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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

A simple model of the effect of ocean ventilation on ocean heat uptake

Presentation includes slides on Earth System Models vs. Simple Climate Models; A Popular SCM: Energy Balance Model of Anomalies; On calibrating against one ESM experiment, the SCM correctly captures that ESM's surface warming response with other forcings; Multi-Model Analysis: Multiple ESMs, Single SCM; Posterior Distributions of ECS; However In Excess of 90% of TOA Energy Imbalance is Sequestered in the World Oceans; Heat Storage in the Two Layer Model; Heat Storage in the Two Layer Model; Including TOA Rad. Imbalance and Ocean Heat in Calibration Improves Repr., but Significant Errors Persist; Improved Vertical Resolution Does Not Fix Problem; A Series of Expts. Confirms That Anomaly-Diffusing Models Cannot Properly Represent Ocean Heat Uptake; Physics of the Thermocline; Outcropping Isopycnals and Horizontally-Averaged Layers; Local interactions between outcropping isopycnals leads to non-local interactions between horizontally-averaged layers; Both Surface Warming and Ocean Heat are Well Represented With Just 4 Layers; A Series of Expts. Confirms That When Non-Local Interactions are Allowed, the SCMs Can Represent Both Surface Warming and Ocean Heat Uptake; and Summary and Conclusions.

58 GEOSCIENCES↗
DOE OSTI2021

Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction Project, Phase I (LS4P-I): organization and experimental design

Subseasonal-to-seasonal (S2S) prediction, especially the prediction of extreme hydroclimate events such as droughts and floods, is not only scientifically challenging, but also has substantial societal impacts. Motivated by preliminary studies, the Global Energy and Water Exchanges (GEWEX)/Global Atmospheric System Study (GASS) has launched a new initiative called “Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction” (LS4P) as the first international grass-roots effort to introduce spring land surface temperature (LST)/subsurface temperature (SUBT) anomalies over high mountain areas as a crucial factor that can lead to significant improvement in precipitation prediction through the remote effects of land–atmosphere interactions. LS4P focuses on process understanding and predictability, and hence it is different from, and complements, other international projects that focus on the operational S2S prediction. More than 40 groups worldwide have participated in this effort, including 21 Earth system models, 9 regional climate models, and 7 data groups.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Robustness of Meteorological Droughts in Dynamically Downscaled Climate Simulations

Abstract We examine the robustness of a suite of regional climate models (RCMs) in simulating meteorological droughts and associated metrics in present‐day climate (1971‐2003) over the conterminous United States (U.S.). The RCMs that are part of North American Regional Climate Change Assessment Program (NARCCAP) simulations are compared with multiple observations over the climatologically homogeneous regions of the U.S. The seasonal precipitation, climatology, drought attributes, and trends have been assessed. The reanalysis‐based multi‐model median RCM reasonably simulates observed statistical attributes of drought and the regional detail due to topographic forcing. However, models fail to simulate significant drying trend over the Southwest and West. Further, reanalysis‐based NARCCAP runs underestimate the observed drought frequency overall, with the exception of the Southwest; whereas they underestimate persistence in the drought‐affected areas over the Southwest and West‐North Central regions. However, global climate model‐driven NARCCAP ensembles tend to overestimate regional drought frequencies. Models exhibit considerable uncertainties while reproducing meteorological drought statistics, as evidenced by a general lack of agreement in the Hurst exponent, which in turn controls drought persistence. Water resources managers need to be aware of the limitations of current climate models, while regional climate modelers may want to fine‐tune their parameters to address impact‐relevant metrics.

