THE DIAGNOSIS OF HEAVY RAINFALL IN CHINA AND CORRELATION OF HEAVY RAINFALL WITH MULTIPLE CLIMATIC FACTORS IN CHINA FROM 1961 TO 2015
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摘要: 气候变化背景下，频发的暴雨事件造成城市内涝、人员伤亡和财产损失，已经成为全社会广泛关注的焦点问题之一。为了诊断中国暴雨的时空变化及其与不同自然因子的关联性，采用1961—2015年中国659个降水站点数据，采用线性趋势、EOF分析等多种统计方法诊断了中国暴雨时空变化特征，结果表明，中国暴雨雨量、雨日和雨强在1961—2015年以胡焕庸线为界呈现出东南高-西北低的气候态空间分布格局；线性趋势分析表明1961—2015年中国暴雨雨量和雨日从东南沿海向西北内陆呈明显“增-减-增”的空间分布格局，且呈增长趋势的站点占主导，分别高达80.88%和79.81%；从西北内陆到东南沿海的年代剖面分析表明中国暴雨雨量和雨日随着年代推移在迅速增长；对低通滤波后的中国暴雨进行EOF分析表明中国暴雨雨量和雨日的增长东南沿海快，内陆地区慢。根据IPCC等已有研究中筛选出对中国地区有影响的28个气候因子，并将其与659个站点的暴雨进行相关分析，结果表明不同气候因子与不同区域暴雨呈现出错综复杂的相关性特征，其中与暴雨雨量呈现以正相关和负相关为主的气候因子分别为15和13个，全局相关因子包含AAO(Antarctic Oscillation)、Pacific Warmpool，而其它气候因子在七大分区中与暴雨的关联性各有突出，表现出明显的空间异质性。Abstract: In recent years many regions are pounded with heavy rainfall, causing flood, casualties, and property damage in many urban areas. Frequent extreme precipitation events under the background of global climate change has caused terrible harm to economic and social development, life security, ecosystem, etc., which has brought profound impact on sustainable development of disaster area. Heavy rainfall has become a key factor of global and regional disasters and environmental risk, and has widely cause concern among academic community and most sectors of the society. According to the existing research results that precipitation in China is greatly affected by various atmospheric, oceanic and other climatic factors. In order to diagnose the temporal and spatial variation characteristics of heavy rainfall in China and its correlation with different climatic indices, here we use daily precipitation observations of 659 meteorological stations in China from 1961 to 2015, based on the heavy rainfall threshold of daily rainfall no less than 50 mm, to calculate heavy rainfall in China. The temporal and spatial variation characteristics of heavy rainfall, including heavy rainfall amounts, heavy rainfall days and heavy rainfall intensity in China, are diagnosed by using various statistical methods such as linear trend, EOF analysis and so on. The results show that China's heavy rainfall amounts, heavy rainfall days and heavy rainfall intensity from 1961 to 2015, by taking the Hu Huanyong line as the dividing line, has shown a significant spatial distribution pattern of being high in the southeast versus low in the northwest. Linear trend analysis result shows that heavy rainfall amounts and heavy rainfall days show an obvious spatial distribution pattern of 'increasing-decreasing-increasing' from the southeast coast to the northwest inland in China. And the meteorological stations number with increasing trend is dominant, which is up to 80.88% in all the meteorological stations in heavy rainfall amounts and 79.81% in heavy rainfall days. The decadal profile analysis result shows that heavy rainfall amounts and heavy rainfall days are increasing rapidly with time from the northwest to the southeast coast in China. The EOF analysis of heavy rainfall after being low-pass filtered shows that heavy rainfall amounts and heavy rainfall days are increasing fast in southeast coastal regions but slowly in inland areas in China. According to the IPCC and other existing research, 28 climatic indices influencing heavy rainfall in China are selected. And the correlation analysis between the 659 meteorological stations and the 28 climatic factors are carried out. The results show that the correlation between different climatic indices and different region's heavy rainfall has significantly intricate features. The climatic indices with the heavy rainfall mainly showing positive and negative correlation are 15 and 13 respectively. Factors have global impact on heavy rainfall in China including AAO and Pacific Warm Pool, while other climate indices associated with heavy rainfall in the seven geographical regions in China prominent exhibit spatial heterogeneity.
