JpGU Meeting 2024
Session Outline| Atmospheric and Hydrospheric Sciences (A) | ||||
|---|---|---|---|---|
| Session Sub-category | Complex & General (CG) | |||
| Session ID | A-CG31 | |||
| Session Title | Climate Variability and Predictability on Subseasonal to Centennial Timescales | |||
| Short Title | Climate Variability and Predictability | |||
| Date & Time | Oral Session |
AM1-AM2 Mon, 27 MAY | ||
| On-site Poster Coretime |
PM3 Mon, 27 MAY | |||
| Main Convener | Name | Hiroyuki Murakami | ||
| Affiliation | Geophysical Fluid Dynamics Laboratory | |||
| Co-Convener 1 | Name | Yushi Morioka | ||
| Affiliation | Japan Agency for Marine-Earth Science and Technology | |||
| Co-Convener 2 | Name | Takahito Kataoka | ||
| Affiliation | ||||
| Co-Convener 3 | Name | Xiaosong Yang | ||
| Affiliation | NOAA Geophysical Fluid Dynamics Laboratory | |||
| Session Language | E | |||
| Scope (Session Description) |
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. |
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| Presentation Format | Oral and Poster | |||
| Collaboration | Joint with | AGU, EGU, AOGS | ||
| Co-sponsoring Society |
The Oceanographic Society of Japan, Meteorological Society of Japan | |||