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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

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 OSTI2012

“Super-parameterization”: A better way to simulate regional extreme precipitation?

Extreme precipitation is generally underestimated by current climate models relative to observations of present-day rainfall distributions. Possible causes of this systematic error include the convective parameterization in these models that have been designed to reproduce measurements of climatological mean precipitation. One possible approach to improve the interaction of subgrid-scale physical processes and large-scale climate is to replace the conventional convective parameterizations with a high-resolution cloud-system resolving model. A "super-parameterized" Community Atmosphere Model (SP-CAM) utilizing this approach is used in this study to investigate the distribution of extreme precipitation in the United States. Results show that SP-CAM better simulates the distributions of both light and intense precipitation compared to the standard version of CAM based upon conventional parameterizations. The improvements are mostly seen in regions dominated by convective precipitation, suggesting that super-parameterization provides a better representation of subgrid convective processes.

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 OSTI2021

Summertime low clouds mediate the impact of the large-scale circulation on Arctic sea ice

The rapid Arctic sea ice retreat in the early 21 st century is believed to be driven by several dynamic and thermodynamic feedbacks, such as ice-albedo feedback and water vapor feedback. However, the role of clouds in these feedbacks remains unclear since the causality between clouds and these processes is complex. Here, we use NASA CERES satellite products and NCAR CESM model simulations to suggest that summertime low clouds have played an important role in driving sea ice melt by amplifying the adiabatic warming induced by a stronger anticyclonic circulation aloft. The upper-level high pressure regulates low clouds through stronger downward motion and increasing lower troposphere relative humidity. The increased low clouds favor more sea ice melt via emitting stronger longwave radiation. Then decreased surface albedo triggers a positive ice-albedo feedback, which further enhances sea ice melt. Considering the importance of summertime low clouds, accurate simulation of this process is a prerequisite for climate models to produce reliable future projections of Arctic sea ice.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2011

Modeling Impacts of Climate Change Mitigation Technologies on Power Grids

This paper describes a modeling approach that simulate the impacts of different climate change mitigation technologies on power grids for power system planning purposes. Because the historical data is less credible when new technologies are being deployed to the system, it is then critical to model them to address their impacts. This paper illustrated how to integrate modeling results obtained from different modeling tools to give a reasonable forecast of the future. Building simulation tools, distribution power grid modeling tools, and power system planning tools are used to model and aggregate the impacts from the end-use to the system level. Electricity generation, production cost, emission, and transmission congestions are used to quantify the influence of different mitigation technologies. Modeling results have shown that the cross-discipline modeling approach provided the modeler with the necessary time resolution and input details to address the variables that influence the modeling results. Different modeling issues are also addressed in the paper.

24 POWER TRANSMISSION AND DISTRIBUTION↗
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 OSTI2015

Multi‐model analysis of the Atlantic influence on Southern Amazon rainfall

Abstract Amazon rainfall is subject to year‐to‐year fluctuation resulting in drought and flood in various intensities. A major climatic driver of the interannual variation of the Amazon rainfall is El Niño/Southern Oscillation. Also, the sea surface temperature over the Atlantic Ocean is identified as an important climatic driver on the Amazon water cycle. Previously, observational data sets were used to support the Atlantic influence on Amazon rainfall. Here, it is found that multiple global climate models do reproduce the Atlantic–Amazon link robustly. However, there exist differences in rainfall response, which primarily depends on the climatological rainfall amount.

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 OSTI2018

Drivers of the Low-Cloud Response to Poleward Jet Shifts in the North Pacific in Observations and Models

The long-standing expectation that poleward shifts of the midlatitude jet under global warming will lead to poleward shifts of clouds and a positive radiative feedback on the climate system has been shown to be misguided by several recent studies. On interannual time scales, free-tropospheric clouds are observed to shift along with the jet, but low clouds increase across a broad expanse of the North Pacific Ocean basin, resulting in negligible changes in total cloud fraction and top-of-atmosphere radiation. Here it is shown that this low-cloud response is consistent across eight independent satellite-derived cloud products. Using multiple linear regression, it is demonstrated that the spatial pattern and magnitude of the low-cloud-coverage response is primarily driven by anomalous surface temperature advection. In the eastern North Pacific, anomalous cold advection by anomalous northerly surface winds enhances sensible and latent heat fluxes from the ocean into the boundary layer, resulting in large increases in low-cloud coverage. Local increases in low-level stability make a smaller contribution to this low-cloud increase. Despite closely capturing the observed response of large-scale meteorology to jet shifts, global climate models largely fail to capture the observed response of clouds and radiation to interannual jet shifts because they systematically underestimate how sensitive low clouds are to surface temperature advection, and to a lesser extent, low-level stability. More realistic model simulations of cloud–radiation–jet interactions require that parameterizations more accurately capture the sensitivity of low clouds to surface temperature advection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Final Technical Report of ASR project entitled “ARM Observations for the Development and Evaluation of Models and Parameterizations of Cloudy Boundary Layers” (DE-SC0000825)