Ganguli, Poulomi↗
DOE OSTI2016

Land-atmosphere coupling and climate prediction over the U.S. Southern Great Plains

Biases in land-atmosphere coupling in climate models can contribute to climate prediction biases, but land models are rarely evaluated in the context of this coupling. Here, we tested land-atmosphere coupling and explored effects of land surface parameterizations on climate prediction in a single-column version of the National Center for Atmospheric Research Community Earth System Model (CESM1.2.2) and an off-line Community Land Model (CLM4.5). The correlation between leaf area index (LAI) and surface evaporative fraction (ratio of latent to total turbulent heat flux) was substantially underpredicted compared to observations in the U.S. Southern Great Plains, while the correlation between soil moisture and evaporative fraction was overpredicted by CLM4.5. To estimate the impacts of these errors on climate prediction, we modified CLM4.5 by prescribing observed LAI, increasing soil resistance to evaporation, increasing minimum stomatal conductance, and increasing leaf reflectance. The modifications improved the predicted soil moisture-evaporative fraction (EF) and LAI-EF correlations in off-line CLM4.5 and reduced the root-mean-square error in summer 2 m air temperature and precipitation in the coupled model. The modifications had the largest effect on prediction during a drought in summer 2006, when a warm bias in daytime 2 m air temperature was reduced from +6°C to a smaller cold bias of -1.3°C, and a corresponding dry bias in precipitation was reduced from -111 mm to -23 mm. The role of vegetation in droughts and heat waves is underpredicted in CESM1.2.2, and improvements in land surface models can improve prediction of climate extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Uncertainty of SW Cloud Radiative Effect in Atmospheric Models Due to the Parameterization of Liquid Cloud Optical Properties

Clouds are largely responsible for the spread of climate models predictions. Here we focus on the uncertainties in cloud shortwave radiative effect due to the parameterization of liquid cloud single scattering properties (SSPs) from liquid water content (LWC) and droplet number concentration (N), named parameterization of cloud optical properties. Uncertainties arise from not accounting for the droplet size distribution (DSD)—which affects the estimation of the effective radius (r eff ) and modulates the r eff -dependency of the SSPs—and from averaging SSPs over wide spectral bands. To assess these uncertainties a series of r eff -dependent SSPs parameterizations corresponding to various DSDs and spectral averaging methods are derived and implemented in a radiative code. Combined with the DSD-dependent estimation of r eff they are used to compute the bulk radiative properties (reflectance, transmittance, absorptance) of various clouds (defined in terms of LWC and N), including a homogeneous cloud, more realistic case studies, and outputs of a climate model. The results show that the cloud radiative forcing can vary up to 20% depending on the assumed DSD. Likewise, differences up to 20% are obtained for heating rates. The estimation of r eff is the main source of uncertainty, while the SSPs parameterization contributes to around 20% of the total uncertainty. Spectral averaging is less an issue, except for atmospheric absorption. Overall, global shortwave cloud radiative effect can vary by 6 W m –2 depending on the assumed DSD shape, which is about 13% of the best observational estimate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Virtual Observation System for Earth System Model: An Application to ACME Land Model Simulations

Investigating and evaluating physical-chemical-biological processes within an Earth system model (EMS) can be very challenging due to the complexity of both model design and software implementation. A virtual observation system (VOS) is presented to enable interactive observation of these processes during system simulation. Based on advance computing technologies, such as compiler-based software analysis, automatic code instrumentation, and high-performance data transport, the VOS provides run-time observation capability, in-situ data analytics for Earth system model simulation, model behavior adjustment opportunities through simulation steering. A VOS for a terrestrial land model simulation within the Accelerated Climate Modeling for Energy model is also presented to demonstrate the implementation details and system innovations.

