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DOE OSTI2026

Examining Cloud Feedback Components in the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM)

Cloud feedback remains the main source of uncertainty in climate sensitivity estimated by global climate models (GCMs), largely because subgrid cloud responses are parameterized in GCMs due to their coarse resolution. Here, this study examines cloud feedback in the global 3.25-km Simple Cloud-Resolving Energy Exascale Earth System Model (E3SM) Atmosphere Model (SCREAM 3 km) through a pair of 1-yr atmosphere-only simulations with control and +4-K sea surface temperature perturbations. SCREAM 3 km produces a positive cloud feedback that falls within but at the upper end of the range of Coupled Model Intercomparison Project phase 5 (CMIP5) and CMIP phase 6 (CMIP6) models and expert judgment. The positive cloud feedback arises from positive contributions from both high- and low-level clouds, with increases in high-cloud altitude and decreases in low-cloud amount and optical depth playing key roles. The stronger-than-CMIP-average feedback is mainly attributable to the high-cloud altitude feedback, owing to cloud tops rising nearly isothermally in SCREAM 3 km. The positive low-cloud amount feedback is weaker in SCREAM than in GCMs because estimated inversion strength (EIS) increases more dramatically with warming. A coarser 12-km resolution version of SCREAM exhibits a weaker positive cloud feedback than SCREAM 3 km, mainly because its low-cloud-radiative flux is more sensitive to EIS, leading to a stronger negative low-cloud amount feedback. With this process-level assessment of cloud feedback, this study reveals where SCREAM aligns with and diverges from conventional GCMs and expert assessment, providing insights to inform further model improvement and future expert assessment.

Cloud radiative effects↗

NASA NTRS2017

Biogeochemical Protocols and Diagnostics for the CMIP6 Ocean Model Intercomparison Project (OMIP)

The Ocean Model Intercomparison Project (OMIP) focuses on the physics and biogeochemistry of the ocean component of Earth system models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6). OMIP aims to provide standard protocols and diagnostics for ocean models, while offering a forum to promote their common assessment and improvement. It also offers to compare solutions of the same ocean models when forced with reanalysis data (OMIP simulations) vs. when integrated within fully coupled Earth system models (CMIP6). Here we detail simulation protocols and diagnostics for OMIP's biogeochemical and inert chemical tracers. These passive-tracer simulations will be coupled to ocean circulation models, initialized with observational data or output from a model spin-up, and forced by repeating the 1948-2009 surface fluxes of heat, fresh water, and momentum. These so-called OMIP-BGC simulations include three inert chemical tracers (CFC-11, CFC-12, SF [subscript] 6) and biogeochemical tracers (e.g., dissolved inorganic carbon, carbon isotopes, alkalinity, nutrients, and oxygen). Modelers will use their preferred prognostic BGC model but should follow common guidelines for gas exchange and carbonate chemistry. Simulations include both natural and total carbon tracers. The required forced simulation (omip1) will be initialized with gridded observational climatologies. An optional forced simulation (omip1-spunup) will be initialized instead with BGC fields from a long model spin-up, preferably for 2000 years or more, and forced by repeating the same 62-year meteorological forcing. That optional run will also include abiotic tracers of total dissolved inorganic carbon and radiocarbon, CTabio and 14CTabio, to assess deep-ocean ventilation and distinguish the role of physics vs. biology. These simulations will be forced by observed atmospheric histories of the three inert gases and CO2 as well as carbon isotope ratios of CO2. OMIP-BGC simulation protocols are founded on those from previous phases of the Ocean Carbon-Cycle Model Intercomparison Project. They have been merged and updated to reflect improvements concerning gas exchange, carbonate chemistry, and new data for initial conditions and atmospheric gas histories. Code is provided to facilitate their implementation.

Carbon↗

NASA NTRSDate not supplied

Uncertainty in the response of sudden stratospheric warmings and stratosphere- troposphere coupling to quadrupled CO2 concentrations in CMIP6 models

