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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Enhancing Discoverability and Management of Atmospheric Data at Scale: Solutions from the ARM Data Center

The Atmospheric Radiation Measurement (ARM) is a multi-laboratory and multi-institutional U.S. Department of Energy (DOE) Office of Science National User Facility. The ARM Data Center (ADC), located at Oak Ridge National Laboratory, collects, archives, and shares vast atmospheric data crucial for climate research. The ADC manages over 7 PB of data from 460 instruments worldwide, processing it into more than 11,000 diverse data products using the Network Common Data Form (NetCDF) for machine-independent accessibility. The primary challenge addressed in this paper is the efficient management and distribution of vast and diverse datasets essential for the climate research community, enhancing accessibility through advanced tools like Data Discovery. The ADC has developed advanced infrastructure and software architecture to handle the continuous influx of heterogeneous data to enhance data discoverability, resulting in increased scientific collaboration. In 2023, users from over 34 countries downloaded and utilized ARM data, resulting in 1,455 publications. The ADC’s efforts have significantly improved the discoverability and usability of atmospheric data, fostering extensive scientific research and collaboration. This paper details the solutions implemented by the ADC team for efficient data discovery and distribution, and it demonstrates ARM’s capability of staging processed data for scientific analysis.

Shah, Chirag [ORNL] (ORCID:0000000203145737)↗
DOE OSTI2024

Research to Confront Climate Change Complexity: Intersectionality, Integration, and Innovative Governance

Abstract Climate impacts increasingly unfold in interlinked systems of people, nature, and infrastructure. The cascading consequences are revealing sometimes surprising connections across sectors and regions, and prospects for climate responses also depend on complex, difficult‐to‐understand interactions. In this commentary, we build on the innovations of the United States Fifth National Climate Assessment to suggest a framework for understanding and responding to complex climate challenges. This approach involves: (a) integration of disciplines and expertise to understand how intersectionality shapes complex climate impacts and the wide‐ranging effects of climate responses, (b) collaborations among diverse knowledge holders to improve responses and better encompass intersectionality, and (c) sustained experimentation with and learning about governance approaches capable of handling the complexity of climate change. Together, these three pillars underscore that usability of climate‐relevant knowledge requires transdisciplinary coordination of research and practice. We outline actionable steps for climate research to incorporate intersectionality, integration, and innovative governance, as is increasingly necessary for confronting climate complexity and sustaining equitable, ideally vibrant climate futures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Towards provision of regularly updated climate data from the Coupled Model Intercomparison Project

The Coupled Model Intercomparison Project (CMIP) is a flagship of the World Climate Research Programme (WCRP). CMIP has become a recognised ‘brand’ in climate circles evolving over the last thirty years from a targeted research activity by a small number of climate modelling centres intercomparing their Earth System Model (ESM) simulations to a broad international coordinated research effort (Durack et al, 2025). CMIP is organized as a research activity leveraging funded and in-kind contributions from experts within modelling centres and the broader scientific community supported more recently by a fully-funded International Project Office. Within CMIP, Model Intercomparison Projects (MIPs) are community-designed to understand past, present and future climate. CMIP data provides a valuable resource for climate research and is routinely used to assess model representation of climate processes and test scientific hypotheses in the context of model uncertainty and (forced and internal) variability as evident from its prolific use in scientific publications1 . The impact relies on enabling infrastructure (most prominently via the Earth System Grid Federation (ESGF)), which allows sharing of simulation output, provision of the boundary conditions used in each simulation, and definition of the data standards that are essential to facilitating wide use of the data. The impact is supplemented by the wide-ranging scrutiny to which model simulations are subjected. Beyond its use in research, CMIP data is a key resource for communities producing derived climate information from downscaling and impact studies, such as the Coordinated Regional Downscaling Experiment (CORDEX; Gutowski et al., 2016) and the Intersectoral Impacts MIP (ISIMIP; Frieler et al., 2024). Government, academic and commercial entities also increasingly rely on CMIP and its downstream data for climate risk assessments and climate services (for example, Copernicus Climate Change Service and World Bank portal). This means that, although CMIP is a research activity, it increasingly serves a secondary and very relevant role as a provider of climate data – a long-recognised dichotomy (Stevens, 2024). Research and applications have distinct needs, with the former requiring flexibility and generality and the latter consistency. Here we explain how the design of the research activity has been adapted to reduce the burdens imposed by applications and how the research infrastructure might evolve to further enable scientific inquiry. We propose one possible approach to consistently providing model information and projections for applications in the future.

