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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling

AI models are criticized as being black boxes, potentially subjecting climate science to greater uncertainty. Explainable artificial intelligence (XAI) has been proposed to probe AI models and increase trust. In this review and perspective paper, we suggest that, in addition to using XAI methods, AI researchers in climate science can learn from past successes in the development of physics-based dynamical climate models. Dynamical models are complex but have gained trust because their successes and failures can sometimes be attributed to specific components or sub-models, such as when model bias is explained by pointing to a particular parameterization. We propose three types of understanding as a basis to evaluate trust in dynamical and AI models alike: (1) instrumental understanding, which is obtained when a model has passed a functional test; (2) statistical understanding, obtained when researchers can make sense of the modeling results using statistical techniques to identify input–output relationships; and (3) component-level understanding, which refers to modelers' ability to point to specific model components or parts in the model architecture as the culprit for erratic model behaviors or as the crucial reason why the model functions well. We demonstrate how component-level understanding has been sought and achieved via climate model intercomparison projects over the past several decades. Such component-level understanding routinely leads to model improvements and may also serve as a template for thinking about AI-driven climate science. Currently, XAI methods can help explain the behaviors of AI models by focusing on the mapping between input and output, thereby increasing the statistical understanding of AI models. Yet, to further increase our understanding of AI models, we will have to build AI models that have interpretable components amenable to component-level understanding. We give recent examples from the AI climate science literature to highlight some recent, albeit limited, successes in achieving component-level understanding and thereby explaining model behavior. The merit of such interpretable AI models is that they serve as a stronger basis for trust in climate modeling and, by extension, downstream uses of climate model data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Physics or Knob‐Tuning? Tropical Anvil Peak Is Captured by GCMs

Tropical anvil clouds peak near 200 hPa and significantly impact Earth's climate, yet its physical realism in coarse‐resolution General Circulation Models (GCMs) remains debated. We examine anvil cloud formation by performing simulations with a GCM with a hierarchy of cloud fraction schemes ranging from a complex prognostic Tiedtke scheme to a simple binary scheme. All schemes consistently reproduce the anvil peak. The robust anvil peak arises because extremely cold temperatures at the upper troposphere facilitate frequent saturation events, producing clouds that disproportionately influence mean cloud fraction. Sensitivity experiments with enhanced evaporation of cloud condensate unexpectedly show increased anvil coverage, highlighting how slight evaporative moistening reinforces local saturation in cold upper‐tropospheric conditions. These results demonstrate that the tropical anvil cloud peak emerges from fundamental thermodynamic constraints, rather than specific cloud fraction parameterization choices.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Computational Modeling of Atmospheric Processes at Texas Southern University

Texas Southern University (TSU) is strengthening its research program in atmospheric chemistry and physics with a climate science emphasis by leveraging partnerships with the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Facility, Brookhaven National Laboratory (BNL), and the Tracking Aerosol Convection Interactions ExpeRiment (TRACER). This RDPP-supported program focuses on secondary organic aerosols (SOAs) and reactive atmospheric species that influence cloud formation, precipitation processes, and radiative forcing. SOAs play a critical role in cloud microphysics and Earth’s energy balance, yet the chemical and physical mechanisms governing SOA–cloud interactions remain a significant source of uncertainty in predictive climate models. Through computational modeling, observational data analysis, and national laboratory collaboration, this program develops a skilled cohort of students trained in atmospheric science, environmental data analysis, and climate-relevant modeling. These research experiences build technical competencies that are transferable to careers in government laboratories, academia, and industry. By engaging students from historically underrepresented communities in high-impact climate research, TSU expands participation in the atmospheric sciences workforce while contributing meaningful scientific insights to DOE-supported ARM research activities. This partnership strengthens national capacity in climate science and supports the development of the next generation of atmospheric researchers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Stratospheric aerosol injection can weaken the carbon dioxide greenhouse effect

