is publically available to the climate analysis and the climate impacts assessment community, through an archiving and data-distribution plan. Some initial results show that the models closely reproduce ENSO-related precipitation variations in coastal California, where the correlation between the simulated and observed monthly time series exceeds 0.94 for all models. The strong El Nino events of 1982-83 and 1997-98 are well reproduced for the Pacific coastal region of the U.S. in all models. ENSO signals are less well reproduced in other regions. The models also produce well extreme monthly precipitation in coastal California and the Upper Midwest. Model performance tends to deteriorate from west to east across the domain, or roughly from the inflow boundary toward the outflow boundary. This deterioration with distance from the inflow boundary is ameliorated to some extent in models formulated such that large-scale information is included in the model solution, whether implemented by spectral nudging or by use of a perturbation form of the governing equations.

Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments.

PubMed

Tao, Fulu; Rötter, Reimund P; Palosuo, Taru; Gregorio Hernández Díaz-Ambrona, Carlos; Mínguez, M Inés; Semenov, Mikhail A; Kersebaum, Kurt Christian; Nendel, Claas; Specka, Xenia; Hoffmann, Holger; Ewert, Frank; Dambreville, Anaelle; Martre, Pierre; Rodríguez, Lucía; Ruiz-Ramos, Margarita; Gaiser, Thomas; Höhn, Jukka G; Salo, Tapio; Ferrise, Roberto; Bindi, Marco; Cammarano, Davide; Schulman, Alan H

2018-03-01

Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was -4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981-2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources. © 2017 John Wiley & Sons Ltd.

Effects of Ensemble Configuration on Estimates of Regional Climate Uncertainties

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

Goldenson, N.; Mauger, G.; Leung, L. R.

Internal variability in the climate system can contribute substantial uncertainty in climate projections, particularly at regional scales. Internal variability can be quantified using large ensembles of simulations that are identical but for perturbed initial conditions. Here we compare methods for quantifying internal variability. Our study region spans the west coast of North America, which is strongly influenced by El Niño and other large-scale dynamics through their contribution to large-scale internal variability. Using a statistical framework to simultaneously account for multiple sources of uncertainty, we find that internal variability can be quantified consistently using a large ensemble or an ensemble ofmore » opportunity that includes small ensembles from multiple models and climate scenarios. The latter also produce estimates of uncertainty due to model differences. We conclude that projection uncertainties are best assessed using small single-model ensembles from as many model-scenario pairings as computationally feasible, which has implications for ensemble design in large modeling efforts.« less

Climate change and watershed mercury export: a multiple projection and model analysis.

PubMed

Golden, Heather E; Knightes, Christopher D; Conrads, Paul A; Feaster, Toby D; Davis, Gary M; Benedict, Stephen T; Bradley, Paul M

2013-09-01

Future shifts in climatic conditions may impact watershed mercury (Hg) dynamics and transport. An ensemble of watershed models was applied in the present study to simulate and evaluate the responses of hydrological and total Hg (THg) fluxes from the landscape to the watershed outlet and in-stream THg concentrations to contrasting climate change projections for a watershed in the southeastern coastal plain of the United States. Simulations were conducted under stationary atmospheric deposition and land cover conditions to explicitly evaluate the effect of projected precipitation and temperature on watershed Hg export (i.e., the flux of Hg at the watershed outlet). Based on downscaled inputs from 2 global circulation models that capture extremes of projected wet (Community Climate System Model, Ver 3 [CCSM3]) and dry (ECHAM4/HOPE-G [ECHO]) conditions for this region, watershed model simulation results suggest a decrease of approximately 19% in ensemble-averaged mean annual watershed THg fluxes using the ECHO climate-change model and an increase of approximately 5% in THg fluxes with the CCSM3 model. Ensemble-averaged mean annual ECHO in-stream THg concentrations increased 20%, while those of CCSM3 decreased by 9% between the baseline and projected simulation periods. Watershed model simulation results using both climate change models suggest that monthly watershed THg fluxes increase during the summer, when projected flow is higher than baseline conditions. The present study's multiple watershed model approach underscores the uncertainty associated with climate change response projections and their use in climate change management decisions. Thus, single-model predictions can be misleading, particularly in developmental stages of watershed Hg modeling. Copyright © 2013 SETAC.

Modeling daily soil temperature over diverse climate conditions in Iran—a comparison of multiple linear regression and support vector regression techniques

NASA Astrophysics Data System (ADS)

Delbari, Masoomeh; Sharifazari, Salman; Mohammadi, Ehsan

2018-02-01

The knowledge of soil temperature at different depths is important for agricultural industry and for understanding climate change. The aim of this study is to evaluate the performance of a support vector regression (SVR)-based model in estimating daily soil temperature at 10, 30 and 100 cm depth at different climate conditions over Iran. The obtained results were compared to those obtained from a more classical multiple linear regression (MLR) model. The correlation sensitivity for the input combinations and periodicity effect were also investigated. Climatic data used as inputs to the models were minimum and maximum air temperature, solar radiation, relative humidity, dew point, and the atmospheric pressure (reduced to see level), collected from five synoptic stations Kerman, Ahvaz, Tabriz, Saghez, and Rasht located respectively in the hyper-arid, arid, semi-arid, Mediterranean, and hyper-humid climate conditions. According to the results, the performance of both MLR and SVR models was quite well at surface layer, i.e., 10-cm depth. However, SVR performed better than MLR in estimating soil temperature at deeper layers especially 100 cm depth. Moreover, both models performed better in humid climate condition than arid and hyper-arid areas. Further, adding a periodicity component into the modeling process considerably improved the models' performance especially in the case of SVR.

The North American Regional Climate Change Assessment Program (NARCCAP): Status and results

NASA Astrophysics Data System (ADS)

Arritt, R.

2009-04-01

NARCCAP is an international program that is generating projections of climate change for the U.S., Canada, and northern Mexico at decision-relevant regional scales. NARCCAP uses multiple limited-area regional climate models (RCMs) nested within multiple atmosphere-ocean general circulation models (AOGCMs). The use of multiple regional and global models allows us to investigate the uncertainty in model responses to future emissions (here, the A2 SRES scenario). The project also includes global time-slice experiments at the same discretization (50 km) using the GFDL atmospheric model (AM2.1) and the NCAR atmospheric model (CAM3). Phase I of the experiment uses the regional models nested within reanalysis in order to establish uncertainty attributable to the RCMs themselves. Phase II of the project then nests the RCMs within results from the current and future runs of the AOGCMs to explore the cascade of uncertainty from the global to the regional models. Phase I has been completed and the results to be shown include findings that spectral nudging is beneficial in some regions but not in others. Phase II is nearing completion and some preliminary results will be shown.

Unraveling multiple changes in complex climate time series using Bayesian inference

NASA Astrophysics Data System (ADS)

Berner, Nadine; Trauth, Martin H.; Holschneider, Matthias

2016-04-01

Change points in time series are perceived as heterogeneities in the statistical or dynamical characteristics of observations. Unraveling such transitions yields essential information for the understanding of the observed system. The precise detection and basic characterization of underlying changes is therefore of particular importance in environmental sciences. We present a kernel-based Bayesian inference approach to investigate direct as well as indirect climate observations for multiple generic transition events. In order to develop a diagnostic approach designed to capture a variety of natural processes, the basic statistical features of central tendency and dispersion are used to locally approximate a complex time series by a generic transition model. A Bayesian inversion approach is developed to robustly infer on the location and the generic patterns of such a transition. To systematically investigate time series for multiple changes occurring at different temporal scales, the Bayesian inversion is extended to a kernel-based inference approach. By introducing basic kernel measures, the kernel inference results are composed into a proxy probability to a posterior distribution of multiple transitions. Thus, based on a generic transition model a probability expression is derived that is capable to indicate multiple changes within a complex time series. We discuss the method's performance by investigating direct and indirect climate observations. The approach is applied to environmental time series (about 100 a), from the weather station in Tuscaloosa, Alabama, and confirms documented instrumentation changes. Moreover, the approach is used to investigate a set of complex terrigenous dust records from the ODP sites 659, 721/722 and 967 interpreted as climate indicators of the African region of the Plio-Pleistocene period (about 5 Ma). The detailed inference unravels multiple transitions underlying the indirect climate observations coinciding with established global climate events.

Climate Science's Globally Distributed Infrastructure

NASA Astrophysics Data System (ADS)

Williams, D. N.

2016-12-01

The Earth System Grid Federation (ESGF) is primarily funded by the Department of Energy's (DOE's) Office of Science (the Office of Biological and Environmental Research [BER] Climate Data Informatics Program and the Office of Advanced Scientific Computing Research Next Generation Network for Science Program), the National Oceanic and Atmospheric Administration (NOAA), the National Aeronautics and Space Administration (NASA), and the National Science Foundation (NSF), the European Infrastructure for the European Network for Earth System Modeling (IS-ENES), and the Australian National University (ANU). Support also comes from other U.S. federal and international agencies. The federation works across multiple worldwide data centers and spans seven international network organizations to provide users with the ability to access, analyze, and visualize data using a globally federated collection of networks, computers, and software. Its architecture employs a series of geographically distributed peer nodes that are independently administered and united by common federation protocols and application programming interfaces (APIs). The full ESGF infrastructure has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the Coupled Model Intercomparison Project (CMIP; output used by the Intergovernmental Panel on Climate Change assessment reports), multiple model intercomparison projects (MIPs; endorsed by the World Climate Research Programme [WCRP]), and the Accelerated Climate Modeling for Energy (ACME; ESGF is included in the overarching ACME workflow process to store model output). ESGF is a successful example of integration of disparate open-source technologies into a cohesive functional system that serves the needs the global climate science community. Data served by ESGF includes not only model output but also observational data from satellites and instruments, reanalysis, and generated images.

Normal forms for reduced stochastic climate models

PubMed Central

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

2009-01-01

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

Pliocene Model Intercomparison (PlioMIP) Phase 2: Scientific Objectives and Experimental Design

NASA Technical Reports Server (NTRS)

Haywood, A. M.; Dowsett, H. J.; Dolan, A. M.; Rowley, D.; Abe-Ouchi, A.; Otto-Bliesner, B.; Chandler, M. A.; Hunter, S. J.; Lunt, D. J.; Pound, M.;

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.

The Climate Response to Stratospheric Aerosol Geoengineering Can Be Tailored Using Multiple Injection Locations

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

MacMartin, Douglas G.; Kravitz, Ben; Tilmes, Simone

The climate response to geoengineering with stratospheric aerosols has the potential to be designed to achieve some chosen objectives. By injecting different amounts of SO2 at multiple different latitudes, the spatial pattern of aerosol optical depth (AOD) can be partially controlled. We use simulations from the fully-coupled whole-atmosphere chemistry-climate model CESM1(WACCM), to demonstrate that three spatial degrees of freedom of AOD can be achieved by appropriately combining injection at different locations: an approximately spatially-uniform AOD distribution, the relative difference in AOD between Northern and Southern hemispheres, and the relative AOD in high versus low latitudes. For forcing levels that yieldmore » 1–2°C cooling, the AOD and surface temperature response are sufficiently linear in this model so that many climate effects can be predicted from single-latitude injection simulations. Optimized injection at multiple locations is predicted to improve compensation of CO2-forced climate change, relative to a case using only equatorial aerosol injection. The additional degrees of freedom can be used, for example, to balance interhemispheric temperature differences and the equator to pole temperature difference in addition to the global mean temperature; this is projected in this model to reduce the mean-square error in temperature compensation by 30%.« less

Evaluation of the multi-model CORDEX-Africa hindcast using RCMES

NASA Astrophysics Data System (ADS)

Kim, J.; Waliser, D. E.; Lean, P.; Mattmann, C. A.; Goodale, C. E.; Hart, A.; Zimdars, P.; Hewitson, B.; Jones, C.

2011-12-01

Recent global climate change studies have concluded with a high confidence level that the observed increasing trend in the global-mean surface air temperatures since mid-20th century is triggered by the emission of anthropogenic greenhouse gases (GHGs). The increase in the global-mean temperature due to anthropogenic emissions is nearly monotonic and may alter the climatological norms resulting in a new climate normal. In the presence of anthropogenic climate change, assessing regional impacts of the altered climate state and developing the plans for mitigating any adverse impacts are an important concern. Assessing future climate state and its impact remains a difficult task largely because of the uncertainties in future emissions and model errors. Uncertainties in climate projections propagates into impact assessment models and result in uncertainties in the impact assessments. In order to facilitate the evaluation of model data, a fundamental step for assessing model errors, the JPL Regional Climate Model Evaluation System (RCMES: Lean et al. 2010; Hart et al. 2011) has been developed through a joint effort of the investigators from UCLA and JPL. RCMES is also a regional climate component of a larger worldwide ExArch project. We will present the evaluation of the surface temperatures and precipitation from multiple RCMs participating in the African component of the Coordinated Regional Climate Downscaling Experiment (CORDEX) that has organized a suite of regional climate projection experiments in which multiple RCMs and GCMs are incorporated. As a part of the project, CORDEX organized a 20-year regional climate hindcast study in order to quantify and understand the uncertainties originating from model errors. Investigators from JPL, UCLA, and the CORDEX-Africa team collaborate to analyze the RCM hindcast data using RCMES. The analysis is focused on measuring the closeness between individual regional climate model outputs as well as their ensembles and observed data. The model evaluation is quantified in terms of widely used metrics. Details on the conceptual outline and architecture of RCMES is presented in two companion papers "The Regional climate model Evaluation System (RCMES) based on contemporary satellite and other observations for assessing regional climate model fidelity" and "A Reusable Framework for Regional Climate Model Evaluation" in GC07 and IN30, respectively.

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.

Simulating water quality and ecological status of Lake Vansjø, Norway, under land-use and climate change by linking process-oriented models with a Bayesian network.

PubMed

Couture, Raoul-Marie; Moe, S Jannicke; Lin, Yan; Kaste, Øyvind; Haande, Sigrid; Lyche Solheim, Anne

2018-04-15

Excess nutrient inputs and climate change are two of multiple stressors affecting many lakes worldwide. Lake Vansjø in southern Norway is one such eutrophic lake impacted by blooms of toxic blue-green algae (cyanobacteria), and classified as moderate ecological status under the EU Water Framework Directive. Future climate change may exacerbate the situation. Here we use a set of chained models (global climate model, hydrological model, catchment phosphorus (P) model, lake model, Bayesian Network) to assess the possible future ecological status of the lake, given the set of climate scenarios and storylines common to the EU project MARS (Managing Aquatic Ecosystems and Water Resources under Multiple Stress). The model simulations indicate that climate change alone will increase precipitation and runoff, and give higher P fluxes to the lake, but cause little increase in phytoplankton biomass or changes in ecological status. For the storylines of future management and land-use, however, the model results indicate that both the phytoplankton biomass and the lake ecological status can be positively or negatively affected. Our results also show the value in predicting a biological indicator of lake ecological status, in this case, cyanobacteria biomass with a BN model. For all scenarios, cyanobacteria contribute to worsening the status assessed by phytoplankton, compared to using chlorophyll-a alone. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Reviewing Bayesian Networks potentials for climate change impacts assessment and management: A multi-risk perspective.

PubMed

Sperotto, Anna; Molina, José-Luis; Torresan, Silvia; Critto, Andrea; Marcomini, Antonio

2017-11-01

The evaluation and management of climate change impacts on natural and human systems required the adoption of a multi-risk perspective in which the effect of multiple stressors, processes and interconnections are simultaneously modelled. Despite Bayesian Networks (BNs) are popular integrated modelling tools to deal with uncertain and complex domains, their application in the context of climate change still represent a limited explored field. The paper, drawing on the review of existing applications in the field of environmental management, discusses the potential and limitation of applying BNs to improve current climate change risk assessment procedures. Main potentials include the advantage to consider multiple stressors and endpoints in the same framework, their flexibility in dealing and communicate with the uncertainty of climate projections and the opportunity to perform scenario analysis. Some limitations (i.e. representation of temporal and spatial dynamics, quantitative validation), however, should be overcome to boost BNs use in climate change impacts assessment and management. Copyright © 2017 Elsevier Ltd. All rights reserved.

Trends and uncertainties in budburst projections of Norway spruce in Northern Europe.

PubMed

Olsson, Cecilia; Olin, Stefan; Lindström, Johan; Jönsson, Anna Maria

2017-12-01

Budburst is regulated by temperature conditions, and a warming climate is associated with earlier budburst. A range of phenology models has been developed to assess climate change effects, and they tend to produce different results. This is mainly caused by different model representations of tree physiology processes, selection of observational data for model parameterization, and selection of climate model data to generate future projections. In this study, we applied (i) Bayesian inference to estimate model parameter values to address uncertainties associated with selection of observational data, (ii) selection of climate model data representative of a larger dataset, and (iii) ensembles modeling over multiple initial conditions, model classes, model parameterizations, and boundary conditions to generate future projections and uncertainty estimates. The ensemble projection indicated that the budburst of Norway spruce in northern Europe will on average take place 10.2 ± 3.7 days earlier in 2051-2080 than in 1971-2000, given climate conditions corresponding to RCP 8.5. Three provenances were assessed separately (one early and two late), and the projections indicated that the relationship among provenance will remain also in a warmer climate. Structurally complex models were more likely to fail predicting budburst for some combinations of site and year than simple models. However, they contributed to the overall picture of current understanding of climate impacts on tree phenology by capturing additional aspects of temperature response, for example, chilling. Model parameterizations based on single sites were more likely to result in model failure than parameterizations based on multiple sites, highlighting that the model parameterization is sensitive to initial conditions and may not perform well under other climate conditions, whether the change is due to a shift in space or over time. By addressing a range of uncertainties, this study showed that ensemble modeling provides a more robust impact assessment than would a single phenology model run.

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.

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.

Impact of Spatial Scales on the Intercomparison of Climate Scenarios

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

Luo, Wei; Steptoe, Michael; Chang, Zheng

2017-01-01

Scenario analysis has been widely applied in climate science to understand the impact of climate change on the future human environment, but intercomparison and similarity analysis of different climate scenarios based on multiple simulation runs remain challenging. Although spatial heterogeneity plays a key role in modeling climate and human systems, little research has been performed to understand the impact of spatial variations and scales on similarity analysis of climate scenarios. To address this issue, the authors developed a geovisual analytics framework that lets users perform similarity analysis of climate scenarios from the Global Change Assessment Model (GCAM) using a hierarchicalmore » clustering approach.« less

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.

Comparing approaches for using climate projections in assessing water resources investments for systems with multiple stakeholder groups

NASA Astrophysics Data System (ADS)

Hurford, Anthony; Harou, Julien

2015-04-01

Climate change has challenged conventional methods of planning water resources infrastructure investment, relying on stationarity of time-series data. It is not clear how to best use projections of future climatic conditions. Many-objective simulation-optimisation and trade-off analysis using evolutionary algorithms has been proposed as an approach to addressing complex planning problems with multiple conflicting objectives. The search for promising assets and policies can be carried out across a range of climate projections, to identify the configurations of infrastructure investment shown by model simulation to be robust under diverse future conditions. Climate projections can be used in different ways within a simulation model to represent the range of possible future conditions and understand how optimal investments vary according to the different hydrological conditions. We compare two approaches, optimising over an ensemble of different 20-year flow and PET timeseries projections, and separately for individual future scenarios built synthetically from the original ensemble. Comparing trade-off curves and surfaces generated by the two approaches helps understand the limits and benefits of optimising under different sets of conditions. The comparison is made for the Tana Basin in Kenya, where climate change combined with multiple conflicting objectives of water management and infrastructure investment mean decision-making is particularly challenging.

Misleading prioritizations from modelling range shifts under climate change

Treesearch

Helen R. Sofaer; Catherine S. Jarnevich; Curtis H. Flather

2018-01-01

Conservation planning requires the prioritization of a subset of taxa and geographical locations to focus monitoring and management efforts. Integration of the threats and opportunities posed by climate change often relies on predictions from species distribution models, particularly for assessments of vulnerability or invasion risk for multiple taxa. We evaluated...

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.

The Impact of Transformational Leadership on Safety Climate and Individual Safety Behavior on Construction Sites.

PubMed

Shen, Yuzhong; Ju, Chuanjing; Koh, Tas Yong; Rowlinson, Steve; Bridge, Adrian J

2017-01-05

Unsafe acts contribute dominantly to construction accidents, and increasing safety behavior is essential to reduce accidents. Previous research conceptualized safety behavior as an interaction between proximal individual differences (safety knowledge and safety motivation) and distal contextual factors (leadership and safety climate). However, relatively little empirical research has examined this conceptualization in the construction sector. Given the cultural background of the sample, this study makes a slight modification to the conceptualization and views transformational leadership as an antecedent of safety climate. Accordingly, this study establishes a multiple mediator model showing the mechanisms through which transformational leadership translates into safety behavior. The multiple mediator model is estimated by the structural equation modeling (SEM) technique, using individual questionnaire responses from a random sample of construction personnel based in Hong Kong. As hypothesized, transformational leadership has a significant impact on safety climate which is mediated by safety-specific leader-member exchange (LMX), and safety climate in turn impacts safety behavior through safety knowledge. The results suggest that future safety climate interventions should be more effective if supervisors exhibit transformational leadership, encourage construction personnel to voice safety concerns without fear of retaliation, and repeatedly remind them about safety on the job.

The Impact of Transformational Leadership on Safety Climate and Individual Safety Behavior on Construction Sites

PubMed Central

Shen, Yuzhong; Ju, Chuanjing; Koh, Tas Yong; Rowlinson, Steve; Bridge, Adrian J.

2017-01-01

Unsafe acts contribute dominantly to construction accidents, and increasing safety behavior is essential to reduce accidents. Previous research conceptualized safety behavior as an interaction between proximal individual differences (safety knowledge and safety motivation) and distal contextual factors (leadership and safety climate). However, relatively little empirical research has examined this conceptualization in the construction sector. Given the cultural background of the sample, this study makes a slight modification to the conceptualization and views transformational leadership as an antecedent of safety climate. Accordingly, this study establishes a multiple mediator model showing the mechanisms through which transformational leadership translates into safety behavior. The multiple mediator model is estimated by the structural equation modeling (SEM) technique, using individual questionnaire responses from a random sample of construction personnel based in Hong Kong. As hypothesized, transformational leadership has a significant impact on safety climate which is mediated by safety-specific leader–member exchange (LMX), and safety climate in turn impacts safety behavior through safety knowledge. The results suggest that future safety climate interventions should be more effective if supervisors exhibit transformational leadership, encourage construction personnel to voice safety concerns without fear of retaliation, and repeatedly remind them about safety on the job. PMID:28067775

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.

Hydrogeologic controls on streamflow sensitivity to climate variation

Treesearch

Anne Jefferson; Anne Nolin; Sarah Lewis; Christina Tague

2008-01-01

Climate models project warmer temperatures for the north-west USA, which will result in reduced snowpacks and decreased summer streamflow. This paper examines how groundwater, snowmelt, and regional climate patterns control discharge at multiple time scales, using historical records from two watersheds with contrasting geological properties and drainage efficiencies....

Fire management, managed relocation, and land conservation options for long-lived obligate seeding plants under global changes in climate, urbanization, and fire regime.

PubMed

Bonebrake, Timothy C; Syphard, Alexandra D; Franklin, Janet; Anderson, Kurt E; Akçakaya, H Resit; Mizerek, Toni; Winchell, Clark; Regan, Helen M

2014-08-01

Most species face multiple anthropogenic disruptions. Few studies have quantified the cumulative influence of multiple threats on species of conservation concern, and far fewer have quantified the potential relative value of multiple conservation interventions in light of these threats. We linked spatial distribution and population viability models to explore conservation interventions under projected climate change, urbanization, and changes in fire regime on a long-lived obligate seeding plant species sensitive to high fire frequencies, a dominant plant functional type in many fire-prone ecosystems, including the biodiversity hotspots of Mediterranean-type ecosystems. First, we investigated the relative risk of population decline for plant populations in landscapes with and without land protection under an existing habitat conservation plan. Second, we modeled the effectiveness of relocating both seedlings and seeds from a large patch with predicted declines in habitat area to 2 unoccupied recipient patches with increasing habitat area under 2 projected climate change scenarios. Finally, we modeled 8 fire return intervals (FRIs) approximating the outcomes of different management strategies that effectively control fire frequency. Invariably, long-lived obligate seeding populations remained viable only when FRIs were maintained at or above a minimum level. Land conservation and seedling relocation efforts lessened the impact of climate change and land-use change on obligate seeding populations to differing degrees depending on the climate change scenario, but neither of these efforts was as generally effective as frequent translocation of seeds. While none of the modeled strategies fully compensated for the effects of land-use and climate change, an integrative approach managing multiple threats may diminish population declines for species in complex landscapes. Conservation plans designed to mitigate the impacts of a single threat are likely to fail if additional threats are ignored. © 2014 Society for Conservation Biology.

Water Isotopes in the GISS GCM: History, Applications and Potential

NASA Astrophysics Data System (ADS)

Schmidt, G. A.; LeGrande, A. N.; Field, R. D.; Nusbaumer, J. M.

2017-12-01

Water isotopes have been incorporated in the GISS GCMs since the pioneering work of Jean Jouzel in the 1980s. Since 2005, this functionality has been maintained within the master branch of the development code and has been usable (and used) in all subsequent versions. This has allowed a wide variety of applications, across multiple time-scales and interests, to be tackled coherently. Water isotope tracers have been used to debug the atmospheric model code, tune parameterisations of moist processes, assess the isotopic fingerprints of multiple climate drivers, produce forward models for remotely sensed isotope products, and validate paleo-climate interpretations from the last millennium to the Eocene. We will present an overview of recent results involving isotope tracers, including improvements in models for the isotopic fractionation processes themselves, and demonstrate the potential for using these tracers and models more systematically in paleo-climate reconstructions and investigations of the modern hydrological cycle.

A geospatial modelling approach to predict seagrass habitat recovery under multiple stressor regimes

EPA Science Inventory

Restoration of estuarine seagrass habitats requires a clear understanding of the modes of action of multiple interacting stressors including nutrients, climate change, coastal land-use change, and habitat modification. We have developed and demonstrated a geospatial modeling a...

Impacts of climate change on the food-energy-water nexus in the upper Yellow River Basin: An integrated hydro-economic modeling approach

NASA Astrophysics Data System (ADS)

Si, Y.; Cai, X.

2017-12-01

The large-scale reservoir system built on the upper Yellow River serves multiple purposes. The generated hydropower supplies over 60% of the entire electricity for the regional power grid while the irrigated crop production feeds almost one-third of the total population throughout the whole river basin. Moreover, the reservoir system also bears the responsibility for controlling ice flood, which occurs during the non-flood season due to winter ice freezing followed by spring thawing process, and could be even more disastrous than the summer flood. The contradiction of water allocation to satisfy multi-sector demands while mitigating ice flood risk has been longstanding. However, few researchers endeavor to employ the nexus thinking to addressing the complexities involved in all the interlinked purposes. In this study, we develop an integrated hydro-economic model that can be used to explore both the tradeoffs and synergies between the multiple purposes, based on which the water infrastructures (e.g., reservoir, diversion canal, pumping well) can be coordinated for maximizing the co-benefits of multiple sectors. The model is based on a node-link schematic of multiple operations including hydropower generation, irrigation scheduling, and the conjunctive use of surface and ground water resources. In particular, the model depicts some details regarding reservoir operation rules during the ice season using two indicators, i.e., flow control period and flow control level. The rules are obtained from historical records using data mining techniques under different climate conditions, and they are added to the model as part of the system constraints. Future reservoir inflow series are generated by a hydrological model with future climate scenarios projected by General Circulation Model (GCM). By analyzing the model results under the various climate scenarios, the future possible shifting trajectory of the food-energy-water system characteristics will be derived compared to the baseline scenario (i.e., the status-quo condition). Thus the model and the results are expected to be useful for enlightening economically efficient water allocation policy coping with climate change.

AgMIP Training in Multiple Crop Models and Tools

NASA Technical Reports Server (NTRS)

Boote, Kenneth J.; Porter, Cheryl H.; Hargreaves, John; Hoogenboom, Gerrit; Thornburn, Peter; Mutter, Carolyn

2015-01-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has the goal of using multiple crop models to evaluate climate impacts on agricultural production and food security in developed and developing countries. There are several major limitations that must be overcome to achieve this goal, including the need to train AgMIP regional research team (RRT) crop modelers to use models other than the ones they are currently familiar with, plus the need to harmonize and interconvert the disparate input file formats used for the various models. Two activities were followed to address these shortcomings among AgMIP RRTs to enable them to use multiple models to evaluate climate impacts on crop production and food security. We designed and conducted courses in which participants trained on two different sets of crop models, with emphasis on the model of least experience. In a second activity, the AgMIP IT group created templates for inputting data on soils, management, weather, and crops into AgMIP harmonized databases, and developed translation tools for converting the harmonized data into files that are ready for multiple crop model simulations. The strategies for creating and conducting the multi-model course and developing entry and translation tools are reviewed in this chapter.

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...

Climate change impact assessments on the water resources of India under extensive human interventions.

PubMed

Madhusoodhanan, C G; Sreeja, K G; Eldho, T I

2016-10-01

Climate change is a major concern in the twenty-first century and its assessments are associated with multiple uncertainties, exacerbated and confounded in the regions where human interventions are prevalent. The present study explores the challenges for climate change impact assessment on the water resources of India, one of the world's largest human-modified systems. The extensive human interventions in the Energy-Land-Water-Climate (ELWC) nexus significantly impact the water resources of the country. The direct human interventions in the landscape may surpass/amplify/mask the impacts of climate change and in the process also affect climate change itself. Uncertainties in climate and resource assessments add to the challenge. Formulating coherent resource and climate change policies in India would therefore require an integrated approach that would assess the multiple interlinkages in the ELWC nexus and distinguish the impacts of global climate change from that of regional human interventions. Concerted research efforts are also needed to incorporate the prominent linkages in the ELWC nexus in climate/earth system modelling.

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.

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...

Tempo-spatial downscaling of multiple GCMs projections for soil erosion risk analysis at El Reno, Oklahoma, USA

USDA-ARS?s Scientific Manuscript database

Proper spatial and temporal treatments of climate change scenarios projected by General Circulation Models (GCMs) are critical to accurate assessment of climatic impacts on natural resources and ecosystems. For accurate prediction of soil erosion risk at a particular farm or field under climate cha...

Accelerating Climate Simulations Through Hybrid Computing

NASA Technical Reports Server (NTRS)

Zhou, Shujia; Sinno, Scott; Cruz, Carlos; Purcell, Mark

2009-01-01

Unconventional multi-core processors (e.g., IBM Cell B/E and NYIDIDA GPU) have emerged as accelerators in climate simulation. However, climate models typically run on parallel computers with conventional processors (e.g., Intel and AMD) using MPI. Connecting accelerators to this architecture efficiently and easily becomes a critical issue. When using MPI for connection, we identified two challenges: (1) identical MPI implementation is required in both systems, and; (2) existing MPI code must be modified to accommodate the accelerators. In response, we have extended and deployed IBM Dynamic Application Virtualization (DAV) in a hybrid computing prototype system (one blade with two Intel quad-core processors, two IBM QS22 Cell blades, connected with Infiniband), allowing for seamlessly offloading compute-intensive functions to remote, heterogeneous accelerators in a scalable, load-balanced manner. Currently, a climate solar radiation model running with multiple MPI processes has been offloaded to multiple Cell blades with approx.10% network overhead.

Projecting Future Land Use Changes in West Africa Driven by Climate and Socioeconomic Factors: Uncertainties and Implications for Adaptation

NASA Astrophysics Data System (ADS)

Wang, G.; Ahmed, K. F.; You, L.

2015-12-01

Land use changes constitute an important regional climate change forcing in West Africa, a region of strong land-atmosphere coupling. At the same time, climate change can be an important driver for land use, although its importance relative to the impact of socio-economic factors may vary significant from region to region. This study compares the contributions of climate change and socioeconomic development to potential future changes of agricultural land use in West Africa and examines various sources of uncertainty using a land use projection model (LandPro) that accounts for the impact of socioeconomic drivers on the demand side and the impact of climate-induced crop yield changes on the supply side. Future crop yield changes were simulated by a process-based crop model driven with future climate projections from a regional climate model, and future changes of food demand is projected using a model for policy analysis of agricultural commodities and trade. The impact of human decision-making on land use was explicitly considered through multiple "what-if" scenarios to examine the range of uncertainties in projecting future land use. Without agricultural intensification, the climate-induced decrease of crop yield together with increase of food demand are found to cause a significant increase in agricultural land use at the expense of forest and grassland by the mid-century, and the resulting land use land cover changes are found to feed back to the regional climate in a way that exacerbates the negative impact of climate on crop yield. Analysis of results from multiple decision-making scenarios suggests that human adaptation characterized by science-informed decision making to minimize land use could be very effective in many parts of the region.

Working with Climate Projections to Estimate Disease Burden: Perspectives from Public Health.

PubMed

Conlon, Kathryn C; Kintziger, Kristina W; Jagger, Meredith; Stefanova, Lydia; Uejio, Christopher K; Konrad, Charles

2016-08-09

There is interest among agencies and public health practitioners in the United States (USA) to estimate the future burden of climate-related health outcomes. Calculating disease burden projections can be especially daunting, given the complexities of climate modeling and the multiple pathways by which climate influences public health. Interdisciplinary coordination between public health practitioners and climate scientists is necessary for scientifically derived estimates. We describe a unique partnership of state and regional climate scientists and public health practitioners assembled by the Florida Building Resilience Against Climate Effects (BRACE) program. We provide a background on climate modeling and projections that has been developed specifically for public health practitioners, describe methodologies for combining climate and health data to project disease burden, and demonstrate three examples of this process used in Florida.

Working with Climate Projections to Estimate Disease Burden: Perspectives from Public Health

PubMed Central

Conlon, Kathryn C.; Kintziger, Kristina W.; Jagger, Meredith; Stefanova, Lydia; Uejio, Christopher K.; Konrad, Charles

2016-01-01

There is interest among agencies and public health practitioners in the United States (USA) to estimate the future burden of climate-related health outcomes. Calculating disease burden projections can be especially daunting, given the complexities of climate modeling and the multiple pathways by which climate influences public health. Interdisciplinary coordination between public health practitioners and climate scientists is necessary for scientifically derived estimates. We describe a unique partnership of state and regional climate scientists and public health practitioners assembled by the Florida Building Resilience Against Climate Effects (BRACE) program. We provide a background on climate modeling and projections that has been developed specifically for public health practitioners, describe methodologies for combining climate and health data to project disease burden, and demonstrate three examples of this process used in Florida. PMID:27517942

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.

Integrating Remote Sensing, Field Observations, and Models to Understand Disturbance and Climate Effects on the Carbon Balance of the West Coast U.S.

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

Cohen, Warren

2014-07-03

As an element of NACP research, the proposed investigation is a two pronged approach that derives and evaluates a regional carbon (C) budget for Oregon, Washington, and California. Objectives are (1) Use multiple data sources, including AmeriFlux data, inventories, and multispectral remote sensing data to investigate trends in carbon storage and exchanges of CO2 and water with variation in climate and disturbance history; (2) Develop and apply regional modeling that relies on these multiple data sources to reduce uncertainty in spatial estimates of carbon storage and NEP, and relative contributions of terrestrial ecosystems and anthropogenic emissions to atmospheric CO2 inmore » the region; (3) Model terrestrial carbon processes across the region, using the Biome-BGC terrestrial ecosystem model, and an atmospheric inverse modeling approach to estimate variation in rate and timing of terrestrial uptake and feedbacks to the atmosphere in response to climate and disturbance.« less

Integrating Remote Sensing, Field Observations, and Models to Understand Disturbance and Climate Effects on the Carbon Balance of the West Coast U.S., Final Report

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

Beverly E. Law

2011-10-05

As an element of NACP research, the proposed investigation is a two pronged approach that derives and evaluates a regional carbon (C) budget for Oregon, Washington, and California. Objectives are (1) Use multiple data sources, including AmeriFlux data, inventories, and multispectral remote sensing data to investigate trends in carbon storage and exchanges of CO2 and water with variation in climate and disturbance history; (2) Develop and apply regional modeling that relies on these multiple data sources to reduce uncertainty in spatial estimates of carbon storage and NEP, and relative contributions of terrestrial ecosystems and anthropogenic emissions to atmospheric CO2 inmore » the region; (3) Model terrestrial carbon processes across the region, using the Biome-BGC terrestrial ecosystem model, and an atmospheric inverse modeling approach to estimate variation in rate and timing of terrestrial uptake and feedbacks to the atmosphere in response to climate and disturbance.« less

Multiple abiotic stimuli are integrated in the regulation of rice gene expression under field conditions.

PubMed

Plessis, Anne; Hafemeister, Christoph; Wilkins, Olivia; Gonzaga, Zennia Jean; Meyer, Rachel Sarah; Pires, Inês; Müller, Christian; Septiningsih, Endang M; Bonneau, Richard; Purugganan, Michael

2015-11-26

Plants rely on transcriptional dynamics to respond to multiple climatic fluctuations and contexts in nature. We analyzed the genome-wide gene expression patterns of rice (Oryza sativa) growing in rainfed and irrigated fields during two distinct tropical seasons and determined simple linear models that relate transcriptomic variation to climatic fluctuations. These models combine multiple environmental parameters to account for patterns of expression in the field of co-expressed gene clusters. We examined the similarities of our environmental models between tropical and temperate field conditions, using previously published data. We found that field type and macroclimate had broad impacts on transcriptional responses to environmental fluctuations, especially for genes involved in photosynthesis and development. Nevertheless, variation in solar radiation and temperature at the timescale of hours had reproducible effects across environmental contexts. These results provide a basis for broad-based predictive modeling of plant gene expression in the field.

Paleoclimate diagnostics: consistent large-scale temperature responses in warm and cold climates

NASA Astrophysics Data System (ADS)

Izumi, Kenji; Bartlein, Patrick; Harrison, Sandy

2015-04-01

The CMIP5 model simulations of the large-scale temperature responses to increased raditative forcing include enhanced land-ocean contrast, stronger response at higher latitudes than in the tropics, and differential responses in warm and cool season climates to uniform forcing. Here we show that these patterns are also characteristic of CMIP5 model simulations of past climates. The differences in the responses over land as opposed to over the ocean, between high and low latitudes, and between summer and winter are remarkably consistent (proportional and nearly linear) across simulations of both cold and warm climates. Similar patterns also appear in historical observations and paleoclimatic reconstructions, implying that such responses are characteristic features of the climate system and not simple model artifacts, thereby increasing our confidence in the ability of climate models to correctly simulate different climatic states. We also show the possibility that a small set of common mechanisms control these large-scale responses of the climate system across multiple states.

Model Independence in Downscaled Climate Projections: a Case Study in the Southeast United States

NASA Astrophysics Data System (ADS)

Gray, G. M. E.; Boyles, R.

2016-12-01

Downscaled climate projections are used to deduce how the climate will change in future decades at local and regional scales. It is important to use multiple models to characterize part of the future uncertainty given the impact on adaptation decision making. This is traditionally employed through an equally-weighted ensemble of multiple GCMs downscaled using one technique. Newer practices include several downscaling techniques in an effort to increase the ensemble's representation of future uncertainty. However, this practice may be adding statistically dependent models to the ensemble. Previous research has shown a dependence problem in the GCM ensemble in multiple generations, but has not been shown in the downscaled ensemble. In this case study, seven downscaled climate projections on the daily time scale are considered: CLAREnCE10, SERAP, BCCA (CMIP5 and CMIP3 versions), Hostetler, CCR, and MACA-LIVNEH. These data represent 83 ensemble members, 44 GCMs, and two generations of GCMs. Baseline periods are compared against the University of Idaho's METDATA gridded observation dataset. Hierarchical agglomerative clustering is applied to the correlated errors to determine dependent clusters. Redundant GCMs across different downscaling techniques show the most dependence, while smaller dependence signals are detected within downscaling datasets and across generations of GCMs. These results indicate that using additional downscaled projections to increase the ensemble size must be done with care to avoid redundant GCMs and the process of downscaling may increase the dependence of those downscaled GCMs. Climate model generation does not appear dissimilar enough to be treated as two separate statistical populations for ensemble building at the local and regional scales.

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

Global Climate Change Adaptation Priorities for Biodiversity and Food Security

PubMed Central

Hannah, Lee; Ikegami, Makihiko; Hole, David G.; Seo, Changwan; Butchart, Stuart H. M.; Peterson, A. Townsend; Roehrdanz, Patrick R.

2013-01-01

International policy is placing increasing emphasis on adaptation to climate change, including the allocation of new funds to assist adaptation efforts. Climate change adaptation funding may be most effective where it meets integrated goals, but global geographic priorities based on multiple development and ecological criteria are not well characterized. Here we show that human and natural adaptation needs related to maintaining agricultural productivity and ecosystem integrity intersect in ten major areas globally, providing a coherent set of international priorities for adaptation funding. An additional seven regional areas are identified as worthy of additional study. The priority areas are locations where changes in crop suitability affecting impoverished farmers intersect with changes in ranges of restricted-range species. Agreement among multiple climate models and emissions scenarios suggests that these priorities are robust. Adaptation funding directed to these areas could simultaneously address multiple international policy goals, including poverty reduction, protecting agricultural production and safeguarding ecosystem services. PMID:23991125

Global climate change adaptation priorities for biodiversity and food security.

PubMed

Hannah, Lee; Ikegami, Makihiko; Hole, David G; Seo, Changwan; Butchart, Stuart H M; Peterson, A Townsend; Roehrdanz, Patrick R

2013-01-01

International policy is placing increasing emphasis on adaptation to climate change, including the allocation of new funds to assist adaptation efforts. Climate change adaptation funding may be most effective where it meets integrated goals, but global geographic priorities based on multiple development and ecological criteria are not well characterized. Here we show that human and natural adaptation needs related to maintaining agricultural productivity and ecosystem integrity intersect in ten major areas globally, providing a coherent set of international priorities for adaptation funding. An additional seven regional areas are identified as worthy of additional study. The priority areas are locations where changes in crop suitability affecting impoverished farmers intersect with changes in ranges of restricted-range species. Agreement among multiple climate models and emissions scenarios suggests that these priorities are robust. Adaptation funding directed to these areas could simultaneously address multiple international policy goals, including poverty reduction, protecting agricultural production and safeguarding ecosystem services.

Identification of the prediction model for dengue incidence in Can Tho city, a Mekong Delta area in Vietnam.

PubMed

Phung, Dung; Huang, Cunrui; Rutherford, Shannon; Chu, Cordia; Wang, Xiaoming; Nguyen, Minh; Nguyen, Nga Huy; Manh, Cuong Do

2015-01-01

The Mekong Delta is highly vulnerable to climate change and a dengue endemic area in Vietnam. This study aims to examine the association between climate factors and dengue incidence and to identify the best climate prediction model for dengue incidence in Can Tho city, the Mekong Delta area in Vietnam. We used three different regression models comprising: standard multiple regression model (SMR), seasonal autoregressive integrated moving average model (SARIMA), and Poisson distributed lag model (PDLM) to examine the association between climate factors and dengue incidence over the period 2003-2010. We validated the models by forecasting dengue cases for the period of January-December, 2011 using the mean absolute percentage error (MAPE). Receiver operating characteristics curves were used to analyze the sensitivity of the forecast of a dengue outbreak. The results indicate that temperature and relative humidity are significantly associated with changes in dengue incidence consistently across the model methods used, but not cumulative rainfall. The Poisson distributed lag model (PDLM) performs the best prediction of dengue incidence for a 6, 9, and 12-month period and diagnosis of an outbreak however the SARIMA model performs a better prediction of dengue incidence for a 3-month period. The simple or standard multiple regression performed highly imprecise prediction of dengue incidence. We recommend a follow-up study to validate the model on a larger scale in the Mekong Delta region and to analyze the possibility of incorporating a climate-based dengue early warning method into the national dengue surveillance system. Copyright © 2014 Elsevier B.V. All rights reserved.

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."

Potential climate change impacts on four biophysical indicators of cattle production from western US rangelands

Treesearch

Matthew Clark Reeves; Karen E. Bagne; John Tanaka

2017-01-01

We examined multiple environmental factors related to climate change that affect cattle production on rangelands to identify sources of vulnerability among seven regions of the western United States. Climate change effects were projected to 2100 using published spatially explicit model output for four indicators of vulnerability: forage quantity, vegetation type...

A Robust, Scalable Framework for Conducting Climate Change Susceptibility Analyses

DTIC Science & Technology

2014-05-01

for identifying areas of heightened risk from varying forms of climate forcings is needed. Based on global climate model projections, deviations from...framework provides an opportunity to easily combine multiple data sources — that are often freely available from many federal, state, and global ...Climate change and extreme weather events: implications for food production, plant diseases, and pests. Global Change and Human Health 2:90–104. ERDC/EL

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.

Assessing the Assessment Methods: Climate Change and Hydrologic Impacts

NASA Astrophysics Data System (ADS)

Brekke, L. D.; Clark, M. P.; Gutmann, E. D.; Mizukami, N.; Mendoza, P. A.; Rasmussen, R.; Ikeda, K.; Pruitt, T.; Arnold, J. R.; Rajagopalan, B.

2014-12-01

The Bureau of Reclamation, the U.S. Army Corps of Engineers, and other water management agencies have an interest in developing reliable, science-based methods for incorporating climate change information into longer-term water resources planning. Such assessments must quantify projections of future climate and hydrology, typically relying on some form of spatial downscaling and bias correction to produce watershed-scale weather information that subsequently drives hydrology and other water resource management analyses (e.g., water demands, water quality, and environmental habitat). Water agencies continue to face challenging method decisions in these endeavors: (1) which downscaling method should be applied and at what resolution; (2) what observational dataset should be used to drive downscaling and hydrologic analysis; (3) what hydrologic model(s) should be used and how should these models be configured and calibrated? There is a critical need to understand the ramification of these method decisions, as they affect the signal and uncertainties produced by climate change assessments and, thus, adaptation planning. This presentation summarizes results from a three-year effort to identify strengths and weaknesses of widely applied methods for downscaling climate projections and assessing hydrologic conditions. Methods were evaluated from two perspectives: historical fidelity, and tendency to modulate a global climate model's climate change signal. On downscaling, four methods were applied at multiple resolutions: statistically using Bias Correction Spatial Disaggregation, Bias Correction Constructed Analogs, and Asynchronous Regression; dynamically using the Weather Research and Forecasting model. Downscaling results were then used to drive hydrologic analyses over the contiguous U.S. using multiple models (VIC, CLM, PRMS), with added focus placed on case study basins within the Colorado Headwaters. The presentation will identify which types of climate changes are expressed robustly across methods versus those that are sensitive to method choice; which method choices seem relatively more important; and where strategic investments in research and development can substantially improve guidance on climate change provided to water managers.

Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes

NASA Astrophysics Data System (ADS)

Mekanik, F.; Imteaz, M. A.; Gato-Trinidad, S.; Elmahdi, A.

2013-10-01

In this study, the application of Artificial Neural Networks (ANN) and Multiple regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors. The use of dual (combined lagged ENSO-IOD) input sets for calibrating and validating ANN and MR Models is proposed to investigate the simultaneous effect of past values of these two major climate modes on long-term spring rainfall prediction. The MR models that did not violate the limits of statistical significance and multicollinearity were selected for future spring rainfall forecast. The ANN was developed in the form of multilayer perceptron using Levenberg-Marquardt algorithm. Both MR and ANN modelling were assessed statistically using mean square error (MSE), mean absolute error (MAE), Pearson correlation (r) and Willmott index of agreement (d). The developed MR and ANN models were tested on out-of-sample test sets; the MR models showed very poor generalisation ability for east Victoria with correlation coefficients of -0.99 to -0.90 compared to ANN with correlation coefficients of 0.42-0.93; ANN models also showed better generalisation ability for central and west Victoria with correlation coefficients of 0.68-0.85 and 0.58-0.97 respectively. The ability of multiple regression models to forecast out-of-sample sets is compatible with ANN for Daylesford in central Victoria and Kaniva in west Victoria (r = 0.92 and 0.67 respectively). The errors of the testing sets for ANN models are generally lower compared to multiple regression models. The statistical analysis suggest the potential of ANN over MR models for rainfall forecasting using large scale climate modes.

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.

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.

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.

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.

Do Responses to Different Anthropogenic Forcings Add Linearly in Climate Models?

NASA Technical Reports Server (NTRS)

Marvel, Kate; Schmidt, Gavin A.; Shindell, Drew; Bonfils, Celine; LeGrande, Allegra N.; Nazarenko, Larissa; Tsigaridis, Kostas

2015-01-01

Many detection and attribution and pattern scaling studies assume that the global climate response to multiple forcings is additive: that the response over the historical period is statistically indistinguishable from the sum of the responses to individual forcings. Here, we use the NASA Goddard Institute for Space Studies (GISS) and National Center for Atmospheric Research Community Climate System Model (CCSM) simulations from the CMIP5 archive to test this assumption for multi-year trends in global-average, annual-average temperature and precipitation at multiple timescales. We find that responses in models forced by pre-computed aerosol and ozone concentrations are generally additive across forcings; however, we demonstrate that there are significant nonlinearities in precipitation responses to di?erent forcings in a configuration of the GISS model that interactively computes these concentrations from precursor emissions. We attribute these to di?erences in ozone forcing arising from interactions between forcing agents. Our results suggest that attribution to specific forcings may be complicated in a model with fully interactive chemistry and may provide motivation for other modeling groups to conduct further single-forcing experiments.

Do responses to different anthropogenic forcings add linearly in climate models?

DOE PAGES

Marvel, Kate; Schmidt, Gavin A.; Shindell, Drew; ...

2015-10-14

Many detection and attribution and pattern scaling studies assume that the global climate response to multiple forcings is additive: that the response over the historical period is statistically indistinguishable from the sum of the responses to individual forcings. Here, we use the NASA Goddard Institute for Space Studies (GISS) and National Center for Atmospheric Research Community Climate System Model (CCSM4) simulations from the CMIP5 archive to test this assumption for multi-year trends in global-average, annual-average temperature and precipitation at multiple timescales. We find that responses in models forced by pre-computed aerosol and ozone concentrations are generally additive across forcings. However,more » we demonstrate that there are significant nonlinearities in precipitation responses to different forcings in a configuration of the GISS model that interactively computes these concentrations from precursor emissions. We attribute these to differences in ozone forcing arising from interactions between forcing agents. Lastly, our results suggest that attribution to specific forcings may be complicated in a model with fully interactive chemistry and may provide motivation for other modeling groups to conduct further single-forcing experiments.« less

Evaluation of the new EMAC-SWIFT chemistry climate model

NASA Astrophysics Data System (ADS)

Scheffler, Janice; Langematz, Ulrike; Wohltmann, Ingo; Rex, Markus

2016-04-01

It is well known that the representation of atmospheric ozone chemistry in weather and climate models is essential for a realistic simulation of the atmospheric state. Including atmospheric ozone chemistry into climate simulations is usually done by prescribing a climatological ozone field, by including a fast linear ozone scheme into the model or by using a climate model with complex interactive chemistry. While prescribed climatological ozone fields are often not aligned with the modelled dynamics, a linear ozone scheme may not be applicable for a wide range of climatological conditions. Although interactive chemistry provides a realistic representation of atmospheric chemistry such model simulations are computationally very expensive and hence not suitable for ensemble simulations or simulations with multiple climate change scenarios. A new approach to represent atmospheric chemistry in climate models which can cope with non-linearities in ozone chemistry and is applicable to a wide range of climatic states is the Semi-empirical Weighted Iterative Fit Technique (SWIFT) that is driven by reanalysis data and has been validated against observational satellite data and runs of a full Chemistry and Transport Model. SWIFT has recently been implemented into the ECHAM/MESSy (EMAC) chemistry climate model that uses a modular approach to climate modelling where individual model components can be switched on and off. Here, we show first results of EMAC-SWIFT simulations and validate these against EMAC simulations using the complex interactive chemistry scheme MECCA, and against observations.

A comparison of large-scale climate signals and the North American Multi-Model Ensemble (NMME) for drought prediction in China

NASA Astrophysics Data System (ADS)

Xu, Lei; Chen, Nengcheng; Zhang, Xiang

2018-02-01

Drought is an extreme natural disaster that can lead to huge socioeconomic losses. Drought prediction ahead of months is helpful for early drought warning and preparations. In this study, we developed a statistical model, two weighted dynamic models and a statistical-dynamic (hybrid) model for 1-6 month lead drought prediction in China. Specifically, statistical component refers to climate signals weighting by support vector regression (SVR), dynamic components consist of the ensemble mean (EM) and Bayesian model averaging (BMA) of the North American Multi-Model Ensemble (NMME) climatic models, and the hybrid part denotes a combination of statistical and dynamic components by assigning weights based on their historical performances. The results indicate that the statistical and hybrid models show better rainfall predictions than NMME-EM and NMME-BMA models, which have good predictability only in southern China. In the 2011 China winter-spring drought event, the statistical model well predicted the spatial extent and severity of drought nationwide, although the severity was underestimated in the mid-lower reaches of Yangtze River (MLRYR) region. The NMME-EM and NMME-BMA models largely overestimated rainfall in northern and western China in 2011 drought. In the 2013 China summer drought, the NMME-EM model forecasted the drought extent and severity in eastern China well, while the statistical and hybrid models falsely detected negative precipitation anomaly (NPA) in some areas. Model ensembles such as multiple statistical approaches, multiple dynamic models or multiple hybrid models for drought predictions were highlighted. These conclusions may be helpful for drought prediction and early drought warnings in China.

Can metric-based approaches really improve multi-model climate projections? A perfect model framework applied to summer temperature change in France.

NASA Astrophysics Data System (ADS)

Boé, Julien; Terray, Laurent

2014-05-01

Ensemble approaches for climate change projections have become ubiquitous. Because of large model-to-model variations and, generally, lack of rationale for the choice of a particular climate model against others, it is widely accepted that future climate change and its impacts should not be estimated based on a single climate model. Generally, as a default approach, the multi-model ensemble mean (MMEM) is considered to provide the best estimate of climate change signals. The MMEM approach is based on the implicit hypothesis that all the models provide equally credible projections of future climate change. This hypothesis is unlikely to be true and ideally one would want to give more weight to more realistic models. A major issue with this alternative approach lies in the assessment of the relative credibility of future climate projections from different climate models, as they can only be evaluated against present-day observations: which present-day metric(s) should be used to decide which models are "good" and which models are "bad" in the future climate? Once a supposedly informative metric has been found, other issues arise. What is the best statistical method to combine multiple models results taking into account their relative credibility measured by a given metric? How to be sure in the end that the metric-based estimate of future climate change is not in fact less realistic than the MMEM? It is impossible to provide strict answers to those questions in the climate change context. Yet, in this presentation, we propose a methodological approach based on a perfect model framework that could bring some useful elements of answer to the questions previously mentioned. The basic idea is to take a random climate model in the ensemble and treat it as if it were the truth (results of this model, in both past and future climate, are called "synthetic observations"). Then, all the other members from the multi-model ensemble are used to derive thanks to a metric-based approach a posterior estimate of climate change, based on the synthetic observation of the metric. Finally, it is possible to compare the posterior estimate to the synthetic observation of future climate change to evaluate the skill of the method. The main objective of this presentation is to describe and apply this perfect model framework to test different methodological issues associated with non-uniform model weighting and similar metric-based approaches. The methodology presented is general, but will be applied to the specific case of summer temperature change in France, for which previous works have suggested potentially useful metrics associated with soil-atmosphere and cloud-temperature interactions. The relative performances of different simple statistical approaches to combine multiple model results based on metrics will be tested. The impact of ensemble size, observational errors, internal variability, and model similarity will be characterized. The potential improvements associated with metric-based approaches compared to the MMEM is terms of errors and uncertainties will be quantified.

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.

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.

Weighing the relative potential impacts of climate change and land-use change on an endangered bird.

PubMed

Bancroft, Betsy A; Lawler, Joshua J; Schumaker, Nathan H

2016-07-01

Climate change and land-use change are projected to be the two greatest drivers of biodiversity loss over the coming century. Land-use change has resulted in extensive habitat loss for many species. Likewise, climate change has affected many species resulting in range shifts, changes in phenology, and altered interactions. We used a spatially explicit, individual-based model to explore the effects of land-use change and climate change on a population of the endangered Red-cockaded Woodpecker (RCW; Picoides borealis). We modeled the effects of land-use change using multiple scenarios representing different spatial arrangements of new training areas for troops across Fort Benning. We used projected climate-driven changes in habitat and changes in reproductive output to explore the potential effects of climate change. We summarized potential changes in habitat based on the output of the dynamic vegetation model LPJ-GUESS, run for multiple climate change scenarios through the year 2100. We projected potential changes in reproduction based on an empirical relationship between spring precipitation and the mean number of successful fledglings produced per nest attempt. As modeled in our study, climate change had virtually no effect on the RCW population. Conversely, simulated effects of land-use change resulted in the loss of up to 28 breeding pairs by 2100. However, the simulated impacts of development depended on where the development occurred and could be completely avoided if the new training areas were placed in poor-quality habitat. Our results demonstrate the flexibility inherent in many systems that allows seemingly incompatible human land uses, such as development, and conservation actions to exist side by side.

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

Recent climate variability and its impacts on soybean yields in Southern Brazil

NASA Astrophysics Data System (ADS)

Ferreira, Danielle Barros; Rao, V. Brahmananda

2011-08-01

Recent climate variability in rainfall, temperatures (maximum and minimum), and the diurnal temperature range is studied with emphasis on its influence over soybean yields in southern Brazil, during 1969 to 2002. The results showed that the soybean ( Glycine max L. Merril) yields are more affected by changes in temperature during summer, while changes in rainfall are more important during the beginning of plantation and at its peak of development. Furthermore, soybean yields in Paraná are more sensitive to rainfall variations, while soybean yields in the Rio Grande do Sul are more sensitive to variations in temperature. Effects of interannual climatic variability on soybean yields are evaluated through three agro-meteorological models: additive Stewart, multiplicative Rao, and multiplicative Jensen. The Jensen model is able to reproduce the interannual behavior of soybean yield reasonably well.

