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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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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Complementing Dynamical Downscaling With Super‐Resolution Convolutional Neural Networks

Despite advancements in Artificial Intelligence (AI) methods for climate downscaling, significant challenges remain for their practicality in climate research. Current AI-methods exhibit notable limitations, such as limited application in downscaling Global Climate Models (GCMs), and accurately representing extremes. To address these challenges, we implement an AI-based methodology using super-resolution convolutional neural networks (SRCNN), trained and evaluated on 40 years of daily precipitation data from a reanalysis and a high-resolution dynamically downscaled counterpart. The dynamical downscaled simulations, constrained using spectral nudging, enable the replication of historical events at a higher resolution. This allows the SRCNN to emulate dynamical downscaling effectively. Modifications, such as incorporating elevation data and data pre-processing enhances overall model performance, while using exponential and quantile loss functions improve the simulation of extremes. Our findings show SRCNN models efficiently and skillfully downscale precipitation from GCMs. Future work will expand this methodology to downscale additional variables for future climate projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Quantifying the impacts of marine aerosols over the southeast Atlantic Ocean using a chemical transport model: implications for aerosol–cloud interactions

The southeast Atlantic region, characterized by persistent stratocumulus clouds, has one of the highest uncertainties in aerosol radiative forcing and significant variability across climate models. In this study, we analyze the seasonally varying role of marine aerosol sources and identify key uncertainties in aerosol composition at cloud-relevant altitudes over the southeast Atlantic using the GEOS-Chem chemical transport model. We evaluate simulated aerosol optical depth (AOD) and speciated aerosol concentrations against those collected from ground observations and aircraft campaigns such as LASIC, ORACLES, and CLARIFY, conducted during 2017. The model consistently underestimates AOD relative to AERONET, particularly at remote locations like Ascension Island. However, when compared with aerosol mass concentrations from aircraft campaigns during the biomass burning period, it performs adequately at cloud-relevant altitudes, with a normalized mean bias (NMB) between -3.5 % (CLARIFY) and -7.5 % (ORACLES). At these altitudes, in the model, organic aerosols (63 %) dominate during the biomass burning period, while sulfate (41 %) prevails during austral summer, when dimethylsulfide (DMS) emissions peak in the model. Our findings indicate that marine sulfate can account for up to 69 % of total sulfate during the high-DMS period. Sensitivity analyses indicate that refining DMS emissions and oxidation chemistry may increase sulfate aerosol produced from marine sources, highlighting that there remains large uncertainty as to the role of DMS emissions in the marine boundary layer. Additionally, we find marine primary organic aerosol emissions may substantially increase total organic aerosol concentrations, particularly during austral summer. This study underscores the imperative need to refine marine emissions and their chemical transformations, as aerosols from marine sources are a major component of total aerosols at cloud-relevant altitudes and may impact uncertainties in aerosol radiative forcing over the southeast Atlantic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Lack of clear standards and usable comparisons of downscaled climate projections pose a roadblock for US climate discovery and adaptation

Abstract The release of global climate projections coupled with the demand for local-resolution climate-forced meteorology has prompted many research groups to downscale these projections using various statistical, dynamical, and current machine learning techniques. Such downscaled datasets are being used to plan infrastructure and other community needs over the coming decades. Faced with roughly a dozen available US downscaled datasets, many practitioners ask, ‘What are the relevant differences between datasets?’ This work highlights the difficulty of comparing downscaled datasets and illustrates ways in which datasets differ even when using identical climate model input data. We show that substantial variability in precipitation projections arises from downscaling alone and that the downscaled dataset agreement varies depending on global climate projection. This analysis emphasizes the need for greater coordination and movement toward rigorous benchmarking of downscaling strategies within the downscaling research community, à la the land-modeling community, to better quantify downscaling dataset differences, strengths, and weaknesses for practitioners.