Abstract Stratospheric aerosol injection is a proposed method for offsetting greenhouse gas-induced warming by introducing scattering aerosols into the lower stratosphere to reflect sunlight. Here we explore a potentially more efficient alternative: weakening the Earth’s greenhouse effect by deploying absorptive aerosols in the upper stratosphere (~10 hPa). These aerosols warm the carbon dioxide emission level—where outgoing longwave radiation is most sensitive to temperature—thereby enhancing top-of-atmosphere infrared emission without altering atmospheric carbon dioxide concentrations. Idealized climate model simulations indicate that this approach can reduce global temperatures an order of magnitude more efficiently per unit aerosol mass than conventional scattering-based interventions. Although based on simplified model experiments lacking interactive aerosol processes and operational constraints, our results identify a distinct physical mechanism for climate intervention, arguing for further research into the impacts—especially potential unintended side effects—of injecting absorptive aerosols into the upper stratosphere as an alternative solar radiation management strategy.

Environmental Sciences & Ecology↗
DOE OSTI2024

Data Imbalance, Uncertainty Quantification, and Transfer Learning in Data‐Driven Parameterizations: Lessons From the Emulation of Gravity Wave Momentum Transport in WACCM

Abstract Neural networks (NNs) are increasingly used for data‐driven subgrid‐scale parameterizations in weather and climate models. While NNs are powerful tools for learning complex non‐linear relationships from data, there are several challenges in using them for parameterizations. Three of these challenges are (a) data imbalance related to learning rare, often large‐amplitude, samples; (b) uncertainty quantification (UQ) of the predictions to provide an accuracy indicator; and (c) generalization to other climates, for example, those with different radiative forcings. Here, we examine the performance of methods for addressing these challenges using NN‐based emulators of the Whole Atmosphere Community Climate Model (WACCM) physics‐based gravity wave (GW) parameterizations as a test case. WACCM has complex, state‐of‐the‐art parameterizations for orography‐, convection‐, and front‐driven GWs. Convection‐ and orography‐driven GWs have significant data imbalance due to the absence of convection or orography in most grid points. We address data imbalance using resampling and/or weighted loss functions, enabling the successful emulation of parameterizations for all three sources. We demonstrate that three UQ methods (Bayesian NNs, variational auto‐encoders, and dropouts) provide ensemble spreads that correspond to accuracy during testing, offering criteria for identifying when an NN gives inaccurate predictions. Finally, we show that the accuracy of these NNs decreases for a warmer climate (4 × CO 2 ). However, their performance is significantly improved by applying transfer learning, for example, re‐training only one layer using ∼1% new data from the warmer climate. The findings of this study offer insights for developing reliable and generalizable data‐driven parameterizations for various processes, including (but not limited to) GWs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

The birth of a field through a marriage of scales: urban meteorological modeling meets regional climate modeling

Urban meteorological modeling and regional climate modeling developed largely independently, with each discipline addressing different aspects of atmospheric processes across space and time. In this review, I show that urban climate modeling did not arise as a simple scaling extension of urban meteorological modeling or an add-on to regional climate modeling, but instead emerged through the selective inheritance of complementary strengths from its parents after both disciplines reached sufficient methodological maturity. By tracing the parallel evolution of these disciplines, I demonstrate that urban climate modeling inherited the physically explicit treatment of the built environment developed within urban meteorological modeling, and the hierarchical scale translation and climatological framing that matured within regional climate modeling, allowing urban effects to influence climate-relevant outcomes. Building on this synthesis, I propose an objective, and methodologically-grounded definition of urban climate modeling that distinguishes it from urban meteorological modeling. This distinction is increasingly important as urban climate data informs decisions with long-lived societal consequences, and as ambiguity in terminology risks conflating fundamentally different modeling frameworks with distinct physical meaning and decision relevance. In addition to a clarifying definition, this review outlines a research framework for advancing urban climate modeling through scale-aware coupling strategies that preserve physically consistent urban-atmosphere interactions.

regional climate↗
DOE OSTI2024

The Potential for Solar Farms to Protect Crops Against Extreme Weather Events

With climate change posing serious risks to crop production, solar PV has the potential to protect agriculture against extreme climate events. This concept has significant implications for agrivoltaics, but large gaps exist in the physical understanding of the climate resiliency that solar PV can bring to agriculture, and current studies focus on one-off designs ideal to protect against specific climate events. This study investigates key climate events that threaten agriculture throughout the year, including winter, spring, and summer climate threats.

