Based on the observational datasets from National Meteorological Information Centre,and NCEP/NCAR reanalysis data,characteristics of spatio-temporal distribution of climate anomalies in the winter of 2023/2024 and possible causes and mechanisms are analyzed.During the 2023/2024 winter,aver-age temperature in China was 0.3℃ higher than climatology.The fluctuation of temperature was signifi-cant,with an intra-seasonal variation feature of being warmer in the early winter but colder in the late win-ter.Average precipitation was more than normal by 19.8％,and most parts of central and eastern China received more precipitation above normal.The power of East Asian winter monsoon and the intensity of Siberian high were near the normal values,and the East Asian trough was in a weakened condition.500 hPa geopotential height in the middle and high latitudes of Eurasia was low in the west but high in the east,featured with significant intra-seasonal fluctuation.Zonal atmospheric circulation prevailed in December 2023 and January 2024,and then it turned into abnormal meridional patterns in February 2024,which was favorable for the southward invasion of cold air in middle and high latitudes into China.In response to the synergistic effect of El Nino mature phase,warm tropical Indian Ocean and Atlantic Ocean,cold phase of Pacific interdecadal oscillation,an anomalous anticyclone was formed in the western North Pacific,being active over the Philippines and the South China Sea in stages.Western Pacific subtropical high intensified and extended westward.A Rossby wave train propagated along westerly jet in the upper troposphere,which was beneficial for the water vapor transport into central and eastern China.Finally,the confluence of this warm-moist air and the southward cold air from the middle and high latitudes led to the frequent snow and rain processes in the central and eastern parts of China.
关键词
东亚冬季风/冷空气/厄尔尼诺/西太平洋副热带高压/季节内变化
Key words
East Asian winter monsoon/cold air/El Nino/western Pacific subtropical high/intra-seasonal variation
维普
万方数据