Abstract

In extreme event attribution, which aims to answer whether and to what extent a particular extreme weather event can be attributed to global warming, the probability of an event is generally estimated through large ensemble simulations, using an atmospheric general circulation model (AGCM). In islands, such as Japan, it has been considered that surface air temperature (SAT) can be significantly affected by the surrounding sea surface temperature (SST), which mostly is affected by atmospheric circulation at mid- and high-latitudes. Therefore, the absence of SST responses to atmospheric variability in AGCMs impacts the estimation of the occurrence of extreme events, such as heat waves in Japan. In this study, we examined the impact of air–sea coupling on the probability of occurrence of severe heat waves that occurred in Japan in the summer of 2010 by analyzing the probability differences obtained from AGCM and coupled general circulation model (CGCM) large-ensemble experiments. The observed ocean temperature, salinity, and sea ice were assimilated in the 100-member CGCM experiments, as they were assigned as boundary conditions in the 100-member AGCM experiments. The SAT around Japan in the northern summer is largely related to the Bonin high, whose interannual variability is largely affected by the Silk Road and Pacific-Japan (PJ) pattern teleconnections in the real world. The SAT anomaly over Japan was related to the pressure variability due to the Silk Road and PJ patterns in the CGCM experiment. By contrast, the SAT over Japan simulated by AGCM was less sensitive to such pressure variability, and the SAT ensemble spread became narrower in AGCM. The results suggest that the probability of occurrence of the 2010 heat wave in Japan would tend to be underestimated by the AGCM ensemble compared to the CGCM ensemble, provided that the ensemble averages of the SAT anomalies were equal between CGCM and AGCM experiments. This study raised the issue of the absence of SST response to atmospheric variability in AGCMs, which can critically impact the estimation of extreme event probability, particularly in mid-latitude islands, such as Japan.

Highlights

  • IntroductionAn atmospheric general circulation model (AGCM) has been used for event attribution (EA) studies because the timescale of the most concerning extreme weather events is usually within 1 month

  • Extreme event attribution (EA) has been developed over the past decade to address questions regarding the impact of global warming on specific extreme weather events

  • By comparing event probability between large ensembles of atmospheric general circulation model (AGCM) and the assimilated coupled general circulation model (CGCM) runs, we evaluated the impact of air–sea coupling on the shape of a probability density function (PDF), on its tail

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Summary

Introduction

An atmospheric general circulation model (AGCM) has been used for EA studies because the timescale of the most concerning extreme weather events is usually within 1 month. Compared to this timescale, dominant intrinsic oceanic or sea ice variability has a considerably longer timescale, represented by interannual, decadal, and multi-decadal variability. The atmospheric model led to unrealistic heat flux at the air–sea interface (Yu and Mechoso 1999) and the absence of important feedback processes (Kitoh and Arakawa 1999) These issues raise the question whether air–sea interaction critically affects the estimation of event probability in the EA approach, for islands surrounded by ocean, such as Japan. It appears to depend on the location of the focus

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