新疆极端气温冷（暖）指数历史变化及未来情景预估

doi:10.12118/j.issn.1000-6060.2024.796

Abstract

Abstract:

To examine the historical and projected variations of cold and warm extreme temperature indices in Xinjiang, China under global warming, this study provides a scientific basis for developing climate adaptation strategies. Using observational data from 52 meteorological stations in Xinjiang (1960—2021) and Coupled model intercomparison project phase 6 (CMIP6) climate model outputs (1960—2100), models with superior simulation accuracy were selected. Multi-model ensemble means were then applied to analyze four cold and four warm extreme temperature indices during the historical period and under three future scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. The results show that: (1) From 1960 to 2021, cold indices significantly decreased, while warm indices increased, with nighttime changes exceeding daytime variations, reflecting an overall regional warming trend. (2) Future projections (2025—2100) indicate continued decreases in cold indices and increases in warm indices across all scenarios, with the SSP5-8.5 pathway exhibiting the most pronounced changes. (3) Spatially, cold and warm indices change consistently across Xinjiang, with both regional differences and commonalities. Summer days and warm nights exhibit strong similarity under all scenarios, whereas frost days, cool nights, cool days, and growing season length show higher consistency under SSP2-4.5 and SSP5-8.5. Xinjiang is already undergoing an extreme warming process, which is expected to intensify. Strengthening adaptive capacity is therefore essential to ensure sustainable regional development.

Key words: CMIP6 models, extreme temperatures events, spatial-temporal differentiation, climate tendency rate, Xinjiang

YANG Yang, CHANG Wei, ZHANG Xingdong. Historical changes and future scenario projections of extreme temperature cold (warm) index in Xinjiang[J].Arid Land Geography, 2025, 48(10): 1747-1759.

