1960~2017年华北地区气候生长季变化特征及成因分析

doi:10.13249/j.cnki.sgs.2019.12.018

地理科学 ›› 2019, Vol. 39 ›› Issue (12): 1990-2000.doi: 10.13249/j.cnki.sgs.2019.12.018

• • 上一篇

1960~2017年华北地区气候生长季变化特征及成因分析

董满宇^1,², 李洁敏², 王磊鑫², 刘佩佩³, 江源^1,², 吴正方⁴

1. 北京师范大学地表过程与资源生态国家重点实验室, 北京100875
2. 北京师范大学地理科学学部, 北京100875
3. 陕西省安康气象局, 陕西安康 725000
4. 东北师范大学地理科学学院, 吉林长春 130024

收稿日期:2018-12-03 修回日期:2019-03-30 出版日期:2019-12-10 发布日期:2020-03-01
作者简介:董满宇（1983-）,男,辽宁兴城人,副教授,博士,主要从事区域气候变化、资源生态研究。E-mail: dongmy@bnu.edu.cn
基金资助:
国家自然科学基金项目资助(41630750);国家自然科学基金项目资助(41771051)

Climatic Characteristics of Climatic Growing Season and Impact Factors in North China During 1960-2017

Dong Manyu^1,², Li Jiemin², Wang Leixin², Liu Peipei³, Jiang Yuan^1,², Wu Zhengfang⁴

1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
2. Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
3. Ankang Meteorology Bureau of Shaanxi Province, Ankang 725000, Shaanxi, China
4. School of Geographical Science, Northeast Normal University, Changchun 130024, Jilin, China

Received:2018-12-03 Revised:2019-03-30 Online:2019-12-10 Published:2020-03-01
Supported by:
National Natural Science Foundation of China(41630750);National Natural Science Foundation of China(41771051)

摘要/Abstract

摘要：

基于华北地区1960~2017年逐日气温数据,运用Mann-Kendall非参数检验、Morlet小波分析和R/S分析等方法,分析了华北地区气候生长季指标生长季开始（GSS）、生长季结束（GSE）、生长季长度（GSL）、生长季内≥10℃活动积温（AT10）及其对应的天数（DT10）的时空变化特征及其影响因素。结果表明:① 华北地区GSS呈显著的提前趋势,变化速率为-2.43 d/10a,GSL呈现出明显延长,AT10和DT10表现为显著增加趋势,变化速率分别为2.95 d/10 a、67.14℃/10a和2.31 d/10a,GSE变化趋势不明显。近60 a来华北地区GSL的延长主要归因于GSS的明显提前。② 生长季指标变化趋势在空间上存在明显差异,其中GSS与GSL,AT10与DT10变化趋势的空间分布格局较为相似。③ 生长季指标普遍在20世纪90年代中后期发生了明显的突变,GSS、GSE和GSL的突变年份为1994~1995年,AT10和DT10的突变年份为1997~1998年。④ 近60 a来华北地区生长季指标变化存在着2~3 a、5~6 a的主周期。生长季指标Hurst指数都大于0.7,表现为较强的持续性,其过去变化趋势将在未来继续延续。⑤ 北大西洋年代际振荡指数（AMO）是影响近60 a来华北地区生长季指标（GSS、GSE与AT10）变化的主要大气环流因子。

关键词: 气候生长季, 趋势分析, 时空变化, 大气环流指数, 华北地区

Abstract:

Variation in climatic growing season indices in North China from 1960-2017 were analyzed based on daily temperature data. The methods of Mann-Kendall test, wavelet transforms and rescaled range (R/S) analysis were employed to delineate the rate of change, abrupt change points, statistical significance of the trends, periodicity, and future trends of growing season indices, including growing season start (GSS), growing season end (GSE), growing season length (GSL), active accumulated temperature ≥10℃ (AT10) and the days of active accumulated temperature ≥10℃(DT10). Important results were obtained as follows: 1) GSS, GSL, AT10 and DT10 showed significant change trend (P<0.05) at the rate of -2.43 d/10a, 2.95 d/10a, 67.14℃/10a and 2.31 d/10a, respectively. GSE presented a non-significant trend with the change rate at 0.53 d/10a. The GSL has extended 17.2 days during the last 60 years, mainly due to the advanced GSS evident in the spring (14.1 d). 2) Spatially, as for GSS, GSL, AT10 and DT10, there were 67.1%, 62.9%, 95.7%, and 92.9% stations showed significant trends (P<0.05). The spatial distribution patterns of the trends in GSS and GSL, AT10 and DT10 were similar. 3) The growth season indices showed obvious mutation in the mid-to-late 1990s. Abrupt change points of GSS, GSE, GSL, AT10 and DT10 were at 1995, 1995 and 2014, 1994, 1998, and 1997. 4 ) The wavelet analysis showed that there were two primary short periods of 2-3 years and 5-6 years for the oscillation of growing season indices in North China. Hurst indexes (H) of growing season indices were all greater than 0.7. It indicated that there will be obvious Hurst phenomenon in the future, and the past trends of GSS, GSE, GSL, AT10 and DT10 will continue in the future period. 5) GSS was negatively related to the atmospheric circulation index of North Atlantic Oscillation (NAO), West Pacific Pattern (WP), Atlantic Multidecadal Oscillation (AMO), and Eastern Atlantic Pattern (EA), and positively related to Polar/ Eurasia Pattern (POL). GSE was positively correlated with AMO. As for AT10, it positively related to AMO and EA, and negatively related to NAO and WP. AMO is the main atmospheric circulation factor affected the growth season indicators (GSS, GSE and AT10) in North China during 1960-2017.

