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DOE OSTI2025

Performance Results from DOE Cold Climate Heat Pump Challenge Field Validation

Space conditioning and water heating consume over 40% of the nation’s primary energy use and represent a significant component of many homeowners’ monthly energy bill. However, in cold climates, performance of heat pumps has traditionally suffered as the units have been unable to efficiently transfer heat from colder outdoor air temperatures to warm the interior space of homes. Optimizing heat pumps for cold climates (5 °F and below) requires coordinated effort to ensure heat pump technologies can be enjoyed by Americans living in these regions. The DOE Cold Climate Heat Pump (CCHP) Challenge sought to address this challenge by partnering with industry to develop, test, and validate the performance of new, highly efficient heat pumps in real homes. The Challenge, launched in 2021, brought together leading heating, ventilation, and air conditioning (HVAC) manufacturers to develop prototype units optimized for performance at cold climates. This report summarizes results from the field validation that occurred 2022-2024.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Aerosol-Cloud Interactions and the Intersection of Climate Forcing and Feedback

The goal of this project has been to understand at the process level how the interaction of aerosols and cloud microphysics may alter the cloud response to climate change from the scale of turbulence to the global scale. Atmospheric aerosols (suspended particulates) have a unique role to play in the climate system. Anthropogenic aerosols may be an air pollution hazard and degrade human health. Their presence also results in the direct scattering or absorption of sunlight, with small net impacts on climate. But aerosols are also a key part of cloud formation, as the locations on which cloud droplets and ice crystals form. Understanding these Aerosol Cloud Interactions (ACI) is a critical uncertainty for climate models, including the Energy Exascale Earth System Model (E3SM), and affects how well models like E3SM can simulate the past and future. This work aims to understand cloud microphysics as well as the interaction of aerosol forcing and cloud feedback through improving our understanding of aerosol-cloud interactions (ACI) by looking at their uncertainty in critical environments at the process level. It will also contribute to the development of E3SM. Specific goals include: first, high level analysis of Perturbed Parameter Ensemble (PPE) simulations already available from the Community Earth System Model (CESM) and E3SM. Second, detailed analysis of recent changes to temperature and the role of aerosols.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

RDPP: Accelerating Diversity in DOE Climate Science and Resilience Research (Final Report)

The scope of the project was set out to accelerate the inclusion of diversity into the Department of Energy (DOE) Earth and Environmental Systems Sciences Division (EESSD) relevant climate science and resilience research to inclusively advance solutions. The Project Objectives were to usher in equitable use-inspired climate-related research with underrepresented Minorities of which this project helped fund 7 HU graduate students work with the DOE (three of which will graduate in Spring 2025). The two key aims underpinning that core goal were AIM1: developing partnerships (18 organized engaged, see partners list) and AIM2: Developing capabilities (3 visits to DOE facilities, 5 DOE partners visits to HU, increased visiting faculty participation in BNL-DOE lab, secured 5 grants together totaling 1.2 million in funds for HU). The project objectives were highly successful as they were designed to ambitiously pull together DOE lab researchers with the long-standing and successful transdisciplinary climate science research programs of the PI and local DC groups. The major outcomes of this RDPP program will be in the new fundamentally inclusive partnerships with DOE and HU tasked to understand the urban-rural impacts due to climate change in the US, Eastern South Atlantic (ESA) Region, related to energy issues driven by heat stress and the water cycle. Overall, this project contributes to the DOE and science community vision for catalyze connections for project-ready underrepresented minorities (URMs) at a prominent HBCU to DOE projects supported by the Biological and Environmental research (BER) Program; particularly, the Earth and Environmental Systems Sciences Division (EESSD).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Stakeholder-driven carbon neutral pathways for Thailand and Bangkok: integrated assessment modeling to inform multilevel climate governance