Figures/Tables 10

Fig. 1

Tab. 1

Tab. 2

Tab. 3

Fig. 2

Fig. 3

Tab. 4

Tab. 5

Fig. 4

Fig. 5

References 39

[1]	IPCC(Intergovernmental Panel on Climate Change). Climate change 2013: Managing the risks of extreme events and disaster to advance climate change adaptation[M]. Cambridge: Cambridge University Press, 2013.
[2]	IPCC(Intergovernmental Panel on Climate Change). Climate change 2021: The physical science basis. Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change[M]. New York: Cambridge University Press, 2021.
[3]	Katz R W, Brown B G. Extreme events in a changing climate: Variability is more important than averages[J]. Climatic Change, 1992, 21(3): 289-302.
[4]	Lobell D B, Hammer G L, Mclean G, et al. The critical role of extreme heat for maize production in the United States[J]. Nature Climate Change, 2013, 3(5): 497-501.
[5]	杨扬, 常伟, 张兴东. 新疆极端气候时空变化及其与棉花生产关联研究[J]. 中国农业资源与区划, 2024, 45(12): 60-74.
	[Yang Yang, Chang Wei, Zhang Xingdong. Spatiotemporal variation of extreme climate and its correlation with cotton production in Xinjiang[J]. China Agricultural Resources and Zoning, 2024, 45(12): 60-74.]
[6]	Stott P A, Christidis N, Oto F, et al. Attribution of extreme weather and climate-related events[J]. Wiley Interdisciplinary Reviews Climate Change, 2016, 7(1): 23-41.
[7]	Founda D, Pierros F, Petrakis M, et al. Variations and trends of the urban heat island in Athens (Greece) and its response to heat waves[J]. Atmospheric Research, 2015, 161: 1-13.
[8]	Alfieri L, Feyen L, Dottori F, et al. Ensemble flood risk assessment in Europe under high end climate scenarios[J]. Global Environmental Change, 2015, 35: 199-212.
[9]	Seneviratne S l, Donat M G, Mueller B, et al. No pause in the increase of hot temperature extremes[J]. Nature Climate Change, 2014, 4(3): 161-163.
[10]	Zhang W, Furtado K, Wu P, et al. Increasing precipitation variability on daily-to-multiyear time scales in a warmer world[J]. Science Advances, 2021, 7(31): eabf8021, doi: 10.1126/sciadv.abf8021.
[11]	任晨辰, 段明铿, 智协飞. 不同气候背景下我国冬夏两季极端气温特征分析[J]. 大气科学学报, 2017, 40(6): 803-813.
	[Ren Chenchen, Duan Mingkeng, Zhi Xiefei. Characteristics of extreme surface air temperature in winter and summer over China under different climate backgrounds[J]. Journal of Atmospheric Sciences, 2017, 40(6): 803-813.]
[12]	任福民, 翟盘茂. 1951—1990年中国极端气温变化分析[J]. 大气科学, 1998, 22(2): 89-96.
	[Ren Fumin, Zhai Panmao. Study on changes of China's extreme temperatures during 1951—1990[J]. Chinese Journal of Atmospheric Sciences, 1998, 22(2): 89-96.]
[13]	索朗塔杰, 施宁, 王艺橙, 等. 我国冬季极端低温指数的年代际变化特征[J]. 大气科学, 2020, 44(5): 1125-1140.
	[Suolang Tajie, Shi Ning, Wang Yicheng, et al. Interdecadal variation characteristics of extreme low temperature index in winter in China[J]. Chinese Journal of Atmospheric Sciences, 2020, 44(5): 1125-1140.]
[14]	李娟, 闫会平, 朱志伟. 中国夏季极端气温与降水事件日数随平均气温变化的定量分析[J]. 高原气象, 2020, 39(3): 532-542. doi: 10.7522/j.issn.1000-0534.2019.00042.
	[Li Juan, Yan Huiping, Zhu Zhiwei. Quantitative analysis of changes of summer extremes temperature and and precipitation days over China with respect to the mean temperature increase[J]. Plateau Meteorology, 2020, 39(3): 532-542.] doi: 10.7522/j.issn.1000-0534.2019.00042.
[15]	李洋, 王玉辉, 吕晓敏, 等. 1961—2013年东北三省极端气候事件时空格局及变化[J]. 资源科学, 2015, 37(12): 2501-2513.
	[Li Yang, Wang Yuhui, Lü Xiaomin, et al. Spatial distribution and temporal change in extreme weather events in three provinces in northeast China[J]. Resources Science, 2015, 37(12): 2501-2513.]
[16]	王琼, 张明军, 王圣杰, 等. 1962—2011年长江流域极端气温事件分析[J]. 地理学报, 2013, 68(5): 611-625.
	[Wang Qiong, Zhang Mingjun, Wang Shengjie, et al. Extreme temperature events in Yangtze River Basin during 1962—2011[J]. Acta Geographica Sinica, 2013, 68(5): 611-625.]
[17]	于水, 张晓龙, 刘志娟, 等. 1961—2020年松花江流域极端气候指数的时空变化特征[J]. 应用生态学报, 2023, 34(4): 1091-1101. doi: 10.13287/j.1001-9332.202304.024
	[Yu Shui, Zhang Xiaolong, Liu Zhijuan, et al. Spatial and temporal variations of extreme climate index in the Songhua River Basin during 1961—2020[J]. Chinese Journal of Applied Ecology, 2023, 34(4): 1091-1101.]
[18]	汪宝龙, 张明军, 魏军林, 等. 西北地区近50 a气温和降水极端事件的变化特征[J]. 自然资源学报, 2012, 27(10): 1720-1733. doi: 10.11849/zrzyxb.2012.10.010
	[Wang Baolong, Zhang Mingjun, Wei Junlin, et al. The change in extreme events of temperature and precipitation over northwest China in recent 50 years[J]. Journal of Natural Resources, 2012, 27(10): 1720-1733.] doi: 10.11849/zrzyxb.2012.10.010
[19]	姚遥, 罗勇, 黄建斌. 8个CMIP5模式对中国极端气温的模拟和预估[J]. 气候变化研究进展, 2012, 8(4): 250-256.
	[Yao Yao, Luo Yong, Huang Jianbin. Evaluation and projection of temperature extremes over China based on 8 modeling data from CMIP5[J]. Advances in Climate Change Research, 2012, 8(4): 250-256.]
[20]	王慧, 肖登攀, 赵彦茜, 等. 基于CMIP6气候模式的华北平原极端气温指数评估和预测[J]. 地理与地理信息科学, 2021, 37(5): 86-94, 142.
	[Wang Hui, Xiao Dengpan, Zhao Yanqian, et al. Evaluation and projection of extreme temperature indices in the North China Plain based on CMIP6 models[J]. Geography and Geo-Information Science, 2021, 37(5): 86-94, 142.]
[21]	李宛鸿, 徐影. CMIP6模式对青藏高原极端气温指数模拟能力评估及预估[J]. 高原气象, 2023, 42(2): 305-319. doi: 10.7522/j.issn.1000-0534.2022.00032
	[Li Wanhong, Xu Ying. Evaluation and projection of extreme temperature indices over the Qinghai-Xizang Plateau by CMIP6 models[J]. Plateau Meteorology, 2023, 42(2): 305-319.] doi: 10.7522/j.issn.1000-0534.2022.00032
