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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

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

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-induced 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.

Zhou, Tian↗
DOE OSTI2020

Regional climate change projections from NA-CORDEX and their relation to climate sensitivity

Abstract The climate sensitivity of global climate models (GCMs) strongly influences projected climate change due to increased atmospheric carbon dioxide. Reasonably, the climate sensitivity of a GCM may be expected to affect dynamically downscaled projections. However, there has been little examination of the effect of the climate sensitivity of GCMs on regional climate model (RCM) ensembles. Therefore, we present projections of temperature and precipitation from the ensemble of projections produced as a part of the North American branch of the international Coordinated Regional Downscaling Experiment (NA-CORDEX) in the context of their relationship to the climate sensitivity of their parent GCMs. NA-CORDEX simulations were produced at 50-km and 25-km resolutions with multiple RCMs which downscaled multiple GCMs that spanned nearly the full range of climate sensitivity available in the CMIP5 archive. We show that climate sensitivity is a very important source of spread in the NA-CORDEX ensemble, particularly for temperature. Temperature projections correlate with driving GCM climate sensitivity annually and seasonally across North America not only at a continental scale but also at a local-to-regional scale. Importantly, the spread in temperature projections would be reduced if only low, mid, or high climate sensitivity simulations were considered, or if only the ensemble mean were considered. Precipitation projections correlate with climate sensitivity, but only at a continental scale during the cold season, due to the increasing influence of other processes at finer scales. Additionally, it is shown that the RCMs do alter the projection space sampled by their driving GCMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Climate Impacts on U.S. Electric Sector Evolution and Water Use Under Varying Market Futures: Are Some Energy Technologies and Regions More Affected by Climate Than Others?

Climate 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 U.S. power system. Different electricity sector configurations are more or less vulnerable to climate-induced changes. For example, once-through cooled thermal facilities are very efficient under cooler and wetter conditions but can be more vulnerable under warmer and drier conditions. Non-thermal renewable technologies are essentially 'drought-proof' but have other reliability challenges. Prior efforts have explored the impacts of climate change on electric sector for a limited set of climate and electricity scenarios. Here, we provide a comprehensive suite of scenarios that evaluate how different electricity sector market futures in the U.S. 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), for twenty unique future climate-water conditions modeled through 2050. The electric sector impacts of these climate-water futures are then evaluated by the Regional Energy Deployment Systems (ReEDS) electricity capacity expansion model. To explore a breadth of possible electricity market futures, four electricity sector scenarios are considered to represent different carbon emission and water usage conditions: (1) business-as-usual, (2) CO2 cap, (3) CO2 cap with high renewable energy costs and extended nuclear lifetimes, and (4) continued coal reliance that maintains the existing coal fleet through 2050. Thus, we examine 84 possible futures in total when including a static climate representation of each market scenario. Results indicate that U.S. electric sector responds to climate change by constructing a combination of gas-fired and low-carbon generating sources, and the balance between technologies are determined by fuel prices, technology costs and policy. The cost for power sector to respond to climate change are generally proportional to the costs of electricity system components, while the relative sensitivity and vulnerability to climate pressures will vary across different generating technologies and different regions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Selection of Global Climate Model Data for Downscaling With Generative Machine Learning and Use in the Power Planning for Alignment of Climate and Energy Systems Project

The range of results from climate models and scenarios is important to the understanding of uncertainty in power planning analysis. A U.S. Department of Energy-funded analytic project called Power Planning for Alignment of Climate and Energy Systems is developing data and analytic methods to reflect the effects of climate change on key variables for power system planning, as part of the Grid Modernization Lab Consortium. This project will select and prepare global climate model results for use in power system planning models. A related report (Evaluation of Global Climate Models for Use in Energy Analysis) assesses the performance of various global climate models from the Coupled Model Intercomparison Project Phase 6 data archive for their historical skill with respect to energy system performance and for their future projections under multiple climate change scenarios. Building from that report, we describe the selection of a climate scenario (Shared Socioeconomic Pathway [SSP] 2-4.5) and five climate models: TaiESM1, EC-Earth3-CC, GFDL-CM4, EC-Earth3-Veg, and MPI-ESM1-2-HR. We describe the model selection criteria, which were based on the quality of the match between model results under historical conditions and on the representation of the range of future values for several variables. These results will be downscaled via an open-source generative machine learning method called Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

What is the importance of climate model bias when projecting the impacts of climate change on land surface processes?

