Articles | Volume 16, issue 13
https://doi.org/10.5194/gmd-16-3953-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-3953-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The fully coupled regionally refined model of E3SM version 2: overview of the atmosphere, land, and river results
Lawrence Livermore National Laboratory, Livermore, CA, USA
Jean-Christophe Golaz
Lawrence Livermore National Laboratory, Livermore, CA, USA
Luke P. Van Roekel
Los Alamos National Laboratory, Los Alamos, NM, USA
Mark A. Taylor
Sandia National Laboratories, Albuquerque, NM, USA
Wuyin Lin
Brookhaven National Laboratory, Upton, NY, USA
Benjamin R. Hillman
Sandia National Laboratories, Albuquerque, NM, USA
Paul A. Ullrich
Department of Land, Air and Water Resources, University of California, Davis, CA, USA
Andrew M. Bradley
Sandia National Laboratories, Albuquerque, NM, USA
Oksana Guba
Sandia National Laboratories, Albuquerque, NM, USA
Jonathan D. Wolfe
Los Alamos National Laboratory, Los Alamos, NM, USA
Tian Zhou
Pacific Northwest National Laboratory, Richland, WA, USA
Kai Zhang
Pacific Northwest National Laboratory, Richland, WA, USA
Xue Zheng
Lawrence Livermore National Laboratory, Livermore, CA, USA
Yunyan Zhang
Lawrence Livermore National Laboratory, Livermore, CA, USA
Meng Zhang
Lawrence Livermore National Laboratory, Livermore, CA, USA
Mingxuan Wu
Pacific Northwest National Laboratory, Richland, WA, USA
Hailong Wang
Pacific Northwest National Laboratory, Richland, WA, USA
Cheng Tao
Lawrence Livermore National Laboratory, Livermore, CA, USA
Balwinder Singh
Pacific Northwest National Laboratory, Richland, WA, USA
Alan M. Rhoades
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Lawrence Livermore National Laboratory, Livermore, CA, USA
Hong-Yi Li
Department of Civil and Environmental Engineering, University of Houston, TX, USA
Argonne National Laboratory, Lemont, IL, USA
Yuying Zhang
Lawrence Livermore National Laboratory, Livermore, CA, USA
Chengzhu Zhang
Lawrence Livermore National Laboratory, Livermore, CA, USA
Charles S. Zender
Departments of Earth System Science and Computer Science, University of California, Irvine, CA, USA
Shaocheng Xie
Lawrence Livermore National Laboratory, Livermore, CA, USA
Erika L. Roesler
Sandia National Laboratories, Albuquerque, NM, USA
Andrew F. Roberts
Los Alamos National Laboratory, Los Alamos, NM, USA
Azamat Mametjanov
Argonne National Laboratory, Lemont, IL, USA
Mathew E. Maltrud
Los Alamos National Laboratory, Los Alamos, NM, USA
Noel D. Keen
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Robert L. Jacob
Argonne National Laboratory, Lemont, IL, USA
Christiane Jablonowski
Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI, USA
Owen K. Hughes
Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI, USA
Ryan M. Forsyth
Lawrence Livermore National Laboratory, Livermore, CA, USA
Alan V. Di Vittorio
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Peter M. Caldwell
Lawrence Livermore National Laboratory, Livermore, CA, USA
Gautam Bisht
Pacific Northwest National Laboratory, Richland, WA, USA
Renata B. McCoy
Lawrence Livermore National Laboratory, Livermore, CA, USA
L. Ruby Leung
Pacific Northwest National Laboratory, Richland, WA, USA
David C. Bader
Lawrence Livermore National Laboratory, Livermore, CA, USA
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Cited
12 citations as recorded by crossref.
- Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model T. Hassan et al. 10.5194/gmd-17-3507-2024
- Atmospheric-river-induced precipitation in California as simulated by the regionally refined Simple Convective Resolving E3SM Atmosphere Model (SCREAM) Version 0 P. Bogenschutz et al. 10.5194/gmd-17-7029-2024
- Present-day correlations are insufficient to predict cloud albedo change by anthropogenic aerosols in E3SM v2 N. Mahfouz et al. 10.5194/acp-24-7253-2024
- Improved subseasonal-to-seasonal precipitation prediction of climate models with nudging approach for better initialization of Tibetan Plateau-Rocky Mountain Circumglobal wave train and land surface conditions Y. Qin et al. 10.1007/s00382-023-07082-1
- Continental United States climate projections based on thermodynamic modification of historical weather A. Jones et al. 10.1038/s41597-023-02485-5
- A seamless approach for evaluating climate models across spatial scales A. Chang et al. 10.3389/feart.2023.1245815
- Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0 J. Zhang et al. 10.5194/gmd-17-3687-2024
- Understanding changes in cloud simulations from E3SM version 1 to version 2 Y. Zhang et al. 10.5194/gmd-17-169-2024
- Recreating the California New Year's Flood Event of 1997 in a Regionally Refined Earth System Model A. Rhoades et al. 10.1029/2023MS003793
- Evaluating the Water Cycle Over CONUS at the Watershed Scale for the Energy Exascale Earth System Model Version 1 (E3SMv1) Across Resolutions B. Harrop et al. 10.1029/2022MS003490
- ENSO Diversity and the Simulation of Its Teleconnections to Winter Precipitation Extremes Over the US in High Resolution Earth System Models S. Mahajan et al. 10.1029/2022GL102657
- Anticipating how rain-on-snow events will change through the 21st century: lessons from the 1997 new year’s flood event A. Rhoades et al. 10.1007/s00382-024-07351-7
8 citations as recorded by crossref.
- Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model T. Hassan et al. 10.5194/gmd-17-3507-2024
- Atmospheric-river-induced precipitation in California as simulated by the regionally refined Simple Convective Resolving E3SM Atmosphere Model (SCREAM) Version 0 P. Bogenschutz et al. 10.5194/gmd-17-7029-2024
- Present-day correlations are insufficient to predict cloud albedo change by anthropogenic aerosols in E3SM v2 N. Mahfouz et al. 10.5194/acp-24-7253-2024
- Improved subseasonal-to-seasonal precipitation prediction of climate models with nudging approach for better initialization of Tibetan Plateau-Rocky Mountain Circumglobal wave train and land surface conditions Y. Qin et al. 10.1007/s00382-023-07082-1
- Continental United States climate projections based on thermodynamic modification of historical weather A. Jones et al. 10.1038/s41597-023-02485-5
- A seamless approach for evaluating climate models across spatial scales A. Chang et al. 10.3389/feart.2023.1245815
- Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0 J. Zhang et al. 10.5194/gmd-17-3687-2024
- Understanding changes in cloud simulations from E3SM version 1 to version 2 Y. Zhang et al. 10.5194/gmd-17-169-2024
4 citations as recorded by crossref.
- Recreating the California New Year's Flood Event of 1997 in a Regionally Refined Earth System Model A. Rhoades et al. 10.1029/2023MS003793
- Evaluating the Water Cycle Over CONUS at the Watershed Scale for the Energy Exascale Earth System Model Version 1 (E3SMv1) Across Resolutions B. Harrop et al. 10.1029/2022MS003490
- ENSO Diversity and the Simulation of Its Teleconnections to Winter Precipitation Extremes Over the US in High Resolution Earth System Models S. Mahajan et al. 10.1029/2022GL102657
- Anticipating how rain-on-snow events will change through the 21st century: lessons from the 1997 new year’s flood event A. Rhoades et al. 10.1007/s00382-024-07351-7
Latest update: 06 Oct 2024
Short summary
High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
High-resolution simulations are superior to low-resolution ones in capturing regional climate...