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http://dx.doi.org/10.14191/Atmos.2020.30.4.361

Evaluation of Performance and Uncertainty for Multi-RCM over CORDEX-East Asia Phase 2 region  

Kim, Jin-Uk (Innovative Meteorological Research Department, National Institute of Meteorological Sciences)
Kim, Tae-Jun (Innovative Meteorological Research Department, National Institute of Meteorological Sciences)
Kim, Do-Hyun (Innovative Meteorological Research Department, National Institute of Meteorological Sciences)
Kim, Jin-Won (Innovative Meteorological Research Department, National Institute of Meteorological Sciences)
Cha, Dong-Hyun (School of Urban and Engineering, Ulsan National Institute of Science and Technology)
Min, Seung-Ki (School Environmental Science and Engineering, Pohang University of Science and Technology)
Kim, Yeon-Hee (Innovative Meteorological Research Department, National Institute of Meteorological Sciences)
Publication Information
Atmosphere / v.30, no.4, 2020 , pp. 361-376 More about this Journal
Abstract
This study evaluates multiple Regional Climate Models (RCMs) in simulating temperature and precipitation over the Far East Asia (FEA) and estimates the portions of the total uncertainty originating in the RCMs and the driving Global Climate Models (GCMs) using nine present-day (1981~2000) climate data obtained from combinations of three GCMs and three RCMs in the CORDEX-EA phase2. Downscaling using the RCMs generally improves the present temperature and precipitation simulated in the GCMs. The mean temperature climate in the RCM simulations is similar to that in the GCMs; however, RCMs yield notably better spatial variability than the GCMs. In particular, the RCMs generally yield positive added values to the variability of the summer temperature and the winter precipitation. Evaluating the uncertainties by the GCMs (VARGCM) and the RCMs (VARRCM) on the basis of two-way ANOVA shows that VARRCM is greater than VARGCM in contrast to previous studies which showed VARGCM is larger. In particular, in the winter temperature, the ocean has a very large VARRCM of up to 30%. Precipitation shows that VARRCM is greater than VARGCM in all seasons, but the difference is insignificant. In the following study, we will analyze how the uncertainty of the climate model in the present-day period affects future climate change prospects.
Keywords
CORDEX-EA; RCM; uncertainty; simulation performance;
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