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Results for “climate models and Earth system models”

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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Intensification of future subsurface marine heatwaves in an eddy-resolving model

A shift in depth range enables marine organisms to adapt to marine heatwaves (MHWs). Subsurface MHWs could limit this pathway, yet their response to climate warming remains unclear. Here, using an eddy-resolving Earth system model forced under a high emission scenario, we project a robust global increase in subsurface MHWs driven by rising subsurface mean temperatures and enhanced temperature variability. Historically, maximum MHW intensity occurs around 100m depth, which shifts to the faster-warming surface under greenhouse warming. However, removing the long-term warming trend yields an increase in subsurface MHW intensity and annual days greater than that at the surface, especially in large marine ecosystem regions, primarily due to increased variability. Additionally, days of the surface and subsurface concurrent event increase ten times more than those of individual events. Our study highlights a heightened threat to marine organisms under global warming, as the increased subsurface heatwaves reduce their refuge options.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Confronting the water potential information gap

Water potential directly controls the function of leaves, roots and microbes, and gradients in water potential drive water flows throughout the soil–plant–atmosphere continuum. Notwithstanding its clear relevance for many ecosystem processes, soil water potential is rarely measured in situ, and plant water potential observations are generally discrete, sparse, and not yet aggregated into accessible databases. These gaps limit our conceptual understanding of biophysical responses to moisture stress and inject large uncertainty into hydrologic and land-surface models. Here, we outline the conceptual and predictive gains that could be made with more continuous and discoverable observations of water potential in soils and plants. We discuss improvements to sensor technologies that facilitate in situ characterization of water potential, as well as strategies for building new networks that aggregate water potential data across sites. Here, we end by highlighting novel opportunities for linking more representative site-level observations of water potential to remotely sensed proxies. Together, these considerations offer a road map for clearer links between ecohydrological processes and the water potential gradients that have the ‘potential’ to substantially reduce conceptual and modelling uncertainties.

58 GEOSCIENCES↗
DOE OSTI2020

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

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

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields

Abstract Efforts to understand and quantify how a changing climate can impact agriculture often rely on bias-corrected and downscaled climate information, making it important to quantify potential biases of this approach. Here, we use a multi-model ensemble of statistically bias-corrected and downscaled climate models, as well as the corresponding parent models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), to drive a statistical panel model of U.S. maize yields that incorporates season-wide measures of temperature and precipitation. We analyze uncertainty in annual yield hindcasts, finding that the CMIP5 models considerably overestimate historical yield variability while the bias-corrected and downscaled versions underestimate the largest weather-induced yield declines. We also find large differences in projected yields and other decision-relevant metrics throughout this century, leaving stakeholders with modeling choices that require navigating trade-offs in resolution, historical accuracy, and projection confidence.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Microbial contribution to post-fire tundra ecosystem recovery over the 21st century

Abstract Tundra ecosystems have experienced an increased frequency of fire, and this trend is predicted to continue throughout the 21 st Century. Post-fire recovery is underpinned by complex interactions between microbial functional groups that drive nutrient cycling. Here we use a mechanistic model to demonstrate an acceleration of the nitrogen cycle post-fire driven by changes in niche space and microbial competitive dynamics. We show that over the first 5-years post-fire, fast-growing bacterial heterotrophs colonize regions of the soil previously occupied by slower-growing saprotrophic fungi. The bacterial heterotrophs mineralize organic matter, releasing nutrients into the soil. This pathway outweighs new sources of nitrogen and facilitates the recovery of plant productivity. We broadly show here that while consideration of distinct microbial metabolisms related to carbon and nutrient cycling remains rare in terrestrial ecosystem models, they are important when considering the rate of ecosystem recovery post-disturbance and the feedback to soil nutrient cycles on centennial timescales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Mapping the global distribution of C 4 vegetation using observations and optimality theory

Plants with the C 4 photosynthesis pathway typically respond to climate change differently from more common C 3 -type plants, due to their distinct anatomical and biochemical characteristics. These different responses are expected to drive changes in global C 4 and C 3 vegetation distributions. However, current C 4 vegetation distribution models may not predict this response as they do not capture multiple interacting factors and often lack observational constraints. Here, we used global observations of plant photosynthetic pathways, satellite remote sensing, and photosynthetic optimality theory to produce an observation-constrained global map of C 4 vegetation. We find that global C 4 vegetation coverage decreased from 17.7% to 17.1% of the land surface during 2001 to 2019. This was the net result of a reduction in C 4 natural grass cover due to elevated CO 2 favoring C 3 -type photosynthesis, and an increase in C 4 crop cover, mainly from corn (maize) expansion. Using an emergent constraint approach, we estimated that C 4 vegetation contributed 19.5% of global photosynthetic carbon assimilation, a value within the range of previous estimates (18–23%) but higher than the ensemble mean of dynamic global vegetation models (14 ± 13%; mean ± one standard deviation). Our study sheds insight on the critical and underappreciated role of C 4 plants in the contemporary global carbon cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