Thailand has established a target of carbon neutrality by 2050. Reaching this goal will require coordination and collaboration between stakeholders spanning sectors and scales, including energy system decision makers, land managers, and city planners. Robust decarbonization scenarios incorporating current plans and targets, additional measures needed, and trade-offs between strategies can help stakeholders make informed decisions in the face of uncertainty. Through iterative engagement with decision makers at the city and national levels, we develop and analyze carbon neutral scenarios for Thailand that incorporate Bangkok’s role using a global integrated assessment model. We find that Thailand can reach carbon neutrality through power sector decarbonization, energy efficiency improvements, widespread electrification, and advanced technologies including carbon capture and storage and hydrogen. Negative emissions technologies will also be needed to offset Thailand and Bangkok’s hardest-to-abate CO 2 emissions. Bangkok, as a major population and economic center, contributes significantly to Thailand’s energy demand and emissions and can therefore play an important role in climate change mitigation. Accordingly, our results underscore the importance of subnational climate action in meeting Thailand’s carbon neutral goal. Our analysis also indicates that without sustained land-based carbon sequestration, much more mitigation effort will be needed in Thailand’s energy sector, including at the subnational scale, to reach carbon neutrality. These insights can help stakeholders identify priorities, consider tradeoffs, and make decisions that will impact Bangkok and Thailand’s long-term climate change mitigation potential. This analysis demonstrates how stakeholder engagement in integrated assessment modeling can facilitate and inform multilevel climate governance.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Investigation of the impact of supplemental reflective films to improve micro-light climate within tomato plant canopy in solar greenhouses

The non-uniform growth and development of crops within Chinese Solar Greenhouses (CSG) is directly related to the micro-light climate within canopy. In practice, reflective films are used to improve micro-light climate within plant canopy by homogenizing light distribution and so increasing total plant light interception. However, as to our knowledge, the contributions to light distribution within canopy have not been investigated for passive reflector like reflective films. Field experiments dealing with light conditions and growth behavior over time, are complicated to carry out, time-consuming and hard to control, while however, accurate measurements of how reflective films influence the micro-light climate of canopy are an essential step to improve the growth conditions for any crop. Here, we propose a supplementary light strategy using reflective films to improve light distribution within plant canopy. Based on the example of CSG, a 3D greenhouse model including a detailed 3D tomato canopy structure was constructed to simulate the influence of supplementary reflective films to improve micro-light climate. Comparison of measured solar radiation intensity with predicted model data demonstrated that the model could precisely predict light radiation intensity over time with different time points and positions in the greenhouse. A series of reflective film configurations were investigated based on features analysis of light distribution in the tomato canopy on sunny days using the proposed model. The reflective film configuration scheme with the highest impact significantly improved the evenness of horizontal and vertical light distribution in tomato canopy. The strategy provided here can be used to configure reflective films that will enhance light conditions in CSG, which can be applied and extended in different scenarios.

Liu, Anhua↗
DOE OSTI2025

The Interplay Between Climate and Urban Expansion on Building Energy Demand in Morocco

Understanding building energy demand is critical for addressing climate uncertainty challenges and ensuring sustainable urban growth. This study develops a building energy demand (BED) model to explore how climate variation and urban expansion affect residential and commercial space heating and cooling demands in Morocco for three scenarios, namely, 2005, 2018, and 2018 + 1.5 °C. The results show that coastal cities have lower heating and cooling needs due to the oceanic influence, while interior cities require significantly higher heating demand per-unit-floorspace. Between 2005 and 2018, urban growth increased total heating and cooling demand by 218.8 GWh, particularly in northern and coastal regions, despite per-unit-floorspace reductions in milder climates and improved building efficiency in 2018. Residential heating remains the dominant energy use, though commercial demand is significant in urban centers. Under the 2018 + 1.5 °C hypothetical scenario, heating demand across Morocco declines by 335.8 GWh compared to 2018, with urban areas amplifying this trend. Meanwhile, cooling demand increases slightly by 44.4 GWh, with major cities experiencing relative increases of up to 50%. These findings highlight a trade-off where reduced winter heating needs are partly offset by increased summer cooling demands in densely urbanized areas. In conclusion, the study identifies key urban hotspots for targeted interventions, emphasizing the need for energy-efficient building designs, climate-adaptive urban planning, and resilient energy management strategies to sustainably address shifting seasonal energy patterns.

