Quantitative Relationship Between Cloud Amount and Precipitation in Summer over China and its Causes
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摘要: 云的形成是产生降雨的必要条件，云和降水之间存在着极为密切而复杂的联系。利用常规站点数据和ISCCP卫星数据等资料分析了夏季中国地区云的多种特征参数的变化与降水变化在时空分布上的联系。站点数据结果表明总云量、低云量与降水的距平在全国范围内表现出显著的正相关关系；在通过0.05水平显著性检验的站点上，云量和降水距平百分率之间的线性关系较明显，总云量每增加1.00%降水增加2.23%，低云量每增加1.00%降水增加0.46%。ISCCP数据结果显示总云云量、光学厚度和云水路径以及高云中的卷层云和深对流云云量与降水距平呈非常好的正相关关系。采用K-means聚类分析方法并参考中国地理气候分布特点，将中国分为9个气候区，以小波相干分析和交叉小波分析对各个气候区夏季云量和降水距平百分率序列在时频域内多尺度特征的关系做了进一步研究。结果显示9个气候区夏季白天总云量和低云量与降水变化在2~4年(a)和5~8 a的尺度周期都具有较强的相干性与共振周期，且处于正相关位相。在时空分布和时频域上，中国地区夏季云和降水的变化之间都存在非常显著的正相关关系，尤其是低云量。云和降水变化之间具有强相干性与共振周期是两者之间正相关联系的原因。Abstract: One of the requirements for producing rainfall is cloud formation, and clouds and precipitation have a very intricate and intimate relationship. First, we used databases from conventional stations and ISCCP (International Satellite Cloud Climatology Project) to evaluate the spatial and temporal relationships between the variation of several characteristic features of clouds and the variance in summertime precipitation over China. According to the station dataset's findings, there is a significant positive correlation between the anomalous percentage of total cloud cover, low cloud cover, and precipitation across the entire nation. For stations that pass the 0.05 level significance test, the linear relationships between the anomalous percentage of clouds and precipitation are particularly obvious. For every 1.00% increase in total cloud cover, precipitation increases by 2.23%, and for every 1.00% increase in low cloud cover, precipitation increases by 0.46%. The ISCCP dataset results demonstrate very strong positive connections between abnormal percentages of cloud amount, optical thickness, cloud-water path, cirrus and deep convective cloud amount in high clouds, and that of precipitation. Second, China was divided into nine climate zones using the k-means cluster analysis method and with reference to the geoclimatic distribution of China. Then wavelet coherence analysis and cross wavelet analysis were used to further investigate the relationship in the time-frequency domain between the anomalous percentage of cloud amounts and precipitation in each climate zone. The findings demonstrate substantial coherence and resonance cycles at the scales of 2 to 4 years and 5 to 8 years, and a positive correlation phase for the anomalous percentage of total and low cloud quantities and daytime precipitation in summer throughout the nine climate zones. In both time-space distributions and time-frequency domains, there are extremely significant positive correlations between the anomalous percentage of cloud amounts (especially the low cloud amount) and precipitation in summer over China. The strong coherence and resonance period between anomalous percentage of cloud amounts and precipitation are the reasons for the positive correlation between them.
