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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Physiological Responses of C4 Perennial Bioenergy Grasses to Climate Change: Causes, Consequences, and Constraints

C 4 perennial bioenergy grasses are an economically and ecologically important group whose responses to climate change will be important to the future bioeconomy. These grasses are highly productive and frequently possess large geographic ranges and broad environmental tolerances, which may contribute to the evolution of ecotypes that differ in physiological acclimation capacity and the evolution of distinct functional strategies. C 4 perennial bioenergy grasses are predicted to thrive under climate change—C 4 photosynthesis likely evolved to enhance photosynthetic efficiency under stressful conditions of low [CO 2 ], high temperature, and drought—although few studies have examined how these species will respond to combined stresses or to extremes of temperature and precipitation. Important targets for C 4 perennial bioenergy production in a changing world, such as sustainability and resilience, can benefit from combining knowledge of C 4 physiology with recent advances in crop improvement, especially genomic selection.

Plant Sciences↗
DOE OSTI2023

Global variance decomposition of downscaled and bias-corrected CMIP6 climate projections

This dataset provides the results of a global variance decomposition of downscaled and bias-corrected CMIP6 climate projections. The total projection variance for a set of climate metrics is partitioned into contributions from: scenario uncertainty, model/GCM uncertainty, downscaling and bias-correction uncertainty, and interannual variability. The contribution from each source is expressed as a percentage of the total variance. Seven climate metrics are analyzed: Annual average temperature (avg_tas.nc) Annual total precipitation (tot_pr.nc) Annual maximum of daily maximum temperature (max_tasmax.nc) Annual maximum 1-day precipitation (max_pr.nc) Annual number of extremely hot days (hot_days.nc) Annual number of extremely wet days (wet_days.nc) Annual number of dry days (dry_days.nc) Extremely hot/wet days are defined to occur when temperature/precipitation exceeds the local 99th percentile defined over 1980-2014. Dry days are defined to occur when daily precipitation is less than 1mm. all_metrics_timesliced.nc gives the results for all metrics averaged over three 20-year periods: 2020-2039, 2050-2069, 2080-2099. For more details on the methods, see: Lafferty & Sriver, Downscaling and bias-correction contribute considerable uncertainty to local climate projections in CMIP6, npj Climate & Atmospheric Science (2023) An interactive visualization of this data can be found at: https://lafferty-sriver-2023-downscaling-uncertainty.msdlive.org

Lafferty, David↗
DOE OSTI2026

Evaluating Atmospheric River Impacts on Energy and Moisture Transport in the Arctic Using Different Detection Algorithms

Atmospheric rivers (ARs) significantly impact the Arctic climate system by enhancing atmospheric heat and moisture transport and altering the local energy budget. Developing AR detection tools (ARDTs) is critical yet challenging. This study evaluates 12 ARDTs in the Arctic to assess their performance in representing atmospheric heat (represented by moist static energy) and moisture transport, as well as surface downward longwave radiation (LWD) and precipitation impacts, spanning 2000 to 2019 using ERA5 reanalysis. We find that AR occurrence frequency in the Arctic varies widely, from less than 1% to over 13%, depending on the ARDT. This variability leads to differences in contributions to poleward atmospheric heat (<1%–33%) and moisture (<1%–49%) transport. The highest AR frequency, and corresponding contributions to atmospheric heat and moisture transport, occurs over the Atlantic sector during non-summer seasons for most ARDTs. This region aligns with the primary poleward moisture pathway and the end of climatological mid-latitude storm tracks, highlighting strong connections between Arctic ARs and mid-latitude cyclones. ARs induce significant LWD anomalies, largest in winter, smallest in summer, and also substantially contribute to the seasonal precipitation. Global ARDTs detect fewer ARs with larger anomalies (>100 W m−2 in higher Arctic), but contribute <1% to seasonal climatological LWD and precipitation. In contrast, polar-specific ARDTs detect higher AR occurrences and account for up to 16% of seasonal LWD and 41% precipitation. This suggests that algorithms emphasizing extreme events with large anomalies do not necessarily indicate a large climate radiative and precipitation impact.

