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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
NASA NTRS2011

Quantifying the Aerosol Semi-Direct Effect in the NASA GEOS-5 AGCM

Aerosols such as black carbon, dust, and some organic carbon species both scatter and absorb incoming solar radiation. This direct aerosol radiative forcing (DARF) redistributes solar energy both by cooling the surface and warming the atmosphere. As a result, these aerosols affect atmospheric stability and cloud cover (the semi-direct effect, or SDE). Furthermore, in regions with persistent high loadings of absorbing aerosols (e.g. Asia), regional circulation patterns may be altered, potentially resulting in changes in precipitation patterns. Here we investigate aerosol-climate coupling using the NASA Goddard Earth Observing System model version 5 (GEOS-5) atmospheric general circulation model (AGCM), in which we have implemented an online version of the Goddard Chemistry, Aerosol, Radiation and Transport (GOCART) model. GOCART includes representations of the sources, sinks, and chemical transformation of externally mixed dust, sea salt, sulfate, and carbonaceous aerosols. We examine a series of free-running ensemble climate simulations of the present-day period (2000-2009) forced by observed sea surface temperatures to determine the impact of aerosols on the model climate. The SDE and response of each simulation is determined by differencing with respect to the control simulation (no aerosol forcing). In a free-running model, any estimate of the SDE includes changes in clouds due both to atmospheric heating from aerosols and changes in circulation. To try and quantify the SDE without these circulation changes we then examine the DARF and SDE in GEOS-5 with prescribed meteorological analyses introduced by the MERRA analysis. By doing so, we are able to examine changes in model clouds that occur on shorter scales (six hours). In the GEOS-5 data assimilation system (DAS), the analysis is defined as the best estimate of the atmospheric state at any given time, and it is determined by optimally combining a first-guess short-term GCM forecast with all available observations. The Incremental Analysis Update (IAU) is added to the model forecast tendencies to align them with the analysis every six hours, thus preventing longer timescale feedbacks due to the aerosol forcing. We calculate the SDE by comparing model runs with and without aerosols, and the difference in the IAU between these runs is a useful metric with which to evaluate the impact of the SDE on the model atmosphere and clouds. Decreasing the IAU indicates that the aerosol direct and semi-direct effects act to reduce the bias between the model and observations and vice versa.

Randles, Cynthia A.↗
DOE OSTI2022

Deep Learning Provides Substantial Improvements to County‐Level Fire Weather Forecasting Over the Western United States

Abstract The recent wildfires in the western United States during 2018 and 2020 caused record‐breaking fire damage and casualties. Despite remarkable advances in fire modeling and weather forecasting, it remains challenging to anticipate catastrophic wildfire events and associated damage. One key missing component is a fire weather prediction system with sufficiently long lead time capable of providing useful regional details. Here, we develop a hybrid prediction model of wildfire danger called CFS with super resolution (CFS‐SR) as a proof of concept to fill that void. The CFS‐SR model is constructed by integrating the Climate Forecast System version 2 with a deep learning (DL) technique from Single Image Super Resolution, a method widely used in enhancing image resolution. We show that for the 2018–2019 fire season, the CFS‐SR model significantly improves accuracy in forecasting fire weather at lead times of up to 7 days with an enhanced spatial resolution up to 4 km. This level of high resolution provides county‐level fire weather forecast, making it more practical for allocating resources to mitigate wildfire danger. Our study demonstrates that a proper combination of ensemble climate predictions with DL techniques can boost predictability at finer spatial scales, increasing the utility of fire weather forecasts for practical applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

On the uncertainty of long-period return values of extreme daily precipitation

Methods for calculating return values of extreme precipitation and their uncertainty are compared using daily precipitation rates over the Western U.S. and Southwestern Canada from a large ensemble of climate model simulations. The roles of return-value estimation procedures and sample size in uncertainty are evaluated for various return periods. We compare two different generalized extreme value (GEV) parameter estimation techniques, namely L-moments and maximum likelihood (MLE), as well as empirical techniques. Even for very large datasets, confidence intervals calculated using GEV techniques are narrower than those calculated using empirical methods. Furthermore, the more efficient L-moments parameter estimation techniques result in narrower confidence intervals than MLE parameter estimation techniques at small sample sizes, but similar best estimates. It should be noted that we do not claim that either parameter fitting technique is better calibrated than the other to estimate long period return values. While a non-stationary MLE methodology is readily available to estimate GEV parameters, it is not for the L-moments method. Comparison of uncertainty quantification methods are found to yield significantly different estimates for small sample sizes but converge to similar results as sample size increases. Finally, practical recommendations about the length and size of climate model ensemble simulations and the choice of statistical methods to robustly estimate long period return values of extreme daily precipitation statistics and quantify their uncertainty.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