ESMs Latent Space Exploration for Uncertainty Quantification and Spatiotemporal Downscaling

This final report for DOE Award DE-SC0023044 presents advances in two key areas of climate modeling: (1) representative climate model selection and (2) Earth System Model (ESM) downscaling using hybrid AI methods. The first section introduces a reordered, three-stage workflow to select representative GCM runs that more effectively balance historical skill with ensemble spread, validated across Texas, Bihar, and New York. The second section introduces two novel super-resolution frameworks, ViSIR and ViFOR, that integrate Vision Transformers with sinusoidal and Fourier-based implicit neural representations. These models achieve state-of-the-art reconstruction accuracy for ESM variables including surface temperature and heat fluxes. The report includes detailed methodology, benchmarks, and results, demonstrating significant gains in uncertainty quantification, spatial fidelity, and scalability for climate-impact studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

An increase in marine heatwaves without significant changes in surface ocean temperature variability

Marine heatwaves (MHWs)—extremely warm, persistent sea surface temperature (SST) anomalies causing substantial ecological and economic consequences—have increased worldwide in recent decades. Concurrent increases in global temperatures suggest that climate change impacted MHW occurrences, beyond random changes arising from natural internal variability. Moreover, the long-term SST warming trend was not constant but instead had more rapid warming in recent decades. Here we show that this nonlinear trend can—on its own—appear to increase SST variance and hence MHW frequency. Using a Linear Inverse Model to separate climate change contributions to SST means and internal variability, both in observations and CMIP6 historical simulations, we find that most MHW increases resulted from regional mean climate trends that alone increased the probability of SSTs exceeding a MHW threshold. Our results suggest the need to carefully attribute global warming-induced changes in climate extremes, which may not always reflect underlying changes in variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Implementation of Framework for Improvement by Vertical Enhancement Into Energy Exascale Earth System Model

Abstract The low cloud bias in global climate models (GCMs) remains an unsolved problem. Coarse vertical resolution in GCMs has been suggested to be a significant cause of low cloud bias because planetary boundary layer parameterizations cannot resolve sharp temperature and moisture gradients often found at the top of subtropical stratocumulus layers. This work aims to ameliorate the low cloud problem by implementing a new computational method, the Framework for Improvement by Vertical Enhancement (FIVE), into the Energy Exascale Earth System Model (E3SM). Three physics schemes representing microphysics, radiation, and turbulence as well as vertical advection are interfaced to vertically enhanced physics (VEP), which allows for these processes to be computed on a higher vertical resolution grid compared to the rest of the E3SM model. We demonstrate the better representation of subtropical boundary layer clouds with FIVE while limiting additional computational cost from the increased number of levels. When the vertical resolution approaches the large eddy simulation‐like vertical resolution in VEP, the climatological low cloud amount shows a significant increase of more than 30% in the southeastern Pacific Ocean. Using FIVE to improve the representation of low‐level clouds does not come with any negative side effects associated with the simulation of mid‐ and high‐level cloud and precipitation, that can occur when running the full model at higher vertical resolution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Deciphering the capricious precipitation response: irrigation impact in the North China Plain

Intensive irrigation in the North China Plain (NCP) raises environmental concerns, yet its climate impacts remain inconsistent among current modeling studies. These inconsistencies may arise from deficiencies in existing models. To better capture irrigation impact, we employed a recently improved irrigation model tailored for the NCP, which significantly enhances simulations of crop growth and irrigation application. Results reveal that irrigation exerts competing effects on precipitation: enhancing it in May and June by increasing rainfall frequency and intensity, while suppressing it in July and August by reducing intensity. Spatial precipitation changes correlate with upper-tropospheric humidity, while inter-annual changes link to geopotential height and wind anomalies. This suggests that irrigation-induced surface cooling and moisturizing modulate atmospheric thermodynamic and dynamic structures, influencing convective precipitation unevenly across regions and time, leading to complex precipitation changes. These findings highlight the complexity of irrigation impacts and emphasize the need for improved land representation in climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Prescreening-Based Subset Selection for Improving Predictions of Earth System Models With Application to Regional Prediction of Red Tide

