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NASA NTRS2015

The AgMIP Coordinated Global and Regional Assessments (CGRA) of Climate Change Impacts on Agriculture and Food Security

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.

models↗

NASA NTRS1997

Applications of Data Assimilation to Analysis of the Ocean on Large Scales

It is commonplace to begin talks on this topic by noting that oceanographic data are too scarce and sparse to provide complete initial and boundary conditions for large-scale ocean models. Even considering the availability of remotely-sensed data such as radar altimetry from the TOPEX and ERS-1 satellites, a glance at a map of available subsurface data should convince most observers that this is still the case. Data are still too sparse for comprehensive treatment of interannual to interdecadal climate change through the use of models, since the new data sets have not been around for very long. In view of the dearth of data, we must note that the overall picture is changing rapidly. Recently, there have been a number of large scale ocean analysis and prediction efforts, some of which now run on an operational or at least quasi-operational basis, most notably the model based analyses of the tropical oceans. These programs are modeled on numerical weather prediction. Aside from the success of the global tide models, assimilation of data in the tropics, in support of prediction and analysis of seasonal to interannual climate change, is probably the area of large scale ocean modeling and data assimilation in which the most progress has been made. Climate change is a problem which is particularly suited to advanced data assimilation methods. Linear models are useful, and the linear theory can be exploited. For the most part, the data are sufficiently sparse that implementation of advanced methods is worthwhile. As an example of a large scale data assimilation experiment with a recent extensive data set, we present results of a tropical ocean experiment in which the Kalman filter was used to assimilate three years of altimetric data from Geosat into a coarsely resolved linearized long wave shallow water model. Since nonlinear processes dominate the local dynamic signal outside the tropics, subsurface dynamical quantities cannot be reliably inferred from surface height anomalies. Because of its potential for large scale synoptic coverage of the deep ocean, acoustic travel time data should be a natural complement to satellite altimetry. Satellite data give us vertical integrals associated with thermodynamic and dynamic processes.

Miller, Robert N.↗

DOE OSTI2017

Collaborative Research: Improving Decadal Prediction of Arctic Climate Variability and Change Using a Regional Arctic

This project developed and applied a regional Arctic System model for enhanced decadal predictions. It built on successful research by four of the current PIs with support from the DOE Climate Change Prediction Program, which has resulted in the development of a fully coupled Regional Arctic Climate Model (RACM) consisting of atmosphere, land-hydrology, ocean and sea ice components. An expanded RACM, a Regional Arctic System Model (RASM), has been set up to include ice sheets, ice caps, mountain glaciers, and dynamic vegetation to allow investigation of coupled physical processes responsible for decadal-scale climate change and variability in the Arctic. RASM can have high spatial resolution (~4-20 times higher than currently practical in global models) to advance modeling of critical processes and determine the need for their explicit representation in Global Earth System Models (GESMs). The pan-Arctic region is a key indicator of the state of global climate through polar amplification. However, a system-level understanding of critical arctic processes and feedbacks needs further development. Rapid climate change has occurred in a number of Arctic System components during the past few decades, including retreat of the perennial sea ice cover, increased surface melting of the Greenland ice sheet, acceleration and thinning of outlet glaciers, reduced snow cover, thawing permafrost, and shifts in vegetation. Such changes could have significant ramifications for global sea level, the ocean thermohaline circulation and heat budget, ecosystems, native communities, natural resource exploration, and commercial transportation. The overarching goal of the RASM project has been to advance understanding of past and present states of arctic climate and to improve seasonal to decadal predictions. To do this the project has focused on variability and long-term change of energy and freshwater flows through the arctic climate system. The three foci of this research are: - Changes in the freshwater flux between arctic climate system components resulting from decadal changes in land and sea ice, seasonal snow, vegetation, and ocean circulation. - Changing energetics due to decadal changes in ice mass, vegetation, and air-sea interactions. - The role of small-scale atmospheric and oceanic processes that influence decadal variability. This research has been addressing modes of natural climate variability as well as extreme and rapid climate change. RASM can facilitate studies of climate impacts (e.g., droughts and fires) and of ecosystem adaptations to these impacts.