58 GEOSCIENCES↗
DOE OSTI2025

Perspectives on Systematic Cloud Microphysics Scheme Development With Machine Learning

Cloud microphysics—the collection of processes that govern the small‐scale formation, evolution, and interactions of liquid droplets and ice crystals in clouds and precipitation—remains a major source of uncertainty in weather and climate models. Although too small in scale to be explicitly resolved in any large‐eddy simulation, weather, or climate model, the representation of cloud microphysical processes has significant impact at the climate scale. Current microphysical schemes are limited by both parametric uncertainty, linked to uncertainty in physical parameter values, and structural uncertainty, arising from incomplete physical understanding of the processes at play or approximations made for computational efficiency. Recent advances in the application of machine learning (ML) to the physical sciences show significant potential for minimizing these limitations by leveraging high‐fidelity simulations and observations. Here we outline the challenges that must be addressed to apply ML toward cloud microphysics scheme development. This perspectives paper synthesizes recent progress in using data‐driven methods, including ML, to improve cloud microphysics parameterizations and highlights opportunities to address key uncertainties. We discuss the roles of aleatoric (irreducible, or statistical) and epistemic (reducible, or systematic) errors in contributing to microphysics parameterization uncertainty. ML can leverage observations to improve microphysical schemes via bottom‐up and top‐down constraints. Methods such as differentiable programming and ML‐enhanced sampling strategies and the creation of large scale benchmark data sets promise to bridge the gap between observations and models and to improve the consistency of cloud microphysical representation across temporal and spatial scales.

Lamb, Kara D. [Columbia Univ., New York, NY (Unite↗
DOE OSTI2023

Antarctic Ice Sheet Freshwater Discharge Drives Substantial Southern Ocean Changes Over the 21st Century

Multidecadal satellite observations indicate that the Antarctic Ice Sheet (AIS) is losing mass at an accelerating rate, which has the potential to impact many aspects of the climate system. While previous studies demonstrated the importance of AIS freshwater (FW) discharge for regional and global climate processes using climate model experiments, many have applied unrealistic FW forcings. Here, we explore potential Southern Ocean (SO) impacts of realistic AIS mass loss over the 21st century in the Community Earth System Model version 2 by applying observation-based historical and ice sheet model-based future AIS FW forcing. The added FW reduces wintertime deep convective area by 72% while retaining 83% more sea ice. Congruent with other studies, we find the increased FW discharge extensively impacts local and remote SO surface and subsurface temperature and stratification. These results demonstrate the necessity of accounting for AIS mass loss in global climate models for projecting future climate.

Geology↗
DOE OSTI2022

Surface Water Mass Transformation in the Southern Ocean: The Role of Eddies Revisited

The water mass transformation (WMT) framework describes how water of one class, such as a discrete interval of density, is converted into another class via air–sea fluxes or interior mixing processes. Here, this paper investigates how this process is modified at the surface when mesoscale ocean eddies are present, using a state-of-the-art high-resolution climate model with reasonable fidelity in the Southern Ocean. The method employed is to coarse-grain the high-resolution model fields to remove eddy signatures, and compare the results with those from the full model fields. This method shows that eddies reduced the WMT by 2–4 Sv (10%–20%; 1 Sv ≡ 10 6 m 3 s −1 ) over a wide range of densities, from typical values of 20 Sv in the smoothed case. The corresponding water mass formation was reduced by 40% at one particular density increment, namely, between 1026.4 and 1026.5 kg m −3 , which corresponds to the lighter end of the range of Indian Ocean Mode Water in the model. The effect of eddies on surface WMT is decomposed into three terms: direct modulation of the density outcrops, then indirectly, by modifying the air–sea density flux, and the combined effect of the two, akin to a covariance. It is found that the first and third terms dominate, i.e., smoothing the outcrops alone has a significant effect, as does the combination of smoothing both outcrops and density flux distributions, but smoothing density flux fields alone has little effect. Results from the coarse-graining method are compared to an alternative approach of temporally averaging the data. Implications for climate model resolution are also discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Impact of Physics Parameterization Ordering in a Global Atmosphere Model