We present the ensemble method of prescreening-based subset selection to improve ensemble predictions of Earth system models (ESMs). In the prescreening step, the independent ensemble members are categorized based on their ability to reproduce physically-interpretable features of interest that are regional and problem-specific. The ensemble size is then updated by selecting the subsets that improve the performance of the ensemble prediction using decision relevant metrics. We apply the method to improve the prediction of red tide along the West Florida Shelf in the Gulf of Mexico, which affects coastal water quality and has substantial environmental and socioeconomic impacts on the State of Florida. Red tide is a common name for harmful algal blooms that occur worldwide, which result from large concentrations of aquatic microorganisms, such as dinoflagellate Karenia brevis, a toxic single celled protist. We present ensemble method for improving red tide prediction using the high resolution ESMs of the Coupled Model Intercomparison Project Phase 6 (CMIP6) and reanalysis data. The study results highlight the importance of prescreening-based subset selection with decision relevant metrics in identifying non-representative models, understanding their impact on ensemble prediction, and improving the ensemble prediction. These findings are pertinent to other regional environmental management applications and climate services. Additionally, our analysis follows the FAIR Guiding Principles for scientific data management and stewardship such that data and analysis tools are findable, accessible, interoperable, and reusable. As such, the interactive Colab notebooks developed for data analysis are annotated in the paper. This allows for efficient and transparent testing of the results’ sensitivity to different modeling assumptions. Moreover, this research serves as a starting point to build upon for red tide management, using the publicly available CMIP, Coordinated Regional Downscaling Experiment (CORDEX), and reanalysis data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Robust but weak winter atmospheric circulation response to future Arctic sea ice loss

The possibility that Arctic sea ice loss weakens mid-latitude westerlies, promoting more severe cold winters, has sparked more than a decade of scientific debate, with apparent support from observations but inconclusive modelling evidence. Here we show that sixteen models contributing to the Polar Amplification Model Intercomparison Project simulate a weakening of mid-latitude westerlies in response to projected Arctic sea ice loss. We develop an emergent constraint based on eddy feedback, which is 1.2 to 3 times too weak in the models, suggesting that the real-world weakening lies towards the higher end of the model simulations. Still, the modelled response to Arctic sea ice loss is weak: the North Atlantic Oscillation response is similar in magnitude and offsets the projected response to increased greenhouse gases, but would only account for around 10% of variations in individual years. We further find that relationships between Arctic sea ice and atmospheric circulation have weakened recently in observations and are no longer inconsistent with those in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Unified 0.25-degree gridded infrastructure-critical extreme weather for the United States from 1979 to 2100

Extreme weather events can severely disrupt critical infrastructure, triggering cascading effects on power, transportation, and essential services. However, standard weather and climate datasets often lack specialized variables necessary for hazard assessments. We present a unified dataset of infrastructure-critical weather and climate variables across the United States at 0.25° resolution, covering daily or sub-daily intervals from 1979 to 2100. The dataset includes temperature, dew point, wind gusts, precipitation partitioned by rain, snow, and freezing rain or ice pellets, lightning, and wildfire metrics. Historical conditions (1979-2023) are synthesized from observations and reanalysis products, while future projections are derived from 14 CMIP6 global climate models (historical, SSP245, and SSP585 experiments). Physically based and data-driven methods are used to estimate variables not directly provided by existing models. By integrating these variables into a single unified dataset, we enable consistent, high-resolution assessments of weather-related infrastructure risks across past and future periods, supporting wide-ranging applications in energy, transportation, water resources, emergency management, and beyond.

Climate and Earth system modelling↗
DOE OSTI2021

Anthropogenic influence on extreme precipitation over global land areas seen in multiple observational datasets

The intensification of extreme precipitation under anthropogenic forcing is robustly projected by global climate models, but highly challenging to detect in the observational record. Large internal variability distorts this anthropogenic signal. Models produce diverse magnitudes of precipitation response to anthropogenic forcing, largely due to differing schemes for parameterizing subgrid-scale processes. Meanwhile, multiple global observational datasets of daily precipitation exist, developed using varying techniques and inhomogeneously sampled data in space and time. Previous attempts to detect human influence on extreme precipitation have not incorporated model uncertainty, and have been limited to specific regions and observational datasets. Using machine learning methods that can account for these uncertainties and capable of identifying the time evolution of the spatial patterns, we find a physically interpretable anthropogenic signal that is detectable in all global observational datasets. Machine learning efficiently generates multiple lines of evidence supporting detection of an anthropogenic signal in global extreme precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Global tropical cyclone precipitation scaling with sea surface temperature