Hartke, Samantha H. (ORCID:0000000202394723)↗
DOE OSTI2024

Convectively Induced Secondary Circulations and Wind‐Driven Heat Fluxes in the Surface Energy Balance Over Land

Increased resolution has enabled kilometer-scale weather and climate models to partially resolve secondary circulations, including horizontal convective rolls (HCRs) and cold pool gust fronts. Although these circulations are ubiquitous in convective boundary layers over land, their impacts on the surface energy balance are largely unknown. Doppler lidar and surface observations were combined with DOE E3SM land model experiments, revealing increased surface winds (5 m/s) and heat fluxes (50 W/m 2 ) in convergent branches of HCRs. Larger wind-driven flux responses (up to 150 W/m 2 ) were found along gust fronts. Surface energy balance shifts to accommodate wind-driven fluxes, reducing ground heat conduction and longwave cooling. Our findings from the US Southern Great Plains are broadly relevant to modeling convective boundary layers. In particular, widely used subgrid wind gust parameterizations were found to be physically inconsistent with resolved secondary circulations and could worsen climate prediction biases at kilometer-scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

UFNet: Joint U-Net and Fully Connected Neural Network to Bias Correct Precipitation Predictions from

Paper information. Shuang Yu, Indrasis Chakraborty, Gemma J. Anderson, Donald D. Lucas, Yannic Lops, and Daniel Galea. UFNet: Joint U-Net and fully connected neural network to bias correct precipitation predictions from climate models. Artificial Intelligence for the Earth Systems, 2024. Overview. This work develops the UFNet methodology to correct E3SM historical precipitation projection bias. The UFNet deep learning framework consists of a two-part architecture: a U-Net convolutional network to capture the spatiotemporal distribution of precipitation and a fully connected network to capture the distribution of higher-order statistics. The joint network, termed UFNet, can simultaneously improve the spatial structure of the modeled precipitation and capture the distribution of extreme precipitation values. Below we provide guidance for applying UFNet to correct the Energy Exascale Earth System Model (E3SM; Golaz et al. 2019) daily precipitation projection over the contiguous United States (CONUS). Getting started 1. Obtain the historical climate simulation and observation data. The E3SM historical simulation data are available through https://aims2.llnl.gov/search/cmip6/. The CPC unified gauge-based analysis of daily precipitation can be found through https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html. The ECMWF atmospheric reanalysis of the 20th century (ERA-20C) data are available through https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-20c. The spatial resolution of E3SM and observed datasets are both regridded to a common 1° resolution grid using conservative interpolation. The regridded E3SM, CPC and ERA-20C with 1° resolution can be found throught ./data/. 2. Train the fully connected network (DNN) Python train_dnn.py 3. Train the UFNet Python train_ufnet.py 4. Evaluation and compared with the baseline Python evaluation.py

Lucas, DonaldD↗
DOE OSTI2025

Early Stages in the Lifecycle of Polar Liquid-Bearing Clouds

Stratiform liquid-bearing clouds are ubiquitous over the polar regions, where they are predominantly mixed-phase. These polar clouds induce substantial radiative forcing on the surface and continuously modify the atmospheric thermodynamic budget, with direct implications for the polar ice pack resilience. However, the physical representation of these clouds is still a major challenge for climate models. A significant part of polar liquid-bearing cloud lifecycle is often manifested in a quasi-steady self-sustaining, persistent, and turbulent state, which is driven by longwave cloud radiative cooling and can last for multiple days. This self-sustaining cloud lifecycle stage has been thoroughly investigated in numerous studies, though some of its aspects such as precipitation still lack robust quantification and evaluation. The preceding cloud lifecycle stages, which may last up to several hours, have nonetheless remained widely overlooked. These preceding stages initiate at cloud formation, often in a stable and non-turbulent atmospheric layer and serve as a key junction between cloud persistence and cloud dissipation. These two contrasting cloud lifecycle trajectories pose the question if general circulation models (GCMs) can capture the full lifecycle accurately for the real physical reasons, a necessary condition to improve our confidence in climate projections given the changing polar climate. The purpose of this project was to improve the characterization and understanding of these early stages in the lifecycle of polar stratiform liquid-bearing cloud and to aid their representation in GCMs. This research relied on measurements from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) field campaign, as well as the observations from the ARM West Antarctic Radiation Experiment (AWARE) and the permanent ARM site at Utqiagvik, North Slope of Alaska (NSA).