Regional climate change impact (CCI) studies have widely involved downscaling and bias-correcting (BC) Global Climate Model (GCM)-projected climate for driving land surface models. However, BC may cause uncertainties in projecting hydrologic and biogeochemical responses to future climate due to the impaired spatiotemporal covariance of climate variables and a breakdown of physical conservation principles. Here we quantify the impact of BC on simulated climate-driven changes in water variables(evapotranspiration, ET; runoff; snow water equivalent, SWE; and water demand for irrigation), crop yield, biogenic volatile organic compounds (BVOC), nitric oxide (NO) emissions, and dissolved inorganic nitrogen (DIN) export over the Pacific Northwest (PNW) Region. We also quantify the impacts on net primary production (NPP) over a small watershed in the region (HJ Andrews). Simulation results from the coupled ECHAM5/MPI-OM model with A1B emission scenario were firstly dynamically downscaled to 12 km resolutions with WRF model. Then a quantile mapping based statistical downscaling model was used to downscale them into 1/16th degree resolution daily climate data over historical and future periods. Two series climate data were generated according to the option of bias-correction (i.e. with bias-correction (BC) and without bias-correction, NBC). Impact models were then applied to estimate hydrologic and biogeochemical responses to both BC and NBC meteorological datasets. These im20 pact models include a macro-scale hydrologic model (VIC), a coupled cropping system model (VIC-CropSyst), an ecohydrologic model (RHESSys), a biogenic emissions model (MEGAN), and a nutrient export model (Global-NEWS). Results demonstrate that the BC and NBC climate data provide consistent estimates of the climate-driven changes in water fluxes (ET, runoff, and water demand), VOCs (isoprene and monoterpenes) and NO emissions, mean crop yield, and river DIN export over the PNW domain. However, significant differences rise from projected SWE, crop yield from dry lands, and HJ Andrews’s ET between BC and NBC data. Even though BC post-processing has no significant impacts on most of the studied variables when taking PNW as a whole, their effects have large spatial variations and some local areas are substantially influenced. In addition, there are months during which BC and NBC post-processing produces significant differences in projected changes, such as summer runoff. Factor-controlled simulations indicate that BC post-processing of precipitation and temperature both substantially contribute to these differences at region scales. We conclude that there are trade-offs between using BC climate data for offline CCI studies vs. direct modeled climate data. These trade-offs should be considered when designing integrated modeling frameworks for specific applications; e.g., BC may be more important when considering impacts on reservoir operations in mountainous watersheds than when investigating impacts on biogenic emissions and air quality (where VOCs are a primary indicator).

Liu, M. L.↗
DOE OSTI2021

Identifying management opportunities to combat climate, land, and marine threats across less climate exposed coral reefs

Conserving coral reefs is critical for maintaining marine biodiversity, protecting coastlines, and supporting livelihoods in many coastal communities. Climate change threatens coral reefs globally, but researchers have identified a portfolio of coral reefs (bioclimatic units [BCUs]) that are relatively less exposed to climate impacts and strongly connected to other coral reef systems. These reefs provide a proactive opportunity to secure a long-term future for coral reefs under climate change. To help guide local management efforts, we quantified marine cumulative human impact (CHI) from climate, marine, and land pressures (2013 and from 2008 to 2013) in BCUs and across countries tasked with BCU management. Additionally, we created a management index based on common management measures and policies for each pressure source (climate, marine, and land) to identify a country's intent and commitment to effectively manage these pressures. Twenty-two countries (79%) had increases in CHI from 2008 to 2013. Climate change pressures had the highest proportional contribution to CHI across all reefs and in all but one country (Singapore), but 18 BCUs (35%) and nine countries containing BCUs (32%) had relatively high land and marine impacts. There was a significant positive relationship between climate impact and the climate management index across countries (R 2 = 0.43, p = 0.02), potentially signifying that countries with greater climate impacts are more committed to managing them. However, this trend was driven by climate management intent in Fiji and Bangladesh. Our results can be used to guide future fine-scale analyses, national policies, and local management decisions, and our management indices reveal areas where management components can be improved. Cost-effectively managing local pressures (e.g., fishing and nutrients) in BCUs is essential for building a climate-ready future that benefits coral reefs and people.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

