The Signal-to-Noise Paradox in Climate Simulations and Prediction

The School of Earth and Atmospheric Sciences Presents Dr. Wei Zhang, Princeton University/GFDL(NOAA)

The Signal-to-Noise Paradox in Climate Simulations and Prediction

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.

Event Details

Date/Time:

  • Thursday, October 21, 2021 - 11:00am to 12:00pm

Location:
Virtual seminar

URL:

Fee(s):
Free

For More Information Contact

Dr. Jie He