秦岭—淮河南北供暖格局变化及其影响因素
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摘要
基于秦岭—淮河南北及其周边196个气象站点观测资料,构建实际和动态供暖指数,对中国南北过渡带供暖格局变化进行分析,并探讨冬季北极涛动(AO)异常与供暖效率的响应关系。结果表明:①固定供暖策略下,1960-2016年秦岭—淮河南北实际供暖能耗偏高,呈现"南多北少,西低东高"的变化特征,且低纬度地区供暖需求下降信号早于高纬度;②对比区域变暖前后,秦岭—淮河南北冬季供暖能耗1960-1990年和1990-2016年两阶段空间特征,发现"整体南高北低,北部东高西低"的格局并未发生变化,供暖南北波动界线依然维持在秦岭山脉—淮河平原中部;③AO强弱波动与区域冬季供暖能耗具有明显的时空响应关系,是影响中国南北过渡带供暖格局变化的重要因素。当AO负相位时,除四川盆地和巫山山区之外,秦岭—淮河南北其他区域实际供暖能耗明显下降,特别是淮河平原和长江下游的过渡地带响应尤为明显,未来应该有针对性制定气候适应对策。
Using daily temperature observations from the National Meteorological Information Center of China, we analyzed the spatiotemporal variation in actual heating energy efficiency in areas south and north of the Qinling Mountains-Huaihe River(hereafter Qinling-Huanhe Line) over the period 1960-2016. The dynamic heating index, defined as the difference between fixed and dynamic heating energy consumption during the entire heating season, was used. Specifically, we analyzed the spatiotemporal response of actual heating energy efficiency to the Arctic Oscillation(AO) index, where changes in the circulation pattern bring frigid winter air to eastern China in the negative phase, resulting in increased heating energy demand.The results showed that:(1) spatial pattern of heating energy consumption in areas south and north of the Qinling-Huaihe Line was high in the eastern and southern regions, but low in the western and northern regions. The signal of decreasing heating energy demand in the lower latitudes of the region occurred earlier than in the higher latitudes.(2) On the whole, actual regional heating energy efficiency showed a decreasing trend that was corresponded with regional warming. In the north, however, the decreasing trend was weaker than in the south during the study period. This implies that residents continued to adopt a fixed-date strategy in the heating season, thus heating energy waste would increase consistently throughout the seven subregions, especially in the south.(3) Comparing the situation before and after climate change, i.e., 1960-1990 versus 1990-2016, we found that substantial changes were not evident in the spatial pattern of heating energy consumption in areas south and north of the Qinling-Huaihe Line. Nevertheless, there were differences in the response of temperature variations to climate change. The lower reaches of the Yangtze River, the Hanjiang River Basin, and the Wushan Mountains were some areas where heating energy waste was slowly increasing. A faster increase in heating energy waste mainly occurred in the eastern Huaihe Plain, the northwestern lower Yellow River Basin, and the Qinling-Bashan Mountains.(4) Actual heating energy efficiency had a close relationship with AO in the south-north transitional zone of China. Over the past 57 years, the AO alternated between positive and negative phases. Starting in the1990 s, the AO tended to be more of a positive phase pattern, in which higher pressure at the mid-latitudes drove warm air farther north, bringing warmer winters to the Qinling-Huaihe region, and thus decreased heating energy consumption. Spatially, the most sensitive responses of heating energy efficiency to climate change occurred in the southern Huaihe Plain and the northern regions of the lower Yangtze River Basin. In future, we should mitigate the risks of extreme climate change in sharply negative phases of the AO warrant attention, and develop policies concerning household heating in the south-north transitional zone of China.
