日本地球惑星科学連合2024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 | 大気海洋・環境科学複合領域・一般 (CG) | |||
| セッション ID | A-CG31 | |||
| 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 | 5/27(月) AM1-AM2 | ||
| 現地 ポスター コアタイム |
5/27(月) PM3 | |||
| 代表コンビーナ | 氏名 | Hiroyuki Murakami | ||
| 所属 | Geophysical Fluid Dynamics Laboratory/University Corporation for Atmospheric Research | |||
| 共同コンビーナ１ | 氏名 | 森岡 優志 | ||
| 所属 | 海洋研究開発機構 | |||
| 共同コンビーナ２ | 氏名 | Takahito Kataoka | ||
| 所属 | ||||
| 共同コンビーナ３ | 氏名 | Xiaosong Yang | ||
| 所属 | NOAA Geophysical Fluid Dynamics Laboratory | |||
| 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 significant impacts on global socioeconomic activities by inducing extreme climate events (e.g., atmospheric and marine heatwaves/coldwaves, hurricanes/typhoons/cyclones, and floods/droughts) and influencing their physical characteristics. Numerous efforts have been made to comprehensively understand and skillfully predict subseasonal to centennial climate variabilities using observation data and dynamical/statistical models. However, most models still undergo systematic biases in the amplitude, spatial patterns, and frequency of these climate variabilities. These model biases often stem from an inadequate grasp of weather and climate interactions across different spatiotemporal scales (e.g., tropical cyclones-ENSO) and incomplete representation of the complex and nonlinear processes within the climate system (e.g., troposphere-stratosphere coupling, atmosphere-ocean-sea ice interactions). Therefore, a seamless approach to climate modeling and observational studies across different spatiotemporal scales is essential. This session welcomes all research activities related to subseasonal to centennial climate variabilities and/or predictability utilizing observational data (e.g., satellite, ship, buoy/float, proxy data), theoretical/modeling approaches, and artificial intelligence/machine learning frameworks. Research topics involving the analysis of the Coupled Model Intercomparison Project Phase 6 (CMIP6) are also welcome. |
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| 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, AOGS | |||