The tropical water and energy cycles in a cumulus ensemble model. Part 1: Equilibrium climate

A cumulus ensemble model is used to study the tropical water and energy cycles and their role in the climate system. The model includes cloud dynamics, radiative processes, and microphysics that incorporate all important production and conversion processes among water vapor and five species of hydrometeors. Radiative transfer in clouds is parameterized based on cloud contents and size distributions of each bulk hydrometeor. Several model integrations have been carried out under a variety of imposed boundary and large-scale conditions. In Part 1 of this paper, the primary focus is on the water and heat budgets of the control experiment, which is designed to simulate the convective - radiative equilibrium response of the model to an imposed vertical velocity and a fixed sea surface temperature at 28 C. The simulated atmosphere is conditionally unstable below the freezing level and close to neutral above the freezing level. The equilibrium water budget shows that the total moisture source, M(sub s), which is contributed by surface evaporation (0.24 M(sub s)) and the large-scale advection (0.76 M(sub s)), all converts to mean surface precipitation bar-P(sub s). Most of M(sub s) is transported verticaly in convective regions where much of the condensate is generated and falls to surface (0.68 bar-P(sub s)). The remaining condensate detrains at a rate of 0.48 bar-P(sub s) and constitutes 65% of the source for stratiform clouds above the melting level. The upper-level stratiform cloud dissipates into clear environment at a rate of 0.14 bar-P(sub s), which is a significant moisture source comparable to the detrained water vapor (0.15 bar-P(sub s)) to the upper troposphere from convective clouds. In the lower troposphere, stratiform clouds evaporate at a rate of 0.41 bar-P(sub s), which is a more dominant moisture source than surface evaporation (0.22 bar-P(sub s)). The precipitation falling to the surface in the stratiform region is about 0.32 bar-P(sub s). The associated latent heating in the water cycle is the dominant source in the heat budget that generates a net upward motion in convective regions, upper stratiform regions (above the freezing level), and a downward motion in the lower stratiform regions. The budgets reveal a cycle of water and energy resulted from radiation-dynamic-convection interactions that maintain equilibrium of the atmosphere.

Sui, C. H.↗
DOE OSTI2025

A multi model ensemble reveals net climate benefits from regenerative practices in US Midwest croplands

Process-based cropping systems models (CSMs) are key components of measurement, monitoring, reporting, and verification frameworks of carbon markets, but model-specific differences limit their applicability across diverse pedo-climatic conditions and agronomic practices. Multi-model ensemble (MME) provides an opportunity to better estimate changes in soil organic carbon (SOC) and nitrous oxide (N 2 O) emissions from agronomic practices at scale. We used an MME across 46 million hectares of US Midwest cropland at a resolution of 4-km 2 to assess the aggregate ability of different regenerative practices to sequester SOC and N 2 O emissions compared to their counterfactual dynamic baselines. MME was validated against long-term trials and compared to its constituent CSMs, showing greater accuracy and lower uncertainty. The results show that adopting no-till combined with cover crops increased SOC stocks by 0.36 ± 0.12 Mg ha -1 yr -1 , corresponding to a net regional SOC gain of 16.4 Tg C yr -1 compared to business-as-usual baselines. These benefits are halved when each management is practiced individually, and the SOC gains are only fully realized with low initial carbon stock. By including N 2 O emissions, we can assess the overall climate mitigation potential, specifically, the extent to which carbon sequestration can offset direct N 2 O emissions. The magnitude of this potential varies depending on management practices and geographic location with net climate benefits on average ranging from 0 to 3 Mg CO 2 -eq ha -1 yr -1 . High-resolution MME results allow for robust estimates of climate mitigation, reducing barriers to carbon market participation and supporting regenerative agriculture initiatives at scale.

