日本地球惑星科学連合2023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 | 大気水圏科学複合領域・一般 (CG) | |||
| セッション ID | A-CG32 | |||
| タイトル | Climate Variability and Predictability on Subseasonal to Centennial Timescales | |||
| タイトル短縮名 | Climate Variability and Predictability | |||
| 開催日時 | ||||
| 口頭 セッション |
5/22(月) AM1, AM2 |
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| 現地 ポスター コアタイム |
5/22(月) PM3 | |||
| オンライン ポスター セッション |
5/24(水) AM1 | |||
| 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 | Takahito Kataoka | ||
| 所属 | ||||
| 共同コンビーナ３ | 氏名 | Liping Zhang | ||
| 所属 | ||||
| セッション言語 | E | |||
| スコープ |
Climate variability on subseasonal to centennial timescales (e.g., Madden-Julian Oscillation, El Nino/Southern Oscillation (ENSO), Indian Ocean Dipole, Pacific Decadal Variability, Atlantic Multidecadal Variability, Southern Ocean Centennial Variability) has huge impacts on global socioeconomic activities by inducing extreme climate events (e.g., atmospheric and marine heatwaves/coldwaves, major hurricanes/typhoons/cyclones, and floods/droughts) and modulating their physical characteristics. Many efforts have been made to accurately understand and skillfully predict subseasonal to centennial climate variabilities using observation data and dynamical/statistical models. However, most models still undergo systematic biases in amplitude, spatial pattern, and frequency of these climate variabilities. The model biases often originate from a lack of understanding of weather and climate interactions across different spatiotemporal scales (e.g., tropical cyclones-ENSO) and incomplete representation of complex and non-linear processes in the climate system (e.g., troposphere-stratosphere coupling, atmosphere-ocean-sea ice interactions). Therefore, seamless climate modeling and observational studies across different spatiotemporal scales are indispensable. This session invites all research activities related to the subseasonal to centennial climate variabilities using observational data (e.g., satellite, ship, buoy/float, proxy data), theoretical/modeling approaches, and artificial intelligence/machine learning frameworks. The research topics through analyzing Coupled Model Intercomparison Project Phase 6 (CMIP6) are also welcomed. |
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| 発表方法 | 口頭およびポスター | |||
| 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, AOGS | |||