Major sudden stratospheric warmings (SSWs), vortex formation and final breakdown dates are key highlight points of the stratospheric polar vortex. These phenomena are relevant for stratosphere-troposphere coupling, which explains the interest in understanding their future changes. However, up to now, there is not a clear consensus on which projected changes to the polar vortex are robust, particularly in the Northern Hemisphere, possibly due to short data record or relatively moderate CO2 forcing. The new simulations performed under the Coupled Model Intercomparison Project, Phase 6, together with the long daily data requirements of the DynVarMIP project in preindustrial and quadrupled CO2 (4xCO2 ) forcing simulations provide a new opportunity to revisit this topic by overcoming the limitations mentioned above. In this study, we analyze this new model output to document the change, if any, in the frequency of SSWs under 4xCO2 forcing. Our analysis reveals a large disagreement across the models as to the sign of this change, even though most models show a statistically significant change. The models, however, are in good agreement as to the impact of SSWs over the North Atlantic: there is no indication of a change under 4xCO2 forcing. Over the Pacific, however, the change is more uncertain. Finally, the models show robust changes to the seasonal cycle in the stratosphere. Specifically, we find a longer duration of the stratospheric polar vortex, and thus a longer season of stratosphere-troposphere coupling.

sudden stratospheric warmings (SSWs)↗

NASA NTRSDate not supplied

Dynamical Sensitivity in Response to a Wide Range of Abrupt CO 2 Forcings

An improved understanding of dynamical variability is important for projecting future changes in extratropical weather and the interaction between the extratropical troposphere and the tropics, stratosphere, cryosphere and ocean. Despite their simplicity, the abrupt 2x- and 4xCO 2 forcing simulations from the Coupled Model Intercomparison Project (CMIP) Phase 6 DECK experiments enable a mechanistic look into the forcing and feedback response characteristics of models that can be unambiguously attributed to an increase in carbon dioxide concentrations. Thus, while typically used to evaluate the climate sensitivity in models, with a primary focus on global surface temperature change, here we focus on several measures of extratropical variability, including projected changes in the storm tracks and in stratospheric polar vortex variability. Results are primarily based on simulations produced using low- and high-top versions of the NASA Goddard Institute for Space Studies Model (ModelE) but results are also presented from the larger CMIP6 multi-model ensemble. In addition to the 2x- and 4xCO 2 simulations, we also explore the linearity of the response of extratropical dynamical variability in ModelE to varying levels of CO 2 spanning the range 1/8-8xCO 2 . In particular, we show that the expansion of the jet streams varies nonlinearly with increasing CO 2 , especially in the Northern Hemisphere, and that this can be interpreted in terms of variations in ocean heat transport. The impact of composition feedbacks on changes in variability is also discussed.

dynamical variability↗

DOE OSTI2024

The Changing Influence of Precipitation on Soil Moisture Drought With Warming in the Mediterranean and Western North America

Abstract Anthropogenic climate change has already affected drought severity and risk across many regions, and climate models project additional increases in drought risk with future warming. Historically, droughts are typically caused by periods of below‐normal precipitation and terminated by average or above‐normal precipitation. In many regions, however, soil moisture is projected to decrease primarily through warming‐driven increases in evaporative demand, potentially affecting the ability of negative precipitation anomalies to cause drought and positive precipitation anomalies to terminate drought. Here, we use climate model simulations from Phase Six of the Coupled Model Intercomparison Project (CMIP6) to investigate how different levels of warming (1, 2, and 3°C) affect the influence of precipitation on soil moisture drought in the Mediterranean and Western North America regions. We demonstrate that the same monthly precipitation deficits (25th percentile relative to a preindustrial baseline) at a global warming level of 2°C increase the probability of both surface and rootzone soil moisture drought by 29% in the Mediterranean and 32% and 6% in Western North America compared to the preindustrial baseline. Furthermore, the probability of a dry (25th percentile relative to a preindustrial baseline) surface soil moisture month given a high (75th percentile relative to a preindustrial baseline) precipitation month is 6 (Mediterranean) and 3 (Western North America) times more likely in a 2°C world compared to the preindustrial baseline. For these regions, warming will likely increase the risk of soil moisture drought during low precipitation periods while simultaneously reducing the efficacy of high precipitation periods to terminate droughts.

Environmental Sciences & Ecology↗

NASA NTRS2024

The Changing Influence of Precipitation on Soil Moisture Drought With Warming in the Mediterranean and Western North America

Anthropogenic climate change has already affected drought severity and risk across many regions, and climate models project additional increases in drought risk with future warming. Historically, droughts are typically caused by periods of below-normal precipitation and terminated by average or above-normal precipitation. In many regions, however, soil moisture is projected to decrease primarily through warming-driven increases in evaporative demand, potentially affecting the ability of negative precipitation anomalies to cause drought and positive precipitation anomalies to terminate drought. Here, we use climate model simulations from Phase Six of the Coupled Model Intercomparison Project (CMIP6) to investigate how different levels of warming (1, 2, and 3°C) affect the influence of precipitation on soil moisture drought in the Mediterranean and Western North America regions. We demonstrate that the same monthly precipitation deficits (25th percentile relative to a preindustrial baseline) at a global warming level of 2°C increase the probability of both surface and rootzone soil moisture drought by 29% in the Mediterranean and 32% and 6% in Western North America compared to the preindustrial baseline. Furthermore, the probability of a dry (25th percentile relative to a preindustrial baseline) surface soil moisture month given a high (75th percentile relative to a preindustrial baseline) precipitation month is 6 (Mediterranean) and 3 (Western North America) times more likely in a 2°C world compared to the preindustrial baseline. For these regions, warming will likely increase the risk of soil moisture drought during low precipitation periods while simultaneously reducing the efficacy of high precipitation periods to terminate droughts.

