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DOE OSTI2025

ORBIT-2 Weather and Climate Downscaling Software Repository

ORBIT-2 is a scalable foundation model for global, hyper-resolution climate and weather downscaling. ORBIT-2 incorporates two key innovations: (1) Residual Slim ViT (Reslim), a lightweight architecture with residual learning and Bayesian regularization for efficient, robust prediction; and (2) TILES, a tile-wise sequence scaling algorithm that reduces self-attention complexity from quadratic to linear, enabling long-sequence processing and massive parallelism. ORBIT-2 scales to 10 billion parameters across 65,536 GPUs, achieving up to 4.1 ExaFLOPS sustained throughput and 74–98% strong scaling efficiency. It supports downscaling to 0.9 km global resolution and processes sequences up to 4.2 billion tokens. On 7 km resolution benchmarks, ORBIT-2 achieves high accuracy with 𝑅2 scores in range of 0.98–0.99 against observation data.

Wang, Xiao [Oak Ridge National Laboratory]↗
DOE OSTI2025

Assessing the impact of climate change on rainfall-triggered landslides: a case study in California

Landslides are widespread natural hazards which take a heavy toll on lives, property, and infrastructure each year. In January 2023 and 2024, widespread landslides occurred in California as a result of extreme winter precipitation. Estimating the regional evolution of landslide hazard in a changing climate is essential for adaptation planning and risk mitigation efforts. The complex effects of climate change on landslide hazard, however, are poorly understood. Here, we use climate and landslide observations to develop a novel data-driven approach for landslide susceptibility assessment under historical and future climate conditions. We apply this framework to investigate the evolution of landslide susceptibility in California and its impacts on the roadway system based on downscaled climate projections under moderate and high greenhouse gas emission scenarios. On average, the results indicate an increase in landslide susceptibility through 2100 in regions where historical susceptibility is moderate to very high. Although the spatio-temporal variations in landslide susceptibility are sensitive to climate model uncertainties in predicting extreme rainfall, the direst impacts are largely circumvented in lower emission scenarios.

Climate Change↗
DOE OSTI2024

Generative network-based approaches to generate stochastic realizations

Generative Adversarial Network (GAN) – based models have been successfully applied in generating different geological models in the literature. However, it is still challenging to use GAN to generate geological realizations with extremely sparse conditioning data (e.g. several well data), which may be regarded as local noise by GAN during the training process. In this work, we propose a novel conditional Generative Adversarial Neural Operator (cGANO) to tackle this challenge. In cGANO, the mapping between conditioning data and output is established through the U-shaped neural operators (UNO), which better preserves local information. Another advantage of using UNO comes from its grid-independent property, which makes the generation of downscaling stochastic geologic realizations possible. We tested the model performance on the IBDP geostatistical dataset with 100 realizations.

58 GEOSCIENCES↗
DOE OSTI2022

Dynamically Downscaled (WRF) 1km, Hourly Meteorological Conditions 1987-2020. East/Taylor Watersheds

This dataset contains meteorological output from the Weather Research and Forecasting (WRF) version 3.8.1. This dataset has been created to 1) investigate hydrometeorological processes impacting the East River and water-delivery to the Critical Zone, and 2) provide meteorological forcing data for distributed Earth-science modeling applications in the East River watershed. Variables have a 1 kilometer spatial resolution and hourly temporal resolution and encompass a rectangular region encompassing the East and Taylor River watersheds, Colorado, near the town of Crested Butte. WRF was forced using Climate Forecast System Reanalysis (CFSR) lateral boundary conditions. Each .zip file contains one "water year" of data (October 1 -- September 30; i.e. water year 2017 starts October 1, 2016 and ends September 30, 2017). Each zip folder contains 12 netcdf (.nc) files containing one month of hourly data each and are approximately 250mb. Model timestamps are in UTC time.The files contain the following data variables:EAST_MASK: binary mask of the watershed regionTAYLOR_MASK: binary mask of the watershed regionGLW downwelling longwave radiation (w/m2)HR_PRCP: Hourly Precipitation Rate (mm/hr). Includes all hydrometeors (solid+liquid). HFX NoahMP LSM total grid-cell modelled sensible heat flux (w/m2) [positive towards atmosphere]LH NoahMP LSM total grid-cell modelled latent heat flux (w/m2) [positive towards atmosphere; can be converted to ET]PSFC Surface Barometric Pressure (hPa)Q2 Two-meter specific humidity (kg/kg)SWDOWN Downwelling shortwave solar radiation (w/m2)SWNORM Terrain-normal downwelling shortwave radiation (w/m2)T2 Two-meter air temperature (deg K)U10 10-m U-component of wind velocity (m/s)V10 10m V-component of wind velocity (m/s)XLAT Latitude of grid-center point XLONG Longitude of grid-center point XTIME Model timestamp, **in UTC**