Abstract Because weather and climate models must capture a wide variety of spatial and temporal scales, they rely heavily on parameterizations of subgrid‐scale processes. The goal of this study is to demonstrate that the assumptions used to couple these parameterizations have an important effect on the climate of version 0 of the Energy Exascale Earth System Model (E3SM) General Circulation Model (GCM), a close relative of version 1 of the Community Earth System Model (CESM1). Like most GCMs, parameterizations in E3SM are sequentially split in the sense that parameterizations are called one after another with each subsequent process feeling the effect of the preceding processes. This coupling strategy is noncommutative in the sense that the order in which processes are called impacts the solution. By examining a suite of 24 simulations with deep convection, shallow convection, macrophysics/microphysics, and radiation parameterizations reordered, process order is shown to have a big impact on predicted climate. In particular, reordering of processes induces differences in net climate feedback that are as big as the intermodel spread in phase 5 of the Coupled Model Intercomparison Project. One reason why process ordering has such a large impact is that the effect of each process is influenced by the processes preceding it. Where output is written is therefore an important control on apparent model behavior. Application of k ‐means clustering demonstrates that the positioning of macro/microphysics and shallow convection plays a critical role on the model solution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

CMIP6-based Multi-model Hydropower Projection over the Conterminous US, Version 1.1

This dataset presents a suite of hydropower projections for the conterminous United States (CONUS), derived from multiple downscaled and bias-corrected Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The CMIP6 GCMs are downscaled using either statistical (DBCCA) or dynamical (RegCM) approaches, based on two meteorological reference datasets (Daymet and Livneh). The resulting downscaled precipitation, temperature, and wind speed data are then used to drive two calibrated hydrologic models (VIC and PRMS), enabling simulations of projected future hydrologic responses across the CONUS. Simulated total runoff is subsequently employed to drive two hydropower models (WMP, now implemented as mosartwmpy-power, and WRES) to evaluate how climate change may affect future hydropower production for both federal and non-federal hydropower fleets. This dataset was developed to support the SECURE Water Act Section 9505 Assessment for the U.S. Department of Energy (DOE) Water Power Technologies Office (WPTO). For further details, see Broman et al. (2024), Thurber et al. (2024), Kao et al. (2022), and Zhou et al. (2023).

Voisin, Nathalie [Pacific Northwest National Labor↗
DOE OSTI2025

Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect

Abstract Predicting forced, long‐term radiative feedbacks from internal climate variability has been a decades‐long quest in climate science. We train a convolutional neural network (CNN) to predict annual‐ and global‐mean top of the atmosphere radiation anomalies from time‐varying maps of near‐surface temperature in climate models. Trained on internal variability alone, the nonlinear CNN can predict radiation under strong climate change, outperforms a regularized linear regression approach, and works within and across different climate models. We show with explainable artificial intelligence methods that the CNN draws predictive skill from physically meaningful regions but at much smaller spatial scales than currently assumed.

Rugenstein, Maria [Colorado State University Fort ↗
DOE OSTI2015

Parameterizing deep convection using the assumed probability density function method

Due to their coarse horizontal resolution, present-day climate models must parameterize deep convection. This paper presents single-column simulations of deep convection using a probability density function (PDF) parameterization. The PDF parameterization predicts the PDF of subgrid variability of turbulence, clouds, and hydrometeors. That variability is interfaced to a prognostic microphysics scheme using a Monte Carlo sampling method.The PDF parameterization is used to simulate tropical deep convection, the transition from shallow to deep convection over land, and mid-latitude deep convection.These parameterized single-column simulations are compared with 3-D reference simulations. The agreement is satisfactory except when the convective forcing is weak. The same PDF parameterization is also used to simulate shallow cumulus and stratocumulus layers. The PDF method is sufficiently general to adequately simulate these five deep, shallow, and stratiform cloud cases with a single equation set. This raises hopes that it may be possible in the future, with further refinements at coarse time step and grid spacing, to parameterize all cloud types in a large-scale model in a unified way.