58 GEOSCIENCES↗

NASA NTRS1988

Global climate changes as forecast by Goddard Institute for Space Studies three-dimensional model

The global climate effects of time-dependent atmospheric trace gas and aerosol variations are simulated by NASA-Goddard's three-dimensional climate model II, which possesses 8 x 10-deg horizontal resolution, for the cases of a 100-year control run and three different atmospheric composition scenarios in which trace gas growth is respectively a continuation of current exponential trends, a reduced linear growth, and a rapid curtailment of emissions due to which net climate forcing no longer increases after the year 2000. The experiments begin in 1958, run to the present, and encompass measured or estimated changes in CO2, CH4, N2O, chlorofluorocarbons, and stratospheric aerosols. It is shown that the greenhouse warming effect may be clearly identifiable in the 1990s.

Hansen, J.↗

DOE OSTI2021

Hydrological analysis in watersheds with a variable-resolution global climate model (VR-CESM)

Traditionally, watershed-scale hydrology is simulated by distributed hydrological models with offline meteorological forcing data, or by regional regional climate models that link atmospheric and land hydrology interactions. Global climate models (GCMs) are rarely used to study watershed-scale hydrology due to the relatively coarse grid resolution, computationally expensive downscaling, and simplified physical processes. Recently, however, watershed-scale hydrology analysis has become possible in GCMs due to the development of variable-resolution GCMs that dynamically couple the hydrological processes between atmosphere and land systems at fine resolutions in selected regions and coarse resolution elsewhere. In this study, we used the variable-resolution Community Earth System Model (VR-CESM) with refined-resolution (14 km) in the western U.S. and eastern China to evaluate watershed-scale hydrology. Furthermore, we compared the historical VR-CESM outputs with gauge measurements and other hydrological models (e.g., National Water Model in the U.S.) and calibrated the subsurface runoff capacities in four mountainous watersheds. An RCP8.5 projection from 2007 to 2050 is used to estimate the impact of changing precipitation and snow climatology on watershed hydrology. We also analyzed the long-term runoff variability and the possibility of extreme runoff events as simulated by the VR-CESM. Although calibration is not possible in larger-scale watersheds, VR-CESM simulates the long-term annual variability of watersheds and provides insights on climate change impacts on hydrology. We conclude that refined-resolution VR-CESM can be used for watershed-scale hydrology analysis to understand water resources and water balance, in addition to traditional watershed-scale hydrological models. It enables hydrological analysis at multiple watersheds in one simulation and can help understand the two-way dynamics between land surface hydrology and atmospheric processes, and is especially practical for projecting climate change impacts. However, it is challenging to apply VR-CESM for hydrologic analysis in regulated watersheds as human factors (e.g., pumping, irrigation, water diversion) have not been fully addressed in VR-CESM.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2005

NASA Earth Sciences Data Support System and Services for the Northern Eurasia Earth Science Partnership Initiative

The presentation describes data management of NASA remote sensing data for Northern Eurasia Earth Science Partnership Initiative (NEESPI). Many types of ground and integrative (e.g., satellite, GIs) data will be needed and many models must be applied, adapted or developed for properly understanding the functioning of Northern Eurasia cold and diverse regional system. Mechanisms for obtaining the requisite data sets and models and sharing them among the participating scientists are essential. The proposed project targets integration of remote sensing data from AVHRR, MODIS, and other NASA instruments on board US- satellites (with potential expansion to data from non-US satellites), customized data products from climatology data sets (e.g., ISCCP, ISLSCP) and model data (e.g., NCEPNCAR) into a single, well-architected data management system. It will utilize two existing components developed by the Goddard Earth Sciences Data & Information Services Center (GES DISC) at the NASA Goddard Space Flight Center: (1) online archiving and distribution system, that allows collection, processing and ingest of data from various sources into the online archive, and (2) user-friendly intelligent web-based online visualization and analysis system, also known as Giovanni. The former includes various kinds of data preparation for seamless interoperability between measurements by different instruments. The latter provides convenient access to various geophysical parameters measured in the Northern Eurasia region without any need to learn complicated remote sensing data formats, or retrieve and process large volumes of NASA data. Initial implementation of this data management system will concentrate on atmospheric data and surface data aggregated to coarse resolution to support collaborative environment and climate change studies and modeling, while at later stages, data from NASA and non-NASA satellites at higher resolution will be integrated into the system.

