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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

High-Resolution Meteorology with Climate Change Impacts from Global Climate Model Data Using Generative Machine Learning

As renewable energy generation increases, the impacts of weather and climate on energy generation and demand become critical to the reliability of the energy system. However, these impacts are often overlooked. Global climate models (GCMs) can be used to understand possible changes to our climate, but their coarse resolution makes them difficult to use in energy system modelling. Here we present open-source generative machine learning methods that produce meteorological data at a nominal spatial resolution of 4 km at an hourly frequency based on inputs from 100 km daily-average GCM data. These methods run 40 times faster than traditional downscaling methods and produce data that have high-resolution spatial and temporal attributes similar to historical datasets. We demonstrate that these methods can be used to downscale projected changes in wind, solar and temperature variables across multiple GCMs including projections for more frequent low-wind and high-temperature events in the Eastern United States.

climate change↗
DOE OSTI2022

Scientific data from precipitation driver response model intercomparison project

This data descriptor reports the main scientific values from General Circulation Models (GCMs) in the Precipitation Driver and Response Model Intercomparison Project (PDRMIP). The purpose of the GCM simulations has been to enhance the scientific understanding of how changes in greenhouse gases, aerosols, and incoming solar radiation perturb the Earth’s radiation balance and its climate response in terms of changes in temperature and precipitation. Here we provide global and annual mean results for a large set of coupled atmospheric-ocean GCM simulations and a description of how to easily extract files from the dataset. The simulations consist of single idealized perturbations to the climate system and have been shown to achieve important insight in complex climate simulations. We therefore expect this data set to be valuable and highly used to understand simulations from complex GCMs and Earth System Models for various phases of the Coupled Model Intercomparison Project.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Floods of Egypt’s Nile in the 21st century

Extreme precipitation and flooding events are rising globally, necessitating a thorough understanding and sustainable management of water resources. One such setting is the Nile River’s source areas, where high precipitation has led to the filling of Lake Nasser (LN) twice (1998–2003; 2019–2022) in the last two decades and the diversion of overflow to depressions west of the Nile, where it is lost mainly to evaporation. Using temporal satellite-based data, climate models, and continuous rainfall-runoff models, we identified the primary contributor to increased runoff that reached LN in the past two decades and assessed the impact of climate change on the LN’s runoff throughout the twenty-first century. Findings include: (1) the Blue Nile subbasin (BNS) is the primary contributor to increased downstream runoff, (2) the BNS runoff was simulated in the twenty-first century using a calibrated (1965–1992) rainfall-runoff model with global circulation models (GCMs), CCSM4, HadGEM3, and GFDL-CM4.0, projections as model inputs, (3) the extreme value analysis for projected runoff driven by GCMs’ output indicates extreme floods are more severe in the twenty-first century, (4) one adaptation for the projected twenty-first century increase in precipitation (25–39%) and flood (2%-20%) extremes is to recharge Egypt’s fossil aquifers during high flood years.

Climate change↗
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 OSTI2024

Bias Correction and Statistical Downscaling of Future Solar Irradiance Projections Using the NSRDB

Assessing renewable energy resources under future climate scenarios has been highlighted to understand potential impacts of future climate change in renewable generation on the power sector. Climate model projection has been recognized by the renewable energy community as a useful data set to analyze the impacts of future climate change on renewable resources. However, future climate projections generated from general circulation models (GCMs) contain inherent biases that need to be corrected for accurate analysis of future projections of climate variables. In addition, the coarse spatiotemporal resolution of GCMs needs to be improved for regional climate studies. In this work, we develop statistical methods to downscale future projections of global horizontal irradiance (GHI) in a computationally efficient way. Our approach builds statistical downscaling models that correct bias of climate projection of GHI and downscale the future GHI projection from daily-scale to hourly-scale. The National Solar Radiation Database (NSRDB) is used to calibrate the statistical models and validate the downscaled GHI projections across the contiguous United State (CONUS). Preliminary results show that the statistical approach efficiently downscales climate projections of GHI with a nBIAS of 3%, nMAE of 34 % and nRMSE of 46% calculated against NSRDB for CONUS. This study describes the implemented methodology and initial results as well as future research to create high-resolution climate data sets for solar energy applications.

analytical models↗
DOE OSTI2020

Reducing the aerosol forcing uncertainty using observational constraints on warm rain processes

