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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 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↗
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↗
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↗
DOE OSTI2022

Long-term trends in storm surge climate derived from an ensemble of global surge reconstructions

We address the challenge, due to sparse observational records, of investigating long-term changes in the storm surge climate globally. We use two centennial and three satellite-era daily storm surge time series from the Global Storm Surge Reconstructions (GSSR) database and assess trends in the magnitude and frequency of extreme storm surge events at 320 tide gauges across the globe from 1930, 1950, and 1980 to present. Before calculating trends, we perform change point analysis to identify and remove data where inhomogeneities in atmospheric reanalysis products could lead to spurious trends in the storm surge data. Even after removing unreliable data, the database still extends existing storm surge records by several decades for most of the tide gauges. Storm surges derived from the centennial 20CR and ERA-20C atmospheric reanalyses show consistently significant positive trends along the southern North Sea and the Kattegat Bay regions during the periods from 1930 and 1950 onwards and negative trends since 1980 period. When comparing all five storm surge reconstructions and observations for the overlapping 1980–2010 period we find overall good agreement, but distinct differences along some coastlines, such as the Bay of Biscay and Australia. We also assess changes in the frequency of extreme surges and find that the number of annual exceedances above the 95th percentile has increased since 1930 and 1950 in several regions such as Western Europe, Kattegat Bay, and the US East Coast.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Differential credibility assessment for statistical downscaling

Climate science is increasingly using (i) ensembles of climate projections from multiple models derived using different assumptions and/or scenarios and (ii) process-oriented diagnostics of model fidelity. Efforts to assign differential credibility to projections and/or models are also rapidly advancing. A framework to quantify and depict the credibility of statistically downscaled model output is presented and demonstrated. Here, the approach employs transfer functions in the form of robust and resilient generalized linear models applied to downscale daily minimum and maximum temperature anomalies at 10 locations using predictors drawn from ERA-Interim reanalysis and two global climate models (GCM; GFDL-ESM2M and MPI-ESM-LR). The downscaled time series are used to derive several impact relevant CLIMDEX temperature indices that are assigned credibility based on (1) the reproduction of relevant large-scale predictors by the GCMs (i.e. fraction of regression beta-weights derived from predictors that are well-reproduced) and (2) the degree of variance in the observations reproduced in the downscaled series following application of a new variance inflation technique. Credibility of the downscaled predictands varies across locations, between the two GCM and is generally higher for minimum temperature than maximum temperature. The differential credibility assessment framework demonstrated here is easy to use and flexible. It can be applied as is to inform decision makers regarding projection confidence, and/or extended to include other components of the transfer functions, and/or used to weight members of a statistically downscaled ensemble.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Bayesian weighting of climate models based on climate sensitivity

Using climate model ensembles containing members that exhibit very high climate sensitivities to increasing CO 2 concentrations can result in biased projections. Various methods have been proposed to ameliorate this ‘hot model’ problem, such as model emulators or model culling. Here, we utilize Bayesian Model Averaging as a framework to address this problem without resorting to outright rejection of models from the ensemble. Taking advantage of multiple lines of evidence used to construct the best estimate of the earth’s climate sensitivity, the Bayesian Model Averaging framework produces an unbiased posterior probability distribution of model weights. The updated multi-model ensemble projects end-of-century global mean surface temperature increases of 2 °C for a low emissions scenario (SSP1-2.6) and 5 °C for a high emissions scenario (SSP5-8.5). These estimates are lower than those produced using a simple multi-model mean for the CMIP6 ensemble. The results are also similar to results from a model culling approach, but retain some weight on low-probability models, allowing for consideration of the possibility that the true value could lie at the extremes of the assessed distribution. Our results showcase Bayesian Model Averaging as a path forward to project future climate change that is commensurate with the available scientific evidence.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Characterizing and quantifying uncertainty in projections of climate change impacts on air quality

Climate change can aggravate air pollution, with important public health and environmental consequences. While major sources of uncertainty in climate change projections - greenhouse gas (GHG) emissions scenario, model response, and internal variability - have been investigated extensively, their propagation to estimates of air quality impacts has not been systematically assessed. Here, we compare these uncertainties using a coupled modeling framework that includes a human activity model, an Earth system model of intermediate complexity, and a global atmospheric chemistry model. Uncertainties in projections of U.S. air quality under 21st century climate change are quantified based on a climate-chemistry ensemble that includes multiple initializations, representations of climate sensitivity, and climate policy scenarios, under constant air pollution emissions. We find that climate-related uncertainties are comparable at mid-century, making it difficult to distinguish the impact of variations in GHG emissions on ozone and particulate matter pollution. While GHG emissions scenario eventually becomes the dominant uncertainty based on the scenarios considered, all sources of uncertainty are significant through the end of the century. The results provide insights into intrinsically different uncertainties in projections of air pollution impacts and the potential for large ensembles to better capture them.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Constraining the Varied Response of Northern Hemisphere Winter Circulation Waviness to Climate Change

Abstract While a large latitudinal displacement of the westerly jet brings about disproportionate socioeconomic impacts over Northern Hemisphere midlatitude continents, it is not well understood as to whether the winter circulation will become wavier or less in response to climate change. Here, using observations and large ensembles of climate models, we show that changes in atmospheric waviness can be estimated from the optimal structures of the westerly jet for wavier circulation, which are obtained from an advection‐diffusion model. Thus, the changes in westerly jet structure in climate models under climate change provide a physical constraint on changes in atmospheric waviness, indicating that the North Atlantic wave activity will experience a robust decline in a warmer climate, while future North Pacific wave activity is obscured by model uncertainty rather than internal variability. These findings highlight the changes to jet stream structure as a constraint for regional circulation waviness in a changing climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Robust evaluation of ENSO in climate models: How many ensemble members are needed?