Leptoukh, Gregory↗

NASA NTRS2006

NASA Earth Sciences Data Support System and Services for the Northern Eurasia Earth Science Partnership Initiative

The presentation describes the recently awarded ACCESS project to provide data management of NASA remote sensing data for the Northern Eurasia Earth Science Partnership Initiative (NEESPI). The project targets integration of remote sensing data from MODIS, and other NASA instruments on board US-satellites (with potential expansion to data from non-US satellites), customized data products from climatology data sets (e.g., ISCCP, ISLSCP) and model data (e.g., NCEP/NCAR) into a single, well-architected data management system. It will utilize two existing components developed by the Goddard Earth Sciences Data & Information Services Center (GES DISC) at the NASA Goddard Space Flight Center: (1) online archiving and distribution system, that allows collection, processing and ingest of data from various sources into the online archive, and (2) user-friendly intelligent web-based online visualization and analysis system, also known as Giovanni. The former includes various kinds of data preparation for seamless interoperability between measurements by different instruments. The latter provides convenient access to various geophysical parameters measured in the Northern Eurasia region without any need to learn complicated remote sensing data formats, or retrieve and process large volumes of NASA data. Initial implementation of this data management system will concentrate on atmospheric data and surface data aggregated to coarse resolution to support collaborative environment and climate change studies and modeling, while at later stages, data from NASA and non-NASA satellites at higher resolution will be integrated into the system.

Leptoukh, Gregory↗

DOE OSTI2023

Arctic ice-ocean interactions in an 8-to-2 kilometer resolution global model

In the last decades, the Arctic climate has changed dramatically, with the loss of multiyear sea ice one of the clearest consequences. These changes have occurred on relatively rapid timescales, and both accurate short-term Arctic prediction (e.g., 10 days to three months) and climate projection of future Arctic scenarios present ongoing challenges. Here we describe a representation of the Arctic ocean and sea ice in a ultrahigh resolution simulation in which the horizontal grid mesh reduces from 8 km at the equator to 2 km at the poles (UH8to2) for the years 2017-2020. We find the simulation reproduces observed distributions of seasonal sea-ice thickness and concentration realistically, although concentration is biased low in the spring and summer and low biases in thickness are found in the central and eastern basins in the fall. Volume, fresh water, and heat transports through key passages are realistic, lying within observationally determined ranges. Climatological comparisons reveal that the UH8to2 Atlantic Water is shallower, warmer, and saltier than the World Ocean Atlas 2018 climatology for 2005-2017 in the eastern basin. Our analysis suggests that these biases, combined with a lack of stratification in the upper 100 m of the simulated ocean, contribute to the winter biases in modeled sea ice thickness. This relationship between biases in the sea ice and ocean points to a potential positive feedback within the model, illuminating challenges for long term model predictive power in a changing Arctic climate.

