水田 亮
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1 Database for Policy Decision making for Future climate change (d4pdf) Hideo Shiogama (NIES, Japan) & the MRI+MIROC joint team
2 Database for Policy Decision making for Future climate change (d4pdf) The joint team of the MRI & MIROC groups has produced the huge ensembles of the 60-km MRI AGCM: member historical runs during member +4K runs of 60-year length SST patterns are taken from RCP8.5 runs of 6 AOGCMs We scaled SST of each AOGCM to adjust the global mean temperature changes to be +4K. We add the SST to the detrended SST pattern of the observations and use the 2090 forcing of RCP initial condition ensembles for each SST pattern member historical DETRENDED runs during We use a detrended SST and the preindustrial forcing. We also downscaled the historical and +4K runs using the 20-km MRI RCM around Japan.
3 Histogram of daily mean precipitation at Tokyo (60km AGCM) (a) Present day (b) Changes in the +4K world Frequency(%) 10yr 100yr Return Period = 1yr Ratio of freq. (+4K/present) 6 patterns of SST (CMIP5 GCMs) & the ensemble mean Daily precipitation (%) Daily precipitation (%) The high resolution MRI AGCM has a good skill on the simulations of daily precipitation. The 100 member ensemble enables us to investigate very rare events (return prd > 100 yr). In the +4K world, heavier precipitation events have larger increases of frequency. Uncertainties due to the different SST patterns are significant. (R. Mizuta, H Shiogama)
4 The continental avraged annual mean surface air temperature changes. Obs.(CRUTEM4) ALL NAT Shading: min-max of 100 members Anomalies from averages. The ALL runs well reproduce the observed warming of the continental averaged annual mean temperature. The NAT runs do not have the warming trends.
5 Attribution of historical changes in frequencies of record breaking temperature and precipitation extreme events Hideo Shiogama 1*, Yukiko Imada 2, Ryo Mizuta 2, Kohei Yoshida 2, Masato Mori 3, Osamu Arakawa 4, Mikiko Ikeda 5, Chiharu Takahashi 3, Miki Arai 3, Masayoshi Ishii 2, Nobuhito Mori 6,Izuru Takayabu 2, Eiichi Nakakita 6, Masahiro Watanabe 3 & Masahide Kimoto 3 1 Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Japan 2 Meteorological Research Institute, Tsukuba, Japan 3 Atmosphere and Ocean Research Institute, the University of Tokyo, Kashiwa, Japan 4 Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan 5 Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan 6 Disaster Prevention Research Institute, Kyoto University, Uji, Japan
6 Area factions of occurrence of record breaking daily extreme events Annual Warmest Nights Shading: 90% confidence intervals Dotted lines: min-max of 100 runs Annual most intense rain Area fractions of record breaking events decline as the observation data accumulate (e.g. Meehl et al. 2009, GRL). Anthropogenic climate change induced significant increases of probability of record breaking annual warmest nights and annul most intense rain after 1990s.
7 PDFs of area fractions of record extreme events during the period. Annual Warmest Nights Annual most intense rain HadEX2 (mean) HadEX2 (mean) 2 times 24 times area fractions of record events (%) area fractions of record events (%) Anthropogenic climate change increase the chance of more area fractions of new record than the observed area fractions during the period by 24 times for the annual warmest nights and 2 times for the annual most intense rain events.
8 Summary The Japanese modelling centers (MRI&MIROC) produced the large ensembles of AGCM and RCM to mainly investigate the uncertainty of internal variability in the D&A analyses and the future projections. The huge ensemble of the high resolution AGCM allows the robust attribution of historical changes in extreme events. The output data will be available at We are transferring the data, but it will take about 6 month due to the 2PB data size! In the next financial year (April 2016-), we will perform +2K ensemble which is closely related to the HAPPI-MIP.
9
10 Anthropogenic changes in probability (ALL minus NAT) of record extreme events during the period Annual Warmest Nights Annual most intense rain
11 trends of HadEX2 (Donat et al. 2013, JGR) Annual Warmest Nights Annual most intense rain
12 Record breaking extreme events
13 Freq. (%) Histogram of daily mean precipitation at Tokyo of OBS and the MRI AGCM OBS Daily mean precipitation (mm/day) The MRI AGCM has amazing skills in simulations of extreme events.
14 Freq. (%) Histogram of daily precipitation at Tokyo of OBS and the MRI AGCM OBS Daily mean precipitation (mm/day)
15 Histogram of daily precipitation at Tokyo of OBS and the MRI AGCM OBS Freq. (%) The large ensemble enable us to investigate very severe events. Daily mean precipitation (mm/day)
16 Global area factions of occurrence of record breaking extreme events Shading: 90% confidence range Dotted lines: min-max