Key words: climatic growing season, trend analysis, spatio-temporal variations, atmospheric circulation index, North China

中图分类号:

P423.3

董满宇, 李洁敏, 王磊鑫, 刘佩佩, 江源, 吴正方. 1960~2017年华北地区气候生长季变化特征及成因分析[J]. 地理科学, 2019, 39(12): 1990-2000.

Dong Manyu, Li Jiemin, Wang Leixin, Liu Peipei, Jiang Yuan, Wu Zhengfang. Climatic Characteristics of Climatic Growing Season and Impact Factors in North China During 1960-2017[J]. SCIENTIA GEOGRAPHICA SINICA, 2019, 39(12): 1990-2000.

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	GSS	GSE	GSL （d）	AT10 （℃）	DT10 (d)
多年均值	60	329	270	4263.1	211
β值（/10a）	-2.43^*	0.53	2.95^*	67.14^*	2.31^*

R/S分析指标	GSS	GSE	GSL	AT10	DT10
H值	0.87	0.71	0.87	0.96	0.94
持续性强度	很强	较强	很强	很强	很强

	GSS	GSE	GSL	研究时段
东北地区^[8]	-1.58	1.10	2.66	1960~2009
内蒙古地区^[6]	-1.30	1.32	2.60	1961~2010
新疆地区^[7]	-0.97	1.47	2.50	1951~2008
青藏高原^[9]	-1.82	1.47	3.29	1960~2009
东部沿海^[10]	-2.49	0.55	3.05	1961~2007
南方地区^[26]	-0.60	0.70	1.30	1951~2007
北方地区^[26]	-1.70	0.70	2.40	1951~2007
全国^[24]	-0.90	1.20	2.10	1960~2011
华北地区 (本文)	-2.43	0.53	2.95	1960~2017

	NAO			AMO			WP			EA			POL
月份	2~3	11~12	4~10	2~3	11~12	4~10	2~3	11~12	4~10	2~3	11~12	4~10	2~3	11~12	4~10
GSS	-0.27^*			-0.34^**			-0.28^*			-0.39^**			0.30^*
GSE		-0.02			0.36^**			0.13			0.08			-0.17
AT10			-0.35^**			0.64^**			-0.30^*			0.73^**			-0.04

		2~3月			11~12月			4~10月
	最高	平均	最低	最高	平均	最低	最高	平均	最低
NAO	0.35^**	0.43^**	0.45^**	-0.07	0.10	0.22	-0.23	-0.29^*	-0.34^**
AMO	0.36^**	0.35^**	0.32^*	0.32^*	0.40^**	0.41^**	0.45^**	0.61^**	0.72^**
WP	0.12	0.23	0.33^*	0.05	0.10	0.12	-0.21	-0.33^*	-0.41^**
EA	0.33^*	0.38^**	0.40^**	-0.02	0.21	0.36^**	0.53^**	0.72^**	0.80^**
POL	-0.26	-0.33^*	-0.38^**	-0.18	-0.14	-0.09	-0.05	-0.05	-0.07

1960~2017年华北地区气候生长季变化特征及成因分析

Climatic Characteristics of Climatic Growing Season and Impact Factors in North China During 1960-2017

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生长季指标	趋势检验通过率（%）	↑趋势个数	显著↑ 个数	↓趋势个数	显著↓ 个数	无趋势个数
GSS	67.1	0	0	68	47	2
GSE	15.7	58	11	0	0	12
GSL	62.9	70	44	0	0	0
AT10	95.7	70	67	0	0	0
DT10	92.9	70	65	0	0	0

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