58 GEOSCIENCES↗

DOE OSTI2017

Workshop on Human Activity at Scale in Earth System Models

Changing human activity within a geographical location may have significant influence on the global climate, but that activity must be parameterized in such a way as to allow these high-resolution sub-grid processes to affect global climate within that modeling framework. Additionally, we must have tools that provide decision support and inform local and regional policies regarding mitigation of and adaptation to climate change. The development of next-generation earth system models, that can produce actionable results with minimum uncertainties, depends on understanding global climate change and human activity interactions at policy implementation scales. Unfortunately, at best we currently have only limited schemes for relating high-resolution sectoral emissions to real-time weather, ultimately to become part of larger regions and well-mixed atmosphere. Moreover, even our understanding of meteorological processes at these scales is imperfect. This workshop addresses these shortcomings by providing a forum for discussion of what we know about these processes, what we can model, where we have gaps in these areas and how we can rise to the challenge to fill these gaps.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

On the variable effects of climate change on Pacific salmon

Pacific salmon are sensitive to changes in water temperature and streamflow. Climate change and water management may alter the streamflow and temperature regimes to which local species have adapted, hence affecting the survival and growth of juvenile salmon. This study estimates the changes in smolt survival rates of Chinook (Oncorhynchus tshawytscha) and Steelhead (Oncorhynchus mykiss) in Columbia River Basin (CRB) due to climatic changes and water management practices (reservoir regulation and water extraction). A multivariate nonlinear regression model is developed between juvenile survival rate and streamflow as well as stream temperature, and incorporates the inherent nonlinearity between survival rate and stream temperature. Using streamflow and stream temperature simulations from a state-of-the-art integrated earth system model, results indicate that climatic changes may reduce basin average juvenile Steelhead survival rates and smolt Chinook survival in Snake River by the 2080s. Water management, on the other hand, can alleviate the reduction in Chinook and Steelhead survival rates induced by climate change through mitigation of stream temperature warming. Integration of the nonlinear response of survival rate to stream temperature in addition to considering water management impacts on streamflow and temperature reduces the overestimate of the negative impacts from climate change. Furthermore, stream warming likely benefits juvenile survival in areas where the current stream temperature is lower than the optimal temperature zone. Our findings add new insights on climate and human impacts on juvenile salmon survival in CRB that are useful for conservation planning.

Zhang, Xiao↗

DOE OSTI2023

Spatiotemporal Super-Resolution with Generative Machine Learning for Creating Renewable Energy Resource Data Under Climate Change Scenarios

As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.

climate change↗

NASA NTRS2017

The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6.

The primary objective of CFMIP is to inform future assessments of cloud feedbacks through improved understanding of cloud-climate feedback mechanisms and better evaluation of cloud processes and cloud feedbacks in climate models. However, the CFMIP approach is also increasingly being used to understand other aspects of climate change, and so a second objective has now been introduced, to improve understanding of circulation, regional-scale precipitation, and non-linear changes. CFMIP is supporting ongoing model inter-comparison activities by coordinating a hierarchy of targeted experiments for CMIP6, along with a set of cloud-related output diagnostics. CFMIP contributes primarily to addressing the CMIP6 questions 'How does the Earth system respond to forcing?' and 'What are the origins and consequences of systematic model biases?' and supports the activities of the WCRP Grand Challenge on Clouds, Circulation and Climate Sensitivity. A compact set of Tier 1 experiments is proposed for CMIP6 to address this question: (1) what are the physical mechanisms underlying the range of cloud feedbacks and cloud adjustments predicted by climate models, and which models have the most credible cloud feedbacks? Additional Tier 2 experiments are proposed to address the following questions. (2) Are cloud feedbacks consistent for climate cooling and warming, and if not, why? (3) How do cloud-radiative effects impact the structure, the strength and the variability of the general atmospheric circulation in present and future climates? (4) How do responses in the climate system due to changes in solar forcing differ from changes due to CO2, and is the response sensitive to the sign of the forcing? (5) To what extent is regional climate change per CO2 doubling state-dependent (non-linear), and why? (6) Are climate feedbacks during the 20th century different to those acting on long-term climate change and climate sensitivity? (7) How do regional climate responses (e.g. in precipitation) and their uncertainties in coupled models arise from the combination of different aspects of CO2 forcing and sea surface warming? CFMIP also proposes a number of additional model outputs in the CMIP DECK, CMIP6 Historical and CMIP6 CFMIP experiments, including COSP simulator outputs and process diagnostics to address the following questions. 1. How well do clouds and other relevant variables simulated by models agree with observations? 2. What physical processes and mechanisms are important for a credible simulation of clouds, cloud feedbacks and cloud adjustments in climate models? 3. Which models have the most credible representations of processes relevant to the simulation of clouds? 4. How do clouds and their changes interact with other elements of the climate system?