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Hydrological analysis in watersheds with a variable-resolution global climate model (VR-CESM)

Traditionally, watershed-scale hydrology is simulated by distributed hydrological models with offline meteorological forcing data, or by regional regional climate models that link atmospheric and land hydrology interactions. Global climate models (GCMs) are rarely used to study watershed-scale hydrology due to the relatively coarse grid resolution, computationally expensive downscaling, and simplified physical processes. Recently, however, watershed-scale hydrology analysis has become possible in GCMs due to the development of variable-resolution GCMs that dynamically couple the hydrological processes between atmosphere and land systems at fine resolutions in selected regions and coarse resolution elsewhere. In this study, we used the variable-resolution Community Earth System Model (VR-CESM) with refined-resolution (14 km) in the western U.S. and eastern China to evaluate watershed-scale hydrology. Furthermore, we compared the historical VR-CESM outputs with gauge measurements and other hydrological models (e.g., National Water Model in the U.S.) and calibrated the subsurface runoff capacities in four mountainous watersheds. An RCP8.5 projection from 2007 to 2050 is used to estimate the impact of changing precipitation and snow climatology on watershed hydrology. We also analyzed the long-term runoff variability and the possibility of extreme runoff events as simulated by the VR-CESM. Although calibration is not possible in larger-scale watersheds, VR-CESM simulates the long-term annual variability of watersheds and provides insights on climate change impacts on hydrology. We conclude that refined-resolution VR-CESM can be used for watershed-scale hydrology analysis to understand water resources and water balance, in addition to traditional watershed-scale hydrological models. It enables hydrological analysis at multiple watersheds in one simulation and can help understand the two-way dynamics between land surface hydrology and atmospheric processes, and is especially practical for projecting climate change impacts. However, it is challenging to apply VR-CESM for hydrologic analysis in regulated watersheds as human factors (e.g., pumping, irrigation, water diversion) have not been fully addressed in VR-CESM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Hydrological projections over the Upper Indus Basin at 1.5 °C and 2.0 °C temperature increase

We analyse an ensemble of statistically downscaled Global Climate Models (GCMs) to investigate future water availability in the Upper Indus Basin (UIB) of Pakistan for the time horizons when the global and/or regional warming levels cross Paris Agreement (PA) targets. The GCMs data is obtained from the 5th Phase of Coupled Model Inter-Comparison Project under two Representative Concentration Pathways (RCP4.5 and RCP8.5). Based on the five best performing GCMs, we note that global 1.5 °C and 2.0 °C warming thresholds are projected in 2026 and 2047 under RCP4.5 and 2022 and 3036 under RCP8.5 respectively while these thresholds are reached much earlier over Pakistan i.e. 2016 and 2030 under RCP4.5 and 2012 and 2025 under RCP8.5 respectively. Interestingly, the GCMs with the earliest emergence at the global scale are not necessarily the ones with the earliest emergence over Pakistan, highlighting spatial non-linearity in GCMs response. The emergence of 2.0 °C warming at global scale across 5 GCMs ranges from 2031 (CCSM4) to 2049 (NorESM) under RCP8.5. Precipitation generally exhibits a progressive increasing trend with stronger changes at higher warming or radiative forcing levels. Hydrological simulations representing the historical, 1.5 °C and 2.0 °C global and region warming time horizons indicate a robust but seasonally varying increase in the inflows. The highest inflows in the baseline and future are witnessed in July. However, the highest future increase in inflows is projected in October under RCP4.5 (37.99% and 65.11% at 1.5 °C and 2.0 °C) and in April under RCP8.5 (37% and 62.05% at 1.5 °C and 2.0 °C). These hydrological changes are driven by increases in the snow and glacial melt contribution, which are more pronounced at 2.0 °C warming level. These findings should help for effective water management in Pakistan over the coming decades.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Bayesian Spatial Models for Projecting Corn Yields

