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-CG38
タイトル 和文 Climate Variability and Predictability on Subseasonal to Centennial Timescales
英文 Climate Variability and Predictability on Subseasonal to Centennial Timescales
タイトル短縮名 和文 Climate Variability and Predictability
英文 Climate Variability and Predictability
代表コンビーナ 氏名 和文 片岡 崇人
英文 Takahito Kataoka
所属 和文 国立研究開発法人 海洋研究開発機構
英文 JAMSTEC Japan Agency for Marine-Earth Science and Technology
共同コンビーナ 1 氏名 和文 Hiroyuki Murakami
英文 Hiroyuki Murakami
所属 和文 Geophysical Fluid Dynamics Laboratory/University Corporation for Atmospheric Research
英文 Geophysical Fluid Dynamics Laboratory
共同コンビーナ 2 氏名 和文 森岡 優志
英文 Yushi Morioka
所属 和文 海洋研究開発機構
英文 Japan Agency for Marine-Earth Science and Technology
共同コンビーナ 3 氏名 和文 Nathaniel C Johnson
英文 Nathaniel C Johnson
所属 和文 NOAA Geophysical Fluid Dynamics Laboratory
英文 NOAA Geophysical Fluid Dynamics Laboratory
発表言語 E
スコープ 和文
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 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.
英文
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 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.
発表方法 口頭および(または)ポスターセッション
ジョイントセッション AGU ,AOGS ,EGU
招待講演 Paul-Arthur Monerie (University of Reading/National Center for Atmospheric Science)
横井 覚 (海洋研究開発機構)
Jong-Seong Kug (Seoul National University)
時間 講演番号 タイトル 発表者
口頭発表 5月28日 AM1
9:00 - 9:15 ACG38-01 Seasonal predictability of mass coral bleaching events between the Pacific Ocean and the East China Sea with a large-ensemble climate model 土井 威志
9:15 - 9:30 ACG38-02 Interannual to multi decadal prediction skill of Summer Monsoon Precipitation Paul-Arthur Monerie
9:30 - 9:45 ACG38-03 低次元モデルによる大循環モデル・ペースメーカー実験の再現性評価と熱帯海盆間相互作用への応用 木戸 晶一郎
9:45 - 10:00 ACG38-04 CS-Colored-LIM: a data-driven linear framework for extended ENSO predictions Lien Justin
10:00 - 10:15 ACG38-05 High-resolution large ensemble simulation with an ocean-assimilated climate model 水田 亮
10:15 - 10:30 ACG38-06 Wasserstein Distance as a Tool for Analyzing Large-Ensemble Datasets 安田 勇輝
口頭発表 5月28日 AM2
10:45 - 11:00 ACG38-07 Intraseasonal Northwest–Southeast Oscillations of the Tropical Easterly Jet Core: Dynamical Mechanisms and Modulation by the Boreal Summer Intraseasonal Oscillation shihua liu
11:00 - 11:15 ACG38-08 熱帯北西太平洋における夏季北進季節内振動の観測的研究 横井 覚
11:15 - 11:30 ACG38-09 The triple-dip La Niña was key to Earth’s extreme heat uptake in 2022-2023 土田 耕
11:30 - 11:45 ACG38-10 Global and Regional Drivers for Exceptional Climate Extremes in 2023-2024: Beyond the New Normal 見延 庄士郎
11:45 - 12:00 ACG38-11 Abrupt shift of El Niño periodicity under CO2 mitigation 岩切 友希
12:00 - 12:15 ACG38-12 Amplified El Niño-induced Global SST Variability in a Warming World Jong-Seong Kug
講演番号 タイトル 発表者
ポスター発表 5月28日 PM3
ACG38-P01 東京における冬季降水に関連した大規模循環場の年々変動 田中 達也
ACG38-P02 Assessment of the temporal variability in precipitation trends in southwestern Russia Mariia Aleshina
ACG38-P03 JMA/MRI-CPS3における熱帯季節内変動の予測可能性に対する夏季豪州モンスーンの影響 関澤 偲温
ACG38-P04 Seasonal Predictability of Marine Heat Waves during Summer around Hawaii 高橋 直也
ACG38-P05 Pacemaker hindcast experiments with MIROC6 contributing to the TBIMIP 片岡 崇人