Understanding the relationship between tropical cyclone (TC) precipitation and sea surface temperature (SST) is essential for both TC hazard forecasting and projecting how these hazards will change in the future due to climate change. This work untangles how global TC precipitation is impacted by present-day SST variability (known as apparent scaling) and by long-term changes in SST caused by climate change (known as climate scaling). A variety of datasets are used including precipitation and SST observations, realistic climate model simulations, and idealized climate model simulations. The apparent scaling rates depend on precipitation metric; examples shown here have ranges of 6.1 to 9.5% per K versus 5.9 to 9.8% per K for two different metrics. The climate scaling is estimated at about 5% per K, which is slightly less than the atmospheric moisture scaling based on thermodynamic principles of about 7% per K (i.e., the Clausius–Clapeyron scaling). The apparent scaling is greater than the climate scaling, which implies that the relationship between TC precipitation and present-day SST variability should not be used to project the long-term response of TC precipitation to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Disproportionate control on aerosol burden by light rain

Atmospheric aerosols are of great climatic and environmental importance due to their effects on the Earth’s radiative energy balance and air quality. Aerosol concentrations are strongly influenced by rainfall via wet removal. Global climate models have been used to quantify their climate and health effects. However, they commonly suffer from a well-known problem of ‘too much light rain and too little heavy rain’. The impact of simulated rainfall intensities on aerosol burden at the global scale is still unclear. In this study, we show that rainfall intensity has profound impacts on aerosol burden, and light rain has a disproportionate control on it. By improving the representation of convection, the light-rain (1–20 mm d –1 ) frequency in two state-of-the-art global climate models is reduced. As a result, the aerosol burden is increased globally, especially over the tropics and subtropics, by as much as 0.3 in aerosol optical depth in tropical rain belts. It is attributed to the dominant contribution of light rain to the accumulated wet removal by its frequent occurrence despite its weak intensity. The implication of these findings is that understanding the nature of aerosol scavenging by rainfall is critical to aerosol–climate interaction and its impact on climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The High-resolution Urban Meteorology for Impacts Dataset (HUMID) daily for the Conterminous United States

Many current gridded surface meteorological datasets are inadequate for quantifying near surface spatiotemporal variability because they do not fully represent the impacts of land surface heterogeneity. Of note, explicit representation of the spatial structure and magnitude of local urban warming are usually lacking. Here we enhance the representation of spatial meteorological variability over urban areas in the conterminous United States (CONUS) by employing the High-Resolution Land Data Assimilation System (HRLDAS), which accounts for the fine-scale impacts of spatiotemporally varying land surfaces on weather. We also synthesize in situ meteorological data including local mesonets to create a 1 km grid spacing model-observation fusion product spanning 1981-2018 over the CONUS. Daily maximum, minimum, and mean values for a variety of temperature estimates, humidity, and surface energy budget terms, among others, are included. This High-resolution Urban Meteorology for Impacts Dataset (HUMID) will be useful for studies examining spatial variability of near surface meteorology and the impacts of urban heat islands across many disciplines including epidemiology, ecology, and climatology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Characterizing Drought Behavior in the Colorado River Basin Using Unsupervised Machine Learning

Drought is a pressing issue for the Colorado River Basin (CRB) due to the social and economic value of water resources in the region and the significant uncertainty of future drought under climate change. Here, we use climate simulations from various Earth System Models (ESMs) to force the Variable Infiltration Capacity hydrologic model and project multiple drought indicators for the sub-watersheds within the CRB. We apply an unsupervised machine learning (ML) based on Non-Negative Matrix Factorization using K-means clustering (NMFk) to synthesize the simulated historical, future, and change in drought indicators. The unsupervised ML approach can identify sub-watersheds where key changes to drought indicator behavior occur, including shifts in snowpack, snowmelt timing, precipitation, and evapotranspiration. While changes in future precipitation vary across ESMs, the results indicate that the Upper CRB will experience increasing evaporative demand and surface-water scarcity, with some locations experiencing a shift from a radiation-limited to a water-limited evaporation regime in the summer. Large shifts in peak runoff are observed in snowmelt-dominant sub-watersheds, with complete disappearance of the snowmelt signal for some sub-watersheds. The work demonstrates the utility of the NMFk algorithm to efficiently identify behavioral changes of drought indicators across space and time and to quickly analyze and interpret hydro climate model results.

54 ENVIRONMENTAL SCIENCES↗