ENSO Precipitation and Temperature Forecasts in the North American Multi-Model Ensemble: Composite Analysis and Validation

Journal of Climate | Chen et al. [2016]


In this study, we examine precipitation and temperature forecasts during El Nino/Southern Oscillation (ENSO) events in six models in the North American Multi-Model Ensemble (NMME), including the CFSv2, CanCM3, CanCM4, FLOR, GEOS5, and CCSM4 models, by comparing the model-based ENSO composites to the observed. The composite analysis is conducted using the 1982-2010 hindcasts for each of the six models with selected ENSO episodes based on the seasonal Oceanic Nino Index just prior to the date the forecasts were initiated. Two types of composites are constructed over the North American continent: one based on mean precipitation and temperature anomalies, the other based on their probability of occurrence in a tercile-based system. The composites apply to monthly mean conditions in November, December, January, February, and March, as well as to the five-month aggregates representing the winter conditions. For anomaly composites, we use the anomaly correlation coefficient and root-mean-square error against the observed composites for evaluation. For probability composites, we develop a probability anomaly correlation measure and a root-mean probability score for assessment. We found that all NMME models predict ENSO precipitation patterns well during wintertime; however, some models have large discrepancies between the model temperature composites and the observed. The fidelity is greater for the multi-model ensemble, as well as for the five-month aggregates. February tends to have higher scores than other winter months. For anomaly composites, most models perform slightly better in predicting El Nino patterns than La Nina patterns. For probability composites, all models have superior performance in predicting ENSO precipitation patterns than temperature patterns.

Full text can be found here.


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