JpGU Meeting 2022
Session outline| Atmospheric and Hydrospheric Sciences (A) | ||
|---|---|---|
| Session Sub-category | Complex & General (CG) | |
| Session ID | A-CG34 | |
| Title | Climate Variability and Predictability on Subseasonal to Multidecadal Timescales | |
| Short Title | Climate Variability and Predictability | |
| Main Convener | Name | Yushi Morioka |
| Affiliation | Japan Agency for Marine-Earth Science and Technology | |
| Co-Convener 1 | Name | Hiroyuki Murakami |
| Affiliation | Geophysical Fluid Dynamics Laboratory/University Corporation for Atmospheric Research | |
| Co-Convener 2 | Name | Tomoe Nasuno |
| Affiliation | Japan Agency for Marine-Earth Science and Technology | |
| Co-Convener 3 | Name | Liping Zhang |
| Affiliation | NOAA GFDL Princeton | |
| Session Language | E | |
| Scope | Climate variability on subseasonal to multidecadal timescales (e.g., Madden-Julian Oscillation, El Nino/Southern Oscillation (ENSO), Indian Ocean Dipole, Pacific Decadal Variability, Atlantic Multidecadal Variability, Southern Ocean Centennial Variability) exerts great influences on global socioeconomic activities by modulating physical characteristics of extreme weather events (e.g., heatwaves/coldwaves, tropical cyclones, and floods/droughts). Many efforts have been made to accurately understand and skillfully predict subseasonal to multidecadal climate variability. However, models have shown systematic biases in amplitude, spatial pattern, and frequency of these climate variabilities. These model biases often stem from multiple factors such as poor 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 interactions) so that seamless studies on climate variability are required. This session invites all research activities related to the subseasonal to multidecadal climate variability 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 session | |
| Joint with | ||
| Co-sponsored | The Oceanographic Society of Japan, Meteorological Society of Japan | |