Zhang, Chen [University of Colorado, Boulder, CO (↗
DOE OSTI2025

Examining Clustered MCSs and Their Precipitation Significance Over Global Land MCS Hotspots

Multiple independent mesoscale convective systems (MCSs) can occur closely in space and time, subjecting common geographic regions to multiple precipitation events over short periods. Motivated by an earlier study highlighting significant hydrological impacts of clustered MCSs in the US, we extend the analysis to seven global continental MCS hotspots using a 20‐year global MCS data set. Our findings reveal that (a) 40%–80% of MCSs occur in clusters, depending on the region; (b) clustered MCSs exhibit larger rainfall areas and higher mean areal rainfall, with the highest values observed in Argentina and India; and (c) clustered MCSs contribute increasing fractions to more extreme 24‐ and 72‐hr precipitation events across all regions. Our results underscore the critical role of clustered MCSs in driving hydrological extremes over global land regions and highlight the need to better understand how their formation mechanisms differ from non‐clustered events across the globe.

Hu, Huancui [Pacific Northwest National Laboratory↗
DOE OSTI2024

Hydrologic applicability of satellite-based precipitation estimates for irrigation water management in the data-scarce region

Reliable precipitation estimates are crucial for planning and managing water resources, monitoring hydrologic extremes, and fulfilling irrigation water requirements. Accurate precipitation estimates are particularly challenging in complex mountain terrains, where monitoring gauges are often sparsely distributed due to their remote locations, and high installation and long-term operation costs. Recent advances in satellite-based precipitation estimates offer promising opportunities to improve our understanding of hydrologic processes and their applications for irrigation water management. Several datasets are available varying considerably in terms of their data sources, quality control methods, estimation procedure, and spatiotemporal resolutions. Choosing the most suitable dataset for a particular application is a complex task. In this study, we (1) evaluate the performance of six satellite-based precipitation estimates (SPEs): i) CHIRPS v2.0, ii) CMORPH v1.0, iii) ERA5, iv) IMERG v6, v) MSWEP v2.8, and vi) PERSIANN-CDR against the gauge precipitation using continuous statistical and categorical indices, (2) integrate SPEs with a calibrated semi-distributed hydrologic model to predict streamflow, and (3) demonstrate practical implications of improved streamflow prediction for irrigation water management in the central Himalayan region, Nepal. Our results illustrate that satellite-based precipitation estimates have competitive performance in capturing a wide range of rainfall characteristics, with demonstrated variability across river basins and time scales. Further, there are no significant discrepancies observed in satellite-based precipitation estimates for estimating irrigation water requirements for the three major crops (maize, wheat, and paddy) during the cropping period across the selected river basins, showing a greater promise for irrigation water management planning and decision making.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Automation is all you need: Faster Earth system models with AI/ML

Focal Area: Data acquisition and assimilation enabled by machine learning (ML), artificial intelligence (AI) and advanced methods. Science Challenge: Tropical cyclones can in- duce extreme water cycle events through dramatic precipitation and storm surge. More reliable models of intensity will translate into better prediction of the impact of extreme events in large scale Earth systems simulations. We demonstrate and describe AI/ML methodologies for rapid assimilation of new, in situ data products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data for Myers-Pigg et al. (2026), "Short-term coastal forest responses to a hurricane-scale freshwater and saltwater flooding experiment"

Coastal upland forests are exposed to intensifying precipitation regimes and sea level rise, increasing tree mortality and transforming these coastal forests into wetland ecosystems. Despite these well-known risks, the differing degrees to which hydrological, biogeochemical, and biological components of upland forests respond to novel salinity exposure is relatively unknown. The Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experiment decouples two distinct disturbances associated with hydrological extremes: (1) flooding from heavy precipitation and (2) exposure to saline conditions from storm surge. This dataset includes data reported in Myers-Pigg et al. (2025), which analyzed data from the first TEMPEST flooding treatment in 2022. This includes: - Colored dissolved organic matter in porewaters - Soil temperature and oxygen - Groundwater temperature and chemistry - Dissolved organic carbon concentrations in porewaters - Soil-to-atmosphere CH4 and CO2 fluxes - Soil temperature, water content, and electrical conductivity - Root-influenced CH4 and CO2 flux - Tree sap flow velocity - The R analytical code and documentation about the computational environmental in which it was run (the "sessionInfo.txt" file) All data files are plain-text comma separated value (CSV) and no special software is required to read them.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Projecting end-of-century climate extremes and their impacts on the hydrology of a representative California watershed