[22]	李纯, 姜彤, 王艳君, 等. 基于CMIP6模式的黄河上游地区未来气温模拟预估[J]. 冰川冻土, 2022, 44(1): 171-178. doi: 10.7522/j.issn.1000-0240.2022.0028
	[Li Chun, Jiang Tong, Wang Yanjun, et al. Simulation and estimation of future air temperature in upper basin of the Yellow River based on CMIP6 models[J]. Journal of Glaciology and Geocryology, 2022, 44(1): 171-178.] doi: 10.7522/j.issn.1000-0240.2022.0028
[23]	晋程绣, 姜超, 张曦月. CMIP6模式对中国西南地区气温的模拟与预估[J]. 中国农业气象, 2022, 43(8): 597-611.
	[Jin Chengxiu, Jiang Chao, Zhang Xiyue. Evaluation and projection of temperature in southwestern China by CMIP6 models[J]. Chinese Journal of Agrometeorology, 2022, 43(8): 597-611.]
[24]	舒章康, 李文鑫, 张建云, 等. 中国极端降水和高温历史变化及未来趋势[J]. 中国工程科学, 2022, 24(5): 116-125. doi: 10.15302/J-SSCAE-2022.05.014
	[Shu Zhangkang, Li Wenxin, Zhang Jianyun, et al. Historical changes and future trends of extreme precipitation and high temperature in China[J]. China Engineering Science, 2022, 24(5): 116-125.]
[25]	康紫薇, 张正勇, 刘琳, 等. 基于MODIS的新疆地表温度时空变化特征分析[J]. 地理研究, 2022, 41(4): 997-1017. doi: 10.11821/dlyj020210232
	[Kang Ziwei, Zhang Zhengyong, Liu Lin, et al. Spatio-temporal variation characteristics of land surface temperature in Xinjiang based on MODIS[J]. Geographical Research, 2022, 41(4): 997-1017.]
[26]	普宗朝, 张山清, 赵逸舟, 等. 气候变暖对新疆冬季采暖的影响[J]. 干旱区资源与环境, 2017, 31(6): 110-116.
	[Pu Zongchao, Zhang Shanqing, Zhao Yizhou, et al. Effects of climate warming on winter heating in Xinjiang during 1961—2014[J]. Journal of Arid Land Resources and Environment, 2017, 31(6): 110-116.]
[27]	玉苏甫·买买提, 买合皮热提·吾拉木, 满苏尔·沙比提. 气候变暖对渭干河—库车河三角洲绿洲棉花生产的影响[J]. 地理研究, 2014, 33(2): 251-259.
	[Mammut Yusufu, Uram Mahpet, Sabit Mansur. Impact of climate warming on cotton production of Ugan-Kuqa River delta oasis[J]. Geographical Research, 2014, 33(2): 251-259.]
[28]	刘璐, 刘普幸, 张旺雄, 等. 1961—2017年新疆极端暖事件变化特征及其未来情景预估[J]. 干旱区研究, 2021, 38(6): 1590-1600. doi: 10.13866/j.azr.2021.06.11
	[Liu Lu, Liu Puxing, Zhang Wangxiong, et al. Variation characteristics of extreme warm events from 1961 to 2017 and projection for future scenarios in Xinjiang, China[J]. Arid Zone Research, 2021, 38(6): 1590-1600.] doi: 10.13866/j.azr.2021.06.11
[29]	李景林, 普宗朝, 张山清. 气候变化对新疆农业的影响及区划[M]. 北京: 气象出版社, 2018.
	[Li Jinglin, Pu Zongchao, Zhang Shanqing. Impacts of climate change on agriculture and regionalization in Xinjiang[M]. Beijing: China Meteorological Press, 2018.]
[30]	Yang Y, Chang W. Analysis of spatial and temporal distribution and changes in extreme climate events in northwest China from 1960 to 2021: A case study of Xinjiang[J]. Sustainability, 2024, 16, 4960, doi: 10.3390/su16124960.
[31]	吴健, 夏军, 曾思栋, 等. CMIP6全球气候模式对长江流域气候变化的模拟评估与未来预估[J]. 长江流域资源与环境, 2023, 32(1): 137-150.
	[Wu Jian, Xia Jun, Zeng Sidong, et al. Evaluation of the performance of CMIP6 models and future changes over the Yangtze River Basin[J]. Resources and Environment in the Yangtze Basin, 2023, 32(1): 137-150.]
[32]	张顺伟, 周自翔, 熊炫晨, 等. 基于WRF模式的无定河流域极端气候特征分析[J]. 干旱区地理, 2024, 47(9): 1482-1495. doi: 10.12118/j.issn.1000-6060.2023.597
	[Zhang Shunwei, Zhou Zixiang, Xiong Xuanchen, et al. Extreme climate characteristics in the Wuding River Basin based on WRF model[J]. Arid Land Geography, 2024, 47(9): 1482-1495.] doi: 10.12118/j.issn.1000-6060.2023.597
[33]	陈跃萍, 武胜利, 赵昕, 等. 近60 a哈密市极端气温时空变化特征[J]. 干旱区地理, 2023, 46(6): 868-879. doi: 10.12118/j.issn.1000-6060.2022.484
	[Chen Yueping, Wu Shengli, Zhao Xin, et al. Spatial and temporal variation characteristics of extreme temperatures in Hami City in the past 60 years[J]. Arid Land Geography, 2023, 46(6): 868-879.] doi: 10.12118/j.issn.1000-6060.2022.484
[34]	高雁鹏, 陈文俊. 1984—2020年辽宁省极端气温时空变化及粮食产量响应研究[J]. 地理科学, 2021, 41(11): 2052-2062. doi: 10.13249/j.cnki.sgs.2021.11.018
	[Gao Yanpeng, Chen Wenjun. Spatial and temporal variation of extreme temperature and grain yield response in Liaoning Province from 1984 to 2020[J]. Scientia Geographica Sinica, 2021, 41(11): 2052-2062.]
[35]	赵安周, 刘宪锋, 朱秀芳, 等. 1965—2013年黄土高原地区极端气温趋势变化及空间差异[J]. 地理研究, 2016, 35(4): 639-652. doi: 10.11821/dlyj201604004
	[Zhao Anzhou, Liu Xianfeng, Zhu Xiufang, et al. Trend variations and spatial difference of extreme air temperature events in the Loess Plateau from 1965 to 2013[J]. Geographical Research, 2016, 35(4): 639-652.]
[36]	李佳瑞, 牛自耕, 冯岚, 等. CMIP5模式对长江和黄河流域极端气温指标的模拟与预估[J]. 地球科学, 2020, 45(6): 1887-1904.
	[Li Jiarui, Niu Zigeng, Feng Lan, et al. Trend variations and spatial difference of extreme air temperature events in the Loess Plateau from 1965 to 2013[J]. Earth Science, 2020, 45(6): 1887-1904.]
[37]	任婧宇, 赵俊侠, 马红斌, 等. 2015—2100年黄土高原四季气候变化的时空分布趋势预测[J]. 水土保持通报, 2019, 39(5): 262-271.
	[Ren Jingyu, Zhao Junxia, Ma Hongbin, et al. Spatiotemporal distribution characteristics of seasonal climate change trends over Loess Plateau during 2015—2100[J]. Soil and Water Conservation Bulletin, 2019, 39(5): 262-271.]
[38]	Miao C Y, Duan Q Y, Sun Q H, et al. Evaluation and application of Bayesian multi-model estimation in temperature simulations[J]. Progress in Physical Geography, 2013, 37(6): 727-744.
[39]	张娇艳, 杨益, 李扬, 等. 优选CMIP6全球气候模式对贵州省未来气温变化的模拟[J]. 中国农业气象, 2024, 45(5): 449-460.
	[Zhang Jiaoyan, Yang Yi, Li Yang, et al. CMIP6 models optimization in simulating future temperature change over Guizhou Province[J]. China Agricultural Meteorology, 2024, 45(5): 449-460.]