Global climate models (GCMs) disagree with other lines of evidence on the rapid adjustments of cloud cover and liquid water path to anthropogenic aerosols. Attempts to use observations to constrain the parameterizations of cloud processes in GCMs have failed to reduce the disagreement. We propose using observations sensitive to the relevant cloud processes rather than only to the atmospheric state and focusing on process realism in the absence of aerosol perturbations in addition to the process susceptibility to aerosols. We show that process-sensitive observations of precipitation can reduce the uncertainty on GCM estimates of rapid cloud adjustments to aerosols. The feasibility of an observational constraint depends on understanding the precipitation intensity spectrum in both observations and models and also on improving methods to compare the two.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Locally narrow droplet size distributions are ubiquitous in stratocumulus clouds

Marine stratocumulus clouds are the “global reflectors,” sharply contrasting with the underlying dark ocean surface and exerting a net cooling on Earth’s climate. The magnitude of this cooling remains uncertain in part owing to the averaged representation of microphysical processes, such as the droplet-to-drizzle transition in global climate models (GCMs). Current GCMs parameterize cloud droplet size distributions as broad, cloud-averaged gammas. Here, using digital holographic measurements of discrete stratocumulus cloud volumes, we found cloud droplet size distributions to be narrower at the centimeter scale, never resembling the cloud average. These local distributions tended to form pockets of similar-looking cloud regions, each characterized by a size distribution shape that is diluted to varying degrees. These observations open the way for new modeling representations of microphysical processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County

Exploring climate-induced impacts on building energy consumption can provide valuable insights for sustainable energy planning and environmental management in the face of a changing climate. By utilizing future weather data statistically downscaled from the Intergovernmental Panel on Climate Change (IPCC) General Circulation Models (GCMs) from 2020-2100, this paper presents a broadening industry-consensus approach for generating future Typical Meteorological Year (fTMY) weather files through a combination of statistical downscaling and high-performance computing that generalizes across decades, multiple locations for a region, and varying climate models. Furthermore, these fTMY files have been generated for 3,128 US counties for capturing potential weather on a 20-year basis.

Building↗
DOE OSTI2023

Use-Inspired, Process-Oriented GCM Selection: Prioritizing Models for Regional Dynamical Downscaling

Dynamical downscaling is a crucial process for providing regional climate information for broad uses, using coarser-resolution global models to drive higher-resolution regional climate simulations. The pool of global climate models (GCMs) providing the fields needed for dynamical downscaling has increased from the previous generations of the Coupled Model Intercomparison Project (CMIP). However, with limited computational resources, the need for prioritizing the GCMs for subsequent downscaling studies remains. GCM selection for dynamical downscaling should focus on evaluating processes relevant for providing boundary conditions to the regional models and be inspired by regional uses such as the response of extremes to changes in the boundary conditions. This leads to the need for metrics representing processes of relevance to diverse stakeholders and subregions of a domain. Procedures to account for metric redundancy and the statistical distinguishability of GCM rankings are required. Further, procedures for selecting realizations from ensembles of top-performing GCM simulations can be used to span the range of climate change signals in multiple ways. As a result, distinct weighting of metrics and prioritization of particular realizations may depend on user needs. We provide high-level guidelines for such region-specific evaluations and address how CMIP7 might enable dynamical downscaling of a representative sample of high-quality models across representative shared socioeconomic pathways (SSPs).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Azimuthally averaged wind and thermodynamic structures of tropical cyclones in global climate models and their sensitivity to horizontal resolution

Characteristics of tropical cyclones (TCs) in global climate models (GCMs) are known to be influenced by details of the model configurations, including horizontal resolution and parameterization schemes. Understanding model-to-model differences in TC characteristics is a prerequisite for reducing uncertainty in future TC activity projections by GCMs. This study performs a process-level examination of TC structures in eight GCM simulations that span a range of horizontal resolutions from 1° to 0.25°. A recently developed set of process-oriented diagnostics is used to examine the azimuthally averaged wind and thermodynamic structures of the GCM-simulated TCs. Results indicate that the inner-core wind structures of simulated TCs are more strongly constrained by the horizontal resolutions of the models than are the thermodynamic structures of those TCs. As expected, the structures of TC circulations become more realistic with smaller horizontal grid spacing, such that the radii of maximum wind (RMW) become smaller, and the maximum vertical velocities occur off the center. However, the RMWs are still too large, especially at higher intensities, and there are rising motions occurring at the storm centers, inconsistently with observations. The distributions of precipitation, moisture, radiative and surface turbulent heat fluxes around TCs are diverse, even across models with similar horizontal resolutions. At the same horizontal resolution, models that produce greater rainfall in the inner-core regions tend to simulate stronger TCs. Further, when TCs are weak, the radial gradient of net column radiative flux convergence is comparable to that of surface turbulent heat fluxes, emphasizing the importance of cloud-radiative feedbacks during the early developmental phases of TCs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Regime-Oriented Approach to Observationally Constraining Extratropical Shortwave Cloud Feedbacks