Morocco↗
DOE OSTI2024

Intensified Positive Arctic–Methane Feedback under IPCC Climate Scenarios in the 21st Century

The positive Arctic–methane (CH 4 ) feedback forms when more CH 4 is released from the Arctic tundra to warm the climate, further stimulating the Arctic to emit CH 4 . This study utilized the CLM-Microbe model to project CH 4 emissions across five distinct Arctic tundra ecosystems on the Alaska North Slope, considering three Shared Socioeconomic Pathway (SSP) scenarios using climate data from three climate models from 2016 to 2100. Employing a hyper-resolution of 5 m × 5 m within 40,000 m 2 domains accounted for the Arctic tundra’s high spatial heterogeneity; three sites were near $Utqia\dot{g}vik$ (US-Beo, US-Bes, and US-Brw), with one each in Atqasuk (US-Atq) and Ivotuk (US-Ivo). Simulated CH 4 emissions substantially increased by a factor of 5.3 to 7.5 under the SSP5–8.5 scenario compared to the SSP1–2.6 and SSP2–4.5 scenarios. The projected CH 4 emissions exhibited a stronger response to rising temperature under the SSP5–8.5 scenario than under the SSP1–2.6 and SSP2–4.5 scenarios, primarily due to strong temperature dependence and the enhanced precipitation-induced expansion of anoxic conditions that promoted methanogenesis. The CH 4 transport via ebullition and plant-mediated transport is projected to increase under all three SSP scenarios, and ebullition dominated CH 4 transport by 2100 across five sites. Projected CH 4 emissions varied in temperature sensitivity, with a Q 10 range of 2.7 to 60.9 under SSP1–2.6, 3.8 to 17.6 under SSP2–4.5, and 5.7 to 17.2 under SSP5–8.5. Compared with the other three sites, US-Atq and US-Ivo were estimated to have greater increases in CH 4 emissions due to warmer temperatures and higher precipitation. The fact that warmer sites and warmer climate scenarios had higher CH 4 emissions suggests an intensified positive Arctic–CH 4 feedback in the 21st century. Microbial physiology and substrate availability dominated the enhanced CH 4 production. The simulated intensified positive feedback underscores the urgent need for a more mechanistic understanding of CH 4 dynamics and the development of strategies to mitigate CH 4 across the Arctic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Dynamic projection of anthropogenic emissions in China: methodology and 2015–2050 emission pathways under a range of socio-economic, climate policy, and pollution control scenarios

Abstract. Future trends in air pollution and greenhouse gas (GHG)emissions for China are of great concern to the community. A set of globalscenarios regarding future socio-economic and climate developments, combiningshared socio-economic pathways (SSPs) with climate forcing outcomes asdescribed by the Representative Concentration Pathways (RCPs), was createdby the Intergovernmental Panel on Climate Change (IPCC). Chinese researchers have also developed various emission scenarios by considering detailed local environmental and climate policies. However, a comprehensive scenario set connecting SSP–RCP scenarios with local policies and representing dynamic emission changes under local policies is still missing. In this work, to fill this gap, we developed a dynamic projection model, the Dynamic Projection model for Emissions in China (DPEC), to explore China'sfuture anthropogenic emission pathways. The DPEC is designed tointegrate the energy system model, emission inventory model, dynamicprojection model, and parameterized scheme of Chinese policies. The modelcontains two main modules, an energy-model-driven activity rate projectionmodule and a sector-based emission projection module. The activity rateprojection module provides the standardized and unified future energyscenarios after reorganizing and refining the outputs from the energy systemmodel. Here we use a new China-focused version of the Global ChangeAssessment Model (GCAM-China) to project future energy demand and supply inChina under different SSP–RCP scenarios at the provincial level. Theemission projection module links a bottom-up emission inventory model, theMulti-resolution Emission Inventory for China (MEIC), to GCAM-China andaccurately tracks the evolution of future combustion and production technologiesand control measures under different environmental policies. We developedtechnology-based turnover models for several key emitting sectors (e.g.coal-fired power plants, key industries, and on-road transportationsectors), which can simulate the dynamic changes in the unit/vehicle fleetturnover process by tracking the lifespan of each unit/vehicle on an annualbasis. With the integrated modelling framework, we connected five SSP scenarios(SSP1–5), five RCP scenarios (RCP8.5, 7.0, 6.0, 4.5, and 2.6), and threepollution control scenarios (business as usual, BAU; enhanced controlpolicy, ECP; and best health effect, BHE) to produce six combined emissionscenarios. With those scenarios, we presented a wide range of China's futureemissions to 2050 under different development and policy pathways. We foundthat, with a combination of strong low-carbon policy and air pollutioncontrol policy (i.e. SSP1-26-BHE scenario), emissions of major airpollutants (i.e. SO 2 , NO x , PM 2.5 , and non-methane volatile organic compounds – NMVOCs) in China willbe reduced by 34%–66% in 2030 and 58%–87% in 2050 compared to 2015. End-of-pipe control measures are more effective for reducing air pollutant emissions before 2030, while low-carbon policy will play a more important rolein continuous emission reduction until 2050. In contrast, China's emissionswill remain at a high level until 2050 under a reference scenario without activeactions (i.e. SSP3-70-BAU). Compared to similar scenarios set from theCMIP6 (Coupled Model Intercomparison Project Phase 6), our estimates ofemission ranges are much lower than the estimates from the harmonized CMIP6 emissions dataset in2020–2030, but their emission ranges become similar in the year 2050.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Opinion: Coordinated development of emission inventories for climate forcers and air pollutants

