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E3SM‐Arctic: Regionally Refined Coupled Model for Advanced Understanding of Arctic Systems Interactions

Journal Article · · Journal of Advances in Modeling Earth Systems
DOI:https://doi.org/10.1029/2024MS004726· OSTI ID:3014012
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  1. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
  2. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
  3. Polish Academy of Sciences, Sopot (Poland)
  4. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  5. Naval Postgraduate School, Monterey, CA (United States)
  6. University of Chicago, IL (United States)
  7. University of Washington, Seattle, WA (United States)
  8. University of Colorado, Boulder, CO (United States)
  9. North Carolina A&T State University, Greensboro, NC (United States)
  10. North Carolina State University, Asheville, NC (United States)
  11. Brookhaven National Laboratory (BNL), Upton, NY (United States)
+ Show Author Affiliations
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.
Research Organization:
Naval Postgraduate School, Monterey, CA (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Science (SC), Biological and Environmental Research (BER). Earth & Environmental Systems Science (EESS)
Grant/Contract Number:
89243019SSC000030; AC02-05CH11231; AC05-76RL01830; SC0024872
OSTI ID:
3014012
Alternate ID(s):
OSTI ID: 2581197
Report Number(s):
PNNL-SA--203932
Journal Information:
Journal of Advances in Modeling Earth Systems, Journal Name: Journal of Advances in Modeling Earth Systems Journal Issue: 6 Vol. 17; ISSN 1942-2466
Publisher:
American Geophysical Union (AGU)Copyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

Energy Exascale Earth System Model (E3SM)
air-sea ice-ocean interactions
arctic modeling
radiative feedbacks
regionally refined mesh