Storer, R. L.↗
DOE OSTI2022

Understanding Air-Sea Feedbacks to the MJO through Process Evaluation of Observations and Models (Final Report)

The goal of our project was to enhance our understanding of the role of coupling in the simulation of the Madden Julian Oscillation (MJO) in coupled climate models. We developed diagnostics of precipitation and Sea Surface Temperature variability in climate models and investigated how they are related to metrics of MJO simulation performance. We found a relationship between models with a high spatial coherence of precipitation and a good propagation of the MJO through the Maritime Continent region, but no relationship between metrics of precipitation organisation and intensity. Analysis of coupled and uncoupled model simulations from the Sixth Coupled Model Intercomparison Project (CMIP6) shows that coupling systematically improved the MJO simulation in both metrics that we investigated. We analysed the role of background moisture gradients in the MJO simulation performance following Lim et al. (2018) and found that although the model to model variation in moisture gradients to the west of Indonesia was related to model MJO performance, in general no single moisture gradient was as good at explaining a large fraction of MJO performance across the models as compared to a linear combination of those metrics, which was able to explain a significant fraction of the variance in the MJO performance. Those relationships were much stronger for coupled simulations than uncoupled simulations and stronger for those metrics that measured propagation of the MJO across the Maritime Continent than for overall MJO activity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Global land carbon sink response to temperature and precipitation varies with ENSO phase

Climate variability associated with the El Niño-Southern Oscillation (ENSO) and its consequent impacts on land carbon sink interannual variability have been used as a basis for investigating carbon cycle responses to climate variability more broadly, and to inform the sensitivity of the tropical carbon budget to climate change. Past studies have presented opposing views about whether temperature or precipitation is the primary factor driving the response of the land carbon sink to ENSO. We show that the dominant driver varies with ENSO phase. And whereas tropical temperature explains sink dynamics following El Niño conditions (r TG,P = 0.59, p < 0.01), the post La Niña sink is driven largely by tropical precipitation (r PG,T= -0.46, p = 0.04). This finding points to an ENSO-phase-dependent interplay between water availability and temperature in controlling the carbon uptake response to climate variations in tropical ecosystems. Furthermore, we find that none of a suite of ten contemporary terrestrial biosphere models captures these ENSO-phase-dependent responses, highlighting a key uncertainty in modeling climate impacts on the future of the global land carbon sink.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Using a Multi‐Institutional Ensemble of Watershed Models to Assess Agricultural Conservation Effectiveness in a Future Climate

This study investigates the combined impacts of climate change and agricultural conservation on the magnitude and uncertainty of nutrient loadings in the Maumee River Watershed, the second-largest watershed of the Laurentian Great Lakes. Two scenarios — baseline agricultural management and increased agricultural conservation — were assessed using an ensemble of five Soil and Water Assessment Tools driven by six climate models. The increased conservation scenario included raising conservation adoption rates from a baseline of existing conservation practices to feasible rates in the near future based on farmer surveys. This increased adoption of winter cover crops on 6%–10% to 60% of cultivated cropland; subsurface placement of phosphorus fertilizers on 35%–60% to 68% of cultivated cropland; and buffer strips intercepting runoff from 29%–34% to 50% of cultivated cropland. Increased conservation resulted in statistically significant (p ≤ 0.05) reductions in annual loads of total phosphorus (41%), dissolved reactive phosphorus (18%), and total nitrogen (14%) under the highest emission climate scenario (RCP 8.5). While nutrient loads decreased with increased conservation relative to baseline management for all watershed models, different conclusions on the true effectiveness of conservation under climate change may be drawn if only one watershed model was used.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Climate hazard indices projections based on CORDEX-CORE, CMIP5 and CMIP6 ensemble