carbon credits↗
NASA NTRS2018

Quantifying the Uncertainties in an Ensemble of Decadal Climate Predictions

Meaningful climate predictions should be accompanied by the corresponding uncertainty range. Common methods for estimating the uncertainty range are based on the spread of ensemble predictions. However, a simulation ensemble is not necessarily a proper sample of the real distribution of the climate, and therefore, the ensemble spread cannot be interpreted as the actual uncertainty. We propose a new method that links between the ensemble spread and the uncertainty without relying on any assumptions regarding the distribution of the ensemble predictions. The method is tested using CMIP5 1981-2010 decadal predictions and is shown to outperform other common methods.

Strobach, Ehud↗
DOE OSTI2025

Uncertainty of 21st Century western U.S. snowfall loss derived from regional climate model large ensemble

Abstract The western United States is dependent on winter snowfall over its major mountain ranges, which gradually melts each year, serving as a natural reservoir for water resources. In a future warmer climate, much of this snowfall could be replaced by rain, making it more challenging to capture and store water. In this study, we utilize an ensemble of dynamically downscaled simulations forced by 14 global climate models (GCMs). These GCMs project wildly different futures, in terms of both temperature and precipitation change, producing significant uncertainty in snowfall projections. Here we exploit the robust statistics of the downscaled ensemble, and diagose the sensitivity of end-of-century snowfall loss across the region to both warming and regional wetting/drying in the driving GCM. The windward slopes of the Sierra Nevada and Cascades are particularly sensitive to warming (losing ~ 15% annual snowfall per degree warming), with little influence of precipitation. By contrast, snowfall loss in the inter-mountain west is less sensitive to warming (~ 5% K −1 ), but is significantly offset/exacerbated by precipitation changes (~ 0.5% snow per 1% precipitation). Combining such sensitivities with the warming and regional precipitation signals in the full CMIP6 ensemble, we can fully quantify likely snowfall loss and its uncertainty at any location, for any emissions scenario. We find that the western U.S. as a whole will lose 34 ± 8% of its total volumetric snowfall by end-of-century under the high-emissions SSP3-7.0 scenario, but 25 ± 6% and 17 ± 6% under the lower-emissions SSP2-4.5 and SSP1-2.6 scenarios.

Norris, Jesse (ORCID:0000000289883326)↗
DOE OSTI2026

Downscaled Daily 1 km Climate Data (NEX-GDDP-CMIP6) for Southeast Texas. Full ensemble of downscaled CMIP6 climate projections at 1 km daily resolution.

For the SETx-UIFL, the daily NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) dataset climate projections were downscaled from approximately 27 km to 1 km. The SETx dataset provides very high-resolution climate data for the historical period (1950–2014) and future scenarios derived from CMIP6 global models under the four Tier 1 Shared Socioeconomic Pathways (SSPs 1.26, 2.45, 3.70, and 5.85), developed for the IPCC Sixth Assessment Report. A subset of ten NEX-GDDP-CMIP6 models was selected to represent a balance of model families, climate sensitivities, and availability across scenarios, ensuring a diverse and reliable ensemble for regional analysis. Selected models: BCC-CSM2-MR, CESM2, CMCC-ESM2, CNRM-ESM2-1, EC-Earth3, FGOALS-g3, GFDL-CM4, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. Daily variables downscaled include tasmax, tasmin, tas, pr, hurs, huss, rsds, rlds, and sfcWind.

