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NASA NTRS2003

Sensitivity of Precipitation Processes of Microphysics and Resolution in a Cloud-Resolving Model

The Goddard Cumulus Ensemble (GCE) model is used to examine the impact of various microphysical schemes, and vertical and horizontal resolution ion the development, intensity and rainfall associated with mesoscale convective systems, idealized hurricanes and an ensemble f clouds. The model variables include horizontal and vertical velocities, potential temperatures, perturbation pressure, turbulent kinetic energy, and mixing ratios of all water phases (vapor, liquid, and ice). The major characteristics of the GCE model are the explicit representation of warm rain and ice microphysical processes, and their complex interactions with solar and infrared radiative transfer processes, and with surface processes. For idealized hurricane, an axisymmetric version of the GCE model was developed and used successfully to simulate the tropical cyclogenesis process using both a Rankin vortex and saturated air within a specified radius as initial conditions. For mesoscale convective systems, the 3-D version of the GCE model was used to simulated squall lines that developed in the western Pacific, eastern Atlantic and central US. For the cloud ensemble, the GCE model was integrated for several days in order to have good sampling of cloud statistics. In this paper, the sensitivities of hurricane intensity to various microphysical processes and model grid resolution will be examined. This will be mainly achieved by performing sensitivity tests using various horizontal (from 1- to 5-km) and vertical resolutions (from 20- to 200-m in the lower troposphere to 200- to 500-m in the middle and upper troposphere). Sensitivity tests using various microphysical schemes (warm rain only, and three ice with either graupel or hail) will also be performed. The thermodynamic and water budget associated with various types of precipitation systems will also be evaluated. The budgets will be calculated for different regions (i.e., convective and stratiform regions).

Tao, Wei-Kuo↗

DOE OSTI2022

Quantifying the Effect of Climate Change on Midlatitude Subseasonal Prediction Skill Provided by the Tropics

Abstract Subseasonal timescales (∼2 weeks–2 months) are known for their lack of predictability, however, specific Earth system states known to have a strong influence on these timescales can be harnessed to improve prediction skill (known as “forecasts of opportunity”). As the climate continues warming, it is hypothesized these states may change and consequently, their importance for subseasonal prediction may also be impacted. Here, we examine changes to midlatitude subseasonal prediction skill provided by the tropics under anthropogenic warming using artificial neural networks to quantify skill. The network is tasked to predict the sign of the 500 hPa geopotential height for historical and future time periods in the Community Earth System Model Version 2 ‐ Large Ensemble across the Northern Hemisphere at a 3 week lead using tropical precipitation. We show prediction skill changes substantially in key midlatitude regions and these changes appear linked to changes in seasonal variability with the largest differences in accuracy occurring during forecasts of opportunity.

Mayer, Kirsten J.↗

DOE OSTI2023

Increased exposure of coastal cities to sea-level rise due to internal climate variability

Adaptation to future sea-level rise is based on projections of continuously improving climate models. These projections are accompanied by inherent uncertainties, including those due to internal climate variability (ICV). The ICV arises from complex and unpredictable interactions within and between climate-system components, rendering its impact irreducible. Although neglecting this uncertainty can lead to an underestimation of future sea-level rise, its estimation and impacts have not been fully explored. Combining the Community Earth System Model version 1 Large Ensemble experiments with power-law statistics, we show that, by 2100, if the ICV uncertainty reaches its upper limit, new sea-level-rise hotspots would appear in Southeast Asian megacities (Chennai, Kolkata, Yangon, Bangkok, Ho Chi Minh City and Manila), in western tropical Pacific Islands and the Western Indian Ocean. Here, the better the ICV uncertainty is taken into account and correctly estimated, the more effective adaptation strategies can be elaborated with confidence and actions to follow.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Interannual fires as a source for subarctic summer decadal climate variability mediated by permafrost thawing