Climate change is predicted to impact corn yields. Previous studies analyzing these impacts differ in data and modeling approaches and, consequently, corn yield projections. We analyze the impacts of climate change on corn yields using two statistical models with different approaches for dealing with county-level effects. The first model, which is novel to modeling corn yields, uses a computationally efficient spatial basis function approach. We use a Bayesian framework to incorporate both parametric and climate model structural uncertainty. We find that the statistical models have similar predictive abilities, but the spatial basis function model is faster and hence potentially a useful tool for crop yield projections. We also explore how different gridded temperature datasets affect the statistical model fit and performance. Compared to the dataset with only weather station data, we find that the dataset composed of satellite and weather station data results in a model with a magnified relationship between temperature and corn yields. For all statistical models, we observe a relationship between temperature and corn yields that is broadly similar to previous studies. We use downscaled and bias-corrected CMIP5 climate model projections to obtain detrended corn yield projections for 2020–2049 and 2069–2098. In both periods, we project a decrease in the mean corn yield production, reinforcing the findings of other studies. However, the magnitude of the decrease and the associated uncertainties we obtain differ from previous studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Current and Projected Future Distribution of Plant Community Types for the Southern Seward Peninsula, Alaska

Landscape scale maps of plant community distribution at 5 m resolution were developed for southern Seward Peninsula. A Random Forest based environmental niche model was developed using topographic and climate data sets. Models were trained for the 2010-2019 period and applied to develop plant community distributions for contemporary (2010-2019) period. Trained model was applied to project the plant community distributions under future climate. Using downscaled projections for RCP8.5 scenario from five climate models (CCSM4, GFDL-CM3, GISS-E2-R, IPSL-CM5A-LR, MRI-CGCM3) and multi-model mean, a six member ensemble of plant community distribution were developed for current decade (2010-2019) and for future decades (2020-2029, 2030-2039, 2040-2049, 2050-2059). Contains *.tif and *.sh files. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Evaluation of precipitation across the contiguous United States, Alaska, and Puerto Rico in multi-decadal convection-permitting simulations

Abstract This study is an early effort to generate a multi-decadal convection-permitting regional climate dataset that covers nearly the entire North American continent. We assessed a 20 year dynamically downscaled regional climate simulation at a 4 km spatial resolution with explicit convection across the contiguous United States (CONUS), Alaska, and Puerto Rico. Specifically, we evaluated the model’s performance in representing mean, 95th percentile, and extreme precipitation across regions. Our findings indicate that when compared with ERA5 reanalysis, the forcing data, convection-permitting simulation improves representations of seasonal, 95th percentile, and extreme precipitation over a large portion of the CONUS, Alaska, and Puerto Rico, particularly in areas where precipitation is heaviest. The simulation adds value over its forcing data (ERA5) in up to 53% of all grid cells in the CONUS, 68.8% in Alaska, and 84.0% in Puerto Rico. It is important to note that, however, despite improvements, model errors in Puerto Rico remain large. Similar improvements are observed in extreme indices, including consecutive dry days, maximum 5 days precipitation, and extreme precipitation. Analysis of the diurnal cycle of mean hourly precipitation suggests that representations of convective processes—including onset, dissipation, suppression, downstream propagation, and local circulation—improved overall.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Improvement of aerosol optical depth data for localized solar resource assessment

Solar irradiance, especially for direct normal irradiance (DNI), is sensitive to the atmospheric aerosols, which can strongly extinguish DNI over areas with elevated aerosol loadings. The National Solar Radiation Database (NSRDB), developed by the National Renewable Energy Laboratory (NREL), uses AOD from MERRA-2 reanalysis and is further downscaled from 0.5° to 2-km based on elevation. However, the elevation-based downscaling may not accurately represent AOD, especially over the areas with large AOD gradients. This study examined whether the 1-km MODIS MAIAC satellite-retrieved AOD product can better represent the AOD distribution and be used to improve solar irradiance assessment. We focused on areas with relatively high AOD over North America (California, and New York City, US, and Mexico City in Mexico, in particular). The evaluation of MAIAC AOD and MERRA-2 AOD against ground-truth AERONET AOD shows that MAIAC AOD exhibits smaller RMSE by about 0.05 and higher correlation coefficient by 0.1-0.6 than MERRA-2 AOD. In addition, MERRA-2 AOD shows larger negative bias. The simulated DNI using MAIAC AOD (DNIMAIAC) and using MERRA-2 AOD (DNINSRDB) were evaluated with DNI observations. The results show the performance of DNIMAIAC is better than that of DNINSRDB with smaller RMSE by 0.5-1.5%, smaller positive mean bias by 0.8-3.1% and comparable correlation for the 3 sites analyzed. Overall, 1-km MAIAC AOD shows higher accuracy than 2-km elevation-based MERRA-2 AOD, leading to better performance of the simulated DNI using MAIAC AOD. Therefore, 1-km MAIAC AOD can be used to improve the accuracy of solar resource assessment.