Using daily temperature observations from the National Meteorological Information Center of China, we analyzed the spatiotemporal variation in actual heating energy efficiency in areas south and north of the Qinling Mountains-Huaihe River(hereafter Qinling-Huanhe Line) over the period 1960-2016. The dynamic heating index, defined as the difference between fixed and dynamic heating energy consumption during the entire heating season, was used. Specifically, we analyzed the spatiotemporal response of actual heating energy efficiency to the Arctic Oscillation(AO) index, where changes in the circulation pattern bring frigid winter air to eastern China in the negative phase, resulting in increased heating energy demand.The results showed that:(1) spatial pattern of heating energy consumption in areas south and north of the Qinling-Huaihe Line was high in the eastern and southern regions, but low in the western and northern regions. The signal of decreasing heating energy demand in the lower latitudes of the region occurred earlier than in the higher latitudes.(2) On the whole, actual regional heating energy efficiency showed a decreasing trend that was corresponded with regional warming. In the north, however, the decreasing trend was weaker than in the south during the study period. This implies that residents continued to adopt a fixed-date strategy in the heating season, thus heating energy waste would increase consistently throughout the seven subregions, especially in the south.(3) Comparing the situation before and after climate change, i.e., 1960-1990 versus 1990-2016, we found that substantial changes were not evident in the spatial pattern of heating energy consumption in areas south and north of the Qinling-Huaihe Line. Nevertheless, there were differences in the response of temperature variations to climate change. The lower reaches of the Yangtze River, the Hanjiang River Basin, and the Wushan Mountains were some areas where heating energy waste was slowly increasing. A faster increase in heating energy waste mainly occurred in the eastern Huaihe Plain, the northwestern lower Yellow River Basin, and the Qinling-Bashan Mountains.(4) Actual heating energy efficiency had a close relationship with AO in the south-north transitional zone of China. Over the past 57 years, the AO alternated between positive and negative phases. Starting in the1990 s, the AO tended to be more of a positive phase pattern, in which higher pressure at the mid-latitudes drove warm air farther north, bringing warmer winters to the Qinling-Huaihe region, and thus decreased heating energy consumption. Spatially, the most sensitive responses of heating energy efficiency to climate change occurred in the southern Huaihe Plain and the northern regions of the lower Yangtze River Basin. In future, we should mitigate the risks of extreme climate change in sharply negative phases of the AO warrant attention, and develop policies concerning household heating in the south-north transitional zone of China.
引文
[1] Bai X M, Dawson R J,ürge-Vorsatz D, et al. Six research priorities for cities and climate change. Nature, 2018, 555(7694):23-25.
[2] Qin Dahe. Extreme Climate Events and Disaster Risk Management and Adaptation to the National Assessment Report of China. Beijing:Science Press, 2015.[秦大河.中国极端气候事件和灾害风险管理及适应国家评估报告.北京:科学出版社, 2015.]
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[5] Li Shuangshuang, Yang Saini, Liu Xianfeng. Spatiotemporal variability of extreme precipitation in north and south of the Qinling-Huaihe region and influencing factors during 1960-2013. Progress in Geography, 2015, 34(3):354-363.[李双双,杨赛霓,刘宪锋. 1960-2013年秦岭—淮河南北极端降水时空变化特征及其影响因素.地理科学进展, 2015, 34(3):354-363.]
[6] Chen Li, Fang Xiuqi, Li Shuai, et al. Comparisons of energy consumption between cold regions in China and the Europe and America. Journal of Natural Resources, 2011, 26(7):1258-1268.[陈莉,方修琦,李帅,等.中国与欧美寒冷地带采暖耗能的比较.自然资源学报, 2011, 26(7):1258-1268.]
[7] Donat M G, Alexander L V. The shifting probability distribution of global daytime and night-time temperatures.Geophysical Research Letters, 2012, 39(14):132-151.
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[9]中华人民共和国建筑部.民用建筑供暖通风与空气调节设计规范(GB50736-2012).北京:中国计划出版社, 2003.[Ministry of Construction of People Republic China. Design Code for Design of Heating, Ventilation and Air Condition of Civil Buildings(GB50736-2012). Beijing:China Planning Press, 2003.]
[10] Yan Dandan. Heating is a'breaking the ice'in South:Advancing household heating system by gas in Hangzhou city. Xin Hua News Agency. http://www.xinhuanet.com/city/2017-02/05/c_129466965.htm, 2017-02-05.[闫丹丹.南方供暖尝试“破冰”:杭州推天然气家庭分户供暖.新华社. http://www.xinhuanet.com/city/201702/05/c_129466965.htm, 2017-02-05.]