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Key words:
- climate change /
- heavy rainfall diagnosis /
- spatiotemporal patterns /
- EOF analysis /
- climate indices /
- correlation
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表 1 与中国区域降水相关的气候因子
序号 英文缩写 指标描述 数据来源 1 WPSH ANNUAL 西太平洋副热带高压年均值 中国气象局国家气候中心 2 WPSH SA 西太平洋副热带高压6—8月夏季均值 中国气象局国家气候中心 3 EASMI 东亚夏季风指数6—8月夏季均值 中国科学院大气物理研究所 4 SCSSMI 南海夏季风指数6—9月均值 中国科学院大气物理研究所 5 SASMI 南亚夏季风指数6—9月均值 中国科学院大气物理研究所 6 ENSO DJF ENSO指数(上一年12和1，2月均值) 美国大气海洋局 7 ENSO MAM ENSO指数3—5月均值 美国大气海洋局 8 ENSO JJA ENSO指数6—8月均值 美国大气海洋局 9 ENSO SOV ENSO指数9—11月均值 美国大气海洋局 10 PDO 太平洋年代际振荡年均值 美国大气海洋局 11 Pacific Warmpool 太平洋暖池年均值 美国大气海洋局 12 NINO3.4 热带太平洋海温年均值 美国大气海洋局 13 AMO US 北大西洋年代际振荡未滑动平均年均值 美国大气海洋局 14 AMO SM 北大西洋年代际振荡滑动平均后年均值 美国大气海洋局 15 Blocking 中纬度阻断事件年平均值6—8月均值 美国大气海洋局 16 IOD MAM 印度洋偶极子指数3—5月均值 日本海洋科学技术中心 17 IOD JJA 印度洋偶极子指数6—8月均值 日本海洋科学技术中心 18 IOD SON 印度洋偶极子指数9—11月均值 日本海洋科学技术中心 19 IOD DJF 印度洋偶极子指数上一年12和1，2月均值 日本海洋科学技术中心 20 IOD ANNUAL 印度洋偶极子指数年均值 日本海洋科学技术中心 21 Tibet 1 青藏高原高压年均值 中国气象局国家气候中心 22 Tibet 2 青藏高原高压年均值 中国气象局国家气候中心 23 QBO 准两年振荡年均值 美国大气海洋局 24 NHPVII 北半球极涡强度指数年均值 中国气象局国家气候中心 25 AO 北极涛动年均值 中国科学院大气物理研究所 26 AAO 南极涛动年均值 中国科学院大气物理研究所 27 NAO 北大西洋涛动年平均值 中国科学院大气物理研究所 28 Sunspots 太阳黑子强度年均值 美国大气海洋局 
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表 2 中国年际暴雨变化趋势站点数统计
暴雨雨量趋势/(mm/(10 a)) 站点数(百分比) 暴雨雨日趋势/(d/(10 a)) 站点数(百分比) < -20 7(1.06%) < -0.4 5(0.76%) -20 ~ -15 5(0.76%) -0.4 ~ -0.3 3(0.46%) -15 ~ -10 8(1.21%) -0.3 ~ -0.2 5(0.76%) -10 ~ -5 10(1.52%) -0.2 ~ -0.1 15(2.28%) -5 ~ 0 96(14.57%) -0.1 ~ 0.0 105(15.93%) 0 ~ 5 259(39.30%) 0.0 ~ 0.1 289(43.85%) 5 ~ 10 65(9.86%) 0.1 ~ 0.2 81(12.29%) 10 ~ 15 42(6.37%) 0.2 ~ 0.3 47(7.13%) 15 ~ 20 34(5.16%) 0.3 ~ 0.4 33(5.01%) > 20 133(20.18%) > 0.4 76(11.53%)
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表 3 中国年际暴雨和气候指数相关性的站点统计
暴雨指标 暴雨雨量 暴雨雨日 暴雨雨强 正相关 百分比/% 负相关 百分比/% 正相关 百分比/% 负相关 百分比/% 正相关 百分比/% 负相关 百分比/% AAO 556 84.37 103 15.63 546 82.85 113 17.15 556 84.37 103 15.63 AMO SM 402 61.00 257 39.00 391 59.33 268 40.67 376 57.06 283 42.94 AMO US 416 63.13 243 36.87 409 62.06 250 37.94 369 55.99 290 44.01 AO 293 44.46 366 55.54 300 45.52 359 54.48 327 49.62 332 50.38 ENSO DFJ 479 72.69 180 27.31 485 73.60 174 26.40 441 66.92 218 33.08 ENSO MAM 225 34.14 434 65.86 223 33.84 436 66.16 204 30.96 455 69.04 ENSO JJA 144 21.85 515 78.15 142 21.55 517 78.45 145 22.00 514 78.00 ENSO SON 130 19.73 529 80.27 118 17.91 541 82.09 131 19.88 528 80.12 NAO 293 44.46 366 55.54 300 45.52 359 54.48 327 49.62 332 50.38 