模式名称	所属研究中心	所属国家	水平分辨率（经纬向格点数）	大气模式分辨率
ACCESS-CM2	澳大利亚联邦科学与工业研究组织和澳大利亚气象局	澳大利亚	360×300	1.9°×1.3°
ACCESS-ESM1-5	澳大利亚联邦科学与工业研究组织和澳大利亚气象局	澳大利亚	360×300	1.9°×1.3°
BCC-CSM2-MR	中国国家气候中心	中国	360×232	1.0°×1.0°
CanESM5	加拿大气候模拟与分析中心	加拿大	361×290	2.8°×2.8°
FIO-ESM-2-0	中国海洋研究所	中国	320×384	1.0°×1.0°
GFDL-ESM4	美国国家海洋和大气管理局地球物理流体动力学实验室	美国	720×576	1.0°×1.0°
INM-CM4-8	俄罗斯科学院数值数学研究所	俄罗斯	360×318	2.0°×1.5°
INM-CM5-0	俄罗斯科学院数值数学研究所	俄罗斯	720×720	2.0°×1.5°
IPSL-CM6A-LR	法国皮埃尔·西蒙·拉普拉斯研究所	法国	362×332	2.5°×1.3°
MIROC6	日本东京大学气候系统研究中心	日本	360×256	1.4°×1.4°
MRI-ESM2-0	日本气象研究所	日本	360×364	1.9°×1.9°
NESM3	中国南京信息工程大学	中国	362×292	1.9°×1.9°