Improving Climate Projections Using "Intelligent" Ensembles

NASA Technical Reports Server (NTRS)

Baker, Noel C.; Taylor, Patrick C.

2015-01-01

Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and that these metrics can be used to evaluate model quality in both current and future climate states. This information will be used to produce new consensus projections and provide communities with improved climate projections for urgent decision-making.

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

NASA Astrophysics Data System (ADS)

Cannon, Alex J.

2018-01-01

Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another—the N-dimensional probability density function transform—is adapted for use as a multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin, particularly for annual maxima of the FWI distribution and spatiotemporal autocorrelation of precipitation fields.

The contribution of future agricultural trends in the US Midwest to global climate change mitigation

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

Thomson, Allison M.; Kyle, G. Page; Zhang, Xuesong

2014-01-19

Land use change is a complex response to changing environmental and socioeconomic systems. Historical drivers of land use change include changes in the natural resource availability of a region, changes in economic conditions for production of certain products and changing policies. Most recently, introduction of policy incentives for biofuel production have influenced land use change in the US Midwest, leading to concerns that bioenergy production systems may compete with food production and land conservation. Here we explore how land use may be impacted by future climate mitigation measures by nesting a high resolution agricultural model (EPIC – Environmental Policy Indicatormore » Climate) for the US Midwest within a global integrated assessment model (GCAM – Global Change Assessment Model). This approach is designed to provide greater spatial resolution and detailed agricultural practice information by focusing on the climate mitigation potential of agriculture and land use in a specific region, while retaining the global economic context necessary to understand the far ranging effects of climate mitigation targets. We find that until the simulated carbon prices are very high, the US Midwest has a comparative advantage in producing traditional food and feed crops over bioenergy crops. Overall, the model responds to multiple pressures by adopting a mix of future responses. We also find that the GCAM model is capable of simulations at multiple spatial scales and agricultural technology resolution, which provides the capability to examine regional response to global policy and economic conditions in the context of climate mitigation.« less

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.

Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison

PubMed Central

Rosenzweig, Cynthia; Elliott, Joshua; Deryng, Delphine; Ruane, Alex C.; Müller, Christoph; Arneth, Almut; Boote, Kenneth J.; Folberth, Christian; Glotter, Michael; Khabarov, Nikolay; Neumann, Kathleen; Piontek, Franziska; Pugh, Thomas A. M.; Schmid, Erwin; Stehfest, Elke; Yang, Hong; Jones, James W.

2014-01-01

Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies. PMID:24344314

Assessing Agricultural Risks of Climate Change in the 21st Century in a Global Gridded Crop Model Intercomparison

NASA Technical Reports Server (NTRS)

Rosenzweig, Cynthia E.; Elliott, Joshua; Deryng, Delphine; Ruane, Alex C.; Mueller, Christoph; Arneth, Almut; Boote, Kenneth J.; Folberth, Christian; Glotter, Michael; Khabarov, Nikolay

2014-01-01

Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies.

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

Multiple abiotic stimuli are integrated in the regulation of rice gene expression under field conditions

PubMed Central

Plessis, Anne; Hafemeister, Christoph; Wilkins, Olivia; Gonzaga, Zennia Jean; Meyer, Rachel Sarah; Pires, Inês; Müller, Christian; Septiningsih, Endang M; Bonneau, Richard; Purugganan, Michael

2015-01-01

Plants rely on transcriptional dynamics to respond to multiple climatic fluctuations and contexts in nature. We analyzed the genome-wide gene expression patterns of rice (Oryza sativa) growing in rainfed and irrigated fields during two distinct tropical seasons and determined simple linear models that relate transcriptomic variation to climatic fluctuations. These models combine multiple environmental parameters to account for patterns of expression in the field of co-expressed gene clusters. We examined the similarities of our environmental models between tropical and temperate field conditions, using previously published data. We found that field type and macroclimate had broad impacts on transcriptional responses to environmental fluctuations, especially for genes involved in photosynthesis and development. Nevertheless, variation in solar radiation and temperature at the timescale of hours had reproducible effects across environmental contexts. These results provide a basis for broad-based predictive modeling of plant gene expression in the field. DOI: http://dx.doi.org/10.7554/eLife.08411.001 PMID:26609814

Landscape-and regional-scale shifts in forest composition under climate change in the Central Hardwood Region of the United States

Treesearch

Wen J. Wang; Hong S. He; Frank R. Thompson; Jacob S. Fraser; William D. Dijak

2016-01-01

Tree species distribution and abundance are affected by forces operating at multiple scales. Niche and biophysical process models have been commonly used to predict climate change effects at regional scales, however, these models have limited capability to include site-scale population dynamics and landscape- scale disturbance and dispersal. We applied a landscape...

Temporal trend and climate factors of hemorrhagic fever with renal syndrome epidemic in Shenyang City, China

PubMed Central

2011-01-01

Background Hemorrhagic fever with renal syndrome (HFRS) is an important infectious disease caused by different species of hantaviruses. As a rodent-borne disease with a seasonal distribution, external environmental factors including climate factors may play a significant role in its transmission. The city of Shenyang is one of the most seriously endemic areas for HFRS. Here, we characterized the dynamic temporal trend of HFRS, and identified climate-related risk factors and their roles in HFRS transmission in Shenyang, China. Methods The annual and monthly cumulative numbers of HFRS cases from 2004 to 2009 were calculated and plotted to show the annual and seasonal fluctuation in Shenyang. Cross-correlation and autocorrelation analyses were performed to detect the lagged effect of climate factors on HFRS transmission and the autocorrelation of monthly HFRS cases. Principal component analysis was constructed by using climate data from 2004 to 2009 to extract principal components of climate factors to reduce co-linearity. The extracted principal components and autocorrelation terms of monthly HFRS cases were added into a multiple regression model called principal components regression model (PCR) to quantify the relationship between climate factors, autocorrelation terms and transmission of HFRS. The PCR model was compared to a general multiple regression model conducted only with climate factors as independent variables. Results A distinctly declining temporal trend of annual HFRS incidence was identified. HFRS cases were reported every month, and the two peak periods occurred in spring (March to May) and winter (November to January), during which, nearly 75% of the HFRS cases were reported. Three principal components were extracted with a cumulative contribution rate of 86.06%. Component 1 represented MinRH0, MT1, RH1, and MWV1; component 2 represented RH2, MaxT3, and MAP3; and component 3 represented MaxT2, MAP2, and MWV2. The PCR model was composed of three principal components and two autocorrelation terms. The association between HFRS epidemics and climate factors was better explained in the PCR model (F = 446.452, P < 0.001, adjusted R2 = 0.75) than in the general multiple regression model (F = 223.670, P < 0.000, adjusted R2 = 0.51). Conclusion The temporal distribution of HFRS in Shenyang varied in different years with a distinctly declining trend. The monthly trends of HFRS were significantly associated with local temperature, relative humidity, precipitation, air pressure, and wind velocity of the different previous months. The model conducted in this study will make HFRS surveillance simpler and the control of HFRS more targeted in Shenyang. PMID:22133347

Organizational Climate of Staff Working Conditions and Safety -- An Integrative Model

DTIC Science & Technology

2005-05-01

fosters innovation, and sustains the use of evidence - based medicine . As other researchers have suggested, there may be multiple climates within an...29. Shortell S, Zazzali J, Burns L, et al. Implementing evidence - based medicine : the role of market pressures, compensation incentives, and culture

Statistical Downscaling and Bias Correction of Climate Model Outputs for Climate Change Impact Assessment in the U.S. Northeast

NASA Technical Reports Server (NTRS)

Ahmed, Kazi Farzan; Wang, Guiling; Silander, John; Wilson, Adam M.; Allen, Jenica M.; Horton, Radley; Anyah, Richard

2013-01-01

Statistical downscaling can be used to efficiently downscale a large number of General Circulation Model (GCM) outputs to a fine temporal and spatial scale. To facilitate regional impact assessments, this study statistically downscales (to 1/8deg spatial resolution) and corrects the bias of daily maximum and minimum temperature and daily precipitation data from six GCMs and four Regional Climate Models (RCMs) for the northeast United States (US) using the Statistical Downscaling and Bias Correction (SDBC) approach. Based on these downscaled data from multiple models, five extreme indices were analyzed for the future climate to quantify future changes of climate extremes. For a subset of models and indices, results based on raw and bias corrected model outputs for the present-day climate were compared with observations, which demonstrated that bias correction is important not only for GCM outputs, but also for RCM outputs. For future climate, bias correction led to a higher level of agreements among the models in predicting the magnitude and capturing the spatial pattern of the extreme climate indices. We found that the incorporation of dynamical downscaling as an intermediate step does not lead to considerable differences in the results of statistical downscaling for the study domain.

Visualizing projected Climate Changes - the CMIP5 Multi-Model Ensemble

NASA Astrophysics Data System (ADS)

Böttinger, Michael; Eyring, Veronika; Lauer, Axel; Meier-Fleischer, Karin

2017-04-01

Large ensembles add an additional dimension to climate model simulations. Internal variability of the climate system can be assessed for example by multiple climate model simulations with small variations in the initial conditions or by analyzing the spread in large ensembles made by multiple climate models under common protocols. This spread is often used as a measure of uncertainty in climate projections. In the context of the fifth phase of the WCRP's Coupled Model Intercomparison Project (CMIP5), more than 40 different coupled climate models were employed to carry out a coordinated set of experiments. Time series of the development of integral quantities such as the global mean temperature change for all models visualize the spread in the multi-model ensemble. A similar approach can be applied to 2D-visualizations of projected climate changes such as latitude-longitude maps showing the multi-model mean of the ensemble by adding a graphical representation of the uncertainty information. This has been demonstrated for example with static figures in chapter 12 of the last IPCC report (AR5) using different so-called stippling and hatching techniques. In this work, we focus on animated visualizations of multi-model ensemble climate projections carried out within CMIP5 as a way of communicating climate change results to the scientific community as well as to the public. We take a closer look at measures of robustness or uncertainty used in recent publications suitable for animated visualizations. Specifically, we use the ESMValTool [1] to process and prepare the CMIP5 multi-model data in combination with standard visualization tools such as NCL and the commercial 3D visualization software Avizo to create the animations. We compare different visualization techniques such as height fields or shading with transparency for creating animated visualization of ensemble mean changes in temperature and precipitation including corresponding robustness measures. [1] Eyring, V., Righi, M., Lauer, A., Evaldsson, M., Wenzel, S., Jones, C., Anav, A., Andrews, O., Cionni, I., Davin, E. L., Deser, C., Ehbrecht, C., Friedlingstein, P., Gleckler, P., Gottschaldt, K.-D., Hagemann, S., Juckes, M., Kindermann, S., Krasting, J., Kunert, D., Levine, R., Loew, A., Mäkelä, J., Martin, G., Mason, E., Phillips, A. S., Read, S., Rio, C., Roehrig, R., Senftleben, D., Sterl, A., van Ulft, L. H., Walton, J., Wang, S., and Williams, K. D.: ESMValTool (v1.0) - a community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP, Geosci. Model Dev., 9, 1747-1802, doi:10.5194/gmd-9-1747-2016, 2016.

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.

Do Leadership Style, Unit Climate, and Safety Climate Contribute to Safe Medication Practices?

PubMed

Farag, Amany; Tullai-McGuinness, Susan; Anthony, Mary K; Burant, Christopher

2017-01-01

This study aims at: examining if leadership style and unit climate predict safety climate; and testing the direct, indirect, and total effect of leadership style, unit climate, and safety climate on nurses' safe medication practices. The Institute of Medicine and nursing scholars propose that safety climate is a prerequisite to safety practices. However, there is limited empirical evidence about factors contributing to the development of safety climate and about the association with nurses' safe medication practices. This cross-sectional study used survey data from 246 RNs working in a Magnet® hospital. Leadership style and unit climate predicted 20% to 50% of variance on all safety climate dimensions. Model testing revealed the indirect impact of leadership style and unit climate on nurses' safe medication practices. Our hypothesized model explained small amount of the variance on nurses' safe medication practices. This finding suggests that nurses' safe medication practices are influenced by multiple contextual and personal factors that should be further examined.

Radiative-convective equilibrium model intercomparison project

NASA Astrophysics Data System (ADS)

Wing, Allison A.; Reed, Kevin A.; Satoh, Masaki; Stevens, Bjorn; Bony, Sandrine; Ohno, Tomoki

2018-03-01

RCEMIP, an intercomparison of multiple types of models configured in radiative-convective equilibrium (RCE), is proposed. RCE is an idealization of the climate system in which there is a balance between radiative cooling of the atmosphere and heating by convection. The scientific objectives of RCEMIP are three-fold. First, clouds and climate sensitivity will be investigated in the RCE setting. This includes determining how cloud fraction changes with warming and the role of self-aggregation of convection in climate sensitivity. Second, RCEMIP will quantify the dependence of the degree of convective aggregation and tropical circulation regimes on temperature. Finally, by providing a common baseline, RCEMIP will allow the robustness of the RCE state across the spectrum of models to be assessed, which is essential for interpreting the results found regarding clouds, climate sensitivity, and aggregation, and more generally, determining which features of tropical climate a RCE framework is useful for. A novel aspect and major advantage of RCEMIP is the accessibility of the RCE framework to a variety of models, including cloud-resolving models, general circulation models, global cloud-resolving models, single-column models, and large-eddy simulation models.

Combining super-ensembles and statistical emulation to improve a regional climate and vegetation model

NASA Astrophysics Data System (ADS)

Hawkins, L. R.; Rupp, D. E.; Li, S.; Sarah, S.; McNeall, D. J.; Mote, P.; Betts, R. A.; Wallom, D.

2017-12-01

Changing regional patterns of surface temperature, precipitation, and humidity may cause ecosystem-scale changes in vegetation, altering the distribution of trees, shrubs, and grasses. A changing vegetation distribution, in turn, alters the albedo, latent heat flux, and carbon exchanged with the atmosphere with resulting feedbacks onto the regional climate. However, a wide range of earth-system processes that affect the carbon, energy, and hydrologic cycles occur at sub grid scales in climate models and must be parameterized. The appropriate parameter values in such parameterizations are often poorly constrained, leading to uncertainty in predictions of how the ecosystem will respond to changes in forcing. To better understand the sensitivity of regional climate to parameter selection and to improve regional climate and vegetation simulations, we used a large perturbed physics ensemble and a suite of statistical emulators. We dynamically downscaled a super-ensemble (multiple parameter sets and multiple initial conditions) of global climate simulations using a 25-km resolution regional climate model HadRM3p with the land-surface scheme MOSES2 and dynamic vegetation module TRIFFID. We simultaneously perturbed land surface parameters relating to the exchange of carbon, water, and energy between the land surface and atmosphere in a large super-ensemble of regional climate simulations over the western US. Statistical emulation was used as a computationally cost-effective tool to explore uncertainties in interactions. Regions of parameter space that did not satisfy observational constraints were eliminated and an ensemble of parameter sets that reduce regional biases and span a range of plausible interactions among earth system processes were selected. This study demonstrated that by combining super-ensemble simulations with statistical emulation, simulations of regional climate could be improved while simultaneously accounting for a range of plausible land-atmosphere feedback strengths.

An assessment of a North American Multi-Model Ensemble (NMME) based global drought early warning forecast system

NASA Astrophysics Data System (ADS)

Wood, E. F.; Yuan, X.; Sheffield, J.; Pan, M.; Roundy, J.

2013-12-01

One of the key recommendations of the WCRP Global Drought Information System (GDIS) workshop is to develop an experimental real-time global monitoring and prediction system. While great advances has been made in global drought monitoring based on satellite observations and model reanalysis data, global drought forecasting has been stranded in part due to the limited skill both in climate forecast models and global hydrologic predictions. Having been working on drought monitoring and forecasting over USA for more than a decade, the Princeton land surface hydrology group is now developing an experimental global drought early warning system that is based on multiple climate forecast models and a calibrated global hydrologic model. In this presentation, we will test its capability in seasonal forecasting of meteorological, agricultural and hydrologic droughts over global major river basins, using precipitation, soil moisture and streamflow forecasts respectively. Based on the joint probability distribution between observations using Princeton's global drought monitoring system and model hindcasts and real-time forecasts from North American Multi-Model Ensemble (NMME) project, we (i) bias correct the monthly precipitation and temperature forecasts from multiple climate forecast models, (ii) downscale them to a daily time scale, and (iii) use them to drive the calibrated VIC model to produce global drought forecasts at a 1-degree resolution. A parallel run using the ESP forecast method, which is based on resampling historical forcings, is also carried out for comparison. Analysis is being conducted over global major river basins, with multiple drought indices that have different time scales and characteristics. The meteorological drought forecast does not have uncertainty from hydrologic models and can be validated directly against observations - making the validation an 'apples-to-apples' comparison. Preliminary results for the evaluation of meteorological drought onset hindcasts indicate that climate models increase drought detectability over ESP by 31%-81%. However, less than 30% of the global drought onsets can be detected by climate models. The missed drought events are associated with weak ENSO signals and lower potential predictability. Due to the high false alarms from climate models, the reliability is more important than sharpness for a skillful probabilistic drought onset forecast. Validations and skill assessments for agricultural and hydrologic drought forecasts are carried out using soil moisture and streamflow output from the VIC land surface model (LSM) forced by a global forcing data set. Given our previous drought forecasting experiences over USA and Africa, validating the hydrologic drought forecasting is a significant challenge for a global drought early warning system.

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.

A changing climate: impacts on human exposures to O3 using ...

EPA Pesticide Factsheets

Predicting the impacts of changing climate on human exposure to air pollution requires future scenarios that account for changes in ambient pollutant concentrations, population sizes and distributions, and housing stocks. An integrated methodology to model changes in human exposures due to these impacts was developed by linking climate, air quality, land-use, and human exposure models. This methodology was then applied to characterize changes in predicted human exposures to O3 under multiple future scenarios. Regional climate projections for the U.S. were developed by downscaling global circulation model (GCM) scenarios for three of the Intergovernmental Panel on Climate Change’s (IPCC’s) Representative Concentration Pathways (RCPs) using the Weather Research and Forecasting (WRF) model. The regional climate results were in turn used to generate air quality (concentration) projections using the Community Multiscale Air Quality (CMAQ) model. For each of the climate change scenarios, future U.S. census-tract level population distributions from the Integrated Climate and Land Use Scenarios (ICLUS) model for four future scenarios based on the IPCC’s Special Report on Emissions Scenarios (SRES) storylines were used. These climate, air quality, and population projections were used as inputs to EPA’s Air Pollutants Exposure (APEX) model for 12 U.S. cities. Probability density functions show changes in the population distribution of 8 h maximum daily O3 exposur

IMPACTS OF CLIMATE-INDUCED CHANGES IN EXTREME EVENTS ON OZONE AND PARTICULATE MATTER AIR QUALITY

EPA Science Inventory

Historical data records of air pollution meteorology from multiple datasets will be compiled and analyzed to identify possible trends in extreme events. Changes in climate and air quality between 2010 and 2050 will be simulated with a suite of models. The consequential effe...

Modeling radiative transfer with the doubling and adding approach in a climate GCM setting

NASA Astrophysics Data System (ADS)

Lacis, A. A.

2017-12-01

The nonlinear dependence of multiply scattered radiation on particle size, optical depth, and solar zenith angle, makes accurate treatment of multiple scattering in the climate GCM setting problematic, due primarily to computational cost issues. In regard to the accurate methods of calculating multiple scattering that are available, their computational cost is far too prohibitive for climate GCM applications. Utilization of two-stream-type radiative transfer approximations may be computationally fast enough, but at the cost of reduced accuracy. We describe here a parameterization of the doubling/adding method that is being used in the GISS climate GCM, which is an adaptation of the doubling/adding formalism configured to operate with a look-up table utilizing a single gauss quadrature point with an extra-angle formulation. It is designed to closely reproduce the accuracy of full-angle doubling and adding for the multiple scattering effects of clouds and aerosols in a realistic atmosphere as a function of particle size, optical depth, and solar zenith angle. With an additional inverse look-up table, this single-gauss-point doubling/adding approach can be adapted to model fractional cloud cover for any GCM grid-box in the independent pixel approximation as a function of the fractional cloud particle sizes, optical depths, and solar zenith angle dependence.

Assessment of the Casualty Risk of Multiple Meteorological Hazards in China

PubMed Central

Xu, Wei; Zhuo, Li; Zheng, Jing; Ge, Yi; Gu, Zhihui; Tian, Yugang

2016-01-01

A study of the frequency, intensity, and risk of extreme climatic events or natural hazards is important for assessing the impacts of climate change. Many models have been developed to assess the risk of multiple hazards, however, most of the existing approaches can only model the relative levels of risk. This paper reports the development of a method for the quantitative assessment of the risk of multiple hazards based on information diffusion. This method was used to assess the risks of loss of human lives from 11 types of meteorological hazards in China at the prefectural and provincial levels. Risk curves of multiple hazards were obtained for each province and the risks of 10-year, 20-year, 50-year, and 100-year return periods were mapped. The results show that the provinces (municipalities, autonomous regions) in southeastern China are at higher risk of multiple meteorological hazards as a result of their geographical location and topography. The results of this study can be used as references for the management of meteorological disasters in China. The model can be used to quantitatively calculate the risks of casualty, direct economic losses, building collapse, and agricultural losses for any hazards at different spatial scales. PMID:26901210

Assessment of the Casualty Risk of Multiple Meteorological Hazards in China.

PubMed

Xu, Wei; Zhuo, Li; Zheng, Jing; Ge, Yi; Gu, Zhihui; Tian, Yugang

2016-02-17

A study of the frequency, intensity, and risk of extreme climatic events or natural hazards is important for assessing the impacts of climate change. Many models have been developed to assess the risk of multiple hazards, however, most of the existing approaches can only model the relative levels of risk. This paper reports the development of a method for the quantitative assessment of the risk of multiple hazards based on information diffusion. This method was used to assess the risks of loss of human lives from 11 types of meteorological hazards in China at the prefectural and provincial levels. Risk curves of multiple hazards were obtained for each province and the risks of 10-year, 20-year, 50-year, and 100-year return periods were mapped. The results show that the provinces (municipalities, autonomous regions) in southeastern China are at higher risk of multiple meteorological hazards as a result of their geographical location and topography. The results of this study can be used as references for the management of meteorological disasters in China. The model can be used to quantitatively calculate the risks of casualty, direct economic losses, building collapse, and agricultural losses for any hazards at different spatial scales.

Agent-based Model for the Coupled Human-Climate System

NASA Astrophysics Data System (ADS)

Zvoleff, A.; Werner, B.

2006-12-01

Integrated assessment models have been used to predict the outcome of coupled economic growth, resource use, greenhouse gas emissions and climate change, both for scientific and policy purposes. These models generally have employed significant simplifications that suppress nonlinearities and the possibility of multiple equilibria in both their economic (DeCanio, 2005) and climate (Schneider and Kuntz-Duriseti, 2002) components. As one step toward exploring general features of the nonlinear dynamics of the coupled system, we have developed a series of variations on the well studied RICE and DICE models, which employ different forms of agent-based market dynamics and "climate surprises." Markets are introduced through the replacement of the production function of the DICE/RICE models with an agent-based market modeling the interactions of producers, policymakers, and consumer agents. Technological change and population growth are treated endogenously. Climate surprises are representations of positive (for example, ice sheet collapse) or negative (for example, increased aerosols from desertification) feedbacks that are turned on with probability depending on warming. Initial results point toward the possibility of large amplitude instabilities in the coupled human-climate system owing to the mismatch between short outlook market dynamics and long term climate responses. Implications for predictability of future climate will be discussed. Supported by the Andrew W Mellon Foundation and the UC Academic Senate.

Effects of multiple stresses hydropower, acid deposition and climate change on water chemistry and salmon populations in the River Otra, Norway.

PubMed

Wright, Richard F; Couture, Raoul-Marie; Christiansen, Anne B; Guerrero, José-Luis; Kaste, Øyvind; Barlaup, Bjørn T

2017-01-01

Many surface waters in Europe suffer from the adverse effects of multiple stresses. The Otra River, southernmost Norway, is impacted by acid deposition, hydropower development and increasingly by climate change. The river holds a unique population of land-locked salmon and anadromous salmon in the lower reaches. Both populations have been severely affected by acidification. The decrease in acid deposition since the 1980s has led to partial recovery of both populations. Climate change with higher temperatures and altered precipitation can potentially further impact fish populations. We used a linked set of process-oriented models to simulate future climate, discharge, and water chemistry at five sub-catchments in the Otra river basin. Projections to year 2100 indicate that future climate change will give a small but measureable improvement in water quality, but that additional reductions in acid deposition are needed to promote full restoration of the fish communities. These results can help guide management decisions to sustain key salmon habitats and carry out effective long-term mitigation strategies such as liming. The Otra River is typical of many rivers in Europe in that it fails to achieve the good ecological status target of the EU Water Framework Directive. The programme of measures needed in the river basin management plan necessarily must consider the multiple stressors of acid deposition, hydropower, and climate change. This is difficult, however, as the synergistic and antagonistic effects are complex and challenging to address with modelling tools currently available. Copyright © 2016 Elsevier B.V. All rights reserved.

Relative importance of climate changes at different time scales on net primary productivity-a case study of the Karst area of northwest Guangxi, China.

PubMed

Liu, Huiyu; Zhang, Mingyang; Lin, Zhenshan

2017-10-05

Climate changes are considered to significantly impact net primary productivity (NPP). However, there are few studies on how climate changes at multiple time scales impact NPP. With MODIS NPP product and station-based observations of sunshine duration, annual average temperature and annual precipitation, impacts of climate changes at different time scales on annual NPP, have been studied with EEMD (ensemble empirical mode decomposition) method in the Karst area of northwest Guangxi, China, during 2000-2013. Moreover, with partial least squares regression (PLSR) model, the relative importance of climatic variables for annual NPP has been explored. The results show that (1) only at quasi 3-year time scale do sunshine duration and temperature have significantly positive relations with NPP. (2) Annual precipitation has no significant relation to NPP by direct comparison, but significantly positive relation at 5-year time scale, which is because 5-year time scale is not the dominant scale of precipitation; (3) the changes of NPP may be dominated by inter-annual variabilities. (4) Multiple time scales analysis will greatly improve the performance of PLSR model for estimating NPP. The variable importance in projection (VIP) scores of sunshine duration and temperature at quasi 3-year time scale, and precipitation at quasi 5-year time scale are greater than 0.8, indicating important for NPP during 2000-2013. However, sunshine duration and temperature at quasi 3-year time scale are much more important. Our results underscore the importance of multiple time scales analysis for revealing the relations of NPP to changing climate.

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.

Clime: analyzing and producing climate data in GIS environment

NASA Astrophysics Data System (ADS)

Cattaneo, Luigi; Rillo, Valeria; Mercogliano, Paola

2014-05-01

In the last years, Impacts on Soil and Coasts Division (ISC) of CMCC (Euro-Mediterranean Center on Climate Change) had several collaboration experiences with impact communities, including IS-ENES (FP7-INF) and SafeLand (FP7-ENV) projects, which involved a study of landslide risk in Europe, and is currently active in GEMINA (FIRB) and ORIENTGATE (SEE Transnational Cooperation Programme) research projects. As a result, it has brought research activities about different impact of climate changes as flood and landslide hazards, based on climate simulation obtained from the high resolution regional climate models COSMO CLM, developed at CMCC as member of the consortium CLM Assembly. ISC-Capua also collaborates with local institutions interested in atmospherical climate change and also of their impacts on the soil, such as river basin authorities in the Campania region, ARPA Emilia Romagna and ARPA Calabria. Impact models (e.g. hydraulic or stability models) are usually developed in a GIS environment, since they need an accurate territory description, so Clime has been designed to bridge the usually existing gap between climate data - both observed and simulated - gathered from different sources, and impact communities. The main goal of Clime, special purpose Geographic Information System (GIS) software integrated in ESRI ArcGIS Desktop 10, is to easily evaluate multiple climate features and study climate changes over specific geographical domains with their related effects on environment, including impacts on soil. Developed as an add-in tool, this software has been conceived for research activities of ISC Division in order to provide a substantial contribution during post-processing and validation phase. Therefore, it is possible to analyze and compare multiple datasets (observations, climate simulations, etc.) through processes involving statistical functions, percentiles, trends test and evaluation of extreme events with a flexible system of temporal and spatial filtering, and to represent results as maps, temporal and statistic plots (time series, seasonal cycles, PDFs, scatter plots, Taylor diagrams) or Excel tables; in addition, it features bias correction techniques for climate model results. Summarizing, Clime is able to provide users a simple and fast way to retrieve analysis over simulated climate data and observations within any geographical site of interest (provinces, regions, countries, etc.).

Variation in Estimated Ozone-Related Health Impacts of Climate Change due to Modeling Choices and Assumptions

PubMed Central

Post, Ellen S.; Grambsch, Anne; Weaver, Chris; Morefield, Philip; Leung, Lai-Yung; Nolte, Christopher G.; Adams, Peter; Liang, Xin-Zhong; Zhu, Jin-Hong; Mahoney, Hardee

2012-01-01

Background: Future climate change may cause air quality degradation via climate-induced changes in meteorology, atmospheric chemistry, and emissions into the air. Few studies have explicitly modeled the potential relationships between climate change, air quality, and human health, and fewer still have investigated the sensitivity of estimates to the underlying modeling choices. Objectives: Our goal was to assess the sensitivity of estimated ozone-related human health impacts of climate change to key modeling choices. Methods: Our analysis included seven modeling systems in which a climate change model is linked to an air quality model, five population projections, and multiple concentration–response functions. Using the U.S. Environmental Protection Agency’s (EPA’s) Environmental Benefits Mapping and Analysis Program (BenMAP), we estimated future ozone (O3)-related health effects in the United States attributable to simulated climate change between the years 2000 and approximately 2050, given each combination of modeling choices. Health effects and concentration–response functions were chosen to match those used in the U.S. EPA’s 2008 Regulatory Impact Analysis of the National Ambient Air Quality Standards for O3. Results: Different combinations of methodological choices produced a range of estimates of national O3-related mortality from roughly 600 deaths avoided as a result of climate change to 2,500 deaths attributable to climate change (although the large majority produced increases in mortality). The choice of the climate change and the air quality model reflected the greatest source of uncertainty, with the other modeling choices having lesser but still substantial effects. Conclusions: Our results highlight the need to use an ensemble approach, instead of relying on any one set of modeling choices, to assess the potential risks associated with O3-related human health effects resulting from climate change. PMID:22796531

Stratigraphic framework for Pliocene paleoclimate reconstruction: The correlation conundrum

USGS Publications Warehouse

Dowsett, H.J.; Robinson, M.M.

2006-01-01

Pre-Holocene paleoclimate reconstructions face a correlation conundrum because complications inherent in the stratigraphic record impede the development of synchronous reconstruction. The Pliocene Research, Interpretation and Synoptic Mapping (PRISM) paleoenvironmental reconstructions have carefully balanced temporal resolution and paleoclimate proxy data to achieve a useful and reliable product and are the most comprehensive pre-Pleistocene data sets available for analysis of warmer-than-present climate and for climate modeling experiments. This paper documents the stratigraphic framework for the mid-Pliocene sea surface temperature (SST) reconstruction of the North Atlantic and explores the relationship between stratigraphic/temporal resolution and various paleoceanographic estimates of SST. The magnetobiostratigraphic framework for the PRISM North Atlantic region is constructed from planktic foraminifer, calcareous nannofossil and paleomagnetic reversal events recorded in deep-sea cores and calibrated to age. Planktic foraminifer census data from multiple samples within the mid-Pliocene yield multiple SST estimates for each site. Extracting a single SST value at each site from multiple estimates, given the limitations of the material and stratigraphic resolution, is problematic but necessary for climate model experiments. The PRISM reconstruction, unprecedented in its integration of many different types of data at a focused stratigraphic interval, utilizes a time slab approach and is based on warm peak average temperatures. A greater understanding of the dynamics of the climate system and significant advances in models now mandate more precise, globally distributed yet temporally synchronous SST estimates than are available through averaging techniques. Regardless of the precision used to correlate between sequences within the midd-Pliocene, a truly synoptic reconstruction in the temporal sense is unlikely. SST estimates from multiple proxies promise to further refine paleoclimate reconstructions but must consider the complications associated with each method, what each proxy actually records, and how these different proxies compare in time-averaged samples.

Emergent climate and CO2 sensitivities of net primary productivity in ecosystem models do not agree with empirical data in temperate forests of eastern North America.

PubMed

Rollinson, Christine R; Liu, Yao; Raiho, Ann; Moore, David J P; McLachlan, Jason; Bishop, Daniel A; Dye, Alex; Matthes, Jaclyn H; Hessl, Amy; Hickler, Thomas; Pederson, Neil; Poulter, Benjamin; Quaife, Tristan; Schaefer, Kevin; Steinkamp, Jörg; Dietze, Michael C

2017-07-01

Ecosystem models show divergent responses of the terrestrial carbon cycle to global change over the next century. Individual model evaluation and multimodel comparisons with data have largely focused on individual processes at subannual to decadal scales. Thus far, data-based evaluations of emergent ecosystem responses to climate and CO 2 at multidecadal and centennial timescales have been rare. We compared the sensitivity of net primary productivity (NPP) to temperature, precipitation, and CO 2 in ten ecosystem models with the sensitivities found in tree-ring reconstructions of NPP and raw ring-width series at six temperate forest sites. These model-data comparisons were evaluated at three temporal extents to determine whether the rapid, directional changes in temperature and CO 2 in the recent past skew our observed responses to multiple drivers of change. All models tested here were more sensitive to low growing season precipitation than tree-ring NPP and ring widths in the past 30 years, although some model precipitation responses were more consistent with tree rings when evaluated over a full century. Similarly, all models had negative or no response to warm-growing season temperatures, while tree-ring data showed consistently positive effects of temperature. Although precipitation responses were least consistent among models, differences among models to CO 2 drive divergence and ensemble uncertainty in relative change in NPP over the past century. Changes in forest composition within models had no effect on climate or CO 2 sensitivity. Fire in model simulations reduced model sensitivity to climate and CO 2 , but only over the course of multiple centuries. Formal evaluation of emergent model behavior at multidecadal and multicentennial timescales is essential to reconciling model projections with observed ecosystem responses to past climate change. Future evaluation should focus on improved representation of disturbance and biomass change as well as the feedbacks with moisture balance and CO 2 in individual models. © 2017 John Wiley & Sons Ltd.

Multiple causes of the Younger Dryas cold period: new insights from coupled model experiments constrained by data assimilation

NASA Astrophysics Data System (ADS)

Renssen, Hans; Mairesse, Aurélien; Goosse, Hugues; Mathiot, Pierre; Heiri, Oliver; Roche, Didier M.; Nisancioglu, Kerim H.; Valdes, Paul J.

2016-04-01

The Younger Dryas cooling event disrupted the overall warming trend in the North Atlantic region during the last deglaciation. Climate change during the Younger Dryas was abrupt, and thus provides insights into the sensitivity of the climate system to perturbations. The sudden Younger Dryas cooling has traditionally been attributed to a shut-down of the Atlantic meridional overturning circulation by meltwater discharges. However, alternative explanations such as strong negative radiative forcing and a shift in atmospheric circulation have also been offered. In this study we investigate the importance of these different forcings in coupled climate model experiments constrained by data assimilation. We find that the Younger Dryas climate signal as registered in proxy evidence is best simulated using a combination of processes: a weakened Atlantic meridional overturning circulation, moderate negative radiative forcing and an altered atmospheric circulation. We conclude that none of the individual mechanisms alone provide a plausible explanation for the Younger Dryas cold period. We suggest that the triggers for abrupt climate changes like the Younger Dryas are more complex than suggested so far, and that studies on the response of the climate system to perturbations should account for this complexity. Reference: Renssen, H. et al. (2015) Multiple causes of the Younger Dryas cold period. Nature Geoscience 8, 946-949.

Transient Simulation of Last Deglaciation with a New Mechanism for B lling-Aller d Warming

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

Erickson, David J

2009-01-01

We conducted the first synchronously coupled atmosphere-ocean general circulation model simulation from the Last Glacial Maximum to the Boelling-Alleroed (BA) warming. Our model reproduces several major features of the deglacial climate evolution, suggesting a good agreement in climate sensitivity between the model and observations. In particular, our model simulates the abrupt BA warming as a transient response of the Atlantic meridional overturning circulation (AMOC) to a sudden termination of freshwater discharge to the North Atlantic before the BA. In contrast to previous mechanisms that invoke AMOC multiple equilibrium and Southern Hemisphere climate forcing, we propose that the BA transition ismore » caused by the superposition of climatic responses to the transient CO{sub 2} forcing, the AMOC recovery from Heinrich Event 1, and an AMOC overshoot.« less

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.

OpenClimateGIS - A Web Service Providing Climate Model Data in Commonly Used Geospatial Formats

NASA Astrophysics Data System (ADS)

Erickson, T. A.; Koziol, B. W.; Rood, R. B.

2011-12-01

The goal of the OpenClimateGIS project is to make climate model datasets readily available in commonly used, modern geospatial formats used by GIS software, browser-based mapping tools, and virtual globes.The climate modeling community typically stores climate data in multidimensional gridded formats capable of efficiently storing large volumes of data (such as netCDF, grib) while the geospatial community typically uses flexible vector and raster formats that are capable of storing small volumes of data (relative to the multidimensional gridded formats). OpenClimateGIS seeks to address this difference in data formats by clipping climate data to user-specified vector geometries (i.e. areas of interest) and translating the gridded data on-the-fly into multiple vector formats. The OpenClimateGIS system does not store climate data archives locally, but rather works in conjunction with external climate archives that expose climate data via the OPeNDAP protocol. OpenClimateGIS provides a RESTful API web service for accessing climate data resources via HTTP, allowing a wide range of applications to access the climate data.The OpenClimateGIS system has been developed using open source development practices and the source code is publicly available. The project integrates libraries from several other open source projects (including Django, PostGIS, numpy, Shapely, and netcdf4-python).OpenClimateGIS development is supported by a grant from NOAA's Climate Program Office.

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.

Software Engineering Support of the Third Round of Scientific Grand Challenge Investigations: An Earth Modeling System Software Framework Strawman Design that Integrates Cactus and UCLA/UCB Distributed Data Broker

NASA Technical Reports Server (NTRS)

Talbot, Bryan; Zhou, Shu-Jia; Higgins, Glenn

2002-01-01

One of the most significant challenges in large-scale climate modeling, as well as in high-performance computing in other scientific fields, is that of effectively integrating many software models from multiple contributors. A software framework facilitates the integration task. both in the development and runtime stages of the simulation. Effective software frameworks reduce the programming burden for the investigators, freeing them to focus more on the science and less on the parallel communication implementation, while maintaining high performance across numerous supercomputer and workstation architectures. This document proposes a strawman framework design for the climate community based on the integration of Cactus, from the relativistic physics community, and UCLA/UCB Distributed Data Broker (DDB) from the climate community. This design is the result of an extensive survey of climate models and frameworks in the climate community as well as frameworks from many other scientific communities. The design addresses fundamental development and runtime needs using Cactus, a framework with interfaces for FORTRAN and C-based languages, and high-performance model communication needs using DDB. This document also specifically explores object-oriented design issues in the context of climate modeling as well as climate modeling issues in terms of object-oriented design.

A multimodel approach to interannual and seasonal prediction of Danube discharge anomalies

NASA Astrophysics Data System (ADS)

Rimbu, Norel; Ionita, Monica; Patrut, Simona; Dima, Mihai

2010-05-01

Interannual and seasonal predictability of Danube river discharge is investigated using three model types: 1) time series models 2) linear regression models of discharge with large-scale climate mode indices and 3) models based on stable teleconnections. All models are calibrated using discharge and climatic data for the period 1901-1977 and validated for the period 1978-2008 . Various time series models, like autoregressive (AR), moving average (MA), autoregressive and moving average (ARMA) or singular spectrum analysis and autoregressive moving average (SSA+ARMA) models have been calibrated and their skills evaluated. The best results were obtained using SSA+ARMA models. SSA+ARMA models proved to have the highest forecast skill also for other European rivers (Gamiz-Fortis et al. 2008). Multiple linear regression models using large-scale climatic mode indices as predictors have a higher forecast skill than the time series models. The best predictors for Danube discharge are the North Atlantic Oscillation (NAO) and the East Atlantic/Western Russia patterns during winter and spring. Other patterns, like Polar/Eurasian or Tropical Northern Hemisphere (TNH) are good predictors for summer and autumn discharge. Based on stable teleconnection approach (Ionita et al. 2008) we construct prediction models through a combination of sea surface temperature (SST), temperature (T) and precipitation (PP) from the regions where discharge and SST, T and PP variations are stable correlated. Forecast skills of these models are higher than forecast skills of the time series and multiple regression models. The models calibrated and validated in our study can be used for operational prediction of interannual and seasonal Danube discharge anomalies. References Gamiz-Fortis, S., D. Pozo-Vazquez, R.M. Trigo, and Y. Castro-Diez, Quantifying the predictability of winter river flow in Iberia. Part I: intearannual predictability. J. Climate, 2484-2501, 2008. Gamiz-Fortis, S., D. Pozo-Vazquez, R.M. Trigo, and Y. Castro-Diez, Quantifying the predictability of winter river flow in Iberia. Part II: seasonal predictability. J. Climate, 2503-2518, 2008. Ionita, M., G. Lohmann, and N. Rimbu, Prediction of spring Elbe river discharge based on stable teleconnections with global temperature and precipitation. J. Climate. 6215-6226, 2008.

Determining the effect of key climate drivers on global hydropower production

NASA Astrophysics Data System (ADS)

Galelli, S.; Ng, J. Y.; Lee, D.; Block, P. J.

2017-12-01

Accounting for about 17% of total global electrical power production, hydropower is arguably the world's main renewable energy source and a key asset to meet Paris climate agreements. A key component of hydropower production is water availability, which depends on both precipitation and multiple drivers of climate variability acting at different spatial and temporal scales. To understand how these drivers impact global hydropower production, we study the relation between four patterns of ocean-atmosphere climate variability (i.e., El Niño Southern Oscillation, Pacific Decadal Oscillation, North Atlantic Oscillation, and Atlantic Multidecadal Oscillation) and monthly time series of electrical power production for over 1,500 hydropower reservoirs—obtained via simulation with a high-fidelity dam model forced with 20th century climate conditions. Notably significant relationships between electrical power productions and climate variability are found in many climate sensitive regions globally, including North and South America, East Asia, West Africa, and Europe. Coupled interactions from multiple, simultaneous climate drivers are also evaluated. Finally, we highlight the importance of using these climate drivers as an additional source of information within reservoir operating rules where the skillful predictability of inflow exists.

A fresh look at the Last Glacial Maximum using Paleoclimate Data Assimilation

NASA Astrophysics Data System (ADS)

Malevich, S. B.; Tierney, J. E.; Hakim, G. J.; Tardif, R.

2017-12-01

Quantifying climate conditions during the Last Glacial Maximum ( 21ka) can help us to understand climate responses to forcing and climate states that are poorly represented in the instrumental record. Paleoclimate proxies may be used to estimate these climate conditions, but proxies are sparsely distributed and possess uncertainties from environmental and biogeochemical processes. Alternatively, climate model simulations provide a full-field view, but may predict unrealistic climate states or states not faithful to proxy records. Here, we use data assimilation - combining climate proxy records with a theoretical understanding from climate models - to produce field reconstructions of the LGM that leverage the information from both data and models. To date, data assimilation has mainly been used to produce reconstructions of climate fields through the last millennium. We expand this approach in order to produce a climate fields for the Last Glacial Maximum using an ensemble Kalman filter assimilation. Ensemble samples were formed from output from multiple models including CCSM3, CESM2.1, and HadCM3. These model simulations are combined with marine sediment proxies for upper ocean temperature (TEX86, UK'37, Mg/Ca and δ18O of foraminifera), utilizing forward models based on a newly developed suite of Bayesian proxy system models. We also incorporate age model and radiocarbon reservoir uncertainty into our reconstructions using Bayesian age modeling software. The resulting fields show familiar patterns based on comparison with previous proxy-based reconstructions, but additionally reveal novel patterns of large-scale shifts in ocean-atmosphere dynamics, as the surface temperature data inform upon atmospheric circulation and precipitation patterns.

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.

A review of multi-risk methodologies for natural hazards: Consequences and challenges for a climate change impact assessment.

PubMed

Gallina, Valentina; Torresan, Silvia; Critto, Andrea; Sperotto, Anna; Glade, Thomas; Marcomini, Antonio

2016-03-01

This paper presents a review of existing multi-risk assessment concepts and tools applied by organisations and projects providing the basis for the development of a multi-risk methodology in a climate change perspective. Relevant initiatives were developed for the assessment of multiple natural hazards (e.g. floods, storm surges, droughts) affecting the same area in a defined timeframe (e.g. year, season, decade). Major research efforts were focused on the identification and aggregation of multiple hazard types (e.g. independent, correlated, cascading hazards) by means of quantitative and semi-quantitative approaches. Moreover, several methodologies aim to assess the vulnerability of multiple targets to specific natural hazards by means of vulnerability functions and indicators at the regional and local scale. The overall results of the review show that multi-risk approaches do not consider the effects of climate change and mostly rely on the analysis of static vulnerability (i.e. no time-dependent vulnerabilities, no changes among exposed elements). A relevant challenge is therefore to develop comprehensive formal approaches for the assessment of different climate-induced hazards and risks, including dynamic exposure and vulnerability. This requires the selection and aggregation of suitable hazard and vulnerability metrics to make a synthesis of information about multiple climate impacts, the spatial analysis and ranking of risks, including their visualization and communication to end-users. To face these issues, climate impact assessors should develop cross-sectorial collaborations among different expertise (e.g. modellers, natural scientists, economists) integrating information on climate change scenarios with sectorial climate impact assessment, towards the development of a comprehensive multi-risk assessment process. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Evaluation of Aerosol Mixing State Classes in the GISS Modele-matrix Climate Model Using Single-particle Mass Spectrometry Measurements

NASA Technical Reports Server (NTRS)

Bauer, Susanne E.; Ault, Andrew; Prather, Kimberly A.

2013-01-01

Aerosol particles in the atmosphere are composed of multiple chemical species. The aerosol mixing state, which describes how chemical species are mixed at the single-particle level, provides critical information on microphysical characteristics that determine the interaction of aerosols with the climate system. The evaluation of mixing state has become the next challenge. This study uses aerosol time-of-flight mass spectrometry (ATOFMS) data and compares the results to those of the Goddard Institute for Space Studies modelE-MATRIX (Multiconfiguration Aerosol TRacker of mIXing state) model, a global climate model that includes a detailed aerosol microphysical scheme. We use data from field campaigns that examine a variety of air mass regimens (urban, rural, and maritime). At all locations, polluted areas in California (Riverside, La Jolla, and Long Beach), a remote location in the Sierra Nevada Mountains (Sugar Pine) and observations from Jeju (South Korea), the majority of aerosol species are internally mixed. Coarse aerosol particles, those above 1 micron, are typically aged, such as coated dust or reacted sea-salt particles. Particles below 1 micron contain large fractions of organic material, internally-mixed with sulfate and black carbon, and few external mixtures. We conclude that observations taken over multiple weeks characterize typical air mass types at a given location well; however, due to the instrumentation, we could not evaluate mass budgets. These results represent the first detailed comparison of single-particle mixing states in a global climate model with real-time single-particle mass spectrometry data, an important step in improving the representation of mixing state in global climate models.

Rainfall and temperatures changes have confounding impacts on Phytophthora cinnamomi occurrence risk in the southwestern USA under climate change scenarios.

PubMed

Thompson, Sally E; Levin, Simon; Rodriguez-Iturbe, Ignacio

2014-04-01

Global change will simultaneously impact many aspects of climate, with the potential to exacerbate the risks posed by plant pathogens to agriculture and the natural environment; yet, most studies that explore climate impacts on plant pathogen ranges consider individual climatic factors separately. In this study, we adopt a stochastic modeling approach to address multiple pathways by which climate can constrain the range of the generalist plant pathogen Phytophthora cinnamomi (Pc): through changing winter soil temperatures affecting pathogen survival; spring soil temperatures and thus pathogen metabolic rates; and changing spring soil moisture conditions and thus pathogen growth rates through host root systems. We apply this model to the southwestern USA for contemporary and plausible future climate scenarios and evaluate the changes in the potential range of Pc. The results indicate that the plausible range of this pathogen in the southwestern USA extends over approximately 200,000 km(2) under contemporary conditions. While warming temperatures as projected by the IPCC A2 and B1 emissions scenarios greatly expand the range over which the pathogen can survive winter, projected reductions in spring rainfall reduce its feasible habitat, leading to spatially complex patterns of changing risk. The study demonstrates that temperature and rainfall changes associated with possible climate futures in the southwestern USA have confounding impacts on the range of Pc, suggesting that projections of future pathogen dynamics and ranges should account for multiple pathways of climate-pathogen interaction. © 2014 John Wiley & Sons Ltd.

Integrating Science and Management to Assess Forest Ecosystem Vulnerability to Climate Change

Treesearch

Leslie A. Brandt; Patricia R. Butler; Stephen D. Handler; Maria K. Janowiak; P. Danielle Shannon; Christopher W. Swanston

2017-01-01

We developed the ecosystem vulnerability assessment approach (EVAA) to help inform potential adaptation actions in response to a changing climate. EVAA combines multiple quantitative models and expert elicitation from scientists and land managers. In each of eight assessment areas, a panel of local experts determined potential vulnerability of forest ecosystems to...

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.

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.

Theoretical electron scattering amplitudes and spin polarizations. Electron energies 100 to 1500 eV Part II. Be, N, O, Al, Cl, V, Co, Cu, As, Nb, Ag, Sn, Sb, I, and Ta targets

NASA Astrophysics Data System (ADS)

Wildhaber, M. L.; Wikle, C. K.; Anderson, C. J.; Franz, K. J.; Moran, E. H.; Dey, R.

2012-12-01

Recent decades have brought substantive changes in land use and climate across the earth, prompting a need to think of population and community ecology not as a static entity, but as a dynamic process. Increasingly there is evidence of ecological changes due to climate change. Although much of this evidence comes from ground-truth observations of biogeographic data, there is increasing reliance on models that relate climate variables to biological systems. Such models can then be used to explore potential changes to population and community level ecological systems in response to climate scenarios as obtained from global climate models (GCMs). A key issue associated with modeling ecosystem response to climate is GCM downscaling to regional and local ecological/biological response models that can be used in vulnerability and risk assessments of the potential effects of climate change. The need is for an explicit means for scaling results up or down multiple hierarchical levels and an effective assessment of the level of uncertainty surrounding current knowledge, data, and data collection methods with these goals identified as in need of acceleration in the U.S. Climate Change Science Program FY2009 Implementation Priorities. In the end, such work should provide the information needed to develop adaptation and mitigation methodologies to minimize the effects of directional and nonlinear climate change on the Nation's land, water, ecosystems, and biological populations. We are working to develop an approach that includes multi-scale and hierarchical Bayesian modeling of Missouri River sturgeon population dynamics. Statistical linkages are defined to quantify implications of climate on fish populations of the Missouri River ecosystem. This approach is a hybrid between physical (deterministic) downscaling and statistical downscaling, recognizing that there is uncertainty in both. The model must include linkages between climate and habitat, and between habitat and population. A key advantage of the hierarchical approach used in this study is that it incorporates various sources of observations and includes established scientific knowledge, and associated uncertainties. The goal is to evaluate the potential distributional changes in an ecological system, given distributional changes implied by a series of linked climate and system models under various emissions/use scenarios. The predictive modeling system being developed will be a powerful tool for evaluating management options for coping with global change consequences and assessing uncertainty of those evaluations. Specifically for the endangered pallid sturgeon (Scaphirhynchus albus), we are already able to assess potential effects of any climate scenario on growth and population size distribution. Future models will incorporate survival and reproduction. Ultimately, these models provide guidance for successful recovery and conservation of the pallid sturgeon. Here we present a basic outline of the approach we are developing and a simple pallid sturgeon example to demonstrate how multiple scales and parameter uncertainty are incorporated.

An Assessment of IPCC 20th Century Climate Simulations Using the 15-year Sea Level Record from Altimetry

NASA Astrophysics Data System (ADS)

Leuliette, E.; Nerem, S.; Jakub, T.

2006-07-01

Recen tly, multiple ensemble climate simulations h ave been produced for th e forthco ming Fourth A ssessment Report of the Intergovernmental Panel on Climate Change (IPCC). N early two dozen coupled ocean- atmo sphere models have contr ibuted output for a variety of climate scen arios. One scenar io, the climate of the 20th century exper imen t (20C3 M), produces model output that can be comp ared to th e long record of sea level provided by altimetry . Generally , the output from the 20C3M runs is used to initialize simulations of future climate scenar ios. Hence, v alidation of the 20 C3 M experiment resu lts is crucial to the goals of th e IPCC. We present compar isons of global mean sea level (G MSL) , global mean steric sea level change, and regional patterns of sea lev el chang e from these models to r esults from altimetry, tide gauge measurements, and reconstructions.

Measuring school climate in high schools: a focus on safety, engagement, and the environment.

PubMed

Bradshaw, Catherine P; Waasdorp, Tracy E; Debnam, Katrina J; Johnson, Sarah Lindstrom

2014-09-01

School climate has been linked to multiple student behavioral, academic, health, and social-emotional outcomes. The US Department of Education (USDOE) developed a 3-factor model of school climate comprised of safety, engagement, and environment. This article examines the factor structure and measurement invariance of the USDOE model. Drawing upon 2 consecutive waves of data from over 25,000 high school students (46% minority), a series of exploratory and confirmatory factor analyses examined the fit of the Maryland Safe and Supportive Schools Climate Survey with the USDOE model. The results indicated adequate model fit with the theorized 3-factor model of school climate, which included 13 subdomains: safety (perceived safety, bullying and aggression, and drug use); engagement (connection to teachers, student connectedness, academic engagement, school connectedness, equity, and parent engagement); environment (rules and consequences, physical comfort, and support, disorder). We also found consistent measurement invariance with regard to student sex, grade level, and ethnicity. School-level interclass correlation coefficients ranged from 0.04 to .10 for the scales. Findings supported the USDOE 3-factor model of school climate and suggest measurement invariance and high internal consistency of the 3 scales and 13 subdomains. These results suggest the 56-item measure may be a potentially efficient, yet comprehensive measure of school climate. © 2014, American School Health Association.

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.

Modeling complex effects of multiple environmental stresses on carbon dynamics of Mid-Atlantic temperate forests

Treesearch

Yude Pan; Richard Birdsey; John Hom; Kevin McCullough

2007-01-01

We used our GIS variant of the PnET-CN model to investigate changes of forest carbon stocks and fluxes in Mid-Atlantic temperate forests over the last century (1900-2000). Forests in this region are affected by multiple environmental changes including climate, atmospheric CO2 concentration, N deposition and tropospheric ozone, and extensive land disturbances. Our...

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.

Multi-objective optimization of GENIE Earth system models.

PubMed

Price, Andrew R; Myerscough, Richard J; Voutchkov, Ivan I; Marsh, Robert; Cox, Simon J

2009-07-13

The tuning of parameters in climate models is essential to provide reliable long-term forecasts of Earth system behaviour. We apply a multi-objective optimization algorithm to the problem of parameter estimation in climate models. This optimization process involves the iterative evaluation of response surface models (RSMs), followed by the execution of multiple Earth system simulations. These computations require an infrastructure that provides high-performance computing for building and searching the RSMs and high-throughput computing for the concurrent evaluation of a large number of models. Grid computing technology is therefore essential to make this algorithm practical for members of the GENIE project.

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.

Weighting climate model projections using observational constraints.

PubMed

Gillett, Nathan P

2015-11-13

Projected climate change integrates the net response to multiple climate feedbacks. Whereas existing long-term climate change projections are typically based on unweighted individual climate model simulations, as observed climate change intensifies it is increasingly becoming possible to constrain the net response to feedbacks and hence projected warming directly from observed climate change. One approach scales simulated future warming based on a fit to observations over the historical period, but this approach is only accurate for near-term projections and for scenarios of continuously increasing radiative forcing. For this reason, the recent Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5) included such observationally constrained projections in its assessment of warming to 2035, but used raw model projections of longer term warming to 2100. Here a simple approach to weighting model projections based on an observational constraint is proposed which does not assume a linear relationship between past and future changes. This approach is used to weight model projections of warming in 2081-2100 relative to 1986-2005 under the Representative Concentration Pathway 4.5 forcing scenario, based on an observationally constrained estimate of the Transient Climate Response derived from a detection and attribution analysis. The resulting observationally constrained 5-95% warming range of 0.8-2.5 K is somewhat lower than the unweighted range of 1.1-2.6 K reported in the IPCC AR5. © 2015 The Authors.

A Regional Climate Model Evaluation System based on contemporary Satellite and other Observations for Assessing Regional Climate Model Fidelity

NASA Astrophysics Data System (ADS)

Waliser, D. E.; Kim, J.; Mattman, C.; Goodale, C.; Hart, A.; Zimdars, P.; Lean, P.

