Yiling Huo, Hailong Wang, Milena Veneziani, Darin Comeau, Robert Osinski, Benjamin R. Hillman, Erika Roesler, Wieslaw Maslowski, Philip J. Rasch, Wilbert Weijer, Ian Baxter, Qiang Fu, Oluwayemi A. Garuba, Weiming Ma, Mark W. Seefeldt, Aodhan Sweeney, Mingxuan Wu, Jing Zhang, Xiangdong Zhang, Yu Zhang, Xylar Asay-Davis, Anthony P. Craig, Younjoo J. Lee, Wuyin Lin, Andrew F. Roberts, Jonathan D. Wolfe, Shixuan Zhang
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This paper provides an overview of the United States (U.S.) Department of Energy's (DOE's) Energy Exascale Earth System Model version 2.1 with an Arctic RRM, hereafter referred to as E3SMv2.1-Arctic, for the atmosphere (25 km), land (25 km), and ocean/ice (10 km) components. We evaluate the atmospheric component and its interactions with land, ocean, and cryosphere by comparing the RRM (E3SM2.1-Arctic) historical simulations (1950–2014) with the uniform low-resolution (LR) counterpart, reanalysis products, and observational data sets. The RRM generally reduces biases in the LR model, improving simulations of Arctic large-scale mean fields, such as precipitation, atmospheric circulation, clouds, atmospheric river frequency, and sea ice thickness. However, it introduces a seasonally dependent surface air temperature bias, reducing the LR cold bias in summer but enhancing the LR warm bias in winter, which contributes to the underestimated winter sea ice area and volume. Radiative feedback analysis shows similar climate feedback strengths in both model configurations, with the RRM exhibiting a more positive surface albedo feedback and contributing to a stronger surface warming than LR. These findings underscore the importance of high-resolution modeling for advancing our understanding of Arctic climate changes and their broader global impacts, although some persistent biases appear to be independent of model resolution at 10–100 km scales.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004726","citationCount":"0","resultStr":"{\"title\":\"E3SM-Arctic: Regionally Refined Coupled Model for Advanced Understanding of Arctic Systems Interactions\",\"authors\":\"Yiling Huo, Hailong Wang, Milena Veneziani, Darin Comeau, Robert Osinski, Benjamin R. Hillman, Erika Roesler, Wieslaw Maslowski, Philip J. Rasch, Wilbert Weijer, Ian Baxter, Qiang Fu, Oluwayemi A. Garuba, Weiming Ma, Mark W. Seefeldt, Aodhan Sweeney, Mingxuan Wu, Jing Zhang, Xiangdong Zhang, Yu Zhang, Xylar Asay-Davis, Anthony P. Craig, Younjoo J. Lee, Wuyin Lin, Andrew F. Roberts, Jonathan D. Wolfe, Shixuan Zhang\",\"doi\":\"10.1029/2024MS004726\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Earth system models are essential tools for climate projections, but coarse resolutions limit regional accuracy, especially in the Arctic. Regionally refined meshes (RRMs) enhance resolution in key areas while maintaining computational efficiency. This paper provides an overview of the United States (U.S.) Department of Energy's (DOE's) Energy Exascale Earth System Model version 2.1 with an Arctic RRM, hereafter referred to as E3SMv2.1-Arctic, for the atmosphere (25 km), land (25 km), and ocean/ice (10 km) components. 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E3SM-Arctic: Regionally Refined Coupled Model for Advanced Understanding of Arctic Systems Interactions
Earth system models are essential tools for climate projections, but coarse resolutions limit regional accuracy, especially in the Arctic. Regionally refined meshes (RRMs) enhance resolution in key areas while maintaining computational efficiency. This paper provides an overview of the United States (U.S.) Department of Energy's (DOE's) Energy Exascale Earth System Model version 2.1 with an Arctic RRM, hereafter referred to as E3SMv2.1-Arctic, for the atmosphere (25 km), land (25 km), and ocean/ice (10 km) components. We evaluate the atmospheric component and its interactions with land, ocean, and cryosphere by comparing the RRM (E3SM2.1-Arctic) historical simulations (1950–2014) with the uniform low-resolution (LR) counterpart, reanalysis products, and observational data sets. The RRM generally reduces biases in the LR model, improving simulations of Arctic large-scale mean fields, such as precipitation, atmospheric circulation, clouds, atmospheric river frequency, and sea ice thickness. However, it introduces a seasonally dependent surface air temperature bias, reducing the LR cold bias in summer but enhancing the LR warm bias in winter, which contributes to the underestimated winter sea ice area and volume. Radiative feedback analysis shows similar climate feedback strengths in both model configurations, with the RRM exhibiting a more positive surface albedo feedback and contributing to a stronger surface warming than LR. These findings underscore the importance of high-resolution modeling for advancing our understanding of Arctic climate changes and their broader global impacts, although some persistent biases appear to be independent of model resolution at 10–100 km scales.
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