分类	指数名称	定义
冷指数	霜冻日数	日最低气温<0 ℃的日数
	结冰日数	日最高气温<0 ℃的日数
	冷夜日数	日最低气温<10%分位值的日数
	冷昼日数	日最高气温<10%分位值的日数
暖指数	生长季长度	生长季结束日和开始日之差
	夏天日数	日最高气温>25 ℃的日数
	暖夜日数	日最低气温>90%分位值的日数
	暖昼日数	日最高气温>90%分位值的日数

分类	指数名称	气候倾向率/d·(10a)^-1	P值
冷指数	霜冻日数	-3.328	0.000
	结冰日数	-0.952	0.079
	冷夜日数	-3.749	0.000
	冷昼日数	-0.875	0.000
暖指数	生长季长度	2.628	0.000
	夏天日数	1.645	0.000
	暖夜日数	3.678	0.000
	暖昼日数	1.836	0.000

模式名称	相关系数	相对误差	相对均方根误差
ACCESS-CM2	0.904	3.691	-0.059
ACCESS-ESM1-5	0.912	3.800	-0.094
BCC-CSM2-MR	0.913	2.776	-0.020
CanESM5	0.884	4.013	0.462
FIO-ESM-2-0	0.906	1.991	0.462
GFDL-ESM4	0.913	2.109	-0.135
INM-CM4-8	0.924	2.903	-0.107
INM-CM5-0	0.922	3.822	-0.078
IPSL-CM6A-LR	0.880	8.578	0.317
MIROC6	0.908	2.176	-0.035
MRI-ESM2-0	0.895	3.054	-0.071
NESM3	0.904	2.231	-0.103

分类	指数名称	SSP1-2.6		SSP2-4.5		SSP5-8.5
分类	指数名称	气候倾向率/d·(10a)^-1	P	气候倾向率/d·(10a)^-1	P	气候倾向率/d·(10a)^-1	P
冷指数	霜冻日数	-0.531	0.031	-2.177	0.000	-5.188	0.000
	结冰日数	-0.509	0.095	-2.400	0.000	-5.227	0.000
	冷夜日数	-1.387	0.000	-5.089	0.000	-8.734	0.000
	冷昼日数	-1.395	0.000	-4.367	0.000	-6.146	0.000
暖指数	生长季长度	0.730	0.016	1.467	0.000	4.488	0.000
	夏天日数	0.562	0.004	2.193	0.000	5.047	0.000
	暖夜日数	1.029	0.007	6.050	0.000	12.384	0.000
	暖昼日数	1.269	0.001	5.071	0.000	8.500	0.000

