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An overview of decadal climate predictability in a multi-model ensemble by climate model MIROC
Authors:Yoshimitsu Chikamoto  Masahide Kimoto  Masayoshi Ishii  Takashi Mochizuki  Takashi T Sakamoto  Hiroaki Tatebe  Yoshiki Komuro  Masahiro Watanabe  Toru Nozawa  Hideo Shiogama  Masato Mori  Sayaka Yasunaka  Yukiko Imada
Institution:1. Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, Chiba, 277-8568, Japan
2. Meteorological Research Institute, Tsukuba, Japan
3. Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
4. National Institute for Environmental Studies, Tsukuba, Japan
5. Department of Civil and Environmental Engineering, Tokyo Institute of Technology, Tokyo, Japan
Abstract:Decadal climate predictability is examined in hindcast experiments by a multi-model ensemble using three versions of the coupled atmosphere-ocean model MIROC. In these hindcast experiments, initial conditions are obtained from an anomaly assimilation procedure using the observed oceanic temperature and salinity with prescribed natural and anthropogenic forcings on the basis of the historical data and future emission scenarios in the Intergovernmental Panel of Climate Change. Results of the multi-model ensemble in our hindcast experiments show that predictability of surface air temperature (SAT) anomalies on decadal timescales mostly originates from externally forced variability. Although the predictable component of internally generated variability has considerably smaller SAT variance than that of externally forced variability, ocean subsurface temperature variability has predictive skills over almost a decade, particularly in the North Pacific and the North Atlantic where dominant signals associated with Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO) are observed. Initialization enhances the predictive skills of AMO and PDO indices and slightly improves those of global mean temperature anomalies. Improvement of these predictive skills in the multi-model ensemble is higher than that in a single-model ensemble.
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