Abstract. In California, it is essential to understand the evolution of water resources in response to a changing climate to sustain its economy and agriculture and to build resilient communities. Although extreme conditions have characterized the historical hydroclimate of California, climate change will likely intensify hydroclimatic extremes by the end of the century (EoC). However, few studies have investigated the impacts of EoC extremes on watershed hydrology. We use cutting-edge global climate and integrated hydrologic models to simulate EoC extremes and their effects on the water-energy balance. We assess the impacts of projected driest, median, and wettest water years under Representative Concentration Pathway (RCP) 8.5 on the hydrodynamics of the Cosumnes River basin. Substantial changes to annual average temperature (>+2.5 ∘C) and precipitation (>+38 %) will characterize the EoC extreme water years compared to their historical counterparts. A shift in the dominant form of precipitation, mostly in the form of rain, is projected to fall earlier. These changes reduce snowpack by more than 90 %, increase peak surface water and groundwater storages up to 75 % and 23 %, respectively, and drive the timing of peak storage to occur earlier in the year. Because EoC temperatures and soil moisture are high, both potential and actual evapotranspiration (ET) increase. The latter, along with the lack of snowmelt in the warm EoC, causes surface water and groundwater storages to significantly decrease in summer, with groundwater showing the highest rates of decrease. These changes result in more ephemeral EoC streams with more focused flow and increased storage in the mainstem of the river network during the summer.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Subseasonal Clustering of Atmospheric Rivers Over the Western United States

Abstract The serial occurrence of atmospheric rivers (ARs) along the US West Coast can lead to prolonged and exacerbated hydrologic impacts, threatening flood‐control and water‐supply infrastructure due to soil saturation and diminished recovery time between storms. Here a statistical approach for quantifying subseasonal temporal clustering among extreme events is applied to a 41‐year (1979–2019) wintertime AR catalog across the western United States (US). Observed AR occurrence, compared against a randomly distributed AR timeseries with the same average event density, reveals temporal clustering at a greater‐than‐random rate across the western US with a distinct geographical pattern. Compared to the Pacific Northwest, significant AR clusters over the northern Coastal Range of California and Sierra Nevada are more frequent and occur over longer time periods. Clusters along the California Coastal Range typically persist for 2 weeks, are composed of 4–5 ARs per cluster, and account for over 85% of total AR occurrence. Across the northwest Coast‐Cascade Ranges, clusters account for ∼50% of total AR occurrence, typically last 8–10 days, and contain 3–4 individual AR events. Based on precipitation data from a high‐resolution dynamical downscaling of reanalysis, the fractions of total and extreme hourly precipitation attributable to AR clusters are largest along the northern California coast and in the Sierra Nevada. Interannual variability among clusters highlights their importance for determining whether a particular water year is anomalously wet or dry. The mechanisms behind this unusual clustering are unclear and require further research.