drought↗

NASA NTRS2021

A multi-model CMIP6-PMIP4 study of Arctic sea ice at 127 ka: sea ice data compilation and model differences

The Last Interglacial period (LIG) is a period with increased summer insolation at high northern latitudes, which results in strong changes in the terrestrial and marine cryosphere. Understanding the mechanisms for this response via climate modelling and comparing the models' representation of climate reconstructions is one of the objectives set up by the Paleoclimate Modelling Intercomparison Project for its contribution to the sixth phase of the Coupled Model Intercomparison Project. Here we analyse the results from 16 climate models in terms of Arctic sea ice. The multi-model mean reduction in minimum sea ice area from the pre industrial period (PI) to the LIG reaches 50 % (multi-model mean LIG area is 3.20×10^6 sq.km, compared to 6.46×10^6 sq.km for the PI). On the other hand, there is little change for the maximum sea ice area (which is 15–16×10^6 sq.km for both the PI and the LIG. To evaluate the model results we synthesise LIG sea ice data from marine cores collected in the Arctic Ocean, Nordic Seas and northern North Atlantic. The reconstructions for the northern North Atlantic show year-round ice-free conditions, and most models yield results in agreement with these reconstructions. Model–data disagreement appear for the sites in the Nordic Seas close to Greenland and at the edge of the Arctic Ocean. The northernmost site with good chronology, for which a sea ice concentration larger than 75 % is reconstructed even in summer, discriminates those models which simulate too little sea ice. However, the remaining models appear to simulate too much sea ice over the two sites south of the northernmost one, for which the reconstructed sea ice cover is seasonal. Hence models either underestimate or overestimate sea ice cover for the LIG, and their bias does not appear to be related to their bias for the pre-industrial period. Drivers for the inter-model differences are different phasing of the up and down short-wave anomalies over the Arctic Ocean, which are associated with differences in model albedo; possible cloud property differences, in terms of optical depth; and LIG ocean circulation changes which occur for some, but not all, LIG simulations. Finally, we note that inter-comparisons between the LIG simulations and simulations for future climate with moderate (1 %/yr) CO2 increase show a relationship between LIG sea ice and sea ice simulated under CO2 increase around the years of doubling CO2. The LIG may therefore yield insight into likely 21st century Arctic sea ice changes using these LIG simulations.

Arctic sea ice↗

NASA NTRS2020

Representation of Modes of Variability in 6 U.S. Climate Models

We compare the performance of several modes of variability across six US climate modeling groups, with a focus on identifying robust improvements in recent models (including those participating in the Coupled Model Intercomparison Project (CMIP) Phase 6) compared to previous versions. In particular, we examine the representation of the Madden-Julian Oscillation (MJO), the El Ni˜no/Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), the Quasi-Biennial Oscillation (QBO) in the tropical stratosphere and the dominant modes of extra-tropical variability, including the Southern Annular Mode (SAM), the Northern Annular Mode (NAM) (and the closely related North Atlantic Oscillation (NAO)), and the Pacific-North American Pattern (PNA). Where feasible, we explore the processes driving these improvements through the use of “intermediary” experiments that utilize model versions between CMIP3/5 and CMIP6 as well as targeted sensitivity experiments in which individual modeling parameters are altered. We find clear and systematic improvements in the MJO and QBO and in the teleconnection patterns associated with the PDO and ENSO. Some gains arise from better process representation, while others (e.g. the QBO) from higher resolution that allows for a greater range of interactions. Our results demonstrate that the incremental development processes in multiple climate model groups lead to more realistic simulations over time.

