Atmospheric and Hydrospheric Sciences (A)
Session Sub-categoryCG
Session IDA-CG32
Title Climate Variability and Predictability on Subseasonal to Centennial Timescales
Short Title Climate Variability and Predictability
Date & Time
AM1, AM2 Mon, 22 MAY
PM3 Mon, 22 MAY
AM1 Wed, 24 MAY
Main ConvenerName Yushi Morioka
Affiliation Japan Agency for Marine-Earth Science and Technology
Co-Convener 1Name Hiroyuki Murakami
Affiliation Geophysical Fluid Dynamics Laboratory/University Corporation for Atmospheric Research
Co-Convener 2Name Takahito Kataoka
Co-Convener 3Name Liping Zhang
Session Language E
Scope 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.
Presentation Format Oral and Poster
Collaboration Joint with AGU, EGU, AOGS
The Oceanographic Society of Japan, Meteorological Society of Japan