Historical changes and future scenario projections of extreme temperature cold (warm) index in Xinjiang

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 39

Related Articles 15

Metrics

Comments

Recommended 0

[1]	LI Liangliang, XIA Yong, WANG Fuhong, GUO Bingxin, ZHAO Lanlan. Muskmelon production pattern and its contributing factors in Xinjiang [J]. Arid Land Geography, 2025, 48(9): 1567-1577.
[2]	RUI Dongsheng, MAO Lu, REN Yanxia, GONG Haoxuan, LI Yanping, FU Zhicong. Spatial distribution characteristics and obstacle factors of relative poverty in Xinjiang [J]. Arid Land Geography, 2025, 48(9): 1672-1682.
[3]	LIU Yixin, PEI Tingting, CHEN Ying, XIE Baopeng. Measurement and spatial-temporal differentiation of rural revitalization development level in Gansu Province based on county scale [J]. Arid Land Geography, 2025, 48(9): 1683-1693.
[4]	LIU Haijun, ZHANG Haihong, YAN Junjie, LI Xiang, LI Gaofeng. Spatiotemporal heterogeneity and its influencing factors of agricultural carbon emission efficiency in Xinjiang [J]. Arid Land Geography, 2025, 48(5): 866-878.
[5]	LI Songrui, LIN Qiuping, YANG Shangguang. Multi-scale evolution characteristics and influencing factors of spatial layout of logistics enterprises in Xinjiang [J]. Arid Land Geography, 2025, 48(4): 739-752.
[6]	WANG Fuhong, XIA Yong. Spatio-temporal pattern evolution and influencing factors of main crops production in arid region: A case of Xinjiang [J]. Arid Land Geography, 2025, 48(3): 444-454.
[7]	LI Mengyuan, PANG Jiapeng, LI Huan. Coordinated development of comprehensive accessibility of provincial typical tourism distribution centers and their spatial relationship: A case of Xinjiang [J]. Arid Land Geography, 2025, 48(3): 539-548.
[8]	Jianiya YERKEN, HOU Jiannan, LIU Sibo. Spatio-temporal characterization of tourism climate comfort in Xinjiang prefectures and cities in the last 30 years [J]. Arid Land Geography, 2025, 48(2): 212-222.
[9]	GUO Jiali, DU Hongru. Network pattern of oasis cities in Xinjiang from the perspective of multiple flows [J]. Arid Land Geography, 2025, 48(2): 323-332.
[10]	BAI Yang, ZHAO Ping, WU Jian, LIU Xiaoyan. Research on the impact of civil aviation opening on tourism economy in Xinjiang [J]. Arid Land Geography, 2025, 48(10): 1866-1877.
[11]	LI Gang. Practice of high quality development in Xinjiang: Construction and measurement of evaluation system [J]. Arid Land Geography, 2025, 48(1): 143-152.
[12]	LIU Hui, SUN Siao, FANG Chuanglin, ZHOU Di, BAO Chao. Spatiotemporal evolutionary patterns and influencing factors of water use in Xinjiang from 1990 to 2020 [J]. Arid Land Geography, 2024, 47(9): 1451-1461.
[13]	SHI Yudong, WANG Shengjie, ZHANG Mingjun, ZHU Chenggang, CHE Yanjun. Spatial distribution characteristics of stable hydrogen and oxygen isotopes in surface waters on the northern slope of the Kunlun Mountains [J]. Arid Land Geography, 2024, 47(7): 1127-1135.
[14]	XIA Wenhao, HUO Yu, LU Yuan, WANG Chaoyi. Spatialtemporal differences and spatial spillover effects of agricultural carbon emissions in Xinjiang [J]. Arid Land Geography, 2024, 47(6): 1084-1096.
[15]	FAN Jing, SHEN Yanbo, CHANG Rui. Impact of climate change on the selection of typical meteorological years in solar energy resource assessment [J]. Arid Land Geography, 2024, 47(6): 922-931.