Meteorology & Atmospheric Sciences↗
DOE OSTI2023

Evaluating the Representations of Atmospheric Rivers and Their Associated Precipitation in Reanalyses With Satellite Observations

Atmospheric rivers (ARs) are filaments of enhanced horizontal moisture transport in the atmosphere. Due to their prominent role in the meridional moisture transport and regional weather extremes, ARs have been studied extensively in recent years. Yet, the representations of ARs and their associated precipitation on a global scale remains largely unknown. In this study, we developed an AR detection algorithm specifically for satellite observations using moisture and the geostrophic winds derived from 3D geopotential height field from the combined retrievals of the Atmospheric Infrared Sounder and the Advanced Microwave Sounding Unit on NASA Aqua satellite. This algorithm enables us to develop the first global AR catalog based solely on satellite observations. The satellite–based AR catalog is then combined with the satellite–based precipitation (Integrated Muti–SatellitE Retrievals for GPM) to evaluate the representations of ARs and AR–induced precipitation in reanalysis products. Here our results show that the spreads in AR frequency and AR length distribution are generally small across data sets, while the spread in AR width is relatively larger. Reanalysis products are found to consistently underestimate both mean and extreme AR–related precipitation. However, all reanalyses tend to precipitate too often under AR conditions, especially over low latitude regions. This finding is consistent with the “drizzling” bias which has plagued generations of climate models. Overall, the findings of this study can help to improve the representations of ARs and associated precipitation in reanalyses and climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Machine learning based algorithms for uncertainty quantification in numerical weather prediction models

Complex numerical weather prediction models incorporate a variety of physical processes, each described by multiple alternative physical schemes with specific parameters. The selection of the physical schemes and the choice of the corresponding physical parameters during model configuration can significantly impact the accuracy of model forecasts. There is no combination of physical schemes that works best for all times, at all locations, and under all conditions. It is therefore of considerable interest to understand the interplay between the choice of physics and the accuracy of the resulting forecasts under different conditions. This paper demonstrates the use of machine learning techniques to study the uncertainty in numerical weather prediction models due to the interaction of multiple physical processes. The first problem addressed herein is the estimation of systematic model errors in output quantities of interest at future times, and the use of this information to improve the model forecasts. The second problem considered is the identification of those specific physical processes that contribute most to the forecast uncertainty in the quantity of interest under specified meteorological conditions. In order to address these questions we employ two machine learning approaches, random forests and artificial neural networks. The discrepancies between model results and observations at past times are used to learn the relationships between the choice of physical processes and the resulting forecast errors. Numerical experiments are carried out with the Weather Research and Forecasting (WRF) model. The output quantity of interest is the model precipitation, a variable that is both extremely important and very challenging to forecast. The physical processes under consideration include various micro-physics schemes, cumulus parameterizations, short wave, and long wave radiation schemes. The experiments demonstrate the strong potential of machine learning approaches to aid the study of model errors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Dose and dose rate effects on the microstructural and mechanical stability of long-range ordered precipitates in Inconel 718

Nickel-based alloys offer exceptional mechanical properties and corrosion resistance for various applications in extreme environments such as high neutron flux, temperature, and stress. Precipitates, resulting from thermal treatments, increase the strength and creep resistance of these alloys. Here, heavy ion irradiation is shown to destabilize the γ' and γ" precipitates in Inconel 718 nickel-based alloy by disordering and dissolution processes. Transmission electron microscopy and atom probe tomography reveal that the disordering and dissolution rates of the precipitates are proportional to both irradiation dose and dose rate. Irradiation induced disordering occurs at a higher rate than the dissolution. From analyzing the hardening contributions, microscale mechanical results suggest radiation-induced disordering is responsible for the majority of the softening response at low dose. In addition, low dose radiation-induced disordering results in strain localization and is believed to be the primary cause for the drastic degradation of uniform elongation and toughness in irradiated Inconel 718.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Synthesis of ARM User Facility Surface Rainfall Datasets to Construct a Best Estimate Value Added Product (PrecipBE)

