Artificial Intelligence-Enhanced CMIP6 Climate Projections Across the Conterminous United States

Rastogi, Deeksha; Niu, Haoran; Kao, Shih-Chieh

doi:10.13139/OLCF/2530405

Title: Artificial Intelligence-Enhanced CMIP6 Climate Projections Across the Conterminous United States

Dataset
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Abstract

This dataset comprises high-resolution climate projections at 1/24 degree grid (~4km) over the conterminous United States (CONUS) based on ten Global Climate Models (GCMs) that are part of the Coupled Models Intercomparison Project phase 6 (CMIP6). The CMIP6 GCMs are downscaled using two artificial intelligence (AI) techniques, primarily based on the computer vision approach called super-resolution. We train two separate networks: super-resolution convolutional neural network (SRCNN) and super-resolution generative adversarial network (SRGAN). The networks are trained using Daymet observations, originally available at a 1 km resolution. For training purposes, the Daymet data is interpolated to 1/24 degree (~4km), 0.25 degree and 1 degree, which serve as high, intermediate and low-resolution inputs respectively. For each of the SRCNN and SRGAN network, we use a two-step resolution enhancement, the first step generates 4x refinement from 1 degree to 0.25 degree and the second step generates 6x refinement from 0.25 degree to 1/24 degree (~4km). We downscale daily scale precipitation, maximum temperature and minimum temperature for the six CMIP6 GCMs for 1980 to 2019 in the historical period and 2020 to 2059 in the near-term future under the shared socioeconomic pathway 585 and 245 (SSP585 and SSP245) emission scenarios. We also perform doublemore »« less

Authors:

Rastogi, Deeksha; Niu, Haoran; Kao, Shih-Chieh

Oak Ridge National Laboratory; ORNL
Oak Ridge National Laboratory

Publication Date:: Thu Mar 27 04:00:00 UTC 2025

DOE Contract Number:: AC05-00OR22725

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: Laboratory Directed Research and Development (LDRD) Program, Oak Ridge National Laboratory; US DOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Water Power Technologies Office

Subject:: 13 HYDRO ENERGY; 54 ENVIRONMENTAL SCIENCES; 58 GEOSCIENCES; 97 MATHEMATICS AND COMPUTING

OSTI Identifier:: 2530405

DOI:: https://doi.org/10.13139/OLCF/2530405

Citation Formats


                    Rastogi, Deeksha, Niu, Haoran, and Kao, Shih-Chieh. Artificial Intelligence-Enhanced CMIP6 Climate Projections Across the Conterminous United States.  United States: N. p., 2025. 
        Web.  doi:10.13139/OLCF/2530405.

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                    Rastogi, Deeksha, Niu, Haoran, & Kao, Shih-Chieh. Artificial Intelligence-Enhanced CMIP6 Climate Projections Across the Conterminous United States.  United States.  doi:https://doi.org/10.13139/OLCF/2530405

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                    Rastogi, Deeksha, Niu, Haoran, and Kao, Shih-Chieh. 2025.  
"Artificial Intelligence-Enhanced CMIP6 Climate Projections Across the Conterminous United States".  United States.  doi:https://doi.org/10.13139/OLCF/2530405.  https://www.osti.gov/servlets/purl/2530405. Pub date:Thu Mar 27 04:00:00 UTC 2025

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@article{osti_2530405,

  title        = {Artificial Intelligence-Enhanced CMIP6 Climate Projections Across the Conterminous United States},

  author       = {Rastogi, Deeksha and Niu, Haoran and Kao, Shih-Chieh},

  abstractNote = {This dataset comprises high-resolution climate projections at 1/24 degree grid (~4km) over the conterminous United States (CONUS) based on ten Global Climate Models (GCMs) that are part of the Coupled Models Intercomparison Project phase 6 (CMIP6). The CMIP6 GCMs are downscaled using two artificial intelligence (AI) techniques, primarily based on the computer vision approach called super-resolution. We train two separate networks: super-resolution convolutional neural network (SRCNN) and super-resolution generative adversarial network (SRGAN). The networks are trained using Daymet observations, originally available at a 1 km resolution. For training purposes, the Daymet data is interpolated to 1/24 degree (~4km), 0.25 degree and 1 degree, which serve as high, intermediate and low-resolution inputs respectively. For each of the SRCNN and SRGAN network, we use a two-step resolution enhancement, the first step generates 4x refinement from 1 degree to 0.25 degree and the second step generates 6x refinement from 0.25 degree to 1/24 degree (~4km). We downscale daily scale precipitation, maximum temperature and minimum temperature for the six CMIP6 GCMs for 1980 to 2019 in the historical period and 2020 to 2059 in the near-term future under the shared socioeconomic pathway 585 and 245 (SSP585 and SSP245) emission scenarios. We also perform double bias-correction with Daymet observations using a quantile mapping approach, first for GCMs prior to making predictions at 1 degree grid and second after making final predictions at ~4km.},

  doi          = {10.13139/OLCF/2530405},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Thu Mar 27 04:00:00 UTC 2025},

  month        = {Thu Mar 27 04:00:00 UTC 2025}

}

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DOI: https://doi.org/10.13139/OLCF/2530405

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