Persad, Geeta↗
DOE OSTI2023

Ensemble Spread Behavior in Coupled Climate Models: Insights From the Energy Exascale Earth System Model Version 1 Large Ensemble

AbstractAssessing uncertainty in future climate projections requires understanding both internal climate variability and external forcing. For this reason, single‐model initial condition large ensembles (SMILEs) run with Earth System Models (ESMs) have recently become popular. Here we present a new 20‐member SMILE with the Energy Exascale Earth System Model version 1 (E3SMv1‐LE), which uses a “macro” initialization strategy choosing coupled atmosphere/ocean states based on inter‐basin contrasts in ocean heat content (OHC). The E3SMv1‐LE simulates tropical climate variability well, albeit with a muted warming trend over the twentieth century due to overly strong aerosol forcing. The E3SMv1‐LE's initial climate spread is comparable to other (larger) SMILEs, suggesting that maximizing inter‐basin ocean heat contrasts may be an efficient method of generating ensemble spread. We also compare different ensemble spread across multiple SMILEs, using surface air temperature and OHC. The Community Earth system Model version 1, the only ensemble which utilizes a “micro” initialization approach perturbing only atmospheric initial conditions, yields lower spread in the first ∼30 years. The E3SMv1‐LE exhibits a relatively large spread, with some evidence for anthropogenic forcing influencing spread in the late twentieth century. However, systematic effects of differing “macro” initialization strategies are difficult to detect, possibly resulting from differing model physics or responses to external forcing. Notably, the method of standardizing results affects ensemble spread: control simulations for most models have either large background trends or multi‐centennial variability in OHC. This spurious disequlibrium behavior is a substantial roadblock to understanding both internal climate variability and its response to forcing.

Stevenson, Samantha↗
DOE OSTI2023

A Large Ensemble Global Dataset for Climate Impact Assessments

We present a self-consistent, large ensemble, high-resolution global dataset of long-term future climate, which accounts for the uncertainty in climate system response to anthropogenic emissions of greenhouse gases and in geographical patterns of climate change. The dataset is developed by applying an integrated spatial disaggregation (SD) - bias-correction (BC) method to climate projections from the MIT Integrated Global System Model (IGSM). Four emission scenarios are considered that represent energy and environmental policies and commitments of potential future pathways, namely, Reference, Paris Forever, Paris 2 °C and Paris 1.5 °C. The dataset contains nine key meteorological variables on a monthly scale from 2021 to 2100 at a spatial resolution of 0.5°x 0.5°, including precipitation, air temperature (mean, minimum and maximum), near-surface wind speed, shortwave and longwave radiation, specific humidity, and relative humidity. We demonstrate the dataset’s ability to represent climate-change responses across various regions of the globe. This dataset can be used to support regional-scale climate-related impact assessments of risk across different applications that include hydropower, water resources, ecosystem, agriculture, and sustainable development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A global ensemble of ocean wave climate projections from CMIP5-driven models

This dataset, produced through the Coordinated Ocean Wave Climate Project (COWCLIP) phase 2, represents the first coordinated multivariate ensemble of 21st Century global wind-wave climate projections available (henceforth COWCLIP2.0). COWCLIP2.0 comprises general and extreme statistics of significant wave height (H S ), mean wave period (T m ), and mean wave direction (θ m ) computed over time-slices 1979–2004 and 2081–2100, at different frequency resolutions (monthly, seasonally and annually). The full ensemble comprising 155 global wave climate simulations is obtained from ten CMIP5-based state-of-the-art wave climate studies and provides data derived from alternative wind-wave downscaling methods, and different climate-model forcing and future emissions scenarios. The data has been produced, and processed, under a specific framework for consistency and quality, and follows CMIP5 Data Reference Syntax, Directory structures, and Metadata requirements. Technical comparison of model skill against 26 years of global satellite measurements of significant wave height has been undertaken at global and regional scales. This new dataset provides support for future broad scale coastal hazard and vulnerability assessments and climate adaptation studies in many offshore and coastal engineering applications.

Science & Technology - Other Topics↗
NASA NTRS2016

Multi-Wheat-Model Ensemble Responses to Interannual Climate Variability

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981e2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-termwarming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.

uncertainty↗
DOE OSTI2023

Advancing research on compound weather and climate events via large ensemble model simulations

Societally relevant weather impacts typically result from compound events, which are rare combinations of weather and climate drivers. Focussing on four event types arising from different combinations of climate variables across space and time, here we illustrate that robust analyses of compound events — such as frequency and uncertainty analysis under present-day and future conditions, event attribution to climate change, and exploration of low-probability-high-impact events — require data with very large sample size. In particular, the required sample is much larger than that needed for analyses of univariate extremes. We demonstrate that Single Model Initial-condition Large Ensemble (SMILE) simulations from multiple climate models, which provide hundreds to thousands of years of weather conditions, are crucial for advancing our assessments of compound events and constructing robust model projections. Combining SMILEs with an improved physical understanding of compound events will ultimately provide practitioners and stakeholders with the best available information on climate risks.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