The effect of climate change, population distribution, and climate mitigation on building energy use in the U.S. and China

A changing climate will affect the energy system in a number of ways, one of which is through changes in demands for heating and cooling in buildings. Understanding the potential effect of climate on heating and cooling demands must take into account not only the manner in which the building sector might evolve over time - including, for example, movements from rural to urban environments in developing countries - but also important uncertainty about the nature of climate change itself and the growth and movements of populations over time. In this study, we explored the uncertainty in climate change impacts on heating and cooling by constructing estimates of heating and cooling degree days for both a reference (no-policy) scenario and a climate mitigation scenario built from 0.5 degree latitude by 0.5 degree longitude resolution output from three different Global Climate Models (GCMs) and three gridded scenarios of population distribution. The implications that changing climate and population distribution might have for building energy consumption in the U.S. and China were then explored by using the heating and cooling degree days results as inputs to a detailed, building energy model, nested in the long-term global integrated assessment framework, Global Change Assessment Model (GCAM). Across the climate models and population distribution scenarios, the results indicate that unabated climate change would cause total final energy consumption to decrease modestly in both U.S. and China buildings by the end of the century, as decreased heating consumption is more than balanced by increased cooling using primarily electricity. However, the results also indicate that when indirect emissions from the power sector are also taken into account, climate change may have negligible effect on building sector CO2 emissions in the two countries. The variation in results due to variation of population distribution is noticeably smaller than variation due to the use of different climate models.

Zhou, Yuyu↗
DOE OSTI2022

The Climatic Impact‐Driver Framework for Assessment of Risk‐Relevant Climate Information

Abstract The climate science and applications communities need a broad and demand‐driven concept to assess physical climate conditions that are relevant for impacts on human and natural systems. Here, we augment the description of the “climatic impact‐driver” (CID) approach adopted in the Working Group I (WGI) contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report. CIDs are broadly defined as “physical climate system conditions (e.g., means, events, and extremes) that affect an element of society or ecosystems. Depending on system tolerance, CIDs and their changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements and regions.” We give background information on the IPCC Report process that led to the development of the 7 CID types (heat and cold, wet and dry, wind, snow and ice, coastal, open ocean, and other) and 33 distinct CID categories, each of which may be evaluated using a variety of CID indices. This inventory of CIDs was co‐developed with WGII to provide a useful collaboration point between physical climate scientists and impacts/risk experts to assess the specific climatic phenomena driving sectoral responses and identify relevant CID indices within each sector. The CID Framework ensures that a comprehensive set of climatic conditions informs adaptation planning and risk management and may also help prioritize improvements in modeling sectoral dynamics that depend on climatic conditions. CIDs contribute to climate services by increasing coherence and neutrality when identifying and communicating relevant findings from physical climate research to risk assessment and planning activities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

