Mon. May 22, 2023 9:00 AM - 10:15 AM 104 (International Conference Hall, Makuhari Messe)

convener:Yushi Morioka(Japan Agency for Marine-Earth Science and Technology), Hiroyuki Murakami(Geophysical Fluid Dynamics Laboratory/University Corporation for Atmospheric Research), Takahito Kataoka, Liping Zhang, Chairperson:Hiroyuki Murakami(Geophysical Fluid Dynamics Laboratory/University Corporation for Atmospheric Research), Takahito Kataoka, Yushi Morioka(Japan Agency for Marine-Earth Science and Technology)

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.