NINO3.4 368 55.84 291 44.16 383 58.12 276 41.88 380 57.66 279 42.34 PDO 278 42.19 381 57.81 285 43.25 374 56.75 299 45.37 360 54.63 Pacific Warmpool 529 80.27 130 19.73 528 80.12 131 19.88 543 82.40 116 17.60 WPSH SA 529 80.27 130 19.73 528 80.12 131 19.88 528 80.12 131 19.88 WPSH ANNUAL 534 81.03 125 18.97 525 79.67 134 20.33 536 81.34 123 18.66 EASMI 204 30.96 455 69.04 209 31.71 450 68.29 204 30.96 455 69.04 SCSSMI 138 20.94 521 79.06 138 20.94 521 79.06 126 19.12 533 80.88 SASMI 227 34.45 432 65.55 222 33.69 437 66.31 208 31.56 451 68.44 NHPVII 255 38.69 404 61.31 263 39.91 396 60.09 298 45.22 361 54.78 Blocking 355 53.87 304 46.13 351 53.26 308 46.74 394 59.79 265 40.21 IOD ANNUAL 250 37.94 409 62.06 235 35.66 424 64.34 261 39.61 398 60.39 IOD MAM 407 61.76 252 38.24 399 60.55 260 39.45 418 63.43 241 36.57 IOD JJA 384 58.27 275 41.73 369 55.99 290 44.01 373 56.60 286 43.40 IOD SON 353 53.57 306 46.43 339 51.44 320 48.56 353 53.57 306 46.43 IOD DJF 267 40.52 392 59.48 263 39.91 396 60.09 267 40.52 392 59.48 QBO 249 37.78 410 62.22 249 37.78 410 62.22 255 38.69 404 61.31 Tibet 1 477 72.38 182 27.62 471 71.47 188 28.53 461 69.95 198 30.05 Tibet 2 488 74.05 171 25.95 480 72.84 179 27.16 488 74.05 171 25.95 Sunspots 408 61.91 251 38.09 416 63.13 243 36.87 432 65.55 227 34.45
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表 4 中国七大地理分区特征[32]
分区 包含省区 面积/万平方公里 人口/万人 人口密度/(人/km2) 站点数(比例/%) 东北 辽宁、吉林、黑龙江 78.83 10 951 139 78(11.84) 华北 北京、天津、河北、山西、内蒙古 155.81 16 832 108 87(13.20) 华中 湖北、湖南、河南、江西 73.16 26 807 366 75(11.38) 华东 山东、江苏、安徽、浙江、福建、上海 63.144 35 098 556 85(12.90) 华南 广东、广西、海南 45.19 15 899 352 58(8.80) 西北 宁夏、新疆、青海、陕西、甘肃 310.82 9 662 31 148(22.46) 西南 四川、云南、贵州、西藏、重庆 235.34 19 634 83 128(19.42)
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表 5 中国不同地区年际暴雨主要气候影响因子
分区 正相关为主 负相关为主 东北 AAO; AMO US; ENSO DJF; EASMI; Pacific Warmpool;
WPSH SA; WPSH ANNUALAO; NAO; Sunspots 华北 AAO; ENSO DJF; Pacific Warmpool; PDO; SASMI; Tibet1;Tibet2;NHPVII; AO; ENSO MAM; ENSO JJA; ENSO SON;
NAO; NINO3.4;Blocking;华中 AAO; ENSO DJF; Pacific Warmpool Blocking; ENSO JJA; ENSO SON; EASMI;
SCSSMI；IOD DJF；IOD JJA；IOD SON华东 AAO; Pacific Warmpool; WPSH SA; WPSH ANNUAL QBO 华南 AAO; AO;IOD DJF; NAO; PDO; Pacific Warmpool;
QBO; WPSH SA; WPSH ANNUALAMO US; NINO3.4 西北 AAO; AMO SM; AMO US; Pacific Warmpool;
WPSH SA; Tibet1;Tibet2;SunspotsENSO MAM; ENSO JJA; ENSO SON; EASMI;
PDO; QBO; SASMI; NHPVII西南 AAO; Pacific Warmpool AMO SM; AMO US; ENSO JJA
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