The extratropical shortwave (SW) cloud feedback is primarily due to increases in extratropical liquid cloud extent and optical depth. Here, we examine the response of extratropical (35°–75°) marine cloud liquid water path (LWP) to a uniform 4-K increase in sea surface temperature (SST) in global climate models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and variants of the HadGEM3-GC3.1 GCM. Compositing is used to partition data into periods inside and out of cyclones. The response of extratropical LWP to a uniform SST increase and associated atmospheric response varies substantially among GCMs, but the sensitivity of LWP to cloud controlling factors (CCFs) is qualitatively similar. When all other predictors are held constant, increasing moisture flux drives an increase in LWP. Increasing SST, holding all other predictors fixed, leads to a decrease in LWP. The combinations of these changes lead to LWP, and by extension reflected SW, increasing with warming in both hemispheres. Observations predict an increase in reflected SW over oceans of 0.8–1.6 W m -2 per kelvin SST increase (35°–75°N) and 1.2–1.9 W m -2 per kelvin SST increase (35°–75°S). This increase in reflected SW is mainly due to increased moisture convergence into cyclones because of increasing available moisture. The efficiency at which converging moisture is converted into precipitation determines the amount of liquid cloud. Thus, cyclone precipitation processes are critical to constraining extratropical cloud feedbacks

58 GEOSCIENCES↗
DOE OSTI2021

Are 100 Ensemble Members Enough to Capture the Remote Atmospheric Response to +2°C Arctic Sea Ice Loss?

This study presents results from the Polar Amplification Multimodel Intercomparison Project (PAMIP) single-year time-slice experiments that aim to isolate the atmospheric response to Arctic sea ice loss at global warming levels of +2°C. Using two general circulation models (GCMs), the ensemble size is increased up to 300 ensemble members, beyond the recommended 100 members. After partitioning the response in groups of 100 ensemble members, the reproducibility of the results is evaluated, with a focus on the response of the midlatitude jet streams in the North Atlantic and North Pacific. Both atmosphere-only and coupled ocean–atmosphere PAMIP experiments are analyzed. Substantial differences in the midlatitude response are found among the different experiment subsets, suggesting that 100-member ensembles are still significantly influenced by internal variability, which can mislead conclusions. Despite an overall stronger response, the coupled ocean–atmosphere runs exhibit greater spread due to additional ENSO-related internal variability when the ocean is interactive. The lack of consistency in the response is true for anomalies that are statistically significant according to Student’s t and false discovery rate tests. This is problematic for the multimodel assessment of the response, as some of the spread may be attributed to different model sensitivities whereas it is due to internal variability. Here, we propose a method to overcome this consistency issue that allows for more robust conclusions when only 100 ensemble members are used.

58 GEOSCIENCES↗
DOE OSTI2023

Observational Constraints on the Cloud Feedback Pattern Effect

Abstract Model evidence for the “pattern effect” assumes that global climate models (GCMs) faithfully simulate how clouds respond to varying sea surface temperature (SST) patterns and associated meteorological perturbations. We exploit time-invariant satellite-based estimates of the sensitivity of marine low clouds to meteorological perturbations to estimate how these clouds responded to time-varying SST patterns and meteorology between 1870 and 2014. GCMs and reanalyses provide estimates of the historical meteorological changes. Observations suggest that increasing estimated inversion strength (EIS) between 1980 and 2014 produced a negative low cloud feedback, opposite to the positive feedback expected from increasing CO 2 . This indicates that the processes responsible for marine cloud changes from 1980 to the near present are distinct from those associated with an increase in CO 2 . We also observationally constrain the difference between the historical near-global marine low cloud feedback, , and that arising from increasing CO 2 , . We find that this cloud feedback pattern effect depends strongly on time period and reanalysis dataset, and that varying changes in EIS and SST with warming explain much of its variability. Between 1980 and 2014, we estimate that (90% confidence) assuming meteorological changes from the Multiple Reanalysis Ensemble, implying a total pattern effect (that arising from all climate feedbacks) of 1.86 ± 0.45 W m −2 K −1 . This observational evidence corroborates previous quantitative estimates of the pattern effect, which heretofore relied largely upon GCM-based cloud changes. However, disparate historical meteorological changes across individual reanalyses contribute to considerable uncertainty in its magnitude.