Williams, Henry J↗
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↗
DOE OSTI2024

An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6

This paper documents the methodology and preliminary results from a perturbed parameter ensemble (PPE) technique, where multiple parameters are varied simultaneously and the parameter values are determined with Latin hypercube sampling. This is done with the Community Atmosphere Model version 6 (CAM6), the atmospheric component of the Community Earth System Model version 2 (CESM2). We apply the PPE method to CESM2–CAM6 to understand climate sensitivity to atmospheric physics parameters. The initial simulations vary 45 parameters in the microphysics, convection, turbulence and aerosol schemes with 263 ensemble members. These atmospheric parameters are typically the most uncertain in many climate models. Control simulations and targeted simulations to understand climate forcing due to aerosols and fast climate feedbacks are analyzed. The use of various emulators is explored in the multi-dimensional space mapping input parameters to output metrics. Parameter impacts on various model outputs, such as radiation, cloud and aerosol properties, are evaluated. Machine learning is also used to probe optimal parameter values against observations. Our findings show that PPE is a valuable tool for climate uncertainty analysis. Furthermore, by varying many parameters simultaneously, we find that many different combinations of parameter values can produce results consistent with observations, and thus careful analysis of tuning is important. The CESM2–CAM6 PPE is publicly available and extensible to other configurations to address questions of other model processes in the atmosphere and other model components (e.g., coupling to the land surface).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Implementation and Evaluation of Physics-Driven Dynamic Entrainment-Mixing Parameterization in a Climate Model and Its Impact on Low-Cloud Simulation

The turbulent entrainment-mixing process in the Community Earth System Model version 1.2 (CESM1.2) is assumed to follow the extremely inhomogeneous entrainment-mixing. However, different entrainment-mixing scenarios can occur in real clouds. To address this deficiency, a unifying parameterization that represents different entrainment-mixing processes is implemented and evaluated in CESM1.2. The results indicate that the homogeneous mixing degree values simulated by the new parameterization in CESM1.2 are predominantly greater than 50%, suggesting a tendency toward homogeneous mixing. Compared to the extremely inhomogeneous mixing mechanism, the new parameterization increases the cloud droplet number concentration (Nc). More importantly, the new parameterization improves low-cloud fraction (CLDLOW) simulation in Northwest Pacific (NWP) and Southeast Pacific (SEP) regions, with relative improvements of 2.95% and 4.17%, respectively. Furthermore, the improvements reach up to 44.6% and 16.2% in the NWP and SEP regions, respectively, when considering the relationship between N c and CLDLOW. Further analysis reveals that the new parameterization enhances cloud optical depth, longwave radiative cooling effect, net condensation rate, cloud water mixing ratio, lower-troposphere stability, and CLDLOW by increasing N c . Additionally, these results underscore the importance of improving entrainment-mixing parameterization in climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Seventh Oregon Climate Assessment

Consistent with its charge under Oregon House Bill 3543, the Oregon Climate Change Research Institute (OCCRI) conducts a biennial assessment of the state of climate change science, including biological, physical, and social science, as it relates to Oregon and the likely effects of climate change on Oregon. This seventh Oregon Climate Assessment, which builds on the previous assessments, is structured with the goal of supporting the state's mitigation planning for natural hazards and implementation of the Oregon Climate Change Adaptation Framework.

17 WIND ENERGY↗
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 OSTI2025

Increased frequency of planetary wave resonance events over the past half-century

We demonstrate a tripling in the frequency of planetary wave resonance events over the past halfcentury, coinciding with the rise in persistent boreal summer weather extremes. This increase aligns with changes in the underlying climate conditions favoring these events, including amplified Arctic warming and land-sea thermal contrast. We also observe increased prevalence of resonant amplification events following the mature phase of strong El Niño events, suggesting that such events may precondition the mean state conditions in ways that favor large-scale quasi-stationary wave patterns and quasi-resonant wave amplification. Since the impact of anthropogenic warming on quasi-resonant amplification is not well captured by current-generation climate models, it is likely that models are underpredicting the potential increase, indicating even greater risk of persistent extreme summer weather events with ongoing warming.

