2024年5月27日(月) 09:00 〜 10:15 201B (幕張メッセ国際会議場)

コンビーナ:Murakami Hiroyuki(Geophysical Fluid Dynamics Laboratory/University Corporation for Atmospheric Research)、森岡 優志(海洋研究開発機構)、Kataoka Takahito、Yang Xiaosong(NOAA Geophysical Fluid Dynamics Laboratory)、Chairperson:Hiroyuki Murakami(NOAA/GFDL)、Xiaosong Yang(NOAA Geophysical Fluid Dynamics Laboratory)

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.