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Webinar: Improving ENSO variability in the Communtiy Earth System model by increasing resolution and introducing stochastic perturbations

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Tuesday, 13 September 2016, 1:00

Tuesday, September 13, 2016. 1:00 PM. Eastern Time. Webinar: Improving ENSO variability in the Communtiy Earth System model by increasing resolution and introducing stochastic perturbations. Judith Berner, NCAR/CGD. Sponsored by National Center for Atmospheric Research. More information here 

 

The El Niño–Southern Oscillation (ENSO) is an irregularly periodical variation in winds and sea surface temperatures over the tropical eastern Pacific Ocean, affecting much of the tropics and subtropics. It is the dominant coupled mode of inter-annual variability in the tropical Pacific. Through many teleconnections it impacts on weather worldwide and a source for extended predictability, so should be well represented in global climate models. However, many climate models have large deficiencies in representing the spatial structure, temporal variability and amplitude of ENSO. At a nominal resolution of 1 degree in the atmospheric and oceanic components, the SSTs variability in the tropical Pacific as simulated by CESM is too strong and ENSO periodicity too regular. At higher resolution, the SST variability is reduced and the power spectrum in theNiño3 region much improved, leading to a much closer match with observations. The same is seen in simulations at 1 degree, but with a stochastic parameterization in the atmospheric component. We hypothesize that added degrees in the atmospheric component are crucial for a better representation of ENSO variability. In both, the high-resolution and stochastically parameterized simulation, the Walker circulation is strengthened and convective intermittency decreased leading to a more realistic forcing for the oceanic component. Since the mechanisms responsible for the improvement are readily controllable in the stochastically perturbed simulations, it is argued that stochastic parameterizations are a useful tool to study model sensitivity in addition to improve mean and variability.

Location  Webcast
Contact  Gaylynn Potemkin, Potemkin(at)ucar.edu