14 SOLAR ENERGY↗
DOE OSTI2021

A hierarchical evaluation framework for assessing climate simulations relevant to the energy-water-land nexus (Final Report)

The overarching goal was to construct a hierarchy of new and well-tested metrics and analysis tools that support both fundamental and use-inspired research. Motivating this goal was a convergence of needs from climate scientists and stakeholders alike for a systematic, robust framework of model evaluation and diagnosis to provide scientific insights, inform model development, support best practices for the use of climate model outputs, and facilitate communication of climate information in the evolving landscapes of multi-model, multi-resolution, and large ensemble simulations that generate terabytes of data for any single climate run. Steps toward attaining these goals benefited from expertise and established capability of the project team, which included leadership of the North American Regional Climate Change Assessment Program (NARCCAP) and the Coordinated Regional Downscaling Experiment (CORDEX), development of hierarchical model evaluation approaches, and successful research in the analysis and diagnosis of climate model skill, as well as the understanding and modeling of regional climate processes in North America. As part of the overarching goal, the project worked to disseminate a suite of methodologies, algorithms, and software components that the wider community can employ to advance climate science and applications. With rigorous demonstration, the evaluation framework and the mix of standard and high risk / high reward approaches helped form the basis for future development of a computationally enabled user-friendly system for community use.

17 WIND ENERGY↗
DOE OSTI2020

Efficacy of the Cell Perturbation Method in Large-Eddy Simulations of Boundary Layer Flow over Complex Terrain

A challenge to simulating turbulent flow in multiscale atmospheric applications is the efficient generation of resolved turbulence motions over an area of interest. One approach is to apply small perturbations to flow variables near the inflow planes of turbulence-resolving simulation domains nested within larger mesoscale domains. While this approach has been examined in numerous idealized and simple terrain cases, its efficacy in complex terrain environments has not yet been fully explored. Here, we examine the benefits of the stochastic cell perturbation method (CPM) over real complex terrain using data from the 2017 Perdigão field campaign, conducted in an approximately 2-km wide valley situated between two nearly parallel ridges. Following a typical configuration for multiscale simulation using nested domains within the Weather Research and Forecasting (WRF) model to downscale from the mesoscale to a large-eddy simulation (LES), we apply the CPM on a domain with horizontal grid spacing of 150 m. At this resolution, spurious coherent structures are often observed under unstable atmospheric conditions with moderate mean wind speeds. Results from such an intermediate resolution grid are often nested down for finer, more detailed LES, where these spurious structures adversely affect the development of turbulence on the subsequent finer grid nest. We therefore examine the impacts of the CPM on the representation of turbulence within the nested LES domain under moderate mean flow conditions in three different stability regimes: weakly convective, strongly convective, and weakly stable. In addition, two different resolutions of the underlying terrain are used to explore the role of the complex topography itself in generating turbulent structures. We demonstrate that the CPM improves the representation of turbulence within the LES domain, relative to the use of high-resolution complex terrain alone. During the convective conditions, the CPM improves the rate at which smaller-scales of turbulence form, while also accelerating the attenuation of the spurious numerically generated roll structures near the inflow boundary. During stable conditions, the coarse mesh spacing of the intermediate LES domain used herein was insufficient to maintain resolved turbulence using CPM as the flow develops downstream, highlighting the need for yet higher resolution under even weakly stable conditions, and the importance of accurate representation of flow on intermediate LES grids.

