Influences of atmospheric circulation patterns on interannual variability of winter precipitation over the northern part of the Korean Peninsula

Explaining the relationship between atmospheric circulation patterns and precipitation over a given area is one of the key problems in predicting climate variables for monthly and seasonal leadtime. We investigated the influence of atmospheric circulation indices on winter precipitation over the northern part of the Korean Peninsula (WP-NPKP) during the period 1949-2020. Data from 37 weather stations were used to calculate the correlation coefficients between WP-NPKP and the Siberian High (SH) and the Aleutian Low (AL), which are thought to dominate the winter weather systems over the study area. We chose those atmospheric circulation indices which are closely connected with the interannual variability of WP-NPKP and explained their possible mechanisms. To identify circulation patterns which most strongly correlated with WP-NPKP, one-point simultaneous correlation maps were created using Pearson correlation analysis, and their monotonic trends were tested with the Mann-Kendall method. We found that interannual variability of WP-NPKP had a closer connection with the difference in area-averaged climate variable anomalies over any 2 areas than with the anomalies themselves. Increased AL index minus decreased SH index had some correlation with winter rainfall; higher sea level pressure over the ocean, rather than that over land, was favorable for the transport of moisture toward the study area. Also, the indices which describe differences in air temperature anomalies at lower and middle latitudes had a relatively high correlation with WP-NPKP, and the difference indices of area-averaged zonal and meridional wind anomalies indicated a considerable linkage with winter precipitation over the study area. Their possible mechanisms, which we discuss in comparison to other studies of winter climate over East Asia, are very useful in selecting the predictors for monthly and seasonal prediction of climate variables over the study area.