This project aims to elucidate the processes governing boundary layer clouds and improve the treatment of cloud processes in Global Climate Models (GCMs). Specifically, we have made research effort in following areas: (1) Developing novel numerical approach of using multiple scale Weather Research & Forecasting (WRF) model simulations for boundary layer cloud research; (2) Addressing issues of PDF schemes for parameterizing sub-grid scale cloud radiative properties; (3) Investigating the impact of mesoscale cloud organizations on the evolution of boundary layer clouds; (4) Evaluating parameterizations of the cumulus induced vertical transport; (5) Limited area model (LAM) intercomparison study of TWP-ICE convective case; (6) Investigating convective invigoration processes at shallow cumulus cold poll boundaries; and (7) Investigating vertical transport processes in moist convection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

GANpiler

Focal Area(s): Rather than augment or replace physical models with machine learning, we instead preserve the existing models and augment the underlying code with faster surrogate models created by generative adversarial networks (GAN). To leverage the performance optimization, we also propose a runtime system and user interfaces that allow prediction and tracking of accumulated error as well as dynamic, per-process decision making as to which model (if any) to use for each iteration. Science Challenge: This proposal sits at the nexus of two hard problems. First, climate models based on machine learning will be, by their nature, difficult to trust once their predictions begin diverging from the consensus. Second, compilers and hardware have been making only incremental performance gains for decades. GPGPUs have provided a welcome performance boost for codes that can take advantage of them, but there is no similar technology on the horizon to provide the next performance leap.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Preliminary Results for Using Uncertainty and Out-of-distribution Detection to Identify Unreliable Predictions.

As machine learning (ML) models are deployed into an ever-diversifying set of application spaces, ranging from self-driving cars to cybersecurity to climate modeling, the need to carefully evaluate model credibility becomes increasingly important. Uncertainty quantification (UQ) provides important information about the ability of a learned model to make sound predictions, often with respect to individual test cases. However, most UQ methods for ML are themselves data-driven and therefore susceptible to the same knowledge gaps as the models themselves. Specifically, UQ helps to identify points near decision boundaries where the models fit the data poorly, yet predictions can score as certain for points that are under-represented by the training data and thus out-of-distribution (OOD). One method for evaluating the quality of both ML models and their associated uncertainty estimates is out-of-distribution detection (OODD). We combine OODD with UQ to provide insights into the reliability of the individual predictions made by an ML model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Analysis of Neural Networks as Random Dynamical Systems

In this report we present our findings and outcomes of the NNRDS (analysis of Neural Networks as Random Dynamical Systems) project. The work is largely motivated by the analogy of a large class of neural networks (NNs) with a discretized ordinary differential equation (ODE) schemes. Namely, residual NNs, or ResNets, can be viewed as a discretization of neural ODEs (NODEs) where the NN depth plays the role of the time evolution. We employ several legacy tools from ODE theory, such as stiffness, nonlocality, autonomicity, to enable regularization of ResNets thus improving their generalization capabilities. Furthermore, armed with NN analysis tools borrowed from the ODE theory, we are able to efficiently augment NN predictions with uncertainty overcoming wellknown dimensionality challenges and adding a degree of trust towards NN predictions. Finally, we have developed a Python library QUiNN (Quantification of Uncertainties in Neural Networks) that incorporates improved-architecture ResNets, besides classical feed-forward NNs, and contains wrappers to PyTorch NN models enabling several major classes of uncertainty quantification methods for NNs. Besides synthetic problems, we demonstrate the methods on datasets from climate modeling and materials science.

97 MATHEMATICS AND COMPUTING↗
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 OSTI2015

Carbon isotopes in the ocean model of the Community Earth System Model (CESM1)