Modes of variability↗

NASA NTRS2020

GISS Model E2.2: A Climate Model Optimized for the Middle Atmosphere. Part 2: Validation of Large-Scale Transport and Evaluation of Climate Response

Here we examine the large‐scale transport characteristics of the new ``Middle Atmosphere" NASA Goddard Institute for Space Studies climate model (E2.2). First we evaluate the stratospheric transport circulation in historical atmosphere‐only simulations integrated with interactive trace gas and aerosol chemistry. Compared to lower vertical resolution model versions, E2.2 exhibits improved tropical ascent and older stratospheric mean ages that are more consistent with observed values. In the troposphere, poleward transport to the Arctic and interhemispheric mean ages in E2.2 are comparable to models participating in the Chemistry Climate Modeling Initiative. In addition to validating E2.2 we also assess its ``transport sensitivity" using the coupled atmosphere‐ocean abrupt 4xCO2 and transient 1%CO2 simulations submitted to the Coupled Model Intercomparison Project, Phase 6, along with a 2xCO2 simulation used to evaluate the linearity of the transport circulation's response to increased CO2. We show that decreases (increases) in a stratospheric mean age (idealized surface loss) tracer scale linearly with increased lower stratospheric upwelling, which also increases linearly with warming tropical sea surface temperatures (SSTs). Abrupt 2xCO2 and 4xCO2 experiments constrained with (fixed) pre‐industrial SSTs are also used to quantify the relative importance of rapid adjustments versus SST feedbacks to the transport circulation responses in the model. Finally, sensitivity experiments are presented to illustrate the impact of changes in the convective parameterization on stratospheric transport.

GISS Model E2.2↗

DOE OSTI2024

Multi-scale impacts of climate change on hydropower for long-term water-energy planning in the contiguous United States

Climate change impacts on watersheds can potentially exacerbate water scarcity issues where water serves multiple purposes including hydropower. The long-term management of water and energy resources is still mostly approached in a siloed manner at different basins or watersheds, failing to consider the potential impacts that may concurrently affect many regions at once. There is a need for a large-scale hydropower modeling framework that can examine climate impacts across adjoining river basins and balancing authorities (BAs) and provide a periodic assessment at regional to national scales. Expanding from our prior assessment only for the United States (US) federal hydropower plants, we enhance and extend two regional hydropower models to cover over 85% of the total hydropower nameplate capacity and present the first contiguous US-wide assessment of future hydropower production under Coupled Model Intercomparison Project phase 6’s high-end Shared Socioeconomic Pathway 5-8.5 emission scenario using an uncertainty-aware multi-model ensemble approach. We present regional hydropower projections, using both BA regions and US Hydrologic Subregions (HUC4s), to consistently inform the energy and water communities for two future periods—the near-term (2020–2039) and the mid-term (2040–2059) relative to a historical baseline period (1980–2019). We find that the median projected changes in annual hydropower generation are typically positive—approximately 5% in the near-term, and 10% in the mid-term. However, since the risk of regional droughts is also projected to increase, future planning cannot overly rely on the ensemble median, as the potential of severe hydropower reductions could be overlooked. The assessment offers an ensemble of future hydropower generation projections, providing regional utilities and power system operators with consistent data to develop drought scenarios, design long duration storage and evaluate energy infrastructure reliability under intensified inter-annual and seasonal variability.

13 HYDRO ENERGY↗

NASA NTRS2021

Uncertainties, Limits, and Benefits of Climate Change Mitigation for Soil Moisture Drought in Southwestern North America

Over the last two decades, southwestern North America (SWNA) has been in the grip of one of the most severe droughts of the last 1,200 years, with one third to nearly one half of its severity attributable to climate change. We analyze how the risk of extreme soil moisture droughts in SWNA, analogous to the most severe 21-year (≥ in magnitude to 2000–2020) and single-year (≥ in magnitude to 2002) events of the last several decades, changes in projections from Phase 6 of the Coupled Model Intercomparison Project. By the end of the 21st century, SWNA experiences robust (R ≥ 0.80) soil moisture drying and substantial increases in extreme single-year drought risk that scale strongly with warming, spanning an 8%–26% probability of occurrence across +2–4 K. Notably, our results show that 21-year droughts analogous to 2000–2020 are up to 5 times more likely than extreme single-year droughts under all levels of warming (≈50%). These high levels of 21-year drought risk are largely invariant across scenarios because of large spring precipitation declines in half the models, shifting SWNA into a drier mean state. Despite projections of this sweeping and ostensibly inevitable increase in 21-year drought risk, climate mitigation reduces their severity by reducing the magnitude of extreme single-year droughts during these events. Our results emphasize both the importance of preparing SWNA for imminent increases in persistent drought events and constraining projected precipitation uncertainty to better resolve future long-term drought risk.