2011-12-01

Evaluation of climate models against observations is an essential part of assessing the impact of climate variations and change on regionally important sectors and improving climate models. Regional climate models (RCMs) are of a particular concern. RCMs provide fine-scale climate needed by the assessment community via downscaling global climate model projections such as those contributing to the Coupled Model Intercomparison Project (CMIP) that form one aspect of the quantitative basis of the IPCC Assessment Reports. The lack of reliable fine-resolution observational data and formal tools and metrics has represented a challenge in evaluating RCMs. Recent satellite observations are particularly useful as they provide a wealth of information and constraints on many different processes within the climate system. Due to their large volume and the difficulties associated with accessing and using contemporary observations, however, these datasets have been generally underutilized in model evaluation studies. Recognizing this problem, NASA JPL and UCLA have developed the Regional Climate Model Evaluation System (RCMES) to help make satellite observations, in conjunction with in-situ and reanalysis datasets, more readily accessible to the regional modeling community. The system includes a central database (Regional Climate Model Evaluation Database: RCMED) to store multiple datasets in a common format and codes for calculating and plotting statistical metrics to assess model performance (Regional Climate Model Evaluation Tool: RCMET). This allows the time taken to compare model data with satellite observations to be reduced from weeks to days. RCMES is a component of the recent ExArch project, an international effort for facilitating the archive and access of massive amounts data for users using cloud-based infrastructure, in this case as applied to the study of climate and climate change. This presentation will describe RCMES and demonstrate its utility using examples from RCMs applied to the southwest US as well as to Africa based on output from the CORDEX activity. Application of RCMES to the evaluation of multi-RCM hindcast for CORDEX-Africa will be presented in a companion paper in A41.

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.

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.

The Agricultural Model Intercomparison and Improvement Project: Phase I Activities by a Global Community of Science. Chapter 1

NASA Technical Reports Server (NTRS)

Rosenzweig, Cynthia E.; Jones, James W.; Hatfield, Jerry L.; Antle, John M.; Ruane, Alexander C.; Mutter, Carolyn Z.

2015-01-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) was founded in 2010. Its mission is to improve substantially the characterization of world food security as affected by climate variability and change, and to enhance adaptation capacity in both developing and developed countries. The objectives of AgMIP are to: Incorporate state-of-the-art climate, crop/livestock, and agricultural economic model improvements into coordinated multi-model regional and global assessments of future climate impacts and adaptation and other key aspects of the food system. Utilize multiple models, scenarios, locations, crops/livestock, and participants to explore uncertainty and the impact of data and methodological choices. Collaborate with regional experts in agronomy, animal sciences, economics, and climate to build a strong basis for model applications, addressing key climate related questions and sustainable intensification farming systems. Improve scientific and adaptive capacity in modeling for major agricultural regions in the developing and developed world, with a focus on vulnerable regions. Improve agricultural data and enhance data-sharing based on their intercomparison and evaluation using best scientific practices. Develop modeling frameworks to identify and evaluate promising adaptation technologies and policies and to prioritize strategies.

A Multi-Model Framework to Achieve Consistent Evaluation of Climate Change Impacts in the United States

NASA Astrophysics Data System (ADS)

Sarofim, M. C.; Martinich, J.; Waldhoff, S.; DeAngelo, B. J.; McFarland, J.; Jantarasami, L.; Shouse, K.; Crimmins, A.; Li, J.

2014-12-01

The Climate Change Impacts and Risk Analysis (CIRA) project establishes a new multi-model framework to systematically assess the physical impacts, economic damages, and risks from climate change. The primary goal of this framework is to estimate the degree to which climate change impacts and damages in the United States are avoided or reduced in the 21st century under multiple greenhouse gas (GHG) emissions mitigation scenarios. The first phase of the CIRA project is a modeling exercise that included two integrated assessment models and 15 sectoral models encompassing five broad impacts sectors: water resources, electric power, infrastructure, human health, and ecosystems. Three consistent socioeconomic and climate scenarios are used to analyze the benefits of global GHG mitigation targets: a reference scenario and two policy scenarios with total radiative forcing targets in 2100 of 4.5 W/m2 and 3.7 W/m2. In this exercise, the implications of key uncertainties are explored, including climate sensitivity, climate model, natural variability, and model structures and parameters. This presentation describes the motivations and goals of the CIRA project; the design and academic contribution of the first CIRA modeling exercise; and briefly summarizes several papers published in a special issue of Climatic Change. The results across impact sectors show that GHG mitigation provides benefits to the United States that increase over time, the effects of climate change can be strongly influenced by near-term policy choices, adaptation can reduce net damages, and impacts exhibit spatial and temporal patterns that may inform mitigation and adaptation policy discussions.

Trend and uncertainty analysis of simulated climate change impacts with multiple GCMs and emission scenarios

USDA-ARS?s Scientific Manuscript database

Impacts of climate change on hydrology, soil erosion, and wheat production during 2010-2039 at El Reno in central Oklahoma, USA, were simulated using the Water Erosion Prediction Project (WEPP) model. Projections from four GCMs (CCSR/NIES, CGCM2, CSIRO-Mk2, and HadCM3) under three emissions scenari...

A multi-model assessment of the co-benefits of climate mitigation for global air quality

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

Rao, Shilpa; Klimont, Zbigniew; Leitao, Joana

The recent International Panel on Climate change (IPCC) report identifies significant co-benefits from climate policies on near-term ambient air pollution and related human health outcomes [1]. This is increasingly relevant for policy making as the health impacts of air pollution are a major global concern- the Global Burden of Disease (GBD) study identifies outdoor air pollution as the sixth major cause of death globally [2]. Integrated assessment models (IAMs) are an effective tool to evaluate future air pollution outcomes across a wide range of assumptions on socio-economic development and policy regimes. The Representative Concentration Pathways (RCPs) [3] were the firstmore » set of long-term global scenarios developed across multiple integrated assessment models that provided detailed estimates of a number of air pollutants until 2100. However these scenarios were primarily designed to cover a defined range of radiative forcing outcomes and thus did not specifically focus on the interactions of long-term climate goals on near-term air pollution impacts. More recently, [4] used the RCP4.5 scenario to evaluate the co-benefits of global GHG reductions on air quality and human health in 2030. [5-7] have further examined the interactions of more diverse pollution control regimes with climate policies. This paper extends the listed studies in a number of ways. Firstly it uses multiple IAMs to look into the co-benefits of a global climate policy for ambient air pollution under harmonized assumptions on near-term air pollution control. Multi-model frameworks have been extensively used in the analysis of climate change mitigation pathways, and the structural uncertainties regarding the underlying mechanisms (see for example [8-10]. This is to our knowledge the first time that a multi-model evaluation has been specifically designed and applied to analyze the co-benefits of climate change policy on ambient air quality, thus enabling a better understanding of at a detailed sector and region level. A second methodological advancement is a quantification of the co-benefits in terms of the associated atmospheric concentrations of fine particulate matter (PM2.5) and consequent mortality related outcomes across different models. This is made possible by the use of state-of the art simplified atmospheric model that allows for the first time a computationally feasible multi-model evaluation of such outcomes.« less

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.

Modelling Coral Reef Futures to Inform Management: Can Reducing Local-Scale Stressors Conserve Reefs under Climate Change?

PubMed Central

Gurney, Georgina G.; Melbourne-Thomas, Jessica; Geronimo, Rollan C.; Aliño, Perry M.; Johnson, Craig R.

2013-01-01

Climate change has emerged as a principal threat to coral reefs, and is expected to exacerbate coral reef degradation caused by more localised stressors. Management of local stressors is widely advocated to bolster coral reef resilience, but the extent to which management of local stressors might affect future trajectories of reef state remains unclear. This is in part because of limited understanding of the cumulative impact of multiple stressors. Models are ideal tools to aid understanding of future reef state under alternative management and climatic scenarios, but to date few have been sufficiently developed to be useful as decision support tools for local management of coral reefs subject to multiple stressors. We used a simulation model of coral reefs to investigate the extent to which the management of local stressors (namely poor water quality and fishing) might influence future reef state under varying climatic scenarios relating to coral bleaching. We parameterised the model for Bolinao, the Philippines, and explored how simulation modelling can be used to provide decision support for local management. We found that management of water quality, and to a lesser extent fishing, can have a significant impact on future reef state, including coral recovery following bleaching-induced mortality. The stressors we examined interacted antagonistically to affect reef state, highlighting the importance of considering the combined impact of multiple stressors rather than considering them individually. Further, by providing explicit guidance for management of Bolinao's reef system, such as which course of management action will most likely to be effective over what time scales and at which sites, we demonstrated the utility of simulation models for supporting management. Aside from providing explicit guidance for management of Bolinao's reef system, our study offers insights which could inform reef management more broadly, as well as general understanding of reef systems. PMID:24260347

Modelling coral reef futures to inform management: can reducing local-scale stressors conserve reefs under climate change?

PubMed

Gurney, Georgina G; Melbourne-Thomas, Jessica; Geronimo, Rollan C; Aliño, Perry M; Johnson, Craig R

2013-01-01

Climate change has emerged as a principal threat to coral reefs, and is expected to exacerbate coral reef degradation caused by more localised stressors. Management of local stressors is widely advocated to bolster coral reef resilience, but the extent to which management of local stressors might affect future trajectories of reef state remains unclear. This is in part because of limited understanding of the cumulative impact of multiple stressors. Models are ideal tools to aid understanding of future reef state under alternative management and climatic scenarios, but to date few have been sufficiently developed to be useful as decision support tools for local management of coral reefs subject to multiple stressors. We used a simulation model of coral reefs to investigate the extent to which the management of local stressors (namely poor water quality and fishing) might influence future reef state under varying climatic scenarios relating to coral bleaching. We parameterised the model for Bolinao, the Philippines, and explored how simulation modelling can be used to provide decision support for local management. We found that management of water quality, and to a lesser extent fishing, can have a significant impact on future reef state, including coral recovery following bleaching-induced mortality. The stressors we examined interacted antagonistically to affect reef state, highlighting the importance of considering the combined impact of multiple stressors rather than considering them individually. Further, by providing explicit guidance for management of Bolinao's reef system, such as which course of management action will most likely to be effective over what time scales and at which sites, we demonstrated the utility of simulation models for supporting management. Aside from providing explicit guidance for management of Bolinao's reef system, our study offers insights which could inform reef management more broadly, as well as general understanding of reef systems.

Creating "Intelligent" Ensemble Averages Using a Process-Based Framework

NASA Astrophysics Data System (ADS)

Baker, Noel; Taylor, Patrick

2014-05-01

The CMIP5 archive contains future climate projections from over 50 models provided by dozens of modeling centers from around the world. Individual model projections, however, are subject to biases created by structural model uncertainties. As a result, ensemble averaging of multiple models is used to add value to individual model projections and construct a consensus projection. Previous reports for the IPCC establish climate change projections based on an equal-weighted average of all model projections. However, individual models reproduce certain climate processes better than other models. Should models be weighted based on performance? Unequal ensemble averages have previously been constructed using a variety of mean state metrics. What metrics are most relevant for constraining future climate projections? This project develops a framework for systematically testing metrics in models to identify optimal metrics for unequal weighting multi-model ensembles. The intention is to produce improved ("intelligent") unequal-weight ensemble averages. A unique aspect of this project is the construction and testing of climate process-based model evaluation metrics. A climate process-based metric is defined as a metric based on the relationship between two physically related climate variables—e.g., outgoing longwave radiation and surface temperature. Several climate process metrics are constructed using high-quality Earth radiation budget data from NASA's Clouds and Earth's Radiant Energy System (CERES) instrument in combination with surface temperature data sets. It is found that regional values of tested quantities can vary significantly when comparing the equal-weighted ensemble average and an ensemble weighted using the process-based metric. Additionally, this study investigates the dependence of the metric weighting scheme on the climate state using a combination of model simulations including a non-forced preindustrial control experiment, historical simulations, and several radiative forcing Representative Concentration Pathway (RCP) scenarios. Ultimately, the goal of the framework is to advise better methods for ensemble averaging models and create better climate predictions.

Increasing atmospheric CO2 overrides the historical legacy of multiple stable biome states in Africa.

PubMed

Moncrieff, Glenn R; Scheiter, Simon; Bond, William J; Higgins, Steven I

2014-02-01

The dominant vegetation over much of the global land surface is not predetermined by contemporary climate, but also influenced by past environmental conditions. This confounds attempts to predict current and future biome distributions, because even a perfect model would project multiple possible biomes without knowledge of the historical vegetation state. Here we compare the distribution of tree- and grass-dominated biomes across Africa simulated using a dynamic global vegetation model (DGVM). We explicitly evaluate where and under what conditions multiple stable biome states are possible for current and projected future climates. Our simulation results show that multiple stable biomes states are possible for vast areas of tropical and subtropical Africa under current conditions. Widespread loss of the potential for multiple stable biomes states is projected in the 21st Century, driven by increasing atmospheric CO2 . Many sites where currently both tree-dominated and grass-dominated biomes are possible become deterministically tree-dominated. Regions with multiple stable biome states are widespread and require consideration when attempting to predict future vegetation changes. Testing for behaviour characteristic of systems with multiple stable equilibria, such as hysteresis and dependence on historical conditions, and the resulting uncertainty in simulated vegetation, will lead to improved projections of global change impacts. © 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.

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.

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.

Future Climate Impacts on Crop Water Demand and Groundwater Longevity in Agricultural Regions

NASA Astrophysics Data System (ADS)

Russo, T. A.; Sahoo, S.; Elliott, J. W.; Foster, I.

2016-12-01

Improving groundwater management practices under future drought conditions in agricultural regions requires three steps: 1) estimating the impacts of climate and drought on crop water demand, 2) projecting groundwater availability given climate and demand forcing, and 3) using this information to develop climate-smart policy and water use practices. We present an innovative combination of models to address the first two steps, and inform the third. Crop water demand was simulated using biophysical crop models forced by multiple climate models and climate scenarios, with one case simulating climate adaptation (e.g. modify planting or harvest time) and another without adaptation. These scenarios were intended to represent a range of drought projections and farm management responses. Nexty, we used projected climate conditions and simulated water demand across the United States as inputs to a novel machine learning-based groundwater model. The model was applied to major agricultural regions relying on the High Plains and Mississippi Alluvial aquifer systems in the US. The groundwater model integrates input data preprocessed using single spectrum analysis, mutual information, and a genetic algorithm, with an artificial neural network model. Model calibration and test results indicate low errors over the 33 year model run, and strong correlations to groundwater levels in hundreds of wells across each aquifer. Model results include a range of projected groundwater level changes from the present to 2050, and in some regions, identification and timeframe of aquifer depletion. These results quantify aquifer longevity under climate and crop scenarios, and provide decision makers with the data needed to compare scenarios of crop water demand, crop yield, and groundwater response, as they aim to balance water sustainability with food security.

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.

The foundation for climate services in Belgium: CORDEX.be

NASA Astrophysics Data System (ADS)

Van Schaeybroeck, Bert; Termonia, Piet; De Ridder, Koen; Fettweis, Xavier; Gobin, Anne; Luyten, Patrick; Marbaix, Philippe; Pottiaux, Eric; Stavrakou, Trissevgeni; Van Lipzig, Nicole; van Ypersele, Jean-Pascal; Willems, Patrick

2017-04-01

According to the Global Framework for Climate Services (GFCS) there are four pillars required to build climate services. As the first step towards the realization of a climate center in Belgium, the national project CORDEX.be focused on one pillar: research modelling and projection. By bringing together the Belgian climate and impact modeling research of nine groups a data-driven capacity development and community building in Belgium based on interactions with users. The project is based on the international CORDEX ("COordinated Regional Climate Downscaling Experiment") project where ".be" indicates it will go beyond for Belgium. Our national effort links to the regional climate initiatives through the contribution of multiple high-resolution climate simulations over Europe following the EURO-CORDEX guidelines. Additionally the same climate simulations were repeated at convection-permitting resolutions over Belgium (3 to 5 km). These were used to drive different local impact models to investigate the impact of climate change on urban effects, storm surges and waves, crop production and changes in emissions from vegetation. Akin to international frameworks such as CMIP and CORDEX a multi-model approach is adopted allowing for uncertainty estimation, a crucial aspect of climate projections for policy-making purposes. However, due to the lack of a large set of high resolution model runs, a combination of all available climate information is supplemented with the statistical downscaling approach. The organization of the project, together with its main results will be outlined. The proposed coordination framework could serve as a demonstration case for regions or countries where the climate-research capacity is present but a structure is required to assemble it coherently. Based on interactions and feedback with stakeholders different applications are planned, demonstrating the use of the climate data.

Improving models to assess impacts of climate change on Mediterranean water resources

NASA Astrophysics Data System (ADS)

Rocha, João; Carvalho Santos, Cláudia; Keizer, Jan Jacob; Alexandre Diogo, Paulo; Nunes, João Pedro

2016-04-01

In recent decades, water availability for human consumption has faced major constraints due to increasing pollution and reduced water availability. Water resources availability can gain additional stresses and pressures in the context of potential climate change scenarios. For the last decades, the climate change paradigm has been the scope of many researchers and the focus of decision makers, policies and environmental/climate legislation. Decision-makers face a wide range of constrains, as they are forced to define new strategies that merge planning, management and climate change adaptations. In turn, decision-makers must create integrated strategies aiming at the sustainable use of resources. There are multiple uncertainties associated with climate change impact assessment and water resources. Typically, most studies have dealt with uncertainties in emission scenarios and resulting socio-economic conditions, including land-use and water use. Less frequently, studies have address the disparities between the future climates generated by climate models for the same greenhouse gas concentrations; and the uncertainties related with the limited knowledge of how watersheds work, which also limits the capacity to simulate them with models. Therefore, the objective of this study is to apply the SWAT (Soil and Water Assessment Tool) hydrological model to a catchment in Alentejo, southern Portugal; and to evaluate the uncertainty associated both to the calibration of hydrological models and the use of different climate change scenarios and models (a combination of 4 GCM (General Circulation Models) and 1 RCM (Regional Circulation Models) for the scenarios RCP 4.5 and 8.5. The Alentejo region is highly vulnerable to the effects of potential climate changes with particular focus on water resources availability, despite several reservoirs used for freshwater supply and agriculture irrigation (e.g. the Alqueva reservoir - the largest artificial lake of the Iberian Peninsula). Here the SWAT2012 model was applied to the catchment of Monte Novo and Vigia. The Monte Novo and Vigia reservoirs were selected due to their importance for the district of Évora, respectively for urban water supply and irrigation. The catchment is a multipurpose reservoir system that covers an area of about 81473 ha and drains into the Alqueva reservoir (25.000 ha). The SWAT2012 model was run for 1973-2012. The calibration routines were conducted on a monthly basis using the SWATCUP. The calibration performance rating is expressed by: NSE 0.89, bR² 0.89, Pbias 7.29 (Vigia) and NSE 0.84, bR² 0.83, Pbias 6.29 (Monte Novo). Expected results are a generalized decrease of water availability in the basin, more intense under the scenario RCP 8.5. However the uncertainty related to the use of different climate change models show different outcomes, which may be considered for the strategies to be adopted. We will take advantage of SWAT's automatic calibration capacities to explore how multiple interpretations of present-day hydrological processes could lead to different outputs in future climate scenarios, and compare this uncertainty with other sources of uncertainty related with future scenarios or different outputs from climate models.

Merging Methods to Manage Uncertainty: Combining Simulation Modeling and Scenario Planning to Inform Resource Management Under Climate Change

NASA Astrophysics Data System (ADS)

Miller, B. W.; Schuurman, G. W.; Symstad, A.; Fisichelli, N. A.; Frid, L.

2017-12-01

Managing natural resources in this era of anthropogenic climate change is fraught with uncertainties around how ecosystems will respond to management actions and a changing climate. Scenario planning (oftentimes implemented as a qualitative, participatory exercise for exploring multiple possible futures) is a valuable tool for addressing this challenge. However, this approach may face limits in resolving responses of complex systems to altered climate and management conditions, and may not provide the scientific credibility that managers often require to support actions that depart from current practice. Quantitative information on projected climate changes and ecological responses is rapidly growing and evolving, but this information is often not at a scale or in a form that is `actionable' for resource managers. We describe a project that sought to create usable information for resource managers in the northern Great Plains by combining qualitative and quantitative methods. In particular, researchers, resource managers, and climate adaptation specialists co-produced a simulation model in conjunction with scenario planning workshops to inform natural resource management in southwest South Dakota. Scenario planning for a wide range of resources facilitated open-minded thinking about a set of divergent and challenging, yet relevant and plausible, climate scenarios and management alternatives that could be implemented in the simulation. With stakeholder input throughout the process, we built a simulation of key vegetation types, grazing, exotic plants, fire, and the effects of climate and management on rangeland productivity and composition. By simulating multiple land management jurisdictions, climate scenarios, and management alternatives, the model highlighted important tradeoffs between herd sizes and vegetation composition, and between the short- versus long-term costs of invasive species management. It also identified impactful uncertainties related to the effects of fire and grazing on vegetation. Ultimately, this integrative and iterative approach yielded counter-intuitive and surprising findings, and resulted in a more tractable set of possible futures for resource management planning.

Estimating uncertainty and its temporal variation related to global climate models in quantifying climate change impacts on hydrology

NASA Astrophysics Data System (ADS)

Shen, Mingxi; Chen, Jie; Zhuan, Meijia; Chen, Hua; Xu, Chong-Yu; Xiong, Lihua

2018-01-01

Uncertainty estimation of climate change impacts on hydrology has received much attention in the research community. The choice of a global climate model (GCM) is usually considered as the largest contributor to the uncertainty of climate change impacts. The temporal variation of GCM uncertainty needs to be investigated for making long-term decisions to deal with climate change. Accordingly, this study investigated the temporal variation (mainly long-term) of uncertainty related to the choice of a GCM in predicting climate change impacts on hydrology by using multi-GCMs over multiple continuous future periods. Specifically, twenty CMIP5 GCMs under RCP4.5 and RCP8.5 emission scenarios were adapted to adequately represent this uncertainty envelope, fifty-one 30-year future periods moving from 2021 to 2100 with 1-year interval were produced to express the temporal variation. Future climatic and hydrological regimes over all future periods were compared to those in the reference period (1971-2000) using a set of metrics, including mean and extremes. The periodicity of climatic and hydrological changes and their uncertainty were analyzed using wavelet analysis, while the trend was analyzed using Mann-Kendall trend test and regression analysis. The results showed that both future climate change (precipitation and temperature) and hydrological response predicted by the twenty GCMs were highly uncertain, and the uncertainty increased significantly over time. For example, the change of mean annual precipitation increased from 1.4% in 2021-2050 to 6.5% in 2071-2100 for RCP4.5 in terms of the median value of multi-models, but the projected uncertainty reached 21.7% in 2021-2050 and 25.1% in 2071-2100 for RCP4.5. The uncertainty under a high emission scenario (RCP8.5) was much larger than that under a relatively low emission scenario (RCP4.5). Almost all climatic and hydrological regimes and their uncertainty did not show significant periodicity at the P = .05 significance level, but their temporal variation could be well modeled by using the fourth-order polynomial. Overall, this study further emphasized the importance of using multiple GCMs for studying climate change impacts on hydrology. Furthermore, the temporal variation of uncertainty sourced from GCMs should be given more attention.

Influence of climate drivers on colonization and extinction dynamics of wetland-dependent species

USGS Publications Warehouse

Ray, Andrew M.; Gould, William R.; Hossack, Blake R.; Sepulveda, Adam; Thoma, David P.; Patla, Debra A.; Daley, Rob; Al-Chokhachy, Robert K.

2016-01-01

Freshwater wetlands are particularly vulnerable to climate change. Specifically, changes in temperature, precipitation, and evapotranspiration (i.e., climate drivers) are likely to alter flooding regimes of wetlands and affect the vital rates, abundance, and distributions of wetland-dependent species. Amphibians may be among the most climate-sensitive wetland-dependent groups, as many species rely on shallow or intermittently flooded wetland habitats for breeding. Here, we integrated multiple years of high-resolution gridded climate and amphibian monitoring data from Grand Teton and Yellowstone National Parks to explicitly model how variations in climate drivers and habitat conditions affect the occurrence and breeding dynamics (i.e., annual extinction and colonization rates) of amphibians. Our results showed that models incorporating climate drivers outperformed models of amphibian breeding dynamics that were exclusively habitat based. Moreover, climate-driven variation in extinction rates, but not colonization rates, disproportionately influenced amphibian occupancy in monitored wetlands. Long-term monitoring from national parks coupled with high-resolution climate data sets will be crucial to describing population dynamics and characterizing the sensitivity of amphibians and other wetland-dependent species to climate change. Further, long-term monitoring of wetlands in national parks will help reduce uncertainty surrounding wetland resources and strengthen opportunities to make informed, science-based decisions that have far-reaching benefits.

Does what you know matter? Investigating the relationship between mental models of climate change and pro-environmental behaviors

NASA Astrophysics Data System (ADS)

Davis, R.

2013-12-01

The purpose of this study is to test the conjecture that environmentally sustainable decisions and behaviors are related to individuals' conceptions of the natural world, in this case climate change; individuals' attitudes towards climate change; and the situations in which these decisions are made. The nature of mental models is an ongoing subject of disagreement. Some argue that mental models are coherent theories, much like scientific theories, that individuals employ systematically when reasoning about the world (Gopnik & Meltzoff, 1998). Others maintain that mental models are cobbled together from fragmented collections of ideas that are only loosely connected and context dependent (Disessa, 1988; Minstrell, 2000). It is likely that individuals sometimes reason about complex phenomena using systematic mental models and at other times reason using knowledge that is organized in fragmented pieces (Steedle & Shavelson, 2009). Thus, in measuring mental models of complex environmental systems, such as climate change, the assumption of systematicity may not be justified. Individuals may apply certain chains of reasoning in some contexts but not in others. The current study hypothesizes that an accurate mental model of climate change enables an individual to make effective evaluative judgments of environmental behavior options. The more an individual's mental model resembles that of an expert, the more consistent, accurate and automatic these judgments become. However, an accurate mental model is not sufficient to change environmental behavior. Real decisions and behaviors are products of a person-situation interaction: an interplay between psychosocial factors (such as knowledge and attitudes) and the situation in which the decision is made. This study investigates the relationship between both psychosocial and situational factors for climate change decisions. Data was collected from 436 adult participants through an online survey. The survey was comprised of demographic questions; three discreet instruments measuring (1) mental models of climate change, (2) attitudes and beliefs about climate change, and (3) self-reported behaviors; and an experimental intervention, followed by a behavioral intention question. Latent class analysis (LCA) and item-response theory (IRT) will be employed to analyze multiple-choice responses to the mental model survey to create groupings of individuals assumed to hold similar mental of climate change. A principal component analysis (PCA) using oblique rotation was employed to identify five scales (Chronbach's alpha > 0.80) within the attitude/belief instrument. Total and sub-scale scores were also calculated for self-reported behaviors. The relationships between mental models, attitudes and behaviors will be analyzed using multiple regression models. This work presents not only the development and validation of three novel instruments for accurately and efficiently measuring mental models, attitudes, and self-reported behaviors, but also provides insight into the types of mental models individuals hold. Understanding how climate change is conceptualized and how such knowledge influences attitudes and behaviors gives educators tools for guiding students towards more expert understandings while also enabling environmentalists to craft more effective messages.

Globally Increased Crop Growth and Cropping Intensity from the Long-Term Satellite-Based Observations

NASA Astrophysics Data System (ADS)

Chen, Bin

2018-04-01

Understanding the spatiotemporal change trend of global crop growth and multiple cropping system under climate change scenarios is a critical requirement for supporting the food security issue that maintains the function of human society. Many studies have predicted the effects of climate changes on crop production using a combination of filed studies and models, but there has been limited evidence relating decadal-scale climate change to global crop growth and the spatiotemporal distribution of multiple cropping system. Using long-term satellite-derived Normalized Difference Vegetation Index (NDVI) and observed climate data from 1982 to 2012, we investigated the crop growth trend, spatiotemporal pattern trend of agricultural cropping intensity, and their potential correlations with respect to the climate change drivers at a global scale. Results show that 82.97 % of global cropland maximum NDVI witnesses an increased trend while 17.03 % of that shows a decreased trend over the past three decades. The spatial distribution of multiple cropping system is observed to expand from lower latitude to higher latitude, and the increased cropping intensity is also witnessed globally. In terms of regional major crop zones, results show that all nine selected zones have an obvious upward trend of crop maximum NDVI (p < 0.001), and as for climatic drivers, the gradual temperature and precipitation changes have had a measurable impact on the crop growth trend.

A Regional Climate Model Evaluation System based on Satellite and other Observations

NASA Astrophysics Data System (ADS)

Lean, P.; Kim, J.; Waliser, D. E.; Hall, A. D.; Mattmann, C. A.; Granger, S. L.; Case, K.; Goodale, C.; Hart, A.; Zimdars, P.; Guan, B.; Molotch, N. P.; Kaki, S.

2010-12-01

Regional climate models are a fundamental tool needed for downscaling global climate simulations and projections, such as those contributing to the Coupled Model Intercomparison Projects (CMIPs) that form the basis of the IPCC Assessment Reports. The regional modeling process provides the means to accommodate higher resolution and a greater complexity of Earth System processes. Evaluation of both the global and regional climate models against observations is essential to identify model weaknesses and to direct future model development efforts focused on reducing the uncertainty associated with climate projections. However, the lack of reliable observational data and the lack of formal tools are among the serious limitations to addressing these objectives. Recent satellite observations are particularly useful as they provide a wealth of information on many different aspects of the climate system, but due to their large volume and the difficulties associated with accessing and using the data, these datasets have been generally underutilized in model evaluation studies. Recognizing this problem, NASA JPL / UCLA is developing a model evaluation system to help make satellite observations, in conjunction with in-situ, assimilated, and reanalysis datasets, more readily accessible to the modeling community. The system includes a central database to store multiple datasets in a common format and codes for calculating predefined statistical metrics to assess model performance. This allows the time taken to compare model simulations with satellite observations to be reduced from weeks to days. Early results from the use this new model evaluation system for evaluating regional climate simulations over California/western US regions will be presented.

Multi-model analysis of the Atlantic influence on Southern Amazon rainfall

DOE PAGES

Yoon, Jin -Ho

2015-12-07

Amazon rainfall is subject to year-to-year fluctuation resulting in drought and flood in various intensities. A major climatic driver of the interannual variation of the Amazon rainfall is El Niño/Southern Oscillation. Also, the Sea Surface Temperature over the Atlantic Ocean is identified as an important climatic driver on the Amazon water cycle. Previously, observational datasets were used to support the Atlantic influence on Amazon rainfall. Furthermore, it is found that multiple global climate models do reproduce the Atlantic-Amazon link robustly. However, there exist differences in rainfall response, which primarily depends on the climatological rainfall amount.

Spatially explicit watershed modeling: tracking water, mercury and nitrogen in multiple systems under diverse conditions

EPA Science Inventory

Environmental decision-making and the influences of various stressors, such as landscape and climate changes on water quantity and quality, requires the application of environmental modeling. Spatially explicit environmental and watershed-scale models using GIS as a base framewor...

Effects of different representations of transport in the new EMAC-SWIFT chemistry climate model

NASA Astrophysics Data System (ADS)

Scheffler, Janice; Langematz, Ulrike; Wohltmann, Ingo; Kreyling, Daniel; Rex, Markus

2017-04-01

It is well known that the representation of atmospheric ozone chemistry in weather and climate models is essential for a realistic simulation of the atmospheric state. Interactively coupled chemistry climate models (CCMs) provide a means to realistically simulate the interaction between atmospheric chemistry and dynamics. The calculation of chemistry in CCMs, however, is computationally expensive which renders the use of complex chemistry models not suitable for ensemble simulations or simulations with multiple climate change scenarios. In these simulations ozone is therefore usually prescribed as a climatological field or included by incorporating a fast linear ozone scheme into the model. While prescribed climatological ozone fields are often not aligned with the modelled dynamics, a linear ozone scheme may not be applicable for a wide range of climatological conditions. An alternative approach to represent atmospheric chemistry in climate models which can cope with non-linearities in ozone chemistry and is applicable to a wide range of climatic states is the Semi-empirical Weighted Iterative Fit Technique (SWIFT) that is driven by reanalysis data and has been validated against observational satellite data and runs of a full Chemistry and Transport Model. SWIFT has been implemented into the ECHAM/MESSy (EMAC) chemistry climate model that uses a modular approach to climate modelling where individual model components can be switched on and off. When using SWIFT in EMAC, there are several possibilities to represent the effect of transport inside the polar vortex: the semi-Lagrangian transport scheme of EMAC and a transport parameterisation that can be useful when using SWIFT in models not having transport of their own. Here, we present results of equivalent simulations with different handling of transport, compare with EMAC simulations with full interactive chemistry and evaluate the results with observations.

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.

Impacts of Future Climate Change on California Perennial Crop Yields: Model Projections with Climate and Crop Uncertainties

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

Lobell, D; Field, C; Cahill, K

2006-01-10

Most research on the agricultural impacts of climate change has focused on the major annual crops, yet perennial cropping systems are less adaptable and thus potentially more susceptible to damage. Improved assessments of yield responses to future climate are needed to prioritize adaptation strategies in the many regions where perennial crops are economically and culturally important. These impact assessments, in turn, must rely on climate and crop models that contain often poorly defined uncertainties. We evaluated the impact of climate change on six major perennial crops in California: wine grapes, almonds, table grapes, oranges, walnuts, and avocados. Outputs from multiplemore » climate models were used to evaluate climate uncertainty, while multiple statistical crop models, derived by resampling historical databases, were used to address crop response uncertainties. We find that, despite these uncertainties, climate change in California is very likely to put downward pressure on yields of almonds, walnuts, avocados, and table grapes by 2050. Without CO{sub 2} fertilization or adaptation measures, projected losses range from 0 to >40% depending on the crop and the trajectory of climate change. Climate change uncertainty generally had a larger impact on projections than crop model uncertainty, although the latter was substantial for several crops. Opportunities for expansion into cooler regions are identified, but this adaptation would require substantial investments and may be limited by non-climatic constraints. Given the long time scales for growth and production of orchards and vineyards ({approx}30 years), climate change should be an important factor in selecting perennial varieties and deciding whether and where perennials should be planted.« less

Global change and terrestrial plant community dynamics

DOE PAGES

Franklin, Janet; Serra-Diaz, Josep M.; Syphard, Alexandra D.; ...

2016-02-29

Anthropogenic drivers of global change include rising atmospheric concentrations of carbon dioxide and other greenhouse gasses and resulting changes in the climate, as well as nitrogen deposition, biotic invasions, altered disturbance regimes, and land-use change. Predicting the effects of global change on terrestrial plant communities is crucial because of the ecosystem services vegetation provides, from climate regulation to forest products. In this article, we present a framework for detecting vegetation changes and attributing them to global change drivers that incorporates multiple lines of evidence from spatially extensive monitoring networks, distributed experiments, remotely sensed data, and historical records. Based on amore » literature review, we summarize observed changes and then describe modeling tools that can forecast the impacts of multiple drivers on plant communities in an era of rapid change. Observed responses to changes in temperature, water, nutrients, land use, and disturbance show strong sensitivity of ecosystem productivity and plant population dynamics to water balance and long-lasting effects of disturbance on plant community dynamics. Persistent effects of land-use change and human-altered fire regimes on vegetation can overshadow or interact with climate change impacts. Models forecasting plant community responses to global change incorporate shifting ecological niches, population dynamics, species interactions, spatially explicit disturbance, ecosystem processes, and plant functional responses. Lastly, monitoring, experiments, and models evaluating multiple change drivers are needed to detect and predict vegetation changes in response to 21st century global change.« less

Modelling marine community responses to climate-driven species redistribution to guide monitoring and adaptive ecosystem-based management.

PubMed

Marzloff, Martin Pierre; Melbourne-Thomas, Jessica; Hamon, Katell G; Hoshino, Eriko; Jennings, Sarah; van Putten, Ingrid E; Pecl, Gretta T

2016-07-01

As a consequence of global climate-driven changes, marine ecosystems are experiencing polewards redistributions of species - or range shifts - across taxa and throughout latitudes worldwide. Research on these range shifts largely focuses on understanding and predicting changes in the distribution of individual species. The ecological effects of marine range shifts on ecosystem structure and functioning, as well as human coastal communities, can be large, yet remain difficult to anticipate and manage. Here, we use qualitative modelling of system feedback to understand the cumulative impacts of multiple species shifts in south-eastern Australia, a global hotspot for ocean warming. We identify range-shifting species that can induce trophic cascades and affect ecosystem dynamics and productivity, and evaluate the potential effectiveness of alternative management interventions to mitigate these impacts. Our results suggest that the negative ecological impacts of multiple simultaneous range shifts generally add up. Thus, implementing whole-of-ecosystem management strategies and regular monitoring of range-shifting species of ecological concern are necessary to effectively intervene against undesirable consequences of marine range shifts at the regional scale. Our study illustrates how modelling system feedback with only limited qualitative information about ecosystem structure and range-shifting species can predict ecological consequences of multiple co-occurring range shifts, guide ecosystem-based adaptation to climate change and help prioritise future research and monitoring. © 2016 John Wiley & Sons Ltd.

Global change and terrestrial plant community dynamics

PubMed Central

Franklin, Janet; Serra-Diaz, Josep M.; Syphard, Alexandra D.; Regan, Helen M.

2016-01-01

Anthropogenic drivers of global change include rising atmospheric concentrations of carbon dioxide and other greenhouse gasses and resulting changes in the climate, as well as nitrogen deposition, biotic invasions, altered disturbance regimes, and land-use change. Predicting the effects of global change on terrestrial plant communities is crucial because of the ecosystem services vegetation provides, from climate regulation to forest products. In this paper, we present a framework for detecting vegetation changes and attributing them to global change drivers that incorporates multiple lines of evidence from spatially extensive monitoring networks, distributed experiments, remotely sensed data, and historical records. Based on a literature review, we summarize observed changes and then describe modeling tools that can forecast the impacts of multiple drivers on plant communities in an era of rapid change. Observed responses to changes in temperature, water, nutrients, land use, and disturbance show strong sensitivity of ecosystem productivity and plant population dynamics to water balance and long-lasting effects of disturbance on plant community dynamics. Persistent effects of land-use change and human-altered fire regimes on vegetation can overshadow or interact with climate change impacts. Models forecasting plant community responses to global change incorporate shifting ecological niches, population dynamics, species interactions, spatially explicit disturbance, ecosystem processes, and plant functional responses. Monitoring, experiments, and models evaluating multiple change drivers are needed to detect and predict vegetation changes in response to 21st century global change. PMID:26929338

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

Franklin, Janet; Serra-Diaz, Josep M.; Syphard, Alexandra D.

Anthropogenic drivers of global change include rising atmospheric concentrations of carbon dioxide and other greenhouse gasses and resulting changes in the climate, as well as nitrogen deposition, biotic invasions, altered disturbance regimes, and land-use change. Predicting the effects of global change on terrestrial plant communities is crucial because of the ecosystem services vegetation provides, from climate regulation to forest products. In this article, we present a framework for detecting vegetation changes and attributing them to global change drivers that incorporates multiple lines of evidence from spatially extensive monitoring networks, distributed experiments, remotely sensed data, and historical records. Based on amore » literature review, we summarize observed changes and then describe modeling tools that can forecast the impacts of multiple drivers on plant communities in an era of rapid change. Observed responses to changes in temperature, water, nutrients, land use, and disturbance show strong sensitivity of ecosystem productivity and plant population dynamics to water balance and long-lasting effects of disturbance on plant community dynamics. Persistent effects of land-use change and human-altered fire regimes on vegetation can overshadow or interact with climate change impacts. Models forecasting plant community responses to global change incorporate shifting ecological niches, population dynamics, species interactions, spatially explicit disturbance, ecosystem processes, and plant functional responses. Lastly, monitoring, experiments, and models evaluating multiple change drivers are needed to detect and predict vegetation changes in response to 21st century global change.« less

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.

A multi-disciplinary approach for the integrated assessment of water alterations under climate change

NASA Astrophysics Data System (ADS)

Sperotto, Anna; Torresan, Silvia; Molina, Jose Luis; Pulido Velazquez, Manuel; Critto, Andrea; Marcomini, Antonio

2017-04-01

Understanding the co-evolution and interrelations between natural and human pressures on water systems is required to ensure a sustainable management of resources under uncertain climate change conditions. To pursue multi-disciplinary research is therefore necessary to consider the multiplicity of stressors affecting water resources, take into account alternative perspectives (i.e. social, economic and environmental objective and priorities) and deal with uncertainty which characterize climate change scenarios. However, approaches commonly adopted in water quality assessment are predominantly mono-disciplinary, single-stressors oriented and apply concepts and models specific of different academic disciplines (e.g. physics, hydrology, ecology, sociology, economy) which, in fact, seldom shed their conceptual blinders failing to provide truly integrated results. In this context, the paper discusses the benefits and limits of adopting a multi-disciplinary approach where different knowledge domains collaborate and quantitative and qualitative information, coming from multiple conceptual and model-based research, are integrated in a harmonic manner. Specifically, Bayesian Networks are used as meta-modelling tool for structuring and combining the probabilistic information available in existing hydrological models, climate change and land use projections, historical observations and expert opinion. The developed network allows to perform a stochastic multi-risk assessment considering the interlacing between climate (i.e. irregularities in water regime) and land use changes (i.e. agriculture, urbanization) and their cascading impacts on water quality parameters (i.e. nutrients loadings). Main objective of the model is the development of multi-risk scenarios to assess and communicate the probability of not meeting a "Good chemical water status" over future timeframe taking into account projected climatic and not climatic conditions. The outcomes are finally used to identify tradeoffs between different water uses and perspectives, thus promoting the implementation of best practices for adaptation and management with ancillary co-benefits and cross-sectoral implications (i.e. tourism, fishing, biodiversity). Some preliminary results, describing the application of the model in the Dese-Zero river estuary, one of the main tributaries of the Venice Lagoon in Italy, will be here presented and discussed.

Common Warming Pattern Emerges Irrespective of Forcing Location

NASA Astrophysics Data System (ADS)

Kang, Sarah M.; Park, Kiwoong; Jin, Fei-Fei; Stuecker, Malte F.

2017-10-01

The Earth's climate is changing due to the existence of multiple radiative forcing agents. It is under question whether different forcing agents perturb the global climate in a distinct way. Previous studies have demonstrated the existence of similar climate response patterns in response to aerosol and greenhouse gas (GHG) forcings. In this study, the sensitivity of tropospheric temperature response patterns to surface heating distributions is assessed by forcing an atmospheric general circulation model coupled to an aquaplanet slab ocean with a wide range of possible forcing patterns. We show that a common climate pattern emerges in response to localized forcing at different locations. This pattern, characterized by enhanced warming in the tropical upper troposphere and the polar lower troposphere, resembles the historical trends from observations and models as well as the future projections. Atmospheric dynamics in combination with thermodynamic air-sea coupling are primarily responsible for shaping this pattern. Identifying this common pattern strengthens our confidence in the projected response to GHG and aerosols in complex climate models.

Integrated assessment of water-power grid systems under changing climate

NASA Astrophysics Data System (ADS)

Yan, E.; Zhou, Z.; Betrie, G.

2017-12-01

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. In this presentation, we are focusing on recent improvement in model development of thermoelectric power plant water use simulator, power grid operation and cost optimization model, and model integration that facilitate interaction among water and electricity generation under extreme climate events. A process based thermoelectric power water use simulator includes heat-balance, climate, and cooling system modules that account for power plant characteristics, fuel types, and cooling technology. The model is validated with more than 800 power plants of fossil-fired, nuclear and gas-turbine power plants with different cooling systems. The power grid operation and cost optimization model was implemented for a selected regional in the Midwest. The case study will be demonstrated to evaluate the sensitivity and resilience of thermoelectricity generation and power grid under various climate and hydrologic extremes and potential economic consequences.

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...

Using the storm water management model to predict urban headwater stream hydrological response to climate and land cover change

Treesearch

J.Y. Wu; J.R. Thompson; R.K. Kolka; K.J. Franz; T.W. Stewart

2013-01-01

Streams are natural features in urban landscapes that can provide ecosystem services for urban residents. However, urban streams are under increasing pressure caused by multiple anthropogenic impacts, including increases in human population and associated impervious surface area, and accelerated climate change. The ability to anticipate these changes and better...

Predicted effects of gypsy moth defoliation and climate change on forest carbon dynamics in the New Jersey Pine Barrens

Treesearch

Alec M. Kretchun; Robert M. Scheller; Melissa S. Lucash; Kenneth L. Clark; John Hom; Steve Van Tuyl; Michael L. Fine

2014-01-01

Disturbance regimes within temperate forests can significantly impact carbon cycling. Additionally, projected climate change in combination with multiple, interacting disturbance effects may disrupt the capacity of forests to act as carbon sinks at large spatial and temporal scales. We used a spatially explicit forest succession and disturbance model, LANDIS-II, to...

Approaches to evaluating climate change impacts on species: A guide to initiating the adaptation planning process

Treesearch

Erika L. Rowland; Jennifer E. Davison; Lisa J. Graumlich

2011-01-01

Assessing the impact of climate change on species and associated management objectives is a critical initial step for engaging in the adaptation planning process. Multiple approaches are available. While all possess limitations to their application associated with the uncertainties inherent in the data and models that inform their results, conducting and incorporating...

Symbiont diversity may help coral reefs survive moderate climate change.

PubMed

Baskett, Marissa L; Gaines, Steven D; Nisbet, Roger M

2009-01-01

Given climate change, thermal stress-related mass coral-bleaching events present one of the greatest anthropogenic threats to coral reefs. While corals and their symbiotic algae may respond to future temperatures through genetic adaptation and shifts in community compositions, the climate may change too rapidly for coral response. To test this potential for response, here we develop a model of coral and symbiont ecological dynamics and symbiont evolutionary dynamics. Model results without variation in symbiont thermal tolerance predict coral reef collapse within decades under multiple future climate scenarios, consistent with previous threshold-based predictions. However, model results with genetic or community-level variation in symbiont thermal tolerance can predict coral reef persistence into the next century, provided low enough greenhouse gas emissions occur. Therefore, the level of greenhouse gas emissions will have a significant effect on the future of coral reefs, and accounting for biodiversity and biological dynamics is vital to estimating the size of this effect.

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.;

Robust Spring Drying in the Southwestern U.S. and Seasonal Migration of Wet/Dry Patterns in a Warmer Climate

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

Gao, Yang; Leung, Lai-Yung R.; Lu, Jian

2014-03-16

This study compares climate simulations over the United States produced by a regional climate model with the driving global climate simulations as well as a large multi-model ensemble of global climate simulations to investigate robust changes in water availability (precipitation (P) – evapotranspiration (E)). A robust spring dry signal across multiple models is identified in the Southwest that results from a decrease in P and an increase in E in the future. In the boreal winter and summer, the prominent changes in P – E are associated with a north – south dipole pattern, while in spring, the prominent changesmore » in P – E appear as an east – west dipole pattern. The progression of the north – south and east – west dipole patterns through the seasons manifests clearly as a seasonal “clockwise” migration of wet/dry patterns, which is shown to be a robust feature of water availability changes in the US consistent across regional and global climate simulations.« less

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.

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.

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.

Benefits of Greenhouse Gas Mitigation on the Supply, Management, and Use of Water Resources in the United States

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

Strzepek, K.; Neumann, Jim; Smith, Joel

Climate change impacts on water resources in the U.S. are likely to be far-reaching and substantial, because the water sector spans many parts of the economy, from supply and demand for agriculture, industry, energy production, transportation and municipal use to damages from natural hazards. This paper provides impact and damage estimates from five water resource-related models in the CIRA frame work, addressing drought risk, flooding damages, water supply and demand, and global water scarcity. The four models differ in the water system assessed, their spatial scale, and the units of assessment, but together they provide a quantitative and descriptive richnessmore » in characterizing water resource sector effects of climate change that no single model can capture. The results also address the sensitivity of these estimates to greenhouse gas emission scenarios, climate sensitivity alternatives, and global climate model selection. While calculating the net impact of climate change on the water sector as a whole may be impractical, because each of the models applied here uses a consistent set of climate scenarios, broad conclusions can be drawn regarding the patterns of change and the benefits of GHG mitigation policies for the water sector. Two key findings emerge: 1) climate mitigation policy substantially reduces the impact of climate change on the water sector across multiple dimensions; and 2) the more managed the water resources system, the more tempered the climate change impacts and the resulting reduction of impacts from climate mitigation policies.« less

Benefits of Greenhouse Gas Mitigation on the Supply, Management, and Use of Water Resources in the United States

DOE PAGES

Strzepek, K.; Neumann, Jim; Smith, Joel; ...

2014-11-29

Climate change impacts on water resources in the U.S. are likely to be far-reaching and substantial, because the water sector spans many parts of the economy, from supply and demand for agriculture, industry, energy production, transportation and municipal use to damages from natural hazards. This paper provides impact and damage estimates from five water resource-related models in the CIRA frame work, addressing drought risk, flooding damages, water supply and demand, and global water scarcity. The four models differ in the water system assessed, their spatial scale, and the units of assessment, but together they provide a quantitative and descriptive richnessmore » in characterizing water resource sector effects of climate change that no single model can capture. The results also address the sensitivity of these estimates to greenhouse gas emission scenarios, climate sensitivity alternatives, and global climate model selection. While calculating the net impact of climate change on the water sector as a whole may be impractical, because each of the models applied here uses a consistent set of climate scenarios, broad conclusions can be drawn regarding the patterns of change and the benefits of GHG mitigation policies for the water sector. Two key findings emerge: 1) climate mitigation policy substantially reduces the impact of climate change on the water sector across multiple dimensions; and 2) the more managed the water resources system, the more tempered the climate change impacts and the resulting reduction of impacts from climate mitigation policies.« less

Computational data sciences for assessment and prediction of climate extremes

NASA Astrophysics Data System (ADS)

Ganguly, A. R.

2011-12-01

Climate extremes may be defined inclusively as severe weather events or large shifts in global or regional weather patterns which may be caused or exacerbated by natural climate variability or climate change. This area of research arguably represents one of the largest knowledge-gaps in climate science which is relevant for informing resource managers and policy makers. While physics-based climate models are essential in view of non-stationary and nonlinear dynamical processes, their current pace of uncertainty reduction may not be adequate for urgent stakeholder needs. The structure of the models may in some cases preclude reduction of uncertainty for critical processes at scales or for the extremes of interest. On the other hand, methods based on complex networks, extreme value statistics, machine learning, and space-time data mining, have demonstrated significant promise to improve scientific understanding and generate enhanced predictions. When combined with conceptual process understanding at multiple spatiotemporal scales and designed to handle massive data, interdisciplinary data science methods and algorithms may complement or supplement physics-based models. Specific examples from the prior literature and our ongoing work suggests how data-guided improvements may be possible, for example, in the context of ocean meteorology, climate oscillators, teleconnections, and atmospheric process understanding, which in turn can improve projections of regional climate, precipitation extremes and tropical cyclones in an useful and interpretable fashion. A community-wide effort is motivated to develop and adapt computational data science tools for translating climate model simulations to information relevant for adaptation and policy, as well as for improving our scientific understanding of climate extremes from both observed and model-simulated data.

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.

Water resources sensitivity to the isolated effects of land use, water demand and climate change under 2 degree global warming

NASA Astrophysics Data System (ADS)

Bisselink, Berny; Bernhard, Jeroen; de Roo, Ad

2017-04-01

One of the key impacts of global change are the future water resources. These water resources are influenced by changes in land use (LU), water demand (WD) and climate change. Recent developments in scenario modelling opened new opportunities for an integrated assessment. However, for identifying water resource management strategies it is helpful to focus on the isolated effects of possible changes in LU, WD and climate that may occur in the near future. In this work, we quantify the isolated contribution of LU, WD and climate to the integrated total water resources assuming a linear model behavior. An ensemble of five EURO-CORDEX RCP8.5 climate projections for the 31-year periods centered on the year of exceeding the global-mean temperature of 2 degree is used to drive the fully distributed hydrological model LISFLOOD for multiple river catchments in Europe. The JRC's Land Use Modelling Platform LUISA was used to obtain a detailed pan-European reference land use scenario until 2050. Water demand is estimated based on socio-economic (GDP, population estimates etc.), land use and climate projections as well. For each climate projection, four model runs have been performed including an integrated (LU, WD and climate) simulation and other three simulations to isolate the effect of LU, WD and climate. Changes relative to the baseline in terms of water resources indicators of the ensemble means of the 2 degree warming period and their associated uncertainties will reveal the integrated and isolated effect of LU, WD and climate change on water resources.

Major challenges for correlational ecological niche model projections to future climate conditions.

PubMed

Peterson, A Townsend; Cobos, Marlon E; Jiménez-García, Daniel

2018-06-20

Species-level forecasts of distributional potential and likely distributional shifts, in the face of changing climates, have become popular in the literature in the past 20 years. Many refinements have been made to the methodology over the years, and the result has been an approach that considers multiple sources of variation in geographic predictions, and how that variation translates into both specific predictions and uncertainty in those predictions. Although numerous previous reviews and overviews of this field have pointed out a series of assumptions and caveats associated with the methodology, three aspects of the methodology have important impacts but have not been treated previously in detail. Here, we assess those three aspects: (1) effects of niche truncation on model transfers to future climate conditions, (2) effects of model selection procedures on future-climate transfers of ecological niche models, and (3) relative contributions of several factors (replicate samples of point data, general circulation models, representative concentration pathways, and alternative model parameterizations) to overall variance in model outcomes. Overall, the view is one of caution: although resulting predictions are fascinating and attractive, this paradigm has pitfalls that may bias and limit confidence in niche model outputs as regards the implications of climate change for species' geographic distributions. © 2018 New York Academy of Sciences.

Application of data on climate extremes for the southwestern United States

NASA Astrophysics Data System (ADS)

Redmond, K. T.; Fleishman, E.; Cayan, D. R.; Daudert, B.; Gershunov, A.

2015-12-01

We are improving the scientific capacity to evaluate responses of natural resources to climate extremes. We also are enhancing a platform for derivation of and access to customized climate information for the full extent or any subset of the southwestern United States. Extreme climate can have substantial effects on species, ecological and evolutionary processes, and the health of visitors to public lands. We are working with federal and state managers and with researchers who collaborate with decision-makers to use data on climate extremes to inform resource management. Current applications include sudden oak death, estuarine management, and fine-resolution manipulation of montane vegetation. To facilitate practical use of data on climate extremes, we are screening global climate models on the basis of their realism in representing natural regional patterns and extremes of temperature and precipitation, including those driven by El Niño and La Niña. We are assessing how well each model represents different climate elements. We also are delivering point and gridded observations and downscaled model projections, all at daily and 6 km resolution, on past and future climate extremes. Additionally, we are using the downscaled outputs to drive a hydrologic model and derive multiple probabilistic measures of water availability, flood, and drought. Moreover, we are extending the capacity of the Southwest Climate and Environmental Information Collaborative (SCENIC; wrcc.dri.edu/csc/scenic), a product developed by the Western Regional Climate Center, to provide access to diverse observed and simulated data on regional weather and climate, particularly on extremes.

Increasing water cycle extremes in California and relation to ENSO cycle under global warming

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

Yoon, Jin -Ho; Wang, S. -Y. Simon; Gillies, Robert R.

California has experienced its most severe drought in recorded history since the winter of 2013-2014. The long duration of drought has stressed statewide water resources and the economy, while fueling an extraordinary increase in wildfires. The effects of global warming on the regional climate include a hotter and drier climate, as well as earlier snowmelt, both of which exacerbate drought conditions. However, connections between a changing climate and how climate oscillations modulate regional water cycle extremes are not well understood. Here we analyze large-ensemble simulations of future climate change in California using the Community Earth System Model version 1 (CESM1)more » and multiple climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Both intense drought and excessive flooding are projected to increase by at least 50% toward the end of the 21st century. Furthermore, the projected increase in water cycle extremes is associated with tighter relation to El Niño and Southern Oscillation (ENSO), particularly extreme El Niño and La Niña events, which modulates California’s climate not only through its warm and cold phases, but also ENSO’s precursor patterns.« less

Increasing water cycle extremes in California and relation to ENSO cycle under global warming

DOE PAGES

Yoon, Jin -Ho; Wang, S. -Y. Simon; Gillies, Robert R.; ...

2015-10-21

California has experienced its most severe drought in recorded history since the winter of 2013-2014. The long duration of drought has stressed statewide water resources and the economy, while fueling an extraordinary increase in wildfires. The effects of global warming on the regional climate include a hotter and drier climate, as well as earlier snowmelt, both of which exacerbate drought conditions. However, connections between a changing climate and how climate oscillations modulate regional water cycle extremes are not well understood. Here we analyze large-ensemble simulations of future climate change in California using the Community Earth System Model version 1 (CESM1)more » and multiple climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Both intense drought and excessive flooding are projected to increase by at least 50% toward the end of the 21st century. Furthermore, the projected increase in water cycle extremes is associated with tighter relation to El Niño and Southern Oscillation (ENSO), particularly extreme El Niño and La Niña events, which modulates California’s climate not only through its warm and cold phases, but also ENSO’s precursor patterns.« less

A multi-model assessment of the co-benefits of climate mitigation for global air quality

NASA Astrophysics Data System (ADS)

Rao, Shilpa; Klimont, Zbigniew; Leitao, Joana; Riahi, Keywan; van Dingenen, Rita; Aleluia Reis, Lara; Calvin, Katherine; Dentener, Frank; Drouet, Laurent; Fujimori, Shinichiro; Harmsen, Mathijs; Luderer, Gunnar; Heyes, Chris; Strefler, Jessica; Tavoni, Massimo; van Vuuren, Detlef P.

