Relating CMIP5 model biases to seasonal forecast skill in the tropical Pacific

We examine links between tropical Pacific mean state biases and El Nino/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 Nino/Southern Oscillation metric for climate simulations.