58 GEOSCIENCES↗
DOE OSTI2022

Intercomparison of Dynamically and Statistically Downscaled Climate Change Projections over the Midwest and Great Lakes Region

Downscaling of global climate model (GCMs) simulations is a key element of regional-to-local-scale climate change projections that can inform impact assessments, long-term planning, and resource management in different sectors. Here, we conduct an intercomparison between statistically and dynamically downscaled GCMs simulations using the hybrid delta (HD) and the Weather Research and Forecast (WRF) Model, respectively, over the Midwest and Great Lakes region to 1) validate their performance in reproducing extreme daily precipitation (P) and daily maximum temperature (T max ) for summer and winter and 2) evaluate projections of extremes in the future. Our results show the HD statistical downscaling approach, which includes large-scale bias correction of GCM inputs, can reproduce observed extreme P and T max reasonably well for both summer and winter. However, raw historical WRF simulations show significant bias in both extreme P and T max for both seasons. Interestingly, the convection-permitting WRF simulation at 4-km grid spacing does not produce better results for seasonal extremes than the WRF simulation at 12 km using a parameterized convection scheme. Despite a broad similarity for winter extreme P projections, the projected changes in the future summer storms are quite different between downscaling methods; WRF simulations show substantial increases in summer extreme precipitation, while the changes projected by the HD approach exhibit moderate decreases overall. The WRF simulations at 4 km also show a pronounced decoupling effect between seasonal totals and extreme daily P for summer, which suggests that there could be more intense summer extremes at two different time scales, with more severe individual convective storms combined with longer summer droughts at the end of the twenty-first century.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

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

This dataset presents a suite of high-resolution downscaled hydro-climate projections over the conterminous United States (CONUS) based on multiple selected Global Climate Models (GCMs) from the Coupled Models Intercomparison Project phase 6 (CMIP6). The CMIP6 GCMs are downscaled using either statistical (DBCCA) or dynamical (RegCM) downscaling approaches based on two meteorological reference datasets (Daymet and Livneh). Subsequently, the downscaled precipitation, temperature, and wind speed are employed to drive two calibrated hydrologic models (VIC and PRMS), enabling the simulation of projected future hydrologic responses across the CONUS. Each ensemble member covers the 1980-2019 baseline and 2020-2059 near-future periods under the high-end (SSP585) emission scenario. Moreover, utilizing only DBCCA and Daymet, the projections are further extended to the 2060-2099 far-future period and across three additional emission scenarios (SSP370, SSP245, and SSP126). This dataset is formulated to support the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). For further details on this dataset, please refer to Kao et al. (2022) and Rastogi et al. (2022).

13 HYDRO ENERGY↗
DOE OSTI2025

Early Stages in the Lifecycle of Polar Liquid-Bearing Clouds

Stratiform liquid-bearing clouds are ubiquitous over the polar regions, where they are predominantly mixed-phase. These polar clouds induce substantial radiative forcing on the surface and continuously modify the atmospheric thermodynamic budget, with direct implications for the polar ice pack resilience. However, the physical representation of these clouds is still a major challenge for climate models. A significant part of polar liquid-bearing cloud lifecycle is often manifested in a quasi-steady self-sustaining, persistent, and turbulent state, which is driven by longwave cloud radiative cooling and can last for multiple days. This self-sustaining cloud lifecycle stage has been thoroughly investigated in numerous studies, though some of its aspects such as precipitation still lack robust quantification and evaluation. The preceding cloud lifecycle stages, which may last up to several hours, have nonetheless remained widely overlooked. These preceding stages initiate at cloud formation, often in a stable and non-turbulent atmospheric layer and serve as a key junction between cloud persistence and cloud dissipation. These two contrasting cloud lifecycle trajectories pose the question if general circulation models (GCMs) can capture the full lifecycle accurately for the real physical reasons, a necessary condition to improve our confidence in climate projections given the changing polar climate. The purpose of this project was to improve the characterization and understanding of these early stages in the lifecycle of polar stratiform liquid-bearing cloud and to aid their representation in GCMs. This research relied on measurements from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) field campaign, as well as the observations from the ARM West Antarctic Radiation Experiment (AWARE) and the permanent ARM site at Utqiagvik, North Slope of Alaska (NSA).