2016-12-01

We present a model comparison study that combines multiple integrated assessment models with a reduced-form global air quality model to assess the potential co-benefits of global climate mitigation policies in relation to the World Health Organization (WHO) goals on air quality and health. We include in our assessment, a range of alternative assumptions on the implementation of current and planned pollution control policies. The resulting air pollution emission ranges significantly extend those in the Representative Concentration Pathways. Climate mitigation policies complement current efforts on air pollution control through technology and fuel transformations in the energy system. A combination of stringent policies on air pollution control and climate change mitigation results in 40% of the global population exposed to PM levels below the WHO air quality guideline; with the largest improvements estimated for India, China, and Middle East. Our results stress the importance of integrated multisector policy approaches to achieve the Sustainable Development Goals.

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.

Uncertainty of future projections of species distributions in mountainous regions.

PubMed

Tang, Ying; Winkler, Julie A; Viña, Andrés; Liu, Jianguo; Zhang, Yuanbin; Zhang, Xiaofeng; Li, Xiaohong; Wang, Fang; Zhang, Jindong; Zhao, Zhiqiang

2018-01-01

Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution.

Uncertainty of future projections of species distributions in mountainous regions

PubMed Central

Tang, Ying; Viña, Andrés; Liu, Jianguo; Zhang, Yuanbin; Zhang, Xiaofeng; Li, Xiaohong; Wang, Fang; Zhang, Jindong; Zhao, Zhiqiang

2018-01-01

Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution. PMID:29320501

Combined measurement and modeling of the hydrological impact of hydraulic redistribution using CLM4.5 at eight AmeriFlux sites

NASA Astrophysics Data System (ADS)

Fu, Congsheng; Wang, Guiling; Goulden, Michael L.; Scott, Russell L.; Bible, Kenneth; Cardon, Zoe G.

2016-05-01

Effects of hydraulic redistribution (HR) on hydrological, biogeochemical, and ecological processes have been demonstrated in the field, but the current generation of standard earth system models does not include a representation of HR. Though recent studies have examined the effect of incorporating HR into land surface models, few (if any) have done cross-site comparisons for contrasting climate regimes and multiple vegetation types via the integration of measurement and modeling. Here, we incorporated the HR scheme of Ryel et al. (2002) into the NCAR Community Land Model Version 4.5 (CLM4.5), and examined the ability of the resulting hybrid model to capture the magnitude of HR flux and/or soil moisture dynamics from which HR can be directly inferred, to assess the impact of HR on land surface water and energy budgets, and to explore how the impact may depend on climate regimes and vegetation conditions. Eight AmeriFlux sites with contrasting climate regimes and multiple vegetation types were studied, including the Wind River Crane site in Washington State, the Santa Rita Mesquite savanna site in southern Arizona, and six sites along the Southern California Climate Gradient. HR flux, evapotranspiration (ET), and soil moisture were properly simulated in the present study, even in the face of various uncertainties. Our cross-ecosystem comparison showed that the timing, magnitude, and direction (upward or downward) of HR vary across ecosystems, and incorporation of HR into CLM4.5 improved the model-measurement matches of evapotranspiration, Bowen ratio, and soil moisture particularly during dry seasons. Our results also reveal that HR has important hydrological impact in ecosystems that have a pronounced dry season but are not overall so dry that sparse vegetation and very low soil moisture limit HR.

Pleistocene climate, phylogeny, and climate envelope models: an integrative approach to better understand species' response to climate change.

PubMed

Lawing, A Michelle; Polly, P David

2011-01-01

Mean annual temperature reported by the Intergovernmental Panel on Climate Change increases at least 1.1°C to 6.4°C over the next 90 years. In context, a change in climate of 6°C is approximately the difference between the mean annual temperature of the Last Glacial Maximum (LGM) and our current warm interglacial. Species have been responding to changing climate throughout Earth's history and their previous biological responses can inform our expectations for future climate change. Here we synthesize geological evidence in the form of stable oxygen isotopes, general circulation paleoclimate models, species' evolutionary relatedness, and species' geographic distributions. We use the stable oxygen isotope record to develop a series of temporally high-resolution paleoclimate reconstructions spanning the Middle Pleistocene to Recent, which we use to map ancestral climatic envelope reconstructions for North American rattlesnakes. A simple linear interpolation between current climate and a general circulation paleoclimate model of the LGM using stable oxygen isotope ratios provides good estimates of paleoclimate at other time periods. We use geologically informed rates of change derived from these reconstructions to predict magnitudes and rates of change in species' suitable habitat over the next century. Our approach to modeling the past suitable habitat of species is general and can be adopted by others. We use multiple lines of evidence of past climate (isotopes and climate models), phylogenetic topology (to correct the models for long-term changes in the suitable habitat of a species), and the fossil record, however sparse, to cross check the models. Our models indicate the annual rate of displacement in a clade of rattlesnakes over the next century will be 2 to 3 orders of magnitude greater (430-2,420 m/yr) than it has been on average for the past 320 ky (2.3 m/yr).

Pleistocene Climate, Phylogeny, and Climate Envelope Models: An Integrative Approach to Better Understand Species' Response to Climate Change

PubMed Central

Lawing, A. Michelle; Polly, P. David

2011-01-01

Mean annual temperature reported by the Intergovernmental Panel on Climate Change increases at least 1.1°C to 6.4°C over the next 90 years. In context, a change in climate of 6°C is approximately the difference between the mean annual temperature of the Last Glacial Maximum (LGM) and our current warm interglacial. Species have been responding to changing climate throughout Earth's history and their previous biological responses can inform our expectations for future climate change. Here we synthesize geological evidence in the form of stable oxygen isotopes, general circulation paleoclimate models, species' evolutionary relatedness, and species' geographic distributions. We use the stable oxygen isotope record to develop a series of temporally high-resolution paleoclimate reconstructions spanning the Middle Pleistocene to Recent, which we use to map ancestral climatic envelope reconstructions for North American rattlesnakes. A simple linear interpolation between current climate and a general circulation paleoclimate model of the LGM using stable oxygen isotope ratios provides good estimates of paleoclimate at other time periods. We use geologically informed rates of change derived from these reconstructions to predict magnitudes and rates of change in species' suitable habitat over the next century. Our approach to modeling the past suitable habitat of species is general and can be adopted by others. We use multiple lines of evidence of past climate (isotopes and climate models), phylogenetic topology (to correct the models for long-term changes in the suitable habitat of a species), and the fossil record, however sparse, to cross check the models. Our models indicate the annual rate of displacement in a clade of rattlesnakes over the next century will be 2 to 3 orders of magnitude greater (430-2,420 m/yr) than it has been on average for the past 320 ky (2.3 m/yr). PMID:22164305

Multiple methods for multiple futures: Integrating qualitative scenario planning and quantitative simulation modeling for natural resource decision making

USGS Publications Warehouse

Symstad, Amy J.; Fisichelli, Nicholas A.; Miller, Brian W.; Rowland, Erika; Schuurman, Gregor W.

2017-01-01

Scenario planning helps managers incorporate climate change into their natural resource decision making through a structured “what-if” process of identifying key uncertainties and potential impacts and responses. Although qualitative scenarios, in which ecosystem responses to climate change are derived via expert opinion, often suffice for managers to begin addressing climate change in their planning, this approach may face limits in resolving the responses of complex systems to altered climate conditions. In addition, this approach may fall short of the scientific credibility managers often require to take actions that differ from current practice. Quantitative simulation modeling of ecosystem response to climate conditions and management actions can provide this credibility, but its utility is limited unless the modeling addresses the most impactful and management-relevant uncertainties and incorporates realistic management actions. We use a case study to compare and contrast management implications derived from qualitative scenario narratives and from scenarios supported by quantitative simulations. We then describe an analytical framework that refines the case study’s integrated approach in order to improve applicability of results to management decisions. The case study illustrates the value of an integrated approach for identifying counterintuitive system dynamics, refining understanding of complex relationships, clarifying the magnitude and timing of changes, identifying and checking the validity of assumptions about resource responses to climate, and refining management directions. Our proposed analytical framework retains qualitative scenario planning as a core element because its participatory approach builds understanding for both managers and scientists, lays the groundwork to focus quantitative simulations on key system dynamics, and clarifies the challenges that subsequent decision making must address.

The Climate Response to Stratospheric Aerosol Geoengineering Can Be Tailored Using Multiple Injection Locations

NASA Astrophysics Data System (ADS)

MacMartin, Douglas G.; Kravitz, Ben; Tilmes, Simone; Richter, Jadwiga H.; Mills, Michael J.; Lamarque, Jean-Francois; Tribbia, Joseph J.; Vitt, Francis

2017-12-01

By injecting different amounts of SO2 at multiple different latitudes, the spatial pattern of aerosol optical depth (AOD) can be partially controlled. This leads to the ability to influence the climate response to geoengineering with stratospheric aerosols, providing the potential for design. We use simulations from the fully coupled whole-atmosphere chemistry climate model CESM1(WACCM) to demonstrate that by appropriately combining injection at just four different locations, 30°S, 15°S, 15°N, and 30°N, then three spatial degrees of freedom of AOD can be achieved: an approximately spatially uniform AOD distribution, the relative difference in AOD between Northern and Southern Hemispheres, and the relative AOD in high versus low latitudes. For forcing levels that yield 1-2°C cooling, the AOD and surface temperature response are sufficiently linear in this model so that the response to different combinations of injection at different latitudes can be estimated from single-latitude injection simulations; nonlinearities associated with both aerosol growth and changes to stratospheric circulation will be increasingly important at higher forcing levels. Optimized injection at multiple locations is predicted to improve compensation of CO2-forced climate change relative to a case using only equatorial aerosol injection (which overcools the tropics relative to high latitudes). The additional degrees of freedom can be used, for example, to balance the interhemispheric temperature gradient and the equator to pole temperature gradient in addition to the global mean temperature. Further research is needed to better quantify the impacts of these strategies on changes to long-term temperature, precipitation, and other climate parameters.

Sensitivity of leaf size and shape to climate: Global patterns and paleoclimatic applications

USGS Publications Warehouse

Peppe, D.J.; Royer, D.L.; Cariglino, B.; Oliver, S.Y.; Newman, S.; Leight, E.; Enikolopov, G.; Fernandez-Burgos, M.; Herrera, F.; Adams, J.M.; Correa, E.; Currano, E.D.; Erickson, J.M.; Hinojosa, L.F.; Hoganson, J.W.; Iglesias, A.; Jaramillo, C.A.; Johnson, K.R.; Jordan, G.J.; Kraft, N.J.B.; Lovelock, E.C.; Lusk, C.H.; Niinemets, U.; Penuelas, J.; Rapson, G.; Wing, S.L.; Wright, I.J.

2011-01-01

Paleobotanists have long used models based on leaf size and shape to reconstruct paleoclimate. However, most models incorporate a single variable or use traits that are not physiologically or functionally linked to climate, limiting their predictive power. Further, they often underestimate paleotemperature relative to other proxies. Here we quantify leaf-climate correlations from 92 globally distributed, climatically diverse sites, and explore potential confounding factors. Multiple linear regression models for mean annual temperature (MAT) and mean annual precipitation (MAP) are developed and applied to nine well-studied fossil floras. We find that leaves in cold climates typically have larger, more numerous teeth, and are more highly dissected. Leaf habit (deciduous vs evergreen), local water availability, and phylogenetic history all affect these relationships. Leaves in wet climates are larger and have fewer, smaller teeth. Our multivariate MAT and MAP models offer moderate improvements in precision over univariate approaches (??4.0 vs 4.8??C for MAT) and strong improvements in accuracy. For example, our provisional MAT estimates for most North American fossil floras are considerably warmer and in better agreement with independent paleoclimate evidence. Our study demonstrates that the inclusion of additional leaf traits that are functionally linked to climate improves paleoclimate reconstructions. This work also illustrates the need for better understanding of the impact of phylogeny and leaf habit on leaf-climate relationships. ?? 2011 The Authors. New Phytologist ?? 2011 New Phytologist Trust.

Estimation of ozone dry deposition over Europe for the period 2071-2100

NASA Astrophysics Data System (ADS)

Komjáthy, Eszter; Gelybó, Györgyi; László Lagzi, István.; Mészáros, Róbert

2010-05-01

Ozone in the lower troposphere is a phytotoxic air pollutant which can cause injury to plant tissues, causing reduction in plant growth and productivity. In the last decades, several investigations have been carried out for the purpose to estimate ozone load over different surface types. At the same time, the changes of atmospheric variables as well as surface/vegetation parameters due to the global climate change could also strongly modify both temporal and spatial variations of ozone load over Europe. In this study, the possible effects of climate change on ozone deposition are analyzed. Using a sophisticated deposition model, ozone deposition was estimated on a regular grid over Europe for the period 2071-2100. Our aim is to determine the uncertainties and the possible degree of change in ozone deposition velocity as an important predictor of total ozone load using climate data from multiple climate models and runs. For these model calculations, results of the PRUDENCE (Predicting of Regional Scenarios and Uncertainties for Defining European Climate Change Risks and Effects) climate prediction project were used. As a first step, seasonal variations of ozone deposition over different vegetation types in case of different climate scenarios are presented in this study. Besides model calculations, in the frame of a sensitivity analyses, the effects of surface/vegetation parameters (e.g. leaf area index or stomatal resistance) on ozone deposition under a modified climate regime have also been analyzed.

Global Potential for Hydro-generated Electricity and Climate Change Impact

NASA Astrophysics Data System (ADS)

Zhou, Y.; Hejazi, M. I.; Leon, C.; Calvin, K. V.; Thomson, A. M.; Li, H. Y.

2014-12-01

Hydropower is a dominant renewable energy source at the global level, accounting for more than 15% of the world's total power supply. It is also very vulnerable to climate change. Improved understanding of climate change impact on hydropower can help develop adaptation measures to increase the resilience of energy system. In this study, we developed a comprehensive estimate of global hydropower potential using runoff and stream flow data derived from a global hydrologic model with a river routing sub-model, along with turbine technology performance, cost assumptions, and environmental consideration (Figure 1). We find that hydropower has the potential to supply a significant portion of the world energy needs, although this potential varies substantially by regions. Resources in a number of countries exceed by multiple folds the total current demand for electricity, e.g., Russia and Indonesia. A sensitivity analysis indicates that hydropower potential can be highly sensitive to a number of parameters including designed flow for capacity, cost and financing, turbine efficiency, and stream flow. The climate change impact on hydropower potential was evaluated by using runoff outputs from 4 climate models (HadCM3, PCM, CGCM2, and CSIRO2). It was found that the climate change on hydropower shows large variation not only by regions, but also climate models, and this demonstrates the importance of incorporating climate change into infrastructure-planning at the regional level though the existing uncertainties.

Optimal selection of MULTI-model downscaled ensembles for interannual and seasonal climate prediction in the eastern seaboard of Thailand

NASA Astrophysics Data System (ADS)

Bejranonda, W.; Koch, M.

2010-12-01

Because of the imminent threat of the water resources of the eastern seaboard of Thailand, a climate impact study has been carried out there. To that avail, a hydrological watershed model is being used to simulate the future water availability in the wake of possible climate change in the region. The hydrological model is forced by predictions from global climate models (GCMs) that are to be downscaled in an appropriate manner. The challenge at that stage of the climate impact analysis lies then the in the choice of the best GCM and the (statistical) downscaling method. In this study the selection of coarse grid resolution output of the GCMs, transferring information to the fine grid of local climate-hydrology is achieved by cross-correlation and multiple linear regression using meteorological data in the eastern seaboard of Thailand observed between 1970-1999. The grids of 20 atmosphere/ocean global climate models (AOGCM), covering latitude 12.5-15.0 N and longitude 100.0-102.5 E were examined using the Climate-Change Scenario Generator (SCENGEN). With that tool the model efficiency of the prediction of daily precipitation and mean temperature was calculated by comparing the 1980-1999 ECMWF reanalysis predictions with the observed data during that time period. The root means square errors of the predictions were considered and ranked to select the top 5 models, namely, BCCR-BCM2.0, GISS-ER, ECHO-G, ECHAM5/MPI-OM and PCM. The daily time-series of 338 predictors in 9 runs of the 5 selected models were gathered from the CMIP3 multi-model database. Monthly time-serial cross-correlations between the climate predictors and the meteorological measurements from 25 rainfall, 4 minimum and maximum temperature, 4 humidity and 2 solar radiation stations in the study area were then computed and ranked. Using the ranked predictors, a multiple-linear regression model (downscaling transfer model) to forecast the local climate was set up. To improve the prediction power of this GCM downscaling approach, the regression equations were considered as a dynamic regression model that can alter the predictor by seasonal variation. The possible seasonal effect was examined for the 1974-1999 period which was equally divided into a calibration and verification sub-period. The calibrated model using the whole observed time-series was compared with the models separated into 2 seasons; dry and wet, 3 seasons; winter, summer and rainy, and 4 seasons; dry, pre-monsoon, first monsoon and second monsoon. The verification power of the various model variants was measured considering Akaike's information criterion (AIC) and the Nash-Sutcliffe coefficient of the corresponding model fit. The results show that the 4-seasons-variation prediction works best. The highest efficiency for the prediction of rainfall is achieved for the dry season, Oct-Mar, whereas the smallest efficiency is obtained in the monsoon seasons. The overall number of predictor giving top efficiency lies between 3 and 20 in the regression models. In the next, still ongoing stage of the climate impact study the predictions from this new, seasonally optimized downscaling transfer model are being used in the simulations of the future hydrological water budget in that region of Thailand.

EXPLAINING FOREST COMPOSITION AND BIOMASS ACROSS MULTIPLE BIOGEOGRAPHIC REGIONS

EPA Science Inventory

Current scientific concerns regarding the impacts of global change include the responses of forest composition and biomass to rapid changes in climate, and forest gap models, have often been used to address this issue. These models reflect the concept that forest composition and...

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.

Testing For The Linearity of Responses To Multiple Anthropogenic Climate Forcings

NASA Astrophysics Data System (ADS)

Forest, C. E.; Stone, P. H.; Sokolov, A. P.

To test whether climate forcings are additive, we compare climate model simulations in which anthropogenic forcings are applied individually and in combination. Tests are performed with different values for climate system properties (climate sensitivity and rate of heat uptake by the deep ocean) as well as for different strengths of the net aerosol forcing, thereby testing for the dependence of linearity on these properties. The MIT 2D Land-Ocean Climate Model used in this study consists of a zonally aver- aged statistical-dynamical atmospheric model coupled to a mixed-layer Q-flux ocean model, with heat anomalies diffused into the deep ocean. Following our previous stud- ies, the anthropogenic forcings are the changes in concentrations of greenhouse gases (1860-1995), sulfate aerosol (1860-1995), and stratospheric and tropospheric ozone (1979-1995). The sulfate aerosol forcing is applied as a surface albedo change. For an aerosol forcing of -1.0 W/m2 and an effective ocean diffusitivity of 2.5 cm2/s, the nonlinearity of the response of global-mean surface temperatures to the combined forcing shows a strong dependence on climate sensitivity. The fractional change in decadal averages ([(TG + TS + TO) - TGSO]/TGSO) for the 1986-1995 period compared to pre-industrial times are 0.43, 0.90, and 1.08 with climate sensitiv- ities of 3.0, 4.5, and 6.2 C, respectively. The values of TGSO for these three cases o are 0.52, 0.62, and 0.76 C. The dependence of linearity on climate system properties, o the role of climate system feedbacks, and the implications for the detection of climate system's response to individual forcings will be presented. Details of the model and forcings can be found at http://web.mit.edu/globalchange/www/.

Testing for the linearity of responses to multiple anthropogenic climate forcings

NASA Astrophysics Data System (ADS)

Forest, C. E.; Stone, P. H.; Sokolov, A. P.

2001-12-01

To test whether climate forcings are additive, we compare climate model simulations in which anthropogenic forcings are applied individually and in combination. Tests are performed with different values for climate system properties (climate sensitivity and rate of heat uptake by the deep ocean) as well as for different strengths of the net aerosol forcing, thereby testing for the dependence of linearity on these properties. The MIT 2D Land-Ocean Climate Model used in this study consists of a zonally averaged statistical-dynamical atmospheric model coupled to a mixed-layer Q-flux ocean model, with heat anomalies diffused into the deep ocean. Following our previous studies, the anthropogenic forcings are the changes in concentrations of greenhouse gases (1860-1995), sulfate aerosol (1860-1995), and stratospheric and tropospheric ozone (1979-1995). The sulfate aerosol forcing is applied as a surface albedo change. For an aerosol forcing of -1.0 W/m2 and an effective ocean diffusitivity of 2.5 cm2/s, the nonlinearity of the response of global-mean surface temperatures to the combined forcing shows a strong dependence on climate sensitivity. The fractional change in decadal averages ([(Δ TG + Δ TS + Δ TO) - Δ TGSO ]/ Δ TGSO) for the 1986-1995 period compared to pre-industrial times are 0.43, 0.90, and 1.08 with climate sensitivities of 3.0, 4.5, and 6.2 oC, respectively. The values of Δ TGSO for these three cases are 0.52, 0.62, and 0.76 oC. The dependence of linearity on climate system properties, the role of climate system feedbacks, and the implications for the detection of climate system's response to individual forcings will be presented. Details of the model and forcings can be found at http://web.mit.edu/globalchange/www/.

Empirically Derived and Simulated Sensitivity of Vegetation to Climate Across Global Gradients of Temperature and Precipitation

NASA Astrophysics Data System (ADS)

Quetin, G. R.; Swann, A. L. S.

2017-12-01

Successfully predicting the state of vegetation in a novel environment is dependent on our process level understanding of the ecosystem and its interactions with the environment. We derive a global empirical map of the sensitivity of vegetation to climate using the response of satellite-observed greenness and leaf area to interannual variations in temperature and precipitation. Our analysis provides observations of ecosystem functioning; the vegetation interactions with the physical environment, across a wide range of climates and provide a functional constraint for hypotheses engendered in process-based models. We infer mechanisms constraining ecosystem functioning by contrasting how the observed and simulated sensitivity of vegetation to climate varies across climate space. Our analysis yields empirical evidence for multiple physical and biological mediators of the sensitivity of vegetation to climate as a systematic change across climate space. Our comparison of remote sensing-based vegetation sensitivity with modeled estimates provides evidence for which physiological mechanisms - photosynthetic efficiency, respiration, water supply, atmospheric water demand, and sunlight availability - dominate the ecosystem functioning in places with different climates. Earth system models are generally successful in reproducing the broad sign and shape of ecosystem functioning across climate space. However, this general agreement breaks down in hot wet climates where models simulate less leaf area during a warmer year, while observations show a mixed response but overall more leaf area during warmer years. In addition, simulated ecosystem interaction with temperature is generally larger and changes more rapidly across a gradient of temperature than is observed. We hypothesize that the amplified interaction and change are both due to a lack of adaptation and acclimation in simulations. This discrepancy with observations suggests that simulated responses of vegetation to global warming, and feedbacks between vegetation and climate, are too strong in the models.

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

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

Analyzing the contribution of climate change to long-term variations in sediment nitrogen sources for reservoirs/lakes.

PubMed

Xia, Xinghui; Wu, Qiong; Zhu, Baotong; Zhao, Pujun; Zhang, Shangwei; Yang, Lingyan

2015-08-01

We applied a mixing model based on stable isotopic δ(13)C, δ(15)N, and C:N ratios to estimate the contributions of multiple sources to sediment nitrogen. We also developed a conceptual model describing and analyzing the impacts of climate change on nitrogen enrichment. These two models were conducted in Miyun Reservoir to analyze the contribution of climate change to the variations in sediment nitrogen sources based on two (210)Pb and (137)Cs dated sediment cores. The results showed that during the past 50years, average contributions of soil and fertilizer, submerged macrophytes, N2-fixing phytoplankton, and non-N2-fixing phytoplankton were 40.7%, 40.3%, 11.8%, and 7.2%, respectively. In addition, total nitrogen (TN) contents in sediment showed significant increasing trends from 1960 to 2010, and sediment nitrogen of both submerged macrophytes and phytoplankton sources exhibited significant increasing trends during the past 50years. In contrast, soil and fertilizer sources showed a significant decreasing trend from 1990 to 2010. According to the changing trend of N2-fixing phytoplankton, changes of temperature and sunshine duration accounted for at least 43% of the trend in the sediment nitrogen enrichment over the past 50years. Regression analysis of the climatic factors on nitrogen sources showed that the contributions of precipitation, temperature, and sunshine duration to the variations in sediment nitrogen sources ranged from 18.5% to 60.3%. The study demonstrates that the mixing model provides a robust method for calculating the contribution of multiple nitrogen sources in sediment, and this study also suggests that N2-fixing phytoplankton could be regarded as an important response factor for assessing the impacts of climate change on nitrogen enrichment. Copyright © 2015 Elsevier B.V. All rights reserved.

Food Crops Response to Climate Change

NASA Astrophysics Data System (ADS)

Butler, E.; Huybers, P.

2009-12-01

Projections of future climate show a warming world and heterogeneous changes in precipitation. Generally, warming temperatures indicate a decrease in crop yields where they are currently grown. However, warmer climate will also open up new areas at high latitudes for crop production. Thus, there is a question whether the warmer climate with decreased yields but potentially increased growing area will produce a net increase or decrease of overall food crop production. We explore this question through a multiple linear regression model linking temperature and precipitation to crop yield. Prior studies have emphasised temporal regression which indicate uniformly decreased yields, but neglect the potentially increased area opened up for crop production. This study provides a compliment to the prior work by exploring this spatial variation. We explore this subject with a multiple linear regression model from temperature, precipitation and crop yield data over the United States. The United States was chosen as the training region for the model because there are good crop data available over the same time frame as climate data and presumably the yield from crops in the United States is optimized with respect to potential yield. We study corn, soybeans, sorghum, hard red winter wheat and soft red winter wheat using monthly averages of temperature and precipitation from NCEP reanalysis and yearly yield data from the National Agriculture Statistics Service for 1948-2008. The use of monthly averaged temperature and precipitation, which neglect extreme events that can have a significant impact on crops limits this study as does the exclusive use of United States agricultural data. The GFDL 2.1 model under a 720ppm CO2 scenario provides temperature and precipitation fields for 2040-2100 which are used to explore how the spatial regions available for crop production will change under these new conditions.

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...

Developing above-ground woody biomass equations for open-grown, multiple-stemmed tree species: shelterbelt-grown Russian-olive

Treesearch

Xinhau Zhour; James R. Brandle; Michele M. Schoeneberger; Tala Awada

2007-01-01

Multiple-stemmed tree species are often used in agricultural settings, playing a significant role in natural resource conservation and carbon sequestration. Biomass estimation, whether for modeling growth under different climate scenarios, accounting for carbon sequestered, or inclusion in natural resource inventories, requires equations that can accurately describe...

Allowable carbon emissions lowered by multiple climate targets.

PubMed

Steinacher, Marco; Joos, Fortunat; Stocker, Thomas F

2013-07-11

Climate targets are designed to inform policies that would limit the magnitude and impacts of climate change caused by anthropogenic emissions of greenhouse gases and other substances. The target that is currently recognized by most world governments places a limit of two degrees Celsius on the global mean warming since preindustrial times. This would require large sustained reductions in carbon dioxide emissions during the twenty-first century and beyond. Such a global temperature target, however, is not sufficient to control many other quantities, such as transient sea level rise, ocean acidification and net primary production on land. Here, using an Earth system model of intermediate complexity (EMIC) in an observation-informed Bayesian approach, we show that allowable carbon emissions are substantially reduced when multiple climate targets are set. We take into account uncertainties in physical and carbon cycle model parameters, radiative efficiencies, climate sensitivity and carbon cycle feedbacks along with a large set of observational constraints. Within this framework, we explore a broad range of economically feasible greenhouse gas scenarios from the integrated assessment community to determine the likelihood of meeting a combination of specific global and regional targets under various assumptions. For any given likelihood of meeting a set of such targets, the allowable cumulative emissions are greatly reduced from those inferred from the temperature target alone. Therefore, temperature targets alone are unable to comprehensively limit the risks from anthropogenic emissions.

Comparing regional precipitation and temperature extremes in climate model and reanalysis products

DOE PAGES

Angélil, Oliver; Perkins-Kirkpatrick, Sarah; Alexander, Lisa V.; ...

2016-07-12

A growing field of research aims to characterise the contribution of anthropogenic emissions to the likelihood of extreme weather and climate events. These analyses can be sensitive to the shapes of the tails of simulated distributions. If tails are found to be unrealistically short or long, the anthropogenic signal emerges more or less clearly, respectively, from the noise of possible weather. Here we compare the chance of daily land-surface precipitation and near-surface temperature extremes generated by three Atmospheric Global Climate Models typically used for event attribution, with distributions from six reanalysis products. The likelihoods of extremes are compared for area-averagesmore » over grid cell and regional sized spatial domains. Results suggest a bias favouring overly strong attribution estimates for hot and cold events over many regions of Africa and Australia, and a bias favouring overly weak attribution estimates over regions of North America and Asia. For rainfall, results are more sensitive to geographic location. Although the three models show similar results over many regions, they do disagree over others. Equally, results highlight the discrepancy amongst reanalyses products. This emphasises the importance of using multiple reanalysis and/or observation products, as well as multiple models in event attribution studies.« less

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...

Landscape-based population viability models demonstrate importance of strategic conservation planning for birds

Treesearch

Thomas W. Bonnot; Frank R. Thompson; Joshua J. Millspaugh; D. Todd Jones-Farland

2013-01-01

Efforts to conserve regional biodiversity in the face of global climate change, habitat loss and fragmentation will depend on approaches that consider population processes at multiple scales. By combining habitat and demographic modeling, landscape-based population viability models effectively relate small-scale habitat and landscape patterns to regional population...

Profiles of Student Perceptions of School Climate: Relations with Risk Behaviors and Academic Outcomes.

PubMed

Shukla, Kathan; Konold, Timothy; Cornell, Dewey

2016-06-01

School climate has been linked to a variety of positive student outcomes, but there may be important within-school differences among students in their experiences of school climate. This study examined within-school heterogeneity among 47,631 high school student ratings of their school climate through multilevel latent class modeling. Student profiles across 323 schools were generated on the basis of multiple indicators of school climate: disciplinary structure, academic expectations, student willingness to seek help, respect for students, affective and cognitive engagement, prevalence of teasing and bullying, general victimization, bullying victimization, and bullying perpetration. Analyses identified four meaningfully different student profile types that were labeled positive climate, medium climate-low bullying, medium climate-high bullying, and negative climate. Contrasts among these profile types on external criteria revealed meaningful differences for race, grade-level, parent education level, educational aspirations, and frequency of risk behaviors. © Society for Community Research and Action 2016.

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.

A crucial step toward realism: responses to climate change from an evolving metacommunity perspective.

PubMed

Urban, Mark C; De Meester, Luc; Vellend, Mark; Stoks, Robby; Vanoverbeke, Joost

2012-02-01

We need to understand joint ecological and evolutionary responses to climate change to predict future threats to biological diversity. The 'evolving metacommunity' framework emphasizes that interactions between ecological and evolutionary mechanisms at both local and regional scales will drive community dynamics during climate change. Theory suggests that ecological and evolutionary dynamics often interact to produce outcomes different from those predicted based on either mechanism alone. We highlight two of these dynamics: (i) species interactions prevent adaptation of nonresident species to new niches and (ii) resident species adapt to changing climates and thereby prevent colonization by nonresident species. The rate of environmental change, level of genetic variation, source-sink structure, and dispersal rates mediate between these potential outcomes. Future models should evaluate multiple species, species interactions other than competition, and multiple traits. Future experiments should manipulate factors such as genetic variation and dispersal to determine their joint effects on responses to climate change. Currently, we know much more about how climates will change across the globe than about how species will respond to these changes despite the profound effects these changes will have on global biological diversity. Integrating evolving metacommunity perspectives into climate change biology should produce more accurate predictions about future changes to species distributions and extinction threats.

A crucial step toward realism: responses to climate change from an evolving metacommunity perspective

PubMed Central

Urban, Mark C; De Meester, Luc; Vellend, Mark; Stoks, Robby; Vanoverbeke, Joost

2012-01-01

We need to understand joint ecological and evolutionary responses to climate change to predict future threats to biological diversity. The ‘evolving metacommunity’ framework emphasizes that interactions between ecological and evolutionary mechanisms at both local and regional scales will drive community dynamics during climate change. Theory suggests that ecological and evolutionary dynamics often interact to produce outcomes different from those predicted based on either mechanism alone. We highlight two of these dynamics: (i) species interactions prevent adaptation of nonresident species to new niches and (ii) resident species adapt to changing climates and thereby prevent colonization by nonresident species. The rate of environmental change, level of genetic variation, source-sink structure, and dispersal rates mediate between these potential outcomes. Future models should evaluate multiple species, species interactions other than competition, and multiple traits. Future experiments should manipulate factors such as genetic variation and dispersal to determine their joint effects on responses to climate change. Currently, we know much more about how climates will change across the globe than about how species will respond to these changes despite the profound effects these changes will have on global biological diversity. Integrating evolving metacommunity perspectives into climate change biology should produce more accurate predictions about future changes to species distributions and extinction threats. PMID:25568038

Creating "Intelligent" Climate Model Ensemble Averages Using a Process-Based Framework

NASA Astrophysics Data System (ADS)

Baker, N. C.; Taylor, P. C.

2014-12-01

The CMIP5 archive contains future climate projections from over 50 models provided by dozens of modeling centers from around the world. Individual model projections, however, are subject to biases created by structural model uncertainties. As a result, ensemble averaging of multiple models is often used to add value to model projections: consensus projections have been shown to consistently outperform individual models. Previous reports for the IPCC establish climate change projections based on an equal-weighted average of all model projections. However, certain models reproduce climate processes better than other models. Should models be weighted based on performance? Unequal ensemble averages have previously been constructed using a variety of mean state metrics. What metrics are most relevant for constraining future climate projections? This project develops a framework for systematically testing metrics in models to identify optimal metrics for unequal weighting multi-model ensembles. A unique aspect of this project is the construction and testing of climate process-based model evaluation metrics. A climate process-based metric is defined as a metric based on the relationship between two physically related climate variables—e.g., outgoing longwave radiation and surface temperature. Metrics are constructed using high-quality Earth radiation budget data from NASA's Clouds and Earth's Radiant Energy System (CERES) instrument and surface temperature data sets. It is found that regional values of tested quantities can vary significantly when comparing weighted and unweighted model ensembles. For example, one tested metric weights the ensemble by how well models reproduce the time-series probability distribution of the cloud forcing component of reflected shortwave radiation. The weighted ensemble for this metric indicates lower simulated precipitation (up to .7 mm/day) in tropical regions than the unweighted ensemble: since CMIP5 models have been shown to overproduce precipitation, this result could indicate that the metric is effective in identifying models which simulate more realistic precipitation. Ultimately, the goal of the framework is to identify performance metrics for advising better methods for ensemble averaging models and create better climate predictions.

Uncertainty squared: Choosing among multiple input probability distributions and interpreting multiple output probability distributions in Monte Carlo climate risk models

NASA Astrophysics Data System (ADS)

Baer, P.; Mastrandrea, M.

2006-12-01

Simple probabilistic models which attempt to estimate likely transient temperature change from specified CO2 emissions scenarios must make assumptions about at least six uncertain aspects of the causal chain between emissions and temperature: current radiative forcing (including but not limited to aerosols), current land use emissions, carbon sinks, future non-CO2 forcing, ocean heat uptake, and climate sensitivity. Of these, multiple PDFs (probability density functions) have been published for the climate sensitivity, a couple for current forcing and ocean heat uptake, one for future non-CO2 forcing, and none for current land use emissions or carbon cycle uncertainty (which are interdependent). Different assumptions about these parameters, as well as different model structures, will lead to different estimates of likely temperature increase from the same emissions pathway. Thus policymakers will be faced with a range of temperature probability distributions for the same emissions scenarios, each described by a central tendency and spread. Because our conventional understanding of uncertainty and probability requires that a probabilistically defined variable of interest have only a single mean (or median, or modal) value and a well-defined spread, this "multidimensional" uncertainty defies straightforward utilization in policymaking. We suggest that there are no simple solutions to the questions raised. Crucially, we must dispel the notion that there is a "true" probability probabilities of this type are necessarily subjective, and reasonable people may disagree. Indeed, we suggest that what is at stake is precisely the question, what is it reasonable to believe, and to act as if we believe? As a preliminary suggestion, we demonstrate how the output of a simple probabilistic climate model might be evaluated regarding the reasonableness of the outputs it calculates with different input PDFs. We suggest further that where there is insufficient evidence to clearly favor one range of probabilistic projections over another, that the choice of results on which to base policy must necessarily involve ethical considerations, as they have inevitable consequences for the distribution of risk In particular, the choice to use a more "optimistic" PDF for climate sensitivity (or other components of the causal chain) leads to the allowance of higher emissions consistent with any specified goal for risk reduction, and thus leads to higher climate impacts, in exchange for lower mitigation costs.

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.

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.

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.

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

Yoon, Jin -Ho

Amazon rainfall is subject to year-to-year fluctuation resulting in drought and flood in various intensities. A major climatic driver of the interannual variation of the Amazon rainfall is El Niño/Southern Oscillation. Also, the Sea Surface Temperature over the Atlantic Ocean is identified as an important climatic driver on the Amazon water cycle. Previously, observational datasets were used to support the Atlantic influence on Amazon rainfall. Furthermore, it is found that multiple global climate models do reproduce the Atlantic-Amazon link robustly. However, there exist differences in rainfall response, which primarily depends on the climatological rainfall amount.

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.

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

Fu, Congsheng; Wang, Guiling; Goulden, Michael L.

Effects of hydraulic redistribution (HR) on hydrological, biogeochemical, and ecological processes have been demonstrated in the field, but the current generation of standard earth system models does not include a representation of HR. Though recent studies have examined the effect of incorporating HR into land surface models, few (if any) have done cross-site comparisons for contrasting climate regimes and multiple vegetation types via the integration of measurement and modeling. Here, we incorporated the HR scheme of Ryel et al. (2002) into the NCAR Community Land Model Version 4.5 (CLM4.5), and examined the ability of the resulting hybrid model to capture themore » magnitude of HR flux and/or soil moisture dynamics from which HR can be directly inferred, to assess the impact of HR on land surface water and energy budgets, and to explore how the impact may depend on climate regimes and vegetation conditions. Eight AmeriFlux sites with contrasting climate regimes and multiple vegetation types were studied, including the Wind River Crane site in Washington State, the Santa Rita Mesquite savanna site in southern Arizona, and six sites along the Southern California Climate Gradient. HR flux, evapotranspiration (ET), and soil moisture were properly simulated in the present study, even in the face of various uncertainties. Our cross-ecosystem comparison showed that the timing, magnitude, and direction (upward or downward) of HR vary across ecosystems, and incorporation of HR into CLM4.5 improved the model-measurement matches of evapotranspiration, Bowen ratio, and soil moisture particularly during dry seasons. Lastly, our results also reveal that HR has important hydrological impact in ecosystems that have a pronounced dry season but are not overall so dry that sparse vegetation and very low soil moisture limit HR.« less

Exploring Climate Niches of Ponderosa Pine (Pinus ponderosa Douglas ex Lawson) Haplotypes in the Western United States: Implications for Evolutionary History and Conservation

PubMed Central

Shinneman, Douglas J.; Potter, Kevin M.; Hipkins, Valerie D.

2016-01-01

Ponderosa pine (Pinus ponderosa Douglas ex Lawson) occupies montane environments throughout western North America, where it is both an ecologically and economically important tree species. A recent study using mitochondrial DNA analysis demonstrated substantial genetic variation among ponderosa pine populations in the western U.S., identifying 10 haplotypes with unique evolutionary lineages that generally correspond spatially with distributions of the Pacific (P. p. var. ponderosa) and Rocky Mountain (P. p. var. scopulorum) varieties. To elucidate the role of climate in shaping the phylogeographic history of ponderosa pine, we used nonparametric multiplicative regression to develop predictive climate niche models for two varieties and 10 haplotypes and to hindcast potential distribution of the varieties during the last glacial maximum (LGM), ~22,000 yr BP. Our climate niche models performed well for the varieties, but haplotype models were constrained in some cases by small datasets and unmeasured microclimate influences. The models suggest strong relationships between genetic lineages and climate. Particularly evident was the role of seasonal precipitation balance in most models, with winter- and summer-dominated precipitation regimes strongly associated with P. p. vars. ponderosa and scopulorum, respectively. Indeed, where present-day climate niches overlap between the varieties, introgression of two haplotypes also occurs along a steep clinal divide in western Montana. Reconstructed climate niches for the LGM suggest potentially suitable climate existed for the Pacific variety in the California Floristic province, the Great Basin, and Arizona highlands, while suitable climate for the Rocky Mountain variety may have existed across the southwestern interior highlands. These findings underscore potentially unique phylogeographic origins of modern ponderosa pine evolutionary lineages, including potential adaptations to Pleistocene climates associated with discrete temporary glacial refugia. Our predictive climate niche models may inform strategies for further genetic research (e.g., sampling design) and conservation that promotes haplotype compatibility with projected changes in future climate. PMID:26985674

Exploring Climate Niches of Ponderosa Pine (Pinus ponderosa Douglas ex Lawson) Haplotypes in the Western United States: Implications for Evolutionary History and Conservation.

PubMed

Shinneman, Douglas J; Means, Robert E; Potter, Kevin M; Hipkins, Valerie D

2016-01-01

Ponderosa pine (Pinus ponderosa Douglas ex Lawson) occupies montane environments throughout western North America, where it is both an ecologically and economically important tree species. A recent study using mitochondrial DNA analysis demonstrated substantial genetic variation among ponderosa pine populations in the western U.S., identifying 10 haplotypes with unique evolutionary lineages that generally correspond spatially with distributions of the Pacific (P. p. var. ponderosa) and Rocky Mountain (P. p. var. scopulorum) varieties. To elucidate the role of climate in shaping the phylogeographic history of ponderosa pine, we used nonparametric multiplicative regression to develop predictive climate niche models for two varieties and 10 haplotypes and to hindcast potential distribution of the varieties during the last glacial maximum (LGM), ~22,000 yr BP. Our climate niche models performed well for the varieties, but haplotype models were constrained in some cases by small datasets and unmeasured microclimate influences. The models suggest strong relationships between genetic lineages and climate. Particularly evident was the role of seasonal precipitation balance in most models, with winter- and summer-dominated precipitation regimes strongly associated with P. p. vars. ponderosa and scopulorum, respectively. Indeed, where present-day climate niches overlap between the varieties, introgression of two haplotypes also occurs along a steep clinal divide in western Montana. Reconstructed climate niches for the LGM suggest potentially suitable climate existed for the Pacific variety in the California Floristic province, the Great Basin, and Arizona highlands, while suitable climate for the Rocky Mountain variety may have existed across the southwestern interior highlands. These findings underscore potentially unique phylogeographic origins of modern ponderosa pine evolutionary lineages, including potential adaptations to Pleistocene climates associated with discrete temporary glacial refugia. Our predictive climate niche models may inform strategies for further genetic research (e.g., sampling design) and conservation that promotes haplotype compatibility with projected changes in future climate.

From Global Climate Model Projections to Local Impacts Assessments: Analyses in Support of Planning for Climate Change

NASA Astrophysics Data System (ADS)

Snover, A. K.; Littell, J. S.; Mantua, N. J.; Salathe, E. P.; Hamlet, A. F.; McGuire Elsner, M.; Tohver, I.; Lee, S.

2010-12-01

Assessing and planning for the impacts of climate change require regionally-specific information. Information is required not only about projected changes in climate but also the resultant changes in natural and human systems at the temporal and spatial scales of management and decision making. Therefore, climate impacts assessment typically results in a series of analyses, in which relatively coarse-resolution global climate model projections of changes in regional climate are downscaled to provide appropriate input to local impacts models. This talk will describe recent examples in which coarse-resolution (~150 to 300km) GCM output was “translated” into information requested by decision makers at relatively small (watershed) and large (multi-state) scales using regional climate modeling, statistical downscaling, hydrologic modeling, and sector-specific impacts modeling. Projected changes in local air temperature, precipitation, streamflow, and stream temperature were developed to support Seattle City Light’s assessment of climate change impacts on hydroelectric operations, future electricity load, and resident fish populations. A state-wide assessment of climate impacts on eight sectors (agriculture, coasts, energy, forests, human health, hydrology and water resources, salmon, and urban stormwater infrastructure) was developed for Washington State to aid adaptation planning. Hydro-climate change scenarios for approximately 300 streamflow locations in the Columbia River basin and selected coastal drainages west of the Cascades were developed in partnership with major water management agencies in the Pacific Northwest to allow planners to consider how hydrologic changes may affect management objectives. Treatment of uncertainty in these assessments included: using “bracketing” scenarios to describe a range of impacts, using ensemble averages to characterize the central estimate of future conditions (given an emissions scenario), and explicitly assessing the impacts of multiple GCM ensemble members. The implications of various approaches to dealing with uncertainty, such as these, must be carefully communicated to decision makers in order for projected climate impacts to be viewed as credible and used appropriately.

Exploring climate niches of ponderosa pine (Pinus ponderosa Douglas ex Lawson) haplotypes in the western United States: Implications for evolutionary history and conservation

USGS Publications Warehouse

Shinneman, Douglas; Means, Robert E.; Potter, Kevin M.; Hipkins, Valerie D.

2016-01-01

Ponderosa pine (Pinus ponderosa Douglas ex Lawson) occupies montane environments throughout western North America, where it is both an ecologically and economically important tree species. A recent study using mitochondrial DNA analysis demonstrated substantial genetic variation among ponderosa pine populations in the western U.S., identifying 10 haplotypes with unique evolutionary lineages that generally correspond spatially with distributions of the Pacific (P. p. var. ponderosa) and Rocky Mountain (P. p. var. scopulorum) varieties. To elucidate the role of climate in shaping the phylogeographic history of ponderosa pine, we used nonparametric multiplicative regression to develop predictive climate niche models for two varieties and 10 haplotypes and to hindcast potential distribution of the varieties during the last glacial maximum (LGM), ~22,000 yr BP. Our climate niche models performed well for the varieties, but haplotype models were constrained in some cases by small datasets and unmeasured microclimate influences. The models suggest strong relationships between genetic lineages and climate. Particularly evident was the role of seasonal precipitation balance in most models, with winter- and summer-dominated precipitation regimes strongly associated with P. p. vars. ponderosa and scopulorum, respectively. Indeed, where present-day climate niches overlap between the varieties, introgression of two haplotypes also occurs along a steep clinal divide in western Montana. Reconstructed climate niches for the LGM suggest potentially suitable climate existed for the Pacific variety in the California Floristic province, the Great Basin, and Arizona highlands, while suitable climate for the Rocky Mountain variety may have existed across the southwestern interior highlands. These findings underscore potentially unique phylogeographic origins of modern ponderosa pine evolutionary lineages, including potential adaptations to Pleistocene climates associated with discrete temporary glacial refugia. Our predictive climate niche models may inform strategies for further genetic research (e.g., sampling design) and conservation that promotes haplotype compatibility with projected changes in future climate.

The Agriculture Model Intercomparison and Improvement Project (AgMIP) (Invited)

NASA Astrophysics Data System (ADS)

Rosenzweig, C.

2010-12-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) is a distributed climate-scenario simulation exercise for historical model intercomparison and future climate change conditions with participation of multiple crop and world agricultural trade modeling groups around the world. The goals of AgMIP are to improve substantially the characterization of risk of hunger and world food security due to climate change and to enhance adaptation capacity in both developing and developed countries. Historical period results will spur model improvement and interaction among major modeling groups, while future period results will lead directly to tests of adaptation and mitigation strategies across a range of scales. AgMIP will consist of a multi-scale impact assessment utilizing the latest methods for climate and agricultural scenario generation. Scenarios and modeling protocols will be distributed on the web, and multi-model results will be collated and analyzed to ensure the widest possible coverage of agricultural crops and regions. AgMIP will place regional changes in agricultural production in a global context that reflects new trading opportunities, imbalances, and shortages in world markets resulting from climate change and other driving forces for food supply. Such projections are essential inputs from the Vulnerability, Impacts, and Adaptation (VIA) research community to the Intergovernmental Panel on Climate Change Fifth Assessment (AR5), now underway, and the UN Framework Convention on Climate Change. They will set the context for local-scale vulnerability and adaptation studies, supply test scenarios for national-scale development of trade policy instruments, provide critical information on changing supply and demand for water resources, and elucidate interactive effects of climate change and land use change. AgMIP will not only provide crucially-needed new global estimates of how climate change will affect food supply and hunger in the agricultural regions of the world, but it will also build the capabilities of developing countries to estimate how climate change will affect their supply and demand for food.

Forest restoration as a strategy to mitigate climate impacts on wildfire, vegetation, and water in semiarid forests.

PubMed

O'Donnell, Frances C; Flatley, William T; Springer, Abraham E; Fulé, Peter Z

2018-06-25

Climate change and wildfire are interacting to drive vegetation change and potentially reduce water quantity and quality in the southwestern United States, Forest restoration is a management approach that could mitigate some of these negative outcomes. However, little information exists on how restoration combined with climate change might influence hydrology across large forest landscapes that incorporate multiple vegetation types and complex fire regimes. We combined spatially explicit vegetation and fire modeling with statistical water and sediment yield models for a large forested landscape (335,000 ha) on the Kaibab Plateau in northern Arizona, USA. Our objective was to assess the impacts of climate change and forest restoration on the future fire regime, forest vegetation, and watershed outputs. Our model results predict that the combination of climate change and high-severity fire will drive forest turnover, biomass declines, and compositional change in future forests. Restoration treatments may reduce the area burned in high-severity fires and reduce conversions from forested to non-forested conditions. Even though mid-elevation forests are the targets of restoration, the treatments are expected to delay the decline of high-elevation spruce-fir, aspen, and mixed conifer forests by reducing the occurrence of high-severity fires that may spread across ecoregions. We estimate that climate-induced vegetation changes will result in annual runoff declines of up to 10%, while restoration reduced or reversed this decline. The hydrologic model suggests that mid-elevation forests, which are the targets of restoration treatments, provide around 80% of runoff in this system and the conservation of mid- to high-elevation forests types provides the greatest benefit in terms of water conservation. We also predict that restoration treatments will conserve water quality by reducing patches of high-severity fire that are associated with high sediment yield. Restoration treatments are a management strategy that may reduce undesirable outcomes for multiple ecosystem services. © 2018 by the Ecological Society of America.

Climatology of salt transitions and implications for stone weathering.

PubMed

Grossi, C M; Brimblecombe, P; Menéndez, B; Benavente, D; Harris, I; Déqué, M

2011-06-01

This work introduces the notion of salt climatology. It shows how climate affects salt thermodynamic and the potential to relate long-term salt damage to climate types. It mainly focuses on specific sites in Western Europe, which include some cities in France and Peninsular Spain. Salt damage was parameterised using the number of dissolution-crystallisation events for unhydrated (sodium chloride) and hydrated (sodium sulphate) systems. These phase transitions have been calculated using daily temperature and relative humidity from observation meteorological data and Climate Change models' output (HadCM3 and ARPEGE). Comparing the number of transitions with meteorological seasonal data allowed us to develop techniques to estimate the frequency of salt transitions based on the local climatology. Results show that it is possible to associate the Köppen-Geiger climate types with potential salt weathering. Temperate fully humid climates seem to offer the highest potential for salt damage and possible higher number of transitions in summer. Climates with dry summers tend to show a lesser frequency of transitions in summer. The analysis of temperature, precipitation and relative output from Climate Change models suggests changes in the Köppen-Geiger climate types and changes in the patterns of salt damage. For instance, West Europe areas with a fully humid climate may change to a more Mediterranean like or dry climates, and consequently the seasonality of different salt transitions. The accuracy and reliability of the projections might be improved by simultaneously running multiple climate models (ensembles). Copyright © 2011 Elsevier B.V. All rights reserved.

An AgMIP framework for improved agricultural representation in integrated assessment models

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

Ruane, Alex C.; Rosenzweig, Cynthia; Asseng, Senthold

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agriculturalmore » Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.« less

An AgMIP framework for improved agricultural representation in integrated assessment models

NASA Astrophysics Data System (ADS)

Ruane, Alex C.; Rosenzweig, Cynthia; Asseng, Senthold; Boote, Kenneth J.; Elliott, Joshua; Ewert, Frank; Jones, James W.; Martre, Pierre; McDermid, Sonali P.; Müller, Christoph; Snyder, Abigail; Thorburn, Peter J.

2017-12-01

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.

The joint space-time statistics of macroweather precipitation, space-time statistical factorization and macroweather models.

PubMed

Lovejoy, S; de Lima, M I P

2015-07-01

Over the range of time scales from about 10 days to 30-100 years, in addition to the familiar weather and climate regimes, there is an intermediate "macroweather" regime characterized by negative temporal fluctuation exponents: implying that fluctuations tend to cancel each other out so that averages tend to converge. We show theoretically and numerically that macroweather precipitation can be modeled by a stochastic weather-climate model (the Climate Extended Fractionally Integrated Flux, model, CEFIF) first proposed for macroweather temperatures and we show numerically that a four parameter space-time CEFIF model can approximately reproduce eight or so empirical space-time exponents. In spite of this success, CEFIF is theoretically and numerically difficult to manage. We therefore propose a simplified stochastic model in which the temporal behavior is modeled as a fractional Gaussian noise but the spatial behaviour as a multifractal (climate) cascade: a spatial extension of the recently introduced ScaLIng Macroweather Model, SLIMM. Both the CEFIF and this spatial SLIMM model have a property often implicitly assumed by climatologists that climate statistics can be "homogenized" by normalizing them with the standard deviation of the anomalies. Physically, it means that the spatial macroweather variability corresponds to different climate zones that multiplicatively modulate the local, temporal statistics. This simplified macroweather model provides a framework for macroweather forecasting that exploits the system's long range memory and spatial correlations; for it, the forecasting problem has been solved. We test this factorization property and the model with the help of three centennial, global scale precipitation products that we analyze jointly in space and in time.

Linking climate projections to performance: A yield-based decision scaling assessment of a large urban water resources system

NASA Astrophysics Data System (ADS)

Turner, Sean W. D.; Marlow, David; Ekström, Marie; Rhodes, Bruce G.; Kularathna, Udaya; Jeffrey, Paul J.

2014-04-01

Despite a decade of research into climate change impacts on water resources, the scientific community has delivered relatively few practical methodological developments for integrating uncertainty into water resources system design. This paper presents an application of the "decision scaling" methodology for assessing climate change impacts on water resources system performance and asks how such an approach might inform planning decisions. The decision scaling method reverses the conventional ethos of climate impact assessment by first establishing the climate conditions that would compel planners to intervene. Climate model projections are introduced at the end of the process to characterize climate risk in such a way that avoids the process of propagating those projections through hydrological models. Here we simulated 1000 multisite synthetic monthly streamflow traces in a model of the Melbourne bulk supply system to test the sensitivity of system performance to variations in streamflow statistics. An empirical relation was derived to convert decision-critical flow statistics to climatic units, against which 138 alternative climate projections were plotted and compared. We defined the decision threshold in terms of a system yield metric constrained by multiple performance criteria. Our approach allows for fast and simple incorporation of demand forecast uncertainty and demonstrates the reach of the decision scaling method through successful execution in a large and complex water resources system. Scope for wider application in urban water resources planning is discussed.

Changes in the potential multiple cropping system in response to climate change in China from 1960-2010.

PubMed

Liu, Luo; Xu, Xinliang; Zhuang, Dafang; Chen, Xi; Li, Shuang

2013-01-01

The multiple cropping practice is essential to agriculture because it has been shown to significantly increase the grain yield and promote agricultural economic development. In this study, potential multiple cropping systems in China are calculated based on meteorological observation data by using the Agricultural Ecology Zone (AEZ) model. Following this, the changes in the potential cropping systems in response to climate change between the 1960s and the 2010s were subsequently analyzed. The results indicate that the changes of potential multiple cropping systems show tremendous heterogeneity in respect to the spatial pattern in China. A key finding is that the magnitude of change of the potential cropping systems showed a pattern of increase both from northern China to southern China and from western China to eastern China. Furthermore, the area found to be suitable only for single cropping decreased, while the area suitable for triple cropping increased significantly from the 1960s to the 2000s. During the studied period, the potential multiple cropping index (PMCI) gap between rain-fed and irrigated scenarios increased from 18% to 24%, which indicated noticeable growth of water supply limitations under the rain-fed scenario. The most significant finding of this research was that from the 1960s to the 2000s climate change had led to a significant increase of PMCI by 13% under irrigated scenario and 7% under rain-fed scenario across the whole of China. Furthermore, the growth of the annual mean temperature is identified as the main reason underlying the increase of PMCI. It has also been noticed that across China the changes of potential multiple cropping systems under climate change were different from region to region.

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...

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 ...

DESYCO: a Decision Support System to provide climate services for coastal stakeholders dealing with climate change impacts.

NASA Astrophysics Data System (ADS)

Torresan, S.; Gallina, V.; Giannini, V.; Rizzi, J.; Zabeo, A.; Critto, A.; Marcomini, A.

2012-04-01

At the international level climate services are recognized as innovative tools aimed at providing and distributing climate data and information according to the needs of end-users. Furthermore, needs-based climate services are extremely effective to manage climate risks and take advantage of the opportunities associated with climate change impacts. To date, climate services are mainly related to climate models that supply climate data (e.g. temperature, precipitations) at different spatial and time scales. However, there is a significant gap of tools aimed at providing information about risks and impacts induced by climate change and allowing non-expert stakeholders to use both climate-model and climate-impact data. DESYCO is a GIS-Decision Support System aimed at the integrated assessment of multiple climate change impacts on vulnerable coastal systems (e.g. beaches, river deltas, estuaries and lagoons, wetlands, agricultural and urban areas). It is an open source software that manages different input data (e.g. raster or shapefiles) coming from climate models (e.g. global and regional climate projections) and high resolution impact models (e.g. hydrodynamic, hydrological and biogeochemical simulations) in order to provide hazard, exposure, susceptibility, risk and damage maps for the identification and prioritization of hot-spot areas and to provide a basis for the definition of coastal adaptation and management strategies. Within the CLIM-RUN project (FP7) DESYCO is proposed as an helpful tool to bridge the gap between climate data and stakeholder needs and will be applied to the coastal area of the North Adriatic Sea (Italy) in order to provide climate services for local authorities involved in coastal zone management. Accordingly, a first workshop was held in Venice (Italy) with coastal authorities, climate experts and climate change risk experts, in order to start an iterative exchange of information about the knowledge related to climate change, climate models and projections, impact and risk parameters and to know what are stakeholder needs related to climate change in a climate service perspective. The preliminary results gained from the workshop showed that DESYCO is an helpful tool for the impact and risk assessment related to climate change that could be improved in order to fulfill stakeholder needs.

