Initialized Seasonal Prediction and ENSO Teleconnections with the NCAR Models in the North American Multi-Model Ensemble (NMME)

This study investigates the seasonal prediction capabilities and ENSO teleconnections of three models, all developed by the National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) and implemented by the University of Miami, within the North American Multi-Model Ensemble (NMME) framework. All three models, CCSM3, CCSM4, and CESM1, are initialized using the Climate Forecast System Reanalysis (CFSR) and have a common period of 1991–2018. The models' performance in predicting key climate variables, including temperature, precipitation, and ENSO (El Niño-Southern Oscillation) teleconnections is assessed. The models' prediction skill is assessed using the sign test, a robust non-parametric method for comparing forecast errors. CCSM4, which succeeded CCSM3 in 2014, demonstrated improvement in predicting precipitation extremes and 2 m temperature over land, and a much more accurate representation of global temperature trends. CESM1, introduced in 2023, shows further improvement relative to CCSM4 in the prediction of sea surface temperature in the tropical Pacific and precipitation extremes over land. The improvement in precipitation prediction skill is encouraging, as this field has seen little improvement over the life of the NMME. The representation of 2 m temperature ENSO teleconnections is somewhat weaker in CESM1 than in CCSM4, although precipitation teleconnection patterns are similar. CCSM4 and CESM1 show stronger surface temperature trends in the tropical Pacific and Southern Ocean than observed trends over the same period, a common problem for current state-of-the-art climate models with implications for prediction and for climate projections.