A Hierarchical Evaluation of Regional Climate Simulations

Global climate models (GCMs) are the primary tools for predicting the evolution of the climate system. Through decades of development, GCMs have demonstrated useful skill in simulating climate at continental to global scales. However, large uncertainties remain in projecting climate change at regional scales, which limit our ability to inform decisions on climate change adaptation and mitigation. To bridge this gap, different modeling approaches including nested regional climate models (RCMs), global stretch-grid models, and global high-resolution atmospheric models have been used to provide regional climate simulations (Leung et al. 2003). In previous efforts to evaluate these approaches, isolating their relative merits was not possible because factors such as dynamical frameworks, physics parameterizations, and model resolutions were not systematically constrained. With advances in high performance computing, it is now feasible to run coupled atmosphere-ocean GCMs at horizontal resolution comparable to what RCMs use today. Global models with local refinement using unstructured grids have become available for modeling regional climate (e.g., Rauscher et al. 2012; Ringler et al. 2013). While they offer opportunities to improve climate simulations, significant efforts are needed to test their veracity for regional-scale climate simulations.

climate↗
DOE OSTI2025

Potential Effects of Climate Change and Solar Radiation Modification on Renewable Energy Resources

Solar radiation modification (SRM) is a possible deliberate approach to decrease or reflect incoming solar radiation with the goal of reducing global temperatures, which have increased over the last decades due to high atmospheric greenhouse gas concentrations. Stratospheric aerosol injection, specifically, has shown potential for successfully reducing global temperatures in climate model simulations. Despite the growing literature in the areas of climate change and SRM, their combined effects on renewable energy generation, a climate change mitigation strategy, have not been addressed. In this review paper, we synthesize previous literature on the possible effects of climate change and SRM on renewable energy resources (i.e., wind energy, solar energy, biomass energy, and hydropower), review the status of climate change and SRM research, and explore potential effects of SRM on renewable energy primarily in the Continental United States (CONUS), but with global perspectives as well. We discuss the research challenges and impacts of SRM on renewable energy and conclude by discussing the potential implications of SRM for renewables for SRM governance and policy. This work is not advocating for or against SRM. It is highlighting an important potential impact for future decision makers.

Kumler, Andrew [National Renewable Energy Laborato↗
DOE OSTI2025

Atmospheric River Detection Under Changing Seasonality and Mean-State Climate: ARTMIP Tier 2 Paleoclimate Experiments

Atmospheric rivers (ARs) are filamentary structures within the atmosphere that account for a substantial portion of poleward moisture transport and play an important role in Earth's hydroclimate. However, there is no one quantitative definition for what constitutes an atmospheric river, leading to uncertainty in quantifying how these systems respond to global change. This study seeks to better understand how different AR detection tools (ARDTs) respond to changes in climate states utilizing single-forcing climate model experiments under the aegis of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP). We compare a simulation with an early Holocene orbital configuration and another with CO2 levels of the Last Glacial Maximum to a preindustrial control simulation to test how the ARDTs respond to changes in seasonality and mean climate state, respectively. We find good agreement among the algorithms in the AR response to the changing orbital configuration, with a poleward shift in AR frequency that tracks seasonal poleward shifts in atmospheric water vapor and zonal winds. In the low CO2 simulation, the algorithms generally agree on the sign of AR changes, but there is substantial spread in their magnitude, indicating that mean-state changes lead to larger uncertainty. This disagreement likely arises primarily from differences between algorithms in their thresholds for water vapor and its transport used for identifying ARs. These findings warrant caution in ARDT selection for paleoclimate and climate change studies in which there is a change to the mean climate state, as ARDT selection contributes substantial uncertainty in such cases.

Atmospheric river, paleoclimate↗
DOE OSTI2014

Integrated assessment of global water scarcity over the 21st century under multiple climate change mitigation policies