Use of NARCCAP Model Projections to Develop a Future Typical Meteorological Year and Estimate the Impact of a Changing Climate on Building Energy Consumption

NASA Astrophysics Data System (ADS)

Patton, S. L.; Takle, E. S.; Passe, U.; Kalvelage, K.

2013-12-01

Current simulations of building energy consumption use weather input files based on the past thirty years of climate observations. These 20th century climate conditions may be inadequate when designing buildings meant to function well into the 21st century. An alternative is using model projections of climate change to estimate future risk to the built environment. In this study, model-projected changes in climate were combined with existing typical meteorological year data to create future typical meteorological year data. These data were then formatted for use in EnergyPlus simulation software to evaluate their potential impact on commercial building energy consumption. The modeled climate data were taken from the North American Regional Climate Change Assessment Program (NARCCAP). NARCCAP uses results of global climate models to drive regional climate models, also known as dynamical downscaling. This downscaling gives higher resolution results over specific locations, and the multiple global/regional climate model combinations provide a unique opportunity to quantify the uncertainty of climate change projections and their impacts. Our results show a projected decrease in heating energy consumption and a projected increase in cooling energy consumption for nine locations across the United States for all model combinations. Warmer locations may expect a decrease in heating load of around 30% to 45% and an increase in cooling load of around 25% to 35%. Colder locations may expect a decrease in heating load of around 15% to 25% and an increase in cooling load of around 40% to 70%. The change in net energy consumption is determined by the balance between the magnitudes of heating change and cooling change. Net energy consumption is projected to increase by an average of 5% for lower-latitude locations and decrease by an average of 5% for higher-latitude locations. With these projected annual and seasonal changes presenting strong evidence for the unsuitable nature of current building practices holding up under future climate change, we recommend using our methods and results to make modifications and adaptations to existing buildings and to aid in the design of future buildings.

Predictive Seagrass Habitat Model

EPA Science Inventory

Restoration of ecosystem services provided by seagrass habitats in estuaries requires a firm understanding of the modes of action of multiple interacting stressors including nutrients, climate change, coastal land-use change, and habitat modification. We explored the application...

Modeled interactive effects of precipitation, temperature, and [CO2] on ecosystem carbon and water dynamics in different climatic zones

Treesearch

Yiqi Luo; Dieter Gerten; Guerric Le Maire; William J. Parton; Ensheng Weng; Xuhui Zhou; Cindy Keough; Claus Beier; Philippe Ciais; Wolfgang Cramer; Jeffrey S. Dukes; Bridget Emmett; Paul J. Hanson; Alan Knapp; Sune Linder; Dan Nepstad; Lindsey. Rustad

2008-01-01

Interactive effects of multiple global change factors on ecosystem processes are complex. It is relatively expensive to explore those interactions in manipulative experiments. We conducted a modeling analysis to identify potentially important interactions and to stimulate hypothesis formulation for experimental research. Four models were used to quantify interactive...

A high-resolution, empirical approach to climate impact assessment for regulatory analysis

NASA Astrophysics Data System (ADS)

Delgado, M.; Simcock, J. G.; Greenstone, M.; Hsiang, S. M.; Kopp, R. E.; Carleton, T.; Hultgren, A.; Jina, A.; Rising, J. A.; Nath, I.; Yuan, J.; Rode, A.; Chong, T.; Dobbels, G.; Hussain, A.; Wang, J.; Song, Y.; Mohan, S.; Larsen, K.; Houser, T.

2017-12-01

Recent breakthroughs in computing, data availability, and methodology have precipitated significant advances in the understanding of the relationship between climate and socioeconomic outcomes [1]. And while the use of estimates of the global marginal costs of greenhouse gas emissions (e.g. the SCC) are a mandatory component of regulatory policy in many jurisdictions, existing SCC-IAMs have lagged advances in impact assessment and valuation [2]. Recent work shows that incorporating high spatial and temporal resolution can significantly affect the observed relationships of economic outcomes to climate and socioeconomic factors [3] and that maintaining this granularity is critical to understanding the sensitivity of aggregate measures of valuation to inequality and risk adjustment methodologies [4]. We propose a novel framework that decomposes uncertainty in the SCC along multiple sources, including aggregate climate response parameters, the translation of global climate into local weather, the effect of weather on physical and economic systems, human and macro-economic responses, and impact valuation methodologies. This work extends Hsiang et al. (2017) [4] to directly estimate local response functions for multiple sectors in each of 24,378 global regions and to estimate impacts at this resolution daily, incorporating endogenous, empirically-estimated adaptation and costs. The goal of this work is to provide insight into the heterogeneity of climate impacts and to work with other modeling teams to enhance the empirical grounding of integrated climate impact assessment in more complex energy-environment-economics models. [1] T. Carleton and S. Hsiang (2016), DOI: 10.1126/science.aad9837. [2] National Academies of Sciences, Engineering, and Medicine (2017), DOI: 10.17226/24651. [3] Burke, M., S. Hsiang, and E. Miguel (2015), DOI: 10.1038/nature15725. [4] S. Hsiang et al. (2017), DOI: 10.1126/science.aal4369.

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;

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 sources of uncertainty in an ensemble of hydrological climate-impact projections

NASA Astrophysics Data System (ADS)

Aryal, Anil; Shrestha, Sangam; Babel, Mukand S.

2018-01-01

The objective of this paper is to quantify the various sources of uncertainty in the assessment of climate change impact on hydrology in the Tamakoshi River Basin, located in the north-eastern part of Nepal. Multiple climate and hydrological models were used to simulate future climate conditions and discharge in the basin. The simulated results of future climate and river discharge were analysed for the quantification of sources of uncertainty using two-way and three-way ANOVA. The results showed that temperature and precipitation in the study area are projected to change in near- (2010-2039), mid- (2040-2069) and far-future (2070-2099) periods. Maximum temperature is likely to rise by 1.75 °C under Representative Concentration Pathway (RCP) 4.5 and by 3.52 °C under RCP 8.5. Similarly, the minimum temperature is expected to rise by 2.10 °C under RCP 4.5 and by 3.73 °C under RCP 8.5 by the end of the twenty-first century. Similarly, the precipitation in the study area is expected to change by - 2.15% under RCP 4.5 and - 2.44% under RCP 8.5 scenarios. The future discharge in the study area was projected using two hydrological models, viz. Soil and Water Assessment Tool (SWAT) and Hydrologic Engineering Center's Hydrologic Modelling System (HEC-HMS). The SWAT model projected discharge is expected to change by small amount, whereas HEC-HMS model projected considerably lower discharge in future compared to the baseline period. The results also show that future climate variables and river hydrology contain uncertainty due to the choice of climate models, RCP scenarios, bias correction methods and hydrological models. During wet days, more uncertainty is observed due to the use of different climate models, whereas during dry days, the use of different hydrological models has a greater effect on uncertainty. Inter-comparison of the impacts of different climate models reveals that the REMO climate model shows higher uncertainty in the prediction of precipitation and, consequently, in the prediction of future discharge and maximum probable flood.

Future discharge drought across climate regions around the world modelled with a synthetic hydrological modelling approach forced by three general circulation models

NASA Astrophysics Data System (ADS)

Wanders, N.; Van Lanen, H. A. J.

2015-03-01

Hydrological drought characteristics (drought in groundwater and streamflow) likely will change in the 21st century as a result of climate change. The magnitude and directionality of these changes and their dependency on climatology and catchment characteristics, however, is uncertain. In this study a conceptual hydrological model was forced by downscaled and bias-corrected outcome from three general circulation models for the SRES A2 emission scenario (GCM forced models), and the WATCH Forcing Data set (reference model). The threshold level method was applied to investigate drought occurrence, duration and severity. Results for the control period (1971-2000) show that the drought characteristics of each GCM forced model reasonably agree with the reference model for most of the climate types, suggesting that the climate models' results after post-processing produce realistic outcomes for global drought analyses. For the near future (2021-2050) and far future (2071-2100) the GCM forced models show a decrease in drought occurrence for all major climates around the world and increase of both average drought duration and deficit volume of the remaining drought events. The largest decrease in hydrological drought occurrence is expected in cold (D) climates where global warming results in a decreased length of the snow season and an increased precipitation. In the dry (B) climates the smallest decrease in drought occurrence is expected to occur, which probably will lead to even more severe water scarcity. However, in the extreme climate regions (desert and polar), the drought analysis for the control period showed that projections of hydrological drought characteristics are most uncertain. On a global scale the increase in hydrological drought duration and severity in multiple regions will lead to a higher impact of drought events, which should motivate water resource managers to timely anticipate the increased risk of more severe drought in groundwater and streamflow and to design pro-active measures.

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.

Annual grass invasion in sagebrush-steppe: The relative importance of climate, soil properties and biotic interactions

USGS Publications Warehouse

Bansal, Sheel; Sheley, Roger L.

2016-01-01

The invasion by winter-annual grasses (AGs) such as Bromus tectorum into sagebrush steppe throughout the western USA is a classic example of a biological invasion with multiple, interacting climate, soil and biotic factors driving the invasion, although few studies have examined all components together. Across a 6000-km2 area of the northern Great Basin, we conducted a field assessment of 100 climate, soil, and biotic (functional group abundances, diversity) factors at each of 90 sites that spanned an invasion gradient ranging from 0 to 100 % AG cover. We first determined which biotic and abiotic factors had the strongest correlative relationships with AGs and each resident functional group. We then used regression and structural equation modeling to explore how multiple ecological factors interact to influence AG abundance. Among biotic interactions, we observed negative relationships between AGs and biodiversity, perennial grass cover, resident species richness, biological soil crust cover and shrub density, whereas perennial and annual forb cover, tree cover and soil microbial biomass had no direct linkage to AG. Among abiotic factors, AG cover was strongly related to climate (increasing cover with increasing temperature and aridity), but had weak relationships with soil factors. Our structural equation model showed negative effects of perennial grasses and biodiversity on AG cover while integrating the negative effects of warmer climate and positive influence of belowground processes on resident functional groups. Our findings illustrate the relative importance of biotic interactions and climate on invasive abundance, while soil properties appear to have stronger relationships with resident biota than with invasives.

Changes in seasonal climate outpace compensatory density-dependence in eastern brook trout

USGS Publications Warehouse

Bassar, Ronald D.; Letcher, Benjamin H.; Nislow, Keith H.; Whiteley, Andrew R.

2016-01-01

Understanding how multiple extrinsic (density-independent) factors and intrinsic (density-dependent) mechanisms influence population dynamics has become increasingly urgent in the face of rapidly changing climates. It is particularly unclear how multiple extrinsic factors with contrasting effects among seasons are related to declines in population numbers and changes in mean body size and whether there is a strong role for density-dependence. The primary goal of this study was to identify the roles of seasonal variation in climate driven environmental direct effects (mean stream flow and temperature) versus density-dependence on population size and mean body size in eastern brook trout (Salvelinus fontinalis). We use data from a 10-year capture-mark-recapture study of eastern brook trout in four streams in Western Massachusetts, USA to parameterize a discrete-time population projection model. The model integrates matrix modeling techniques used to characterize discrete population structures (age, habitat type and season) with integral projection models (IPMs) that characterize demographic rates as continuous functions of organismal traits (in this case body size). Using both stochastic and deterministic analyses we show that decreases in population size are due to changes in stream flow and temperature and that these changes are larger than what can be compensated for through density-dependent responses. We also show that the declines are due mostly to increasing mean stream temperatures decreasing the survival of the youngest age class. In contrast, increases in mean body size over the same period are the result of indirect changes in density with a lesser direct role of climate-driven environmental change.

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

PubMed Central

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

2016-01-01

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

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

PubMed

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

2016-01-01

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

Pan evaporation modeling using six different heuristic computing methods in different climates of China

NASA Astrophysics Data System (ADS)

Wang, Lunche; Kisi, Ozgur; Zounemat-Kermani, Mohammad; Li, Hui

2017-01-01

Pan evaporation (Ep) plays important roles in agricultural water resources management. One of the basic challenges is modeling Ep using limited climatic parameters because there are a number of factors affecting the evaporation rate. This study investigated the abilities of six different soft computing methods, multi-layer perceptron (MLP), generalized regression neural network (GRNN), fuzzy genetic (FG), least square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), adaptive neuro-fuzzy inference systems with grid partition (ANFIS-GP), and two regression methods, multiple linear regression (MLR) and Stephens and Stewart model (SS) in predicting monthly Ep. Long-term climatic data at various sites crossing a wide range of climates during 1961-2000 are used for model development and validation. The results showed that the models have different accuracies in different climates and the MLP model performed superior to the other models in predicting monthly Ep at most stations using local input combinations (for example, the MAE (mean absolute errors), RMSE (root mean square errors), and determination coefficient (R2) are 0.314 mm/day, 0.405 mm/day and 0.988, respectively for HEB station), while GRNN model performed better in Tibetan Plateau (MAE, RMSE and R2 are 0.459 mm/day, 0.592 mm/day and 0.932, respectively). The accuracies of above models ranked as: MLP, GRNN, LSSVM, FG, ANFIS-GP, MARS and MLR. The overall results indicated that the soft computing techniques generally performed better than the regression methods, but MLR and SS models can be more preferred at some climatic zones instead of complex nonlinear models, for example, the BJ (Beijing), CQ (Chongqing) and HK (Haikou) stations. Therefore, it can be concluded that Ep could be successfully predicted using above models in hydrological modeling studies.

The Multitrophic Effects of Climate Change and Glacier Retreat in Mountain Rivers

PubMed Central

2017-01-01

Abstract Climate change is driving the thinning and retreat of many glaciers globally. Reductions of ice-melt inputs to mountain rivers are changing their physicochemical characteristics and, in turn, aquatic communities. Glacier-fed rivers can serve as model systems for investigations of climate-change effects on ecosystems because of their strong atmospheric–cryospheric links, high biodiversity of multiple taxonomic groups, and significant conservation interest concerning endemic species. From a synthesis of existing knowledge, we develop a new conceptual understanding of how reducing glacier cover affects organisms spanning multiple trophic groups. Although the response of macroinvertebrates to glacier retreat has been well described, we show that there remains a relative paucity of information for biofilm, microinvertebrate, and vertebrate taxa. Enhanced understanding of whole river food webs will improve the prediction of river-ecosystem responses to deglaciation while offering the potential to identify and protect a wider range of sensitive and threatened species. PMID:29599537

The Multitrophic Effects of Climate Change and Glacier Retreat in Mountain Rivers.

PubMed

Fell, Sarah C; Carrivick, Jonathan L; Brown, Lee E

2017-10-01

Climate change is driving the thinning and retreat of many glaciers globally. Reductions of ice-melt inputs to mountain rivers are changing their physicochemical characteristics and, in turn, aquatic communities. Glacier-fed rivers can serve as model systems for investigations of climate-change effects on ecosystems because of their strong atmospheric-cryospheric links, high biodiversity of multiple taxonomic groups, and significant conservation interest concerning endemic species. From a synthesis of existing knowledge, we develop a new conceptual understanding of how reducing glacier cover affects organisms spanning multiple trophic groups. Although the response of macroinvertebrates to glacier retreat has been well described, we show that there remains a relative paucity of information for biofilm, microinvertebrate, and vertebrate taxa. Enhanced understanding of whole river food webs will improve the prediction of river-ecosystem responses to deglaciation while offering the potential to identify and protect a wider range of sensitive and threatened species.

Incorporating spatial autocorrelation into species distribution models alters forecasts of climate-mediated range shifts.

PubMed

Crase, Beth; Liedloff, Adam; Vesk, Peter A; Fukuda, Yusuke; Wintle, Brendan A

2014-08-01

Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad-scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment-only models (most frequently applied in species' range forecasts), and two approaches that incorporate SA; autologistic models and residuals autocovariate (RAC) models. Differences in forecasts among modeling approaches and climate scenarios were quantified. While all model predictions at the current time closely matched that of the actual current distribution of the mangrove communities, under the climate change scenarios environment-only models forecast substantially greater range shifts than models incorporating SA. Furthermore, the magnitude of these differences intensified with increasing increments of climate change across the scenarios. When models do not account for SA, forecasts of species' range shifts indicate more extreme impacts of climate change, compared to models that explicitly account for SA. Therefore, where biological or population processes induce substantial autocorrelation in the distribution of organisms, and this is not modeled, model predictions will be inaccurate. These results have global importance for conservation efforts as inaccurate forecasts lead to ineffective prioritization of conservation activities and potentially to avoidable species extinctions. © 2014 John Wiley & Sons Ltd.

Assessment of soil organic carbon stocks under future climate and land cover changes in Europe.

PubMed

Yigini, Yusuf; Panagos, Panos

2016-07-01

Soil organic carbon plays an important role in the carbon cycling of terrestrial ecosystems, variations in soil organic carbon stocks are very important for the ecosystem. In this study, a geostatistical model was used for predicting current and future soil organic carbon (SOC) stocks in Europe. The first phase of the study predicts current soil organic carbon content by using stepwise multiple linear regression and ordinary kriging and the second phase of the study projects the soil organic carbon to the near future (2050) by using a set of environmental predictors. We demonstrate here an approach to predict present and future soil organic carbon stocks by using climate, land cover, terrain and soil data and their projections. The covariates were selected for their role in the carbon cycle and their availability for the future model. The regression-kriging as a base model is predicting current SOC stocks in Europe by using a set of covariates and dense SOC measurements coming from LUCAS Soil Database. The base model delivers coefficients for each of the covariates to the future model. The overall model produced soil organic carbon maps which reflect the present and the future predictions (2050) based on climate and land cover projections. The data of the present climate conditions (long-term average (1950-2000)) and the future projections for 2050 were obtained from WorldClim data portal. The future climate projections are the recent climate projections mentioned in the Fifth Assessment IPCC report. These projections were extracted from the global climate models (GCMs) for four representative concentration pathways (RCPs). The results suggest an overall increase in SOC stocks by 2050 in Europe (EU26) under all climate and land cover scenarios, but the extent of the increase varies between the climate model and emissions scenarios. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Examining the Performance of Statistical Downscaling Methods: Toward Matching Applications to Data Products

NASA Astrophysics Data System (ADS)

Dixon, K. W.; Lanzante, J. R.; Adams-Smith, D.

2017-12-01

Several challenges exist when seeking to use future climate model projections in a climate impacts study. A not uncommon approach is to utilize climate projection data sets derived from more than one future emissions scenario and from multiple global climate models (GCMs). The range of future climate responses represented in the set is sometimes taken to be indicative of levels of uncertainty in the projections. Yet, GCM outputs are deemed to be unsuitable for direct use in many climate impacts applications. GCM grids typically are viewed as being too coarse. Additionally, regional or local-scale biases in a GCM's simulation of the contemporary climate that may not be problematic from a global climate modeling perspective may be unacceptably large for a climate impacts application. Statistical downscaling (SD) of climate projections - a type of post-processing that uses observations to inform the refinement of GCM projections - is often used in an attempt to account for GCM biases and to provide additional spatial detail. "What downscaled climate projection is the best one to use" is a frequently asked question, but one that is not always easy to answer, as it can be dependent on stakeholder needs and expectations. Here we present results from a perfect model experimental design illustrating how SD method performance can vary not only by SD method, but how performance can also vary by location, season, climate variable of interest, amount of projected climate change, SD configuration choices, and whether one is interested in central tendencies or the tails of the distribution. Awareness of these factors can be helpful when seeking to determine the suitability of downscaled climate projections for specific climate impacts applications. It also points to the potential value of considering more than one SD data product in a study, so as to acknowledge uncertainties associated with the strengths and weaknesses of different downscaling methods.

Paleoclimate reconstruction through Bayesian data assimilation

NASA Astrophysics Data System (ADS)

Fer, I.; Raiho, A.; Rollinson, C.; Dietze, M.

2017-12-01

Methods of paleoclimate reconstruction from plant-based proxy data rely on assumptions of static vegetation-climate link which is often established between modern climate and vegetation. This approach might result in biased climate constructions as it does not account for vegetation dynamics. Predictive tools such as process-based dynamic vegetation models (DVM) and their Bayesian inversion could be used to construct the link between plant-based proxy data and palaeoclimate more realistically. In other words, given the proxy data, it is possible to infer the climate that could result in that particular vegetation composition, by comparing the DVM outputs to the proxy data within a Bayesian state data assimilation framework. In this study, using fossil pollen data from five sites across the northern hardwood region of the US, we assimilate fractional composition and aboveground biomass into dynamic vegetation models, LINKAGES, LPJ-GUESS and ED2. To do this, starting from 4 Global Climate Model outputs, we generate an ensemble of downscaled meteorological drivers for the period 850-2015. Then, as a first pass, we weigh these ensembles based on their fidelity with independent paleoclimate proxies. Next, we run the models with this ensemble of drivers, and comparing the ensemble model output to the vegetation data, adjust the model state estimates towards the data. At each iteration, we also reweight the climate values that make the model and data consistent, producing a reconstructed climate time-series dataset. We validated the method using present-day datasets, as well as a synthetic dataset, and then assessed the consistency of results across ecosystem models. Our method allows the combination of multiple data types to reconstruct the paleoclimate, with associated uncertainty estimates, based on ecophysiological and ecological processes rather than phenomenological correlations with proxy data.

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

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.

Impacts of uncertainties in European gridded precipitation observations on regional climate analysis.

PubMed

Prein, Andreas F; Gobiet, Andreas

2017-01-01

Gridded precipitation data sets are frequently used to evaluate climate models or to remove model output biases. Although precipitation data are error prone due to the high spatio-temporal variability of precipitation and due to considerable measurement errors, relatively few attempts have been made to account for observational uncertainty in model evaluation or in bias correction studies. In this study, we compare three types of European daily data sets featuring two Pan-European data sets and a set that combines eight very high-resolution station-based regional data sets. Furthermore, we investigate seven widely used, larger scale global data sets. Our results demonstrate that the differences between these data sets have the same magnitude as precipitation errors found in regional climate models. Therefore, including observational uncertainties is essential for climate studies, climate model evaluation, and statistical post-processing. Following our results, we suggest the following guidelines for regional precipitation assessments. (1) Include multiple observational data sets from different sources (e.g. station, satellite, reanalysis based) to estimate observational uncertainties. (2) Use data sets with high station densities to minimize the effect of precipitation undersampling (may induce about 60% error in data sparse regions). The information content of a gridded data set is mainly related to its underlying station density and not to its grid spacing. (3) Consider undercatch errors of up to 80% in high latitudes and mountainous regions. (4) Analyses of small-scale features and extremes are especially uncertain in gridded data sets. For higher confidence, use climate-mean and larger scale statistics. In conclusion, neglecting observational uncertainties potentially misguides climate model development and can severely affect the results of climate change impact assessments.

Changes in Concurrent Risk of Warm and Dry Years under Impact of Climate Change

NASA Astrophysics Data System (ADS)

Sarhadi, A.; Wiper, M.; Touma, D. E.; Ausín, M. C.; Diffenbaugh, N. S.

2017-12-01

Anthropogenic global warming has changed the nature and the risk of extreme climate phenomena. The changing concurrence of multiple climatic extremes (warm and dry years) may result in intensification of undesirable consequences for water resources, human and ecosystem health, and environmental equity. The present study assesses how global warming influences the probability that warm and dry years co-occur in a global scale. In the first step of the study a designed multivariate Mann-Kendall trend analysis is used to detect the areas in which the concurrence of warm and dry years has increased in the historical climate records and also climate models in the global scale. The next step investigates the concurrent risk of the extremes under dynamic nonstationary conditions. A fully generalized multivariate risk framework is designed to evolve through time under dynamic nonstationary conditions. In this methodology, Bayesian, dynamic copulas are developed to model the time-varying dependence structure between the two different climate extremes (warm and dry years). The results reveal an increasing trend in the concurrence risk of warm and dry years, which are in agreement with the multivariate trend analysis from historical and climate models. In addition to providing a novel quantification of the changing probability of compound extreme events, the results of this study can help decision makers develop short- and long-term strategies to prepare for climate stresses now and in the future.

The Saskatchewan River Basin - a large scale observatory for water security research (Invited)

NASA Astrophysics Data System (ADS)

Wheater, H. S.

2013-12-01

The 336,000 km2 Saskatchewan River Basin (SaskRB) in Western Canada illustrates many of the issues of Water Security faced world-wide. It poses globally-important science challenges due to the diversity in its hydro-climate and ecological zones. With one of the world's more extreme climates, it embodies environments of global significance, including the Rocky Mountains (source of the major rivers in Western Canada), the Boreal Forest (representing 30% of Canada's land area) and the Prairies (home to 80% of Canada's agriculture). Management concerns include: provision of water resources to more than three million inhabitants, including indigenous communities; balancing competing needs for water between different uses, such as urban centres, industry, agriculture, hydropower and environmental flows; issues of water allocation between upstream and downstream users in the three prairie provinces; managing the risks of flood and droughts; and assessing water quality impacts of discharges from major cities and intensive agricultural production. Superimposed on these issues is the need to understand and manage uncertain water futures, including effects of economic growth and environmental change, in a highly fragmented water governance environment. Key science questions focus on understanding and predicting the effects of land and water management and environmental change on water quantity and quality. To address the science challenges, observational data are necessary across multiple scales. This requires focussed research at intensively monitored sites and small watersheds to improve process understanding and fine-scale models. To understand large-scale effects on river flows and quality, land-atmosphere feedbacks, and regional climate, integrated monitoring, modelling and analysis is needed at large basin scale. And to support water management, new tools are needed for operational management and scenario-based planning that can be implemented across multiple scales and multiple jurisdictions. The SaskRB has therefore been developed as a large scale observatory, now a Regional Hydroclimate Project of the World Climate Research Programme's GEWEX project, and is available to contribute to the emerging North American Water Program. State-of-the-art hydro-ecological experimental sites have been developed for the key biomes, and a river and lake biogeochemical research facility, focussed on impacts of nutrients and exotic chemicals. Data are integrated at SaskRB scale to support the development of improved large scale climate and hydrological modelling products, the development of DSS systems for local, provincial and basin-scale management, and the development of related social science research, engaging stakeholders in the research and exploring their values and priorities for water security. The observatory provides multiple scales of observation and modelling required to develop: a) new climate, hydrological and ecological science and modelling tools to address environmental change in key environments, and their integrated effects and feedbacks at large catchment scale, b) new tools needed to support river basin management under uncertainty, including anthropogenic controls on land and water management and c) the place-based focus for the development of new transdisciplinary science.

Gone with the trees: Phylogeography of Rhodiola sect. Trifida (Crassulaceae) reveals multiple refugia on the Qinghai-Tibetan Plateau.

PubMed

Li, Yuan-Cong; Zhong, Da-Lv; Rao, Guang-Yuan; Wen, Jun; Ren, Yi; Zhang, Jian-Qiang

2018-04-01

Quaternary climatic oscillations have had tremendous effects on current distribution of species. Previous studies unraveled multiple microrefugia on the Qinghai-Tibetan Plateau (QTP) in two woody plants. Still we know little whether herbs growing in forests responded to climatic oscillations similarly. We herein conducted a phylogeographic study on Rhodiola sect. Trifida, an herbaceous group endemic to the QTP, which mainly growing on the forest floors, using plastid and ITS sequences as well as ecological niche modeling. The origin and divergence of major clades of sect. Trifida were in accordance with the last phase of the QTP uplifts. Mismatch distribution analysis indicated a range expansion dated to ca. 135 thousand years ago. A high frequency and an even distribution of private haplotypes in both plastid and ITS data sets throughout the distribution of sect. Trifida were detected. The ecological niche modeling results showed that there were suitable habitats on the QTP platform during the LGM. Our results found that multiple microrefugia existed on the QTP platform, supporting the hypothesis that species with similar geographic distribution and inhabiting the same community had similar responses to the Quaternary climatic oscillations. Furthermore, species delimitations in sect. Trifida need to be tested based on integrative evidence from morphological, ecological and genetic data. Copyright © 2018 Elsevier Inc. All rights reserved.

A review of multimodel superensemble forecasting for weather, seasonal climate, and hurricanes

NASA Astrophysics Data System (ADS)

Krishnamurti, T. N.; Kumar, V.; Simon, A.; Bhardwaj, A.; Ghosh, T.; Ross, R.

2016-06-01

This review provides a summary of work in the area of ensemble forecasts for weather, climate, oceans, and hurricanes. This includes a combination of multiple forecast model results that does not dwell on the ensemble mean but uses a unique collective bias reduction procedure. A theoretical framework for this procedure is provided, utilizing a suite of models that is constructed from the well-known Lorenz low-order nonlinear system. A tutorial that includes a walk-through table and illustrates the inner workings of the multimodel superensemble's principle is provided. Systematic errors in a single deterministic model arise from a host of features that range from the model's initial state (data assimilation), resolution, representation of physics, dynamics, and ocean processes, local aspects of orography, water bodies, and details of the land surface. Models, in their diversity of representation of such features, end up leaving unique signatures of systematic errors. The multimodel superensemble utilizes as many as 10 million weights to take into account the bias errors arising from these diverse features of multimodels. The design of a single deterministic forecast models that utilizes multiple features from the use of the large volume of weights is provided here. This has led to a better understanding of the error growths and the collective bias reductions for several of the physical parameterizations within diverse models, such as cumulus convection, planetary boundary layer physics, and radiative transfer. A number of examples for weather, seasonal climate, hurricanes and sub surface oceanic forecast skills of member models, the ensemble mean, and the superensemble are provided.

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.

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

Assessing Ecosystem Service Provision Under Climate Change to Support Conservation and Development Planning in Myanmar

NASA Technical Reports Server (NTRS)

Mandle, Lisa; Wolny, Stacie; Bhagabati, Nirmal; Helsingen, Hanna; Hamel, Perrine; Bartlett, Ryan; Dixon, Adam; Horton, Radley M.; Lesk, Corey; Manley, Danielle;

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.

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.

Bluetongue virus spread in Europe is a consequence of climatic, landscape and vertebrate host factors as revealed by phylogeographic inference

PubMed Central

Palmarini, Massimo; Mertens, Peter

2017-01-01

Spatio-temporal patterns of the spread of infectious diseases are commonly driven by environmental and ecological factors. This is particularly true for vector-borne diseases because vector populations can be strongly affected by host distribution as well as by climatic and landscape variables. Here, we aim to identify environmental drivers for bluetongue virus (BTV), the causative agent of a major vector-borne disease of ruminants that has emerged multiple times in Europe in recent decades. In order to determine the importance of climatic, landscape and host-related factors affecting BTV diffusion across Europe, we fitted different phylogeographic models to a dataset of 113 time-stamped and geo-referenced BTV genomes, representing multiple strains and serotypes. Diffusion models using continuous space revealed that terrestrial habitat below 300 m altitude, wind direction and higher livestock densities were associated with faster BTV movement. Results of discrete phylogeographic analysis involving generalized linear models broadly supported these findings, but varied considerably with the level of spatial partitioning. Contrary to common perception, we found no evidence for average temperature having a positive effect on BTV diffusion, though both methodological and biological reasons could be responsible for this result. Our study provides important insights into the drivers of BTV transmission at the landscape scale that could inform predictive models of viral spread and have implications for designing control strategies. PMID:29021180

The joint space-time statistics of macroweather precipitation, space-time statistical factorization and macroweather models

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

Lovejoy, S., E-mail: lovejoy@physics.mcgill.ca; Lima, M. I. P. de; Department of Civil Engineering, University of Coimbra, 3030-788 Coimbra

2015-07-15

Over the range of time scales from about 10 days to 30–100 years, in addition to the familiar weather and climate regimes, there is an intermediate “macroweather” regime characterized by negative temporal fluctuation exponents: implying that fluctuations tend to cancel each other out so that averages tend to converge. We show theoretically and numerically that macroweather precipitation can be modeled by a stochastic weather-climate model (the Climate Extended Fractionally Integrated Flux, model, CEFIF) first proposed for macroweather temperatures and we show numerically that a four parameter space-time CEFIF model can approximately reproduce eight or so empirical space-time exponents. In spitemore » of this success, CEFIF is theoretically and numerically difficult to manage. We therefore propose a simplified stochastic model in which the temporal behavior is modeled as a fractional Gaussian noise but the spatial behaviour as a multifractal (climate) cascade: a spatial extension of the recently introduced ScaLIng Macroweather Model, SLIMM. Both the CEFIF and this spatial SLIMM model have a property often implicitly assumed by climatologists that climate statistics can be “homogenized” by normalizing them with the standard deviation of the anomalies. Physically, it means that the spatial macroweather variability corresponds to different climate zones that multiplicatively modulate the local, temporal statistics. This simplified macroweather model provides a framework for macroweather forecasting that exploits the system's long range memory and spatial correlations; for it, the forecasting problem has been solved. We test this factorization property and the model with the help of three centennial, global scale precipitation products that we analyze jointly in space and in time.« less

More robust regional precipitation projection from selected CMIP5 models based on multiple-dimensional metrics

NASA Astrophysics Data System (ADS)

Qian, Y.; Wang, L.; Leung, L. R.; Lin, G.; Lu, J.; Gao, Y.; Zhang, Y.

2017-12-01

Projecting precipitation changes is challenging because of incomplete understanding of the climate system and biases and uncertainty in climate models. In East Asia where summer precipitation is dominantly influenced by the monsoon circulation and the global models from Coupled Model Intercomparison Project Phase 5 (CMIP5), however, give various projection of precipitation change for 21th century. It is critical for community to know which models' projection are more reliable in response to natural and anthropogenic forcings. In this study we defined multiple-dimensional metrics, measuring the model performance in simulating the present-day of large-scale circulation, regional precipitation and relationship between them. The large-scale circulation features examined in this study include the lower tropospheric southwesterly winds, the western North Pacific subtropical high, the South China Sea Subtropical High, and the East Asian westerly jet in the upper troposphere. Each of these circulation features transport moisture to East Asia, enhancing the moist static energy and strengthening the Meiyu moisture front that is the primary mechanism for precipitation generation in eastern China. Based on these metrics, 30 models in CMIP5 ensemble are classified into three groups. Models in the top performing group projected regional precipitation patterns that are more similar to each other than the bottom or middle performing group and consistently projected statistically significant increasing trends in two of the large-scale circulation indices and precipitation. In contrast, models in the bottom or middle performing group projected small drying or no trends in precipitation. We also find the models that only reasonably reproduce the observed precipitation climatology does not guarantee more reliable projection of future precipitation because good simulation skill could be achieved through compensating errors from multiple sources. Herein the potential for more robust projections of precipitation changes at regional scale is demonstrated through the use of discriminating metric to subsample the multi-model ensemble. The results from this study provides insights for how to select models from CMIP ensemble to project regional climate and hydrological cycle changes.

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).

(Un)certainty in climate change impacts on global energy consumption

NASA Astrophysics Data System (ADS)

van Ruijven, B. J.; De Cian, E.; Sue Wing, I.

2017-12-01

Climate change is expected to have an influence on the energy sector, especially on energy demand. For many locations, this change in energy demand is a balance between increase of demand for space cooling and a decrease of space heating demand. We perform a large-scale uncertainty analysis to characterize climate change risk on energy consumption as driven by climate and socioeconomic uncertainty. We combine a dynamic econometric model1 with multiple realizations of temperature projections from all 21 CMIP5 models (from the NASA Earth Exchange Global Daily Downscaled Projections2) under moderate (RCP4.5) and vigorous (RCP8.5) warming. Global spatial population projections for five SSPs are combined with GDP projections to construct scenarios for future energy demand driven by socioeconomic change. Between the climate models, we find a median global increase in climate-related energy demand of around 24% by 2050 under RCP8.5 with an interquartile range of 18-38%. Most climate models agree on increases in energy demand of more than 25% or 50% in tropical regions, the Southern USA and Southern China (see Figure). With respect to socioeconomic scenarios, we find wide variations between the SSPs for the number of people in low-income countries who are exposed to increases in energy demand. Figure attached: Number of models that agree on total climate-related energy consumption to increase or decrease by more than 0, 10, 25 or 50% by 2050 under RCP8.5 and SSP5 as result of the CMIP5 ensemble of temperature projections. References1. De Cian, E. & Sue Wing, I. Global Energy Demand in a Warming Climate. (FEEM, 2016). 2. Thrasher, B., Maurer, E. P., McKellar, C. & Duffy, P. B. Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrol Earth Syst Sci 16, 3309-3314 (2012).

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.

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.

Downscaled climate projections for the Southeast United States: evaluation and use for ecological applications

USGS Publications Warehouse

Wootten, Adrienne; Smith, Kara; Boyles, Ryan; Terando, Adam; Stefanova, Lydia; Misra, Vasru; Smith, Tom; Blodgett, David L.; Semazzi, Fredrick

2014-01-01

Climate change is likely to have many effects on natural ecosystems in the Southeast U.S. The National Climate Assessment Southeast Technical Report (SETR) indicates that natural ecosystems in the Southeast are likely to be affected by warming temperatures, ocean acidification, sea-level rise, and changes in rainfall and evapotranspiration. To better assess these how climate changes could affect multiple sectors, including ecosystems, climatologists have created several downscaled climate projections (or downscaled datasets) that contain information from the global climate models (GCMs) translated to regional or local scales. The process of creating these downscaled datasets, known as downscaling, can be carried out using a broad range of statistical or numerical modeling techniques. The rapid proliferation of techniques that can be used for downscaling and the number of downscaled datasets produced in recent years present many challenges for scientists and decisionmakers in assessing the impact or vulnerability of a given species or ecosystem to climate change. Given the number of available downscaled datasets, how do these model outputs compare to each other? Which variables are available, and are certain downscaled datasets more appropriate for assessing vulnerability of a particular species? Given the desire to use these datasets for impact and vulnerability assessments and the lack of comparison between these datasets, the goal of this report is to synthesize the information available in these downscaled datasets and provide guidance to scientists and natural resource managers with specific interests in ecological modeling and conservation planning related to climate change in the Southeast U.S. This report enables the Southeast Climate Science Center (SECSC) to address an important strategic goal of providing scientific information and guidance that will enable resource managers and other participants in Landscape Conservation Cooperatives to make science-based climate change adaptation decisions.

Integrating a Detailed Agricultural Model in a Global Economic Framework: New methods for assessment of climate mitigation and adaptation opportunities

NASA Astrophysics Data System (ADS)

Thomson, A. M.; Izaurralde, R. C.; Calvin, K.; Zhang, X.; Wise, M.; West, T. O.

2010-12-01

Climate change and food security are global issues increasingly linked through human decision making that takes place across all scales from on-farm management actions to international climate negotiations. Understanding how agricultural systems can respond to climate change, through mitigation or adaptation, while still supplying sufficient food to feed a growing global population, thus requires a multi-sector tool in a global economic framework. Integrated assessment models are one such tool, however they are typically driven by historical aggregate statistics of production in combination with exogenous assumptions of future trends in agricultural productivity; they are not yet capable of exploring agricultural management practices as climate adaptation or mitigation strategies. Yet there are agricultural models capable of detailed biophysical modeling of farm management and climate impacts on crop yield, soil erosion and C and greenhouse gas emissions, although these are typically applied at point scales that are incompatible with coarse resolution integrated assessment modeling. To combine the relative strengths of these modeling systems, we are using the agricultural model EPIC (Environmental Policy Integrated Climate), applied in a geographic data framework for regional analyses, to provide input to the global economic model GCAM (Global Change Assessment Model). The initial phase of our approach focuses on a pilot region of the Midwest United States, a highly productive agricultural area. We apply EPIC, a point based biophysical process model, at 60 m spatial resolution within this domain and aggregate the results to GCAM agriculture and land use subregions for the United States. GCAM is then initialized with multiple management options for key food and bioenergy crops. Using EPIC to distinguish these management options based on grain yield, residue yield, soil C change and cost differences, GCAM then simulates the optimum distribution of the available management options to meet demands for food and energy over the next century. The coupled models provide a new platform for evaluating future changes in agricultural management based on food demand, bioenergy demand, and changes in crop yield and soil C under a changing climate. This framework can be applied to evaluate the economically and biophysically optimal distribution of management under future climates.

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.

School environments and obesity: The mediating role of personal stress.

PubMed

Milam, Adam J; Jones, Chandria D; Debnam, Katrina J; Bradshaw, Catherine P

2017-01-01

Youth spend a large amount of time in the school environment. Given the multiple influences of teachers, peers, and food and physical activity options, youth are likely to experience stressors that can influence their weight. This study examines the association between school climate and weight status. Students ( n = 28,582; 58 schools) completed an online, anonymous school climate survey as part of the Maryland Safe and Supportive Schools Project. Multilevel structural equation modeling was used to explore the association between school climate, personal stress, and obesity. Analyses were stratified by gender. At the individual level, poor school climate (bullying, physical safety, and lack of whole-school connectedness) was associated with an increased likelihood of being overweight among females ( β =.115, p = .019) but not males ( β = .138; p =.244), after controlling for age, race, and physical activity. There was no association between school climate at the school level and being overweight among males or females. A second model included stress as a potential mediator; stress attenuated the relationship between poor school-related climate and being overweight ( β = .039; p = .048) among females. Findings suggest that stress related to school climate can play a role in the health and weight status of youth.

The Impact of School Climate and School Identification on Academic Achievement: Multilevel Modeling with Student and Teacher Data

PubMed Central

Maxwell, Sophie; Reynolds, Katherine J.; Lee, Eunro; Subasic, Emina; Bromhead, David

2017-01-01

School climate is a leading factor in explaining student learning and achievement. Less work has explored the impact of both staff and student perceptions of school climate raising interesting questions about whether staff school climate experiences can add “value” to students' achievement. In the current research, multiple sources were integrated into a multilevel model, including staff self-reports, student self-reports, objective school records of academic achievement, and socio-economic demographics. Achievement was assessed using a national literacy and numeracy tests (N = 760 staff and 2,257 students from 17 secondary schools). In addition, guided by the “social identity approach,” school identification is investigated as a possible psychological mechanism to explain the relationship between school climate and achievement. In line with predictions, results show that students' perceptions of school climate significantly explain writing and numeracy achievement and this effect is mediated by students' psychological identification with the school. Furthermore, staff perceptions of school climate explain students' achievement on numeracy, writing and reading tests (while accounting for students' responses). However, staff's school identification did not play a significant role. Implications of these findings for organizational, social, and educational research are discussed. PMID:29259564

The Impact of School Climate and School Identification on Academic Achievement: Multilevel Modeling with Student and Teacher Data.

PubMed

Maxwell, Sophie; Reynolds, Katherine J; Lee, Eunro; Subasic, Emina; Bromhead, David

2017-01-01

School climate is a leading factor in explaining student learning and achievement. Less work has explored the impact of both staff and student perceptions of school climate raising interesting questions about whether staff school climate experiences can add "value" to students' achievement. In the current research, multiple sources were integrated into a multilevel model, including staff self-reports, student self-reports, objective school records of academic achievement, and socio-economic demographics. Achievement was assessed using a national literacy and numeracy tests ( N = 760 staff and 2,257 students from 17 secondary schools). In addition, guided by the "social identity approach," school identification is investigated as a possible psychological mechanism to explain the relationship between school climate and achievement. In line with predictions, results show that students' perceptions of school climate significantly explain writing and numeracy achievement and this effect is mediated by students' psychological identification with the school. Furthermore, staff perceptions of school climate explain students' achievement on numeracy, writing and reading tests (while accounting for students' responses). However, staff's school identification did not play a significant role. Implications of these findings for organizational, social, and educational research are discussed.

Visual Reconciliation of Alternative Similarity Spaces in Climate Modeling.

PubMed

Poco, Jorge; Dasgupta, Aritra; Wei, Yaxing; Hargrove, William; Schwalm, Christopher R; Huntzinger, Deborah N; Cook, Robert; Bertini, Enrico; Silva, Claudio T

2014-12-01

Visual data analysis often requires grouping of data objects based on their similarity. In many application domains researchers use algorithms and techniques like clustering and multidimensional scaling to extract groupings from data. While extracting these groups using a single similarity criteria is relatively straightforward, comparing alternative criteria poses additional challenges. In this paper we define visual reconciliation as the problem of reconciling multiple alternative similarity spaces through visualization and interaction. We derive this problem from our work on model comparison in climate science where climate modelers are faced with the challenge of making sense of alternative ways to describe their models: one through the output they generate, another through the large set of properties that describe them. Ideally, they want to understand whether groups of models with similar spatio-temporal behaviors share similar sets of criteria or, conversely, whether similar criteria lead to similar behaviors. We propose a visual analytics solution based on linked views, that addresses this problem by allowing the user to dynamically create, modify and observe the interaction among groupings, thereby making the potential explanations apparent. We present case studies that demonstrate the usefulness of our technique in the area of climate science.

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.

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.

Time-lag effects of global vegetation responses to climate change.

PubMed

Wu, Donghai; Zhao, Xiang; Liang, Shunlin; Zhou, Tao; Huang, Kaicheng; Tang, Bijian; Zhao, Wenqian

2015-09-01

Climate conditions significantly affect vegetation growth in terrestrial ecosystems. Due to the spatial heterogeneity of ecosystems, the vegetation responses to climate vary considerably with the diverse spatial patterns and the time-lag effects, which are the most important mechanism of climate-vegetation interactive effects. Extensive studies focused on large-scale vegetation-climate interactions use the simultaneous meteorological and vegetation indicators to develop models; however, the time-lag effects are less considered, which tends to increase uncertainty. In this study, we aim to quantitatively determine the time-lag effects of global vegetation responses to different climatic factors using the GIMMS3g NDVI time series and the CRU temperature, precipitation, and solar radiation datasets. First, this study analyzed the time-lag effects of global vegetation responses to different climatic factors. Then, a multiple linear regression model and partial correlation model were established to statistically analyze the roles of different climatic factors on vegetation responses, from which the primary climate-driving factors for different vegetation types were determined. The results showed that (i) both the time-lag effects of the vegetation responses and the major climate-driving factors that significantly affect vegetation growth varied significantly at the global scale, which was related to the diverse vegetation and climate characteristics; (ii) regarding the time-lag effects, the climatic factors explained 64% variation of the global vegetation growth, which was 11% relatively higher than the model ignoring the time-lag effects; (iii) for the area with a significant change trend (for the period 1982-2008) in the global GIMMS3g NDVI (P < 0.05), the primary driving factor was temperature; and (iv) at the regional scale, the variation in vegetation growth was also related to human activities and natural disturbances. Considering the time-lag effects is quite important for better predicting and evaluating the vegetation dynamics under the background of global climate change. © 2015 John Wiley & Sons Ltd.

Association of climate drivers with rainfall in New South Wales, Australia, using Bayesian Model Averaging

NASA Astrophysics Data System (ADS)

Duc, Hiep Nguyen; Rivett, Kelly; MacSween, Katrina; Le-Anh, Linh

2017-01-01

Rainfall in New South Wales (NSW), located in the southeast of the Australian continent, is known to be influenced by four major climate drivers: the El Niño/Southern Oscillation (ENSO), the Interdecadal Pacific Oscillation (IPO), the Southern Annular Mode (SAM) and the Indian Ocean Dipole (IOD). Many studies have shown the influences of ENSO, IPO modulation, SAM and IOD on rainfall in Australia and on southeast Australia in particular. However, only limited work has been undertaken using a multiple regression framework to examine the extent of the combined effect of these climate drivers on rainfall. This paper analysed the role of these combined climate drivers and their interaction on the rainfall in NSW using Bayesian Model Averaging (BMA) to account for model uncertainty by considering each of the linear models across the whole model space which is equal to the set of all possible combinations of predictors to find the model posterior probabilities and their expected predictor coefficients. Using BMA for linear regression models, we are able to corroborate and confirm the results from many previous studies. In addition, the method gives the ranking order of importance and the probability of the association of each of the climate drivers and their interaction on the rainfall at a site. The ability to quantify the relative contribution of the climate drivers offers the key to understand the complex interaction of drivers on rainfall, or lack of rainfall in a region, such as the three big droughts in southeastern Australia which have been the subject of discussion and debate recently on their causes.

Building an ensemble of climate scenarios for decision-making in hydrology: benefits, pitfalls and uncertainties

NASA Astrophysics Data System (ADS)

Braun, Marco; Chaumont, Diane

2013-04-01

Using climate model output to explore climate change impacts on hydrology requires several considerations, choices and methods in the post treatment of the datasets. In the effort of producing a comprehensive data base of climate change scenarios for over 300 watersheds in the Canadian province of Québec, a selection of state of the art procedures were applied to an ensemble comprising 87 climate simulations. The climate data ensemble is based on global climate simulations from the Coupled Model Intercomparison Project - Phase 3 (CMIP3) and regional climate simulations from the North American Regional Climate Change Assessment Program (NARCCAP) and operational simulations produced at Ouranos. Information on the response of hydrological systems to changing climate conditions can be derived by linking climate simulations with hydrological models. However, the direct use of raw climate model output variables as drivers for hydrological models is limited by issues such as spatial resolution and the calibration of hydro models with observations. Methods for downscaling and bias correcting the data are required to achieve seamless integration of climate simulations with hydro models. The effects on the results of four different approaches to data post processing were explored and compared. We present the lessons learned from building the largest data base yet for multiple stakeholders in the hydro power and water management sector in Québec putting an emphasis on the benefits and pitfalls in choosing simulations, extracting the data, performing bias corrections and documenting the results. A discussion of the sources and significance of uncertainties in the data will also be included. The climatological data base was subsequently used by the state owned hydro power company Hydro-Québec and the Centre d'expertise hydrique du Québec (CEHQ), the provincial water authority, to simulate future stream flows and analyse the impacts on hydrological indicators. While this submission focuses on the production of climatic scenarios for application in hydrology, the submission « The (cQ)2 project: assessing watershed scale hydrological changes for the province of Québec at the 2050 horizon, a collaborative framework » by Catherine Guay describes how Hydro-Québec and CEHQ put the data into use.

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.

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.

Earth System Modeling and Field Experiments in the Arctic-Boreal Zone - Report from a NASA Workshop

NASA Technical Reports Server (NTRS)

Sellers, Piers; Rienecker Michele; Randall, David; Frolking, Steve

2012-01-01

Early climate modeling studies predicted that the Arctic Ocean and surrounding circumpolar land masses would heat up earlier and faster than other parts of the planet as a result of greenhouse gas-induced climate change, augmented by the sea-ice albedo feedback effect. These predictions have been largely borne out by observations over the last thirty years. However, despite constant improvement, global climate models have greater difficulty in reproducing the current climate in the Arctic than elsewhere and the scatter between projections from different climate models is much larger in the Arctic than for other regions. Biogeochemical cycle (BGC) models indicate that the warming in the Arctic-Boreal Zone (ABZ) could lead to widespread thawing of the permafrost, along with massive releases of CO2 and CH4, and large-scale changes in the vegetation cover in the ABZ. However, the uncertainties associated with these BGC model predictions are even larger than those associated with the physical climate system models used to describe climate change. These deficiencies in climate and BGC models reflect, at least in part, an incomplete understanding of the Arctic climate system and can be related to inadequate observational data or analyses of existing data. A workshop was held at NASA/GSFC, May 22-24 2012, to assess the predictive capability of the models, prioritize the critical science questions; and make recommendations regarding new field experiments needed to improve model subcomponents. This presentation will summarize the findings and recommendations of the workshop, including the need for aircraft and flux tower measurements and extension of existing in-situ measurements to improve process modeling of both the physical climate and biogeochemical cycle systems. Studies should be directly linked to remote sensing investigations with a view to scaling up the improved process models to the Earth System Model scale. Data assimilation and observing system simulation studies should be used to guide the deployment pattern and schedule for inversion studies as well. Synthesis and integration of previously funded Arctic-Boreal projects (e.g., ABLE, BOREAS, ICESCAPE, ICEBRIDGE, ARCTAS) should also be undertaken. Such an effort would include the integration of multiple remotely sensed products from the EOS satellites and other resources.

Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models.

PubMed

Jenouvrier, Stéphanie; Holland, Marika; Stroeve, Julienne; Barbraud, Christophe; Weimerskirch, Henri; Serreze, Mark; Caswell, Hal

2012-09-01

Sea ice conditions in the Antarctic affect the life cycle of the emperor penguin (Aptenodytes forsteri). We present a population projection for the emperor penguin population of Terre Adélie, Antarctica, by linking demographic models (stage-structured, seasonal, nonlinear, two-sex matrix population models) to sea ice forecasts from an ensemble of IPCC climate models. Based on maximum likelihood capture-mark-recapture analysis, we find that seasonal sea ice concentration anomalies (SICa ) affect adult survival and breeding success. Demographic models show that both deterministic and stochastic population growth rates are maximized at intermediate values of annual SICa , because neither the complete absence of sea ice, nor heavy and persistent sea ice, would provide satisfactory conditions for the emperor penguin. We show that under some conditions the stochastic growth rate is positively affected by the variance in SICa . We identify an ensemble of five general circulation climate models whose output closely matches the historical record of sea ice concentration in Terre Adélie. The output of this ensemble is used to produce stochastic forecasts of SICa , which in turn drive the population model. Uncertainty is included by incorporating multiple climate models and by a parametric bootstrap procedure that includes parameter uncertainty due to both model selection and estimation error. The median of these simulations predicts a decline of the Terre Adélie emperor penguin population of 81% by the year 2100. We find a 43% chance of an even greater decline, of 90% or more. The uncertainty in population projections reflects large differences among climate models in their forecasts of future sea ice conditions. One such model predicts population increases over much of the century, but overall, the ensemble of models predicts that population declines are far more likely than population increases. We conclude that climate change is a significant risk for the emperor penguin. Our analytical approach, in which demographic models are linked to IPCC climate models, is powerful and generally applicable to other species and systems. © 2012 Blackwell Publishing Ltd.

Decision-relevant evaluation of climate models: A case study of chill hours in California

NASA Astrophysics Data System (ADS)

Jagannathan, K. A.; Jones, A. D.; Kerr, A. C.

2017-12-01

The past decade has seen a proliferation of different climate datasets with over 60 climate models currently in use. Comparative evaluation and validation of models can assist practitioners chose the most appropriate models for adaptation planning. However, such assessments are usually conducted for `climate metrics' such as seasonal temperature, while sectoral decisions are often based on `decision-relevant outcome metrics' such as growing degree days or chill hours. Since climate models predict different metrics with varying skill, the goal of this research is to conduct a bottom-up evaluation of model skill for `outcome-based' metrics. Using chill hours (number of hours in winter months where temperature is lesser than 45 deg F) in Fresno, CA as a case, we assess how well different GCMs predict the historical mean and slope of chill hours, and whether and to what extent projections differ based on model selection. We then compare our results with other climate-based evaluations of the region, to identify similarities and differences. For the model skill evaluation, historically observed chill hours were compared with simulations from 27 GCMs (and multiple ensembles). Model skill scores were generated based on a statistical hypothesis test of the comparative assessment. Future projections from RCP 8.5 runs were evaluated, and a simple bias correction was also conducted. Our analysis indicates that model skill in predicting chill hour slope is dependent on its skill in predicting mean chill hours, which results from the non-linear nature of the chill metric. However, there was no clear relationship between the models that performed well for the chill hour metric and those that performed well in other temperature-based evaluations (such winter minimum temperature or diurnal temperature range). Further, contrary to conclusions from other studies, we also found that the multi-model mean or large ensemble mean results may not always be most appropriate for this outcome metric. Our assessment sheds light on key differences between global versus local skill, and broad versus specific skill of climate models, highlighting that decision-relevant model evaluation may be crucial for providing practitioners with the best available climate information for their specific needs.

The impacts of climate, land use, and demography on fires during the 21st century simulated by CLM-CN

NASA Astrophysics Data System (ADS)

Kloster, S.; Mahowald, N. M.; Randerson, J. T.; Lawrence, P. J.

2012-01-01

Landscape fires during the 21st century are expected to change in response to multiple agents of global change. Important controlling factors include climate controls on the length and intensity of the fire season, fuel availability, and fire management, which are already anthropogenically perturbed today and are predicted to change further in the future. An improved understanding of future fires will contribute to an improved ability to project future anthropogenic climate change, as changes in fire activity will in turn impact climate. In the present study we used a coupled-carbon-fire model to investigate how changes in climate, demography, and land use may alter fire emissions. We used climate projections following the SRES A1B scenario from two different climate models (ECHAM5/MPI-OM and CCSM) and changes in population. Land use and harvest rates were prescribed according to the RCP 45 scenario. In response to the combined effect of all these drivers, our model estimated, depending on our choice of climate projection, an increase in future (2075-2099) fire carbon emissions by 17 and 62% compared to present day (1985-2009). The largest increase in fire emissions was predicted for Southern Hemisphere South America for both climate projections. For Northern Hemisphere Africa, a region that contributed significantly to the global total fire carbon emissions, the response varied between a decrease and an increase depending on the climate projection. We disentangled the contribution of the single forcing factors to the overall response by conducting an additional set of simulations in which each factor was individually held constant at pre-industrial levels. The two different projections of future climate change evaluated in this study led to increases in global fire carbon emissions by 22% (CCSM) and 66% (ECHAM5/MPI-OM). The RCP 45 projection of harvest and land use led to a decrease in fire carbon emissions by -5%. The RCP 26 and RCP 60 harvest and landuse projections caused decreases around -20%. Changes in human ignition led to an increase of 20%. When we also included changes in fire management efforts to suppress fires in densely populated areas, global fire carbon emission decreased by -6% in response to changes in population density. We concluded from this study that changes in fire emissions in the future are controlled by multiple interacting factors. Although changes in climate led to an increase in future fire emissions this could be globally counterbalanced by coupled changes in land use, harvest, and demography.

