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.
Model description paper
| 
13 Jul 2023
Model description paper |
| 13 Jul 2023
| 13 Jul 2023
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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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...
