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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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DOE OSTI2025

Tropical High Cloud Feedback Relationships to Climate Sensitivity

Clouds constitute a large portion of uncertainty in predictions of equilibrium climate sensitivity (ECS). While low cloud feedbacks have been the focus of intermodel studies due to their high variability among global climate models, tropical high cloud feedbacks also exhibit considerable uncertainty. Here, we apply the cloud radiative kernel technique of Zelinka et al. to 22 models across the CMIP5 and CMIP6 ensembles to survey tropical high cloud feedbacks and analyze their relationship to ECS. We find that the net high cloud feedback and its altitude and optical depth feedback components are significantly positively correlated with ECS in the tropical mean. On the other hand, the tropical mean high cloud amount feedback is not correlated with ECS. These relationships are most pronounced outside of areas of strong climatological ascent, suggesting the importance of thin cirrus feedbacks. Finally, we explore connections between high cloud feedbacks, climate sensitivity, and mean state high cloud properties. In general, high ECS models are cloudier in the upper troposphere but have a thinner high cloud population. Furthermore, we find that having more thin cirrus in the mean state relates to more positive high cloud altitude and optical depth feedbacks, and it either amplifies or dampens the high cloud amount feedback depending on the large-scale dynamical regime (amplifying in descent and dampening in ascent). In summary, our analysis highlights the importance of tropical high cloud feedbacks for driving intermodel spread in ECS and suggests that mean state high cloud characteristics might provide a unique opportunity for observationally constraining high cloud feedbacks.

atmosphere↗
DOE OSTI2022

Non‐Linear Dimensionality Reduction With a Variational Encoder Decoder to Understand Convective Processes in Climate Models

Abstract Deep learning can accurately represent sub‐grid‐scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack interpretability due to large internal dimensionality, resulting in reduced trustworthiness in these methods. Here, we use Variational Encoder Decoder structures (VED), a non‐linear dimensionality reduction technique, to learn and understand convective processes in an aquaplanet superparameterized climate model simulation, where deep convective processes are simulated explicitly. We show that similar to previous deep learning studies based on feed‐forward neural nets, the VED is capable of learning and accurately reproducing convective processes. In contrast to past work, we show this can be achieved by compressing the original information into only five latent nodes. As a result, the VED can be used to understand convective processes and delineate modes of convection through the exploration of its latent dimensions. A close investigation of the latent space enables the identification of different convective regimes: (a) stable conditions are clearly distinguished from deep convection with low outgoing longwave radiation and strong precipitation; (b) high optically thin cirrus‐like clouds are separated from low optically thick cumulus clouds; and (c) shallow convective processes are associated with large‐scale moisture content and surface diabatic heating. Our results demonstrate that VEDs can accurately represent convective processes in climate models, while enabling interpretability and better understanding of sub‐grid‐scale physical processes, paving the way to increasingly interpretable machine learning parameterizations with promising generative properties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

New Frontiers in Geoengineering Research

In late summer 2019, the ninth annual workshop of the Geoengineering Model Intercomparison Project (GeoMIP; http://climate.envsci.rutgers.edu/GeoMIP) convened at Beijing Normal University (Fig. 1). This meeting was held in conjunction with a summer school in which 89 participants from five continents learned about climate modeling of solar geoengineering, also known as climate engineering or climate intervention. Solar geoengineering refers to “radiation management” methods, which involve deliberate manipulations of the radiation budget through potential technologies such as stratospheric sulfate aerosols, marine cloud brightening, and cirrus thinning, but not CO2 removal. The students at the summer school were mainly physical science majors, with many from China, but also included some social science people. The summer school was focused on impact modeling (e.g., on agriculture and the cryosphere) and consideration of extremes, rather than just looking at climate modeloutput. There were also lectures on governance and economics. This event signifies the first major international geoengineering meeting held in Asia.

Kravitz, Benjamin S.↗
DOE OSTI2021

Reflecting Sunlight: Recommendations for Solar Geoengineering Research and Research Governance

The National Academies of Sciences, Engineering, and Medicine (NASEM) report "Reflecting Sunlight: Recommendations for Solar Geoengineering Research and Research Governance" focused on three atmospheric-based Solar Geoengineering strategies: Stratospheric Aerosol Injection, Marine Cloud Brightening, Cirrus Cloud Thinning. The study recommends that a national (and internationally-coordinated) Solar Geoengineering research program be established and coordinated by the U.S. Global Change Research Program—with a focus on developing information needed by decision makers, not advancing a path to deployment. The study recommends a detailed framework for governance of this research, at both national and international levels. The study also recommends a detailed research agenda for this program, encompassing over a dozen transdisciplinary research topics that can be loosely grouped into the categories of (i) technical dimensions and impacts, (ii) social dimensions, and (iii) context and goals for solar geoengineering research itself.

54 ENVIRONMENTAL SCIENCES↗