2017
DOI: 10.2151/jmsj.2017-013
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Future Changes in Atmospheric Conditions for the Seasonal Evolution of the Baiu as Revealed from Projected AGCM Experiments

Abstract: This study investigates future changes in atmospheric circulation during the Baiu in Japan using 20-km-mesh atmospheric general circulation model (AGCM) simulations for the present-day and the future (2075-2099) climates under the Representative Concentration Pathways 8.5 scenario. The simulated future climates include the outputs obtained with one control sea surface temperature (SST) and three different SST patterns.

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Cited by 22 publications
(24 citation statements)
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References 41 publications
(84 reference statements)
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“…Some regions showed reasonably high contributions from the difference in future SST patterns to the total uncertainties, for example, the Nansei Islands in June and western Japan during July-August. Recently, Okada et al (2017) revealed that the differences in future SST patterns in July and August have significant impact on the termination timing of the baiu season, whereas the mean circulation changes in June are similar across all SST patterns. Figure 8 also shows future changes in climatological-mean moisture flux during June-August for three representatives out of six SST patterns according to Mizuta et al (2014), which support the results of Okada et al (2017).…”
Section: Discussionmentioning
confidence: 99%
“…Some regions showed reasonably high contributions from the difference in future SST patterns to the total uncertainties, for example, the Nansei Islands in June and western Japan during July-August. Recently, Okada et al (2017) revealed that the differences in future SST patterns in July and August have significant impact on the termination timing of the baiu season, whereas the mean circulation changes in June are similar across all SST patterns. Figure 8 also shows future changes in climatological-mean moisture flux during June-August for three representatives out of six SST patterns according to Mizuta et al (2014), which support the results of Okada et al (2017).…”
Section: Discussionmentioning
confidence: 99%
“…Consequently, the term "future change" is defined here as the difference between 2075 -2099 and 1980 -2004 mean values, whereas the term "climatology" refers to the 25-year mean. Although the results are dependent to some degree on the prescribed future SST changes (e.g., Endo et al 2012;Kusunoki 2017;Okada et al 2017), the present study concerns the three-model ensemble experiments using the CMIP5 ensemble mean SST. The Global Precipitation Climatology Project (GPCP) (Adler et al 2003) and the Japanese 55-year Reanalysis (JRA-55 reanalysis) (Kobayashi et al 2015) are used for the comparison of precipitation and atmospheric elements between the simulated and the observed present-day climatology.…”
Section: Experimental Data For Analysismentioning
confidence: 99%
“…Consequently, MRI-AGCM3.2 has been employed in conjunction with CMIP-derived and observed sea surface temperature (SST) data to explore the detailed patterns of regional climate under global warming scenarios (Kitoh et al 2016). Okada et al (2017) analyzed global warming experiments using the 20 km mesh version of MRI-AGCM 3.2 (MRI-AGCM3.2S) to assess seasonal evolution of atmospheric conditions for the Baiu in the future. They showed that the Baiu frontal zone remains south of Japan in June and that the end date of the Baiu depends on the future distribution of SSTs.…”
Section: Introductionmentioning
confidence: 99%
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“…Although the ensemble number of the MRI-AGCM runs is not sufficient to derive reliable probabilities, uncertainty and robustness of the projected changes are derived. Okada et al (2016) used the outputs from the presentclimate simulation and the 4 ensemble future-climate projections from MRI-AGCM to investigate the projected changes in atmospheric circulation during the Baiu season. They indicated the delayed northward shift of the Baiu front in June and the resulting decrease in rainfall in western Japan in June.…”
Section: Probabilistic Analysismentioning
confidence: 99%