JpGU-AGU Joint Meeting 2017

講演情報

[JJ] 口頭発表

セッション記号 A (大気水圏科学) » A-CG 大気水圏科学複合領域・一般

[A-CG53] [JJ] 気候変動への適応とその社会実装

2017年5月20日(土) 09:00 〜 10:30 104 (国際会議場 1F)

コンビーナ:石川 洋一(海洋研究開発機構)、渡邉 真吾(国立研究開発法人海洋研究開発機構)、大楽 浩司(防災科学技術研究所)、座長:渡邉 真吾(海洋研究開発機構)

09:45 〜 10:00

[ACG53-04] 都道府県スケールでの都市気候の将来予測における不確実性の評価

*原 政之1嶋田 知英1 (1.埼玉県環境科学国際センター)

キーワード:都市ヒートアイランド、気候変動、不確実性、適応策

Tokyo Metropolitan area (i.e., southern part of Kanto district) is known for one of the hottest areas in summer in Japan. Especially in Saitama prefecture (north of Tokyo), the daily maximum surface air temperature (SAT) at screen height sometimes reached in 40 ℃. In the last decade, the summer heat environment in Japan is getting worse, and the number of emergency transportations due to heat stroke is rapidly increasing.

The japan meteorological agency reported that increase in annual mean SAT from 1931 to 2015 is 3.2 ℃ in Tokyo, while the one averaged over 15 suburban cities is only 1.5 ℃. Increase in SAT is caused by both the global warming and urban heat island.

The increase in temperature widely discussed in COP21 (such as +1.5 and/or 2 ℃ world), is globally-averaged SAT. Under the +1.5 and/or 2 ℃ world, the increase in SAT in local scale is not 1.5 and/or 2 ℃ because of the global warming and urban heat island. We need to perform downscaling to estimate the increase in prefectural- (or provincial-) scale SAT under +1.5 and/or 2 ℃ world.

Moreover, in making environmental policies in local government, prefectural (or provincial ) scale future climate information is required to estimate the cost and benefit affected by climate adaptation strategies. So, policy maker requires the climate prediction, including its uncertainty information. But the future climate information provided by climate scientists contains uncertainty from various sources.

In this study, we evaluate the due to global climate change, regional climate change and land use change. To evaluate the uncertainty in regional climate prediction, we performed a series of present climate simulations using the Weather Research and Forecasting (WRF) model with high horizontal resolution, including an urban canopy sub-model. We also analyze global future climate predictions of CMIP5 CGCMs to evaluate the uncertainty in global climate change prediction.