[11] Chen Li, Li Shuai, Qin Xue, et al. Change characteristics and predication of climate conditions of heating in northeast China. Journal of Natural Resources, 2014, 29(7):1185-1195.[陈莉,李帅,覃雪,等.东北地区采暖气候条件变化特征及预测.自然资源学报, 2014, 29(7):1185-1195.]
[12] Jiang D C, Xiao W H, Wang J H, et al. Evaluation of the effects of one cold wave on heating energy consumption in different regions of northern China. Energy, 2018, 142(1):331-338.
[13] Li M C, Cao J F, Guo J, et al. Response of energy consumption for building heating to climatic change and variability in Tianjin City, China. Meteorological Applications, 2016, 23(1):123-131.
[14] You Q L, Fraedrich K, Sielmann F, et al. Present and projected degree days in China from observation, reanalysis and simulations. Climate Dynamics, 2014, 43(5/6):449-1462.
[15] Shen X J, Liu B H. Changes in the timing, length and heating degree days of the heating season in central heating zone of China. Scientific Reports, 2016, 6:33384-33393.
[16] Meng F C, Li M C, Cao J F, et al. The effects of climate change on heating energy consumption of office buildings in different climate zones in China. Theoretical and Applied Climatology, 2018, 133:521-530.
[17] Liu Yulian, Ren Guoyu. Analysis of heating intensity in Harbin based on degree-hours method. Journal of Natural Resources,2018, 33(1):139-148.[刘玉莲,任国玉.基于度—时法的哈尔滨冬季采暖强度评价.自然资源学报. 2018, 33(1):139-148.]
[18] Yan Zhongwei, Li Zhen, Xia Jiangjiang. Homogenization of climate series:The basis for assessing climate changes.Science China:Earth Sciences, 2014, 44(10):2101-2111.[严中伟,李珍,夏江江.气候序列的均一化:定量评估气候变化的基础.中国科学:地球科学, 2014, 44(10):2101-2111.]
[19] Pranab Kumar Sen. Estimates of the regression coefficient based on Kendall's Tau. Publications of the American Statistical Association, 1968, 63(324):1379-1389.
[20] Xu Yan, Tang Guoli, Zhang Qiang. Analysis of the variation of the air temperature over China during the global warming hiatus period. Climate Change Research, 2017, 13(6):569-577.[许艳,唐国利,张强.基于均一化格点资料的全球变暖趋缓期中国气温变化特征分析.气候变化研究进展, 2017, 13(6):569-577.]
[21] National Climate Committee of China Meteorological Administration. China Climate Bulletin in 2017. Beijing:National Climate Committee of the China Meteorological Adminstration, 2018.[中国气象局国家气候委员会. 2017.中国气候公报.北京.中国气候局国家气候委员会, 2018.]
[22] Ren G Y, Ding Y H, Tang G L. An overview of mainland China temperature change research. Journal of Meteorological Research, 2017, 31(1):3-16.
[23] Wu Shaohong, Liu Wenzheng, Pan Tao, et al. Amplitude and velocity of the shifts in the Chinese terrestrial surface regions from 1960 to 2011. Chinese Science Bulletin, 2016, 61(19):2187-2197.[吴绍洪,刘文政,潘韬,等. 1960-2011年中国陆地表层区域变动幅度与速率.科学通报, 2016, 61(19):2187-2197.]
[24] Wu J, Gao X J, Giorgi F, et al. Changes of effective temperature and cold/hot days in late decades over China based on a high-resolution gridded observation dataset. International Journal of Climatology, 2017, 37(S1):788-800.
[25] Thompson D W J, Wallace J M. The Arctic oscillation signature in the wintertime geopotential height and temperature fields. Geophysical Research Letters, 1998, 25(9):1297-1300.
[26] Zhang Leying, Xu Haiming, Shi Ning. Influence of the spring Arctic oscillation on midsummer surface air temperature over the Yangtze River valley. Chinese Journal of Atmospheric Sciences, 2015, 19(5):1049-1058.[张乐英,徐海明,施宁.春季北极涛动对盛夏长江流域地表气温的影响.大气科学, 2015, 19(5):1049-1058.]