Environmental sciences↗
DOE OSTI2020

Evaluation of Methods for Causal Discovery in Hydrometeorological Systems

Understanding causal relations is of utmost importance in hydrology and climate research for systems identification, prediction, and understanding systems behavior in a changing climate. Traditionally, researchers in hydrometeorology attempted to study causal questions by conducting controlled experiments using numerical models. This approach, however, in most cases of interest provides uncertain results because the models are approximate representation of the natural system. An alternative approach that has recently drawn significant attention in several fields is to infer causal relations from purely observational data. It possesses several traits to its utility particularly in hydrometeorology due to the rapid accumulation of in situ and remotely sensed data records. The first objective of this study is to present a brief description of four causal discovery methods (Granger causality, Transfer Entropy, graph-based algorithms, and Convergent Cross Mapping) with special emphasis on the assumptions on which they are built. Second, using synthetic data generated from a hydrological model, we assess their performance in retrieving causal information taking into account sensitivity to sample size and presence of noise. Last, we use causal analysis to examine and formulate hypotheses on causal drivers of evapotranspiration in a shrubland region during summer and winter seasons. An interpretation of the hypotheses based on canopy seasonal dynamics and evapotranspiration processes is presented. It is hoped that the results presented here can be useful in guiding researchers studying hydrometeorological systems as to which causal method is most appropriate to the characteristics of the system under study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Microbes and Climate Change: a Research Prospectus for the Future

Climate change is the most serious challenge facing humanity. Microbes produce and consume three major greenhouse gases—carbon dioxide, methane, and nitrous oxide—and some microbes cause human, animal, and plant diseases that can be exacerbated by climate change. Hence, microbial research is needed to help ameliorate the warming trajectory and cascading effects resulting from heat, drought, and severe storms. We present a brief summary of what is known about microbial responses to climate change in three major ecosystems: terrestrial, ocean, and urban. We also offer suggestions for new research directions to reduce microbial greenhouse gases and mitigate the pathogenic impacts of microbes. These include performing more controlled studies on the climate impact on microbial processes, system interdependencies, and responses to human interventions, using microbes and their carbon and nitrogen transformations for useful stable products, improving microbial process data for climate models, and taking the One Health approach to study microbes and climate change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

The Need for Urban‐Resolving Climate Modeling Across Scales

Abstract Increasing urbanization, evolving urban landscapes, and growing populations require an accurate representation of urban areas and urban processes at microscale, regional, and global scales and their feedback across the scales. Important urban processes should be represented in climate models, and current approaches used are discussed. We discuss strategies for urban‐resolving climate research to capture the effect of climate on urban regions and the non‐linear urban processes on climate. Advancing urban‐resolving climate modeling will take us closer to capabilities that can truly capture relevant natural and human components.

Sharma, Ashish↗
DOE OSTI2026

American cities in a time of global environmental change: the case of the Baltimore Social-Environmental Collaborative

The Baltimore Social-Environmental Collaborative (BSEC) Urban Integrated Field Laboratory seeks a new paradigm for urban climate research. Motivated by deep uncertainties in urban climate and the future of urban systems, BSEC works collaboratively across institutions and stakeholder groups to co-generate the science needed to advance energy security and resilience to extreme events across the city of Baltimore, Maryland, USA, and to do so in a manner that can inform similar efforts in other cities. BSEC begins with stakeholder priorities (health, affordable energy, etc) and designs observation networks and models to deliver climate science to address them. This takes the form of an iterative collaborative cycle, in which an initial research strategy is repeatedly updated in conversation with community partners, and researchers and stakeholders learn from each other. To date, this cycle has included multiple rounds of collaborative deliberation on urban heat mitigation, in which a multicriteria decision tool has been updated with more community-relevant spatial structure and modified optimization metrics. The guiding objective of this cycle is to inform potential ‘secure and resilient pathways’ for energy and infrastructure. In doing so, BSEC addresses fundamental urban science questions in natural and social sciences. It also tests our ability to integrate this science in a manner that advances participatory decision-making for urban resilience.

climate↗
DOE OSTI2025

MSD CoP Webinar: "Generative agents: A new frontier for representing human actors and their behavior in MSD models"