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Key words:
- cloud /
- precipitation /
- correlation analysis /
- wavelet coherence /
- cross wavelet analysis
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图 3 1984—2009年夏季站点降水和ISCCP云量距平百分率的相关
通过0.05水平显著性检验的区域打点标注。a. 总云量；b. 高云量；c. 卷层云云量；d. 深对流云云量。
表 1 1961—2010年的夏季9个气候区的云量和降水序列的共振周期
区域 XWT共振周期(a) 总云量和降水 低云量和降水 NE132 2~4*、5~8*、8~12 2~4*、5~8、8~12 EA192 2~4*、4~6、7~12* 2~4*、4~6、7~12 WAS75 2~4*、5~8*、8~14 2~3*、5~7、10~12 NC294 2~4*、4~6、10~16 2~4*、4~6、10~16 CCN131 2~4*、5~7*、7~10* 2~4*、5~7、7~10* CCS194 2~4*、4~8*、10~16* 2~4*、4~8、10~16* SC283 2~4*、6~8、8~16 2~4*、6~8、8~16 SW289 2~4*、4~7*、8~16* 2~4*、4~7、8~16 QT63 2~4*、4~8*、8~14 2~4*、4~8*、8~14 注：带*表示该周期含通过0.05水平显著性检验的时域。 
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表 2 1961—2010年的夏季9个气候区的云量和降水序列的相关系数
区域 总云量和降水 低云量和降水 R P R P NE132 0.652 7** 2.80×10-7 0.673 4** 8.40×10-8 EA192 0.536 1** 6.01×10-5 0.788 8** 1.00×10-11 WAS75 0.438 1** 1.46×10-3 0.564 0** 2.00×10-5 NC294 0.599 7** 4.18×10-6 0.724 9** 2.64×10-9 CCN131 0.716 7** 4.82×10-9 0.448 9** 1.08×10-3 CCS194 0.587 3** 7.33×10-6 0.750 8** 3.41×10-10 SC283 0.341 5* 1.52×10-2 0.769 0** 6.90×10-11 SW289 0.395 5** 4.47×10-3 0.473 9** 5.08×10-4 QT63 0.505 2** 1.83×10-4 0.528 9** 7.59×10-5 注：带**表示通过0.005的显著性检验，带*表示通过0.05的显著性检验。
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[1] 胡亮, 李耀东, 何金海. 东亚热带季风与副热带季风降水特征研究的回顾与展望[J]. 热带气象学报, 2010, 26(6): 813-818. [2] 杜振彩, 黄荣辉, 黄刚, 等. 亚洲季风区积云降水和层云降水时空分布特征及其可能成因分析[J]. 大气科学, 2011, 35(6): 993-1 008. [3] 李跃清, 张琪. 西南地区夏季云量与降水的关系特征分析[J]. 自然资源学报, 2014, 29(3): 441-453. [4] 陈勇航, 黄建平, 王天河, 等. 西北地区不同类型云的时空分布及其与降水的关系[J]. 应用气象学报, 2005, 16(6): 717-727. [5] 周毓荃, 蔡淼, 欧建军, 等. 云特征参数与降水相关性的研究[J]. 大气科学学报, 2011, 34(6): 641-652. [6] 张中波, 黎祖贤, 唐林. 湖南省云特征参数与降水相关性研究[J]. 气候变化研究快报, 2017, 6(2): 74-82. [7] 王磊, 周毓荃, 蔡淼, 等. 华北云特征参数与降水相关性的研究[J]. 气象与环境科学, 2019, 42(3): 9-16. [8] 曾昭美, 严中伟. 近40年中国云量变化的分析[J]. 大气科学, 1993, 17(6): 50-58. [9] 卢爱刚, 康世昌, 庞德谦, 等. 1951-1994年中国各地云量变化对ENSO事件的响应[J]. 干旱区资源与环境, 2009, 23(4): 131-135. [10] DU J, CHENG L L, ZHANG Q. Spatiotemporal variability and trends in the hydrology of the Xiang River basin, China: extreme precipitation and streamflow[J]. Arabian Journal of Geosciences, 2019, 12(18): 566. [11] LI F X, HE L. The effects of dominant driving forces in summer precipitation during different periods in Beijing[J]. Atmosphere, 2017, 8 (44): 1-13. [12] LIU F, CHEN H, CAI H Y, et al. Impacts of ENSO on multi-scale variations in sediment discharge from the Pearl River to the South China Sea[J]. Geomorphology, 2017, 293(A15): 24-36. [13] ZHANG R, XU Z X, ZUO D P, et al. Hydro-meteorological trends