Evaluating El Niño Southern Oscillation Simulations in the Climate Forecast System Model

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Sunday, 2 February 2014
Hall C3 (The Georgia World Congress Center )
Kelly M. Nunez Ocasio, University of Puerto Rico, Mayagüez, PR; and A. Kumar

El Niño- Southern Oscillation (ENSO) is a naturally occurring fluctuation over the Tropical Pacific Region. Its oceanic component; El Niño and La Niña, represent opposite extremes in the ENSO cycle. ENSO's peak phase occurs in December, January and February months (DJF), during which time tropical and extratropical teleconnections in the atmosphere are the strongest. Predicting ENSO is extremely important because, it makes possible for society to prepare for changes in climate variability worldwide. The intensity of El Niño is classified based on SST anomalies exceeding a pre-selected threshold over a certain region of the Equatorial Pacific. Niño 3.4 Region is preferred because; the Sea Surface Temperatures (SST) variability in this region has the strongest effect on shifting the rainfall in the Western Pacific. Currently dynamical coupled models are preferred tools for ENSO and climate forecasting. However, model biases exist and need to be documented based on comparison with observations. The objective of this research is to validate characteristics of ENSO variability in the Climate Forecast System version 1 (CFSv1) against its observational counterpart; the Extended Reconstructed Sea Surface Temperatures (ERSST) SST variability for the DJF season and for the Niño 3.4 SST index. Our analysis was based on comparing standard deviations, skewness, correlations and others, over 50- year moving windows. Result showed skewness and standard deviation have the best match between model and observations. Model captured more El Niño events than observations though observation events were more peaked. Moving average trend lines showed a pronounced upward trend for observations due to climate change. Model does not simulate this trend because of fixed CO2. There is also evidence for statistical difference between model and observations; colder SSTs in model simulations, for example. The results documented the strength and weakness of CFSv1 in simulating ENSO against ERSST, and can be used for future investigations to study forecast reliability and to assess future model improvements.