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Talk #1: Behavioral Generative Agents for Energy Operations Presenter: Dr. Cong Chen (Thayer School of Engineering, Dartmouth College) Abstract: Accurately modeling consumer behavior in energy operations remains challenging due to inherent uncertainties, behavioral complexities, and limited empirical data. This talk introduces a novel approach leveraging generative agents--artificial agents powered by large language models--to realistically simulate customer decision-making in dynamic energy operations. Talk #2: Simulating multiple human perspectives in socio-ecological systems using large language models Presenter: Dr. Yongchao Zeng (Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU) of the Karlsruhe Institute of Technology in Germany) Abstract: Understanding socio-ecological systems requires insights from diverse stakeholder perspectives. This talk describes a novel simulation system called HoPeS (Human-oriented Perspective Shifting). HoPeS enables model users to not only explore simulated socio-ecological systems (SESs) from a third-person observer's perspective but also take any of the simulated stakeholder roles, like playing an RPG game. By shifting multiple perspectives, model users can reflect and integrate the situated knowledge learned through the participatory simulation, approximating a more holistic and less biased understanding of SESs. Moderators: Jim Yoon (MSD CoP Human Systems Modeling Working Group Co-Chair); Stefano Galelli (MSD CoP Using AI to Enhance MSD Research Working Group Co-Chair); Patrick M. Reed (MSD CoP Facilitation Team) This webinar was held on: November 13th, 2025 from 12-1 PM EST.

Artificial Intelligence↗
DOE OSTI2023

Water isotopes, climate variability, and the hydrological cycle: recent advances and new frontiers

Abstract The hydrologic cycle is a fundamental component of the climate system with critical societal and ecological relevance. Yet gaps persist in our understanding of water fluxes and their response to increased greenhouse gas forcing. The stable isotope ratios of oxygen and hydrogen in water provide a unique opportunity to evaluate hydrological processes and investigate their role in the variability of the climate system and its sensitivity to change. Water isotopes also form the basis of many paleoclimate proxies in a variety of archives, including ice cores, lake and marine sediments, corals, and speleothems. These records hold most of the available information about past hydrologic variability prior to instrumental observations. Water isotopes thus provide a ‘common currency’ that links paleoclimate archives to modern observations, allowing us to evaluate hydrologic processes and their effects on climate variability on a wide range of time and length scales. Building on previous literature summarizing advancements in water isotopic measurements and modeling and describe water isotopic applications for understanding hydrological processes, this topical review reflects on new insights about climate variability from isotopic studies. We highlight new work and opportunities to enhance our understanding and predictive skill and offer a set of recommendations to advance observational and model-based tools for climate research. Finally, we highlight opportunities to better constrain climate sensitivity and identify anthropogenically-driven hydrologic changes within the inherently noisy background of natural climate variability.

Dee, Sylvia (ORCID:000000022140785X)↗
DOE OSTI2022

Understanding Recent Global Hydroclimate Change Using Multivariate Detection and Attribution Techniques and GCM Experiments

The observed slowdown in the rate of global average surface temperature (T) warming during the post-1998 decade (referred to at the time as the “global warming hiatus”) posed a challenge for climate researchers because it was not captured in global climate models projection. Observational and modeling evidence hinted that that leveling of the T trend was linked with the simultaneous appearance of cold surface temperature in the eastern equatorial Pacific that formed a protracted La Niña-like state, a phenomenon known as the Inter-decadal Pacific Oscillation (IPO). It was however not clear whether the concurrent equatorial Pacific cooling was a regional response to the rise in atmospheric greenhouse gas levels or anthropogenic aerosols that is not captured by the climate models or whether it was an unrelated, serendipitous manifestation of a natural phenomenon, internal to the climate system. To resolve this issue, we proposed in this project to develop and apply fingerprinting methods to differentiate between the observed climate response to various external forcing agents and the manifestations of internal climate variability. The project also sought to find out if climate models are able to reproduce the change signals detected in key observational phenomena and if they can be used to investigate the underlying processes. As such we examined changes in global hydroclimate variability and in an outstanding example of regional hydroclimate change, namely the multi-decadal 20 th century change in Sahel rainfall.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset

CRU TS (Climatic Research Unit gridded Time Series) is a widely used climate dataset on a 0.5° latitude by 0.5° longitude grid over all land domains of the world except Antarctica. It is derived by the interpolation of monthly climate anomalies from extensive networks of weather station observations. Here we describe the construction of a major new version, CRU TS v4. It is updated to span 1901–2018 by the inclusion of additional station observations, and it will be updated annually. The interpolation process has been changed to use angular-distance weighting (ADW), and the production of secondary variables has been revised to better suit this approach. This implementation of ADW provides improved traceability between each gridded value and the input observations, and allows more informative diagnostics that dataset users can utilise to assess how dataset quality might vary geographically.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Wetlands Delineation Report and Classification: PNNL – Sequim (formerly MSL) Estuary Wetland Delineation