58 GEOSCIENCES↗
DOE OSTI2024

More high-impact atmospheric river-induced extreme precipitation events under warming in a high-resolution model

Extreme precipitation events, as occurred in Europe 2021, or western North America 2023, with an intensity of 75 mm/day or 100 mm/day, respectively, exert a catastrophic impact. Lower-resolution (~100 km) climate models cannot simulate the intensity, nor their response to greenhouse warming. Using a high-resolution (~25 km) model capable of simulating such events, here we show that frequency-weighted area of such events over western Europe and the west coast of North America likely expands by more than 80% under an approximately 4°C of global warming from the historical levels. Along the west coasts of Europe and North America, area impacted by atmospheric rivers-induced extreme precipitation events is projected to double, driven by intensified landfalling atmospheric rivers. Thermodynamic processes drive the increase, whereas dynamic processes reduce the intensity over western Europe but enhance it along the west coast of North America. Here our findings provide policy-relevant information for climate adaptation strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The vertical-velocity skewness in the inertial sublayer of turbulent wall flows

Empirical evidence is provided that within the inertial sublayer (i.e. logarithmic region) of adiabatic turbulent flows over smooth walls, the skewness of the vertical-velocity component 𝑆 𝑤 displays universal behaviour, being a positive constant and constrained within the range 𝑆 𝑤 ≈ 0.1–0.16, regardless of flow configuration and Reynolds number. A theoretical model is then proposed to explain this behaviour, including the observed range of variations of 𝑆 𝑤 . The proposed model clarifies why 𝑆 𝑤 cannot be predicted from down-gradient closure approximations routinely employed in large-scale meteorological and climate models. The proposed model also offers an alternative and implementable approach for such large-scale models.

turbulent boundary layers↗
DOE OSTI2025

Near-term heatwave risk in HighResMIP models across different temperature zones of West Africa

This study projects near-future (2031–2050) changes in heatwave (HW) risk across West Africa (WA) using an ensemble of eight high-resolution global climate models from the High-Resolution Model Intercomparison Project under a high-emission scenario. Using K-means clustering, we divided WA into four unique temperature zones and examined projected changes in extreme temperatures, HW occurrence and magnitude. Our results indicate a statistically significant increase in future HW events across most parts of WA, although considerable spread exists over the region and among individual models. The most pronounced increases are evident in the Sahel/Sahara and the Guinea Highlands subregions, with an ensemble mean increase of ∼10 HW events per year. In contrast, the lowest increase in HW events is projected in central WA, with increases ranging between 1 and 5 events per year. Similarly, the magnitude of HW events is projected to increase in most models, with Sahel/Sahara exhibiting the largest increases. Additionally, projections suggest that the strongest HWs will become more frequent, particularly in northern and southwestern WA. These findings highlight significant spatial heterogeneity in future HW risk across WA, emphasizing the need for targeted adaptation strategies.