drought↗

DOE OSTI2024

Estimating Uncertainty in Simulated ENSO Statistics

Abstract Large ensembles of model simulations are frequently used to reduce the impact of internal variability when evaluating climate models and assessing climate change induced trends. However, the optimal number of ensemble members required to distinguish model biases and climate change signals from internal variability varies across models and metrics. Here we analyze the mean, variance and skewness of precipitation and sea surface temperature in the eastern equatorial Pacific region often used to describe the El Niño–Southern Oscillation (ENSO), obtained from large ensembles of Coupled model intercomparison project phase 6 climate simulations. Leveraging established statistical theory, we develop and assess equations to estimate, a priori, the ensemble size or simulation length required to limit sampling‐based uncertainties in ENSO statistics to within a desired tolerance. Our results confirm that the uncertainty of these statistics decreases with the square root of the time series length and/or ensemble size. Moreover, we demonstrate that uncertainties of these statistics are generally comparable when computed using either pre‐industrial control or historical runs. This suggests that pre‐industrial runs can sometimes be used to estimate the expected uncertainty of statistics computed from an existing historical member or ensemble, and the number of simulation years (run duration and/or ensemble size) required to adequately characterize the statistic. This advance allows us to use existing simulations (e.g., control runs that are performed during model development) to design ensembles that can sufficiently limit diagnostic uncertainties arising from simulated internal variability. These results may well be applicable to variables and regions beyond ENSO.

54 ENVIRONMENTAL SCIENCES↗

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 OSTI2025

Projected evolution of droughts and human exposure in the contiguous United States under SSP5-8.5: a regional downscaling perspective

The increasingly unpredictable and extreme weather patterns under a warming climate underscore the urgency of accurate regional assessments of future drought risk. This study evaluates the projected drought evolution in the contiguous United States under the high-emission shared socioeconomic pathway 5–8.5 climate scenario for the coming decades. Using a multi-model ensemble of six Coupled Model Intercomparison Project Phase 6 global climate models combined with dynamical downscaling techniques, we analyzed near-term (2020–2039) and mid-term (2040–2059) drought patterns using the self-calibrating palmer drought severity index (ScPDSI), the standardized precipitation index (SPI-12), and the Standardized Precipitation-Evapotranspiration Index (SPEI-12). Results reveal a widespread increase in abnormally dry (D0) and moderate drought (D1) conditions, particularly in urban areas, while severe (D2), extreme (D3), and exceptional (D4) droughts are expected to become less common in many regions. Meanwhile, persistent and intensifying droughts are projected in the western and southwestern U.S., driven by long-term soil moisture deficits. The ScPDSI projects that 1.1 million urban residents will be affected by D0 conditions in 2050, while SPI-12 suggests a decrease in the total affected populations after 2040. ScPDSI indicates prolonged droughts in the West, and SPI-12 captures transient variability. Although the total drought-exposed population is expected to decrease, urban areas will continue to bear a greater burden, particularly for mild droughts (D0, D1). These findings highlight a shift toward more frequent mild droughts, fewer severe droughts, and persistent drying in the Southwest, emphasizing the need for region-specific adaptation strategies.

CMIP6↗

DOE OSTI2025

Remote Influence of Andean Convection on Amazonian Rainfall and Its Mechanisms

Models from Coupled Model Intercomparison Project Phase 6 produce too much precipitation over the Andes but too little over the Amazon or the Wet Andes-Dry Amazon (WADA) bias pattern. Unlike the conventional view that convection parameterization and land model deficiencies can contribute to Amazonian rainfall biases, we approach this long-standing biased model behavior through the lens of Andean convection. Using Community Earth System Model v1.1 and focusing on the wet season, our mechanism-denial experiments demonstrate that Andean convection notably reduces precipitation over the Amazon during austral summer. The Andean forced Amazonian response operates on weather timescale. Furthermore, the reduction of Amazonian rainfall is detectable within a few hours after initial Andean forcing. The precipitation response is primarily driven by variations in the moisture budget and is moderated by changes in convective available potential energy over the Amazon. Changes in the total advection of moisture over the Amazon are dominated by the vertical advection term and can be attributed to discrepancies in the dynamic omega field. In the experiments, the Andean east flank region is scrutinized where the vertical velocity and moisture fields play an intermediary role for the Andean driven WADA connection. The Andean forcing induces descending anomalies on the Andean east flank. The disturbances of wind and geopotential fields over the Andean east flank propagate eastward via Kelvin waves. Over the Amazon, descending anomalies and advective drying lead to reduction of mid-to-high level cloud, increase of shortwave cloud forcing and surface net radiation, and enhancement of themodynamic stability and rainfall reduction.

58 GEOSCIENCES↗