[27] Song L, Wu R G, Jiao Y. Relative contributions of synoptic and intraseasonal variations to strong cold events over eastern China. Climate Dynamics, 2018, 50(11/12):4619-4634.
[28] He S P, Gao Y Q, Li F, et al. Impact of Arctic oscillation on the East Asian climate:A review. Earth-Science Reviews,2017, 164:48-62.
[29] Chen S, Wu R, Chen W. A strengthened impact of november Arctic oscillation on subsequent tropical Pacific sea surface temperature variation since the late-1970s. Climate Dynamics, 2018, 51(1/2):511-529.
[30] Li Shuangshuang, Lu Jiayu, Yan Junping, et al. Spatiotemporal variability of temperature in northern and southern Qinling Mountains and its influence on climatic boundary. Acta Geographica Sinica, 2018, 73(1):13-24.[李双双,芦佳玉,延军平,等. 1970-2015年秦岭南北气温时空变化及其气候分界意义.地理学报, 2018, 73(1):13-24.]
[31] Ding Yihui, Liu Yanju, Liang Sujie, et al. Interdecadal variability of the East Asian winter monsoon and its possible links to global climate change. Acta Meteorologica Sinica, 2014, 72(5):835-852.[丁一汇,柳艳菊,梁苏洁,等.东亚冬季风的年代际变化及其与全球气候变化的可能联系.气象学报, 2014, 72(5):835-852.]
[32] Li Shuangshuang, Yan Junping, Kong Feng, et al. The nonlinear trends of high temperature weather in Xi'an by extremepoint symmetric mode decomposition method. Geographical Research, 2018, 37(1):209-219.[李双双,延军平,孔锋,等.极点对称模态分解下西安高温天气的趋势特征.地理研究, 2018, 37(1):209-219.]
[33] Mu Mu, Ren Hongli. Enlightenments form researches and predictions of 2014-2016 super El Ni?o event. Science China Earth Sciences, 2017, 47(9):993-995.[穆穆,任宏利. 2014-2016年超强厄尔尼诺事件研究及其预测给予我们的启示.中国科学:地球科学, 2017, 47(9):993-995.]
[34] Wu Shaohong, Gao Hongbo, Deng Yuhao, et al. Climate change risk and methodology for its quantitative assessment.Progress in Geography, 2018, 37(1):28-35.[吴绍洪,高江波,邓浩宇,等.气候变化风险及其定量评估方法.地理科学进展, 2018, 37(1):28-35.]
[2] Qin Dahe. Extreme Climate Events and Disaster Risk Management and Adaptation to the National Assessment Report of China. Beijing:Science Press, 2015.[秦大河.中国极端气候事件和灾害风险管理及适应国家评估报告.北京:科学出版社, 2015.]
[3] Wu Shaohong, Pan Tao, Liu Yanhua, et al. Comprehensive climate change risk regionalization of China. Acta Geographica Sinica, 2017, 72(1):3-17.[吴绍洪,潘韬,刘燕华,等.中国综合气候变化风险区划.地理学报, 2017, 72(1):3-17.]
[4] Li Shuangshuang, Yan Junping, Yang Saini, et al. Spatiotemporal variability of heat waves and influencing factors in the Qinling-Huaihe region, 1960-2016. Progress in Geography, 2018, 37(4):504-514.[李双双,延军平,杨赛霓,等. 1960-2016年秦岭—淮河地区热浪时空变化特征及其影响因素.地理科学进展, 2018, 37(4):504-514.]
[5] Li Shuangshuang, Yang Saini, Liu Xianfeng. Spatiotemporal variability of extreme precipitation in north and south of the Qinling-Huaihe region and influencing factors during 1960-2013. Progress in Geography, 2015, 34(3):354-363.[李双双,杨赛霓,刘宪锋. 1960-2013年秦岭—淮河南北极端降水时空变化特征及其影响因素.地理科学进展, 2015, 34(3):354-363.]