The CORDEX-CORE initiative was developed with the aim of producing homogeneous regional climate model (RCM) projections over domains world wide. In its first phase, two RCMs were run at 0.22° resolution downscaling 3 global climate models (GCMs) from the CMIP5 program for 9 CORDEX domains and two climate scenarios, the RCP2.6 and RCP8.5. The CORDEX-CORE simulations along with the CMIP5 GCM ensemble and the most recently produced CMIP6 GCM ensemble are analyzed, with focus on several temperature, heat, wet and dry hazard indicators for present day and mid-century and far future time slices. The CORDEX-CORE ensemble shows a better performance than the driving GCMs for several hazard indices due to its higher spatial resolution. For the far future time slice the 3 ensembles project an increase in all temperature and heat indices analyzed under the RCP8.5 scenario. The largest increases are always shown by the CMIP6 ensemble, except for Tx > 35 °C, for which the CORDEX-CORE projects higher warming. Extreme wet and flood prone maxima are projected to increase by the RCM ensemble over the la Plata basin in South America, the Congo basin in Africa, east North America, north east Europe, India and Indochina, regions where a better performance is obtained, whereas the GCM ensembles show small or negligible signals. Furthermore, compound hazard hotspots based on heat, drought and wet indicators are detected in each continent worldwide in region like Central America, the Amazon, the Mediterranean, South Africa and Australia, where a linear relation is shown between the heatwave and drought change signal, and region like Arabian peninsula, the central and south east Africa region (SEAF), the north west America (NWN), south east Asia, India, China and central and northern European regions (WCE, NEU) where the same linear relation is found for extreme precipitation and HW increases. Although still limited, the CORDEX-CORE initiative was able to produce high resolution climate projections with almost global coverage and can provide an important resource for impact assessment and climate service activities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Validating a microphysical prognostic stratospheric aerosol implementation in E3SMv2 using observations after the Mount Pinatubo eruption

Abstract. This paper describes the addition of a stratospheric prognostic aerosol (SPA) capability – developed with the goal of accurately simulating sulfate aerosol formation and evolution in the stratosphere – in the Department of Energy (DOE) Energy Exascale Earth System Model, version 2 (E3SMv2). The implementation includes changes to the four-mode Modal Aerosol Module microphysics in the stratosphere to allow for larger particle growth and more accurate stratospheric aerosol lifetime following the Pinatubo eruption. E3SMv2-SPA reasonably reproduces stratospheric aerosol lifetime, burden, aerosol optical depth, and top-of-atmosphere flux when compared to remote sensing observations. E3SMv2-SPA also has close agreement with the interactive chemistry–climate model CESM2-WACCM (Community Earth System Model version 2–Whole Atmosphere Community Climate Model) – which has a more complete chemical treatment – and the observationally constrained, prescribed volcanic aerosol treatment in E3SMv2. Global stratospheric aerosol size distributions identify the nucleation and growth of sulfate aerosol from volcanically injected SO2 from both major and minor volcanic eruptions from 1991 to 1993. The modeled aerosol effective radius is consistently lower than satellite and in situ measurements (max differences of ∼ 30 %). Comparisons with in situ size distribution samples indicate that this simulated underestimation in both E3SMv2-SPA and CESM2-WACCM is due to overly small accumulation and coarse-mode aerosols 6–18 months post-eruption, with E3SMv2-SPA simulating ∼ 50 % of the coarse-mode geometric mean diameters of observations 11 months post-eruption. Effective radii from the models and observations are used to calculate offline scattering and absorption efficiencies to explore the implications of smaller simulated aerosol size for the Pinatubo climate impacts. Scattering efficiencies at wavelengths of peak solar irradiance (∼ 0.5 µm) are 10 %–80 % higher for daily samples in models relative to observations through 1993, suggesting higher diffuse radiation at the surface and a larger cooling effect in the models due to the smaller simulated aerosol; absorption efficiencies at the peak wavelengths of outgoing terrestrial radiation (∼ 10 µm) are 15 %–40 % lower for daily samples in models relative to observations, suggesting an underestimation in stratospheric heating in the models due to the smaller simulated aerosol. These potential biases are based on aerosol size alone and do not take into account differences in the aerosol number. The overall agreement of E3SMv2-SPA with observations and its similar performance to the well-validated CESM2-WACCM makes E3SMv2-SPA a viable alternative to simulating climate impacts from stratospheric sulfate aerosols.

Brown, Hunter York (ORCID:0000000218147874)↗