An improved model-based analogue forecasting for the prediction of the tropical Indo-Pacific Sea surface temperature in a coupled climate model

Abstract

Analogue forecasting is a method with a long history in weather and climate prediction. Recently, model-based analogue forecasting (MAF) was developed and applied in Indo-Pacific sea surface temperature (SST) with the long-term (>1,000 years) run of the coupled general circulation model as the prediction library dataset. Here, we use the MAF method for Indo-Pacific SST prediction with a 1,600-year uninitialized control run of ICMv2 as the library datasets to select the analogues. In the improved analogue strategy, subsurface thermal conditions are valid predictors besides the surface conditions. The anomaly of SST, subsurface temperature at 5- and 105-m depth as the best-performing predictors can better simulate the overall thermal conditions of the ocean. In addition, by increasing the length of the model library dataset, the number of selected analogue cases for the ensemble, using root-mean-square and pattern correlation combined analogue metric and subtracting the long-term trend, the improved MAF can produce a skillful SST prediction in the tropical Indian Ocean, southwest and northwest Pacific Ocean across 1- to 9-month lead times. The ACC of the improved MAF prediction in these regions significantly increases by more than 0.18 relatively to the SST-initialized hindcast of ICMv2 with considerable savings in terms of computational resource.