Climate model simulations run under the Coupled Model Intercomparison Project Phase 6 (CMIP6) use an inhomogeneous biomass burning aerosol (BBA) emission dataset, which exhibits pronounced interannual variability from 1997–2014 due to the infusion of satellite data. Using the Community Earth System Model version 2 Large Ensemble (CESM2-LE) with original and smoothed CMIP6 BBA forcings, we show that the CMIP6 data inhomogeneity causes spurious decadal subarctic land warming. During years with reduced aerosol concentrations, increased solar radiation can trigger abrupt subarctic permafrost thawing, increased soil water drainage, upper soil drying, and subsequent surface warming. This slow process, which is further amplified by nonlinear cloud-aerosol interactions, cannot be completely offset during years of increased aerosol fluxes, thereby reddening surface temperature spectra in response to large-amplitude interannual aerosol forcing. More generally, our CESM2 experiments identify a pathway for generating decadal variability in high latitudes, involving interannual shortwave forcing and slow nonlinear soil responses.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Isolating the effect of biomass burning aerosol emissions on 20th century hydroclimate in South America and Southeast Asia

Abstract Biomass burning is a significant source of aerosol emissions in some regions and has a considerable impact on regional climate. Earth system model simulations indicate that increased biomass burning aerosol emissions contributed to statistically significant decreases in tropical precipitation over the 20th century. In this study, we use the Community Earth System Model version 1 Large Ensemble (CESM1-LENS) experiment to evaluate the mechanisms by which biomass burning aerosol contributed to decreased tropical precipitation, with a focus on South America and Southeast Asia. We analyze the all-but-one forcing simulations in which biomass burning aerosol emissions are held constant while other forcings (e.g., greenhouse gas concentrations) vary throughout the 20th century. This allows us to isolate the influence of biomass burning aerosol on processes that contribute to decreasing precipitation, including cloud microphysics, the radiative effects of absorbing aerosol particles, and alterations in regional circulation. We also show that the 20th century reduction in precipitation identified in the CESM1-LENS historical and biomass burning experiments is consistent across Coupled Model Intercomparison Project Phase 5 models with interactive aerosol schemes and the CESM2 single-forcing experiment. Our results demonstrate that higher concentrations of biomass burning aerosol increases the quantity of cloud condensation nuclei and cloud droplets, limiting cloud droplet size and precipitation formation. Additionally, absorbing aerosols (e.g., black carbon) contribute to a warmer cloud layer, which promotes cloud evaporation, increases atmospheric stability, and alters regional circulation patterns. Corresponding convectively coupled circulation responses, particularly over the tropical Andes, contribute to further reducing the flow of moisture and moisture convergence over tropical land. These results elucidate the processes that affect the water cycle in regions prone to biomass burning and inform our understanding of how future changes in aerosol emissions may impact tropical precipitation over the 21st century.

Environmental Sciences & Ecology↗

DOE OSTI2025

Evaluating an Earth system model from a water manager perspective

Abstract. The large spatial scale of global Earth system models (ESMs) is often cited as an obstacle to using the output by water resource managers in localized decisions. Recent advances in computing have improved the fidelity of hydrological responses in ESMs through increased connectivity between model components. However, the models are seldom evaluated for their ability to reproduce metrics that are important for and resonate with practitioners or that allow practitioners to situate higher-resolution model outputs within a cascade of uncertainty stemming from different models and scenarios. We draw on the combined experience of the author team and water manager workshop participants to identify salient water management metrics and evaluate whether they are credibly reproduced over the conterminous USA by the Community Earth System Model v2 (CESM2) Large Ensemble. We find that, while the exact values may not match the observations, aspects such as interannual variability can be reproduced by CESM2 for the mean wet day precipitation and length of dry spells. CESM2 also captures the proportion of total annual precipitation that derives from the heaviest rain days in watersheds that are not snow-dominated. Aggregating the 7 d mean daily runoff to two-digit Hydrological Unit Code (HUC2) watersheds also shows that rain-dominated regions capture the timing and interannual variability of annual maximum and minimum flows. We conclude that there is potential for far greater use of large-ensemble ESMs, such as CESM2, in long-range water management decisions to supplement high-resolution regional projections.