carbon dioxide↗

NASA NTRS2012

The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and Pilot Studies

The Agricultural Model Intercomparison and Improvement Project (AgMIP) is a major international effort linking the climate, crop, and economic modeling communities with cutting-edge information technology to produce improved crop and economic models and the next generation of climate impact projections for the agricultural sector. The goals of AgMIP are to improve substantially the characterization of world food security due to climate change and to enhance adaptation capacity in both developing and developed countries. Analyses of the agricultural impacts of climate variability and change require a transdisciplinary effort to consistently link state-of-the-art climate scenarios to crop and economic models. Crop model outputs are aggregated as inputs to regional and global economic models to determine regional vulnerabilities, changes in comparative advantage, price effects, and potential adaptation strategies in the agricultural sector. Climate, Crop Modeling, Economics, and Information Technology Team Protocols are presented to guide coordinated climate, crop modeling, economics, and information technology research activities around the world, along with AgMIP Cross-Cutting Themes that address uncertainty, aggregation and scaling, and the development of Representative Agricultural Pathways (RAPs) to enable testing of climate change adaptations in the context of other regional and global trends. The organization of research activities by geographic region and specific crops is described, along with project milestones. Pilot results demonstrate AgMIP's role in assessing climate impacts with explicit representation of uncertainties in climate scenarios and simulations using crop and economic models. An intercomparison of wheat model simulations near Obregón, Mexico reveals inter-model differences in yield sensitivity to [CO2] with model uncertainty holding approximately steady as concentrations rise, while uncertainty related to choice of crop model increases with rising temperatures. Wheat model simulations with midcentury climate scenarios project a slight decline in absolute yields that is more sensitive to selection of crop model than to global climate model, emissions scenario, or climate scenario downscaling method. A comparison of regional and national-scale economic simulations finds a large sensitivity of projected yield changes to the simulations' resolved scales. Finally, a global economic model intercomparison example demonstrates that improvements in the understanding of agriculture futures arise from integration of the range of uncertainty in crop, climate, and economic modeling results in multi-model assessments.

Rosenzweig, C.↗

DOE OSTI2019

Variability, timescales, and nonlinearity in climate responses to black carbon emissions

Black carbon (BC) particles exert a potentially large warming influence onthe Earth system. Reductions in BC emissions have attracted attention as apossible means to moderate near-term temperature changes. For the first time,we evaluate regional climate responses, nonlinearity, and short-termtransient responses to BC emission perturbations in the Arctic, midlatitudes,and globally based on a comprehensive set of emission-driven experimentsusing the Community Earth System Model (CESM). Surface temperature responsesto BC emissions are complex, with surface warming over land from midlatitudeBC perturbations partially offset by ocean cooling. Climate responses do notscale linearly with emissions. While stronger BC emission perturbations havea higher burden efficiency, their temperature sensitivity is lower. BCimpacts temperature much faster than greenhouse gas forcing, with transienttemperature responses in the Arctic and midlatitudes approaching aquasi-equilibrium state with a timescale of 2–3 years. We find largevariability in BC-induced climate changes due to background model noise. As aresult, removing present-day BC emissions results in discernible surfacetemperature changes for only limited regions of the globe. In order to betterunderstand the climatic impacts of BC emissions, both the drivers ofnonlinear responses and response variability need to be assessed acrossclimate models.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2015