The Pacific Northwest National Laboratory (PNNL) – Sequim, historically known as the Marine Sciences Laboratory (MSL) in Sequim, Washington, is managed and operated by Battelle on behalf of the U.S. Department of Energy (DOE) Pacific Northwest Site Office (PNSO). The site provides capabilities for future energy research, climate change effects analyses, wetland and coastal ecosystem restoration, other environmental research involving marine resources and hosts the only marine research facilities in the Department of Energy National Laboratory Complex. In order to support campus development, maintenance, and potential research activities, a wetland delineation was conducted on the northern portion of campus in accordance with state and federal wetland regulations. This technical report details out the delineation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

MSD CoP Webinar: Confronting Climate Change Complexity: Intersectionality, Integration, and Innovative Governance

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Abstract: Climate impacts increasingly unfold in interlinked systems of people, nature, and infrastructure. The cascading consequences are revealing sometimes surprising connections, and prospects for climate responses also depend on complex interactions. In this presentation, we will introduce key findings from the United States Fifth National Climate Assessment and build upon them to suggest a framework for understanding and responding to complex climate challenges. This approach involves: (1) integration of disciplines and expertise to understand how intersectionality shapes complex climate impacts and the wide-ranging effects of climate responses, (2) collaborations among diverse knowledge holders to improve responses and better encompass intersectionality, and (3) sustained experimentation with and learning about governance approaches capable of handling the complexity of climate change. We outline actionable steps for climate research to incorporate these pillars, as is increasingly necessary for confronting climate complexity and sustaining equitable, ideally vibrant climate futures. Presenters : Katharine J. Mach, Kripa Jagannathan, Carlos E. Martín, and Linda Shi Moderator: Pat M. Reed (MSD CoP Facilitation Team) This webinar was held on: March 28th, 2024 from 1-2 PM ET

Mach, Katharine J.↗
DOE OSTI2020

Projected increase in compound dry and hot events over global land areas

Abstract Under global warming, dry and hot events have been increasing in recent decades and are projected to increase in the future across global land areas. The impacts of compound dry and hot events may lead to increased stress to the natural and human systems than separate dry or hot events. Thus, quantitative assessments of global land areas affected by these compound events are needed to understand their risks. This study focuses on the variation in global land and cropland areas affected by compound dry and hot events for both historical and future periods using observations from Climatic Research Unit (CRU) and simulations from Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. Based on historical observations and simulations, a substantial increase in the spatial extent of these compound events was detected, especially since the 1980s. Climate model projections under the Representative Concentration Pathway (RCP) 8.5 scenario reveal that both global land and cropland areas affected by compound dry and hot events will increase to approximately 1.7–1.8 times by the end of the 21st century. Based on different thresholds of compound events, the spatial extent of global land areas during the June–July–August (December–January–February) season will increase by 12.38–17.20% (7.83–11.19%) in 2050–2099 relative to that in 1950–1999, and the spatial extent of global cropland areas will increase by 14.69–19.63% (9.60–14.48%). The increase in areas affected by compound dry and hot events may lead to more losses in different sectors in the future and more efforts are needed to cope with their potential impacts.

Wu, Xinying↗
DOE OSTI2020

Summertime Post-Cold-Frontal Marine Stratocumulus Transition Processes over the Eastern North Atlantic

Abstract Marine boundary layer (MBL) cloud morphology associated with two summertime cold fronts over the eastern North Atlantic (ENA) is investigated using high-resolution simulations from the Weather Research and Forecasting (WRF) Model and observations from the Atmospheric Radiation Measurement (ARM) ENA Climate Research Facility. Lagrangian trajectories are used to study the evolution of post-cold-frontal MBL clouds from solid stratocumulus to broken cumulus. Clouds within specified domains in the vicinity of transitions are classified according to their degree of decoupling, and cloud-base and cloud-top breakup processes are evaluated. The Lagrangian derivative of the surface latent heat flux is found to be strongly correlated with that of the cloud fraction at cloud base in the simulations. Cloud-top entrainment instability (CTEI) is shown to operate only in the decoupled MBL. A new indicator of inversion strength at cloud top that employs the vertical gradients of equivalent potential temperature and saturation equivalent potential temperature, which can be computed directly from soundings, is proposed as an alternative to CTEI. Overall, results suggest that the deepening–warming hypothesis suggested by Bretherton and Wyant explains many of the characteristics of the summertime postfrontal MBL evolution of cloud structure over the ENA, thereby widening the phase space over which the hypothesis may be applied. A subset of the deepening–warming hypothesis involving warming initially dominating over moistening is proposed. It is postulated that changes in climate change–induced modifications in cold-frontal structure over the ENA may be accompanied by coincident changes in the location and timing of MBL cloud transitions in the post-cold-frontal environment.

54 ENVIRONMENTAL SCIENCES↗
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↗