Carbon isotopes in the ocean are frequently used as paleoclimate proxies and as present-day geochemical ocean tracers. In order to allow a more direct comparison of climate model results with this large and currently underutilized data set, we added a carbon isotope module to the ocean model of the Community Earth System Model (CESM), containing the cycling of the stable isotope 13 C and the radioactive isotope 14 C. We implemented the 14 C tracer in two ways: in the "abiotic" case, the 14 C tracer is only subject to air–sea gas exchange, physical transport, and radioactive decay, while in the "biotic" version, the 14 C additionally follows the 13 C tracer through all biogeochemical and ecological processes. Thus, the abiotic 14 C tracer can be run without the ecosystem module, requiring significantly fewer computational resources. The carbon isotope module calculates the carbon isotopic fractionation during gas exchange, photosynthesis, and calcium carbonate formation, while any subsequent biological process such as remineralization as well as any external inputs are assumed to occur without fractionation. Given the uncertainty associated with the biological fractionation during photosynthesis, we implemented and tested three parameterizations of different complexity. Compared to present-day observations, the model is able to simulate the oceanic 14 C bomb uptake and the 13 C Suess effect reasonably well compared to observations and other model studies. Lastly, at the same time, the carbon isotopes reveal biases in the physical model, for example, too sluggish ventilation of the deep Pacific Ocean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

MACv2-SP: a parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6

A simple plume implementation of the second version (v2) of the Max Planck Institute Aerosol Climatology, MACv2-SP, is described. MACv2-SP provides a prescription of anthropogenic aerosol optical properties and an associated Twomey effect. It was created to provide a harmonized description of post-1850 anthropogenic aerosol radiative forcing for climate modeling studies. MACv2-SP has been designed to be easy to implement, change and use, and thereby enable studies exploring the climatic effects of different patterns of aerosol radiative forcing, including a Twomey effect. MACv2-SP is formulated in terms of nine spatial plumes associated with different major anthropogenic source regions. The shape of the plumes is fit to the Max Planck Institute Aerosol Climatology, version 2, whose present-day (2005) distribution is anchored by surface-based observations. Two types of plumes are considered: one predominantly associated with biomass burning, the other with industrial emissions. These differ in the prescription of their annual cycle and in their optical properties, thereby implicitly accounting for different contributions of absorbing aerosol to the different plumes. A Twomey effect for each plume is prescribed as a change in the host model's background cloud-droplet population density using relationships derived from satellite data. Year-to-year variations in the amplitude of the plumes over the historical period (1850–2016) are derived by scaling the plumes with associated national emission sources of SO 2 and NH 3 . Experiments using MACv2-SP are performed with the Max Planck Institute Earth System Model. The globally and annually averaged instantaneous and effective aerosol radiative forcings are estimated to be -0.6 and -0.5 W m -2 , respectively. Forcing from aerosol–cloud interactions (the Twomey effect) offsets the reduction of clear-sky forcing by clouds, so that the net effect of clouds on the aerosol forcing is small; hence, the clear-sky forcing, which is more readily measurable, provides a good estimate of the total aerosol forcing.

Stevens, Bjorn↗
DOE OSTI2018

Decline and poleward shift in Indian summer monsoon synoptic activity in a warming climate

Cyclonic atmospheric vortices of varying intensity, collectively known as low-pressure systems (LPS), travel northwest across central India and produce more than half of the precipitation received by that fertile region and its ~600 million inhabitants. Yet, future changes in LPS activity are poorly understood, due in part to inadequate representation of these storms in current climate models. Using a high-resolution atmospheric general circulation model that realistically simulates the genesis distribution of LPS, here we show that Indian monsoon LPS activity declines about 45% by the late 21st century in simulations of a business-as-usual emission scenario. The distribution of LPS genesis shifts poleward as it weakens, with oceanic genesis decreasing by ~60% and continental genesis increasing by ~10%; over land the increase in storm counts is accompanied by a shift toward lower storm wind speeds. The weakening and poleward shift of the genesis distribution in a warmer climate are confirmed and attributed, via a statistical model, to the reduction and poleward shift of low-level absolute vorticity over the monsoon region, which in turn are robust features of most coupled model projections. The poleward shift in LPS activity results in an increased frequency of extreme precipitation events over northern India.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Stochastic nucleation processes and substrate abundance explain time-dependent freezing in supercooled droplets

Atmospheric immersion freezing (IF), a heterogeneous ice nucleation process where an ice nucleating particle (INP) is immersed in supercooled water, is a dominant ice formation pathway impacting the hydrological cycle and climate. Implementation of IF derived from field and laboratory data in cloud and climate models is difficult due to the high variability in spatio-temporal scales, INP composition, and morphological complexity. We demonstrate that IF can be consistently described by a stochastic nucleation process accounting for uncertainties in the INP surface area. This approach accounts for time-dependent freezing, a wide range of surface areas and challenges phenomenological descriptions typically used to interpret IF. The results have an immediate impact on the current description, interpretation, and experiments of IF and its implementation in models. The findings are in accord with nucleation theory, and thus should hold for any supercooled liquid material that nucleates in contact with a substrate.

54 ENVIRONMENTAL SCIENCES↗