The underestimated potential of solar energy to mitigate climate change

NASA Astrophysics Data System (ADS)

Creutzig, Felix; Agoston, Peter; Goldschmidt, Jan Christoph; Luderer, Gunnar; Nemet, Gregory; Pietzcker, Robert C.

2017-09-01

The Intergovernmental Panel on Climate Change's fifth assessment report emphasizes the importance of bioenergy and carbon capture and storage for achieving climate goals, but it does not identify solar energy as a strategically important technology option. That is surprising given the strong growth, large resource, and low environmental footprint of photovoltaics (PV). Here we explore how models have consistently underestimated PV deployment and identify the reasons for underlying bias in models. Our analysis reveals that rapid technological learning and technology-specific policy support were crucial to PV deployment in the past, but that future success will depend on adequate financing instruments and the management of system integration. We propose that with coordinated advances in multiple components of the energy system, PV could supply 30-50% of electricity in competitive markets.

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.

Multiple mechanisms of Amazonian forest biomass losses in three dynamic global vegetation models under climate change.

PubMed

Galbraith, David; Levy, Peter E; Sitch, Stephen; Huntingford, Chris; Cox, Peter; Williams, Mathew; Meir, Patrick

2010-08-01

*The large-scale loss of Amazonian rainforest under some future climate scenarios has generally been considered to be driven by increased drying over Amazonia predicted by some general circulation models (GCMs). However, the importance of rainfall relative to other drivers has never been formally examined. *Here, we conducted factorial simulations to ascertain the contributions of four environmental drivers (precipitation, temperature, humidity and CO(2)) to simulated changes in Amazonian vegetation carbon (C(veg)), in three dynamic global vegetation models (DGVMs) forced with climate data based on HadCM3 for four SRES scenarios. *Increased temperature was found to be more important than precipitation reduction in causing losses of Amazonian C(veg) in two DGVMs (Hyland and TRIFFID), and as important as precipitation reduction in a third DGVM (LPJ). Increases in plant respiration, direct declines in photosynthesis and increases in vapour pressure deficit (VPD) all contributed to reduce C(veg) under high temperature, but the contribution of each mechanism varied greatly across models. Rising CO(2) mitigated much of the climate-driven biomass losses in the models. *Additional work is required to constrain model behaviour with experimental data under conditions of high temperature and drought. Current models may be overly sensitive to long-term elevated temperatures as they do not account for physiological acclimation.

Combined measurement and modeling of the hydrological impact of hydraulic redistribution using CLM4.5 at eight AmeriFlux sites

DOE PAGES

Fu, Congsheng; Wang, Guiling; Goulden, Michael L.; ...

2016-05-17

Effects of hydraulic redistribution (HR) on hydrological, biogeochemical, and ecological processes have been demonstrated in the field, but the current generation of standard earth system models does not include a representation of HR. Though recent studies have examined the effect of incorporating HR into land surface models, few (if any) have done cross-site comparisons for contrasting climate regimes and multiple vegetation types via the integration of measurement and modeling. Here, we incorporated the HR scheme of Ryel et al. (2002) into the NCAR Community Land Model Version 4.5 (CLM4.5), and examined the ability of the resulting hybrid model to capture themore » magnitude of HR flux and/or soil moisture dynamics from which HR can be directly inferred, to assess the impact of HR on land surface water and energy budgets, and to explore how the impact may depend on climate regimes and vegetation conditions. Eight AmeriFlux sites with contrasting climate regimes and multiple vegetation types were studied, including the Wind River Crane site in Washington State, the Santa Rita Mesquite savanna site in southern Arizona, and six sites along the Southern California Climate Gradient. HR flux, evapotranspiration (ET), and soil moisture were properly simulated in the present study, even in the face of various uncertainties. Our cross-ecosystem comparison showed that the timing, magnitude, and direction (upward or downward) of HR vary across ecosystems, and incorporation of HR into CLM4.5 improved the model-measurement matches of evapotranspiration, Bowen ratio, and soil moisture particularly during dry seasons. Lastly, our results also reveal that HR has important hydrological impact in ecosystems that have a pronounced dry season but are not overall so dry that sparse vegetation and very low soil moisture limit HR.« less

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.

Stochastic Hourly Weather Generator HOWGH: Validation and its Use in Pest Modelling under Present and Future Climates

NASA Astrophysics Data System (ADS)

Dubrovsky, M.; Hirschi, M.; Spirig, C.

2014-12-01

To quantify impact of the climate change on a specific pest (or any weather-dependent process) in a specific site, we may use a site-calibrated pest (or other) model and compare its outputs obtained with site-specific weather data representing present vs. perturbed climates. The input weather data may be produced by the stochastic weather generator. Apart from the quality of the pest model, the reliability of the results obtained in such experiment depend on an ability of the generator to represent the statistical structure of the real world weather series, and on the sensitivity of the pest model to possible imperfections of the generator. This contribution deals with the multivariate HOWGH weather generator, which is based on a combination of parametric and non-parametric statistical methods. Here, HOWGH is used to generate synthetic hourly series of three weather variables (solar radiation, temperature and precipitation) required by a dynamic pest model SOPRA to simulate the development of codling moth. The contribution presents results of the direct and indirect validation of HOWGH. In the direct validation, the synthetic series generated by HOWGH (various settings of its underlying model are assumed) are validated in terms of multiple climatic characteristics, focusing on the subdaily wet/dry and hot/cold spells. In the indirect validation, we assess the generator in terms of characteristics derived from the outputs of SOPRA model fed by the observed vs. synthetic series. The weather generator may be used to produce weather series representing present and future climates. In the latter case, the parameters of the generator may be modified by the climate change scenarios based on Global or Regional Climate Models. To demonstrate this feature, the results of codling moth simulations for future climate will be shown. Acknowledgements: The weather generator is developed and validated within the frame of projects WG4VALUE (project LD12029 sponsored by the Ministry of Education, Youth and Sports of CR), and VALUE (COST ES 1102 action).

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.

Integrated monitoring and information systems for managing aquatic invasive species in a changing climate.

PubMed

Lee, Henry; Reusser, Deborah A; Olden, Julian D; Smith, Scott S; Graham, Jim; Burkett, Virginia; Dukes, Jeffrey S; Piorkowski, Robert J; McPhedran, John

2008-06-01

Changes in temperature, precipitation, and other climatic drivers and sea-level rise will affect populations of existing native and non-native aquatic species and the vulnerability of aquatic environments to new invasions. Monitoring surveys provide the foundation for assessing the combined effects of climate change and invasions by providing baseline biotic and environmental conditions, although the utility of a survey depends on whether the results are quantitative or qualitative, and other design considerations. The results from a variety of monitoring programs in the United States are available in integrated biological information systems, although many include only non-native species, not native species. Besides including natives, we suggest these systems could be improved through the development of standardized methods that capture habitat and physiological requirements and link regional and national biological databases into distributed Web portals that allow drawing information from multiple sources. Combining the outputs from these biological information systems with environmental data would allow the development of ecological-niche models that predict the potential distribution or abundance of native and non-native species on the basis of current environmental conditions. Environmental projections from climate models can be used in these niche models to project changes in species distributions or abundances under altered climatic conditions and to identify potential high-risk invaders. There are, however, a number of challenges, such as uncertainties associated with projections from climate and niche models and difficulty in integrating data with different temporal and spatial granularity. Even with these uncertainties, integration of biological and environmental information systems, niche models, and climate projections would improve management of aquatic ecosystems under the dual threats of biotic invasions and climate change.

Integrated monitoring and information systems for managing aquatic invasive species in a changing climate

USGS Publications Warehouse

Lee, Henry; Reusser, Deborah A.; Olden, Julian D.; Smith, Scott S.; Graham, Jim; Burkett, Virginia; Dukes, Jeffrey S.; Piorkowski, Robert J.; Mcphedran, John

2008-01-01

Changes in temperature, precipitation, and other climatic drivers and sea-level rise will affect populations of existing native and non-native aquatic species and the vulnerability of aquatic environments to new invasions. Monitoring surveys provide the foundation for assessing the combined effects of climate change and invasions by providing baseline biotic and environmental conditions, although the utility of a survey depends on whether the results are quantitative or qualitative, and other design considerations. The results from a variety of monitoring programs in the United States are available in integrated biological information systems, although many include only non-native species, not native species. Besides including natives, we suggest these systems could be improved through the development of standardized methods that capture habitat and physiological requirements and link regional and national biological databases into distributed Web portals that allow drawing information from multiple sources. Combining the outputs from these biological information systems with environmental data would allow the development of ecological-niche models that predict the potential distribution or abundance of native and non-native species on the basis of current environmental conditions. Environmental projections from climate models can be used in these niche models to project changes in species distributions or abundances under altered climatic conditions and to identify potential high-risk invaders. There are, however, a number of challenges, such as uncertainties associated with projections from climate and niche models and difficulty in integrating data with different temporal and spatial granularity. Even with these uncertainties, integration of biological and environmental information systems, niche models, and climate projections would improve management of aquatic ecosystems under the dual threats of biotic invasions and climate change

Projections of annual rainfall and surface temperature from CMIP5 models over the BIMSTEC countries

NASA Astrophysics Data System (ADS)

Pattnayak, K. C.; Kar, S. C.; Dalal, Mamta; Pattnayak, R. K.

2017-05-01

Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) comprising Bangladesh, Bhutan, India, Myanmar, Nepal, Sri Lanka and Thailand brings together 21% of the world population. Thus the impact of climate change in this region is a major concern for all. To study the climate change, fifth phase of Climate Model Inter-comparison Project (CMIP5) models have been used to project the climate for the 21st century under the Representative Concentration Pathways (RCPs) 4.5 and 8.5 over the BIMSTEC countries for the period 1901 to 2100 (initial 105 years are historical period and the later 95 years are projected period). Climate change in the projected period has been examined with respect to the historical period. In order to validate the models, the mean annual rainfall has been compared with observations from multiple sources and temperature has been compared with the data from Climatic Research Unit (CRU) during the historical period. Comparison reveals that ensemble mean of the models is able to represent the observed spatial distribution of rainfall and temperature over the BIMSTEC countries. Therefore, data from these models may be used to study the future changes in the 21st century. Four out of six models show that the rainfall over India, Thailand and Myanmar has decreasing trend and Bangladesh, Bhutan, Nepal and Sri Lanka show an increasing trend in both the RCP scenarios. In case of temperature, all the models show an increasing trend over all the BIMSTEC countries in both the scenarios, however, the rate of increase is relatively less over Sri Lanka than the other countries. The rate of increase/decrease in rainfall and temperature are relatively more in RCP8.5 than RCP4.5 over all these countries. Inter-model comparison show that there are uncertainties within the CMIP5 model projections. More similar studies are required to be done for better understanding the model uncertainties in climate projections over this region.

The Paleoclimate Uncertainty Cascade: Tracking Proxy Errors Via Proxy System Models.

NASA Astrophysics Data System (ADS)

Emile-Geay, J.; Dee, S. G.; Evans, M. N.; Adkins, J. F.

2014-12-01

Paleoclimatic observations are, by nature, imperfect recorders of climate variables. Empirical approaches to their calibration are challenged by the presence of multiple sources of uncertainty, which may confound the interpretation of signals and the identifiability of the noise. In this talk, I will demonstrate the utility of proxy system models (PSMs, Evans et al, 2013, 10.1016/j.quascirev.2013.05.024) to quantify the impact of all known sources of uncertainty. PSMs explicitly encode the mechanistic knowledge of the physical, chemical, biological and geological processes from which paleoclimatic observations arise. PSMs may be divided into sensor, archive and observation components, all of which may conspire to obscure climate signals in actual paleo-observations. As an example, we couple a PSM for the δ18O of speleothem calcite to an isotope-enabled climate model (Dee et al, submitted) to analyze the potential of this measurement as a proxy for precipitation amount. A simple soil/karst model (Partin et al, 2013, 10.1130/G34718.1) is used as sensor model, while a hiatus-permitting chronological model (Haslett & Parnell, 2008, 10.1111/j.1467-9876.2008.00623.x) is used as part of the observation model. This subdivision allows us to explicitly model the transformation from precipitation amount to speleothem calcite δ18O as a multi-stage process via a physical and chemical sensor model, and a stochastic archive model. By illustrating the PSM's behavior within the context of the climate simulations, we show how estimates of climate variability may be affected by each submodel's transformation of the signal. By specifying idealized climate signals(periodic vs. episodic, slow vs. fast) to the PSM, we investigate how frequency and amplitude patterns are modulated by sensor and archive submodels. To the extent that the PSM and the climate models are representative of real world processes, then the results may help us more accurately interpret existing paleodata, characterize their uncertainties, and design sampling strategies that exploit their strengths while mitigating their weaknesses.

Spatial diffusion of influenza outbreak-related climate factors in Chiang Mai Province, Thailand.

PubMed

Nakapan, Supachai; Tripathi, Nitin Kumar; Tipdecho, Taravudh; Souris, Marc

2012-10-24

Influenza is one of the most important leading causes of respiratory illness in the countries located in the tropical areas of South East Asia and Thailand. In this study the climate factors associated with influenza incidence in Chiang Mai Province, Northern Thailand, were investigated. Identification of factors responsible for influenza outbreaks and the mapping of potential risk areas in Chiang Mai are long overdue. This work examines the association between yearly climate patterns between 2001 and 2008 and influenza outbreaks in the Chiang Mai Province. The climatic factors included the amount of rainfall, percent of rainy days, relative humidity, maximum, minimum temperatures and temperature difference. The study develops a statistical analysis to quantitatively assess the relationship between climate and influenza outbreaks and then evaluate its suitability for predicting influenza outbreaks. A multiple linear regression technique was used to fit the statistical model. The Inverse Distance Weighted (IDW) interpolation and Geographic Information System (GIS) techniques were used in mapping the spatial diffusion of influenza risk zones. The results show that there is a significance correlation between influenza outbreaks and climate factors for the majority of the studied area. A statistical analysis was conducted to assess the validity of the model comparing model outputs and actual outbreaks.

Sensitivity of Regulated Flow Regimes to Climate Change in the Western United States

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

Zhou, Tian; Voisin, Nathalie; Leng, Guoyong

Water management activities or flow regulations modify water fluxes at the land surface and affect water resources in space and time. We hypothesize that flow regulations change the sensitivity of river flow to climate change with respect to unmanaged water resources. Quantifying these changes in sensitivity could help elucidate the impacts of water management at different spatiotemporal scales and inform climate adaptation decisions. In this study, we compared the emergence of significant changes in natural and regulated river flow regimes across the Western United States from simulations driven by multiple climate models and scenarios. We find that significant climate change-inducedmore » alterations in natural flow do not cascade linearly through water management activities. At the annual time scale, 50% of the Hydrologic Unit Code 4 (HUC4) sub-basins over the Western U.S. regions tend to have regulated flow regime more sensitive to the climate change than natural flow regime. Seasonality analyses show that the sensitivity varies remarkably across the seasons. We also find that the sensitivity is related to the level of water management. For 35% of the HUC4 sub-basins with the highest level of water management, the summer and winter flows tend to show a heightened sensitivity to climate change due to the complexity of joint reservoir operations. We further demonstrate that the impacts of considering water management in models are comparable to those that arises from uncertainties across climate models and emission scenarios. This prompts further climate adaptation studies research about nonlinearity effects of climate change through water management activities.« less

Transient climate simulations of the deglaciation 21-9 thousand years before present (version 1) - PMIP4 Core experiment design and boundary conditions

NASA Astrophysics Data System (ADS)

Ivanovic, Ruza F.; Gregoire, Lauren J.; Kageyama, Masa; Roche, Didier M.; Valdes, Paul J.; Burke, Andrea; Drummond, Rosemarie; Peltier, W. Richard; Tarasov, Lev

2016-07-01

The last deglaciation, which marked the transition between the last glacial and present interglacial periods, was punctuated by a series of rapid (centennial and decadal) climate changes. Numerical climate models are useful for investigating mechanisms that underpin the climate change events, especially now that some of the complex models can be run for multiple millennia. We have set up a Paleoclimate Modelling Intercomparison Project (PMIP) working group to coordinate efforts to run transient simulations of the last deglaciation, and to facilitate the dissemination of expertise between modellers and those engaged with reconstructing the climate of the last 21 000 years. Here, we present the design of a coordinated Core experiment over the period 21-9 thousand years before present (ka) with time-varying orbital forcing, greenhouse gases, ice sheets and other geographical changes. A choice of two ice sheet reconstructions is given, and we make recommendations for prescribing ice meltwater (or not) in the Core experiment. Additional focussed simulations will also be coordinated on an ad hoc basis by the working group, for example to investigate more thoroughly the effect of ice meltwater on climate system evolution, and to examine the uncertainty in other forcings. Some of these focussed simulations will target shorter durations around specific events in order to understand them in more detail and allow for the more computationally expensive models to take part.

Using Water Quality Models in Management - A Multiple Model Assessment, Analysis of Confidence, and Evaluation of Climate Change Impacts

NASA Astrophysics Data System (ADS)

Irby, Isaac David

Human impacts on the Chesapeake Bay through increased nutrient run-off as a result of land-use change, urbanization, and industrialization, have resulted in a degradation of water quality over the last half-century. These direct impacts, compounded with human-induced climate changes such as warming, rising sea-level, and changes in precipitation, have elevated the conversation surrounding the future of water quality in the Bay. The overall goal of this dissertation project is to use a combination of models and data to better understand and quantify the impact of changes in nutrient loads and climate on water quality in the Chesapeake Bay. This research achieves that goal in three parts. First, a set of eight water quality models is used to establish a model mean and assess model skill. All models were found to exhibit similar skill in resolving dissolved oxygen concentrations as well as a number of dissolved oxygen-influencing variables (temperature, salinity, stratification, chlorophyll and nitrate) and the model mean exhibited the highest individual skill. The location of stratification within the water column was found to be a limiting factor in the models' ability to adequately simulate habitat compression resulting from low-oxygen conditions. Second, two of the previous models underwent the regulatory Chesapeake Bay pollution diet mandated by the Environmental Protection Agency. Both models exhibited a similar relative improvement in dissolved oxygen concentrations as a result of the reduction of nutrients stipulated in the pollution diet. A Confidence Index was developed to identify the locations of the Bay where the models are in agreement and disagreement regarding the impacts of the pollution diet. The models were least certain in the deep part of the upper main stem of the Bay and the uncertainty primarily stemmed from the post-processing methodology. Finally, by projecting the impacts of climate change in 2050 on the Bay, the potential success of the pollution diet in light of future projections for air temperature, sea level, and precipitation was examined. While a changing climate will reduce the ability of the nutrient reduction to improve oxygen concentrations, that effect is trumped by the improvements in dissolved oxygen stemming from the pollution diet itself. However, climate change still has the potential to cause the current level of nutrient reduction to be inadequate. This is primarily due to the fact that low-oxygen conditions are predicted to start one week earlier, on average, in the future, with the primary changes resulting from the increase in temperature. Overall, this research lends an increased degree of confidence in the water quality modeling of the potential impact of the Chesapeake Bay pollution diet. This research also establishes the efficacy of utilizing a multiple model approach to examining projected changes in water quality while establishing that the pollution diet trumps the impact from climate change. This work will lead directly to advances in scientific understanding of the response of water quality, ecosystem health, and ecological resilience to the impacts of nutrient reduction and climate change.

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

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.

Global water resources affected by human interventions and climate change.

PubMed

Haddeland, Ingjerd; Heinke, Jens; Biemans, Hester; Eisner, Stephanie; Flörke, Martina; Hanasaki, Naota; Konzmann, Markus; Ludwig, Fulco; Masaki, Yoshimitsu; Schewe, Jacob; Stacke, Tobias; Tessler, Zachary D; Wada, Yoshihide; Wisser, Dominik

2014-03-04

Humans directly change the dynamics of the water cycle through dams constructed for water storage, and through water withdrawals for industrial, agricultural, or domestic purposes. Climate change is expected to additionally affect water supply and demand. Here, analyses of climate change and direct human impacts on the terrestrial water cycle are presented and compared using a multimodel approach. Seven global hydrological models have been forced with multiple climate projections, and with and without taking into account impacts of human interventions such as dams and water withdrawals on the hydrological cycle. Model results are analyzed for different levels of global warming, allowing for analyses in line with temperature targets for climate change mitigation. The results indicate that direct human impacts on the water cycle in some regions, e.g., parts of Asia and in the western United States, are of the same order of magnitude, or even exceed impacts to be expected for moderate levels of global warming (+2 K). Despite some spread in model projections, irrigation water consumption is generally projected to increase with higher global mean temperatures. Irrigation water scarcity is particularly large in parts of southern and eastern Asia, and is expected to become even larger in the future.

Global water resources affected by human interventions and climate change

PubMed Central

Haddeland, Ingjerd; Heinke, Jens; Biemans, Hester; Eisner, Stephanie; Flörke, Martina; Hanasaki, Naota; Konzmann, Markus; Ludwig, Fulco; Masaki, Yoshimitsu; Schewe, Jacob; Stacke, Tobias; Tessler, Zachary D.; Wada, Yoshihide; Wisser, Dominik

2014-01-01

Humans directly change the dynamics of the water cycle through dams constructed for water storage, and through water withdrawals for industrial, agricultural, or domestic purposes. Climate change is expected to additionally affect water supply and demand. Here, analyses of climate change and direct human impacts on the terrestrial water cycle are presented and compared using a multimodel approach. Seven global hydrological models have been forced with multiple climate projections, and with and without taking into account impacts of human interventions such as dams and water withdrawals on the hydrological cycle. Model results are analyzed for different levels of global warming, allowing for analyses in line with temperature targets for climate change mitigation. The results indicate that direct human impacts on the water cycle in some regions, e.g., parts of Asia and in the western United States, are of the same order of magnitude, or even exceed impacts to be expected for moderate levels of global warming (+2 K). Despite some spread in model projections, irrigation water consumption is generally projected to increase with higher global mean temperatures. Irrigation water scarcity is particularly large in parts of southern and eastern Asia, and is expected to become even larger in the future. PMID:24344275

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

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

Hinzman, Larry D.; Bolton, William Robert; Young-Robertson, Jessica

This project improves meso-scale hydrologic modeling in the boreal forest by: (1) demonstrating the importance of capturing the heterogeneity of the landscape using small scale datasets for parameterization for both small and large basins; (2) demonstrating that in drier parts of the landscape and as the boreal forest dries with climate change, modeling approaches must consider the sensitivity of simulations to soil hydraulic parameters - such as residual water content - that are usually held constant. Thus, variability / flexibility in residual water content must be considered for accurate simulation of hydrologic processes in the boreal forest; (3) demonstrating thatmore » assessing climate change impacts on boreal forest hydrology through multiple model integration must account for direct effects of climate change (temperature and precipitation), and indirect effects from climate impacts on landscape characteristics (permafrost and vegetation distribution). Simulations demonstrated that climate change will increase runoff, but will increase ET to a greater extent and result in a drying of the landscape; and (4) vegetation plays a significant role in boreal hydrologic processes in permafrost free areas that have deciduous trees. This landscape type results in a decoupling of ET and precipitation, a tight coupling of ET and temperature, low runoff, and overall soil drying.« less

The contrasting climate response to tropical and extratropical energy perturbations

NASA Astrophysics Data System (ADS)

Hawcroft, Matt; Haywood, Jim M.; Collins, Mat; Jones, Andy

2018-01-01

The link between cross-equatorial energy transport, the double-intertropical convergence zone (DI) problem and biases in tropical and extratropical albedo and energy budgets in climate models have been investigated in multiple studies, though DI biases persist in many models. Here, a coupled climate model, HadGEM2-ES, is used to investigate the response to idealised energy perturbations in the tropics and extratropics, in both the northern and southern hemispheres, through the imposition of stratospheric aerosols that reflect incoming radiation. The impact on the tropical climate of high and low latitude forcing strongly contrasts, with large changes in tropical precipitation and modulation of the DI bias when the tropics are cooled as precipitation moves away from the cooled hemisphere. These responses are muted when the extratropics are cooled, as the meridional energy transport anomalies that are excited by these energy budget anomalies are partitioned between the atmosphere and ocean. The results here highlight the persistence of the DI bias in HadGEM2-ES, indicating why little progress has been made in rectifying these problems through many generations of climate models. A highly linear relationship between cross-equatorial atmospheric energy transport, tropical precipitation asymmetry and tropical sea surface temperature biases is also demonstrated, giving some suggestion as to where improvements in these large scale, persistent biases may be achieved.

Methodology for the assessment of the impacts of climate change on land degradation at multiple scales: Use of high resolution satellite imagery, modelling, and ground measurements for the assessment in Ethiopia

NASA Astrophysics Data System (ADS)

Ahmed, Oumer

In this study, a new multi-scalar methodology for assessing land degradation response to climate change is presented by analyzing 22 years of both climatic data and satellite observations, together with future projections from modelling, for Ethiopia. A comprehensive analysis of the impacts of climate change on land degradation was performed as evidenced from the integration of a host of land degradation indicators, namely: normalized difference vegetation Index (NDVI), net primary productivity (NPP), crop yield, biomass, length of growing period (LGP), rainfall use efficiency (RUE), energy use efficiency (EUE) and aridity index (AI). The results from the national level assessment indicate that over the period of 1984-2006, NPP decreased overall. Degrading areas occupy 30% of the country and suffer an average loss of NPP 10.3 kg C ha-1 y-1. The crop yield prediction results indicate a wide range of outcomes is to be expected for the country, due to the heterogeneity of the agro-climatic resources as well as of projected climate change. The results of the sub-national level assessment show that about 29% of the Awash watershed is degrading, and these degrading areas experience an average loss of NPP 4.6 kg C ha-1 y-1. Further, about 33.8% of the degrading area in the watershed is associated with bare land and 25% with agricultural land. Finally, since remotely sensed estimates are frequently used to assess land degradation at multiple scales, scale transfer methods are evaluated in this study to provide a tool to rank both upscaling and downscaling procedures.

Assess and Adapt: Coordinated Ecoregional Forest Vulnerability Assessments Covering the Upper Midwest and Northeast in Support of Climate-informed Decision-making

NASA Astrophysics Data System (ADS)

Swanston, C.; Janowiak, M.; Handler, S.; Butler, P.; Brandt, L.; Iverson, L.; Thompson, F.; Ontl, T.; Shannon, D.

2016-12-01

Forest ecosystem vulnerability assessments are rapidly becoming an integral component of forest management planning, in which there is increasing public expectation that even near-term activities explicitly incorporate information about anticipated climate impacts and risks. There is a clear desire among forest managers for targeted assessments that address critical questions about species and ecosystem vulnerabilities while delivering this information in an accessible format. We developed the Ecosystem Vulnerability Assessment Approach (EVAA), which combines multiple quantitative models, expert elicitation from scientists and land managers, and a templated report structure oriented to natural resource managers. The report structure includes relevant information on the contemporary landscape, past climate, future climate projections, impact model results, and a transparent vulnerability assessment of species and ecosystems. We have used EVAA in seven ecoregional assessments covering 246 million acres of forestland across the upper Midwest and Northeast (www.forestadaptation.org; five published, two in review). We convened a panel of local forest ecology and management experts in each assessment area to examine projected climate effects on system drivers, stressors, and dominant species, as well as the current adaptive capacity of the major ecoregional forest ecosystems. The panels provided a qualitative assessment of the vulnerability of forest ecosystems to climate change over the next century. Over 130 authors from dozens of organizations collaborated on these peer-reviewed assessment publications, which are delivered to thousands of stakeholders through live and recorded webinars, online briefs, and in-person trainings and seminars. The assessments are designed to be used with the Adaptation Workbook (www.adaptationworkbook.org), a planning tool that works at multiple scales and has generated more than 200 real-world forest adaptation demonstration projects.

Weighted Iterative Bayesian Compressive Sensing (WIBCS) for High Dimensional Polynomial Surrogate Construction

NASA Astrophysics Data System (ADS)

Sargsyan, K.; Ricciuto, D. M.; Safta, C.; Debusschere, B.; Najm, H. N.; Thornton, P. E.

2016-12-01

Surrogate construction has become a routine procedure when facing computationally intensive studies requiring multiple evaluations of complex models. In particular, surrogate models, otherwise called emulators or response surfaces, replace complex models in uncertainty quantification (UQ) studies, including uncertainty propagation (forward UQ) and parameter estimation (inverse UQ). Further, surrogates based on Polynomial Chaos (PC) expansions are especially convenient for forward UQ and global sensitivity analysis, also known as variance-based decomposition. However, the PC surrogate construction strongly suffers from the curse of dimensionality. With a large number of input parameters, the number of model simulations required for accurate surrogate construction is prohibitively large. Relatedly, non-adaptive PC expansions typically include infeasibly large number of basis terms far exceeding the number of available model evaluations. We develop Weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm for adaptive basis growth and PC surrogate construction leading to a sparse, high-dimensional PC surrogate with a very few model evaluations. The surrogate is then readily employed for global sensitivity analysis leading to further dimensionality reduction. Besides numerical tests, we demonstrate the construction on the example of Accelerated Climate Model for Energy (ACME) Land Model for several output QoIs at nearly 100 FLUXNET sites covering multiple plant functional types and climates, varying 65 input parameters over broad ranges of possible values. This work is supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research, Accelerated Climate Modeling for Energy (ACME) project. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

Combining surface reanalysis and remote sensing data for monitoring evapotranspiration

USGS Publications Warehouse

Marshall, M.; Tu, K.; Funk, C.; Michaelsen, J.; Williams, Pat; Williams, C.; Ardö, J.; Marie, B.; Cappelaere, B.; Grandcourt, A.; Nickless, A.; Noubellon, Y.; Scholes, R.; Kutsch, W.

2012-01-01

Climate change is expected to have the greatest impact on the world's poor. In the Sahel, a climatically sensitive region where rain-fed agriculture is the primary livelihood, expected decreases in water supply will increase food insecurity. Studies on climate change and the intensification of the water cycle in sub-Saharan Africa are few. This is due in part to poor calibration of modeled actual evapotranspiration (AET), a key input in continental-scale hydrologic models. In this study, a model driven by dynamic canopy AET was combined with the Global Land Data Assimilation System realization of the NOAH Land Surface Model (GNOAH) wet canopy and soil AET for monitoring purposes in sub-Saharan Africa. The performance of the hybrid model was compared against AET from the GNOAH model and dynamic model using eight eddy flux towers representing major biomes of sub-Saharan Africa. The greatest improvements in model performance are at humid sites with dense vegetation, while performance at semi-arid sites is poor, but better than individual models. The reduction in errors using the hybrid model can be attributed to the integration of a dynamic vegetation component with land surface model estimates, improved model parameterization, and reduction of multiplicative effects of uncertain data.

Competition-interaction landscapes for the joint response of forests to climate change.

PubMed

Clark, James S; Bell, David M; Kwit, Matthew C; Zhu, Kai

2014-06-01

The recent global increase in forest mortality episodes could not have been predicted from current vegetation models that are calibrated to regional climate data. Physiological studies show that mortality results from interactions between climate and competition at the individual scale. Models of forest response to climate do not include interactions because they are hard to estimate and require long-term observations on individual trees obtained at frequent (annual) intervals. Interactions involve multiple tree responses that can only be quantified if these responses are estimated as a joint distribution. A new approach provides estimates of climate–competition interactions in two critical ways, (i) among individuals, as a joint distribution of responses to combinations of inputs, such as resources and climate, and (ii) within individuals, due to allocation requirements that control outputs, such as demographic rates. Application to 20 years of data from climate and competition gradients shows that interactions control forest responses, and their omission from models leads to inaccurate predictions. Species most vulnerable to increasing aridity are not those that show the largest growth response to precipitation, but rather depend on interactions with the local resource environment. This first assessment of regional species vulnerability that is based on the scale at which climate operates, individual trees competing for carbon and water, supports predictions of potential savannification in the southeastern US.

Integrated surface/subsurface permafrost thermal hydrology: Model formulation and proof-of-concept simulations

DOE PAGES

Painter, Scott L.; Coon, Ethan T.; Atchley, Adam L.; ...

2016-08-11

The need to understand potential climate impacts and feedbacks in Arctic regions has prompted recent interest in modeling of permafrost dynamics in a warming climate. A new fine-scale integrated surface/subsurface thermal hydrology modeling capability is described and demonstrated in proof-of-concept simulations. The new modeling capability combines a surface energy balance model with recently developed three-dimensional subsurface thermal hydrology models and new models for nonisothermal surface water flows and snow distribution in the microtopography. Surface water flows are modeled using the diffusion wave equation extended to include energy transport and phase change of ponded water. Variation of snow depth in themore » microtopography, physically the result of wind scour, is also modeled heuristically with a diffusion wave equation. The multiple surface and subsurface processes are implemented by leveraging highly parallel community software. Fully integrated thermal hydrology simulations on the tilted open book catchment, an important test case for integrated surface/subsurface flow modeling, are presented. Fine-scale 100-year projections of the integrated permafrost thermal hydrological system on an ice wedge polygon at Barrow Alaska in a warming climate are also presented. Finally, these simulations demonstrate the feasibility of microtopography-resolving, process-rich simulations as a tool to help understand possible future evolution of the carbon-rich Arctic tundra in a warming climate.« less

Impacts of uncertainties in European gridded precipitation observations on regional climate analysis

PubMed Central

Gobiet, Andreas

2016-01-01

ABSTRACT Gridded precipitation data sets are frequently used to evaluate climate models or to remove model output biases. Although precipitation data are error prone due to the high spatio‐temporal variability of precipitation and due to considerable measurement errors, relatively few attempts have been made to account for observational uncertainty in model evaluation or in bias correction studies. In this study, we compare three types of European daily data sets featuring two Pan‐European data sets and a set that combines eight very high‐resolution station‐based regional data sets. Furthermore, we investigate seven widely used, larger scale global data sets. Our results demonstrate that the differences between these data sets have the same magnitude as precipitation errors found in regional climate models. Therefore, including observational uncertainties is essential for climate studies, climate model evaluation, and statistical post‐processing. Following our results, we suggest the following guidelines for regional precipitation assessments. (1) Include multiple observational data sets from different sources (e.g. station, satellite, reanalysis based) to estimate observational uncertainties. (2) Use data sets with high station densities to minimize the effect of precipitation undersampling (may induce about 60% error in data sparse regions). The information content of a gridded data set is mainly related to its underlying station density and not to its grid spacing. (3) Consider undercatch errors of up to 80% in high latitudes and mountainous regions. (4) Analyses of small‐scale features and extremes are especially uncertain in gridded data sets. For higher confidence, use climate‐mean and larger scale statistics. In conclusion, neglecting observational uncertainties potentially misguides climate model development and can severely affect the results of climate change impact assessments. PMID:28111497

Benchmarking novel approaches for modelling species range dynamics

PubMed Central

Zurell, Damaris; Thuiller, Wilfried; Pagel, Jörn; Cabral, Juliano S; Münkemüller, Tamara; Gravel, Dominique; Dullinger, Stefan; Normand, Signe; Schiffers, Katja H.; Moore, Kara A.; Zimmermann, Niklaus E.

2016-01-01

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species’ range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process-based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process-based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species’ response to climate change but also emphasise several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species. PMID:26872305

Benchmarking novel approaches for modelling species range dynamics.

PubMed

Zurell, Damaris; Thuiller, Wilfried; Pagel, Jörn; Cabral, Juliano S; Münkemüller, Tamara; Gravel, Dominique; Dullinger, Stefan; Normand, Signe; Schiffers, Katja H; Moore, Kara A; Zimmermann, Niklaus E

2016-08-01

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species' range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process-based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process-based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species' response to climate change but also emphasize several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species. © 2016 John Wiley & Sons Ltd.

Building a foundation for continued dialogue between climate science and water resource communities

NASA Astrophysics Data System (ADS)

Vano, J. A.; Arnold, J.; Clark, M. P.; Gutmann, E. D.; Hamman, J.; Nijssen, B.; Wood, A.

2017-12-01

Research into climate change has led to the development of many global climate models, downscaling techniques, and impacts models. This proliferation of information has resulted in insights into how climate change will impact hydrology that are more robust than any single approach, which is helpful for advancing the science. However, the variety of approaches makes navigating what information to use in water resource planning and management challenging. Each technique has strengths and weaknesses and associated uncertainties, and approaches are always being updated. Here we provide a user-focused, modularly framed guidance that is designed to be expandable and where updates can be targeted. This includes describing dos and don'ts for how to use climate change information in water resource planning and management that can be read at multiple levels. It can provide context for those seeking to understand the general need, opportunities, and challenges of including climate change information. It also provides details (frequently asked questions and examples) and direction to further guidance and resources for those engaged in the technical work. This guidance is intended to provide a foundation for continued dialogue within and between the climate science and application communities, to increase the utility and appropriate use of climate change information.

Geospatial interface and model for predicting potential seagrass habitat

EPA Science Inventory

Restoration of ecosystem services provided by seagrass habitats in estuaries requires a clear understanding of the modes of action of multiple interacting stressors including nutrients, climate change, coastal land-use change, and habitat modification. We have developed a geos...

Quantitative Models for the Narragansett Bay Estuary, Rhode Island/Massachusetts, USA

EPA Science Inventory

Multiple drivers, including nutrient loading and climate change, affect the Narragansett Bay ecosystem in Rhode Island/Massachusetts, USA. Managers are interested in understanding the timing and magnitude of these effects, and ecosystem responses to restoration actions. To provid...

GFDL's unified regional-global weather-climate modeling system with variable resolution capability for severe weather predictions and regional climate simulations

NASA Astrophysics Data System (ADS)

Lin, S. J.

2015-12-01

The NOAA/Geophysical Fluid Dynamics Laboratory has been developing a unified regional-global modeling system with variable resolution capabilities that can be used for severe weather predictions (e.g., tornado outbreak events and cat-5 hurricanes) and ultra-high-resolution (1-km) regional climate simulations within a consistent global modeling framework. The fundation of this flexible regional-global modeling system is the non-hydrostatic extension of the vertically Lagrangian dynamical core (Lin 2004, Monthly Weather Review) known in the community as FV3 (finite-volume on the cubed-sphere). Because of its flexability and computational efficiency, the FV3 is one of the final candidates of NOAA's Next Generation Global Prediction System (NGGPS). We have built into the modeling system a stretched (single) grid capability, a two-way (regional-global) multiple nested grid capability, and the combination of the stretched and two-way nests, so as to make convection-resolving regional climate simulation within a consistent global modeling system feasible using today's High Performance Computing System. One of our main scientific goals is to enable simulations of high impact weather phenomena (such as tornadoes, thunderstorms, category-5 hurricanes) within an IPCC-class climate modeling system previously regarded as impossible. In this presentation I will demonstrate that it is computationally feasible to simulate not only super-cell thunderstorms, but also the subsequent genesis of tornadoes using a global model that was originally designed for century long climate simulations. As a unified weather-climate modeling system, we evaluated the performance of the model with horizontal resolution ranging from 1 km to as low as 200 km. In particular, for downscaling studies, we have developed various tests to ensure that the large-scale circulation within the global varaible resolution system is well simulated while at the same time the small-scale can be accurately captured within the targeted high resolution region.

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

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.

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

The Software Architecture of Global Climate Models

NASA Astrophysics Data System (ADS)

Alexander, K. A.; Easterbrook, S. M.

2011-12-01

It has become common to compare and contrast the output of multiple global climate models (GCMs), such as in the Climate Model Intercomparison Project Phase 5 (CMIP5). However, intercomparisons of the software architecture of GCMs are almost nonexistent. In this qualitative study of seven GCMs from Canada, the United States, and Europe, we attempt to fill this gap in research. We describe the various representations of the climate system as computer programs, and account for architectural differences between models. Most GCMs now practice component-based software engineering, where Earth system components (such as the atmosphere or land surface) are present as highly encapsulated sub-models. This architecture facilitates a mix-and-match approach to climate modelling that allows for convenient sharing of model components between institutions, but it also leads to difficulty when choosing where to draw the lines between systems that are not encapsulated in the real world, such as sea ice. We also examine different styles of couplers in GCMs, which manage interaction and data flow between components. Finally, we pay particular attention to the varying levels of complexity in GCMs, both between and within models. Many GCMs have some components that are significantly more complex than others, a phenomenon which can be explained by the respective institution's research goals as well as the origin of the model components. In conclusion, although some features of software architecture have been adopted by every GCM we examined, other features show a wide range of different design choices and strategies. These architectural differences may provide new insights into variability and spread between models.

ClimateSpark: An in-memory distributed computing framework for big climate data analytics

NASA Astrophysics Data System (ADS)

Hu, Fei; Yang, Chaowei; Schnase, John L.; Duffy, Daniel Q.; Xu, Mengchao; Bowen, Michael K.; Lee, Tsengdar; Song, Weiwei

2018-06-01

The unprecedented growth of climate data creates new opportunities for climate studies, and yet big climate data pose a grand challenge to climatologists to efficiently manage and analyze big data. The complexity of climate data content and analytical algorithms increases the difficulty of implementing algorithms on high performance computing systems. This paper proposes an in-memory, distributed computing framework, ClimateSpark, to facilitate complex big data analytics and time-consuming computational tasks. Chunking data structure improves parallel I/O efficiency, while a spatiotemporal index is built for the chunks to avoid unnecessary data reading and preprocessing. An integrated, multi-dimensional, array-based data model (ClimateRDD) and ETL operations are developed to address big climate data variety by integrating the processing components of the climate data lifecycle. ClimateSpark utilizes Spark SQL and Apache Zeppelin to develop a web portal to facilitate the interaction among climatologists, climate data, analytic operations and computing resources (e.g., using SQL query and Scala/Python notebook). Experimental results show that ClimateSpark conducts different spatiotemporal data queries/analytics with high efficiency and data locality. ClimateSpark is easily adaptable to other big multiple-dimensional, array-based datasets in various geoscience domains.

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.

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...

Modeling species’ realized climatic niche space and predicting their response to global warming for several western forest species with small geographic distributions

Treesearch

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

2010-01-01

The Random Forests multiple regression tree was used to develop an empirically based bioclimatic model of the presence-absence of species occupying small geographic distributions in western North America. The species assessed were subalpine larch (Larix lyallii), smooth Arizona cypress (Cupressus arizonica ssp. glabra...

Rift Valley fever risk map model and seroprevalence in selected wild ungulates and camels from Kenya

USDA-ARS?s Scientific Manuscript database

Since the first isolation of Rift Valley fever virus (RVFV) in the 1930s, there have been multiple epizootics and epidemics in animals and humans in sub-Saharan Africa. Prospective climate-based models have recently been developed that flag areas at risk of RVFV transmission in endemic regions based...

Modeling potential evapotranspiration of two forested watersheds in the southern Appalachians

Treesearch

L.Y. Rao; G. Sun; C.R. Ford; J.M. Vose

2011-01-01

Global climate change has direct impacts on watershed hydrology through altering evapotranspiration (ET) processes at multiple scales. There are many methods to estimate forest ET with models, but the most practical and the most popular one is the potential ET (PET) based method. However, the choice of PET methods for AET estimation remains challenging. This study...

Integrated Modeling to Assess the Impacts of Changes in Climate and Socio Economics on Agriculture in the Columbia River Basin

NASA Astrophysics Data System (ADS)

Rajagopalan, K.; Chinnayakanahalli, K.; Adam, J. C.; Malek, K.; Nelson, R.; Stockle, C.; Brady, M.; Dinesh, S.; Barber, M. E.; Yorgey, G.; Kruger, C.

2012-12-01

The objective of this work is to assess the impacts of climate change and socio economics on agriculture in the Columbia River basin (CRB) in the Pacific Northwest region of the U.S. and a portion of Southwestern Canada. The water resources of the CRB are managed to satisfy multiple objectives including agricultural withdrawal, which is the largest consumptive user of CRB water with 14,000 square kilometers of irrigated area. Agriculture is an important component of the region's economy, with an annual value over 5 billion in Washington State alone. Therefore, the region is relevant for applying a modeling framework that can aid agriculture decision making in the context of a changing climate. To do this, we created an integrated biophysical and socio-economic regional modeling framework that includes human and natural systems. The modeling framework captures the interactions between climate, hydrology, crop growth dynamics, water management and socio economics. The biophysical framework includes a coupled macro-scale physically-based hydrology model (the Variable Infiltration Capacity, VIC model), and crop growth model (CropSyst), as well as a reservoir operations simulation model. Water rights data and instream flow target requirements are also incorporated in the model to simulate the process of curtailment during water shortage. The economics model informs the biophysical model of the short term agricultural producer response to water shortage as well as the long term agricultural producer response to domestic growth and international trade in terms of an altered cropping pattern. The modeling framework was applied over the CRB for the historical period 1976-2006 and compared to a future 30-year period centered on the 2030s. Impacts of climate change on irrigation water availability, crop irrigation demand, frequency of curtailment, and crop yields are quantified and presented. Sensitivity associated with estimates of water availability, irrigation demand, crop yields, unmet demand and available instream flows due to climate inputs, hydrology and crop model parameterization, water management assumptions, model integration assumptions, as well as multiple socio economic alternatives are also presented. Compared to historical conditions, for the 2030s time period, our results show an average additional irrigation water demand requirement of 370 million cubic meters in the CRB, an increased frequency of curtailment and a revenue impact between 70 and $150 million resulting from adverse crop yield impacts due to curtailment in the state of Washington. The impacts vary spatially and some of the CRB tributary watersheds are impacted more than others, e.g., unmet demand in the Yakima River basin is expected to increase by 50%. Increased irrigation demand, coupled with decreased seasonal supply poses difficult water resources management questions in the region.

Estimating greenhouse gas emissions of European cities--modeling emissions with only one spatial and one socioeconomic variable.

PubMed

Baur, Albert H; Lauf, Steffen; Förster, Michael; Kleinschmit, Birgit

2015-07-01

Substantive and concerted action is needed to mitigate climate change. However, international negotiations struggle to adopt ambitious legislation and to anticipate more climate-friendly developments. Thus, stronger actions are needed from other players. Cities, being greenhouse gas emission centers, play a key role in promoting the climate change mitigation movement by becoming hubs for smart and low-carbon lifestyles. In this context, a stronger linkage between greenhouse gas emissions and urban development and policy-making seems promising. Therefore, simple approaches are needed to objectively identify crucial emission drivers for deriving appropriate emission reduction strategies. In analyzing 44 European cities, the authors investigate possible socioeconomic and spatial determinants of urban greenhouse gas emissions. Multiple statistical analyses reveal that the average household size and the edge density of discontinuous dense urban fabric explain up to 86% of the total variance of greenhouse gas emissions of EU cities (when controlled for varying electricity carbon intensities). Finally, based on these findings, a multiple regression model is presented to determine greenhouse gas emissions. It is independently evaluated with ten further EU cities. The reliance on only two indicators shows that the model can be easily applied in addressing important greenhouse gas emission sources of European urbanites, when varying power generations are considered. This knowledge can help cities develop adequate climate change mitigation strategies and promote respective policies on the EU or the regional level. The results can further be used to derive first estimates of urban greenhouse gas emissions, if no other analyses are available. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

Effectiveness and Tradeoffs between Portfolios of Adaptation Strategies Addressing Future Climate and Socioeconomic Uncertainties in California's Central Valley

NASA Astrophysics Data System (ADS)

Tansey, M. K.; Van Lienden, B.; Das, T.; Munevar, A.; Young, C. A.; Flores-Lopez, F.; Huntington, J. L.

2013-12-01

The Central Valley of California is one of the major agricultural areas in the United States. The Central Valley Project (CVP) is operated by the Bureau of Reclamation to serve multiple purposes including generating approximately 4.3 million gigawatt hours of hydropower and providing, on average, 5 million acre-feet of water per year to irrigate approximately 3 million acres of land in the Sacramento, San Joaquin, and Tulare Lake basins, 600,000 acre-feet per year of water for urban users, and 800,000 acre-feet of annual supplies for environmental purposes. The development of effective adaptation and mitigation strategies requires assessing multiple risks including potential climate changes as well as uncertainties in future socioeconomic conditions. In this study, a scenario-based analytical approach was employed by combining three potential 21st century socioeconomic futures with six representative climate and sea level change projections developed using a transient hybrid delta ensemble method from an archive of 112 bias corrected spatially downscaled CMIP3 global climate model simulations to form 18 future socioeconomic-climate scenarios. To better simulate the effects of climate changes on agricultural water demands, analyses of historical agricultural meteorological station records were employed to develop estimates of future changes in solar radiation and atmospheric humidity from the GCM simulated temperature and precipitation. Projected changes in atmospheric carbon dioxide were computed directly by weighting SRES emissions scenarios included in each representative climate projection. These results were used as inputs to a calibrated crop water use, growth and yield model to simulate the effects of climate changes on the evapotranspiration and yields of major crops grown in the Central Valley. Existing hydrologic, reservoir operations, water quality, hydropower, greenhouse gas (GHG) emissions and both urban and agricultural economic models were integrated into a suite of decision support tools to assess the impacts of future socioeconomic-climate uncertainties on key performance metrics for the CVP, State Water Project and other Central Valley water management systems under current regulatory requirements. Four thematic portfolios consisting of regional and local adaptation strategies including changes in reservoir operations, increased water conservation, storage and conveyance were developed and simulated to evaluate their potential effectiveness in meeting delivery reliability, water quality, environmental, hydropower, GHG, urban and agricultural economic performance criteria. The results indicate that the portfolios exhibit a considerable range of effectiveness depending on the socioeconomic-climate scenario. For most criteria, the portfolios were more sensitive to climate projections than socioeconomic assumptions. However, the results demonstrate that important tradeoffs occur between portfolios depending on the performance criteria considered.

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.

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

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.

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.

The effects of climate downscaling technique and observational data set on modeled ecological responses.

PubMed

Pourmokhtarian, Afshin; Driscoll, Charles T; Campbell, John L; Hayhoe, Katharine; Stoner, Anne M K

2016-07-01

Assessments of future climate change impacts on ecosystems typically rely on multiple climate model projections, but often utilize only one downscaling approach trained on one set of observations. Here, we explore the extent to which modeled biogeochemical responses to changing climate are affected by the selection of the climate downscaling method and training observations used at the montane landscape of the Hubbard Brook Experimental Forest, New Hampshire, USA. We evaluated three downscaling methods: the delta method (or the change factor method), monthly quantile mapping (Bias Correction-Spatial Disaggregation, or BCSD), and daily quantile regression (Asynchronous Regional Regression Model, or ARRM). Additionally, we trained outputs from four atmosphere-ocean general circulation models (AOGCMs) (CCSM3, HadCM3, PCM, and GFDL-CM2.1) driven by higher (A1fi) and lower (B1) future emissions scenarios on two sets of observations (1/8º resolution grid vs. individual weather station) to generate the high-resolution climate input for the forest biogeochemical model PnET-BGC (eight ensembles of six runs).The choice of downscaling approach and spatial resolution of the observations used to train the downscaling model impacted modeled soil moisture and streamflow, which in turn affected forest growth, net N mineralization, net soil nitrification, and stream chemistry. All three downscaling methods were highly sensitive to the observations used, resulting in projections that were significantly different between station-based and grid-based observations. The choice of downscaling method also slightly affected the results, however not as much as the choice of observations. Using spatially smoothed gridded observations and/or methods that do not resolve sub-monthly shifts in the distribution of temperature and/or precipitation can produce biased results in model applications run at greater temporal and/or spatial resolutions. These results underscore the importance of carefully considering field observations used for training, as well as the downscaling method used to generate climate change projections, for smaller-scale modeling studies. Different sources of variability including selection of AOGCM, emissions scenario, downscaling technique, and data used for training downscaling models, result in a wide range of projected forest ecosystem responses to future climate change. © 2016 by the Ecological Society of America.

Parsing multiple processes of high temperature impacts on corn/soybean yield using a newly developed CLM-APSIM modeling framework

NASA Astrophysics Data System (ADS)

Peng, B.; Guan, K.; Chen, M.

2016-12-01

Future agricultural production faces a grand challenge of higher temperature under climate change. There are multiple physiological or metabolic processes of how high temperature affects crop yield. Specifically, we consider the following major processes: (1) direct temperature effects on photosynthesis and respiration; (2) speed-up growth rate and the shortening of growing season; (3) heat stress during reproductive stage (flowering and grain-filling); (4) high-temperature induced increase of atmospheric water demands. In this work, we use a newly developed modeling framework (CLM-APSIM) to simulate the corn and soybean growth and explicitly parse the above four processes. By combining the strength of CLM in modeling surface biophysical (e.g., hydrology and energy balance) and biogeochemical (e.g., photosynthesis and carbon-nitrogen interactions), as well as that of APSIM in modeling crop phenology and reproductive stress, the newly developed CLM-APSIM modeling framework enables us to diagnose the impacts of high temperature stress through different processes at various crop phenology stages. Ground measurements from the advanced SoyFACE facility at University of Illinois is used here to calibrate, validate, and improve the CLM-APSIM modeling framework at the site level. We finally use the CLM-APSIM modeling framework to project crop yield for the whole US Corn Belt under different climate scenarios.

Climate and Land Use Controls on Soil Organic Carbon in the Loess Plateau Region of China

PubMed Central

Tao, Bo; Chen, Guangsheng; Lu, Chaoqun; Yang, Jia; Pan, Shufen; Wang, Guodong; Li, Shiqing; Tian, Hanqin

2014-01-01

The Loess Plateau of China has the highest soil erosion rate in the world where billion tons of soil is annually washed into Yellow River. In recent decades this region has experienced significant climate change and policy-driven land conversion. However, it has not yet been well investigated how these changes in climate and land use have affected soil organic carbon (SOC) storage on the Loess Plateau. By using the Dynamic Land Ecosystem Model (DLEM), we quantified the effects of climate and land use on SOC storage on the Loess Plateau in the context of multiple environmental factors during the period of 1961–2005. Our results show that SOC storage increased by 0.27 Pg C on the Loess Plateau as a result of multiple environmental factors during the study period. About 55% (0.14 Pg C) of the SOC increase was caused by land conversion from cropland to grassland/forest owing to the government efforts to reduce soil erosion and improve the ecological conditions in the region. Historical climate change reduced SOC by 0.05 Pg C (approximately 19% of the total change) primarily due to a significant climate warming and a slight reduction in precipitation. Our results imply that the implementation of “Grain for Green” policy may effectively enhance regional soil carbon storage and hence starve off further soil erosion on the Loess Plateau. PMID:24788559

Climate and land use controls on soil organic carbon in the loess plateau region of China.

PubMed

Dang, Yaai; Ren, Wei; Tao, Bo; Chen, Guangsheng; Lu, Chaoqun; Yang, Jia; Pan, Shufen; Wang, Guodong; Li, Shiqing; Tian, Hanqin

2014-01-01

The Loess Plateau of China has the highest soil erosion rate in the world where billion tons of soil is annually washed into Yellow River. In recent decades this region has experienced significant climate change and policy-driven land conversion. However, it has not yet been well investigated how these changes in climate and land use have affected soil organic carbon (SOC) storage on the Loess Plateau. By using the Dynamic Land Ecosystem Model (DLEM), we quantified the effects of climate and land use on SOC storage on the Loess Plateau in the context of multiple environmental factors during the period of 1961-2005. Our results show that SOC storage increased by 0.27 Pg C on the Loess Plateau as a result of multiple environmental factors during the study period. About 55% (0.14 Pg C) of the SOC increase was caused by land conversion from cropland to grassland/forest owing to the government efforts to reduce soil erosion and improve the ecological conditions in the region. Historical climate change reduced SOC by 0.05 Pg C (approximately 19% of the total change) primarily due to a significant climate warming and a slight reduction in precipitation. Our results imply that the implementation of "Grain for Green" policy may effectively enhance regional soil carbon storage and hence starve off further soil erosion on the Loess Plateau.

Climate change, estuaries and anadromous fish habitat in the northeastern United States: models, downscaling and uncertainty

NASA Astrophysics Data System (ADS)

Muhling, B.; Gaitan, C. F.; Tommasi, D.; Saba, V. S.; Stock, C. A.; Dixon, K. W.

2016-02-01

Estuaries of the northeastern United States provide essential habitat for many anadromous fishes, across a range of life stages. Climate change is likely to impact estuarine environments and habitats through multiple pathways. Increasing air temperatures will result in a warming water column, and potentially increased stratification. In addition, changes to precipitation patterns may alter freshwater inflow dynamics, leading to altered seasonal salinity regimes. However, the spatial resolution of global climate models is generally insufficient to resolve these processes at the scale of individual estuaries. Global models can be downscaled to a regional resolution using a variety of dynamical and statistical methods. In this study, we examined projections of estuarine conditions, and future habitat extent, for several anadromous fishes in the Chesapeake Bay using different statistical downscaling methods. Sources of error from physical and biological models were quantified, and key areas of uncertainty were highlighted. Results suggested that future projections of the distribution and recruitment of species most strongly linked to freshwater flow dynamics had the highest levels of uncertainty. The sensitivity of different life stages to environmental conditions, and the population-level responses of anadromous species to climate change, were identified as important areas for further research.

Signs of the Land: Reaching Arctic Communities Facing Climate Change

NASA Astrophysics Data System (ADS)

Sparrow, E. B.; Chase, M. J.; Demientieff, S.; Pfirman, S. L.; Brunacini, J.