HighResMIP↗
DOE OSTI2024

Deep Learning-based Non-Stationary Bias Correction (NSBC)

This work develops the NSBC (non-stationary bias correction) methodology to correct temperature projection bias from E3SM. The NSBC deep learning framework consists of a three-part architecture: an auto-encoder for compressing the spatial information, an LSTM for predicting annual temperature mean, and a U-Net for capturing the residual bias in temperature. The non-stationary bias correction (NSBC) framework can correct the non-stationarity of the biases of the climate models, which significantly improves the accuracy of future temperature prediction and improves the overestimation of extreme high temperatures that many existing bias correction methods suffer from. Getting started 1. Obtain the historical climate simulation and observation data. The E3SM simulation data are available through https://aims2.llnl.gov/search/cmip6/. The pseudo observations, the Geophysical Fluid Dynamics Laboratory (GFDL)-ESM4 model (Krasting et al., 2018) are available through https://aims2.llnl.gov/search/cmip6/. The spatial resolution of E3SM and pseudo observation datasets are both regridded to a common 1° resolution grid using conservative interpolation. The regridded E3SM and pseudo observation with 1° resolution can be found throught ./data/. 2. Train the Auto-encoder model. Python 0-autoencoder.py 3. Train the LSTM Python 1-LSTM.py 4. Generate the annual mean temperature based on trained LSTM Python 2-generate_annual_mean_LSTM.py 5. Train the U-Net. Python 3-unet.py 6. Evaluation and compared with the baseline Python 4_evaluation.py Is there a deadline approaching that requires the release of yo

Lucas, Donald↗
DOE OSTI2024

Understanding the Cascade: Removing GCM Biases Improves Dynamically Downscaled Climate Projections

Polarization surrounding bias correction (BC) in creating climate projections arises from its lack of physicality. Here, we perform and analyze 18 dynamical downscaling simulations (with and without BC) to better understand the physical impacts of BC, applied before downscaling, on regional climate output across the western United States. Without BC, downscaled precipitation is systematically and unrealistically wet biased compared to a hierarchy of observationally based datasets over the 1980–2014 period due to cascading mean–state Global Climate Model (GCM) biases: (a) overly strong lower–tropospheric lapse rates (5 K/km), (b) overly cold (2 K) tropospheric temperatures, and (c) anomalous mid–tropospheric cyclonic vorticity advection. With BC, downscaled precipitation (snow) biases are virtually eliminated (halved). Identified GCM biases are common to the broader Coupled Model Intercomparison Project ensemble. Physical effects of BC on the quality of the regionalized projections, pending an evaluation of BC's distortion of the downscaled climate response, may motivate its broader application by dynamical downscalers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Can we rely on drought–ending “miracles” in the Colorado River Basin?

Unexpected and large spring precipitation events in the Colorado River Basin (CRB) that significantly alleviated an otherwise severe water shortage have been observed for over a century, such as the “Miracle May” of 2015. Although these events are often termed as “drought-busting” or “miracle events” by water managers and the media, they have not been extensively researched or characterized. In this collaborative study with water managers across the CRB, we propose a definition for these hard-to-predict, ultra-high precipitation events occurring during the late-snow or snowmelt season. This characterization provides a framework for quantifying the frequency and intensity of extreme dry-to-wet springtime transitions. Despite limitations of climate model simulations due to uncertainties and the inhomogeneous qualities, our findings suggest that such transitions may become less frequent and less intense in a warming climate. Finally, in view of the potentially wetter but less-snowy climate in the basin, the need for future research to more quantitatively assess these “miracle events” is emphasized.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Standardising the “Gregory method” for calculating equilibrium climate sensitivity

