Relating CMIP5 Model Biases to Seasonal Forecast Skill in the Tropical Pacific

Abstract

We examine links between tropical Pacific mean state biases and El Niño/Southern Oscillation forecast skill, using model-analog hindcasts of sea surface temperature (SST; 1961–2015) and precipitation (1979–2015) at leads of 0–12 months, generated by 28 different models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Model-analog forecast skill has been demonstrated to match or even exceed traditional assimilation-initialized forecast skill in a given model. Models with the most realistic mean states and interannual variability for SST, precipitation, and 10-m zonal winds in the equatorial Pacific also generate the most skillful precipitation forecasts in the central equatorial Pacific and the best SST forecasts at 6-month or longer leads. These results show direct links between model climatological biases and seasonal forecast errors, demonstrating that model-analog hindcast skill—that is, how well a model can capture the observed evolution of tropical Pacific anomalies—is an informative El Niño/Southern Oscillation metric for climate simulations.