日本地球惑星科学連合2021年大会
セッション概要| 大気水圏科学 (A) | ||||
|---|---|---|---|---|
| セッション小記号 | 海洋科学・海洋環境 (OS) | |||
| セッション ID | A-OS09 | |||
| タイトル | Climate variability and predictability on subseasonal to multidecadal timescales | |||
| タイトル短縮名 | Climate variability and predictability | |||
| 開催日時 | 口頭セッション | 6/3 (木) PM1, PM2 | チャンネル | 09 |
| ポスターセッション | 6/3 (木) PM3 | 代表コンビーナ | 氏名 | 森岡 優志 |
| 所属 | 海洋研究開発機構 | |||
| 共同コンビーナ 1 | 氏名 | Hiroyuki Murakami | ||
| 所属 | Geophysical Fluid Dynamics Laboratory/University Corporation for Atmospheric Research | |||
| 共同コンビーナ 2 | 氏名 | 中野 満寿男 | ||
| 所属 | 海洋研究開発機構 | |||
| 共同コンビーナ 3 | 氏名 | V Ramaswamy | ||
| 所属 | NOAA GFDL | |||
| セッション言語 | E | |||
| スコープ | Climate variability on subseasonal-multidecadal timescales (e.g. Madden-Julian Oscillation, ENSO, Indian Ocean Dipole, Pacific Decadal Variability, Atlantic Multidecadal Variability) has serious impacts on global and regional socioeconomic activities through changes in intensity and frequency of extreme weather events (e.g. cold/heat waves, tropical storms, and floods/droughts). Efforts have been made to understand and predict subseasonal-multidecadal climate variability, but climate simulations and predictions using state-of-the-art coupled general circulation models have biases that represent large uncertainties in amplitude and spatial patterns of the climate variability. The model uncertainties arise from multiple factors such as inadequate understanding of weather and climate interactions across different spatiotemporal scales (e.g. tropical cyclones-ENSO) and insufficient representation of the complex and non-linear climate system (e.g. troposphere-stratosphere coupling, atmosphere-ocean-sea ice-land interactions). Recently, Coupled Model Intercomparison Project Phase 6 (CMIP6) has opened up new model simulation datasets to the public, which can be expected to further advance our understanding and prediction of subseasonal-multidecadal climate variability under the changing climate. This session invites all research related to the subseasonal-multidecadal climate variability using observational, theoretical, modelling and AI/ML frameworks and especially novel approaches. | |||
| 発表方法 | 口頭およびポスターセッション | 共催情報 | 学協会 | 日本海洋学会, 日本気象学会 |
| ジョイント | AGU, EGU | |||