Tye, Mari R. (ORCID:0000000324911020)↗

NASA NTRSDate not supplied

High Latitude Atmospheric Rivers: Teleconnections and Impacts

Atmospheric rivers (ARs) are long, narrow synoptic scale features that act as moisture transport vehicles by moving water vapor poleward. They are typically found in storm tracks and often have profound implications for regional hydroclimate. For high latitude locales such as Antarctica and the Arctic, ARs are fewer in number but often have an outsized impact by either producing significant snow accumulation events, or, depending on their thermal characteristics, initiating melt events on ice sheets, ice shelves, and sea ice. In this study, we apply both reanalysis and climate model data to evaluate high latitude ARs for both the Arctic and Antarctic in the context of teleconnections and modes of variability. Specifically, we fold in both MERRA-2 and ERA5 reanalysis as well as historical and climate change simulations from the CESM2 (Community Earth System Model, Version 2) and E3SMv2 (Energy Exascale Earth System Model, v2) with multiple ensemble members to better understand natural variability versus climate change signals. An overview of AR climatology and impacts will be presented, including a baseline uncertainty quantification analysis using data from ARTMIP (the Atmospheric River Tracking Method Intercomparison Project). AR impacts during specific modes of variability will be characterized by diagnosing precipitation and boundary layer temperature associated with Ars influencing the cryosphere. Modes of variability investigated include the SAM (Southern Annular Mode), PDO (Pacific Decadal Oscillation), PSA2 (Pacific South American Mode 2), the IOD (Indian Ocean Dipole), and the AO (Arctic Oscillation).

Atmospheric Rivers↗

NASA NTRSDate not supplied

Aerosols Delay the Emergence of Greenhouse Gas Forcing on 21st Century South Asian Monsoon Precipitation By Several Decades

Anthropogenic aerosols (AERs) affect several aspects of the climate system across the world through radiative forcing and microphysical effects. These influences are particularly strong across South Asia, where AER concentrations are highest and further projected to increase in coming decades. Using large ensemble experiments from Earth system model, we examine how AERs shape the evolution of seasonal precipitation over South Asia inlate 20th century and 21st century climate in the presence of rising greenhouse gases (GHGs) concentrations. We find that AERs strongly reduce monsoon precipitation, moderately reduce post-monsoon precipitation, and negligibly influence pre-monsoon precipitation. Consequently, AERs delay the emergence of GHG-forced increases in precipitation by ~5 decades in the monsoon season and ~1 decade in the post-monsoon season. However, GHGs are projected to outpace the influence of AERs by mid 21st century, causing a steep intensification of monsoon and post-monsoon precipitation. We further show that local AERs have the strongest influence on precipitation in the monsoon and post monsoon seasons in the near-future (2020-2049). However, the contribution from remote AERs changes is also important in shaping the monsoon precipitation changes over northwestern South Asia. Further, the influence of local AERs monsoon precipitation remains stationary throughout the 21st century, indicating the insensitivity of relationship between local AOD and precipitation to the projected warming. A better understanding of aerosol-climate interactions and associated precipitation responses in is pertinent for policymakers to address the critical aspect of regional consequences over South Asia induced by externally forced climate change.

Aerosols↗

NASA NTRS2017

Role of Forcing Uncertainty and Background Model Error Characterization in Snow Data Assimilation

Accurate specification of the model error covariances in data assimilation systems is a challenging issue. Ensemble land data assimilation methods rely on stochastic perturbations of input forcing and model prognostic fields for developing representations of input model error covariances. This article examines the limitations of using a single forcing dataset for specifying forcing uncertainty inputs for assimilating snow depth retrievals. Using an idealized data assimilation experiment, the article demonstrates that the use of hybrid forcing input strategies (either through the use of an ensemble of forcing products or through the added use of the forcing climatology) provide a better characterization of the background model error, which leads to improved data assimilation results, especially during the snow accumulation and melt-time periods. The use of hybrid forcing ensembles is then employed for assimilating snow depth retrievals from the AMSR2 (Advanced Microwave Scanning Radiometer 2) instrument over two domains in the continental USA with different snow evolution characteristics. Over a region near the Great Lakes, where the snow evolution tends to be ephemeral, the use of hybrid forcing ensembles provides significant improvements relative to the use of a single forcing dataset. Over the Colorado headwaters characterized by large snow accumulation, the impact of using the forcing ensemble is less prominent and is largely limited to the snow transition time periods. The results of the article demonstrate that improving the background model error through the use of a forcing ensemble enables the assimilation system to better incorporate the observational information.