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects.

climate↗

DOE OSTI2013

Implications of simultaneously mitigating and adapting to climate change: Initial experiments using GCAM

Historically climate impacts research and climate mitigation research have been two separate and independent domains of inquiry. Climate mitigation research has investigated greenhouse gas emissions assuming that climate is unchanging. At the same time climate mitigation research has investigated the implications of climate change on the assumption that climate mitigation will proceed without affecting the degree of climate impacts or the ability of human and natural systems to adapt. The Global Change Assessment Model (GCAM) has largely been employed to study climate mitigation. Here we explore the development of capabilities to assess climate change impacts and adaptation within the GCAM model. These capabilities are being developed so as to be able to simultaneously reconcile the joint implications of climate change mitigation, impacts and adaptive potential. This is an important step forward in that it enables direct comparison between climate mitigation activities and climate impacts and the opportunity to understand interactions between the two.

Calvin, Katherine V↗

DOE OSTI2021

Quantifying the Uncertainty of the Future Hydrological Impacts of Climate Change: Comparative Analysis of an Advanced Hierarchical Sensitivity in Humid and Semiarid Basins

Comparison and quantification of different uncertainties of future climate change involved in the modeling of a hydrological system are highly important for both hydrological modelers and policy-makers. However, few studies have accurately estimated the relative importance of different sources of uncertainty at different spatiotemporal scales. Here, a hierarchical sensitivity analysis framework (HSAF) incorporated with a variance-based global sensitivity analysis is developed to quantify the spatiotemporal contributions of different uncertainties in hydrological impacts of climate change in two different climatic (humid and semiarid) basins in China. The uncertainty sources include three emission scenarios (ESs), 20 global climate models (GCs), three hydrological models (HMs), and the associated sensitive hydrological parameters (PAs) screened and sampled by the Morris and Latin hypercube sampling methods, respectively. Further, the results indicate that the overall trend of uncertainty is PA > HM > GC > ES, but their uncertainties have discrepancies in projections of different hydrological variables. The HM uncertainty in annual and monthly discharge projections is generally larger than the PA uncertainty in the humid basin than semiarid basin. The PA has greater uncertainty in extreme hydrological event (annual peak discharge) projections than in annual discharge projections for both basins (particularly for the humid basin), but contributes larger uncertainty to annual and monthly discharge projections in the semiarid basin than humid basin. The GC contributes larger uncertainty in all the hydrological variables projections in the humid basin than semiarid basin, while the ES uncertainty is rather limited in both basins. Overall, our results suggest there is greater spatiotemporal variability of hydrological uncertainty in more arid regions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Predicting future coexistence in a North American ant community

Global climate change will remodel ecological communities worldwide. However, as a consequence of biotic interactions, communities may respond to climate change in idiosyncratic ways. This makes predictive models that incorporate biotic interactions necessary. We show how such models can be constructed based on empirical studies in combination with predictions or assumptions regarding the abiotic consequences of climate change. Specifically, we consider a well-studied ant community in North America. First, we use historical data to parameterize a basic model for species coexistence. Using this model, we determine the importance of various factors, including thermal niches, food discovery rates, and food removal rates, to historical species coexistence. We then extend the model to predict how the community will restructure in response to several climate-related changes, such as increased temperature, shifts in species phenology, and altered resource availability. Interestingly, our mechanistic model suggests that increased temperature and shifts in species phenology can have contrasting effects. Nevertheless, for almost all scenarios considered, we find that the most subordinate ant species suffers most as a result of climate change. More generally, our analysis shows that community composition can respond to climate warming in nonintuitive ways. For example, in the context of a community, it is not necessarily the most heatsensitive species that are most at risk. Our results demonstrate how models that account for niche partitioning and interspecific trade-offs among species can be used to predict the likely idiosyncratic responses of local communities to climate change.

54 ENVIRONMENTAL SCIENCES↗