17 WIND ENERGY↗
DOE OSTI2022

Meso- to microscale modeling of atmospheric stability effects on wind turbine wake behavior in complex terrain

Abstract. Terrain-induced flow phenomena modulate wind turbine performance and wake behavior in ways that are not adequately accounted for in typical wind turbine wake and wind plant design models. In this work, we simulate flow over two parallel ridges with a wind turbine on one of the ridges, focusing on conditions observed during the Perdigão field campaign in 2017. Two case studies are selected to be representative of typical flow conditions at the site, including the effects of atmospheric stability: a stable case where a mountain wave occurs (as in ∼ 50 % of the nights observed) and a convective case where a recirculation zone forms in the lee of the ridge with the turbine (as occurred over 50 % of the time with upstream winds normal to the ridgeline). We use the Weather Research and Forecasting Model (WRF), dynamically downscaled from the mesoscale (6.75 km resolution) to microscale large-eddy simulation (LES) at 10 m resolution, where a generalized actuator disk (GAD) wind turbine parameterization is used to simulate turbine wakes. We compare the WRF–LES–GAD model results to data from meteorological towers, lidars, and a tethered lifting system, showing good qualitative and quantitative agreement for both case studies. Significantly, the wind turbine wake shows different amounts of vertical deflection from the terrain and persistence downstream in the two stability regimes. In the stable case, the wake follows the terrain along with the mountain wave and deflects downwards by nearly 100 m below hub height at four rotor diameters downstream. In the convective case, the wake deflects above the recirculation zone over 40 m above hub height at the same downstream distance. Overall, the WRF–LES–GAD model is able to capture the observed behavior of the wind turbine wakes, demonstrating the model's ability to represent wakes over complex terrain for two distinct and representative atmospheric stability classes, and, potentially, to improve wind turbine siting and operation in hilly landscapes.

17 WIND ENERGY↗
DOE OSTI2026

CLM5 Simulations of Soil Moisture and Gross Primary Productivity for CONUS at 0.125 degrees

This dataset provides 0.125-degree gridded simulations of soil moisture and gross primary productivity (GPP) for the Contiguous United States (CONUS), generated using the Community Land Model version 5 (CLM5) with the biogeochemistry module enabled. The data covers a historical baseline (1980-2015) and mid-century future projections (2020-2055). Future projections are organized into two sets of scenarios to distinguish the impacts of different drivers: (1) Atmospheric Only (ATM): These scenarios apply future atmospheric forcings while holding land use and land cover (LULC) at historical baseline levels. The atmospheric forcings represent moderately versus severely hotter/drier atmospheric conditions (dynamically downscaled perturbed thermodynamics simulations based on CMIP6 SSP245 and SSP585 warming signals), each with cooler versus hotter Earth System Model temperature sensitivity instantiations. These scenarios are identified in the folder names as rcp45_cooler_near, rcp45_hotter_near, rcp85_cooler_near, and rcp85_hotter_near. (2) Coupled Atmospheric and Land-Use (LAND+ATM): These scenarios apply future atmospheric forcing together with future LULC by pairing atmospheric pathways with lower versus higher population/economic growth scenarios representing Shared Socioeconomic Pathways 3 and 5 (SSP3 and SSP5). These scenarios are identified in the file names as ssp3_rcp45_cooler_near, ssp3_rcp45_hotter_near, ssp5_rcp85_cooler_near, and ssp5_rcp85_hotter_near. Please refer to the "README_first.md" file for detailed information on file structure, variables, units, and data formats.

drought↗
DOE OSTI2026

CLM5 Simulations of Soil Moisture and Grain Carbon for CONUS at 0.125 degrees

This dataset provides 0.125° gridded simulations of soil moisture and crop grain carbon for the Contiguous United States (CONUS), generated using the Community Land Model version 5 (CLM5) with the biogeochemistry module enabled. The data covers a historical baseline (1980–2015) and mid-century future projections (2020–2055). Future projections are organized into two sets of scenarios to distinguish the impacts of different drivers: (1) Atmospheric Only (ATM-only): These scenarios apply future atmospheric forcings while holding land use and land cover change (LULCC) at historical baseline levels. The atmospheric forcings represent moderately versus severely hotter/drier atmospheric conditions (dynamically downscaled perturbed thermodynamics simulations based on CMIP6 SSP245 and SSP585 warming signals), each with cooler versus hotter Earth System Model temperature sensitivity instantiations. These scenarios are identified in the file names as atm45cooler, atm45hotter, atm85cooler, and atm85hotter. (2) Coupled Atmospheric and Land-Use (LAND+ATM): These scenarios apply future atmospheric forcing together with future LULCC by pairing atmospheric pathways with lower versus higher population/economic growth scenarios representing Shared Socioeconomic Pathways 3 and 5 (SSP3 and SSP5). These scenarios are identified in the file names as atm45cooler_ssp3, atm45hotter_ssp3, atm45cooler_ssp5, atm45hotter_ssp5, atm85cooler_ssp3, atm85hotter_ssp3, atm85cooler_ssp5, and atm85hotter_ssp5. Please refer to the README file for detailed information on file structure, variables, units, and data formats.

