Articles | Volume 16, issue 13
https://doi.org/10.5194/gmd-16-3953-2023
https://doi.org/10.5194/gmd-16-3953-2023
Model description paper
 | 
13 Jul 2023
Model description paper |  | 13 Jul 2023

The fully coupled regionally refined model of E3SM version 2: overview of the atmosphere, land, and river results

Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader

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Cited articles

Adler, R., Sapiano, M., Huffman, G., Wang, J.-J., Gu, G., Bolvin, D., Chiu, L., Schneider, U., Becker, A., Nelkin, E., Xie, P., Ferraro, R., and Shin, D.-B.: The Global Precipitation Climatology Project (GPCP) Monthly Analysis (New Version 2.3) and a Review of 2017 Global Precipitation, Atmosphere, 9, 138, https://doi.org/10.3390/atmos9040138, 2018. a
Bambach, N. E., Rhoades, A. M., Hatchett, B. J., Jones, A. D., Ullrich, P. A., and Zarzycki, C. M.: Projecting climate change in South America using variable-resolution Community Earth System Model: An application to Chile, Int. J. Climatol., 42, 2514–2542, https://doi.org/10.1002/joc.7379, 2022. a, b, c
Bengtsson, Y., Hodges, K. I., and Roeckner, R.: Storm tracks and climate change, J. Climate, 19, 3518–3543, https://doi.org/10.1175/JCLI3815.1, 2006. a, b
Beres, J. H., Alexander, M. J., and Holton, J. R.: A Method of Specifying the Gravity Wave Spectrum above Convection Based on Latent Heating Properties and Background Wind, J. Atmos. Sci., 61, 324–337, https://doi.org/10.1175/1520-0469(2004)061<0324:AMOSTG>2.0.CO;2, 2004. a
Blender, R. and Schubert, M.: Cyclone tracking in different spatial and temporal resolutions, Mon. Weather Rev., 128, 377–384, https://doi.org/10.1175/1520-0493(2000)128<0377:CTIDSA>2.0.CO;2, 2000.  a, b
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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.