2014-12-01

In July 2014, a diverse and intergenerational group of Alaskan Natives came together on Howard Luke's Galee'ya Camp by the Tanana River in Fairbanks, Alaska to talk about climate change and it's impacts on local communities. Over a period of four days, the Signs of the Land Climate Change Camp wove together traditional knowledge, local observations, Native language, and climate science through a mix of storytelling, presentations, dialogue, and hands-on, community-building activities. This camp adapted the model developed several years ago under the Association for Interior Native Educators (AINE)'s Elder Academy. Part of the Polar Learning and Responding Climate Change Education Partnership, the Signs of the Land Climate Change Camp was developed and conducted collaboratively with multiple partners to test a model for engaging indigenous communities in the co-production of climate change knowledge, communication tools, and solutions-building. Native Alaskans have strong subsistence and cultural connections to the land and its resources, and, in addition to being keen observers of their environment, have a long history of adapting to changing conditions. Participants in the camp included Elders, classroom teachers, local resource managers and planners, community members, and climate scientists. Based on their experiences during the camp, participants designed individualized outreach plans for bringing culturally-responsive climate learning to their communities and classrooms throughout the upcoming year. Plans included small group discussions, student projects, teacher training, and conference presentations.

Climate and air pollution impacts on habitat suitability of Austrian forest ecosystems

PubMed Central

Djukic, Ika; Kitzler, Barbara; Kobler, Johannes; Mol-Dijkstra, Janet P.; Posch, Max; Reinds, Gert Jan; Schlutow, Angela; Starlinger, Franz; Wamelink, Wieger G. W.

2017-01-01

Climate change and excess deposition of airborne nitrogen (N) are among the main stressors to floristic biodiversity. One particular concern is the deterioration of valuable habitats such as those protected under the European Habitat Directive. In future, climate-driven shifts (and losses) in the species potential distribution, but also N driven nutrient enrichment may threaten these habitats. We applied a dynamic geochemical soil model (VSD+) together with a novel niche-based plant response model (PROPS) to 5 forest habitat types (18 forest sites) protected under the EU Directive in Austria. We assessed how future climate change and N deposition might affect habitat suitability, defined as the capacity of a site to host its typical plant species. Our evaluation indicates that climate change will be the main driver of a decrease in habitat suitability in the future in Austria. The expected climate change will increase the occurrence of thermophilic plant species while decreasing cold-tolerant species. In addition to these direct impacts, climate change scenarios caused an increase of the occurrence probability of oligotrophic species due to a higher N immobilisation in woody biomass leading to soil N depletion. As a consequence, climate change did offset eutrophication from N deposition, even when no further reduction in N emissions was assumed. Our results show that climate change may have positive side-effects in forest habitats when multiple drivers of change are considered. PMID:28898262

Climate and air pollution impacts on habitat suitability of Austrian forest ecosystems.

PubMed

Dirnböck, Thomas; Djukic, Ika; Kitzler, Barbara; Kobler, Johannes; Mol-Dijkstra, Janet P; Posch, Max; Reinds, Gert Jan; Schlutow, Angela; Starlinger, Franz; Wamelink, Wieger G W

2017-01-01

Climate change and excess deposition of airborne nitrogen (N) are among the main stressors to floristic biodiversity. One particular concern is the deterioration of valuable habitats such as those protected under the European Habitat Directive. In future, climate-driven shifts (and losses) in the species potential distribution, but also N driven nutrient enrichment may threaten these habitats. We applied a dynamic geochemical soil model (VSD+) together with a novel niche-based plant response model (PROPS) to 5 forest habitat types (18 forest sites) protected under the EU Directive in Austria. We assessed how future climate change and N deposition might affect habitat suitability, defined as the capacity of a site to host its typical plant species. Our evaluation indicates that climate change will be the main driver of a decrease in habitat suitability in the future in Austria. The expected climate change will increase the occurrence of thermophilic plant species while decreasing cold-tolerant species. In addition to these direct impacts, climate change scenarios caused an increase of the occurrence probability of oligotrophic species due to a higher N immobilisation in woody biomass leading to soil N depletion. As a consequence, climate change did offset eutrophication from N deposition, even when no further reduction in N emissions was assumed. Our results show that climate change may have positive side-effects in forest habitats when multiple drivers of change are considered.

Ecosystem evapotranspiration: Challenges in measurements, estimates, and modeling

USDA-ARS?s Scientific Manuscript database

Evapotranspiration (ET) processes at the leaf-to-landscape scales in multiple land uses have important controls and feedbacks for the local, regional and global climate and water resource systems. Innovative methods, tools, and technologies for improved understanding and quantification of ET and cro...

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.

Addressing socioeconomic and political challenges posed by climate change

NASA Astrophysics Data System (ADS)

Fernando, Harindra Joseph; Klaic, Zvjezdana Bencetic

2011-08-01

NATO Advanced Research Workshop: Climate Change, Human Health and National Security; Dubrovnik, Croatia, 28-30 April 2011; Climate change has been identified as one of the most serious threats to humanity. It not only causes sea level rise, drought, crop failure, vector-borne diseases, extreme events, degradation of water and air quality, heat waves, and other phenomena, but it is also a threat multiplier wherein concatenation of multiple events may lead to frequent human catastrophes and intranational and international conflicts. In particular, urban areas may bear the brunt of climate change because of the amplification of climate effects that cascade down from global to urban scales, but current modeling and downscaling capabilities are unable to predict these effects with confidence. These were the main conclusions of a NATO Advanced Research Workshop (ARW) sponsored by the NATO Science for Peace and Security program. Thirty-two invitees from 17 counties, including leading modelers; natural, political, and social scientists; engineers; politicians; military experts; urban planners; industry analysts; epidemiologists; and health care professionals, parsed the topic on a common platform.

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 ecosystem service provision under climate change to support conservation and development planning in Myanmar

PubMed Central

Wolny, Stacie; Bhagabati, Nirmal; Helsingen, Hanna; Hamel, Perrine; Bartlett, Ryan; Dixon, Adam; Horton, Radley; Lesk, Corey; Manley, Danielle; De Mel, Manishka; Bader, Daniel; Nay Won Myint, Sai; Myint, Win; Su Mon, Myat

2017-01-01

Inclusion of ecosystem services (ES) information into national-scale development and climate adaptation planning has yet to become common practice, despite demand from decision makers. Identifying where ES originate and to whom the benefits flow–under current and future climate conditions–is especially critical in rapidly developing countries, where the risk of ES loss is high. Here, using Myanmar as a case study, we assess where and how ecosystems provide key benefits to the country’s people and infrastructure. We model the supply of and demand for sediment retention, dry-season baseflows, flood risk reduction and coastal storm protection from multiple beneficiaries. We find that locations currently providing the greatest amount of services are likely to remain important under the range of climate conditions considered, demonstrating their importance in planning for climate resilience. Overlap between priority areas for ES provision and biodiversity conservation is higher than expected by chance overall, but the areas important for multiple ES are underrepresented in currently designated protected areas and Key Biodiversity Areas. Our results are contributing to development planning in Myanmar, and our approach could be extended to other contexts where there is demand for national-scale natural capital information to shape development plans and policies. PMID:28934282

Development and Performance of Alternative Electricity Sector Pathways Subject to Multiple Climate and Water Projections

NASA Astrophysics Data System (ADS)

Newmark, R. L.; Vorosmarty, C. J.; Miara, A.; Cohen, S.; Macknick, J.; Sun, Y.; Corsi, F.; Fekete, B. M.; Tidwell, V. C.

2017-12-01

Climate change impacts on air temperatures and water availability have the potential to alter future electricity sector investment decisions as well as the reliability and performance of the power sector. Different electricity sector configurations are more or less vulnerable to climate-induced changes. For example, once-through cooled thermal facilities are the most cost-effective and efficient technologies under cooler and wetter conditions, but can be substantially affected by and vulnerable to warmer and drier conditions. Non-thermal renewable technologies, such as PV and wind, are essentially "drought-proof" but have other integration and reliability challenges. Prior efforts have explored the impacts of climate change on electric sector development for a limited set of climate and electricity scenarios. Here, we provide a comprehensive suite of scenarios that evaluate how different electricity sector pathways could be affected by a range of climate and water resource conditions. We use four representative concentration pathway (RCP) scenarios under five global circulation models (GCM) as climate drivers to a Water Balance Model (WBM), to provide twenty separate future climate-water conditions. These climate-water conditions influence electricity sector development from present day to 2050 as determined using the Regional Energy Deployment Systems (ReEDS) model. Four unique electricity sector pathways will be considered, including business-as-usual, carbon cap, high renewable energy technology costs, and coal reliance scenarios. The combination of climate-water and electricity sector pathway scenarios leads to 80 potential future cases resulting in different national and regional electricity infrastructure configurations. The vulnerability of these configurations in relation to climate change (including in-stream thermal pollution impacts and environmental regulations) is evaluated using the Thermoelectric Power and Thermal Pollution (TP2M) model, providing quantitative estimates of the power sector's ability to meet loads, given changes in air temperature, water temperature, and water availability.

An iceberg model implementation in ACME.

NASA Astrophysics Data System (ADS)

Comeau, D.; Turner, A. K.; Hunke, E. C.

2017-12-01

Icebergs represent approximately half of the mass flux from the Antarctic ice sheet, transporting freshwater and nutrients away from the coast to the Southern Ocean. Icebergs impact the surrounding ocean and sea ice environment, and serve as nutrient sources for biogeochemical activity, yet these processes are typically not resolved in current climate models. We have implemented a parameterization for iceberg drift and decay into the Department of Energy's Accelerated Climate Model for Energy (ACME), where the ocean, sea ice, and land ice components are based on the unstructured grid modeling framework Multiple Prediction Across Scales (MPAS), to improve the representation of Antarctic mass flux to the Southern Ocean and its impacts on ocean stratification and circulation, sea ice, and biogeochemical processes in a fully coupled global climate model. The iceberg model is implemented in two frameworks: Lagrangian and Eulerian. The Lagrangian framework embeds individual icebergs into the ocean and sea ice grids, and will be useful in modeling `giant' (>10 nautical miles) iceberg events, which may have highly localized impacts on ocean and sea ice. The Eulerian framework allows us to model a realistic population of Antarctic icebergs without the computational expense of individual particle tracking to simulate the aggregate impact on the Southern Ocean climate system. This capability, together with under ice-shelf ocean cavities and dynamic ice-shelf fronts, will allow for extremely high fidelity simulation of the southern cryosphere within ACME.

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

Great plains regional climate assessment technical report

USDA-ARS?s Scientific Manuscript database

The Great Plains region (GP) plays important role in providing food and energy to the economy of the United States. Multiple climatic and non-climatic stressors put multiple sectors, livelihoods and communities at risk, including agriculture, water, ecosystems and rural and tribal communities. The G...

The Impacts of Multiple Simultaneous Climate Variations

DTIC Science & Technology

2016-12-01

MULTIPLE SIMULTANEOUS CLIMATE VARIATIONS by Richard E. Ilczuk Jr. December 2016 Thesis Advisor: Tom Murphree Co-Advisor: Megan Hutchins......13. ABSTRACT (maximum 200 words) Climate variations—such as El Niño–La Niña (ENLN), the Madden–Julian Oscillation (MJO), and the Arctic

Constructing Scientific Arguments Using Evidence from Dynamic Computational Climate Models

NASA Astrophysics Data System (ADS)

Pallant, Amy; Lee, Hee-Sun

2015-04-01

Modeling and argumentation are two important scientific practices students need to develop throughout school years. In this paper, we investigated how middle and high school students ( N = 512) construct a scientific argument based on evidence from computational models with which they simulated climate change. We designed scientific argumentation tasks with three increasingly complex dynamic climate models. Each scientific argumentation task consisted of four parts: multiple-choice claim, openended explanation, five-point Likert scale uncertainty rating, and open-ended uncertainty rationale. We coded 1,294 scientific arguments in terms of a claim's consistency with current scientific consensus, whether explanations were model based or knowledge based and categorized the sources of uncertainty (personal vs. scientific). We used chi-square and ANOVA tests to identify significant patterns. Results indicate that (1) a majority of students incorporated models as evidence to support their claims, (2) most students used model output results shown on graphs to confirm their claim rather than to explain simulated molecular processes, (3) students' dependence on model results and their uncertainty rating diminished as the dynamic climate models became more and more complex, (4) some students' misconceptions interfered with observing and interpreting model results or simulated processes, and (5) students' uncertainty sources reflected more frequently on their assessment of personal knowledge or abilities related to the tasks than on their critical examination of scientific evidence resulting from models. These findings have implications for teaching and research related to the integration of scientific argumentation and modeling practices to address complex Earth systems.

Stochastic modeling of soil salinity

NASA Astrophysics Data System (ADS)

Suweis, S.; Porporato, A. M.; Daly, E.; van der Zee, S.; Maritan, A.; Rinaldo, A.

2010-12-01

A minimalist stochastic model of primary soil salinity is proposed, in which the rate of soil salinization is determined by the balance between dry and wet salt deposition and the intermittent leaching events caused by rainfall events. The equations for the probability density functions of salt mass and concentration are found by reducing the coupled soil moisture and salt mass balance equations to a single stochastic differential equation (generalized Langevin equation) driven by multiplicative Poisson noise. Generalized Langevin equations with multiplicative white Poisson noise pose the usual Ito (I) or Stratonovich (S) prescription dilemma. Different interpretations lead to different results and then choosing between the I and S prescriptions is crucial to describe correctly the dynamics of the model systems. We show how this choice can be determined by physical information about the timescales involved in the process. We also show that when the multiplicative noise is at most linear in the random variable one prescription can be made equivalent to the other by a suitable transformation in the jump probability distribution. We then apply these results to the generalized Langevin equation that drives the salt mass dynamics. The stationary analytical solutions for the probability density functions of salt mass and concentration provide insight on the interplay of the main soil, plant and climate parameters responsible for long term soil salinization. In particular, they show the existence of two distinct regimes, one where the mean salt mass remains nearly constant (or decreases) with increasing rainfall frequency, and another where mean salt content increases markedly with increasing rainfall frequency. As a result, relatively small reductions of rainfall in drier climates may entail dramatic shifts in longterm soil salinization trends, with significant consequences, e.g. for climate change impacts on rain fed agriculture.

Understanding uncertainty in precipitation changes in a balanced perturbed-physics ensemble under multiple climate forcings

NASA Astrophysics Data System (ADS)

Millar, R.; Ingram, W.; Allen, M. R.; Lowe, J.

2013-12-01

Temperature and precipitation patterns are the climate variables with the greatest impacts on both natural and human systems. Due to the small spatial scales and the many interactions involved in the global hydrological cycle, in general circulation models (GCMs) representations of precipitation changes are subject to considerable uncertainty. Quantifying and understanding the causes of uncertainty (and identifying robust features of predictions) in both global and local precipitation change is an essential challenge of climate science. We have used the huge distributed computing capacity of the climateprediction.net citizen science project to examine parametric uncertainty in an ensemble of 20,000 perturbed-physics versions of the HadCM3 general circulation model. The ensemble has been selected to have a control climate in top-of-atmosphere energy balance [Yamazaki et al. 2013, J.G.R.]. We force this ensemble with several idealised climate-forcing scenarios including carbon dioxide step and transient profiles, solar radiation management geoengineering experiments with stratospheric aerosols, and short-lived climate forcing agents. We will present the results from several of these forcing scenarios under GCM parametric uncertainty. We examine the global mean precipitation energy budget to understand the robustness of a simple non-linear global precipitation model [Good et al. 2012, Clim. Dyn.] as a better explanation of precipitation changes in transient climate projections under GCM parametric uncertainty than a simple linear tropospheric energy balance model. We will also present work investigating robust conclusions about precipitation changes in a balanced ensemble of idealised solar radiation management scenarios [Kravitz et al. 2011, Atmos. Sci. Let.].

A paradigm shift toward a consistent modeling framework to assess climate impacts

NASA Astrophysics Data System (ADS)

Monier, E.; Paltsev, S.; Sokolov, A. P.; Fant, C.; Chen, H.; Gao, X.; Schlosser, C. A.; Scott, J. R.; Dutkiewicz, S.; Ejaz, Q.; Couzo, E. A.; Prinn, R. G.; Haigh, M.

2017-12-01

Estimates of physical and economic impacts of future climate change are subject to substantial challenges. To enrich the currently popular approaches of assessing climate impacts by evaluating a damage function or by multi-model comparisons based on the Representative Concentration Pathways (RCPs), we focus here on integrating impacts into a self-consistent coupled human and Earth system modeling framework that includes modules that represent multiple physical impacts. In a sample application we show that this framework is capable of investigating the physical impacts of climate change and socio-economic stressors. The projected climate impacts vary dramatically across the globe in a set of scenarios with global mean warming ranging between 2.4°C and 3.6°C above pre-industrial by 2100. Unabated emissions lead to substantial sea level rise, acidification that impacts the base of the oceanic food chain, air pollution that exceeds health standards by tenfold, water stress that impacts an additional 1 to 2 billion people globally and agricultural productivity that decreases substantially in many parts of the world. We compare the outcomes from these forward-looking scenarios against the common goal described by the target-driven scenario of 2°C, which results in much smaller impacts. It is challenging for large internationally coordinated exercises to respond quickly to new policy targets. We propose that a paradigm shift toward a self-consistent modeling framework to assess climate impacts is needed to produce information relevant to evolving global climate policy and mitigation strategies in a timely way.

Can Earth System Model Provide Reasonable Natural Runoff Estimates to Support Water Management Studies?

NASA Astrophysics Data System (ADS)

Kao, S. C.; Shi, X.; Kumar, J.; Ricciuto, D. M.; Mao, J.; Thornton, P. E.

2017-12-01

With the concern of changing hydrologic regime, there is a crucial need to better understand how water availability may change and influence water management decisions in the projected future climate conditions. Despite that surface hydrology has long been simulated by land model within the Earth System modeling (ESM) framework, given the coarser horizontal resolution and lack of engineering-level calibration, raw runoff from ESM is generally discarded by water resource managers when conducting hydro-climate impact assessments. To identify a likely path to improve the credibility of ESM-simulated natural runoff, we conducted regional model simulation using the land component (ALM) of the Accelerated Climate Modeling for Energy (ACME) version 1 focusing on the conterminous United States (CONUS). Two very different forcing data sets, including (1) the conventional 0.5° CRUNCEP (v5, 1901-2013) and (2) the 1-km Daymet (v3, 1980-2013) aggregated to 0.5°, were used to conduct 20th century transient simulation with satellite phenology. Additional meteorologic and hydrologic observations, including PRISM precipitation and U.S. Geological Survey WaterWatch runoff, were used for model evaluation. For various CONUS hydrologic regions (such as Pacific Northwest), we found that Daymet can significantly improve the reasonableness of simulated ALM runoff even without intensive calibration. The large dry bias of CRUNCEP precipitation (evaluated by PRISM) in multiple CONUS hydrologic regions is believed to be the main reason causing runoff underestimation. The results suggest that when driving with skillful precipitation estimates, ESM has the ability to produce reasonable natural runoff estimates to support further water management studies. Nevertheless, model calibration will be required for regions (such as Upper Colorado) where ill performance is showed for multiple different forcings.

The Agricultural Model Intercomparison and Improvement Project (AgMIP): Progress and Preliminary Results

NASA Astrophysics Data System (ADS)

Rosenzweig, C.

2011-12-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) is a distributed climate-scenario simulation exercise for historical model intercomparison and future climate change conditions with participation of multiple crop and agricultural trade modeling groups around the world. The goals of AgMIP are to improve substantially the characterization of risk of hunger and world food security due to climate change and to enhance adaptation capacity in both developing and developed countries. Recent progress and the current status of AgMIP will be presented, highlighting three areas of activity: preliminary results from crop pilot studies, outcomes from regional workshops, and emerging scientific challenges. AgMIP crop modeling efforts are being led by pilot studies, which have been established for wheat, maize, rice, and sugarcane. These crop-specific initiatives have proven instrumental in testing and contributing to AgMIP protocols, as well as creating preliminary results for aggregation and input to agricultural trade models. Regional workshops are being held to encourage collaborations and set research activities in motion for key agricultural areas. The first of these workshops was hosted by Embrapa and UNICAMP and held in Campinas, Brazil. Outcomes from this meeting have informed crop modeling research activities within South America, AgMIP protocols, and future regional workshops. Several scientific challenges have emerged and are currently being addressed by AgMIP researchers. Areas of particular interest include geospatial weather generation, ensemble methods for climate scenarios and crop models, spatial aggregation of field-scale yields to regional and global production, and characterization of future changes in climate variability.

Towards a New Food System Assessment: AgMIP Coordinated Global and Regional Assessments of Climate Change

NASA Technical Reports Server (NTRS)

Rosenzweig, Cynthia E.; Thorburn, Peter

2017-01-01

Agricultural stakeholders need more credible information on which to base adaptation and mitigation policy decisions. In order to provide this, we must improve the rigor of agricultural modelling. Ensemble approaches can be used to address scale issues and integrated teams can overcome disciplinary silos. The AgMIP Coordinated Global and Regional Assessments of Climate Change and Food Security (CGRA) has the goal to link agricultural systems models using common protocols and scenarios to significantly improve understanding of climate effects on crops, livestock and livelihoods across multiple scales. The AgMIP CGRA assessment brings together experts in climate, crop, livestock, economics, and food security to develop Protocols to guide the process throughout the assessment. Scenarios are designed to consistently combine elements of intertwined storylines of future society including, socioeconomic development, greenhouse gas concentrations, and specific pathways of agricultural sector development. Through these approaches, AgMIP partners around the world are providing an evidence base for their stakeholders as they make decisions and investments.

Crop connectivity under climate change: future environmental and geographic risks of potato late blight in Scotland.

PubMed

Skelsey, Peter; Cooke, David E L; Lynott, James S; Lees, Alison K

2016-11-01

The impact of climate change on dispersal processes is largely ignored in risk assessments for crop diseases, as inoculum is generally assumed to be ubiquitous and nonlimiting. We suggest that consideration of the impact of climate change on the connectivity of crops for inoculum transmission may provide additional explanatory and predictive power in disease risk assessments, leading to improved recommendations for agricultural adaptation to climate change. In this study, a crop-growth model was combined with aerobiological models and a newly developed infection risk model to provide a framework for quantifying the impact of future climates on the risk of disease occurrence and spread. The integrated model uses standard meteorological variables and can be easily adapted to various crop pathosystems characterized by airborne inoculum. In a case study, the framework was used with data defining the spatial distribution of potato crops in Scotland and spatially coherent, probabilistic climate change data to project the future connectivity of crop distributions for Phytophthora infestans (causal agent of potato late blight) inoculum and the subsequent risk of infection. Projections and control recommendations are provided for multiple combinations of potato cultivar and CO 2 emissions scenario, and temporal and spatial averaging schemes. Overall, we found that relative to current climatic conditions, the risk of late blight will increase in Scotland during the first half of the potato growing season and decrease during the second half. To guide adaptation strategies, we also investigated the potential impact of climate change-driven shifts in the cropping season. Advancing the start of the potato growing season by 1 month proved to be an effective strategy from both an agronomic and late blight management perspective. © 2016 John Wiley & Sons Ltd.

Are there pre-Quaternary geological analogues for a future greenhouse warming?

USGS Publications Warehouse

Haywood, A.M.; Ridgwell, A.; Lunt, D.J.; Hill, D.J.; Pound, M.J.; Dowsett, H.J.; Dolan, A.M.; Francis, J.E.; Williams, M.

2011-01-01

Given the inherent uncertainties in predicting how climate and environments will respond to anthropogenic emissions of greenhouse gases, it would be beneficial to society if science could identify geological analogues to the human race's current grand climate experiment. This has been a focus of the geological and palaeoclimate communities over the last 30 years, with many scientific papers claiming that intervals in Earth history can be used as an analogue for future climate change. Using a coupled ocean-atmosphere modelling approach, we test this assertion for the most probable pre-Quaternary candidates of the last 100 million years: the Mid- and Late Cretaceous, the Palaeocene-Eocene Thermal Maximum (PETM), the Early Eocene, as well as warm intervals within the Miocene and Pliocene epochs. These intervals fail as true direct analogues since they either represent equilibrium climate states to a long-term CO2 forcing-whereas anthropogenic emissions of greenhouse gases provide a progressive (transient) forcing on climate-or the sensitivity of the climate system itself to CO2 was different. While no close geological analogue exists, past warm intervals in Earth history provide a unique opportunity to investigate processes that operated during warm (high CO2) climate states. Palaeoclimate and environmental reconstruction/modelling are facilitating the assessment and calculation of the response of global temperatures to increasing CO2 concentrations in the longer term (multiple centuries); this is now referred to as the Earth System Sensitivity, which is critical in identifying CO2 thresholds in the atmosphere that must not be crossed to avoid dangerous levels of climate change in the long term. Palaeoclimatology also provides a unique and independent way to evaluate the qualities of climate and Earth system models used to predict future climate. ?? 2011 The Royal Society.

Are there pre-Quaternary geological analogues for a future greenhouse warming?

PubMed

Haywood, Alan M; Ridgwell, Andy; Lunt, Daniel J; Hill, Daniel J; Pound, Matthew J; Dowsett, Harry J; Dolan, Aisling M; Francis, Jane E; Williams, Mark

2011-03-13

Given the inherent uncertainties in predicting how climate and environments will respond to anthropogenic emissions of greenhouse gases, it would be beneficial to society if science could identify geological analogues to the human race's current grand climate experiment. This has been a focus of the geological and palaeoclimate communities over the last 30 years, with many scientific papers claiming that intervals in Earth history can be used as an analogue for future climate change. Using a coupled ocean-atmosphere modelling approach, we test this assertion for the most probable pre-Quaternary candidates of the last 100 million years: the Mid- and Late Cretaceous, the Palaeocene-Eocene Thermal Maximum (PETM), the Early Eocene, as well as warm intervals within the Miocene and Pliocene epochs. These intervals fail as true direct analogues since they either represent equilibrium climate states to a long-term CO(2) forcing--whereas anthropogenic emissions of greenhouse gases provide a progressive (transient) forcing on climate--or the sensitivity of the climate system itself to CO(2) was different. While no close geological analogue exists, past warm intervals in Earth history provide a unique opportunity to investigate processes that operated during warm (high CO(2)) climate states. Palaeoclimate and environmental reconstruction/modelling are facilitating the assessment and calculation of the response of global temperatures to increasing CO(2) concentrations in the longer term (multiple centuries); this is now referred to as the Earth System Sensitivity, which is critical in identifying CO(2) thresholds in the atmosphere that must not be crossed to avoid dangerous levels of climate change in the long term. Palaeoclimatology also provides a unique and independent way to evaluate the qualities of climate and Earth system models used to predict future climate.

Using multiple climate projections for assessing hydrological response to climate change in the Thukela River Basin, South Africa

NASA Astrophysics Data System (ADS)

Graham, L. Phil; Andersson, Lotta; Horan, Mark; Kunz, Richard; Lumsden, Trevor; Schulze, Roland; Warburton, Michele; Wilk, Julie; Yang, Wei

This study used climate change projections from different regional approaches to assess hydrological effects on the Thukela River Basin in KwaZulu-Natal, South Africa. Projecting impacts of future climate change onto hydrological systems can be undertaken in different ways and a variety of effects can be expected. Although simulation results from global climate models (GCMs) are typically used to project future climate, different outcomes from these projections may be obtained depending on the GCMs themselves and how they are applied, including different ways of downscaling from global to regional scales. Projections of climate change from different downscaling methods, different global climate models and different future emissions scenarios were used as input to simulations in a hydrological model to assess climate change impacts on hydrology. A total of 10 hydrological change simulations were made, resulting in a matrix of hydrological response results. This matrix included results from dynamically downscaled climate change projections from the same regional climate model (RCM) using an ensemble of three GCMs and three global emissions scenarios, and from statistically downscaled projections using results from five GCMs with the same emissions scenario. Although the matrix of results does not provide complete and consistent coverage of potential uncertainties from the different methods, some robust results were identified. In some regards, the results were in agreement and consistent for the different simulations. For others, particularly rainfall, the simulations showed divergence. For example, all of the statistically downscaled simulations showed an annual increase in precipitation and corresponding increase in river runoff, while the RCM downscaled simulations showed both increases and decreases in runoff. According to the two projections that best represent runoff for the observed climate, increased runoff would generally be expected for this basin in the future. Dealing with such variability in results is not atypical for assessing climate change impacts in Africa and practitioners are faced with how to interpret them. This work highlights the need for additional, well-coordinated regional climate downscaling for the region to further define the range of uncertainties involved.

An empirical test of the relative and combined effects of land-cover and climate change on local colonization and extinction.

PubMed

Yalcin, Semra; Leroux, Shawn James

2018-04-14

Land-cover and climate change are two main drivers of changes in species ranges. Yet, the majority of studies investigating the impacts of global change on biodiversity focus on one global change driver and usually use simulations to project biodiversity responses to future conditions. We conduct an empirical test of the relative and combined effects of land-cover and climate change on species occurrence changes. Specifically, we examine whether observed local colonization and extinctions of North American birds between 1981-1985 and 2001-2005 are correlated with land-cover and climate change and whether bird life history and ecological traits explain interspecific variation in observed occurrence changes. We fit logistic regression models to test the impact of physical land-cover change, changes in net primary productivity, winter precipitation, mean summer temperature, and mean winter temperature on the probability of Ontario breeding bird local colonization and extinction. Models with climate change, land-cover change, and the combination of these two drivers were the top ranked models of local colonization for 30%, 27%, and 29% of species, respectively. Conversely, models with climate change, land-cover change, and the combination of these two drivers were the top ranked models of local extinction for 61%, 7%, and 9% of species, respectively. The quantitative impacts of land-cover and climate change variables also vary among bird species. We then fit linear regression models to test whether the variation in regional colonization and extinction rate could be explained by mean body mass, migratory strategy, and habitat preference of birds. Overall, species traits were weakly correlated with heterogeneity in species occurrence changes. We provide empirical evidence showing that land-cover change, climate change, and the combination of multiple global change drivers can differentially explain observed species local colonization and extinction. © 2018 John Wiley & Sons Ltd.

The evolution of scaling laws in the sea ice floe size distribution

NASA Astrophysics Data System (ADS)

Horvat, Christopher; Tziperman, Eli

2017-09-01

The sub-gridscale floe size and thickness distribution (FSTD) is an emerging climate variable, playing a leading-order role in the coupling between sea ice, the ocean, and the atmosphere. The FSTD, however, is difficult to measure given the vast range of horizontal scales of individual floes, leading to the common use of power-law scaling to describe it. The evolution of a coupled mixed-layer-FSTD model of a typical marginal ice zone is explicitly simulated here, to develop a deeper understanding of how processes active at the floe scale may or may not lead to scaling laws in the floe size distribution. The time evolution of mean quantities obtained from the FSTD (sea ice concentration, mean thickness, volume) is complex even in simple scenarios, suggesting that these quantities, which affect climate feedbacks, should be carefully calculated in climate models. The emergence of FSTDs with multiple separate power-law regimes, as seen in observations, is found to be due to the combination of multiple scale-selective processes. Limitations in assuming a power-law FSTD are carefully analyzed, applying methods used in observations to FSTD model output. Two important sources of error are identified that may lead to model biases: one when observing an insufficiently small range of floe sizes, and one from the fact that floe-scale processes often do not produce power-law behavior. These two sources of error may easily lead to biases in mean quantities derived from the FSTD of greater than 100%, and therefore biases in modeled sea ice evolution.

School environments and obesity: The mediating role of personal stress

PubMed Central

Milam, Adam J.; Jones, Chandria D.; Debnam, Katrina J.; Bradshaw, Catherine P.

2018-01-01

Background Youth spend a large amount of time in the school environment. Given the multiple influences of teachers, peers, and food and physical activity options, youth are likely to experience stressors that can influence their weight. This study examines the association between school climate and weight status. Method Students (n = 28,582; 58 schools) completed an online, anonymous school climate survey as part of the Maryland Safe and Supportive Schools Project. Multilevel structural equation modeling was used to explore the association between school climate, personal stress, and obesity. Analyses were stratified by gender. Results At the individual level, poor school climate (bullying, physical safety, and lack of whole-school connectedness) was associated with an increased likelihood of being overweight among females (β =.115, p = .019) but not males (β = .138; p =.244), after controlling for age, race, and physical activity. There was no association between school climate at the school level and being overweight among males or females. A second model included stress as a potential mediator; stress attenuated the relationship between poor school-related climate and being overweight (β = .039; p = .048) among females. Conclusion Findings suggest that stress related to school climate can play a role in the health and weight status of youth. PMID:29731524

Modelling the combined impacts of climate change and direct anthropogenic drivers on the ecosystem of the northwest European continental shelf

NASA Astrophysics Data System (ADS)

Wakelin, Sarah L.; Artioli, Yuri; Butenschön, Momme; Allen, J. Icarus; Holt, Jason T.

2015-12-01

The potential response of the marine ecosystem of the northwest European continental shelf to climate change under a medium emissions scenario (SRES A1B) is investigated using the coupled hydrodynamics-ecosystem model POLCOMS-ERSEM. Changes in the near future (2030-2040) and the far future (2082-2099) are compared to the recent past (1983-2000). The sensitivity of the ecosystem to potential changes in multiple anthropogenic drivers (river nutrient loads and benthic trawling) in the near future is compared to the impact of changes in climate. With the exception of the biomass of benthic organisms, the influence of the anthropogenic drivers only exceeds the impact of climate change in coastal regions. Increasing river nitrogen loads has a limited impact on the ecosystem whilst reducing river nitrogen and phosphate concentrations affects net primary production (netPP) and phytoplankton and zooplankton biomass. Direct anthropogenic forcing is seen to mitigate/amplify the effects of climate change. Increasing river nitrogen has the potential to amplify the effects of climate change at the coast by increasing netPP. Reducing river nitrogen and phosphate mitigates the effects of climate change for netPP and the biomass of small phytoplankton and large zooplankton species but amplifies changes in the biomass of large phytoplankton and small zooplankton.

A trait-based approach for predicting species responses to environmental change from sparse data: how well might terrestrial mammals track climate change?

PubMed

Santini, Luca; Cornulier, Thomas; Bullock, James M; Palmer, Stephen C F; White, Steven M; Hodgson, Jenny A; Bocedi, Greta; Travis, Justin M J

2016-07-01

Estimating population spread rates across multiple species is vital for projecting biodiversity responses to climate change. A major challenge is to parameterise spread models for many species. We introduce an approach that addresses this challenge, coupling a trait-based analysis with spatial population modelling to project spread rates for 15 000 virtual mammals with life histories that reflect those seen in the real world. Covariances among life-history traits are estimated from an extensive terrestrial mammal data set using Bayesian inference. We elucidate the relative roles of different life-history traits in driving modelled spread rates, demonstrating that any one alone will be a poor predictor. We also estimate that around 30% of mammal species have potential spread rates slower than the global mean velocity of climate change. This novel trait-space-demographic modelling approach has broad applicability for tackling many key ecological questions for which we have the models but are hindered by data availability. © 2016 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

Projecting demographic responses to climate change: adult and juvenile survival respond differently to direct and indirect effects of weather in a passerine population

USGS Publications Warehouse

Dybala, Kristen E.; Eadie, John M.; Gardali, Thomas; Seavy, Nathaniel E.; Herzog, Mark P.

2013-01-01

Few studies have quantitatively projected changes in demography in response to climate change, yet doing so can provide important insights into the processes that may lead to population declines and changes in species distributions. Using a long-term mark-recapture data set, we examined the influence of multiple direct and indirect effects of weather on adult and juvenile survival for a population of Song Sparrows (Melospiza melodia) in California. We found evidence for a positive, direct effect of winter temperature on adult survival, and a positive, indirect effect of prior rainy season precipitation on juvenile survival, which was consistent with an effect of precipitation on food availability during the breeding season. We used these relationships, and climate projections of significantly warmer and slightly drier winter weather by the year 2100, to project a significant increase in mean adult survival (12-17%) and a slight decrease in mean juvenile survival (4-6%) under the B1 and A2 climate change scenarios. Together with results from previous studies on seasonal fecundity and postfledging survival in this population, we integrated these results in a population model and projected increases in the population growth rate under both climate change scenarios. Our results underscore the importance of considering multiple, direct, and indirect effects of weather throughout the annual cycle, as well as differences in the responses of each life stage to climate change. Projecting demographic responses to climate change can identify not only how populations will be affected by climate change but also indicate the demographic process(es) and specific mechanisms that may be responsible. This information can, in turn, inform climate change adaptation plans, help prioritize future research, and identify where limited conservation resources will be most effectively and efficiently spent.

NACP Synthesis: Evaluating modeled carbon state and flux variables against multiple observational constraints (Invited)

NASA Astrophysics Data System (ADS)

Thornton, P. E.; Nacp Site Synthesis Participants

2010-12-01

The North American Carbon Program (NACP) synthesis effort includes an extensive intercomparison of modeled and observed ecosystem states and fluxes preformed with multiple models across multiple sites. The participating models span a range of complexity and intended application, while the participating sites cover a broad range of natural and managed ecosystems in North America, from the subtropics to arctic tundra, and coastal to interior climates. A unique characteristic of this collaborative effort is that multiple independent observations are available at all sites: fluxes are measured with the eddy covariance technique, and standard biometric and field sampling methods provide estimates of standing stock and annual production in multiple categories. In addition, multiple modeling approaches are employed to make predictions at each site, varying, for example, in the use of diagnostic vs. prognostic leaf area index. Given multiple independent observational constraints and multiple classes of model, we evaluate the internal consistency of observations at each site, and use this information to extend previously derived estimates of uncertainty in the flux observations. Model results are then compared with all available observations and models are ranked according to their consistency with each type of observation (high frequency flux measurement, carbon stock, annual production). We demonstrate a range of internal consistency across the sites, and show that some models which perform well against one observational metric perform poorly against others. We use this analysis to construct a hypothesis for combining eddy covariance, biometrics, and other standard physiological and ecological measurements which, as data collection proceeded over several years, would present an increasingly challenging target for next generation models.

Beyond Adapting to Climate Change: Embedding Adaptation in Responses to Multiple Threats and Stresses

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

Wilbanks, Thomas J; Kates, Dr. Robert W.

2010-01-01

Climate change impacts are already being experienced in every region of the United States and every part of the world most severely in Arctic regions and adaptation is needed now. Although climate change adaptation research is still in its infancy, significant adaptation planning in the United States has already begun in a number of localities. This article seeks to broaden the adaptation effort by integrating it with broader frameworks of hazards research, sustainability science, and community and regional resilience. To extend the range of experience, we draw from ongoing case studies in the Southeastern United States and the environmental historymore » of New Orleans to consider the multiple threats and stresses that all communities and regions experience. Embedding climate adaptation in responses to multiple threats and stresses helps us to understand climate change impacts, themselves often products of multiple stresses, to achieve community acceptance of needed adaptations as co-benefits of addressing multiple threats, and to mainstream the process of climate adaptation through the larger envelope of social relationships, communication channels, and broad-based awareness of needs for risk management that accompany community resilience.« less

Toward Seasonal Forecasting of Global Droughts: Evaluation over USA and Africa

NASA Astrophysics Data System (ADS)

Wood, Eric; Yuan, Xing; Roundy, Joshua; Sheffield, Justin; Pan, Ming

2013-04-01

Extreme hydrologic events in the form of droughts are significant sources of social and economic damage. In the United States according to the National Climatic Data Center, the losses from drought exceed US210 billion during 1980-2011, and account for about 24% of all losses from major weather disasters. Internationally, especially for the developing world, drought has had devastating impacts on local populations through food insecurity and famine. Providing reliable drought forecasts with sufficient early warning will help the governments to move from the management of drought crises to the management of drought risk. After working on drought monitoring and forecasting over the USA for over 10 years, the Princeton land surface hydrology group is now developing a global drought monitoring and forecasting system using a dynamical seasonal climate-hydrologic LSM-model (CHM) approach. Currently there is an active debate on the merits of the CHM-based seasonal hydrologic forecasts as compared to Ensemble Streamflow Prediction (ESP). We use NCEP's operational forecast system, the Climate Forecast System version 2 (CFSv2) and its previous version CFSv1, to investigate the value of seasonal climate model forecasts by conducting a set of 27-year seasonal hydrologic hindcasts over the USA. Through Bayesian downscaling, climate models have higher squared correlation (R2) and smaller error than ESP for monthly precipitation averaged over major river basins across the USA, and the forecasts conditional on ENSO show further improvements (out to four months) over river basins in the southern USA. All three approaches have plausible predictions of soil moisture drought frequency over central USA out to six months because of strong soil moisture memory, and seasonal climate models provide better results over central and eastern USA. The R2 of drought extent is higher for arid basins and for the forecasts initiated during dry seasons, but significant improvements from CFSv2 occur in different seasons for different basins. The R2 of drought severity accumulated over USA is higher during winter, and climate models present added value especially at long leads. For countries with sparse networks and weak reporting systems, remote sensing observations can provide the realtime data for the monitoring of drought. More importantly, these datasets are now available for at least a decade, which allows for estimating a climatology against which current conditions can be compared. Based on our established experimental African Drought Monitor (ADM) (see http://hydrology.princeton.edu/~nchaney/ADM_ML), we use the downscaled CFSv2 climate forcings to drive the re-calibrated VIC model and produce 6-month, 20-member ensemble hydrologic forecasts over Africa starting on the 1st of each calendar month during 1982-2007. Our CHM-based seasonal hydrologic forecasts are now being analyzed for its skill in predicting short-term soil moisture droughts over Africa. Besides relying on a single seasonal climate model or a single drought index, preliminary forecast results will be presented using multiple seasonal climate models based on the NOAA-supported National Multi-Model Ensemble (NMME) project, and with multiple drought indices. Results will be presented for the USA NIDIS test beds such as Southeast US and Colorado NIDIS (National Integrated Drought Information System) test beds, and potentially for other regions of the globe.

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.

Integrated Modeling and Participatory Scenario Planning for Climate Adaptation: the Maui Groundwater Project

NASA Astrophysics Data System (ADS)

Keener, V. W.; Finucane, M.; Brewington, L.

2014-12-01

For the last century, the island of Maui, Hawaii, has been the center of environmental, agricultural, and legal conflict with respect to surface and groundwater allocation. Planning for adequate future freshwater resources requires flexible and adaptive policies that emphasize partnerships and knowledge transfer between scientists and non-scientists. In 2012 the Hawai'i state legislature passed the Climate Change Adaptation Priority Guidelines (Act 286) law requiring county and state policy makers to include island-wide climate change scenarios in their planning processes. This research details the ongoing work by researchers in the NOAA funded Pacific RISA to support the development of Hawaii's first island-wide water use plan under the new climate adaptation directive. This integrated project combines several models with participatory future scenario planning. The dynamically downscaled triply nested Hawaii Regional Climate Model (HRCM) was modified from the WRF community model and calibrated to simulate the many microclimates on the Hawaiian archipelago. For the island of Maui, the HRCM was validated using 20 years of hindcast data, and daily projections were created at a 1 km scale to capture the steep topography and diverse rainfall regimes. Downscaled climate data are input into a USGS hydrological model to quantify groundwater recharge. This model was previously used for groundwater management, and is being expanded utilizing future climate projections, current land use maps and future scenario maps informed by stakeholder input. Participatory scenario planning began in 2012 to bring together a diverse group of over 50 decision-makers in government, conservation, and agriculture to 1) determine the type of information they would find helpful in planning for climate change, and 2) develop a set of scenarios that represent alternative climate/management futures. This is an iterative process, resulting in flexible and transparent narratives at multiple scales. The resulting climate, land use, and groundwater recharge maps give stakeholders a common set of future scenarios that they understand through the participatory scenario process, and identify the vulnerabilities, trade-offs, and adaptive priorities for different groundwater management and land uses in an uncertain future.

Climate change and the eco-hydrology of fire: Will area burned increase in a warming western USA?

Treesearch

Donald McKenzie; Jeremy S. Littell

2017-01-01

Wildfire area is predicted to increase with global warming. Empirical statistical models and process-based simulations agree almost universally. The key relationship for this unanimity, observed at multiple spatial and temporal scales, is between drought and fire. Predictive models often focus on ecosystems in which this relationship appears to be particularly strong,...

Logit-normal mixed model for Indian monsoon precipitation

NASA Astrophysics Data System (ADS)

Dietz, L. R.; Chatterjee, S.

2014-09-01

Describing the nature and variability of Indian monsoon precipitation is a topic of much debate in the current literature. We suggest the use of a generalized linear mixed model (GLMM), specifically, the logit-normal mixed model, to describe the underlying structure of this complex climatic event. Four GLMM algorithms are described and simulations are performed to vet these algorithms before applying them to the Indian precipitation data. The logit-normal model was applied to light, moderate, and extreme rainfall. Findings indicated that physical constructs were preserved by the models, and random effects were significant in many cases. We also found GLMM estimation methods were sensitive to tuning parameters and assumptions and therefore, recommend use of multiple methods in applications. This work provides a novel use of GLMM and promotes its addition to the gamut of tools for analysis in studying climate phenomena.

Modelling exploration of non-stationary hydrological system

NASA Astrophysics Data System (ADS)

Kim, Kue Bum; Kwon, Hyun-Han; Han, Dawei

2015-04-01

Traditional hydrological modelling assumes that the catchment does not change with time (i.e., stationary conditions) which means the model calibrated for the historical period is valid for the future period. However, in reality, due to change of climate and catchment conditions this stationarity assumption may not be valid in the future. It is a challenge to make the hydrological model adaptive to the future climate and catchment conditions that are not observable at the present time. In this study a lumped conceptual rainfall-runoff model called IHACRES was applied to a catchment in southwest England. Long observation data from 1961 to 2008 were used and seasonal calibration (in this study only summer period is further explored because it is more sensitive to climate and land cover change than the other three seasons) has been done since there are significant seasonal rainfall patterns. We expect that the model performance can be improved by calibrating the model based on individual seasons. The data is split into calibration and validation periods with the intention of using the validation period to represent the future unobserved situations. The success of the non-stationary model will depend not only on good performance during the calibration period but also the validation period. Initially, the calibration is based on changing the model parameters with time. Methodology is proposed to adapt the parameters using the step forward and backward selection schemes. However, in the validation both the forward and backward multiple parameter changing models failed. One problem is that the regression with time is not reliable since the trend may not be in a monotonic linear relationship with time. The second issue is that changing multiple parameters makes the selection process very complex which is time consuming and not effective in the validation period. As a result, two new concepts are explored. First, only one parameter is selected for adjustment while the other parameters are set as constant. Secondly, regression is made against climate condition instead of against time. It has been found that such a new approach is very effective and this non-stationary model worked very well both in the calibration and validation period. Although the catchment is specific in southwest England and the data are for only the summer period, the methodology proposed in this study is general and applicable to other catchments. We hope this study will stimulate the hydrological community to explore a variety of sites so that valuable experiences and knowledge could be gained to improve our understanding of such a complex modelling issue in climate change impact assessment.

An imperative need for global change research in tropical forests.

PubMed

Zhou, Xuhui; Fu, Yuling; Zhou, Lingyan; Li, Bo; Luo, Yiqi

2013-09-01

Tropical forests play a crucial role in regulating regional and global climate dynamics, and model projections suggest that rapid climate change may result in forest dieback or savannization. However, these predictions are largely based on results from leaf-level studies. How tropical forests respond and feedback to climate change is largely unknown at the ecosystem level. Several complementary approaches have been used to evaluate the effects of climate change on tropical forests, but the results are conflicting, largely due to confounding effects of multiple factors. Although altered precipitation and nitrogen deposition experiments have been conducted in tropical forests, large-scale warming and elevated carbon dioxide (CO2) manipulations are completely lacking, leaving many hypotheses and model predictions untested. Ecosystem-scale experiments to manipulate temperature and CO2 concentration individually or in combination are thus urgently needed to examine their main and interactive effects on tropical forests. Such experiments will provide indispensable data and help gain essential knowledge on biogeochemical, hydrological and biophysical responses and feedbacks of tropical forests to climate change. These datasets can also inform regional and global models for predicting future states of tropical forests and climate systems. The success of such large-scale experiments in natural tropical forests will require an international framework to coordinate collaboration so as to meet the challenges in cost, technological infrastructure and scientific endeavor.

Hydrologic extremes - an intercomparison of multiple gridded statistical downscaling methods

NASA Astrophysics Data System (ADS)

Werner, A. T.; Cannon, A. J.

2015-06-01

Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e., correlation tests) and distributional properties (i.e., tests for equality of probability distributions). Outputs from seven downscaling methods - bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), climate imprint delta method (CI), and bias corrected CI (BCCI) - are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3 day peak flow and 7 day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational datasets are used as downscaling target data. The skill of the downscaling methods generally depended on reanalysis and gridded observational dataset. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7 day low flow events, regardless of reanalysis or observational dataset. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event-scale spatial gradients, passed the greatest number of tests for hydrologic extremes. Non-stationarity in the observational/reanalysis datasets complicated the evaluation of downscaling performance. Comparing temporal homogeneity and trends in climate indices and hydrological model outputs calculated from downscaled reanalyses and gridded observations was useful for diagnosing the reliability of the various historical datasets. We recommend that such analyses be conducted before such data are used to construct future hydro-climatic change scenarios.

Hydrologic extremes - an intercomparison of multiple gridded statistical downscaling methods

NASA Astrophysics Data System (ADS)

Werner, Arelia T.; Cannon, Alex J.

2016-04-01

Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e. correlation tests) and distributional properties (i.e. tests for equality of probability distributions). Outputs from seven downscaling methods - bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), the climate imprint delta method (CI), and bias corrected CI (BCCI) - are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3-day peak flow and 7-day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational data sets are used as downscaling target data. The skill of the downscaling methods generally depended on reanalysis and gridded observational data set. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7-day low-flow events, regardless of reanalysis or observational data set. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event-scale spatial gradients, passed the greatest number of tests for hydrologic extremes. Non-stationarity in the observational/reanalysis data sets complicated the evaluation of downscaling performance. Comparing temporal homogeneity and trends in climate indices and hydrological model outputs calculated from downscaled reanalyses and gridded observations was useful for diagnosing the reliability of the various historical data sets. We recommend that such analyses be conducted before such data are used to construct future hydro-climatic change scenarios.

Effects of climate change, CO2 and O3 on wheat productivity in Eastern China, singly and in combination

NASA Astrophysics Data System (ADS)

Tao, Fulu; Feng, Zhaozhong; Tang, Haoye; Chen, Yi; Kobayashi, Kazuhiko

2017-03-01

Air pollution and climate change are increasing threats to agricultural production and food security. Extensive studies have focused on the effect of climate change, but the interactive effects of multiple global change factors are poorly understood. Here, we incorporate the interactions between climate change, carbon dioxide (CO2) and ozone (O3) into an eco-physiological mechanistic model based on three years of O3 Free-Air Concentration Elevation (O3-FACE) experiments. We then investigate the effects of climate change, elevated CO2 concentration ([CO2]) and rising O3 concentration ([O3]) on wheat growth and productivity in eastern China in 1996-2005 (2000s) and 2016-2025 (2020s) under two climate change scenarios, singly and in combination. We find the interactive effects of climate change, CO2 and O3 on wheat productivity have spatially explicit patterns; the effect of climate change dominates the general pattern, which is however subject to the large uncertainties of climate change scenarios. Wheat productivity is estimated to increase by 2.8-9.0% due to elevated [CO2] however decline by 2.8-11.7% due to rising [O3] in the 2020s, relative to the 2000s. The combined effects of CO2 and O3 are less than that of O3 only, on average by 4.6-5.2%, however with O3 damage outweighing CO2 benefit in most of the region. This study demonstrates a more biologically meaningful and appropriate approach for assessing the interactive effects of climate change, CO2 and O3 on crop growth and productivity. Our findings promote the understanding on the interactive effects of multiple global change factors across contrasting climate conditions, cast doubt on the potential of CO2 fertilization effect in offsetting possible negative effect of climate change on crop productivity as suggested by many previous studies.

Misleading prioritizations from modelling range shifts under climate change

USGS Publications Warehouse

Sofaer, Helen R.; Jarnevich, Catherine S.; Flather, Curtis H.

2018-01-01

AimConservation planning requires the prioritization of a subset of taxa and geographical locations to focus monitoring and management efforts. Integration of the threats and opportunities posed by climate change often relies on predictions from species distribution models, particularly for assessments of vulnerability or invasion risk for multiple taxa. We evaluated whether species distribution models could reliably rank changes in species range size under climate and land use change.LocationConterminous U.S.A.Time period1977–2014.Major taxa studiedPasserine birds.MethodsWe estimated ensembles of species distribution models based on historical North American Breeding Bird Survey occurrences for 190 songbirds, and generated predictions to recent years given c. 35 years of observed land use and climate change. We evaluated model predictions using standard metrics of discrimination performance and a more detailed assessment of the ability of models to rank species vulnerability to climate change based on predicted range loss, range gain, and overall change in range size.ResultsSpecies distribution models yielded unreliable and misleading assessments of relative vulnerability to climate and land use change. Models could not accurately predict range expansion or contraction, and therefore failed to anticipate patterns of range change among species. These failures occurred despite excellent overall discrimination ability and transferability to the validation time period, which reflected strong performance at the majority of locations that were either always or never occupied by each species.Main conclusionsModels failed for the questions and at the locations of greatest interest to conservation and management. This highlights potential pitfalls of multi-taxa impact assessments under global change; in our case, models provided misleading rankings of the most impacted species, and spatial information about range changes was not credible. As modelling methods and frameworks continue to be refined, performance assessments and validation efforts should focus on the measures of risk and vulnerability useful for decision-making.

Multiple greenhouse gas feedbacks from the land biosphere under future climate change scenarios

NASA Astrophysics Data System (ADS)

Stocker, Benjamin; Roth, Raphael; Joos, Fortunat; Spahni, Renato; Steinacher, Marco; Zaehle, Soenke; Bouwman, Lex; Xu-Ri, Xu-Ri; Prentice, Colin

2013-04-01

Atmospheric concentrations of the three important greenhouse gases (GHG) CO2, CH4, and N2O are mediated by processes in the terrestrial biosphere. The sensitivity of terrestrial GHG emissions to climate and CO2 contributed to the sharp rise in atmospheric GHG concentrations since preindustrial times and leads to multiple feedbacks between the terrestrial biosphere and the climate system. The strength of these feedbacks is determined by (i) the sensitivity of terrestrial GHG emissions to climate and CO2 and (ii) the greenhouse warming potential of the respective gas. Here, we quantify feedbacks from CO2, CH4, N2O, and land surface albedo in a consistent and comprehensive framework based on a large set of simulations conducted with an Earth System Model of Intermediate Complexity. The modeled sensitivities of CH4 and N2O emissions are tested, demonstrating that independent data for non-land (anthropogenic, oceanic, etc.) GHG emissions, combined with simulated emissions from natural and agricultural land reproduces historical atmospheric budgets within their uncertainties. 21st-century scenarios for climate, land use change and reactive nitrogen inputs (Nr) are applied to investigate future GHG emissions. Results suggest that in a business-as-usual scenario, terrestrial N2O emissions increase from 9.0 by today to 9.8-11.1 (RCP 2.6) and 14.2-17.0 TgN2O-N/yr by 2100 (RCP 8.5). Without anthropogenic Nr inputs, the amplification is reduced by 24-32%. Soil CH4 emissions increase from 221 at present to 228-245 in RCP 2.6 and to 303-343 TgCH4/yr in RCP 8.5, and the land becomes a net source of C by 2100 AD. Feedbacks from land imply an additional warming of 1.3-1.5°C by 2300 in RCP 8.5, 0.4-0.5°C of which are due to N2O and CH4. The combined effect of multiple GHGs and albedo represents an increasingly positive total feedback to anthropogenic climate change with positive individual feedbacks from CH4, N2O, and albedo outweighing the diminishing negative feedback from CO2 fertilisation of terrestrial C storage. This positive feedback from terrestrial biogeochemistry amplifies the traditionally defined physical equilibrium climate sensitivity by 23-28%, Strong mitigation, reducing Nr inputs and preserving natural vegetation limits the amplification of terrestrial GHG emissions and prevents the land biosphere from acting as an increasingly strong amplifier of anthropogenic climate change.

Heat stress increase under climate change twice as large in cities as in rural areas: A study for a densely populated midlatitude maritime region

NASA Astrophysics Data System (ADS)

Wouters, Hendrik; De Ridder, Koen; Poelmans, Lien; Willems, Patrick; Brouwers, Johan; Hosseinzadehtalaei, Parisa; Tabari, Hossein; Vanden Broucke, Sam; van Lipzig, Nicole P. M.; Demuzere, Matthias

2017-09-01

Urban areas are usually warmer than their surrounding natural areas, an effect known as the urban heat island effect. As such, they are particularly vulnerable to global warming and associated increases in extreme temperatures. Yet ensemble climate-model projections are generally performed on a scale that is too coarse to represent the evolution of temperatures in cities. Here, for the first time, we combine unprecedented long-term (35 years) urban climate model integrations at the convection-permitting scale (2.8 km resolution) with information from an ensemble of general circulation models to assess temperature-based heat stress for Belgium, a densely populated midlatitude maritime region. We discover that the heat stress increase toward the mid-21st century is twice as large in cities compared to their surrounding rural areas. The exacerbation is driven by the urban heat island itself, its concurrence with heat waves, and urban expansion. Cities experience a heat stress multiplication by a factor 1.4 and 15 depending on the scenario. Remarkably, the future heat stress surpasses everywhere the urban hot spots of today. Our results demonstrate the need to combine information from climate models, acting on different scales, for climate change risk assessment in heterogeneous regions. Moreover, these results highlight the necessity for adaptation to increasing heat stress, especially in urban areas.

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.