58 GEOSCIENCES↗
DOE OSTI2020

GCAM-Demeter-LU

This GCAM-Demeter-LU is made available under the Open Data Commons Attribution License:http://opendatacommons.org/licenses/by/1.0/. The dataset includes the projected global gridded land cover (excluding the Antarctic) for the period of 2015-2100 at 0.05-degree resolution and 5-year time step under fifteen SSP-RCP scenarios driven by five GCMs (i.e., gfdl, hadgem, ipsl, miroc, and noresm), using the Global Change Analysis Model (GCAM) and a geospatial downscaling model (Demeter). The data has been stored in self-describing NetCDF format. The dataset also includes the mean and standard deviation of the results driven by the five GCMs. More specifically, the data in each year includes grid-explicit fraction (in percent) of each of the 32 plant functional types that are widely used in current Earth system models. The NetCDF files are named as “GCAM_Demeter_LU_SSP_RCP_Model_Year.nc”, where “SSP” and “RCP” denote the SSP and RCP scenarios, including 'ssp1_rcp26', 'ssp1_rcp45', 'ssp1_rcp60', 'ssp2_rcp26', 'ssp2_rcp45', 'ssp2_rcp60', 'ssp3_rcp45', 'ssp3_rcp60', 'ssp4_rcp26', 'ssp4_rcp45', 'ssp4_rcp60', 'ssp5_rcp26', 'ssp5_rcp45', 'ssp5_rcp60', and 'ssp5_rcp85'. “Year” denotes the year of the land use data, and “GCM” denotes the source driving forcing data from five global climate models (gfdl, hadgem, ipsl, miroc, and noresm), or the mean (“modelmean”) and the standard deviation (“modelstd”) of the results from the five GCMs.See https://github.com/JGCRI/chen_et_al_2020a for details on how to reproduce this data. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

A seamless approach for evaluating climate models across spatial scales

In regions of the world where topography varies significantly with distance, most global climate models (GCMs) have spatial resolutions that are too coarse to accurately simulate key meteorological variables that are influenced by topography, such as clouds, precipitation, and surface temperatures. One approach to tackle this challenge is to run climate models of sufficiently high resolution in those topographically complex regions such as the North American Regionally Refined Model (NARRM) subset of the Department of Energy’s (DOE) Energy Exascale Earth System Model version 2 (E3SM v2). Although high-resolution simulations are expected to provide unprecedented details of atmospheric processes, running models at such high resolutions remains computationally expensive compared to lower-resolution models such as the E3SM Low Resolution (LR). Moreover, because regionally refined and high-resolution GCMs are relatively new, there are a limited number of observational datasets and frameworks available for evaluating climate models with regionally varying spatial resolutions. As such, we developed a new framework to quantify the added value of high spatial resolution in simulating precipitation over the contiguous United States (CONUS). To determine its viability, we applied the framework to two model simulations and an observational dataset. We first remapped all the data into Hierarchical Equal-Area Iso-Latitude Pixelization (HEALPix) pixels. HEALPix offers several mathematical properties that enable seamless evaluation of climate models across different spatial resolutions including its equal-area and partitioning properties. The remapped HEALPix-based data are used to show how the spatial variability of both observed and simulated precipitation changes with resolution increases. This study provides valuable insights into the requirements for achieving accurate simulations of precipitation patterns over the CONUS. It highlights the importance of allocating sufficient computational resources to run climate models at higher temporal and spatial resolutions to capture spatial patterns effectively. Furthermore, the study demonstrates the effectiveness of the HEALPix framework in evaluating precipitation simulations across different spatial resolutions. This framework offers a viable approach for comparing observed and simulated data when dealing with datasets of varying spatial resolutions. By employing this framework, researchers can extend its usage to other climate variables, datasets, and disciplines that require comparing datasets with different spatial resolutions.

54 ENVIRONMENTAL SCIENCES↗