The equilibrium climate sensitivity (ECS) – the equilibrium global mean temperature response to a doubling of atmospheric CO 2 – is a high-profile metric for quantifying the Earth system's response to human-induced climate change. A widely applied approach to estimating the ECS is the “Gregory method” (Gregory et al., 2004), which uses an ordinary least squares (OLS) regression between the net radiative flux, N, and surface air temperature anomalies, ΔT, from a 150 year experiment in which atmospheric CO 2 concentrations are quadrupled. The ECS is determined by extrapolating the linear fit to N=0, i.e. the ΔT-intercept, indicating the point at which the system is back in equilibrium. This method has been used to compare ECS estimates across the CMIP5 and CMIP6 ensembles and will likely be a key diagnostic for CMIP7. Despite its widespread application, there is little consistency or transparency between studies in how the climate model data is processed prior to the regression, leading to potential discrepancies in ECS estimates. We identify 32 alternative data processing pathways, varying by differences in global mean weighting, net radiative flux variable, anomaly calculation method, and linear regression fit. Using 44 CMIP6 models, we systematically assess the impact of these choices on ECS estimates and calculate uncertainty ranges using two bootstrap approaches. While the inter-model ECS range is insensitive to the data processing pathway, individual outlier models exhibit notable differences. Approximating a model's native grid cell area (if irregular) with cosine of the latitude can decrease the ECS by 11 %, the choice of N-variable can change the ECS by 6 %, and some anomaly calculation methods can introduce spurious temporal correlations in the processed data. Beyond data processing choices, we also evaluate an alternative linear regression method – total least squares (TLS) – which has a more statistically robust basis than OLS. However, for consistency with previous literature, and given TLS may reduce the ECS compared to OLS (by up to 24 %), thereby making a known bias in the Gregory method worse, we do not feel there is sufficient clarity to recommend a transition to TLS in all cases. To improve reproducibility and comparability in future studies, we recommend a standardised Gregory method: weighting the global mean by cell area, using the top of the atmosphere (as opposed to the top of model) N-variable, and calculating anomalies by first applying a rolling average to the preindustrial control timeseries then subtracting from the raw CO 2 quadrupling experiment. This approach accounts for model drift while reducing noise in the data to best meet the pre-conditions of the linear regression. While CMIP6 results of the multi-model mean ECS appear insensitive to these processing choices, similar assumptions may not hold for CMIP7, underscoring the need for standardised data preparation in future climate sensitivity assessments.

Geosciences↗
DOE OSTI2026

Model-observation discrepancies in Arctic moisture intrusions: causes and pathways for improved simulation

Arctic moisture intrusions (MIs), narrow filaments of strong moisture transport, are key drivers of poleward moisture flux and Arctic weather extremes, yet their representation in climate models is poorly understood. Using a new Arctic MI detection algorithm, we document persistent biases across three CMIP generations (CMIP3–CMIP6): models overestimate MI occurrence over the Pacific sector and underestimate it over the Atlantic sector. These errors stem from misrepresented midlatitude westerly jets, with an equatorward North Atlantic jet associated with too few Atlantic MIs, and a poleward, weakened North Pacific jet linked to too many Pacific MIs. Experiments that correct sea surface temperature and sea ice concentration biases and increase atmospheric resolution improve jet structure and MI statistics, while a cloud-locking simulation indicates that better high-frequency cloud–radiation–circulation interactions can yield further gains. Our results clarify pathways to reducing long-standing MI and jet biases, providing guidance for improving simulations of Arctic and midlatitude climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Unified 0.25-degree gridded infrastructure-critical extreme weather for the United States from 1979 to 2100

Extreme weather events can severely disrupt critical infrastructure, triggering cascading effects on power, transportation, and essential services. However, standard weather and climate datasets often lack specialized variables necessary for hazard assessments. We present a unified dataset of infrastructure-critical weather and climate variables across the United States at 0.25° resolution, covering daily or sub-daily intervals from 1979 to 2100. The dataset includes temperature, dew point, wind gusts, precipitation partitioned by rain, snow, and freezing rain or ice pellets, lightning, and wildfire metrics. Historical conditions (1979-2023) are synthesized from observations and reanalysis products, while future projections are derived from 14 CMIP6 global climate models (historical, SSP245, and SSP585 experiments). Physically based and data-driven methods are used to estimate variables not directly provided by existing models. By integrating these variables into a single unified dataset, we enable consistent, high-resolution assessments of weather-related infrastructure risks across past and future periods, supporting wide-ranging applications in energy, transportation, water resources, emergency management, and beyond.

