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Home » Toward Improving ENSO Modeling
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Toward Improving ENSO Modeling

A fundamental attribute of tropical Pacific variability is the asymmetry in equatorial SST anomalies between the extreme phases of ENSO. Such ENSO asymmetry has implications for U.S. seasonal forecasting for which ENSO is the primary skill source. Also, given ENSO’s global impacts, ensuring accurate simulation of ENSO asymmetry in climate models is important for increasing reliability in projections of regional climate change.

The proposed research attempts to understand ENSO in climate models by focusing on ENSO asymmetry and seeking causes for the inability of most state-of-the–art coupled models to simulate ENSO asymmetry. We hypothesize that ENSO asymmetry is fundamentally linked to the climatological tropical winds, and that model biases in the former might be linked to biases in the latter through wind impacts on mean SST states.

We propose to conduct model data analysis and perform reduced model experiments (atmosphere-only, and ocean-only, regional, NCAR Pacific basin model) to address the following questions related to our hypothesis:

(1) What is the relative role of the biases in mean winds and biases in their interannual variability in simulating ENSO asymmetry?

(2) In which regions (equatorial, Pacific basin, etc.) would an improvement in mean winds and their interannual variability be most effective in simulating ENSO asymmetry?

We will utilize the latest version of NCAR’s CESM1 and the atmospheric component CAM5 for which long coupled runs and AMIP runs are available. We will also analyze CCSM4 and the corresponding CAM4 model experiments to understand the origin of changes in ENSO asymmetry between CCSM4 and CESM1.

Answers to these questions will guide the development of a new metric for the representation of ENSO asymmetry in coupled models that can better elucidate the main processes contributing to biases in ENSO features. This proposal will thus directly relate to the 2014 goal of ESS/CVP to conduct reduced-model experiments that can better isolate the sources and amplifiers of biases in climate models, and thus improve predictions and projections. It is also relevant to NOAA’s long-term climate goal—an improved scientific understanding of the changing climate system and its impact, which requires to support understanding and modeling core capabilities.

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