Surface precipitation measurements are essential for Earth system model (ESM) evaluation and understanding cloud processes. An ever-growing need for robust, temporally evolving, and easy-to-use statistical datasets provides motivation for a baseline ground-based precipitation properties data product. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility operates an extensive suite of precipitation instruments with various sensitivities and operating mechanisms, which render the decision of which instrument to use based on one or more fixed thresholds challenging and prone to errors and bias. Using a long-term instrument inter-comparison from a unique per-precipitation event perspective, rather than instantaneous sample comparison, we demonstrate that ARM rainfall-measuring instruments are generally consistent with each other at the statistical level. Inter-instrument deviations at the single event level can be large, especially for specific rainfall event properties such as maximum precipitation rates. A machine-learning (ML) analysis using a random forest regressor indicates that in some cases, depending on instrument, local site climatology, and/or specific deployment configuration, certain atmospheric state variables influence the measured quantities in an unpredictable manner. Thus, a-priori weighting of different instruments does not necessarily lead to more accurate and less biased synthesis of instrument data. These results motivate the design of the ARM precipitation best-estimate (PrecipBE) value-added product, which incorporates all valid precipitation data while considering data quality and other instrument limitations. PrecipBE consists of time series and tabular statistics datasets in an easy-to-use and insightful per-precipitation event format. It provides a large set of precipitation event properties supplemented with ancillary data from ARM datasets that correspond to the detected precipitation events. We describe the PrecipBE algorithm and demonstrate its use via the examination of a single-day output as well as a long-term trend analysis of precipitation events at the ARM Southern Great Plains (SGP) site, covering more than 30 years of data. The trend analysis tentatively suggests a long-term temporal tendency for mainly shorter and less intense precipitation events at the SGP site, but a long-term increase in annual rainfall by more than 36 mm (5 %) per decade. This rainfall trend is catalyzed primarily by more extreme event properties of relatively rare, intense precipitation events, with event total and 1 min maximum precipitation rate at a 1 year timeframe increasing up to 5 mm and 9 mm h −1 (several percent) per decade, respectively. While the currently available PrecipBE datasets (at https://adc.arm.gov/discovery/, last access: 8 December 2025) cover rainfall from multiple ARM deployments up to March 2025, PrecipBE is planned to be expanded to include solid-phase precipitation and will soon become an operational product with a several-day lag from real-time. We invite the ARM user community to leverage this new product and welcome user feedback to further enhance the dataset.

Silber, Israel [Pacific Northwest National Laborat↗
DOE OSTI2024

The GFDL Variable-Resolution Global Chemistry-Climate Model for Research at the Nexus of US Climate and Air Quality Extremes

We present a variable-resolution global chemistry-climate model (AM4VR) developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) for research at the nexus of US climate and air quality extremes. AM4VR has a horizontal resolution of 13 km over the US, allowing it to resolve urban-to-rural chemical regimes, mesoscale convective systems, and land-surface heterogeneity. With the resolution gradually reducing to 100 km over the Indian Ocean, we achieve multi-decadal simulations driven by observed sea surface temperatures at 50% of the computational cost for a 25-km uniform-resolution grid. In contrast with GFDL's AM4.1 contributing to the sixth Coupled Model Intercomparison Project at 100 km resolution, AM4VR features much improved US climate mean patterns and variability. In particular, AM4VR shows improved representation of: precipitation seasonal-to-diurnal cycles and extremes, notably reducing the central US dry-and-warm bias; western US snowpack and summer drought, with implications for wildfires; and the North American monsoon, affecting dust storms. AM4VR exhibits excellent representation of winter precipitation, summer drought, and air pollution meteorology in California with complex terrain, enabling skillful prediction of both extreme summer ozone pollution and winter haze events in the Central Valley. AM4VR also provides vast improvements in the process-level representations of biogenic volatile organic compound emissions, interactive dust emissions from land, and removal of air pollutants by terrestrial ecosystems. We highlight the value of increased model resolution in representing climate–air quality interactions through land-biosphere feedbacks. AM4VR offers a novel opportunity to study global dimensions to US air quality, especially the role of Earth system feedbacks in a changing climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Vertical Structure of Clouds and Precipitation During Arctic Cold–Air Outbreaks and Warm–Air Intrusions: Observations From COMBLE