Climate and Earth system modelling↗
DOE OSTI2026

Exposing and Reducing Biases of Simulating Mixed-Phase Clouds in the Convection-Permitting E3SM Atmosphere Model: Lessons From an Arctic Cold-Air Outbreak

Mixed-phase clouds modulate the water and energy cycles of high-latitude regions, yet their liquid-ice phase partitioning has long been poorly simulated in climate models. Here, simulations of Arctic mixed-phase clouds by the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) are assessed against large-eddy simulations, satellite data, and ground-based observations during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment field campaign. SCREAM simulates nearly completely frozen clouds, which is attributed largely to the unreasonably strong Wegener–Bergeron–Findeisen (WBF) process that converts liquid to ice excessively and partly to the early over-abundant ice production at cold temperatures from a temperature-deterministic deposition ice nucleation scheme. Assuming no subgrid variation for the WBF process in the original formulation particularly conflicts with the instantaneous saturation adjustment assumption in the condensation scheme that assumes subgrid variability, leading to exaggerated WBF process rates. A proposed simple physically-based improvement on the treatment of subgrid cloud overlap substantially increases supercooled liquid water content and notably improves cloud-top phase partitioning, aligning better with observations. Improvement of supercooled liquid water content also converges with increasing horizontal resolution. The deposition ice nucleation scheme is found responsible for a falsely-produced ice cloud aloft that is not observed, biasing the simulated cloud radiative effects and top-of-atmosphere radiative fluxes. This study identifies key deficiencies in cloud parameterizations that continue to challenge convection-permitting models.

Geosciences↗
DOE OSTI2026

New framework for benchmarking decadal predictions leveraging the PCMDI Metric Package with interactive visualization

Reliable climate predictions across multiple timescales are increasingly critical as climate-related risks continue to rise. With the growing number and diversity of climate prediction systems, systematic intercomparison has become essential. Here, we present a comprehensive evaluation framework based on the PCMDI Metric Package to assess the performance of multiple decadal climate prediction systems. Unlike uninitialized simulations, initialized predictions exhibit bias and predictive skill that evolve with forecast lead time. To address this, we introduce (1) model-by-lead-time portrait plots, which efficiently summarize metrics of global temperature, precipitation, and Arctic/Antarctic sea-ice extent, and (2) an HTML-based interactive visualization platform that provides detailed regional and seasonal diagnostics of model bias, skill scores, and ensemble spread for each model and lead time. Comparisons with uninitialized simulations further quantify the relative impacts of initialization and external forcing on prediction skill. The proposed framework provides a scalable and transparent approach for multi-model climate prediction assessments and can be readily extended to a wide range of operational and research forecasting systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Stable Simulation of the Community Atmosphere Model Using Machine‐Learning Physical Parameterization Trained With Experience Replay

In recent years, machine learning (ML) models have been used to improve physical parameterizations of general circulation models (GCMs). A significant challenge of integrating ML models into GCMs is the online instability when they are coupled for long‐term simulation. We present a new strategy that demonstrates robust online stability when the physical parameterization package of an atmospheric GCM is replaced by a deep ML model. The method uses experience replay with a multistep training scheme of the ML model in which the model's own output at the previous time step is used in the training. Predicted physics tendencies in the replay buffer with the most recent errors in the training iterations are reused, making the ML model learn from its own errors. The training method reduces the gap between the offline and online environments of the ML model. The method is used to train the ML model as the physical parameterization of the Community Atmosphere Model (CAM5) with training data from the Multi‐scale Modeling Framework high resolution simulations. Three 6‐year online simulations of the CAM5 are carried out by using the ML physics package. The simulated spatial distributions of precipitation, surface temperature and zonally averaged atmospheric fields demonstrate overall better accuracy than that of the standard CAM5 and benchmark model even without the use of additional physical constraints or tuning. This work is the first to demonstrate a solution to address the online instability problem in climate modeling with ML physics by using experience replay.

54 ENVIRONMENTAL SCIENCES↗