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dc.contributor.authorBrunke, Michael A.
dc.contributor.authorCassano, John J.
dc.contributor.authorDawson, Nicholas
dc.contributor.authorDuVivier, Alice K.
dc.contributor.authorGutowski , William J. Jr
dc.contributor.authorHamman, Joseph
dc.contributor.authorMaslowski, Wieslaw
dc.contributor.authorNijssen, Bart
dc.contributor.authorEyre, J.E. Jack Reeves
dc.contributor.authorRenteria, José C.
dc.contributor.authorRoberts, Andrew
dc.contributor.authorZeng, Xubin
dc.date04 Dec 2018
dc.date.accessioned2020-01-14T01:34:58Z
dc.date.available2020-01-14T01:34:58Z
dc.date.issued2018-12-04
dc.identifier.citationBrunke, Michael A., et al. "Evaluation of the atmosphere–land–ocean–sea ice interface processes in the Regional Arctic System Model version 1 (RASM1) using local and globally gridded observations." Geoscientific Model Development 11.12 (2018).en_US
dc.identifier.urihttp://hdl.handle.net/10945/63804
dc.descriptionThe article of record as published may be found at https://doi.org/10.5194/gmd-11-4817-2018en_US
dc.description.abstractThe 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.en_US
dc.description.sponsorshipThis multi-institutional work was funded by the U.S. Department of Energy (DE-SC0006693, DE-SC0006178, DE-SC0006643, DE-FG02-07ER64460, DE-SC0006856, DE- SC0005783, and DE-SC0005522), by the U.S. National Science Foundation (PLR-1107788, PLR-1417818, and ARC1023369), and by the National Aeronautics and Space Administration (NNX14AM02G). Computing resources were provided via a Chal- lenge Grant from the U.S. Department of Defense (DoD) High Performance Computing Modernization Program (HPCMP).en_US
dc.format.extent25 p.en_US
dc.publisherEuropean Geosciences Union (EGU)en_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleEvaluation of the atmosphere–land–ocean–sea ice interface processes in the Regional Arctic System Model version 1 (RASM1) using local and globally gridded observationsen_US
dc.typeArticleen_US
dc.contributor.departmentOceanographyen_US
dc.description.funderThis multi-institutional work was funded by the U.S. Department of Energy (DE-SC0006693, DE-SC0006178, DE-SC0006643, DE-FG02-07ER64460, DE-SC0006856, DE- SC0005783, and DE-SC0005522), by the U.S. National Science Foundation (PLR-1107788, PLR-1417818, and ARC1023369), and by the National Aeronautics and Space Administration (NNX14AM02G). Computing resources were provided via a Chal- lenge Grant from the U.S. Department of Defense (DoD) High Performance Computing Modernization Program (HPCMP).en_US


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