Abstract

This talk will review the recent advances of GFDL’s high-resolution seasonal climate prediction system in terms of modeling, initialization, seasonal prediction, and predictability sources. Beyond the primary roles of ocean state (e.g., ENSO) in seasonal climate prediction, we find that the atmosphere initial state plays significant roles in improving the short-term seasonal climate prediction. The physical processes translating the observed atmosphere initialization into predictive skill, such as stratosphere-troposphere interaction and air-sea coupling, will be discussed. As a case study, we will discuss the roles of atmospheric initial state in predicting the winter precipitation anomalies over the western United States during the major 2015/16 El Nino event.

Presenter Profile

Dr. Xiaosong Yang is a project scientist with the National Oceanic and Atmospheric Administration's Geophysical Fluid Dynamics Laboratory (GFDL). His research focuses on seasonal to decadal time scale prediction and predictability of the climate system, including the hydroclimate variations and extratropical weather extreme events. Dr. Yang’s research combines the climate model initialization development for the climate prediction and the scientific understanding of the physical mechanism controlling the predictability. After earning his doctorate in atmospheric and oceanic sciences at Stony Brook University in 2006, Dr. Yang served as a Postdoctoral research scientist at the Center of Ocean-Land-Atmosphere Studies (COLA). Prior to joining GFDL in 2011, Dr. Yang was a research scientist at COLA.