[6] Chen Li, Fang Xiuqi, Li Shuai, et al. Comparisons of energy consumption between cold regions in China and the Europe and America. Journal of Natural Resources, 2011, 26(7):1258-1268.[陈莉,方修琦,李帅,等.中国与欧美寒冷地带采暖耗能的比较.自然资源学报, 2011, 26(7):1258-1268.]
[7] Donat M G, Alexander L V. The shifting probability distribution of global daytime and night-time temperatures.Geophysical Research Letters, 2012, 39(14):132-151.
[8] Shi Y, Gao X J, Xu Y, et al. Effects of climate change on heating and cooling degree days and potential energy demand in the household sector of China. Climate Research, 2016, 67(2):135-149.
[9]中华人民共和国建筑部.民用建筑供暖通风与空气调节设计规范(GB50736-2012).北京:中国计划出版社, 2003.[Ministry of Construction of People Republic China. Design Code for Design of Heating, Ventilation and Air Condition of Civil Buildings(GB50736-2012). Beijing:China Planning Press, 2003.]
[10] Yan Dandan. Heating is a'breaking the ice'in South:Advancing household heating system by gas in Hangzhou city. Xin Hua News Agency. http://www.xinhuanet.com/city/2017-02/05/c_129466965.htm, 2017-02-05.[闫丹丹.南方供暖尝试“破冰”:杭州推天然气家庭分户供暖.新华社. http://www.xinhuanet.com/city/201702/05/c_129466965.htm, 2017-02-05.]
[11] Chen Li, Li Shuai, Qin Xue, et al. Change characteristics and predication of climate conditions of heating in northeast China. Journal of Natural Resources, 2014, 29(7):1185-1195.[陈莉,李帅,覃雪,等.东北地区采暖气候条件变化特征及预测.自然资源学报, 2014, 29(7):1185-1195.]
[12] Jiang D C, Xiao W H, Wang J H, et al. Evaluation of the effects of one cold wave on heating energy consumption in different regions of northern China. Energy, 2018, 142(1):331-338.
[13] Li M C, Cao J F, Guo J, et al. Response of energy consumption for building heating to climatic change and variability in Tianjin City, China. Meteorological Applications, 2016, 23(1):123-131.
[14] You Q L, Fraedrich K, Sielmann F, et al. Present and projected degree days in China from observation, reanalysis and simulations. Climate Dynamics, 2014, 43(5/6):449-1462.
[15] Shen X J, Liu B H. Changes in the timing, length and heating degree days of the heating season in central heating zone of China. Scientific Reports, 2016, 6:33384-33393.
[16] Meng F C, Li M C, Cao J F, et al. The effects of climate change on heating energy consumption of office buildings in different climate zones in China. Theoretical and Applied Climatology, 2018, 133:521-530.
[17] Liu Yulian, Ren Guoyu. Analysis of heating intensity in Harbin based on degree-hours method. Journal of Natural Resources,2018, 33(1):139-148.[刘玉莲,任国玉.基于度—时法的哈尔滨冬季采暖强度评价.自然资源学报. 2018, 33(1):139-148.]
[18] Yan Zhongwei, Li Zhen, Xia Jiangjiang. Homogenization of climate series:The basis for assessing climate changes.Science China:Earth Sciences, 2014, 44(10):2101-2111.[严中伟,李珍,夏江江.气候序列的均一化:定量评估气候变化的基础.中国科学:地球科学, 2014, 44(10):2101-2111.]
[19] Pranab Kumar Sen. Estimates of the regression coefficient based on Kendall's Tau. Publications of the American Statistical Association, 1968, 63(324):1379-1389.
[20] Xu Yan, Tang Guoli, Zhang Qiang. Analysis of the variation of the air temperature over China during the global warming hiatus period. Climate Change Research, 2017, 13(6):569-577.[许艳,唐国利,张强.基于均一化格点资料的全球变暖趋缓期中国气温变化特征分析.气候变化研究进展, 2017, 13(6):569-577.]
