CMIP6 model-based analog forecasting for the seasonal prediction of sea surface temperature in the offshore area of China

Seasonal forecasts at lead times of 1-12 months for sea surface temperature (SST) anomalies (SSTAs) in the offshore area of China are a considerable challenge for climate prediction in China. Previous research suggests that a model-based analog forecasting (MAF) method based on the simulations of coupled global climate models provide skillful climate forecasts of tropical Indo-Pacific SSTAs. This MAF method selects the model-simulated cases close to the observed initial state as a model-analog ensemble, and then uses the subsequent evolution of the SSTA to generate the forecasts. In this study, the MAF method is applied to the offshore area of China (0 degrees-45 degrees N, 105 degrees-135 degrees E) based on the simulations of 23 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the period 1981-2010. By optimizing the key factors in the MAF method, we suggest that the optimal initial field for the analog criteria should be concentrated in the western North Pacific. The multi-model ensemble of the optimized MAF prediction using these 23 CMIP6 models shows anomaly correlation coefficients exceeding 0.6 at the 3-month lead time, which is much improved relative to previous SST-initialized hindcasts and appears practical for operational forecasting.

Automated summary

Seasonal forecasts for sea surface temperature anomalies in the offshore area of China pose a challenge for climate prediction in China. Research shows that using simulations from global climate models can provide accurate climate forecasts. The optimized multi-model ensemble can achieve high anomaly correlation coefficients at a 3-month lead time.