assimilation↗

DOE OSTI2023

Modeling single-molecule stretching experiments using statistical thermodynamics

Single-molecule stretching experiments are widely utilized within the fields of physics and chemistry to characterize the mechanics of individual bonds or molecules, as well as chemical reactions. Analytic relations describing these experiments are valuable, and these relations can be obtained through the statistical thermodynamics of idealized model systems representing the experiments. Since the specific thermodynamic ensembles manifested by the experiments affect the outcome, primarily for small molecules, the stretching device must be included in the idealized model system. Though the model for the stretched molecule might be exactly solvable, including the device in the model often prevents analytic solutions. In the limit of large or small device stiffness, the isometric or isotensional ensembles can provide effective approximations, but the device effects are missing. Here a dual set of asymptotically correct statistical thermodynamic theories are applied to develop accurate approximations for the full model system that includes both the molecule and the device. In conclusion, the asymptotic theories are first demonstrated to be accurate using the freely jointed chain model and then using molecular dynamics calculations of a single polyethylene chain.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2023

Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles

Classical problems in computational physics such as data-driven forecasting and signal reconstruction from sparse sensors have recently seen an explosion in deep neural network (DNN) based algorithmic approaches. However, most DNN models do not provide uncertainty estimates, which are crucial for establishing the trustworthiness of these techniques in downstream decision making tasks and scenarios. In recent years, ensemble-based methods have achieved significant success for the uncertainty quantification in DNNs on a number of benchmark problems. However, their performance on real-world applications remains under-explored. In this work, we present an automated approach to DNN discovery and demonstrate how this may also be utilized for ensemble-based uncertainty quantification. Specifically, we propose the use of a scalable neural and hyperparameter architecture search for discovering an ensemble of DNN models for complex dynamical systems. We highlight how the proposed method not only discovers high-performing neural network ensembles for our tasks, but also quantifies uncertainty seamlessly. This is achieved by using genetic algorithms and Bayesian optimization for sampling the search space of neural network architectures and hyperparameters. Subsequently, a model selection approach is used to identify candidate models for an ensemble set construction. Afterwards, a variance decomposition approach is used to estimate the uncertainty of the predictions from the ensemble. We demonstrate the feasibility of this framework for two tasks — forecasting from historical data and flow reconstruction from sparse sensors for the sea-surface temperature. In conclusion, we demonstrate superior performance from the ensemble in contrast with individual high-performing models and other benchmarks.

Deep ensembles↗

NASA NTRS2003

Various Numerical Applications on Tropical Convective Systems Using a Cloud Resolving Model

