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Influence of external forcing factors on first frost date and its predition in Ningxia
HUANG Ying, YANG Jianling, SUN Yinchuan, WANG Fan, WANG Suyan, WANG Dai
Journal of Arid Meteorology    2021, 39 (06): 957-965.   DOI: 10.11755/j.issn.1006-7639(2021)-06-0957
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Based on data such as first frost date in Ningxia, geopotential height, sea surface temperature (SST), snow cover area, and sea ice area from 1981 to 2019, the influence of external forcing factors including sea surface temperature, sea ice area, and snow cover area on the abnormally early and late first frost in Ningxia was studied. On the basis of above, a physical conceptual model and an objective prediction model for predicting first frost date were established. The results are as follows: (1) In the early years of first frost, the SST in the equatorial central and eastern Pacific continued to be significantly warmer in the early period, and the SST anomaly presented an obvious ENSO model. When the SST of the equatorial central and eastern Pacific was warmer in the early period, the east Asian trough was stronger and the subtropical high was weaker, which was conducive to cold air activity. So, first frost date was early, otherwise it was late. (2) The snow cover area in the northern hemisphere from May to August in the early period and the sea ice area of Greenland from January to July had a continuously and significantly negative correlation with the date of first frost. When the snow cover in the northern hemisphere decreased or the sea ice in Greenland decreased, the east Asian trough was weaker and the western Pacific subtropical high was relatively stronger, which was not conducive to active cold air, causing first frost to be late, and vice versa. (3) The main factors affecting the date of first frost in Ningxia were the intensity of the east Asian trough, the SST anomaly in the NINO3.4 area, the SST anomaly in the tropical south Atlantic, the snow area in the northern hemisphere, the intensity of the western Pacific subtropical high, and the area of Greenland sea ice. The objective prediction model established by using the multiple regression equation had a good prediction effect.

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