Water scarcity conditions over the 21st century both globally and regionally are assessed in the context of climate change and climate mitigation policies, by estimating both water availability and water demand within the Global Change Assessment Model (GCAM), a leading community integrated assessment model of energy, agriculture, climate, and water. To quantify changes in future water availability, a new gridded water-balance global hydrologic model – namely, the Global Water Availability Model (GWAM) – is developed and evaluated. Global water demands for six major demand sectors (irrigation, livestock, domestic, electricity generation, primary energy production, and manufacturing) are modeled in GCAM at the regional scale (14 geopolitical regions, 151 sub-regions) and then spatially downscaled to 0.5 o x 0.5o resolution to match the scale of GWAM. Using a baseline scenario (i.e., no climate change mitigation policy) with radiative forcing reaching 8.8 W/m2 (equivalent to the SRES A1Fi emission scenario) and three climate policy scenarios with increasing mitigation stringency of 7.7, 5.5, and 4.2 W/m2 (equivalent to the SRES A2, B2, and B1 emission scenarios, respectively), we investigate the effects of emission mitigation policies on water scarcity. Two carbon tax regimes (a universal carbon tax (UCT) which includes land use change emissions, and a fossil fuel and industrial emissions carbon tax (FFICT) which excludes land use change emissions) are analyzed. The baseline scenario results in more than half of the world population living under extreme water scarcity by the end of the 21st century. Additionally, in years 2050 and 2095, 36% (28%) and 44% (39%) of the global population, respectively, is projected to live in grid cells (in basins) that will experience greater water demands than the amount of available water in a year (i.e., the water scarcity index (WSI) > 1.0). When comparing the climate policy scenarios to the baseline scenario while maintaining the same baseline socioeconomic assumptions, water scarcity declines under a UCT mitigation policy but increases with a FFICT mitigation scenario by the year 2095 particularly with more stringent climate mitigation targets. Under the FFICT scenario, water scarcity is projected to increase driven by higher water demands for bio-energy crops.

climate policy↗
DOE OSTI2016

Collaborative Project: Understanding Climate Model Biases in Tropical Atlantic and Their Impact on Simulations of Extreme Climate Events

Recent studies have revealed that among all the tropical oceans, the tropical Atlantic has experienced the most pronounced warming trend over the 20th century. Many extreme climate events affecting the U.S., such as hurricanes, severe precipitation and drought events, are influenced by conditions in the Gulf of Mexico and the Atlantic Ocean. It is therefore imperative to have accurate simulations of the climatic mean and variability in the Atlantic region to be able to make credible projections of future climate change affecting the U.S. and other countries adjoining the Atlantic Ocean. Unfortunately, almost all global climate models exhibit large biases in their simulations of tropical Atlantic climate. The atmospheric convection simulation errors in the Amazon region and the associated errors in the trade wind simulations are hypothesized to be a leading cause of the tropical Atlantic biases in climate models. As global climate models have resolutions that are too coarse to resolve some of the atmospheric and oceanic processes responsible for the model biases, we propose to use a high-resolution coupled regional climate model (CRCM) framework to address the tropical bias issue. We propose to combine the expertise in tropical coupled atmosphere-ocean modeling at Texas A&M University (TAMU) and the coupled land-atmosphere modeling expertise at Pacific Northwest National Laboratory (PNNL) to develop a comprehensive CRCM for the Atlantic sector within a general and flexible modeling framework. The atmospheric component of the CRCM will be the NCAR WRF model and the oceanic component will be the Rutgers/UCLA ROMS. For the land component, we will use CLM modified at PNNL to include more detailed representations of vegetation and soil hydrology processes. The combined TAMU-PNNL CRCM model will be used to simulate the Atlantic climate, and the associated land-atmosphere-ocean interactions at a horizontal resolution of 9 km or finer. A particular focus of the model development effort will be to optimize the performance of WRF and ROMS over several thousand of cores by focusing on both the parallel communication libraries and the I/O interfaces, in order to achieve the sustained throughput needed to perform simulations on such fine resolution grids. The CRCM model will be developed within the framework of the Coupler (CPL7) software that is part of the NCAR Community Earth System Model (CESM). Through efforts at PNNL and within the community, WRF and CLM have already been coupled via CPL7. Using the flux coupler approach for the whole CRCM model will allow us to flexibly couple WRF, ROMS, and CLM with each model running on its own grid at different resolutions. In addition, this framework will allow us to easily port parameterizations between CESM and the CRCM, and potentially allow partial coupling between the CESM and the CRCM. TAMU and PNNL will contribute cooperatively to this research endeavor. The TAMU team led by Chang and Saravanan has considerable experience in studying atmosphere-ocean interactions within tropical Atlantic sector and will focus on modeling issues that relate to coupling WRF and ROMS. The PNNL team led by Leung has extensive expertise in atmosphere-land interaction and will be responsible for improving the land surface parameterization. Both teams will jointly work on integrating WRF-ROMS and WRF-CLM to couple WRF, ROMS, and CLM through CPL7. Montuoro of the TAMU Supercomputing Center will be responsible for improving the MPI and Parallel IO interfaces of the CRCM. Both teams will contribute to the design and execution of the proposed numerical experiments and jointly perform analysis of the numerical experiments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Functional approach to exploring climatic and landscape controls of runoff generation: 1. Behavioral constraints on runoff volume

