The reversal of surface air temperature anomalies in China between early and late winter 2021/2022: Observations and predictions

Abstract

During winter of 2021/2022, the temperature in China is characterized by a warm-to-cold transition, and the average temperature anomaly in February 2022 is −1.6 °C, the coldest February in 2013–2022. We revealed the circulation regimes and physical mechanisms associated with this reversal event and demonstrated the advantage of a regional model downscaling over the use of the global model alone in predicting. In early winter, the warm anomalies are mainly related to an anomalous anticyclonic system downstream of a PNA-like (Pacific–North America) Rossby-wave train induced by La Niña. In late winter, due to the circulation response to the central Pacific warming and negative tropical Indian Ocean Dipole (TIOD), two ‘−＋−’ Rossby-wave trains from high latitudes and the tropical Indian Ocean jointly lead to an anomalous cyclonic system in China. Meanwhile, an anticyclonic blocking system on the northern side of Baikal brings strong and cold air to China. These two systems together cause a significant drop in surface air temperature anomaly in China during the late winter. The Beijing Climate Center climate system model (BCC_CSM1.1 m) can essentially predict this temperature reversal in China about five months in advance. However, the reversal amplitude is weaker due to warm deviations over the tropical Pacific Ocean and equatorial Indian Ocean. Using dynamic downscaling, a regional Climate–Weather Research and Forecasting (CWRF) model correctly predicts the cold SAT anomalies in late winter 2021/2022. The regional model depicts more realistic circulation patterns in East Asia; the anomalous cyclonic system in Inner Mongolia accompanied by the northerly anomalies contribute to a lower-than-normal SAT over China. This study reveals the cooperative effect of wave trains from high latitudes and the tropics on the subseasonal temperature reversal and demonstrates a possible solution to improve the forecast skill by dynamic downscaling according to precise characterization of local surface information.