The Arctic is marked by deep intrusions of warm, moist air, alternating with outbreaks of cold air down to lower latitudes. The typical vertical structure of clouds and precipitation during these two synoptic weather extremes is examined at a coastal site at 69°N in Norway. The Norwegian Sea is a corridor for warm-air intrusions (WAIs) and frequently witnesses cold-air outbreaks (CAOs). This study uses data from profiling radar, lidar, and microwave radiometer, radiosondes and other probes that were collected during the CAOs in the Marine Boundary Layer Experiment (COMBLE) between 1 December 2019 and 31 May 2020. Marine CAOs are defined in terms of thermal instability relative to the sea surface temperature, and WAIs in terms of equivalent potential temperature stratification between the surface and 850 hPa. Cloud structures in CAOs are convective, driven by strong surface heat fluxes over a long fetch of open water, with cloud tops rarely exceeding 6 km above sea level. The mostly open-cellular convection produces intermittent moderately-heavy precipitation at the observational site, notwithstanding the low precipitable water vapor (PWV). In contrast, WAIs are marked by high values of PWV and integrated vapor transport. Here, WAI clouds are synoptically driven, stratiform, with cloud tops often exceeding 5 km, sometimes layered, and generally producing persistent precipitation that can be heavier than in CAOs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Changes in atmospheric circulation amplify extreme snowfall fueled by Arctic sea ice loss over high-latitude land

Arctic sea-ice retreat has been linked to increased winter precipitation and heavy snowfall over land, likely due to a combination of enhanced evaporation from ice-free Arctic marginal seas (AMS) and changes in atmospheric circulation. However, their relative roles and contributions remain uncertain. Here, we show that a greater proportion of AMS evaporative moisture reached high-latitude land during the cold seasons from 1980–1989 to 2012–2021. Atmospheric circulation changes added an additional 13 % increase in the AMS moisture contribution, accounting for 11 % of the total increase in AMS-sourced land precipitation. Notably, 46 % of the increase in AMS-sourced extreme snowfall is attributed to circulation-driven landward moisture transport, representing an 84 % increase beyond the effect of enhanced AMS evaporation alone. Further analysis indicates that both the rise in Arctic moisture and the atmospheric circulation shifts are primarily driven by anthropogenic forcing. These findings highlight how atmospheric circulation changes amplify extreme snowfall fueled by AMS evaporation, underscoring the synergistic effects of Arctic sea ice loss and circulation change on high-latitude winter precipitation.

Anthropogenic forcing↗
DOE OSTI2024

Primer: Physical Factors of Agricultural Production & Climate Change

This white paper is a primer on physical factors that influence agricultural production and associated touchpoints to climate change. Agricultural production (which includes both crop and livestock production) is critical for food security and supports other economic products, such as textiles and generation of fuels for energy. Various physical factors influence agricultural production, including the crop types being cultivated and livestock being raised; land area and quality; water access and control; fertilizers, pesticides, and antibiotics; labor; and infrastructure associated with processing, storage, and transportation. These factors are impacted by climate change in both chronic and acute ways, from changing temperatures and precipitation patterns to increased prevalence of extreme events and diseases. We draw on examples from around the world to show the complex ways that agricultural production factors and climate interact with local capacities to influence regions around the world.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A storyline analysis of Hurricane Irma’s precipitation under various levels of climate warming

Abstract Understanding how extreme weather, such as tropical cyclones, will change with future climate warming is an interesting computational challenge. Here, the hindcast approach is used to create different storylines of a particular tropical cyclone, Hurricane Irma (2017). Using the community atmosphere model, we explore how Irma’s precipitation would change under various levels of climate warming. Analysis is focused on a 48 h period where the simulated hurricane tracks reasonably represent Irma’s observed track. Under future scenarios of 2 K, 3 K, and 4 K global average surface temperature increase above pre-industrial levels, the mean 3-hourly rainfall rates in the simulated storms increase by 3–7% K −1 compared to present. This change increases in magnitude for the 95th and 99th percentile 3-hourly rates, which intensify by 10–13% K −1 and 17–21% K −1 , respectively. Over Florida, the simulated mean rainfall accumulations increase by 16–26% K −1 , with local maxima increasing by 18–43% K −1 . All percent changes increase monotonically with warming level.

Environmental Sciences & Ecology↗