Attribution of multi-annual to decadal changes in the climate system: The Large Ensemble Single Forcing Model Intercomparison Project (LESFMIP)

Multi-annual to decadal changes in climate are accompanied by changes in extreme events that cause major impacts on society and severe challenges for adaptation. Early warnings of such changes are now potentially possible through operational decadal predictions. However, improved understanding of the causes of regional changes in climate on these timescales is needed both to attribute recent events and to gain further confidence in forecasts. Here we document the Large Ensemble Single Forcing Model Intercomparison Project that will address this need through coordinated model experiments enabling the impacts of different external drivers to be isolated. We highlight the need to account for model errors and propose an attribution approach that exploits differences between models to diagnose the real-world situation and overcomes potential errors in atmospheric circulation changes. The experiments and analysis proposed here will provide substantial improvements to our ability to understand near-term changes in climate and will support the World Climate Research Program Lighthouse Activity on Explaining and Predicting Earth System Change.

decadal climate↗
DOE OSTI2023

Amplified drought trends in Nepal increase the potential for Himalayan wildfires

Here we report in the spring 2021, Nepal underwent a record wildfire season in which active fires were detected at a rate 10 times greater than the 2002–2020 average. Prior to these major wildfire events, the country experienced a prolonged precipitation deficit and extreme drought during the post-monsoon period (starting in October 2020). An analysis using observational, reanalysis, and climate model ensemble data indicates that both climate variability and climate change-induced severe drought conditions were at play. Further analysis of climate model outputs suggests the likely reoccurrence of drought conditions, thus favoring active wildfire seasons in Nepal throughout the twenty-first century. While the inter-model uncertainty is large and direct modeling of wildfire spread and suppression has not been completed, the demonstrated relationship between a drought index (the standardized precipitation and evapotranspiration index) and subsequent fire activity may offer actionable opportunities for forest managers to employ the monitoring and projection of climate anomalies at sub-seasonal to decadal timescales to inform their management strategies for Nepal’s wildlands.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2020

The Pliocene Model Intercomparison Project Phase 2: Large-scale Climate Features and Climate Sensitivity

The Pliocene epoch has great potential to improve our understanding of the long-term climatic and environmental consequences of an atmospheric CO2 concentration near ∼400 parts per million by volume. Here we present the large-scale features of Pliocene climate as simulated by a new ensemble of climate models of varying complexity and spatial resolution based on new reconstructions of boundary conditions (the Pliocene Model Intercomparison Project Phase 2; PlioMIP2). As a global annual average, modelled surface air temperatures increase by between 1.7 and 5.2 ∘C relative to the pre-industrial era with a multi-model mean value of 3.2 ∘C. Annual mean total precipitation rates increase by 7 % (range: 2 %–13 %). On average, surface air temperature (SAT) increases by 4.3 ∘C over land and 2.8 ∘C over the oceans. There is a clear pattern of polar amplification with warming polewards of 60∘ N and 60∘ S exceeding the global mean warming by a factor of 2.3. In the Atlantic and Pacific oceans, meridional temperature gradients are reduced, while tropical zonal gradients remain largely unchanged. There is a statistically significant relationship between a model's climate response associated with a doubling in CO2 (equilibrium climate sensitivity; ECS) and its simulated Pliocene surface temperature response. The mean ensemble Earth system response to a doubling of CO2 (including ice sheet feedbacks) is 67 % greater than ECS; this is larger than the increase of 47 % obtained from the PlioMIP1 ensemble. Proxy-derived estimates of Pliocene sea surface temperatures are used to assess model estimates of ECS and give an ECS range of 2.6–4.8 ∘C. This result is in general accord with the ECS range presented by previous Intergovernmental Panel on Climate Change (IPCC) Assessment Reports.

Pliocene Model Intercomparison Project↗