Abstract Inspired by the Dunne diagram, the climatic and landscape controls on the partitioning of annual runoff into its various components (Hortonian and Dunne overland flow and subsurface stormflow) are assessed quantitatively, from a purely theoretical perspective. A simple distributed hydrologic model has been built sufficient to simulate the effects of different combinations of climate, soil, and topography on the runoff generation processes. The model is driven by a sequence of simple hypothetical precipitation events, for a large combination of climate and landscape properties, and hydrologic responses at the catchment scale are obtained through aggregation of grid‐scale responses. It is found, first, that the water balance responses, including relative contributions of different runoff generation mechanisms, could be related to a small set of dimensionless similarity parameters. These capture the competition between the wetting, drying, storage, and drainage functions underlying the catchment responses, and in this way, provide a quantitative approximation of the conceptual Dunne diagram. Second, only a subset of all hypothetical catchment/climate combinations is found to be “behavioral,” in terms of falling sufficiently close to the Budyko curve, describing mean annual runoff as a function of climate aridity. Furthermore, these behavioral combinations are mostly consistent with the qualitative picture presented in the Dunne diagram, indicating clearly the commonality between the Budyko curve and the Dunne diagram. These analyses also suggest clear interrelationships amongst the “behavioral” climate, soil, and topography parameter combinations, implying these catchment properties may be constrained to be codependent in order to satisfy the Budyko curve.

Li, Hong‐Yi↗
DOE OSTI2017

Recent advances in understanding secondary organic aerosol: Implications for global climate forcing: Advances in Secondary Organic Aerosol

Anthropogenic emissions and land use changes have modified atmospheric aerosol concentrations and size distributions over time. Understanding preindustrial conditions and changes in organic aerosol due to anthropogenic activities is important because these features (1) influence estimates of aerosol radiative forcing and (2) can confound estimates of the historical response of climate to increases in greenhouse gases. Secondary organic aerosol (SOA), formed in the atmosphere by oxidation of organic gases, represents a major fraction of global submicron-sized atmospheric organic aerosol. Over the past decade, significant advances in understanding SOA properties and formation mechanisms have occurred through measurements, yet current climate models typically do not comprehensively include all important processes. Our review summarizes some of the important developments during the past decade in understanding SOA formation. We also highlight the importance of some processes that influence the growth of SOA particles to sizes relevant for clouds and radiative forcing, including formation of extremely low volatility organics in the gas phase, acid-catalyzed multiphase chemistry of isoprene epoxydiols, particle-phase oligomerization, and physical properties such as volatility and viscosity. Several SOA processes highlighted in this review are complex and interdependent and have nonlinear effects on the properties, formation, and evolution of SOA. Current global models neglect this complexity and nonlinearity and thus are less likely to accurately predict the climate forcing of SOA and project future climate sensitivity to greenhouse gases. Efforts are also needed to rank the most influential processes and nonlinear process-related interactions, so that these processes can be accurately represented in atmospheric chemistry-climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Observed and Simulated Sensitivities of Spring Greenup to Preseason Climate in Northern Temperate and Boreal Regions: Climate control on spring phenology