Yao, Lili [Pacific Northwest National Laboratory] ↗
DOE OSTI2023

Climate Change Signal in Atlantic Tropical Cyclones Today and Near Future

This manuscript discusses the challenges in detecting and attributing recently observed trends in the Atlantic tropical cyclone (TC) and the epistemic uncertainty we face in assessing future risk. We use synthetic storms downscaled from five CMIP5 models by the Columbia HAZard model (CHAZ), and directly simulated storms from high-resolution climate models. We examine three aspects of recent TC activity: the upward trend and multi-decadal oscillation of the annual frequency, the increase in storm wind intensity, and the decrease in forward speed. Some data sets suggest that these trends and oscillation are forced while others suggest that they can be explained by natural variability. Projections under warming climate scenarios also show a wide range of possibilities, especially for the annual frequencies, which increase or decrease depending on the choice of moisture variable used in the CHAZ model and on the choice of climate model. The uncertainties in the annual frequency lead to epistemic uncertainties in TC risk assessment. Here, we investigate the potential for reduction of these epistemic uncertainties through a statistical practice, namely likelihood analysis. We find that historical observations are more consistent with the simulations with increasing frequency than those with decreasing frequency, but we are not able to rule out the latter. We argue that the most rational way to treat epistemic uncertainty is to consider all outcomes contained in the results. In the context of risk assessment, since the results contain possible outcomes in which TC risk is increasing, this view implies that the risk is increasing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Adversarial super-resolution of climatological wind and solar data

Accurate and high-resolution data reflecting different climate scenarios are vital for policy makers when deciding on the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally important systems. However, state-of-the-art long-term global climate simulations are unable to resolve the spatiotemporal characteristics necessary for resource assessment or operational planning. We introduce an adversarial deep learning approach to super resolve wind velocity and solar irradiance outputs from global climate models to scales sufficient for renewable energy resource assessment. Using adversarial training to improve the physical and perceptual performance of our networks, we demonstrate up to a 50 × resolution enhancement of wind and solar data. In validation studies, the inferred fields are robust to input noise, possess the correct small-scale properties of atmospheric turbulent flow and solar irradiance, and retain consistency at large scales with coarse data. An additional advantage of our fully convolutional architecture is that it allows for training on small domains and evaluation on arbitrarily-sized inputs, including global scale. We conclude with a super-resolution study of renewable energy resources based on climate scenario data from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report.

14 SOLAR ENERGY↗
DOE OSTI2023

Future Projections of EURO‐CORDEX Raw and Bias‐Corrected Daily Maximum Wind Speeds Over Scandinavia

Twenty historical and future Coordinated Regional Climate Downscaling Experiment ensemble for Europe simulations are bias corrected to investigate the future changes in the daily maximum wind speed over Scandinavia. We use quantile mapping to adjust the wind for the historical period (1985–2014) and quantile delta mapping for two future periods (2041–2070, 2071–2100, RCP8.5) with the 3-km spatial resolution NORA3 hindcast as the reference data set. Decomposing the variance, we find that most of the inter-model spread in the bias and the response to climate change is due to the regional climate model over land and mainly to the general climate model over sea. On average, the mean daily maximum wind speed is projected to increase everywhere except along the western coast of Norway and Denmark. In summer, we see an opposite sign over Sweden and Finland. The Norwegian Sea experiences weaker mean and high wind whereas the Baltic Sea experiences a strengthening. Bias correction influences the amplitude of the response, not the response pattern. Wind speed distributions can have different shapes in the future, with, for example, a flattening of the distribution off the coast of Norway with more frequent weak and very strong winds. Apart from a few locations in Norway, we find an increase in the number of days in the local highest historical wind category. Overall, summer exhibits opposite signals compared to the three other seasons. At country scale, the sign of the change in the mean and 98th percentile varies greatly depending on the region and among the simulations, especially for Norway.

54 ENVIRONMENTAL SCIENCES↗