In recent years, increasing attention has been given to cloud resolving models (CRMs or cloud ensemble models-CEMs) for their ability to simulate the radiative-convective system, which plays a significant role in determining the regional heat and moisture budgets in the Tropics. The growing popularity of CRM usage can be credited to its inclusion of crucial and physically relatively realistic features such as explicit cloud-scale dynamics, sophisticated microphysical processes, and explicit cloud-radiation interaction. On the other hand, impacts of the environmental conditions (for example, the large-scale wind fields, heat and moisture advections as well as sea surface temperature) on the convective system can also be plausibly investigated using the CRMs with imposed explicit forcing. In this paper, by basically using a Goddard Cumulus Ensemble (GCE) model, three different studies on tropical convective systems are briefly presented. Each of these studies serves a different goal as well as uses a different approach. In the first study, which uses more of an idealized approach, the respective impacts of the large-scale horizontal wind shear and surface fluxes on the modeled tropical quasi-equilibrium states of temperature and water vapor are examined. In this 2-D study, the imposed large-scale horizontal wind shear is ideally either nudged (wind shear maintained strong) or mixed (wind shear weakened), while the minimum surface wind speed used for computing surface fluxes varies among various numerical experiments. For the second study, a handful of real tropical episodes (TRMM Kwajalein Experiment - KWAJEX, 1999; TRMM South China Sea Monsoon Experiment - SCSMEX, 1998) have been simulated such that several major atmospheric characteristics such as the rainfall amount and its associated stratiform contribution, the Qlheat and Q2/moisture budgets are investigated. In this study, the observed large-scale heat and moisture advections are continuously applied to the 2-D model. The modeled cloud generated from such an approach is termed continuously forced convection or continuous large-scale forced convection. A third study, which focuses on the respective impact of atmospheric components on upper Ocean heat and salt budgets, will be presented in the end. Unlike the two previous 2-D studies, this study employs the 3-D GCE-simulated diabatic source terms (using TOGA COARE observations) - radiation (longwave and shortwave), surface fluxes (sensible and latent heat, and wind stress), and precipitation as input for the Ocean mixed-layer (OML) model.

Shie, C.-L.↗

NASA NTRS2003

Sensitivity of Precipitation Processes to Microphysics and Resolution in a Cloud-Resolving Model

The Goddard Cumulus Ensemble (GCE) model is used to examine the impact of various microphysical schemes, and vertical and horizontal resolution on the development, intensity and rainfall associated with mesoscale convective systems, idealized hurricanes and an ensemble of clouds. The model variables include horizontal and vertical velocities, potential temperature, perturbation pressure, turbulent kinetic energy, and mixing ratios of all water phases (vapor, liquid, and ice). The major characteristics of the GCE model are the explicit representation of warm rain and ice microphysical processes, and their complex interactions with solar and infrared radiative transfer processes, and with surface processes. For idealized hurricane, an axisymmetric version of the GCE model was developed and used successfully to simulate the tropical cyclogenesis process using both a Rankin vortex and saturated air within a specified radius as initial conditions. For mesoscale convective systems, the 3-D version of the GCE model was used to simulate squall lines that developed in the western Pacific, eastern Atlantic and central US. For the cloud ensemble, the GCE model was integrated for several days in order to have good sampling of cloud statistics. In this paper, the sensitivities of hurricane intensity to various microphysical processes and model grid resolution will be examined.

Tao, Wei-Kuo↗

NASA NTRS2003

Sensitivity of Precipitation Processes to Microphysics and Resolution in a Cloud-resolving Model

Tao, Wei-Kuo↗

NASA NTRS2003

Sensitivity of Precipitation Processes to Microphysics and Resolution in a Cloud-Resolving Model

The Goddard Cumulus Ensemble (GCE) model is used to examine the impact of various microphysical schemes, and vertical and horizontal resolution on the development, intensity and rainfall associated with mesoscale convective systems, idealized hurricanes and an ensemble of clouds. The model variables include horizontal and vertical velocities, potential temperature, perturbation pressure, turbulent kinetic energy, and mixing ratios of all water phases (vapor, liquid, and ice). The major characteristics of the GCE model are the explicit representation of warm rain and ice microphysical processes, and their complex interactions with solar and infrared radiative transfer processes, and with surface processes. For idealized hurricane, an axisymmetric version of the GCE model was developed and used successfully to simulate the tropical cyclogenesis process using both a Rankin vortex and saturated air within a specified radius as initial conditions. For mesoscale convective systems, the 3-D version of the GCE model was used to simulate squall lines that developed in the western Pacific, South China Sea, eastern Atlantic, South America and central U.S. For the cloud ensemble, the GCE model was integrated for several days in order to have good sampling of cloud statistics. In this paper, the sensitivities of hurricane intensity to various microphysical processes and model grid resolution will be examined. This will be mainly achieved by performing sensitivity tests using various horizontal (from 1- to 5-kilometers) and vertical resolutions (from 20- to 200-meters in the lower troposphere to 200- to 500-m in the middle and upper troposphere). Sensitivity tests using various microphysical schemes (warm rain only, and three ice with either graupel or hail) will also be performed. The PBL and diurnal variation of precipitation processes will also be evaluated. The budgets will be calculated for different regions (i.e., convective and stratiform regions).

