Spatio-temporal variation in China’s climatic seasons from 1951 to 2017

doi:10.1007/s11442-020-1788-6

Abstract

Abstract:

In this paper, meteorological industry standard, daily mean temperature, and an improved multiple regression model are used to calculate China’s climatic seasons, not only to help understand their spatio-temporal distribution, but also to provide a reference for China’s climatic regionalization and crop production. It is found that the improved multiple regression model can accurately show the spatial distribution of climatic seasons. The main results are as follows. There are four climatic seasonal regions in China, namely, the perennial-winter, no-winter, no-summer and discernible regions, and their ranges basically remained stable from 1951 to 2017. The cumulative anomaly curve of the four climatic seasonal regions clarifies that the trend of China’s climatic seasonal regions turned in 1994, after which the area of the perennial-winter and no-summer regions narrowed and the no-winter and discernible regions expanded. The number of sites with significantly reduced winter duration is the largest, followed by the number of sites with increased summer duration, and the number of sites with large changes in spring and autumn is the least. Spring advances and autumn is postponed due to the shortened winter and lengthened summer durations. Sites with significant change in seasonal duration are mainly distributed in Northwest China, the Sichuan Basin, the Huanghe-Huaihe-Haihe (Huang-Huai-Hai) Plain, the Northeast China Plain, and the Southeast Coast.

Key words: climatic seasons, revised multiple regression model, spatio-temporal variation, China

MA Bin, ZHANG Bo, JIA Lige. Spatio-temporal variation in China’s climatic seasons from 1951 to 2017[J].Journal of Geographical Sciences, 2020, 30(9): 1387-1400.

Figures/Tables 11

Figure 1

Table 1

Table 2

Table 3

Multiple regression models for climatic seasonal length simulation in China for different Climate Normals"

Climate Normal	Threshold	Multiple regression model	R²	Significance level
1951-1980	10 ℃	$\begin{align} & Y=4014.82\text{+}41.71\times \theta \text{+}0.003\times {{\theta }^{3}}-4.32\times \text{sin}\theta -1280.2\times \\ & \ \ \ \ \ \ \text{ln}\theta -119.38\times \text{ln}\varphi -0.02\times h \\ \end{align}$	0.97	0.001
1951-1980	22 ℃	$\begin{align} & Y=3461.58+93.51\times \theta -0.65\times {{\theta }^{2}}\text{+}2.42\times \text{sin}\theta -1759.02\times \\ & \ \ \ \ \ \ \text{ln}\theta -121.4\times \text{ln}\varphi -0.09\times h \\ \end{align}$	0.958	0.001
1961-1990	10 ℃	$\begin{align} & Y=4025.87\text{+}41.94\times \theta -0.003\times {{\theta }^{3}}-4.28\times \text{sin}\theta -1285.43\times \\ & \ \ \ \ \ \ \text{ln}\theta -119.33\times \text{ln}\varphi -0.02\times h \\ \end{align}$	0.97	0.001
1961-1990	22 ℃	$\begin{align} & Y=3461.57\text{+}93.51\times \theta -0.65\times {{\theta }^{2}}\text{+}2.42\times \text{sin}\theta -1759.02\times \\ & \ \ \ \ \ \ \text{ln}\theta -121.4\times \text{ln}\varphi -0.09\times h \\ \end{align}$	0.958	0.001
1971-2000	10 ℃	$\begin{align} & Y=4009.6\text{+}41.7\times \theta -0.003\times {{\theta }^{3}}-5.39\times \text{sin}\theta -1280.05\times \\ & \ \ \ \ \ \ \text{ln}\theta -118.44\times \text{ln}\varphi -0.02\times h \\ \end{align}$	0.97	0.001
1971-2000	22 ℃	$\begin{align} & Y=3682.63\text{+}105.4\times \theta -0.74\times {{\theta }^{2}}\text{+}1.75\times \text{sin}\theta -1964.13\times \\ & \ \ \ \ \ \ \text{ln}\theta -165.82\times \text{ln}\varphi -0.1\times h \\ \end{align}$	0.958	0.001
1981-2010	10 ℃	$\begin{align} & Y=3801.72\text{+}38.03\times \theta -0.003\times {{\theta }^{3}}-5.44\times \text{sin}\theta -1193.74\times \\ & \ \ \ \ \ \ \text{ln}\theta -114.2\times \text{ln}\varphi -0.02\times h \\ \end{align}$	0.97	0.001
1981-2010	22 ℃	$\begin{align} & Y=3395.83\text{+}67.93\times \theta -0.006\times {{\theta }^{3}}\text{+}2.1\times \text{sin}\theta -1671.14\times \\ & \ \ \ \ \ \ \text{ln}\theta -147\times \text{ln}\varphi -0.1\times h \\ \end{align}$	0.959	0.001

Table 3

Table 4

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

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Climatic seasons regions	Abbreviation	Threshold for the temperature of tenderness
Perennial-winter region	PWR	$T{{\overline{M}}_{j}}<10$℃
Perennial-summer region	PSuR	$T{{\overline{M}}_{j}}\ge 22$℃
Perennial-spring region	PSpR	$10\le T{{\overline{M}}_{j}}<22$℃
No-winter region	NWR	$T{{\overline{M}}_{j}}\ge 10$℃
No-summer region	NSR	$T{{\overline{M}}_{j}}<22$℃
Discernible region	DR	Except the above five cases

Station	R	S	RSME	Station	R	S	RSME
Hailar	0.99	61.21	5.05	Dardo	0.99	79.33	22.77
Karamay	0.98	47.87	23.47	Yanyuan	0.99	109.76	17.15
Korla	0.99	56.08	19.86	Zhanyi	0.99	124.65	24.16
Yarkant	0.99	62.22	5.61	Gaoping	0.99	77.55	4.40
Naomaohu	0.98	44.79	17.10	Shuangfeng	0.99	63.33	4.76
Jiuquan	0.99	72.03	15.55	Xuzhou	0.99	57.50	3.45
Gonghe	0.99	61.20	10.06	Anqing	0.99	62.44	3.26
Urad Zhongqi	0.99	68.65	6.08	Wenzhou	0.99	77.66	6.83
Houma	0.99	58.18	3.94	Du’an	0.99	88.15	3.00
Changchun	0.99	56.58	4.68	Meixian	0.99	87.59	6.98
Qinglong	0.99	60.40	4.03	Tainan	0.99	76.59	48.13
Sog	0.98	35.70	8.39	Qionghai	0.99	60.24	16.26