Development of a Winter Abnormal Cold Climate Index and Its Influencing Factors

Abstract

\n The prediction of extreme weather and climate events is a difficulty in the field of climate prediction. In practice, seasonal and monthly mean temperature has been used as the climate prediction index. However, the mean temperature forecast on seasonal and annual time scales cannot truly reflect the real climate situation as the mean temperature on a certain time scale smoothes abnormal cold and warm climate events. For instance, an abnormal cold climate event may occur even if the winter mean temperature is forecasted to be normal in a certain year. An abnormal warm climate event may also occur at the same time. Both affect the accuracy of climate prediction. Based on extreme weather events, this paper constructs a winter abnormal cold climate index, named WACCI, to represent the characteristics of extremely low temperature events. The index is composed of three factors, including cold duration, extremely cold temperature anomaly, and temperature cumulative anomaly. Taking Heilongjiang Province in China as the study area, relationships between WACCI and winter mean temperature, atmospheric circulation, and sea surface temperature (SST) are analyzed and compared. The analysis shows that the polar and mid-high latitude circulations have a significant impact on WACCI. The meridional circulation is dominant in the mid-high latitudes of Eurasia when the polar vortex area in the Northern Hemisphere is large, the intensity of the polar vortex is weak, and the polar vortex splits southward. Additionally, the intensity of the East Asian trough is strong and arctic oscillation (AO) is in an abnormal negative phase, which tends to result in a large WACCI. According to those atmospheric circulation factors, the regional and even global abnormal cold climate can be predicted in practice instead of using the prediction of winter mean temperature. The abnormal cold climate index proposed in this paper provides a new way for the extreme climate event occurrence trend, mechanism research, and short-term climate prediction (i.e., monthly or seasonal).