17 PDFs of area fractions of new record extreme events during the period.
18 Changes in probability (ALL minus NAT) of new record extreme events during the period.
19 地球温暖化対策に資するアンサンブル気候予測実験データベース 日本の温暖化施策決定のための統一シナリオ 環境省 適応 データセット を大幅拡充気温将来変化現在 温暖化時確率密度地球シミュレーター 特別推進課題 最新高解像度大気モデル実験 創生 P テーマ間連携 ES 最新機を利用 4 上昇した将来気候を予測 大量アンサンブルで高精度の統計情報 多様な影響評価に活用可能
20 気候モデルを用いた地球温暖化予測における不確実性 排出シナリオ 気候モデル 内部変動 (IPCC AR5) 発生頻度の低い異常天候や極端気象の変化の不確実性を十分に評価できていない Global, Large-scale: CMIP5 実験 Extremes, Regional-scale: 60km モデル実験 ( 創生プロ C 実験 + 環境省 気象庁気候変動予測データ ) でカバー 高解像度 大量アンサンブルで統計情報が必要
21 モデルと実験設定 1 60km 全球大気モデル : MRI-AGCM3.2 (Mizuta et al. 2012) 文部科学省の 21 世紀気候変動予測革新プログラム 環境省の 地域気候変動予測データ でも使用 日本域は 20km 領域気候モデルへダウンスケーリング 過去実験 : の 60 年 100 メンバー SST は COBE-SST2(Hirahara et al. 2014) に時空間的に連続な 100 種類の摂動を加算 摂動は観測の不確実性 ( 解析誤差 ) の情報から生成 非温暖化過去実験 : の 60 年 100 メンバー 過去実験において温暖化トレンドを除いた SST を使用 温暖化トレンドを含む過去 60 年の時間変動 ( 赤線 ;COBE-SST2) 観測不確実性を表す摂動 (δt)
22 モデルと実験設定 2 将来実験 : 産業革命前から 4 昇温した状態を 60 年 6 15=90 メンバー 海面水温の温暖化パターンとして CMIP5 の 6 種類の CGCM で地上気温が 4 上昇したときの海面水温変化を算出し 温暖化トレンドを除いた過去 60 年の海面水温に上乗せ 過去実験と同様の摂動を 15 種類 温室効果ガス濃度は RCP8.5 シナリオの 2090 年相当 6 種の温暖化パターン (CMIP5) (ΔT) 温暖化トレンドを除いた過去 60 年の時間変動 ( 青線 ;COBE-SST2) 観測不確実性を表す 15 摂動 (δt)
23 東京での日降水量頻度分布 (60km model) (a) 現在の東京の日降水量頻度分布 (b) +4 で頻度が何倍になるか 頻度 (%) 1 年に 1 度 10 年に 1 度 100 年に 1 度 頻度の比 ( 将来 / 現在 ) 日平均降水量 (mm/day) 日平均降水量 (mm/day) (H Shiogama, R. Mizuta)
24 熱帯低気圧全球年発生数の確率分布 発生確率 [%] 観測 [1] N=84.3 (32 年 ) 過去実験 [100] N=84.6 (60 年 ) 過去実験 [1] N=84.9 (60 年 ) 将来実験 [90] N=54.7 (60 年 ) 将来実験 [1] N=55.7 (60 年 ) [ ] はメンバー数 年々変動の標準偏差 HPB( ): (±0.90) HPB( ): 9.74 (±1.24) 観測 ( ): 8.91 HFB( ): 8.76 (±0.88) 熱帯低気圧の 1 年あたりの発生数 (by K. Yoshida)
25 10 年に 1 度の日降水量 メンバー数を増やすことによって よりはっきりした増減の分布が得られる 1 member 10 members 90 members Past +4K Change Rate ( 将来の 10 年に 1 度 )/( 現在の 10 年に 1 度 ) (R. Mizuta)
26 We selected the periods (year 2040), (2090) and (2140) for the SST warming patterns (forcing conditions) in the +2 C, +4 C and +6 C warmer world.
27 CMIP5 GCM names scaling factors for +2 C scaling factors for +4 C scaling factors for +6 C CCSM GFDL-CM HadGEM2-AO MIROC MPI-ESM-MR MRI-CGCM
28 XX 年に 1 度の日降水量 過去 将来変化とも分布は大きく変わらないが Return period が長いほど増加が大きい Once in 10 years Once in 30 years Once in 100 years Past +4K Change Rate 将来において現在の頻度の何倍になるか
![熱帯低気圧の 10 年あたり通過頻度 [number/10 year] [number/10 year] SST](/docs-images/75/72334885/images/29-0.jpg "昇温のモデル間の違いで頻度が異なる 西太平洋では MIROC5 と MIR-CGCM3 の違いが明瞭 (by K.")
29 熱帯低気圧の 10 年あたり通過頻度 [number/10 year] [number/10 year] SST 昇温のモデル間の違いで頻度が異なる 西太平洋では MIROC5 と MIR-CGCM3 の違いが明瞭 (by K. Yoshida)
IPCC 第 5 次報告書における排出ガスの抑制シナリオ 最新の IPCC 第 5 次報告書 (AR5) では 温室効果ガス濃度の推移の違いによる 4 つの RCP シナリオが用意されている パリ協定における将来の気温上昇を 2 以下に抑えるという目標に相当する排出量の最も低い RCP2.6 や最大
資料 5 気候変動を踏まえた治水計画の前提となる外力の設定手法 平成 30 年 4 月 12 日 1 IPCC 第 5 次報告書における排出ガスの抑制シナリオ 最新の IPCC 第 5 次報告書 (AR5) では 温室効果ガス濃度の推移の違いによる 4 つの RCP シナリオが用意されている パリ協定における将来の気温上昇を 2 以下に抑えるという目標に相当する排出量の最も低い RCP2.6 や最大排出量に相当する
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1 2 4 6 6 7 8 10 11 11 12 14 Page Page Page Page Page Page Page Page Page Hi everyone! My name is Martin Dusinberre, and I come from the UK. I first came to Iwaishima six years ago, when I taught English
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