Seasonal prediction of high-resolution temperature at 2-m height over Mongolia during boreal winter using both coupled general circulation model and artificial neural network

Abstract

The hindcast data of Pusan National University coupled general circulation model (PNU CGCM), a participant model of the Asia-Pacific Economic Cooperation Climate Center (APCC) Multi-Model Ensemble Climate Prediction System, and August–October sea-surface temperature (SST) in the northern Barents–Kara Sea (BKI) and the sea-ice extent (SIE) in the Chukchi Sea (East Siberian Sea index [ESI]) are used for predicting 20 × 20-km-resolution anomalous surface air temperature at 2-m height (aT2m) over Mongolia for boreal winter. For this purpose, area-averaged surface air temperature (TI) and sea-level pressure (SLP) over Mongolia are defined. Then four large-scale indices, TImdl and SHImdl obtained from PNU CGCM, and TIMLR and SHIMLR obtained from multiple linear regressions on BKI and ESI, are incorporated using the artificial neural network (ANN) method for the prediction and statistical downscaling to obtain the monthly and seasonal 20 × 20-km-resolution aT2m over Mongolia in winter. An additional statistical method, which uses BKI and ESI as predictors of TI and SHI together with dynamic prediction by the CGCM, is used because of the relatively low skill of seasonal predictions by most of the state-of-the-art models and the multi-model ensemble systems over high-latitude landlocked Eurasian regions such as Mongolia. The results show that the predictabilities of monthly and seasonal 20 × 20-km-resolution aT2m over Mongolia in winter are improved by applying ANN to both statistical and dynamical predictions compared to utilizing only dynamic prediction. The predictability gained by the proposed method is also demonstrated by the probabilistic forecast implying that the method forecasts aT2m over Mongolia in winter reasonably well.