Development of the Regional Arctic System Model (RASM): Near-Surface Atmospheric Climate Sensitivity
Author
Cassano, John J.DuVivier, Alice
Roberts, Andrew
Hughes, Mimi
Seefeldt, Mark
Brunke, Michael
Craig, Anthony
Fisel, Brandon
Gutowski, William
Hamman, Joseph
Higgins, Matthew
Maslowski, Wieslaw
Nijssen, Bart
Osinski, Robert
Zeng, Xubin
Affiliation
Univ Arizona, Dept Atmospher SciIssue Date
2017-08
Metadata
Show full item recordPublisher
AMER METEOROLOGICAL SOCCitation
Development of the Regional Arctic System Model (RASM): Near-Surface Atmospheric Climate Sensitivity 2017, 30 (15):5729 Journal of ClimateJournal
Journal of ClimateRights
© 2017 American Meteorological Society.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
The near-surface climate, including the atmosphere, ocean, sea ice, and land state and fluxes, in the initial version of the Regional Arctic System Model (RASM) are presented. The sensitivity of the RASM near-surface climate to changes in atmosphere, ocean, and sea ice parameters and physics is evaluated in four simulations. The near-surface atmospheric circulation is well simulated in all four RASM simulations but biases in surface temperature are caused by biases in downward surface radiative fluxes. Errors in radiative fluxes are due to biases in simulated clouds with different versions of RASM simulating either too much or too little cloud radiative impact over open ocean regions and all versions simulating too little cloud radiative impact over land areas. Cold surface temperature biases in the central Arctic in winter are likely due to too few or too radiatively thin clouds. The precipitation simulated by RASM is sensitive to changes in evaporation that were linked to sea surface temperature biases. Future work will explore changes in model microphysics aimed at minimizing the cloud and radiation biases identified in this work.Note
6 month embargo; Published Online: 29 June 2017ISSN
0894-87551520-0442
Version
Final published versionSponsors
United States Department of Energy [DE-FG02-07ER64462, DE-SC0006178, DE-FG02-07ER64460, DE-SC0006856, DE-FG02-07ER64463, DE-SC0006693]; National Science Foundation [PLR-1107788, PLR-1417818]Additional Links
http://journals.ametsoc.org/doi/10.1175/JCLI-D-15-0775.1ae974a485f413a2113503eed53cd6c53
10.1175/JCLI-D-15-0775.1