Document Type

Article

Publication Date

1-1-2018

Publication Title

Geoscientific Model Development Discussions

DOI

10.5194/gmd-2018-104

Abstract

The Regional Arctic System Model version 1 (RASM1) has been developed to provide high-resolution simulations of the Arctic atmosphere–ocean–sea ice–land system. Here, we provide a baseline for the capability of RASM to simulate interface processes by comparing retrospective simulations from RASM1 for 1990–2014 with the Community Earth System Model version 1 (CESM1) and the spread across three recent reanalyses. Evaluations of surface and 2 m air temperature, surface radiative and turbulent fluxes, precipitation, and snow depth in the various models and reanalyses are performed using global and regional datasets and a variety of in situ datasets, including flux towers over land, ship cruises over oceans, and a field experiment over sea ice. These evaluations reveal that RASM1 simulates precipitation that is similar to CESM1, reanalyses, and satellite gauge combined precipitation datasets over all river basins within the RASM domain. Snow depth in RASM is closer to upscaled surface observations over a flatter region than in more mountainous terrain in Alaska. The sea ice–atmosphere interface is well simulated in regards to radiation fluxes, which generally fall within observational uncertainty. RASM1 monthly mean surface temperature and radiation biases are shown to be due to biases in the simulated mean diurnal cycle. At some locations, a minimal monthly mean bias is shown to be due to the compensation of roughly equal but opposite biases between daytime and nighttime, whereas this is not the case at locations where the monthly mean bias is higher in magnitude. These biases are derived from errors in the diurnal cycle of the energy balance (radiative and turbulent flux) components. Therefore, the key to advancing the simulation of SAT and the surface energy budget would be to improve the representation of the diurnal cycle of radiative and turbulent fluxes. The development of RASM2 aims to address these biases. Still, an advantage of RASM1 is that it captures the interannual and interdecadal variability in the climate of the Arctic region, which global models like CESM cannot do.

Comments

Michael A. Brunke1 , John J. Cassano2 , Nicholas Dawson3 , Alice K. DuVivier4 , William J. Gutowski Jr.5 , Joseph Hamman4 , Wieslaw Maslowski7 , Bart Nijssen6 , J. E. Jack Reeves Eyre1 , José C. Renteria8 , Andrew Roberts7 , and Xubin Zeng1

1Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ 85719, USA 2Cooperative Institute for Research in Environmental Sciences and Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO 80309, USA 3 Idaho Power, Boise, ID 83702, USA 4National Center for Atmospheric Research, Boulder, CO 80305, USA 5Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA 50011, USA 6Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA 7Department of Oceanography, Naval Postgraduate School, Monterey, CA 93943, USA 8U.S. Department of Defense, High Performance Computing Modernization Program, Lorton, VA 22079, USA

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