[21] National Climate Committee of China Meteorological Administration. China Climate Bulletin in 2017. Beijing:National Climate Committee of the China Meteorological Adminstration, 2018.[中国气象局国家气候委员会. 2017.中国气候公报.北京.中国气候局国家气候委员会, 2018.]
[22] Ren G Y, Ding Y H, Tang G L. An overview of mainland China temperature change research. Journal of Meteorological Research, 2017, 31(1):3-16.
[23] Wu Shaohong, Liu Wenzheng, Pan Tao, et al. Amplitude and velocity of the shifts in the Chinese terrestrial surface regions from 1960 to 2011. Chinese Science Bulletin, 2016, 61(19):2187-2197.[吴绍洪,刘文政,潘韬,等. 1960-2011年中国陆地表层区域变动幅度与速率.科学通报, 2016, 61(19):2187-2197.]
[24] Wu J, Gao X J, Giorgi F, et al. Changes of effective temperature and cold/hot days in late decades over China based on a high-resolution gridded observation dataset. International Journal of Climatology, 2017, 37(S1):788-800.
[25] Thompson D W J, Wallace J M. The Arctic oscillation signature in the wintertime geopotential height and temperature fields. Geophysical Research Letters, 1998, 25(9):1297-1300.
[26] Zhang Leying, Xu Haiming, Shi Ning. Influence of the spring Arctic oscillation on midsummer surface air temperature over the Yangtze River valley. Chinese Journal of Atmospheric Sciences, 2015, 19(5):1049-1058.[张乐英,徐海明,施宁.春季北极涛动对盛夏长江流域地表气温的影响.大气科学, 2015, 19(5):1049-1058.]
[27] Song L, Wu R G, Jiao Y. Relative contributions of synoptic and intraseasonal variations to strong cold events over eastern China. Climate Dynamics, 2018, 50(11/12):4619-4634.
[28] He S P, Gao Y Q, Li F, et al. Impact of Arctic oscillation on the East Asian climate:A review. Earth-Science Reviews,2017, 164:48-62.
[29] Chen S, Wu R, Chen W. A strengthened impact of november Arctic oscillation on subsequent tropical Pacific sea surface temperature variation since the late-1970s. Climate Dynamics, 2018, 51(1/2):511-529.
[30] Li Shuangshuang, Lu Jiayu, Yan Junping, et al. Spatiotemporal variability of temperature in northern and southern Qinling Mountains and its influence on climatic boundary. Acta Geographica Sinica, 2018, 73(1):13-24.[李双双,芦佳玉,延军平,等. 1970-2015年秦岭南北气温时空变化及其气候分界意义.地理学报, 2018, 73(1):13-24.]
[31] Ding Yihui, Liu Yanju, Liang Sujie, et al. Interdecadal variability of the East Asian winter monsoon and its possible links to global climate change. Acta Meteorologica Sinica, 2014, 72(5):835-852.[丁一汇,柳艳菊,梁苏洁,等.东亚冬季风的年代际变化及其与全球气候变化的可能联系.气象学报, 2014, 72(5):835-852.]
[32] Li Shuangshuang, Yan Junping, Kong Feng, et al. The nonlinear trends of high temperature weather in Xi'an by extremepoint symmetric mode decomposition method. Geographical Research, 2018, 37(1):209-219.[李双双,延军平,孔锋,等.极点对称模态分解下西安高温天气的趋势特征.地理研究, 2018, 37(1):209-219.]
[33] Mu Mu, Ren Hongli. Enlightenments form researches and predictions of 2014-2016 super El Ni?o event. Science China Earth Sciences, 2017, 47(9):993-995.[穆穆,任宏利. 2014-2016年超强厄尔尼诺事件研究及其预测给予我们的启示.中国科学:地球科学, 2017, 47(9):993-995.]
[34] Wu Shaohong, Gao Hongbo, Deng Yuhao, et al. Climate change risk and methodology for its quantitative assessment.Progress in Geography, 2018, 37(1):28-35.[吴绍洪,高江波,邓浩宇,等.气候变化风险及其定量评估方法.地理科学进展, 2018, 37(1):28-35.]