2021年6月3日(木) 15:30 〜 17:00 Ch.09 (Zoom会場09)

コンビーナ:森岡 優志(海洋研究開発機構)、Hiroyuki Murakami(Geophysical Fluid Dynamics Laboratory/University Corporation for Atmospheric Research)、中野 満寿男(海洋研究開発機構)、V Ramaswamy(NOAA GFDL)、座長:V Ramaswamy(NOAA GFDL)、森岡 優志(海洋研究開発機構)

Climate variability on subseasonal-multidecadal timescales (e.g. Madden-Julian Oscillation, ENSO, Indian Ocean Dipole, Pacific Decadal Variability, Atlantic Multidecadal Variability) has serious impacts on global and regional socioeconomic activities through changes in intensity and frequency of extreme weather events (e.g. cold/heat waves, tropical storms, and floods/droughts). Efforts have been made to understand and predict subseasonal-multidecadal climate variability, but climate simulations and predictions using state-of-the-art coupled general circulation models have biases that represent large uncertainties in amplitude and spatial patterns of the climate variability. The model uncertainties arise from multiple factors such as inadequate understanding of weather and climate interactions across different spatiotemporal scales (e.g. tropical cyclones-ENSO) and insufficient representation of the complex and non-linear climate system (e.g. troposphere-stratosphere coupling, atmosphere-ocean-sea ice-land interactions). Recently, Coupled Model Intercomparison Project Phase 6 (CMIP6) has opened up new model simulation datasets to the public, which can be expected to further advance our understanding and prediction of subseasonal-multidecadal climate variability under the changing climate. This session invites all research related to the subseasonal-multidecadal climate variability using observational, theoretical, modelling and AI/ML frameworks and especially novel approaches.