Large ensembles of model simulations require considerable resources, and thus defining an appropriate ensemble size for a particular application is an important experimental design criterion. We estimate the ensemble size (N) needed to assess a model's ability to capture observed El Niño-Southern Oscillation (ENSO) behavior by utilizing the recently developed International CLIVAR ENSO Metrics Package (Planton et al., 2021). Using the larger ensembles available from CMIP6 and the US CLIVAR Large Ensemble Working Group, we find that larger ensembles are needed to robustly capture baseline ENSO characteristics (N > 50) and physical processes (N > 50) than the background climatology (N ≥ 12) and remote ENSO teleconnections (N ≥ 6). While these results vary somewhat across metrics and models, our study quantifies how larger ensembles are required to robustly evaluate simulated ENSO behavior, thereby providing some guidance for the design of model ensembles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Increasing large wildfires over the western United States linked to diminishing sea ice in the Arctic

Abstract The compound nature of large wildfires in combination with complex physical and biophysical processes affecting variations in hydroclimate and fuel conditions makes it difficult to directly connect wildfire changes over fire-prone regions like the western United States (U.S.) with anthropogenic climate change. Here we show that increasing large wildfires during autumn over the western U.S. are fueled by more fire-favorable weather associated with declines in Arctic sea ice during preceding months on both interannual and interdecadal time scales. Our analysis (based on observations, climate model sensitivity experiments, and a multi-model ensemble of climate simulations) demonstrates and explains the Arctic-driven teleconnection through regional circulation changes with the poleward-shifted polar jet stream and enhanced fire-favorable surface weather conditions. The fire weather changes driven by declining Arctic sea ice during the past four decades are of similar magnitude to other leading modes of climate variability such as the El Niño-Southern Oscillation that also influence fire weather in the western U.S.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Statistical Generation of Ocean Forcing With Spatiotemporal Variability for Ice Sheet Models

Melting of ice at the base of floating ice shelves that fringe the Antarctic ice sheet has been identified as a significant source of uncertainty in sea level rise projections. Part of this uncertainty derives from chaotic internal variability of the coupled ocean-atmosphere system. For numerical ice sheet model projections, this uncertainty has not previously been quantified because of the prohibitive computational expense of running large climate model ensembles. Here, we develop and demonstrate a technique that generates independent realizations of internal climate variability from a single climate model simulation. Building on previous developments in model emulation, this technique uses empirical orthogonal function decomposition and Fourier-phase randomization to generate statistically consistent realizations of spatiotemporal variability fields for the target climate variable. The method facilitates efficient sampling of a wide range of climate trajectories, which can also be incorporated within ice sheet or other physical models to represent feedback processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Separating the Influences of Low-Latitude Warming and Sea Ice Loss on Northern Hemisphere Climate Change

Abstract Analyzing a multimodel ensemble of coupled climate model simulations forced with Arctic sea ice loss using a two-parameter pattern-scaling technique to remove the cross-coupling between low- and high-latitude responses, the sensitivity to high-latitude sea ice loss is isolated and contrasted to the sensitivity to low-latitude warming. Despite some differences in experimental design, the Northern Hemisphere near-surface atmospheric sensitivity to sea ice loss is found to be robust across models in the cold season; however, a larger intermodel spread is found at the surface in boreal summer, and in the free tropospheric circulation. In contrast, the sensitivity to low-latitude warming is most robust in the free troposphere and in the warm season, with more intermodel spread in the surface ocean and surface heat flux over the Northern Hemisphere. The robust signals associated with sea ice loss include upward turbulent and longwave heat fluxes where sea ice is lost, warming and freshening of the Arctic Ocean, warming of the eastern North Pacific Ocean relative to the western North Pacific with upward turbulent heat fluxes in the Kuroshio Extension, and salinification of the shallow shelf seas of the Arctic Ocean alongside freshening in the subpolar North Atlantic Ocean. In contrast, the robust signals associated with low-latitude warming include intensified ocean warming and upward latent heat fluxes near the western boundary currents, freshening of the Pacific Ocean, salinification of the North Atlantic, and downward sensible and longwave fluxes over the ocean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Assessing Prior Emergent Constraints on Surface Albedo Feedback in CMIP6

An emergent constraint (EC) is a popular model evaluation technique, which offers the potential to reduce intermodel variability in projections of climate change. Two examples have previously been laid out for future surface albedo feedbacks (SAF) stemming from loss of Northern Hemisphere (NH) snow cover (SAF snow ) and sea ice (SAF ice ). These processes also have a modern-day analog that occurs each year as snow and sea ice retreat from their seasonal maxima, which is strongly correlated with future SAF across an ensemble of climate models. The newly released CMIP6 ensemble offers the chance to test prior constraints through out-of-sample verification, an important examination of EC robustness. Here, we show that the SAF snow EC is equally strong in CMIP6 as it was in past generations, while the SAF ice EC is also shown to exist in CMIP6, but with different, slightly weaker characteristics. We find that the CMIP6 mean NH SAF exhibits a global feedback of 0.25 ± 0.05 W m -2 K -1 , or ~61% of the total global albedo feedback, largely in line with prior generations despite its increased climate sensitivity. Additionally, the NH SAF can be broken down into similar contributions from snow and sea ice over the twenty-first century in CMIP6. Crucially, intermodel variability in seasonal SAF snow and SAF ice is largely unchanged from CMIP5 because of poor outlier simulations of snow cover, surface albedo, and sea ice thickness. These outliers act to mask the noted improvement from many models when it comes to SAF ice , and to a lesser extent SAF snow .

58 GEOSCIENCES↗