Emissions into the atmosphere of fine particulate matter, its precursors, and precursors to tropospheric ozone impact not only human health and ecosystems, but also the climate by altering Earth's radiative balance. Accurately quantifying these impacts across local to global scales historically and in future scenarios requires emission inventories that are accurate, transparent, complete, comparable, and consistent. In an effort to better quantify the emissions and impacts of these pollutants, also called short-lived climate forcers (SLCFs), the Intergovernmental Panel on Climate Change (IPCC) is developing a new SLCF emissions methodology report. This report would supplement existing IPCC reporting guidance on greenhouse gas (GHG) emission inventories, which are currently used by inventory compilers to fulfill national reporting requirements under the United Nations Framework Convention on Climate Change (UNFCCC) and new requirements of the Enhanced Transparency Framework (ETF) under the Paris Agreement starting in 2024. We review the relevant issues, including how air pollutant and GHG inventory activities have historically been structured, as well as potential benefits, challenges, and recommendations for coordinating GHG and air pollutant inventory efforts. We argue that, while there are potential benefits to increasing coordination between air pollutant and GHG inventory development efforts, we also caution that there are differences in appropriate methodologies and applications that must jointly be considered.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Climate-driven deterioration of future ozone pollution in Asia predicted by machine learning with multi-source data

Ozone (O 3 ) is a secondary pollutant in the atmosphere formed by photochemical reactions that endangers human health and ecosystems. O 3 has aggravated in Asia in recent decades and will vary in the future. In this study, to quantify the impacts of future climate change on O 3 pollution, near-surface O 3 concentrations over Asia in 2020–2100 are projected using a machine learning (ML) method along with multi-source data. The ML model is trained with combined O 3 data from a global atmospheric chemical transport model and real-time observations. The ML model is then used to estimate future O 3 with meteorological fields from multi-model simulations under various climate scenarios. The near-surface O 3 concentrations are projected to increase by 5 %–20 % over South China, Southeast Asia, and South India and less than 10 % over North China and the Gangetic Plains under the high-forcing scenarios in the last decade of 21st century, compared to the first decade of 2020–2100. The O 3 increases are primarily owing to the favorable meteorological conditions for O 3 photochemical formation in most Asian regions. We also find that the summertime O 3 pollution over eastern China will expand from North China to South China and extend into the cold season in a warmer future. Our results demonstrate the important role of a climate change penalty on Asian O 3 in the future, which provides implications for environmental and climate strategies of adaptation and mitigation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A full year of aerosol size distribution data from the central Arctic under an extreme positive Arctic Oscillation: insights from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition

Abstract. The Arctic environment is rapidly changing due to accelerated warming in the region. The warming trend is driving a decline in sea ice extent, which thereby enhances feedback loops in the surface energy budget in the Arctic. Arctic aerosols play an important role in the radiative balance and hence the climate response in the region, yet direct observations of aerosols over the Arctic Ocean are limited. In this study, we investigate the annual cycle in the aerosol particle number size distribution (PNSD), particle number concentration (PNC), and black carbon (BC) mass concentration in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This is the first continuous, year-long data set of aerosol PNSD ever collected over the sea ice in the central Arctic Ocean. We use a k-means cluster analysis, FLEXPART simulations, and inverse modeling to evaluate seasonal patterns and the influence of different source regions on the Arctic aerosol population. Furthermore, we compare the aerosol observations to land-based sites across the Arctic, using both long-term measurements and observations during the year of the MOSAiC expedition (2019–2020), to investigate interannual variability and to give context to the aerosol characteristics from within the central Arctic. Our analysis identifies that, overall, the central Arctic exhibits typical seasonal patterns of aerosols, including anthropogenic influence from Arctic haze in winter and secondary aerosol processes in summer. The seasonal pattern corresponds to the global radiation, surface air temperature, and timing of sea ice melting/freezing, which drive changes in transport patterns and secondary aerosol processes. In winter, the Norilsk region in Russia/Siberia was the dominant source of Arctic haze signals in the PNSD and BC observations, which contributed to higher accumulation-mode PNC and BC mass concentrations in the central Arctic than at land-based observatories. We also show that the wintertime Arctic Oscillation (AO) phenomenon, which was reported to achieve a record-breaking positive phase during January–March 2020, explains the unusual timing and magnitude of Arctic haze across the Arctic region compared to longer-term observations. In summer, the aerosol PNCs of the nucleation and Aitken modes are enhanced; however, concentrations were notably lower in the central Arctic over the ice pack than at land-based sites further south. The analysis presented herein provides a current snapshot of Arctic aerosol processes in an environment that is characterized by rapid changes, which will be crucial for improving climate model predictions, understanding linkages between different environmental processes, and investigating the impacts of climate change in future Arctic aerosol studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The effect of geographic sampling on evaluation of extreme precipitation in high-resolution climate models

Traditional approaches for comparing global climate models and observational data products typically fail to account for the geographic location of the underlying weather station data. For modern global high-resolution models with a horizontal resolution of tens of kilometers, this is an oversight since there are likely grid cells where the physical output of a climate model is compared with a statistically interpolated quantity instead of actual measurements of the climate system. In this paper, we quantify the impact of geographic sampling on the relative performance of high-resolution climate model representations of precipitation extremes in boreal winter (December–January–February) over the contiguous United States (CONUS), comparing model output from five early submissions to the HighResMIP subproject of the CMIP6 experiment. We find that properly accounting for the geographic sampling of weather stations can significantly change the assessment of model performance. Across the models considered, failing to account for sampling impacts the different metrics (extreme bias, spatial pattern correlation, and spatial variability) in different ways (both increasing and decreasing). We argue that the geographic sampling of weather stations should be accounted for in order to yield a more straightforward and appropriate comparison between models and observational data sets, particularly for high-resolution models with a horizontal resolution of tens of kilometers. While we focus on the CONUS in this paper, our results have important implications for other global land regions where the sampling problem is more severe.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models

Results from the fully and biogeochemically coupled simulations in which CO 2 increases at a rate of 1 % yr –1 (1pctCO2) from its preindustrial value are analyzed to quantify the magnitude of carbon–concentration and carbon–climate feedback parameters which measure the response of ocean and terrestrial carbon pools to changes in atmospheric CO 2 concentration and the resulting change in global climate, respectively. The results are based on 11 comprehensive Earth system models from the most recent (sixth) Coupled Model Intercomparison Project (CMIP6) and compared with eight models from the fifth CMIP (CMIP5). The strength of the carbon–concentration feedback is of comparable magnitudes over land (mean ± standard deviation = 0.97 ± 0.40 PgC ppm –1 ) and ocean (0.79 ± 0.07 PgC ppm –1 ), while the carbon–climate feedback over land (–45.1 ± 50.6 PgC °C –1 ) is about 3 times larger than over ocean (–17.2 ± 5.0 PgC °C –1 ). The strength of both feedbacks is an order of magnitude more uncertain over land than over ocean as has been seen in existing studies. These values and their spread from 11 CMIP6 models have not changed significantly compared to CMIP5 models. The absolute values of feedback parameters are lower for land with models that include a representation of nitrogen cycle. The transient climate response to cumulative emissions (TCRE) from the 11 CMIP6 models considered here is 1.77 ± 0.37 °C EgC –1 and is similar to that found in CMIP5 models (1.63 ± 0.48 °C EgC –1 ) but with somewhat reduced model spread. The expressions for feedback parameters based on the fully and biogeochemically coupled configurations of the 1pctCO2 simulation are simplified when the small temperature change in the biogeochemically coupled simulation is ignored. Decomposition of the terms of these simplified expressions for the feedback parameters is used to gain insight into the reasons for differing responses among ocean and land carbon cycle models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Assessing climate change impacts on live fuel moisture and wildfire risk using a hydrodynamic vegetation model

