Abstract: 

One of the emerging topics in climate prediction is the issue of the so-called “signal-to-noise paradox”, characterized by too small signal-to-noise ratio in current model predictions that cannot reproduce the signal in the real world. Recent studies have suggested that seasonal-to-decadal climate can be more predictable than ever expected due to this paradox. However, the mechanism behind the signal-to-noise paradox has yet to be fully understood. This study introduces a Markov model framework to represent the ensemble forecasts and the signal-to-noise paradox. The simulations suggest that the paradox is primarily due to the shorter persistence or overestimated noise variance in models than the observational estimates. The Markov model framework is applied to determine the existence of the paradox in CMIP5 and CMIP6 models, with respect to the NAO index and surface climate, including sea level pressure, precipitation and sea surface temperature. The results suggest that the signal-to-noise paradox is widespread in current global climate models but can potentially be ameliorated with high-resolution ocean models.

Bio：

Dr. Wei Zhang is a recipient of the CIMES postdoctoral research fellowship at Princeton University's Atmospheric and Oceanic Sciences (AOS) Program and NOAA Geophysical Fluid Dynamics Laboratory (GFDL), and a visiting research scientist at NOAA Global System Laboratory (GSL). He is also a colleague of the 2021 Nobel Prize recipient Dr. Suki Manabe at the AOS program, Princeton University.

Dr. Wei Zhang holds a bachelor's degree in Ocean Science from Nanjing University and a Ph.D. degree in Meteorology and Physical Oceanography from the University of Miami. His research expertise is climate dynamics and prediction, air-sea interaction, and high-resolution modeling. His current research projects include (1) using statistical and dynamical models to understand climate variability and predictability over sub-seasonal to decadal timescales; and (2) developing deep learning methods to improve ENSO prediction (funded by the Microsoft AI for Earth Grant).

Conference ID (For Tencent)：https://meeting.tencent.com/dm/35SOzBHEnR5b

Tencent (腾讯会议) Link：404 591 870