Arctic amplification↗
DOE OSTI2026

ARM Data-Oriented Metrics and Diagnostics Package for Climate Model Evaluation

A Python-based metrics and diagnostics package is currently being developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Infrastructure Team at Lawrence Livermore National Laboratory (LLNL) to facilitate the use of long-term, high-frequency measurements from the ARM Facility in evaluating the regional climate simulation of clouds, radiation, and precipitation. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The Coupled Model Intercomparison Project (CMIP) model data sets are also included in the package to enable model intercomparison as demonstrated in Zhang et al. (2017). The mean of the CMIP model can serve as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, and radiative fluxes, with plan to extend to more fields, such as aerosol and microphysics properties. Process-oriented diagnostics focusing on individual cloud- and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. The version 1.0 package is designed based on data collected at ARM’s Southern Great Plains (SGP) Research Facility, with the plan to extend to other ARM sites. The metrics and diagnostics package is currently built upon standard Python libraries and additional Python packages developed by DOE (such as CDMS and CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first present the input data, which constitutes the core content of the metrics and diagnostics package in section 2, and a user's guide documenting the workflow/structure of the version 1.0 codes, and including step-by-step instruction for running the package in section 3.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Spatio-temporal multivariate cluster evolution analysis for detecting and tracking climate impacts

Recent years have seen a growing concern about climate change and its impacts. While Earth System Models (ESMs) can be invaluable tools for studying the impacts of climate change, the complex coupling processes encoded in ESMs and the large amounts of data produced by these models, together with the high internal variability of the Earth system, can obscure important source-to-impact relationships. Here, this paper presents a novel and efficient unsupervised data-driven approach for detecting statistically-significant impacts and tracing spatio-temporal source-impact pathways in the climate through a unique combination of ideas from anomaly detection, clustering and Natural Language Processing (NLP). Using as an exemplar the 1991 eruption of Mount Pinatubo in the Philippines, we demonstrate that the proposed approach is capable of detecting known post-eruption impacts/events. We additionally describe a methodology for extracting meaningful sequences of post-eruption impacts/events by using NLP to efficiently mine frequent multivariate cluster evolutions, which can be used to confirm or discover the chain of physical processes between a climate source and its impact(s).

Anomaly detection↗
DOE OSTI2024

Regional climate change: consensus, discrepancies, and ways forward

Climate change has emerged across many regions. Some observed regional climate changes, such as amplified Arctic warming and land-sea warming contrasts have been predicted by climate models. However, many other observed regional changes, such as changes in tropical sea surface temperature and monsoon rainfall are not well simulated by climate model ensembles even when taking into account natural internal variability and structural uncertainties in the response of models to anthropogenic radiative forcing. This suggests climate model predictions may not fully reflect what our future will look like. The discrepancies between models and observations are not well understood due to several real and apparent puzzles and limitations such as the “signal-to-noise paradox” and real-world record-shattering extremes falling outside of the possible range predicted by models. Addressing these discrepancies, puzzles and limitations is essential, because understanding and reliably predicting regional climate change is necessary in order to communicate effectively about the underlying drivers of change, provide reliable information to stakeholders, enable societies to adapt, and increase resilience and reduce vulnerability. The challenges of achieving this are greater in the Global South, especially because of the lack of observational data over long time periods and a lack of scientific focus on Global South climate change. To address discrepancies between observations and models, it is important to prioritize resources for understanding regional climate predictions and analyzing where and why models and observations disagree via testing hypotheses of drivers of biases using observations and models. Gaps in understanding can be discovered and filled by exploiting new tools, such as artificial intelligence/machine learning, high-resolution models, new modeling experiments in the model hierarchy, better quantification of forcing, and new observations. Conscious efforts are needed toward creating opportunities that allow regional experts, particularly those from the Global South, to take the lead in regional climate research. This includes co-learning in technical aspects of analyzing simulations and in the physics and dynamics of regional climate change. Finally, improved methods of regional climate communication are needed, which account for the underlying uncertainties, in order to provide reliable and actionable information to stakeholders and the media.

54 ENVIRONMENTAL SCIENCES↗