Abstract. Live fuel moisture content (LFMC) plays a critical role in wildfire dynamics, but little is known about responses of LFMC to multivariate climate change, e.g., warming temperature, CO2 fertilization, and altered precipitation patterns, leading to a limited prediction ability of future wildfire risks. Here, we use a hydrodynamic demographic vegetation model to estimate LFMC dynamics of chaparral shrubs, a dominant vegetation type in fire-prone southern California. We parameterize the model based on observed shrub allometry and hydraulic traits and evaluate the model's accuracy through comparisons between observed and simulated LFMC of three plant functional types (PFTs) under current climate conditions. Moreover, we estimate the number of days per year of LFMC below 79 % (which is a critical threshold for wildfire danger rating of southern California chaparral shrubs) from 1960 to 2099 for each PFT and compare the number of days below the threshold for medium and high greenhouse gas emission scenarios (RCP4.5 and 8.5). We find that climate change could lead to more days per year (5.2 %–14.8 % increase) with LFMC below 79 % between the historical (1960–1999) and future (2080–2099) periods, implying an increase in wildfire danger for chaparral shrubs in southern California. Under the high greenhouse gas emission scenario during the dry season, we find that the future LFMC reductions mainly result from a warming temperature, which leads to 9.1 %–18.6 % reduction in LFMC. Lower precipitation in the spring leads to a 6.3 %–8.1 % reduction in LFMC. The combined impacts of warming and precipitation change on fire season length are equal to the additive impacts of warming and precipitation change individually. Our results show that the CO2 fertilization will mitigate fire risk by causing a 3.5 %–4.8 % increase in LFMC. Our results suggest that multivariate climate change could cause a significant net reduction in LFMC and thus exacerbate future wildfire danger in chaparral shrub systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Towards an improved representation of carbonaceous aerosols over the Indian monsoon region in a regional climate model: RegCM

Mitigation of carbonaceous aerosol emissions is expected to provide climate and health co-benefits. The accurate representation of carbonaceous aerosols in climate models is critical for reducing uncertainties in their climate feedback. In this regard, emission fluxes and aerosol life cycle processes are the two primary sources of uncertainties. Here, we demonstrate that the incorporation of a dynamic ageing scheme and emission estimates that are updated for the local sources improves the representation of carbonaceous aerosols over the Indian monsoon region in a regional climate model, RegCM, compared with its default configuration. The respective mean black carbon (BC) and organic carbon (OC) surface concentrations in 2010 are estimated to be 4.25 and 10.35 µg m -3 over the Indo-Gangetic Plain (IGP) in the augmented model. The BC column burden over the polluted IGP is found to be 2.47 mg m -2 , 69.95 % higher than in the default model configuration and much closer to available observations. The anthropogenic aerosol optical depth (AOD) increases by more than 19 % over the IGP due to the model enhancement, also leading to a better agreement with observed AOD. The respective top-of-the-atmosphere, surface, and atmospheric anthropogenic aerosol short-wave radiative forcing are estimated at -0.3, -9.3, and 9.0 W m -2 over the IGP and -0.89, -5.33, and 4.44 W m -2 over Peninsular India (PI). Our results suggest that the combined effect of two modifications leads to maximum improvements in the model performance in regions where emissions play a dominant role.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evaluating 3 decades of precipitation in the Upper Colorado River basin from a high-resolution regional climate model