A National Energy-Water System Assessment Framework (NEWS): Synopsis of Stage 1 Research Strategy and Results

NASA Astrophysics Data System (ADS)

Vorosmarty, C. J.; Miara, A.; Macknick, J.; Newmark, R. L.; Cohen, S.; Sun, Y.; Tidwell, V. C.; Corsi, F.; Melillo, J. M.; Fekete, B. M.; Proussevitch, A. A.; Glidden, S.; Suh, S.

2017-12-01

The focus of this talk is on climate adaptation and the reliability of power supply infrastructure when viewed through the lens of strategic water issues. Power supply is critically dependent upon water resources, particularly to cool thermoelectric plants, making the sector particularly sensitive to any shifts in the geography or seasonality of water supply. We report on results from an NSF-Funded Water Sustainability and Climate effort aimed at uncovering key energy and economic system vulnerabilities. We have developed the National Energy-Water System assessment framework (NEWS) to systematically evaluate: a) the performance of the nation's electricity sector under multiple climate scenarios; b) the feasibility of alternative pathways to improve climate adaptation; and, c) the impacts of energy technology and investment tradeoffs on the economic productivity, water availability and aquatic ecosystem condition. Our project combines core engineering and geophysical models (ReEDS [Regional Energy Deployment System], TP2M [Thermoelectric Power and Thermal Pollution], and WBM [Water Balance]) through unique digital "handshake" protocols that operate across different institutions and modeling platforms. Combined system outputs are fed into a regional-to-national scale economic input/output model to evaluate economic consequences of climate constraints, technology choices, and environmental regulation. The impact assessments in NEWS are carried out through a series of climate/energy policy scenario studies to 2050. We find that despite significant climate-water impacts on individual plants, the current US power supply infrastructure shows potential for adaptation to future climates by capitalizing on the size of regional power systems, grid configuration and improvements in thermal efficiencies. However, the magnitude and implications of climate-water impacts vary depending on the configuration of the future power sector. To evaluate future power supply performance, we model alternative electricity sector pathways in combination with varying climate-water conditions. Further, water-linked disruptions in electricity supply yield substantial impacts on regional economies yet system-level shocks can be attenuated through different technology mixes and infrastructure.

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

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 the contribution of different factors in RegCM4.3 regional climate model projections using the Factor Separation method over the Med-CORDEX domain

NASA Astrophysics Data System (ADS)

Zsolt Torma, Csaba; Giorgi, Filippo

2014-05-01

A set of regional climate model (RCM) simulations applying dynamical downscaling of global climate model (GCM) simulations over the Mediterranean domain specified by the international initiative Coordinated Regional Downscaling Experiment (CORDEX) were completed with the Regional Climate Model RegCM, version RegCM4.3. Two GCMs were selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble to provide the driving fields for the RegCM: HadGEM2-ES (HadGEM) and MPI-ESM-MR (MPI). The simulations consist of an ensemble including multiple physics configurations and different "Reference Concentration Pathways" (RCP4.5 and RCP8.5). In total 15 simulations were carried out with 7 model physics configurations with varying convection and land surface schemes. The horizontal grid spacing of the RCM simulations is 50 km and the simulated period in all cases is 1970-2100 (1970-2099 in case of HadGEM driven simulations). This ensemble includes a combination of experiments in which different model components are changed individually and in combination, and thus lends itself optimally to the application of the Factor Separation (FS) method. This study applies the FS method to investigate the contributions of different factors, along with their synergy, on a set of regional climate model (RCM) projections for the Mediterranean region. The FS method is applied to 6 projections for the period 1970-2100 performed with the regional model RegCM4.3 over the Med-CORDEX domain. Two different sets of factors are intercompared, namely the driving global climate model (HadGEM and MPI) boundary conditions against two model physics settings (convection scheme and irrigation). We find that both the GCM driving conditions and the model physics provide important contributions, depending on the variable analyzed (surface air temperature and precipitation), season (winter vs. summer) and time horizon into the future, while the synergy term mostly tends to counterbalance the contributions of the individual factors. We demonstrate the usefulness of the FS method to assess different sources of uncertainty in RCM-based regional climate projections.

Influence of three common calibration metrics on the diagnosis of climate change impacts on water resources

NASA Astrophysics Data System (ADS)

Seiller, G.; Roy, R.; Anctil, F.

2017-04-01

Uncertainties associated to the evaluation of the impacts of climate change on water resources are broad, from multiple sources, and lead to diagnoses sometimes difficult to interpret. Quantification of these uncertainties is a key element to yield confidence in the analyses and to provide water managers with valuable information. This work specifically evaluates the influence of hydrological modeling calibration metrics on future water resources projections, on thirty-seven watersheds in the Province of Québec, Canada. Twelve lumped hydrologic models, representing a wide range of operational options, are calibrated with three common objective functions derived from the Nash-Sutcliffe efficiency. The hydrologic models are forced with climate simulations corresponding to two RCP, twenty-nine GCM from CMIP5 (Coupled Model Intercomparison Project phase 5) and two post-treatment techniques, leading to future projections in the 2041-2070 period. Results show that the diagnosis of the impacts of climate change on water resources are quite affected by the hydrologic models selection and calibration metrics. Indeed, for the four selected hydrological indicators, dedicated to water management, parameters from the three objective functions can provide different interpretations in terms of absolute and relative changes, as well as projected changes direction and climatic ensemble consensus. The GR4J model and a multimodel approach offer the best modeling options, based on calibration performance and robustness. Overall, these results illustrate the need to provide water managers with detailed information on relative changes analysis, but also absolute change values, especially for hydrological indicators acting as security policy thresholds.

Predicted effects of gypsy moth defoliation and climate change on forest carbon dynamics in the New Jersey pine barrens.

PubMed

Kretchun, Alec M; Scheller, Robert M; Lucash, Melissa S; Clark, Kenneth L; Hom, John; Van Tuyl, Steve

2014-01-01

Disturbance regimes within temperate forests can significantly impact carbon cycling. Additionally, projected climate change in combination with multiple, interacting disturbance effects may disrupt the capacity of forests to act as carbon sinks at large spatial and temporal scales. We used a spatially explicit forest succession and disturbance model, LANDIS-II, to model the effects of climate change, gypsy moth (Lymantria dispar L.) defoliation, and wildfire on the C dynamics of the forests of the New Jersey Pine Barrens over the next century. Climate scenarios were simulated using current climate conditions (baseline), as well as a high emissions scenario (HadCM3 A2 emissions scenario). Our results suggest that long-term changes in C cycling will be driven more by climate change than by fire or gypsy moths over the next century. We also found that simulated disturbances will affect species composition more than tree growth or C sequestration rates at the landscape level. Projected changes in tree species biomass indicate a potential increase in oaks with climate change and gypsy moth defoliation over the course of the 100-year simulation, exacerbating current successional trends towards increased oak abundance. Our research suggests that defoliation under climate change may play a critical role in increasing the variability of tree growth rates and in determining landscape species composition over the next 100 years.

Predicted Effects of Gypsy Moth Defoliation and Climate Change on Forest Carbon Dynamics in the New Jersey Pine Barrens

PubMed Central

Kretchun, Alec M.; Scheller, Robert M.; Lucash, Melissa S.; Clark, Kenneth L.; Hom, John; Van Tuyl, Steve

2014-01-01

Disturbance regimes within temperate forests can significantly impact carbon cycling. Additionally, projected climate change in combination with multiple, interacting disturbance effects may disrupt the capacity of forests to act as carbon sinks at large spatial and temporal scales. We used a spatially explicit forest succession and disturbance model, LANDIS-II, to model the effects of climate change, gypsy moth (Lymantria dispar L.) defoliation, and wildfire on the C dynamics of the forests of the New Jersey Pine Barrens over the next century. Climate scenarios were simulated using current climate conditions (baseline), as well as a high emissions scenario (HadCM3 A2 emissions scenario). Our results suggest that long-term changes in C cycling will be driven more by climate change than by fire or gypsy moths over the next century. We also found that simulated disturbances will affect species composition more than tree growth or C sequestration rates at the landscape level. Projected changes in tree species biomass indicate a potential increase in oaks with climate change and gypsy moth defoliation over the course of the 100-year simulation, exacerbating current successional trends towards increased oak abundance. Our research suggests that defoliation under climate change may play a critical role in increasing the variability of tree growth rates and in determining landscape species composition over the next 100 years. PMID:25119162

Quantifying climate change impacts emphasises the importance of managing regional threats in the endangered Yellow-eyed penguin.

PubMed

Mattern, Thomas; Meyer, Stefan; Ellenberg, Ursula; Houston, David M; Darby, John T; Young, Melanie; van Heezik, Yolanda; Seddon, Philip J

2017-01-01

Climate change is a global issue with effects that are difficult to manage at a regional scale. Yet more often than not climate factors are just some of multiple stressors affecting species on a population level. Non-climatic factors-especially those of anthropogenic origins-may play equally important roles with regard to impacts on species and are often more feasible to address. Here we assess the influence of climate change on population trends of the endangered Yellow-eyed penguin ( Megadyptes antipodes ) over the last 30 years, using a Bayesian model. Sea surface temperature (SST) proved to be the dominating factor influencing survival of both adult birds and fledglings. Increasing SST since the mid-1990s was accompanied by a reduction in survival rates and population decline. The population model showed that 33% of the variation in population numbers could be explained by SST alone, significantly increasing pressure on the penguin population. Consequently, the population becomes less resilient to non-climate related impacts, such as fisheries interactions, habitat degradation and human disturbance. However, the extent of the contribution of these factors to declining population trends is extremely difficult to assess principally due to the absence of quantifiable data, creating a discussion bias towards climate variables, and effectively distracting from non-climate factors that can be managed on a regional scale to ensure the viability of the population.

Participatory Scenario Planning for Climate Change Adaptation: the Maui Groundwater Project

NASA Astrophysics Data System (ADS)

Keener, V. W.; Brewington, L.; Finucane, M.

2015-12-01

For the last century, the island of Maui in Hawai'i has been the center of environmental, agricultural, and legal conflict with respect to both surface and groundwater allocation. Planning for sustainable future freshwater supply in Hawai'i requires adaptive policies and decision-making that emphasizes private and public partnerships and knowledge transfer between scientists and non-scientists. We have downscaled dynamical climate models to 1 km resolution in Maui and coupled them with a USGS Water Budget model and a participatory scenario building process to quantify future changes in island-scale climate and groundwater recharge under different land uses. Although these projections are uncertain, the integrated nature of the Pacific RISA research program has allowed us to take a multi-pronged approach to facilitate the uptake of climate information into policy and management. This presentation details the ongoing work to support the development of Hawai'i's first island-wide water use plan under the new climate adaptation directive. Participatory scenario planning began in 2012 to bring together a diverse group of ~100 decision-makers in state and local government, watershed restoration, agriculture, and conservation to 1) determine the type of information (climate variables, land use and development, agricultural practices) they would find helpful in planning for climate change, and 2) develop a set of nested scenarios that represent alternative climate and management futures. This integration of knowledge is an iterative process, resulting in flexible and transparent narratives of complex futures comprised of information at multiple scales. We will present an overview of the downscaling, scenario building, hydrological modeling processes, and stakeholder response.

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.

Diversity, Adaptability and Ecosystem Resilience

NASA Astrophysics Data System (ADS)

Keribin, Rozenn; Friend, Andrew

2013-04-01

Our ability to predict climate change and anticipate its impacts depends on Earth System Models (ESMs) and their ability to account for the high number of interacting components of the Earth System and to gauge both their influence on the climate and the feedbacks they induce. The land carbon cycle is a component of ESMs that is still poorly constrained. Since the 1990s dynamic global vegetation models (DGVMs) have become the main tool through which we understand the interactions between plant ecosystems and the climate. While DGVMs have made it clear the impacts of climate change on vegetation could be dramatic, predicting the dieback of rainforests and massive carbon losses from various ecosystems, they are highly variable both in their composition and their predictions. Their treatment of plant diversity and competition in particular vary widely and are based on highly-simplified relationships that do not account for the multiple levels of diversity and adaptability found in real plant ecosystems. The aim of this GREENCYCLES II project is to extend an individual-based DGVM to treat the diversity of physiologies found in plant communities and evaluate their effect if any on the ecosystem's transient dynamics and resilience. In the context of the InterSectoral Impacts Model Intercomparison Project (ISI-MIP), an initiative coordinated by a team at the Potsdam Institute for Climate Impact Research (PIK) that aims to provide fast-track global impact assessments for the IPCC's Fifth Assessment Report, we compare 6 vegetation models including 4 DGVMs under different climate change scenarios and analyse how the very different treatments of plant diversity and interactions from one model to the next affect the models' results. We then investigate a new, more mechanistic method of incorporating plant diversity into the DGVM "Hybrid" based on ecological tradeoffs mediated by plant traits and individual-based competition for light.

The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols

NASA Technical Reports Server (NTRS)

Shukla, Sonali P.; Ruane, Alexander Clark

2014-01-01

Climate change is expected to alter a multitude of factors important to agricultural systems, including pests, diseases, weeds, extreme climate events, water resources, soil degradation, and socio-economic pressures. Changes to carbon dioxide concentration ([CO2]), temperature, and water (CTW) will be the primary drivers of change in crop growth and agricultural systems. Therefore, establishing the CTW-change sensitivity of crop yields is an urgent research need and warrants diverse methods of investigation. Crop models provide a biophysical, process-based tool to investigate crop responses across varying environmental conditions and farm management techniques, and have been applied in climate impact assessment by using a variety of methods (White et al., 2011, and references therein). However, there is a significant amount of divergence between various crop models' responses to CTW changes (Rotter et al., 2011). While the application of a site-based crop model is relatively simple, the coordination of such agricultural impact assessments on larger scales requires consistent and timely contributions from a large number of crop modelers, each time a new global climate model (GCM) scenario or downscaling technique is created. A coordinated, global effort to rapidly examine CTW sensitivity across multiple crops, crop models, and sites is needed to aid model development and enhance the assessment of climate impacts (Deser et al., 2012). To fulfill this need, the Coordinated Climate-Crop Modeling Project (C3MP) (Ruane et al., 2014) was initiated within the Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013). The submitted results from C3MP Phase 1 (February 15, 2013-December 31, 2013) are currently being analyzed. This chapter serves to present and update the C3MP protocols, discuss the initial participation and general findings, comment on needed adjustments, and describe continued and future development. AgMIP aims to improve substantially the climate, crop, and economic simulation tools that are used to characterize the agricultural sector, to assess future world food security under changing climate conditions, and to enhance adaptation capacity both globally and regionally. To understand better and improve the modeled crop responses, AgMIP has conducted detailed crop model intercomparisons at closely observed field sites for wheat (Asseng et al., 2013), rice (Li et al., in review), maize (Bassu et al., 2014), and sugarcane (Singels et al., 2013). A coordinated modeling exercise was one of the original motivations for AgMIP, and C3MP provides rapid estimation of crop responses to CO2, water, and temperature (CTW) changes, adding dimension and insight into the crop model intercomparisons, while facilitating interactions within the global community of modelers. C3MP also contributes a fast-track, multi-model climate sensitivity assessment for the AgMIP climate and crop modeling teams on Research Track 2 (Fig. 1), which seeks to understand the impact of projected climatic changes on crop production and food security (Rosenzweig et al., 2013; Ruane et al., 2014).

A Portable Regional Weather and Climate Downscaling System Using GEOS-5, LIS-6, WRF, and the NASA Workflow Tool

NASA Astrophysics Data System (ADS)

Kemp, E. M.; Putman, W. M.; Gurganus, J.; Burns, R. W.; Damon, M. R.; McConaughy, G. R.; Seablom, M. S.; Wojcik, G. S.

2009-12-01

We present a regional downscaling system (RDS) suitable for high-resolution weather and climate simulations in multiple supercomputing environments. The RDS is built on the NASA Workflow Tool, a software framework for configuring, running, and managing computer models on multiple platforms with a graphical user interface. The Workflow Tool is used to run the NASA Goddard Earth Observing System Model Version 5 (GEOS-5), a global atmospheric-ocean model for weather and climate simulations down to 1/4 degree resolution; the NASA Land Information System Version 6 (LIS-6), a land surface modeling system that can simulate soil temperature and moisture profiles; and the Weather Research and Forecasting (WRF) community model, a limited-area atmospheric model for weather and climate simulations down to 1-km resolution. The Workflow Tool allows users to customize model settings to user needs; saves and organizes simulation experiments; distributes model runs across different computer clusters (e.g., the DISCOVER cluster at Goddard Space Flight Center, the Cray CX-1 Desktop Supercomputer, etc.); and handles all file transfers and network communications (e.g., scp connections). Together, the RDS is intended to aid researchers by making simulations as easy as possible to generate on the computer resources available. Initial conditions for LIS-6 and GEOS-5 are provided by Modern Era Retrospective-Analysis for Research and Applications (MERRA) reanalysis data stored on DISCOVER. The LIS-6 is first run for 2-4 years forced by MERRA atmospheric analyses, generating initial conditions for the WRF soil physics. GEOS-5 is then initialized from MERRA data and run for the period of interest. Large-scale atmospheric data, sea-surface temperatures, and sea ice coverage from GEOS-5 are used as boundary conditions for WRF, which is run for the same period of interest. Multiply nested grids are used for both LIS-6 and WRF, with the innermost grid run at a resolution sufficient for typical local weather features (terrain, convection, etc.) All model runs, restarts, and file transfers are coordinated by the Workflow Tool. Two use cases are being pursued. First, the RDS generates regional climate simulations down to 4-km for the Chesapeake Bay region, with WRF output provided as input to more specialized models (e.g., ocean/lake, hydrological, marine biology, and air pollution). This will allow assessment of climate impact on local interests (e.g., changes in Bay water levels and temperatures, innundation, fish kills, etc.) Second, the RDS generates high-resolution hurricane simulations in the tropical North Atlantic. This use case will support Observing System Simulation Experiments (OSSEs) of dynamically-targeted lidar observations as part of the NASA Sensor Web Simulator project. Sample results will be presented at the AGU Fall Meeting.

Scientist-Practitioner Engagement to Inform Regional Hydroclimate Model Evaluation

NASA Astrophysics Data System (ADS)

Jones, A. D.; Jagannathan, K. A.; Ullrich, P. A.

2017-12-01

Water mangers face significant challenges in planning for the coming decades as previously stationary aspects of the regional hydroclimate shift in response to global climate change. Providing scientific insights that enable appropriate use of regional hydroclimate projections for planning is a non-trivial problem. The system of data, models, and methods used to produce regional hydroclimate projections is subject to multiple interacting uncertainties and biases, including uncertainties that arise from general circulation models, re-analysis data products, regional climate models, hydrologic models, and statistical downscaling methods. Moreover, many components of this system were not designed with the information needs of water managers in mind. To address this problem and provide actionable insights into the sources of uncertainty present in regional hydroclimate data products, Project Hyperion has undertaken a stakeholder engagement process in four case study water basins across the US. Teams of water managers and scientists are interacting in a structured manner to identify decision-relevant metrics of model performance. These metrics are in turn being used to drive scientific investigations to uncover the sources of uncertainty in these quantities. Thus far, we have found that identification of climate phenomena of interest to stakeholders is relatively easy, but translating these into specific quantifiable metrics and prioritizing metrics is more challenging. Iterative feedback among scientists and stakeholders has proven critical in resolving these challenges, as has the roles played by boundary spanners who understand and can speak to the perspectives of multiple professional communities. Here we describe the structured format of our engagement process and the lessons learned so far, as we aim to improve the decision-relevance of hydroclimate projections through a collaborative process.

Climate Narratives: Combing multiple sources of information to develop risk management strategies for a municipal water utility

NASA Astrophysics Data System (ADS)

Yates, D. N.; Basdekas, L.; Rajagopalan, B.; Stewart, N.

2013-12-01

Municipal water utilities often develop Integrated Water Resource Plans (IWRP), with the goal of providing a reliable, sustainable water supply to customers in a cost-effective manner. Colorado Springs Utilities, a 5-service provider (potable and waste water, solid waste, natural gas and electricity) in Colorado USA, recently undertook an IWRP. where they incorporated water supply, water demand, water quality, infrastructure reliability, environmental protection, and other measures within the context of complex water rights, such as their critically important 'exchange potential'. The IWRP noted that an uncertain climate was one of the greatest sources of uncertainty to achieving a sustainable water supply to a growing community of users. We describe how historic drought, paleo-climate, and climate change projections were blended together into climate narratives that informed a suite of water resource systems models used by the utility to explore the vulnerabilities of their water systems.

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.

Combining state-and-transition simulations and species distribution models to anticipate the effects of climate change

USGS Publications Warehouse

Miller, Brian W.; Frid, Leonardo; Chang, Tony; Piekielek, N. B.; Hansen, Andrew J.; Morisette, Jeffrey T.

2015-01-01

State-and-transition simulation models (STSMs) are known for their ability to explore the combined effects of multiple disturbances, ecological dynamics, and management actions on vegetation. However, integrating the additional impacts of climate change into STSMs remains a challenge. We address this challenge by combining an STSM with species distribution modeling (SDM). SDMs estimate the probability of occurrence of a given species based on observed presence and absence locations as well as environmental and climatic covariates. Thus, in order to account for changes in habitat suitability due to climate change, we used SDM to generate continuous surfaces of species occurrence probabilities. These data were imported into ST-Sim, an STSM platform, where they dictated the probability of each cell transitioning between alternate potential vegetation types at each time step. The STSM was parameterized to capture additional processes of vegetation growth and disturbance that are relevant to a keystone species in the Greater Yellowstone Ecosystem—whitebark pine (Pinus albicaulis). We compared historical model runs against historical observations of whitebark pine and a key disturbance agent (mountain pine beetle, Dendroctonus ponderosae), and then projected the simulation into the future. Using this combination of correlative and stochastic simulation models, we were able to reproduce historical observations and identify key data gaps. Results indicated that SDMs and STSMs are complementary tools, and combining them is an effective way to account for the anticipated impacts of climate change, biotic interactions, and disturbances, while also allowing for the exploration of management options.

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.

Co-producing simulation models to inform resource management: a case study from southwest South Dakota

USGS Publications Warehouse

Miller, Brian W.; Symstad, Amy J.; Frid, Leonardo; Fisichelli, Nicholas A.; Schuurman, Gregor W.

2017-01-01

Simulation models can represent complexities of the real world and serve as virtual laboratories for asking “what if…?” questions about how systems might respond to different scenarios. However, simulation models have limited relevance to real-world applications when designed without input from people who could use the simulated scenarios to inform their decisions. Here, we report on a state-and-transition simulation model of vegetation dynamics that was coupled to a scenario planning process and co-produced by researchers, resource managers, local subject-matter experts, and climate change adaptation specialists to explore potential effects of climate scenarios and management alternatives on key resources in southwest South Dakota. Input from management partners and local experts was critical for representing key vegetation types, bison and cattle grazing, exotic plants, fire, and the effects of climate change and management on rangeland productivity and composition given the paucity of published data on many of these topics. By simulating multiple land management jurisdictions, climate scenarios, and management alternatives, the model highlighted important tradeoffs between grazer density and vegetation composition, as well as between the short- and long-term costs of invasive species management. It also pointed to impactful uncertainties related to the effects of fire and grazing on vegetation. More broadly, a scenario-based approach to model co-production bracketed the uncertainty associated with climate change and ensured that the most important (and impactful) uncertainties related to resource management were addressed. This cooperative study demonstrates six opportunities for scientists to engage users throughout the modeling process to improve model utility and relevance: (1) identifying focal dynamics and variables, (2) developing conceptual model(s), (3) parameterizing the simulation, (4) identifying relevant climate scenarios and management alternatives, (5) evaluating and refining the simulation, and (6) interpreting the results. We also reflect on lessons learned and offer several recommendations for future co-production efforts, with the aim of advancing the pursuit of usable science.

Disentangling climatic and anthropogenic controls on global terrestrial evapotranspiration trends

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

Mao, Jiafu; Shi, Xiaoying; Ricciuto, Daniel M.

Here, we examined natural and anthropogenic controls on terrestrial evapotranspiration (ET) changes from 1982-2010 using multiple estimates from remote sensing-based datasets and process-oriented land surface models. A significant increased trend of ET in each hemisphere was consistently revealed by observationally-constrained data and multi-model ensembles that considered historic natural and anthropogenic drivers. The climate impacts were simulated to determine the spatiotemporal variations in ET. Globally, rising CO 2 ranked second in these models after the predominant climatic influences, and yielded a decreasing trend in canopy transpiration and ET, especially for tropical forests and high-latitude shrub land. Increased nitrogen deposition slightly amplifiedmore » global ET via enhanced plant growth. Land-use-induced ET responses, albeit with substantial uncertainties across the factorial analysis, were minor globally, but pronounced locally, particularly over regions with intensive land-cover changes. Our study highlights the importance of employing multi-stream ET and ET-component estimates to quantify the strengthening anthropogenic fingerprint in the global hydrologic cycle.« less

Statistical Forecasting of Current and Future Circum-Arctic Ground Temperatures and Active Layer Thickness

NASA Astrophysics Data System (ADS)

Aalto, J.; Karjalainen, O.; Hjort, J.; Luoto, M.

2018-05-01

Mean annual ground temperature (MAGT) and active layer thickness (ALT) are key to understanding the evolution of the ground thermal state across the Arctic under climate change. Here a statistical modeling approach is presented to forecast current and future circum-Arctic MAGT and ALT in relation to climatic and local environmental factors, at spatial scales unreachable with contemporary transient modeling. After deploying an ensemble of multiple statistical techniques, distance-blocked cross validation between observations and predictions suggested excellent and reasonable transferability of the MAGT and ALT models, respectively. The MAGT forecasts indicated currently suitable conditions for permafrost to prevail over an area of 15.1 ± 2.8 × 106 km2. This extent is likely to dramatically contract in the future, as the results showed consistent, but region-specific, changes in ground thermal regime due to climate change. The forecasts provide new opportunities to assess future Arctic changes in ground thermal state and biogeochemical feedback.

Disentangling climatic and anthropogenic controls on global terrestrial evapotranspiration trends

DOE PAGES

Mao, Jiafu; Shi, Xiaoying; Ricciuto, Daniel M.; ...

2015-09-08

Here, we examined natural and anthropogenic controls on terrestrial evapotranspiration (ET) changes from 1982-2010 using multiple estimates from remote sensing-based datasets and process-oriented land surface models. A significant increased trend of ET in each hemisphere was consistently revealed by observationally-constrained data and multi-model ensembles that considered historic natural and anthropogenic drivers. The climate impacts were simulated to determine the spatiotemporal variations in ET. Globally, rising CO 2 ranked second in these models after the predominant climatic influences, and yielded a decreasing trend in canopy transpiration and ET, especially for tropical forests and high-latitude shrub land. Increased nitrogen deposition slightly amplifiedmore » global ET via enhanced plant growth. Land-use-induced ET responses, albeit with substantial uncertainties across the factorial analysis, were minor globally, but pronounced locally, particularly over regions with intensive land-cover changes. Our study highlights the importance of employing multi-stream ET and ET-component estimates to quantify the strengthening anthropogenic fingerprint in the global hydrologic cycle.« less

An Integrated Modeling Framework Forecasting Ecosystem Exposure— A Systems Approach to the Cumulative Impacts of Multiple Stressors

EPA Science Inventory

Freshwater habitats provide fishable, swimmable and drinkable resources and are a nexus of geophysical and biological processes. These processes in turn influence the persistence and sustainability of populations, communities and ecosystems. Climate change and landuse change enco...

Quantitative Models for Ecosystem Assessment in Narragansett Bay: Response to Nutrient Loading and Other Stressors

EPA Science Inventory

Multiple drivers, including nutrient loading and climate change, affect the Narragansett Bay ecosystem. Managers are interested in understanding the timing and magnitude of these effects, as well as ecosystem responses to restoration actions, such as the capacity and potential fo...

Regionalisation of statistical model outputs creating gridded data sets for Germany

NASA Astrophysics Data System (ADS)

Höpp, Simona Andrea; Rauthe, Monika; Deutschländer, Thomas

2016-04-01

The goal of the German research program ReKliEs-De (regional climate projection ensembles for Germany, http://.reklies.hlug.de) is to distribute robust information about the range and the extremes of future climate for Germany and its neighbouring river catchment areas. This joint research project is supported by the German Federal Ministry of Education and Research (BMBF) and was initiated by the German Federal States. The Project results are meant to support the development of adaptation strategies to mitigate the impacts of future climate change. The aim of our part of the project is to adapt and transfer the regionalisation methods of the gridded hydrological data set (HYRAS) from daily station data to the station based statistical regional climate model output of WETTREG (regionalisation method based on weather patterns). The WETTREG model output covers the period of 1951 to 2100 with a daily temporal resolution. For this, we generate a gridded data set of the WETTREG output for precipitation, air temperature and relative humidity with a spatial resolution of 12.5 km x 12.5 km, which is common for regional climate models. Thus, this regionalisation allows comparing statistical to dynamical climate model outputs. The HYRAS data set was developed by the German Meteorological Service within the German research program KLIWAS (www.kliwas.de) and consists of daily gridded data for Germany and its neighbouring river catchment areas. It has a spatial resolution of 5 km x 5 km for the entire domain for the hydro-meteorological elements precipitation, air temperature and relative humidity and covers the period of 1951 to 2006. After conservative remapping the HYRAS data set is also convenient for the validation of climate models. The presentation will consist of two parts to present the actual state of the adaptation of the HYRAS regionalisation methods to the statistical regional climate model WETTREG: First, an overview of the HYRAS data set and the regionalisation methods for precipitation (REGNIE method based on a combination of multiple linear regression with 5 predictors and inverse distance weighting), air temperature and relative humidity (optimal interpolation) will be given. Finally, results of the regionalisation of WETTREG model output will be shown.

Climate Change Impacts on Crop Production in Nigeria

NASA Astrophysics Data System (ADS)

Mereu, V.; Gallo, A.; Carboni, G.; Spano, D.

2011-12-01

The agricultural sector in Nigeria is particularly important for the country's food security, natural resources, and growth agenda. The cultivable areas comprise more than 70% of the total area; however, the cultivated area is about the 35% of the total area. The most important components in the food basket of the nation are cereals and tubers, which include rice, maize, corn, millet, sorghum, yam, and cassava. These crops represent about 80% of the total agricultural product in Nigeria (from NPAFS). The major crops grown in the country can be divided into food crops (produced for consumption) and export products. Despite the importance of the export crops, the primary policy of agriculture is to make Nigeria self-sufficient in its food and fiber requirements. The projected impacts of future climate change on agriculture and water resources are expected to be adverse and extensive in these area. This implies the need for actions and measures to adapt to climate change impacts, and especially as they affect agriculture, the primary sector for Nigerian economy. In the framework of the Project Climate Risk Analysis in Nigeria (founded by World Bank Contract n.7157826), a study was made to assess the potential impact of climate change on the main crops that characterize Nigerian agriculture. The DSSAT-CSM (Decision Support System for Agrotechnology Transfer - Cropping System Model) software, version 4.5 was used for the analysis. Crop simulation models included in DSSAT are tools that simulate physiological processes of crop growth, development and production by combining genetic crop characteristics and environmental (soil and weather) conditions. For each selected crop, the models were calibrated to evaluate climate change impacts on crop production. The climate data used for the analysis are derived by the Regional Circulation Model COSMO-CLM, from 1971 to 2065, at 8 km of spatial resolution. The RCM model output was "perturbed" with 10 Global Climate Models to have a wide variety of possible climate projections for the impact analysis. Multiple combinations of soil and climate conditions and crop management and varieties were considered for each Agro-Ecological Zone (AEZ) of Nigeria. A sensitivity analysis was made to evaluate the model response to changes in precipitation and temperature. The climate impact assessment was made by comparing the yield obtained with the climate data for the present period and the yield obtainable under future climate conditions. The results were analyzed at state, AEZ and country levels. The analysis shows a general reduction in crop yields in particular in the dryer regions of northern Nigeria.

On a collision course: competition and dispersal differences create no-analogue communities and cause extinctions during climate change

PubMed Central

Urban, Mark C.; Tewksbury, Josh J.; Sheldon, Kimberly S.

2012-01-01

Most climate change predictions omit species interactions and interspecific variation in dispersal. Here, we develop a model of multiple competing species along a warming climatic gradient that includes temperature-dependent competition, differences in niche breadth and interspecific differences in dispersal ability. Competition and dispersal differences decreased diversity and produced so-called ‘no-analogue’ communities, defined as a novel combination of species that does not currently co-occur. Climate change altered community richness the most when species had narrow niches, when mean community-wide dispersal rates were low and when species differed in dispersal abilities. With high interspecific dispersal variance, the best dispersers tracked climate change, out-competed slower dispersers and caused their extinction. Overall, competition slowed the advance of colonists into newly suitable habitats, creating lags in climate tracking. We predict that climate change will most threaten communities of species that have narrow niches (e.g. tropics), vary in dispersal (most communities) and compete strongly. Current forecasts probably underestimate climate change impacts on biodiversity by neglecting competition and dispersal differences. PMID:22217718

The AgMIP Coordinated Global and Regional Assessments (CGRA) of Climate Change Impacts on Agriculture and Food Security

NASA Technical Reports Server (NTRS)

Ruane, Alex; Rosenzweig, Cynthia; Elliott, Joshua; Antle, John

2015-01-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has been working since 2010 to construct a protocol-based framework enabling regional assessments (led by regional experts and modelers) that can provide consistent inputs to global economic and integrated assessment models. These global models can then relay important global-level information that drive regional decision-making and outcomes throughout an interconnected agricultural system. AgMIPs community of nearly 800 climate, crop, livestock, economics, and IT experts has improved the state-of-the-art through model intercomparisons, validation exercises, regional integrated assessments, and the launch of AgMIP programs on all six arable continents. AgMIP is now launching Coordinated Global and Regional Assessments (CGRA) of climate change impacts on agriculture and food security to link global and regional crop and economic models using a protocol-based framework. The CGRA protocols are being developed to utilize historical observations, climate projections, and RCPsSSPs from CMIP5 (and potentially CMIP6), and will examine stakeholder-driven agricultural development and adaptation scenarios to provide cutting-edge assessments of climate changes impact on agriculture and food security. These protocols will build on the foundation of established protocols from AgMIPs 30+ activities, and will emphasize the use of multiple models, scenarios, and scales to enable an accurate assessment of related uncertainties. The CGRA is also designed to provide the outputs necessary to feed into integrated assessment models (IAMs), nutrition and food security assessments, nitrogen and carbon cycle models, and additional impact-sector assessments (e.g., water resources, land-use, biomes, urban areas). This presentation will describe the current status of CGRA planning and initial prototype experiments to demonstrate key aspects of the protocols before wider implementation ahead of the IPCC Sixth Assessment Report.

The AgMIP Wheat Pilot: A multi-model approach for climate change impact assessments.

NASA Astrophysics Data System (ADS)

Asseng, S.

2012-12-01

Asseng S., F. Ewert, C. Rosenzweig, J.W. Jones, J.L. Hatfield, A. Ruane, K.J. Boote, P. Thorburn, R.P. Rötter, D. Cammarano, N. Brisson, B. Basso, P. Martre, D. Ripoche, P. Bertuzzi, P. Steduto, L. Heng, M.A. Semenov, P. Stratonovitch, C. Stockle, G. O'Leary, P.K. Aggarwal, S. Naresh Kumar, C. Izaurralde, J.W. White, L.A. Hunt, R. Grant, K.C. Kersebaum, T. Palosuo, J. Hooker, T. Osborne, J. Wolf, I. Supit, J.E. Olesen, J. Doltra, C. Nendel, S. Gayler, J. Ingwersen, E. Priesack, T. Streck, F. Tao, C. Müller, K. Waha, R. Goldberg, C. Angulo, I. Shcherbak, C. Biernath, D. Wallach, M. Travasso, A. Challinor. Abstract: Crop simulation models have been used to assess the impact of climate change on agriculture. These assessments are often carried out with a single model in a limited number of environments and without determining the uncertainty of simulated impacts. There is a need for a coordinated effort bringing together multiple modeling teams which has been recognized by the Agricultural Model Intercomparison and Improvement Project (AgMIP; www.agmip.org). AgMIP aims to provide more robust estimates of climate impacts on crop yields and agricultural trade, including estimates of associated uncertainties. Here, we present the AgMIP Wheat Pilot Study, the most comprehensive model intercomparison of the response of wheat crops to climate change to date, including 27 wheat models. Crop model uncertainties in assessing climate change impacts are explored and compared with field experimental and Global Circulation Model uncertainties. Causes of impact uncertainties and ways to reduce these are discussed.

The AgMIP Coordinated Global and Regional Assessments (CGRA) of Climate Change Impacts on Agriculture and Food Security

NASA Astrophysics Data System (ADS)

Ruane, A. C.; Rosenzweig, C.; Antle, J. M.; Elliott, J. W.

2015-12-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has been working since 2010 to construct a protocol-based framework enabling regional assessments (led by regional experts and modelers) that can provide consistent inputs to global economic and integrated assessment models. These global models can then relay important global-level information that drive regional decision-making and outcomes throughout an interconnected agricultural system. AgMIP's community of nearly 800 climate, crop, livestock, economics, and IT experts has improved the state-of-the-art through model intercomparisons, validation exercises, regional integrated assessments, and the launch of AgMIP programs on all six arable continents. AgMIP is now launching Coordinated Global and Regional Assessments (CGRA) of climate change impacts on agriculture and food security to link global and regional crop and economic models using a protocol-based framework. The CGRA protocols are being developed to utilize historical observations, climate projections, and RCPs/SSPs from CMIP5 (and potentially CMIP6), and will examine stakeholder-driven agricultural development and adaptation scenarios to provide cutting-edge assessments of climate change's impact on agriculture and food security. These protocols will build on the foundation of established protocols from AgMIP's 30+ activities, and will emphasize the use of multiple models, scenarios, and scales to enable an accurate assessment of related uncertainties. The CGRA is also designed to provide the outputs necessary to feed into integrated assessment models (IAMs), nutrition and food security assessments, nitrogen and carbon cycle models, and additional impact-sector assessments (e.g., water resources, land-use, biomes, urban areas). This presentation will describe the current status of CGRA planning and initial prototype experiments to demonstrate key aspects of the protocols before wider implementation ahead of the IPCC Sixth Assessment Report.

POTENTIAL DROUGHT IMPACTS ON ELECTRICITY GENERATION IN TEXAS

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

Yan, Y. Eugene; Demissie, Yonas K.; Wigmosta, Mark S.

2013-09-30

Many power plants in the Electric Reliability Council of Texas (ERCOT) region require a large amount of water for system cooling. To improve the understanding of potential risks of electricity generation curtailment due to drought, an assessment of water availability and its potential impacts on generation during drought was performed. For this impact analysis, we identified three drought scenarios based on historical drought records and projected climate data from the Geophysical Fluid Dynamics Laboratory global climate model, for greenhouse gas emission scenario A2 defined by the Intergovernmental Panel on Climate Change . The three drought scenarios are (1) 2011 droughtmore » conditions (the worst drought in history), with the current level of water use; (2) a single-year drought (2022) projected for the period of 2020-2030, with the assumed projected water use level for 2030; and (3) a multiple-year drought constructed with climate data for 1950-1957 and water demand projected for 2030. The projected drought scenario in 2022 and the historical droughts in 2011 and 1950-1957 represent two different precipitation patterns in the Texas-Gulf river basin. The hydrologic model constructed for the Texas-Gulf river basin covers most of the ERCOT region. The model incorporates climate and water use data that correspond to three drought scenarios, respectively, to estimate evapotranspiration, water yield from watersheds, stream flow and water storage in reservoirs. Using criteria based on observed (< 50% storage) and predicted (< 55% storage) reservoir data, we identified 15 low-storage reservoirs in 2011, 10 in 2022, and 20 in 1956 (the last year of the multiple-year drought). The power plants that are supported by these reservoirs would be potentially at risk of being derated for thermoelectric cooling because of a lack of water supply. These power plants are located mainly in watersheds near and between Houston and Austin, as well as surrounding Dallas.« less

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.

Effects of climate change, invasive species, and disease on the distribution of native European crayfishes.

PubMed

Capinha, César; Larson, Eric R; Tricarico, Elena; Olden, Julian D; Gherardi, Francesca

2013-08-01

Climate change will require species to adapt to new conditions or follow preferred climates to higher latitudes or elevations, but many dispersal-limited freshwater species may be unable to move due to barriers imposed by watershed boundaries. In addition, invasive nonnative species may expand into new regions under future climate conditions and contribute to the decline of native species. We evaluated future distributions for the threatened European crayfish fauna in response to climate change, watershed boundaries, and the spread of invasive crayfishes, which transmit the crayfish plague, a lethal disease for native European crayfishes. We used climate projections from general circulation models and statistical models based on Mahalanobis distance to predict climate-suitable regions for native and invasive crayfishes in the middle and at the end of the 21st century. We identified these suitable regions as accessible or inaccessible on the basis of major watershed boundaries and present occurrences and evaluated potential future overlap with 3 invasive North American crayfishes. Climate-suitable areas decreased for native crayfishes by 19% to 72%, and the majority of future suitable areas for most of these species were inaccessible relative to native and current distributions. Overlap with invasive crayfish plague-transmitting species was predicted to increase. Some native crayfish species (e.g., noble crayfish [Astacus astacus]) had no future refugia that were unsuitable for the modeled nonnative species. Our results emphasize the importance of preventing additional introductions and spread of invasive crayfishes in Europe to minimize interactions between the multiple stressors of climate change and invasive species, while suggesting candidate regions for the debatable management option of assisted colonization. © 2013 Society for Conservation Biology.

The non-linear, interactive effects of population density and climate drive the geographical patterns of waterfowl survival

USGS Publications Warehouse

Zhao, Qing; Boomer, G. Scott; Kendall, William L.

2018-01-01

On-going climate change has major impacts on ecological processes and patterns. Understanding the impacts of climate on the geographical patterns of survival can provide insights to how population dynamics respond to climate change and provide important information for the development of appropriate conservation strategies at regional scales. It is challenging to understand the impacts of climate on survival, however, due to the fact that the non-linear relationship between survival and climate can be modified by density-dependent processes. In this study we extended the Brownie model to partition hunting and non-hunting mortalities and linked non-hunting survival to covariates. We applied this model to four decades (1972–2014) of waterfowl band-recovery, breeding population survey, and precipitation and temperature data covering multiple ecological regions to examine the non-linear, interactive effects of population density and climate on waterfowl non-hunting survival at a regional scale. Our results showed that the non-linear effect of temperature on waterfowl non-hunting survival was modified by breeding population density. The concave relationship between non-hunting survival and temperature suggested that the effects of warming on waterfowl survival might be multifaceted. Furthermore, the relationship between non-hunting survival and temperature was stronger when population density was higher, suggesting that high-density populations may be less buffered against warming than low-density populations. Our study revealed distinct relationships between waterfowl non-hunting survival and climate across and within ecological regions, highlighting the importance of considering different conservation strategies according to region-specific population and climate conditions. Our findings and associated novel modelling approach have wide implications in conservation practice.

Using Bayesian networks to assess the vulnerability of Hawaiian terrestrial biota to climate change

NASA Astrophysics Data System (ADS)

Fortini, L.; Jacobi, J.; Price, J.; Vorsino, A.; Paxton, E.; Amidon, F.; 'Ohukani'ohi'a Gon, S., III; Koob, G.; Brink, K.; Burgett, J.; Miller, S.

2012-12-01

As the effects of climate change on individual species become increasingly apparent, there is a clear need for effective adaptation planning to prevent an increase in species extinctions worldwide. Given the limited understanding of species responses to climate change, vulnerability assessments and species distribution models (SDMs) have been two common tools used to jump-start climate change adaptation efforts. However, although these two approaches generally serve the same purpose of understanding species future responses to climate change, they have rarely mixed. In collaboration with research and management partners from federal, state and non-profit organizations, we are conducting a climate change vulnerability assessment for hundreds of plant and forest bird species of the Main Hawaiian Islands. This assessment is the first to comprehensively consider the potential threats of climate change to a significant portion of Hawaii's fauna and flora (over one thousand species considered) and thus fills a critical gap defined by natural resource scientists and managers in the region. We have devised a flexible approach that effectively integrates species distribution models into a vulnerability assessment framework that can be easily updated with improved models and data. This tailors our assessment approach to the Pacific Island reality of often limited and fragmented information on species and large future climate uncertainties, This vulnerability assessment is based on a Bayesian network-based approach that integrates multiple landscape (e.g., topographic diversity, dispersal barriers), species trait (e.g., generation length, fecundity) and expert-knowledge based information (e.g., capacity to colonize restored habitat) relevant to long-term persistence of species under climate change. Our presentation will highlight some of the results from our assessment but will mainly focus on the utility of the flexible approach we have developed and its potential application in other settings.

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.

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.

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.

Landscape genomic analysis of candidate genes for climate adaptation in a California endemic oak, Quercus lobata.

PubMed

Sork, Victoria L; Squire, Kevin; Gugger, Paul F; Steele, Stephanie E; Levy, Eric D; Eckert, Andrew J

2016-01-01

The ability of California tree populations to survive anthropogenic climate change will be shaped by the geographic structure of adaptive genetic variation. Our goal is to test whether climate-associated candidate genes show evidence of spatially divergent selection in natural populations of valley oak, Quercus lobata, as preliminary indication of local adaptation. Using DNA from 45 individuals from 13 localities across the species' range, we sequenced portions of 40 candidate genes related to budburst/flowering, growth, osmotic stress, and temperature stress. Using 195 single nucleotide polymorphisms (SNPs), we estimated genetic differentiation across populations and correlated allele frequencies with climate gradients using single-locus and multivariate models. The top 5% of FST estimates ranged from 0.25 to 0.68, yielding loci potentially under spatially divergent selection. Environmental analyses of SNP frequencies with climate gradients revealed three significantly correlated SNPs within budburst/flowering genes and two SNPs within temperature stress genes with mean annual precipitation, after controlling for multiple testing. A redundancy model showed a significant association between SNPs and climate variables and revealed a similar set of SNPs with high loadings on the first axis. In the RDA, climate accounted for 67% of the explained variation, when holding climate constant, in contrast to a putatively neutral SSR data set where climate accounted for only 33%. Population differentiation and geographic gradients of allele frequencies in climate-associated functional genes in Q. lobata provide initial evidence of adaptive genetic variation and background for predicting population response to climate change. © 2016 Botanical Society of America.

Microbial dormancy improves development and experimental validation of ecosystem model

DOE PAGES

Wang, Gangsheng; Jagadamma, Sindhu; Mayes, Melanie; ...

2014-07-11

Climate feedbacks from soils can result from environmental change followed by response of plant and microbial communities, and/or associated changes in nutrient cycling. Explicit consideration of microbial life history traits and functions may be necessary to predict climate feedbacks due to changes in the physiology and community composition of microbes and their associated effect on carbon cycling. Here, we enhanced the Microbial-Enzyme-mediated Decomposition (MEND) model by incorporating microbial dormancy and the ability to track multiple isotopes of carbon. We tested two versions of MEND, i.e., MEND with dormancy and MEND without dormancy, against long-term (270 d) lab incubations of fourmore » soils with isotopically-labeled substrates. MEND without dormancy adequately fitted multiple observations (total and 14C respiration, and dissolved organic carbon), but at the cost of significantly underestimating the total microbial biomass. The MEND with dormancy improved estimates of microbial biomass by 20 71% over the MEND without dormancy. We observed large differences for two fitted model parameters, the specific maintenance and growth rates for active microbes, depending on whether dormancy was considered. Together our model extrapolations of the incubation study show that long-term soil incubations with observations in multiple carbon pools are necessary to estimate both decomposition and microbial parameters. These efforts should provide essential support to future field- and global-scale simulations and enable more confident predictions of feedbacks between environmental change and carbon cycling.« less

Near Real-Time Event Detection & Prediction Using Intelligent Software Agents

DTIC Science & Technology

2006-03-01

value was 0.06743. Multiple autoregressive integrated moving average ( ARIMA ) models were then build to see if the raw data, differenced data, or...slight improvement. The best adjusted r^2 value was found to be 0.1814. Successful results were not expected from linear or ARIMA -based modelling ...appear, 2005. [63] Mora-Lopez, L., Mora, J., Morales-Bueno, R., et al. Modelling time series of climatic parameters with probabilistic finite

The Ophidia Stack: Toward Large Scale, Big Data Analytics Experiments for Climate Change

NASA Astrophysics Data System (ADS)

Fiore, S.; Williams, D. N.; D'Anca, A.; Nassisi, P.; Aloisio, G.

2015-12-01

The Ophidia project is a research effort on big data analytics facing scientific data analysis challenges in multiple domains (e.g. climate change). It provides a "datacube-oriented" framework responsible for atomically processing and manipulating scientific datasets, by providing a common way to run distributive tasks on large set of data fragments (chunks). Ophidia provides declarative, server-side, and parallel data analysis, jointly with an internal storage model able to efficiently deal with multidimensional data and a hierarchical data organization to manage large data volumes. The project relies on a strong background on high performance database management and On-Line Analytical Processing (OLAP) systems to manage large scientific datasets. The Ophidia analytics platform provides several data operators to manipulate datacubes (about 50), and array-based primitives (more than 100) to perform data analysis on large scientific data arrays. To address interoperability, Ophidia provides multiple server interfaces (e.g. OGC-WPS). From a client standpoint, a Python interface enables the exploitation of the framework into Python-based eco-systems/applications (e.g. IPython) and the straightforward adoption of a strong set of related libraries (e.g. SciPy, NumPy). The talk will highlight a key feature of the Ophidia framework stack: the "Analytics Workflow Management System" (AWfMS). The Ophidia AWfMS coordinates, orchestrates, optimises and monitors the execution of multiple scientific data analytics and visualization tasks, thus supporting "complex analytics experiments". Some real use cases related to the CMIP5 experiment will be discussed. In particular, with regard to the "Climate models intercomparison data analysis" case study proposed in the EU H2020 INDIGO-DataCloud project, workflows related to (i) anomalies, (ii) trend, and (iii) climate change signal analysis will be presented. Such workflows will be distributed across multiple sites - according to the datasets distribution - and will include intercomparison, ensemble, and outlier analysis. The two-level workflow solution envisioned in INDIGO (coarse grain for distributed tasks orchestration, and fine grain, at the level of a single data analytics cluster instance) will be presented and discussed.

Connecting medieval megadroughts and surface climate in the Last Millennium Reanalysis

NASA Astrophysics Data System (ADS)

Erb, M. P.; Emile-Geay, J.; Anderson, D. M.; Hakim, G. J.; Horlick, K. A.; Noone, D.; Perkins, W. A.; Steig, E. J.; Tardif, R.

2016-12-01

The North American Drought Atlas shows severe, long-lasting droughts during the Medieval Climate Anomaly. Because drought frequency and severity over the coming century is an area of vital interest, better understanding the causes of these historic droughts is crucial. A variety of research has suggested that a La Niña state was important for producing medieval megadroughts [1], and other work has indicated the potential roles of the Atlantic Multidecadal Oscillation [2] and internal atmospheric variability [3]. Correlations between drought and large-scale climate patterns also exist in the instrumental record [4], but understanding these relationships is far from complete. To investigate these relationships further, a data assimilation approach is employed. Proxy records - including tree rings, corals, and ice cores - are used to constrain climate states over the Common Era. By using general circulation model (GCM) output to quantify the covariances in the climate system, climate can be constrained not just at proxy sites but for all covarying locations and climate fields. Multiple GCMs will be employed to offset the limitations of imperfect model physics. This "Last Millennium Reanalysis" will be used to quantify relationships between North American medieval megadroughts and sea surface temperature patterns in the Atlantic and Pacific. 1. Cook, E. R., et al., Earth-Sci. Rev. 81, 93 (2007). 2. Oglesby, R., et al., Global Planet. Change 84-85, 56 (2012). 3. Stevenson, S., et al., J. Climate 28, 1865 (2015). 4. Cook, B. I., et al., J. Climate 27, 383 (2014).

Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model

NASA Astrophysics Data System (ADS)

Williamson, Daniel B.; Blaker, Adam T.; Sinha, Bablu

2017-04-01

In this paper we discuss climate model tuning and present an iterative automatic tuning method from the statistical science literature. The method, which we refer to here as iterative refocussing (though also known as history matching), avoids many of the common pitfalls of automatic tuning procedures that are based on optimisation of a cost function, principally the over-tuning of a climate model due to using only partial observations. This avoidance comes by seeking to rule out parameter choices that we are confident could not reproduce the observations, rather than seeking the model that is closest to them (a procedure that risks over-tuning). We comment on the state of climate model tuning and illustrate our approach through three waves of iterative refocussing of the NEMO (Nucleus for European Modelling of the Ocean) ORCA2 global ocean model run at 2° resolution. We show how at certain depths the anomalies of global mean temperature and salinity in a standard configuration of the model exceeds 10 standard deviations away from observations and show the extent to which this can be alleviated by iterative refocussing without compromising model performance spatially. We show how model improvements can be achieved by simultaneously perturbing multiple parameters, and illustrate the potential of using low-resolution ensembles to tune NEMO ORCA configurations at higher resolutions.

The Relative Impact of Climate Change on the Extinction Risk of Tree Species in the Montane Tropical Andes.

PubMed

Tejedor Garavito, Natalia; Newton, Adrian C; Golicher, Duncan; Oldfield, Sara

2015-01-01

There are widespread concerns that anthropogenic climate change will become a major cause of global biodiversity loss. However, the potential impact of climate change on the extinction risk of species remains poorly understood, particularly in comparison to other current threats. The objective of this research was to examine the relative impact of climate change on extinction risk of upper montane tree species in the tropical Andes, an area of high biodiversity value that is particularly vulnerable to climate change impacts. The extinction risk of 129 tree species endemic to the region was evaluated according to the IUCN Red List criteria, both with and without the potential impacts of climate change. Evaluations were supported by development of species distribution models, using three methods (generalized additive models, recursive partitioning, and support vector machines), all of which produced similarly high AUC values when averaged across all species evaluated (0.82, 0.86, and 0.88, respectively). Inclusion of climate change increased the risk of extinction of 18-20% of the tree species evaluated, depending on the climate scenario. The relative impact of climate change was further illustrated by calculating the Red List Index, an indicator that shows changes in the overall extinction risk of sets of species over time. A 15% decline in the Red List Index was obtained when climate change was included in this evaluation. While these results suggest that climate change represents a significant threat to tree species in the tropical Andes, they contradict previous suggestions that climate change will become the most important cause of biodiversity loss in coming decades. Conservation strategies should therefore focus on addressing the multiple threatening processes currently affecting biodiversity, rather than focusing primarily on potential climate change impacts.

Vulnerability-based evaluation of water supply design under climate change

NASA Astrophysics Data System (ADS)

Umit Taner, Mehmet; Ray, Patrick; Brown, Casey

2015-04-01

Long-lived water supply infrastructures are strategic investments in the developing world, serving the purpose of balancing water deficits compounded by both population growth and socio-economic development. Robust infrastructure design under climate change is compelling, and often addressed by focusing on the outcomes of climate model projections ('scenario-led' planning), or by identifying design options that are less vulnerable to a wide range of plausible futures ('vulnerability-based' planning). Decision-Scaling framework combines these two approaches by first applying a climate stress test on the system to explore vulnerabilities across many traces of the future, and then employing climate projections to inform the decision-making process. In this work, we develop decision scaling's nascent risk management concepts further, directing actions on vulnerabilities identified during the climate stress test. In the process, we present a new way to inform climate vulnerability space using climate projections, and demonstrate the use of multiple decision criteria to guide to a final design recommendation. The concepts are demonstrated for a water supply project in the Mombasa Province of Kenya, planned to provide domestic and irrigation supply. Six storage design capacities (from 40 to 140 million cubic meters) are explored through a stress test, under a large number climate traces representing both natural climate variability and plausible climate changes. Design outcomes are simulated over a 40-year planning period with a coupled hydrologic-water resources systems model and using standard reservoir operation rules. Resulting performance is expressed in terms of water supply reliability and economic efficiency. Ensemble climate projections are used for assigning conditional likelihoods to the climate traces using a statistical distance measure. The final design recommendations are presented and discussed for the decision criteria of expected regret, satisficing, and conditional value-at-risk (CVaR).

The Relative Impact of Climate Change on the Extinction Risk of Tree Species in the Montane Tropical Andes

PubMed Central

Tejedor Garavito, Natalia; Newton, Adrian C.; Golicher, Duncan; Oldfield, Sara

2015-01-01

There are widespread concerns that anthropogenic climate change will become a major cause of global biodiversity loss. However, the potential impact of climate change on the extinction risk of species remains poorly understood, particularly in comparison to other current threats. The objective of this research was to examine the relative impact of climate change on extinction risk of upper montane tree species in the tropical Andes, an area of high biodiversity value that is particularly vulnerable to climate change impacts. The extinction risk of 129 tree species endemic to the region was evaluated according to the IUCN Red List criteria, both with and without the potential impacts of climate change. Evaluations were supported by development of species distribution models, using three methods (generalized additive models, recursive partitioning, and support vector machines), all of which produced similarly high AUC values when averaged across all species evaluated (0.82, 0.86, and 0.88, respectively). Inclusion of climate change increased the risk of extinction of 18–20% of the tree species evaluated, depending on the climate scenario. The relative impact of climate change was further illustrated by calculating the Red List Index, an indicator that shows changes in the overall extinction risk of sets of species over time. A 15% decline in the Red List Index was obtained when climate change was included in this evaluation. While these results suggest that climate change represents a significant threat to tree species in the tropical Andes, they contradict previous suggestions that climate change will become the most important cause of biodiversity loss in coming decades. Conservation strategies should therefore focus on addressing the multiple threatening processes currently affecting biodiversity, rather than focusing primarily on potential climate change impacts. PMID:26177097