Tao, Wei-Kao↗

DOE OSTI2021

Crack fault diagnosis of rotating machine in nuclear power plant based on ensemble learning

Crack faults in rotating machines can cause machine shutdown or scrapping, endangering the normal operation and safety of nuclear power plants. Intelligent diagnostic techniques based on machine learning have the potential to diagnose crack faults. However, problems such as scarcity of field fault data and high noise of plant measurements pose challenges to the application of machine learning. Here this study proposes an ensemble learning approach to mitigate the negative impacts of the problems. Ensemble learning is a strategy for combining multiple machine learning models into a composite model. The basic idea of ensemble learning is that even if one model makes a mistake, other models can correct it. Case studies based on bearing and gear system fault experiments show that the proposed ensemble learning models have better diagnostic results than the single model in the presence of noise and small data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

A Greening Future Elevates Flash Drought Risk in Northern Mid‐to‐High Latitudes

Flash droughts have become a growing concern, as they can emerge rapidly and increase the risk of crop failure. Although past studies have investigated the meteorological drivers and future changes of flash drought, why flash drought is more frequent over humid and vegetated regions remains underexplored. This study delves further into the mechanism by which vegetation regulates flash drought and its future change using observations from multiple data sets and large ensemble simulations from three Earth system models. On an interannual timescale, both observations and simulations show robust increases in flash drought frequency and a higher flash-to-sub-seasonal drought ratio during spring or antecedent conditions with dense vegetation, supporting the important role of vegetation in flash drought occurrence, especially in the northern mid-to-high latitudes. In the latter regions, the large ensemble simulations show robust increases in flash drought (e.g., 67% and 46% increases in Eastern U.S. and North Asia in 2050–2100 relative to 1950–2000 under the high emission scenario), where the growing season is lengthening. Although greening might suggest reduced drought stress, it drives precipitation-soil moisture-evapotranspiration decoupling by increasing evapotranspiration partitioning to transpiration. As transpiration can access deep soil water through the plant root system, its increased portion can weaken the constraints of concurrent precipitation on evapotranspiration, thus accelerating soil moisture depletion under high evaporative demand, driving a slow-to-rapid drought transition. How vegetation regulates flash drought by regulating surface moisture budget is supported by observations and simulations. Although warming supports early planting, agriculture may increasingly be threatened by surging flash drought risk.

Drought↗

DOE OSTI2023

Ensemble models for circuit topology estimation, fault detection and classification in distribution systems

This paper presents a methodology for simultaneous fault detection, classification, and topology estimation for adaptive protection of distribution systems. The methodology estimates the probability of the occurrence of each one of these events by using a hybrid structure that combines three sub-systems, a convolutional neural network for topology estimation, a fault detection based on predictive residual analysis, and a standard support vector machine with probabilistic output for fault classification. The input to all these sub-systems is the local voltage and current measurements. A convolutional neural network uses these local measurements in the form of sequential data to extract features and estimate the topology conditions. The fault detector is constructed with a Bayesian stage (a multitask Gaussian process) that computes a predictive distribution (assumed to be Gaussian) of the residuals using the input. Since the distribution is known, these residuals can be transformed into a Standard distribution, whose values are then introduced into a one-class support vector machine. The structure allows using a one-class support vector machine without parameter cross-validation, so the fault detector is fully unsupervised. Finally, a support vector machine uses the input to perform the classification of the fault types. All three sub-systems can work in a parallel setup for both performance and computation efficiency. In conclusion, we test all three sub-systems included in the structure on a modified IEEE123 bus system, and we compare and evaluate the results with standard approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