Abstract. Convection-permitting regional climate models (RCMs) have recently become tractable for applications at multi-decadal timescales. These types of models have tremendous utility for water resource studies, but better characterization of precipitation biases is needed, particularly for water-resource-critical mountain regions, where precipitation is highly variable in space, observations are sparse, and the societal water need is great. This study examines 34 years (1987–2020) of RCM precipitation from the Weather Research and Forecasting model (WRF; v3.8.1), using the Climate Forecast System Reanalysis (CFS; CFSv2) initial and lateral boundary conditions and a 1 km × 1 km innermost grid spacing. The RCM is centered over the Upper Colorado River basin, with a focus on the high-elevation, 750 km2 East River watershed (ERW), where a variety of high-impact scientific activities are currently ongoing. Precipitation is compared against point observations (Natural Resources Conservation Service Snow Telemetry or SNOTEL), gridded climate datasets (Newman, Livneh, and PRISM), and Bayesian reconstructions of watershed mean precipitation conditioned on streamflow and high-resolution snow remote-sensing products. We find that the cool-season precipitation percent error between WRF and 23 SNOTEL gauges has a low overall bias (x^ = 0.25 %, s = 13.63 %) and that WRF has a higher percent error during the warm season (x^ = 10.37 %, s = 12.79 %). Warm-season bias manifests as a high number of low-precipitation days, though the low-resolution or SNOTEL gauges limit some of the conclusions that can be drawn. Regional comparisons between WRF precipitation accumulation and three different gridded datasets show differences on the order of ± 20 %, particularly at the highest elevations and in keeping with findings from other studies. We find that WRF agrees slightly better with the Bayesian reconstruction of precipitation in the ERW compared to the gridded precipitation datasets, particularly when changing SNOTEL densities are taken into account. The conclusions are that the RCM reasonably captures orographic precipitation in this region and demonstrates that leveraging additional hydrologic information (streamflow and snow remote-sensing data) improves the ability to characterize biases in RCM precipitation fields. Error characteristics reported in this study are essential for leveraging the RCM model outputs for studies of past and future climates and water resource applications. The methods developed in this study can be applied to other watersheds and model configurations. Hourly 1 km × 1 km precipitation and other meteorological outputs from this dataset are publicly available and suitable for a wide variety of applications.

Geology↗
DOE OSTI2024

Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0

Abstract. The spatial heterogeneity related to complex topography in California demands high-resolution (< 5 km) modeling, but global convection-permitting climate models are computationally too expensive to run multi-decadal simulations. We developed a 3.25 km California climate modeling framework by leveraging regional mesh refinement (CARRM) using the U.S. Department of Energy (DOE)'s global Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) version 0. Four 5-year time periods (2015–2020, 2029–2034, 2044–2049, and 2094–2099) were simulated by nudging CARRM outside California to 1° coupled simulation of E3SMv1 under the Shared Socioeconomic Pathways (SSP)5-8.5 future scenario. The 3.25 km grid spacing adds considerable value to the prediction of the California climate changes, including more realistic high temperatures in the Central Valley and much improved spatial distributions of precipitation and snowpack in the Sierra Nevada and coastal stratocumulus. Under the SSP5-8.5 scenario, CARRM simulation predicts widespread warming of 6–10 °C over most of California, a 38 % increase in statewide average 30 d winter–spring precipitation, a near-complete loss of the alpine snowpack, and a sharp reduction in shortwave cloud radiative forcing associated with marine stratocumulus by the end of the 21st century. We note a climatological wet precipitation bias for the CARRM and discuss possible reasons. We conclude that SCREAM RRM is a technically feasible and scientifically valid tool for climate simulations in regions of interest, providing an excellent bridge to global convection-permitting simulations.

58 GEOSCIENCES↗
DOE OSTI2021

Assessing climate-change-induced flood risk in the Conasauga River watershed: an application of ensemble hydrodynamic inundation modeling

Abstract. This study evaluates the impact of potential future climate change on flood regimes, floodplain protection, and electricity infrastructures across the Conasauga River watershed in the southeastern United States through ensemble hydrodynamic inundation modeling. The ensemble streamflow scenarios were simulated by the Distributed Hydrology Soil Vegetation Model (DHSVM) driven by (1) 1981–2012 Daymet meteorological observations and (2) 11 sets of downscaled global climate models (GCMs) during the 1966–2005 historical and 2011–2050 future periods. Surface inundation was simulated using a GPU-accelerated Two-dimensional Runoff Inundation Toolkit for Operational Needs (TRITON) hydrodynamic model. A total of 9 out of the 11 GCMs exhibit an increase in the mean ensemble flood inundation areas. Moreover, at the 1 % annual exceedance probability level, the flood inundation frequency curves indicate a ∼ 16 km2 increase in floodplain area. The assessment also shows that even after flood-proofing, four of the substations could still be affected in the projected future period. The increase in floodplain area and substation vulnerability highlights the need to account for climate change in floodplain management. Overall, this study provides a proof-of-concept demonstration of how the computationally intensive hydrodynamic inundation modeling can be used to enhance flood frequency maps and vulnerability assessment under the changing climatic conditions.

54 ENVIRONMENTAL SCIENCES↗