Global and Regional Variations in Mean Temperature and Warm Extremes in Large-Member Historical AGCM Simulation

Thursday, 17 December 2015: 17:30
3012 (Moscone West)
Youichi Kamae1, Hideo Shiogama2, Yukiko Imada3, Masato Mori4, Osamu Arakawa1, Ryo Mizuta5, Kohei Yoshida6, Masayoshi Ishii7, Masahiro Watanabe8, Masahide Kimoto9 and Hiroaki Ueda1, (1)University of Tsukuba, Tsukuba, Japan, (2)NIES National Institute for Environmental Studies, Tsukuba, Japan, (3)Meteorological Research Institute, Ibaraki, Japan, (4)Atmosphere and Ocean Research Institute University of Tokyo, Chiba, Japan, (5)Meteorological Research Inst., Tsukuba, Japan, (6)MRI/JMA, Tsukuba, Japan, (7)Japan Meteorological Agency, Tsukuba, Japan, (8)University of Tokyo, Atmosphere and Ocean Research Institute, Bunkyo-ku, Japan, (9)Atmosphere and Ocean Research Institute University of Tokyo, Tokyo, Japan
Frequency of heat extremes during the summer season has increased continuously since the late 20th century despite the global warming hiatus. In previous studies, anthropogenic influences, natural variation in sea surface temperature (SST), and internal atmospheric variabilities are suggested to be factors contributing to the increase in the frequency of warm extremes. Here 100-member ensemble historical simulations were performed (called “database for Probabilistic Description of Future climate”; d4PDF) to examine physical mechanisms responsible for the increasing hot summers and attribute to the anthropogenic influences or natural climate variability. 60km resolution MRI-AGCM ensemble simulations can reproduce historical variations in the mean temperature and warm extremes. Natural SST variability in the Pacific and Atlantic Oceans contribute to the decadal variation in the frequency of hot summers in the Northern Hemisphere middle latitude. For example, the surface temperature over western North America, including California, is largely influenced by anomalous atmospheric circulation pattern associated with Pacific SST variability. Future projections based on anomalous SST patterns derived from coupled climate model simulations will also be introduced.