Vegetation phenology plays an important role in regulating land-atmosphere energy, water, and trace-gas exchanges. Changes in spring greenup (SG) have been documented in the past half-century in response to ongoing climate change. We use normalized difference vegetation index generated from NOAA's advanced very high resolution radiometer data in the Global Inventory Modeling and Monitoring Study project over the 1982–2005 period, coupled with climate reanalysis (Climate Research Unit-National Centers for Environmental Prediction) to investigate the SG responses to preseason climate change in northern temperate and boreal regions. We compared these observed responses to the simulated SG responses to preseason climate inferred from the Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) over 1982–2005. The observationally inferred SG suggests that there has been an advance of about 1 days per decade between 1982 and 2005 in the northern midlatitude to high latitude, with significant spatial heterogeneity. The spatial heterogeneity of the SG advance results from heterogeneity in the change of the preseason climate as well as varied vegetation responses to the preseason climate across biomes. The SG to preseason temperature sensitivity is highest in forests other than deciduous needleleaf forests, followed by temperate grasslands and woody savannas. The SG in deciduous needleleaf forests, open shrublands, and tundra is relatively insensitive to preseason temperature. Although the extent of regions where the SG is sensitive to preseason precipitation is smaller than the extent of regions where the SG is sensitive to preseason temperature, the biomes that are more sensitive to temperature are also more sensitive to precipitation, suggesting the interactive control of temperature and precipitation. Finally, in the mean, the CMIP5 ESMs reproduced the dominant latitudinal preseason climate trends and SG advances. However, large biases in individual ESMs for the preseason period, climate, and SG sensitivity imply needed model improvements to climate prediction and phenological process parameterizations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The climate change mitigation potential of annual grasslands under future climates

Abstract Composted manure and green waste amendments have been shown to increase net carbon (C) sequestration in rangeland soils and have been proposed as a means to help lower atmospheric CO 2 concentrations. However, the effect of climate change on soil organic C (SOC) stocks and greenhouse gas emissions in rangelands is not well understood, and the viability of climate change mitigation strategies under future conditions is even less certain. We used a process‐based biogeochemical model (DayCent) at a daily time step to explore the long‐term effects of potential future climate changes on C and greenhouse gas dynamics in annual grassland ecosystems. We then used the model to explore how the same ecosystems might respond to climate change following compost amendments to soils and determined the long‐term viability of net SOC sequestration under changing climates. We simulated net primary productivity (NPP), SOC, and greenhouse gas fluxes across seven California annual grasslands with and without compost amendments. We drove the DayCent simulations with field data and with site‐specific daily climate data from two Earth system models (CanESM2 and HadGEM‐ES) and two representative concentration pathways (RCP4.5 and RCP8.5) through 2100. NPP and SOC stocks in unamended and amended ecosystems were surprisingly insensitive to projected climate changes. A one‐time amendment of compost to rangeland acted as a slow‐release organic fertilizer and increased NPP by up to 390–814 kg C ha −1 year −1 across sites. The amendment effect on NPP was not sensitive to Earth system model or emissions scenario and endured through the end of the century. Net SOC sequestration amounted to 1.96 ± 0.02 Mg C ha −1 relative to unamended soils at the maximum amendment effect. Averaged across sites and scenarios, SOC sequestration peaked 22 ± 1 years after amendment and declined but remained positive throughout the century. Though compost stimulated nitrous oxide (N 2 O) emissions, the cumulative net emissions (in CO 2 equivalents) due to compost were far less than the amount of SOC sequestered. Compost amendments resulted in a net climate benefit of 69.6 ± 0.5 Tg CO 2 e 20 ± 1 years after amendment if applied to similar ecosystems across the state, amounting to 39% of California's rangeland. These results suggest that the biogeochemical benefits of a single amendment of compost to rangelands in California are insensitive to climate change and could contribute to decadal‐scale climate change mitigation goals alongside emissions reductions.

Mayer, Allegra↗