Influence of external forcing factors on first frost date and its predition in Ningxia

doi:10.11755/j.issn.1006-7639(2021)-06-0957

Abstract

Abstract:

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.

Key words: first frost date, external forcing factor, multiple regression equation, prediction model

摘要：

利用1981—2019年宁夏初霜冻日期资料及同期位势高度场、海表面温度(SST)、积雪面积、海冰面积等资料,研究SST、海冰面积、积雪面积等外强迫因子对宁夏初霜冻日期异常偏早、偏晚的影响,在此基础上,建立了初霜冻日期的物理概念模型和客观预测模型。结果表明:(1)偏早(偏晚)年,前期赤道中东太平洋SST持续显著偏暖(冷),SST异常形态为明显的ENSO模态。当前期赤道中东太平洋SST偏暖时,东亚槽偏强,副热带高压偏弱,异常环流形势有利于冷空气活动,初霜冻日期易偏早,反之则偏晚。(2)5—8月北半球积雪面积、1—7月格陵兰海冰面积与初霜冻日期存在持续显著负相关关系。当前期北半球积雪面积或格陵兰海冰面积减少时,东亚槽偏弱,西太平洋副高偏强,不利于冷空气活跃,初霜冻日期易偏晚,反之则偏早。(3)影响宁夏初霜冻日期的主要因子为东亚槽强度、NINO3.4区SST异常、热带南大西洋SST异常、北半球积雪面积、西太平洋副高强度以及格陵兰海冰面积等,基于以上因子用多元回归方程建立的客观化预测模型具有良好的预测效果。

关键词: 初霜冻日期, 外强迫因子, 多元线性回归, 预测模型

CLC Number:

P468.0⁺21

HUANG Ying, YANG Jianling, SUN Yinchuan, WANG Fan, WANG Suyan, WANG Dai. Influence of external forcing factors on first frost date and its predition in Ningxia[J]. Journal of Arid Meteorology, 2021, 39(06): 957-965.

黄莹, 杨建玲, 孙银川, 王璠, 王素艳, 王岱. 外强迫因子对宁夏初霜冻日期的影响及其预测[J]. 干旱气象, 2021, 39(06): 957-965.

Figures/Tables 10

Fig.1 The composite anomaly distribution of SST in July (a, d, g), August (b, e, h) and September (c, f, i) in late (a, b, c) and early (d, e, f) first frost years and their difference (g, h, i) during 1981-2019 (Unit: ℃) (The black dots area passed the significance test at 0.05)

Fig.2 Distribution of correlation coefficients between NINO3.4 index (a, b), tropical south Atlantic index (c, d) from July to September and geopotential height on 200 hPa in September (a, c), 500 hPa in October (b, d) during 1981-2019 (The black dots area passed the significance test at 0.1)

Tab.1 The correlation coefficients between the snow area anomaly in the northern hemisphere, Greenland sea ice area anomaly and first frost date anomaly in Ningxia from January to September during 1981-2019

特征量	1月	2月	3月	4月	5月	6月	7月	8月	9月
北半球积雪面积	0.31^**	0.20	0.01	-0.22	-0.40^**	-0.40^**	-0.39^**	-0.36^**	0.00
格陵兰海冰面积	-0.31^**	-0.55^**	-0.46^**	-0.31^**	-0.38^**	-0.39^**	-0.33^**	-0.18	-0.25

Fig.3 The inter-annual variation of snow area anomaly from May to August in the northern hemisphere and first frost date anomaly in Ningxia (a), Greenland sea ice area anomaly from January to July and first frost date anomaly in Ningxia (b) during 1981-2019

Fig.4 The 21-year sliding correlation coefficients between snow area anomaly in the northern hemisphere from May to August, Greenland sea ice area anomaly from January to July and first frost date anomaly in Ningxia during 1981-2019

Fig.5 Distribution of correlation coefficients between snow area anomaly in the northern hemisphere from May to August (a, b), Greenland sea ice area anomaly from January to July (c, d) and the geopotential height fields on 200 hPa in September (a, c), 500 hPa in October (b, d) during 1981-2019 (The black dots area passed the significance test at 0.1)

Tab.2 The correlation coefficients between the various factors and first frost date in Ningxia from January to August during 1981-2019

因子特征量	1月	2月	3月	4月	5月	6月	7月	8月
东亚槽强度指数	-0.14	-0.10	0.10	-0.09	0.64^**	0.27^*	0.35^**	0.25
热带南大西洋SST指数	0.44^**	0.44^**	0.43^**	0.43^**	0.43^**	0.36^**	0.32^**	0.38^**
北半球积雪面积指数	0.31^**	0.20	0.01	-0.22	-0.40^**	-0.40^**	-0.39^**	-0.36^**
NINO3.4区SST距平指数	-0.02	-0.05	-0.04	-0.10	-0.21	-0.33^**	-0.37^**	-0.41^**
西太平洋副高强度指数	0.31^**	0.31^**	0.31^**	0.48^**	0.40^**	0.32^**	0.36^**	0.40^**
格陵兰海冰面积指数	-0.31^**	-0.55^**	-0.46^**	-0.31^**	-0.38^**	-0.39^**	-0.33^**	-0.18

Fig.6 The prediction model of physical concept for first frost date in Ningxia

Fig.7 The 21-year sliding correlation coefficients between the various factors and first frost date in Ningxia during 1981-2019