in the Yarlung Zangbo River Basin and possible associations with largescale circulation[J]. Water, 2020, 12(144): 1-20. [14] FANG G H, LI X, XU M, et al. Spatiotemporal variability of drought and its multi-scale linkages with climate indices in the Huaihe River Basin, Central China and East China[J]. Atmosphere, 2021, 12(11): 1 446. [15] YANG R T, XING B. Possible Linkages of Hydrological Variables to Ocean-Atmosphere Signals and Sunspot Activity in the Upstream Yangtze River Basin[J]. Atmosphere, 2021, 12(10): 1 361. [16] WEI Y J, ZHU L, CHEN Y, et al. Spatiotemporal Variations in Drought and Vegetation Response in Inner Mongolia from 1982 to 2019[J]. Remote Sensing, 2022, 14(15): 3 803. [17] AMANTAI N, DING J L. Analysis on the Spatio-Temporal Changes of LST and Its Influencing Factors Based on VIC Model in the Arid Region from 1960 to 2017: An Example of the Ebinur Lake Watershed, Xinjiang, China[J]. Remote Sensing, 2021, 13(23): 4 867. [18] 孙鹏, 张强, 陈晓宏. 鄱阳湖流域水沙周期特征及其影响因素[J]. 武汉大学学报: 理学版, 2011, 57(4): 298-304. [19] 叶许春, 许崇育, 张丹, 等. 长江中下游夏季降水变化与亚洲夏季风系统的关系[J]. 地理科学, 2018, 38(7): 1 174-1 182. [20] 刘占明, 陈子燊, 路剑飞, 等. 广东北江流域降水时空分布及其与Niño 3区SST相关性分析[J]. 自然资源学报, 2013, 28(5): 786-798. [21] 侯迎, 郑芳, 穆红雪. 全球SPEI数据的应用及其与环流因子的多尺度分析[J]. 水资源研究, 2017, 6(1): 9-17. [22] 侯迎, 郑芳, 邵议. 基于交叉小波的多尺度气候变化及其对径流的影响[J]. 水资源研究, 2016, 5(6): 564-572. [23] 王俊杰, 拾兵, 卢仲翰. 黄河入海径流量周期变化与东亚夏季风的关系研究[J]. 海洋通报, 2020, 39(3): 316-323. [24] 王俊杰, 拾兵, 巴彦斌. 近70年黄河入海水沙通量演变特征[J]. 水土保持研究, 2020, 27(3): 57-62, 69. [25] 洪梅, 刘科峰, 张栋, 等. 基于交叉小波分析方法的西太平洋副热带高压年际变率与热带海温及大气环流异常的相关性研究[J]. 热带气象学报, 2020, 36(2): 166-179. [26] GRINSTED A, MOORE J C, JEVREJEVA S. Application of the cross wavelet transform and wavelet coherence to geophysical time series [J]. Nonlinear Processes in Geophysics, 2004, 11: 561-566. [27] 邵骏. 基于交叉小波变换的水文多尺度相关分析[J]. 水力发电学报, 2013, 32(2): 23-26, 42. [28] 黄嘉佑. 气象统计分析与预报方法(第3版)[M]. 北京: 气象出版社, 2004. [29] HARTIGANM. A, WONG J A. Algorithm AS 136: A K-Means clustering algorithm[J]. Journal of the Royal Statistical Society, 1979, 28(1): 100-108. [30] 王澄海, 李健, 李小兰, 等. 近50a中国降水变化的准周期性特征及未来的变化趋势[J]. 干旱区研究, 2012, 29(1): 1-10. [31] SHEN Z X, ZHANG Q, SINGH V P, et al. Station-based non-linear regression downscaling approach: A new monthly precipitation downscaling technique[J]. International Journal of Climatology, 2021, 41(13): 5 879-5 898. [32] 刘引鸽, 王宁练, 武小波, 等. 1951-2009年中国低云量的时空特征及其影响因素[J]. 高原气象, 2013, 32(6): 1 608-1 616. [33] 高翠翠, 方乐锌, 李昀英, 等. 1985—2011年中国不同类型云发生频率、持续时数及伴随降水概率[J]. 暴雨灾害, 2015, 34(3): 206-214. [34] 刘瑞霞, 刘玉洁, 杜秉玉. 中国云气候特征的分析[J]. 应用气象学报, 2004, 15(4): 468-476. -
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