[A-CG31] Climate Variability and Predictability on Subseasonal to Centennial Timescales

Mon. May 27, 2024 5:15 PM - 6:45 PM Poster Hall (Exhibition Hall 6, Makuhari Messe)

convener:Hiroyuki Murakami(Geophysical Fluid Dynamics Laboratory), Yushi Morioka(Japan Agency for Marine-Earth Science and Technology), Takahito Kataoka, Xiaosong Yang(NOAA Geophysical Fluid Dynamics Laboratory)

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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5:15 PM - 6:45 PM

[ACG31-P01] Multi-year predictive skill of the wintertime heavy rainfall potentials in western Japan

*Takashi Mochizuki¹ (1.Department of Earth and Planetary Sciences, Kyushu University)

5:15 PM - 6:45 PM

[ACG31-P02] Seasonal predictable signals of the East Asian summer monsoon rainfall

*Kairan Ying¹, Dabang Jiang², Xiaogu Zheng³, Carsten S. Frederiksen⁴ (1.National Institute of Natural Hazards, 2.Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China, 3.International Global Change Institute, Hamilton, New Zealand, 4.The Bureau of Meteorology, Melbourne, Australia)

5:15 PM - 6:45 PM

[ACG31-P03] Skillful Seasonal Prediction of Wind Energy Resources in the contiguous United States

*Xiaosong Yang¹, Thomas Delworth¹, Liwei Jia¹, Nathaniel Johnson¹, Feiyu Lu¹, Colleen McHugh¹ (1.NOAA Geophysical Fluid Dynamics Laboratory)

5:15 PM - 6:45 PM

[ACG31-P04] Heat budget in the surface layer related to the Pacific Decadal Oscillation

*Satoshi Osafune¹, Shinya Kouketsu¹ (1.Japan Agency for Marine-Earth Science and Technology)

5:15 PM - 6:45 PM

[ACG31-P05] Antarctic sea ice multidecadal variability revealed by reconstructed data and model simulations

*Yushi Morioka¹, Syukuro Manabe², Liping Zhang^3,4, Thomas L. Delworth³, William Cooke³, Masami Nonaka¹, Swadhin Behera¹ (1.Japan Agency for Marine-Earth Science and Technology, 2.Atmospheric and Oceanic Sciences Program, Princeton University, 3.Geophysical Fluid Dynamics Laboratory, NOAA, 4.University Corporation for Atmospheric Research, Boulder, Colorado)

5:15 PM - 6:45 PM

[ACG31-P06] Investigating the multiscale variability of Taiwan extreme precipitation with emphasis on weather types

*Yi-chao Wu¹, Li-Huan Hsu¹, Shao-Chin Huang¹, Wan-Ru Huang², Jung-Lien Chu¹, Yi-Chiang Yu¹ (1.National Science and Technology Center for Disaster Reduction, 2.Department of Earth Sciences, National Taiwan Normal University)

5:15 PM - 6:45 PM

[ACG31-P07] Early Warning of the Indian Ocean Dipole Using Climate Network Analysis

*Zhenghui Lu¹ (1.National Institute of Natural Hazards, Ministry of Emergency Management of China)

5:15 PM - 6:45 PM

[ACG31-P08] Future marine heatwaves around Japan based on high-resolution ensemble simulations

*Yuma Kawakami¹, Hideyuki Nakano¹, Shogo Urakawa¹, Takahiro Toyoda¹, Kei Sakamoto², Shiro Nisikawa³, Toru Sugiyama³, Masao Kurogi³, Yoichi Ishikawa³, Katsunari Sato², Goro Yamanaka¹ (1.Meteorological Research Institute, 2.Japan Meteorological Agency, 3.Japan Agency for Marine-Earth Science and Technology)

5:15 PM - 6:45 PM

[ACG31-P09] Robust future projections of global spatial distribution of major tropical cyclones and sea level pressure gradients

*Hiroyuki Murakami¹, Cooke F William¹, Ryo Mizuta², Hirokazu Endo², Kohei Yoshida², Shuai Wang³, Pang-Chi Hsu⁴ (1.Geophysical Fluid Dynamics Laboratory, 2.Meteorological Research Institute, 3.Univeristy of Delaware, 4.Nanjing University of Information Science and Technology)

5:15 PM - 6:45 PM

[ACG31-P10] Modeling the kinetics of cell enlargement of larch trees growing in the permafrost zone of Siberia

*Margarita Popkova¹, Vladimir Shishov¹ (1.Siberian Federal University)

Session information

[A-CG31] Climate Variability and Predictability on Subseasonal to Centennial Timescales

[ACG31-P01] Multi-year predictive skill of the wintertime heavy rainfall potentials in western Japan

[ACG31-P02] Seasonal predictable signals of the East Asian summer monsoon rainfall

[ACG31-P03] Skillful Seasonal Prediction of Wind Energy Resources in the contiguous United States

[ACG31-P04] Heat budget in the surface layer related to the Pacific Decadal Oscillation

[ACG31-P05] Antarctic sea ice multidecadal variability revealed by reconstructed data and model simulations

[ACG31-P06] Investigating the multiscale variability of Taiwan extreme precipitation with emphasis on weather types

[ACG31-P07] Early Warning of the Indian Ocean Dipole Using Climate Network Analysis

[ACG31-P08] Future marine heatwaves around Japan based on high-resolution ensemble simulations

[ACG31-P09] Robust future projections of global spatial distribution of major tropical cyclones and sea level pressure gradients

[ACG31-P10] Modeling the kinetics of cell enlargement of larch trees growing in the permafrost zone of Siberia

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