Fig.8 The fitted value, predicted value and observed value of first frost date in the whole area of Ningxia (a), irrigation region (b), middle arid region (c) and southern mountainous region (d) during 1981-2019

References 29

[1]	陈乾金, 张永山. 华北异常初终霜冻气候特征的研究[J]. 自然灾害学报, 1995(3):33-39.
[2]	LIU Q, PIAO S L, JANSSENS I A, et al. Extension of the growing season increases vegetation exposure to frost[J]. Nature Communications, 2018, 9(1):426-430. DOI URL
[3]	朱红蕊, 于宏敏, 姚俊英, 等. 黑龙江省水稻初霜冻灾害致灾因子危险性分析[J]. 灾害学, 2012, 103(2):96-99.
[4]	李凤琴, 李茂春, 胡秀茹, 等. 新疆哈密市初、终霜冻日变化及其对棉花生长的影响[J]. 中国棉花, 2020, 47(10):18-20.
[5]	李阳, 刘静, 马力文, 等. 宁夏中南部山区马铃薯气候适宜度时空变化特征[J]. 干旱气象, 2020, 38(6):1001-1008.
[6]	朱永宁, 冯东溥, 李红英, 等. 宁夏春霜冻期最低气温和霜冻日数时空变化特征[J]. 干旱气象, 2020, 38(2):256-262.
[7]	张健, 许丽玲, 班晋, 等. 黑龙江省初霜冻变化特征研究及预测方法[J]. 冰川冻土, 2019, 41(3):629-636.
[8]	吴幸毓, 林毅, 陈文键, 等. 福建霜冻时空分布特征及环流背景分析[J]. 大气科学学报, 2016, 173(4):501-509.
[9]	李芬, 张建新. 山西近50年初霜冻的区域特征[J]. 灾害学, 2013, 28(4):104-109.
[10]	LI Fen, ZHANG J. Effects of geographic and topographic factors on first frost in Shanxi Province from 1961 to 2010[J]. Agricultural Science & Technology, 2014, 15(7):1223-1228.
[11]	HUANG R, ZANG X, ZHANG R, et al. The westerly anomalies over the tropical Pacific and their dynamical effect on the ENSO cycles during 1980-1994[J]. Advances in Atmospheric Sciences, 1998, 15(2):135-151. DOI URL
[12]	张雯, 马阳, 李欣, 等. 赤道太平洋海温异常对宁夏7月降水的影响[J]. 干旱气象, 2020, 38(4):543-551.
[13]	WU B Y, WANG J, WALSH J. Possible feedback of winter sea ice in Greenland and the Barents sea on the local atmosphere[J]. Monthly Weather Review, 2004, 132:1868-1876. DOI URL
[14]	DESER C, WALSH J E, TIMLIN M S. Arctic sea ice variability in the context of recent atmospheric circulation trends[J]. Journal of Climate, 2000, 13(2):617-633. DOI URL
[15]	魏立新. 北极海冰变化及其气候效应研究[D]. 青岛: 中国海洋大学, 2011.
[16]	张人禾, 张若楠, 左志燕. 中国冬季积雪特征及欧亚大陆积雪对中国气候影响[J]. 应用气象学报, 2016, 27(5):513-526.
[17]	陈海山, 孙照渤, 朱伟军. 欧亚积雪异常分布对冬季大气环流的影响II. 数值模拟[J]. 大气科学, 2003(5):847-860.
[18]	杨明珠, 陈丽娟, 宋文玲. 黑潮区SST对中国北方初霜冻日期的影响研究[J]. 气象, 2013, 39(9):1125-1132.
[19]	韩荣青, 李维京. 中国北方初霜冻日期预测方法研究[C]// 中国气象学会. 2011年第28届中国气象学会年会论文集. 北京: 中国气象学会, 2011.
[20]	秦俊义. 初霜冻预报方法探讨[J]. 内蒙古气象, 1997(5):22-23.
[21]	蔡冰, 隋明, 孙蕾, 等. 抚顺地区初霜冻特征分析及预报方法研究[J]. 现代农业科技, 2014(21):218-219.
[22]	陆忠艳, 韩江文, 李萍, 等. 辽宁省初霜特征及预报技术指标初探[J]. 中国农学通报, 2014, 30(36):257-263.
[23]	彭九慧, 王多, 陆倩, 等. 承德初霜冻天气概念模型及其检验[J]. 沙漠与绿洲气象, 2016, 10(3):15-20.
[24]	吕新, 罗先平, 李晔. 霜冻灰色马尔柯夫链预测模型及其应用[J]. 中国农业气象, 1997(5):47-49.
[25]	曲成军, 林嘉楠, 赵广娜. 近50年黑龙江省初霜日变化影响因子及预测模型建立的研究[J]. 自然灾害学报, 2019, 28(3):205-213.
[26]	王鹏祥, 杨建玲, 李栋梁, 等. 中国西北地区东部汛期降水异常成因及预测研究[M]. 北京: 气象出版社, 2020.
[27]	中国气象局. 初霜冻日期早晚等级: QX/T 456—2018[S]. 北京: 中国标准出版社, 2018.
[28]	魏凤英. 现代气候统计诊断方法与预测技术[M]. 北京: 气象出版社, 2007.
[29]	陈少勇, 郑延祥, 楼望萍, 等. 中国西北地区初霜冻的气候变化特征[J]. 资源科学, 2013(1):165-172.