全球中低纬度降水量时空特征分析

doi:10.3976/j.issn.1002-4026.20240013

Abstract

Abstract:

Based on the monthly average global precipitation data from January 1982 to April 2022, this study discusses the spatial distribution and time series of mode one and mode two using the cyclostationary empirical orthogonal function (CSEOF) method; analyzes the spatial distribution, seasonal variation characteristics, and interannual variation characteristics of global marine precipitation at low and middle latitudes; and discusses the possible causes of these variations. Results show that the interannual variation of precipitation exhibits periodic characteristics, with the main areas experiencing variations being distributed in the tropical Pacific region; additionally, the spatial field of mode one demonstrates an east-west inverse phase distribution, which is stronger in winter than in summer. The seasonal variation of the spatial field of mode two is more complex than that of mode one, with the high-value positive and negative variability regions demonstrating an east-west inverse phase distribution in winter and negative variability regions dominating in summer with weaker intensity. ENSO has an important impact on the interannual variations of precipitation. The spatiotemporal variation characteristics of mode one are primarily affected by the ENSO phenomenon, while those of mode two are mainly affected by the El Niño Modoki phenomenon. The main precipitation variation characteristics are affected by the superposition of these two phenomena.

Key words: CSEOF, global precipitation, ENSO, CMAP database

CLC Number:

P426.611

WANG Qiuyan, SONG Xiuyu, YANG Fan, LI Yaoru. Analysis of spatiotemporal characteristics of global precipitation at middle and low latitudes[J].Shandong Science, 2024, 37(6): 125-134.

Figures/Tables 6

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

References 32

[1]	ALLANR P, ARIAS P A, BERGER S, et al. IPCC, 2021: Summary for policymakers in: climate change 2021: the physical science basis. Contribution of working group i to the sixth assessment report of the intergovernmental panel on climate change[EB/OL].[2024-01-05]. https://archive.connect.h1.co/article/740620545/#eval793587812.
[2]	刘希洋, 蔡勤禹. 近二十年中国海洋灾害史研究的进展与问题[J]. 海洋湖沼通报, 2019(6): 157-165.
[3]	SILLMANN J, KHARIN VV, ZWIERS F W, et al. Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections[J]. Journal of Geophysical Research: Atmospheres, 2013, 118(6): 2473-2493. DOI: 10.1002/jgrd.50188.
[4]	杨小欣, 吴晓芬, 许建平. 热带太平洋海域上层海洋热盐含量研究概述[J]. 海洋湖沼通报, 2017(5): 18-30.
[5]	CHEN H P, SUN J Q, LIN W Q, et al. Comparison of CMIP6 and CMIP5 models in simulating climate extremes[J]. Science Bulletin, 2020, 65(17): 1415-1418. DOI: 10.1016/j.scib.2020.05.015. pmid: 36747394
[6]	王丹阳, 苏涵, 张春玲. Argo数据同化方法及其网格化产品研究进展[J]. 海洋湖沼通报, 2022, 44(3): 158-165. DOI: 10.13984/j.cnki.cn37-1141.2022.03.021.
[7]	林益同, 赵春雨, 房一禾, 等. 东北初夏和盛夏降水时空变化及大气环流因子新特征分析[J]. 气象与环境学报, 2021, 37(5): 63-71. DOI: 10.3969/j.issn.1673-503X.2021.05.010.
[8]	赵京华. 热带降水年际变化时空分布特征研究[D]. 青岛: 中国海洋大学, 2008.
[9]	任湘湘, 夏冬冬. 印度海洋观测预报发展现状和思考[J]. 海洋预报, 2022, 39(3): 107-116. DOI: 10.11737/j.issn.1003-0239.2022.03.011.
[10]	邹美常, 鄢波, 黄子建. 中国沿海11省驱动海洋经济发展的多元路径分析[J]. 海洋湖沼通报, 2022, 44(6): 157-163. DOI: 10.13984/j.cnki.cn37-1141.2022.06.020.
[11]	KONAPALA G, MISHRA A K, WADA Y, et al. Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation[J]. Nature Communications, 2020, 11(1): 3044. DOI: 10.1038/s41467-020-16757-w.
[12]	CHANG X Y, WANG B B, YAN Y, et al. Characterizing effects of monsoons and climate teleconnections on precipitation in China using wavelet coherence and global coherence[J]. Climate Dynamics, 2019, 52(9): 5213-5228. DOI: 10.1007/s00382-018-4439-1.
[13]	AN Q, HE H X, GAO J J, et al. Analysis of temporal-spatial variation characteristics of drought: a case study from Xinjiang, China[J]. Water, 2020, 12(3): 741. DOI: 10.3390/w12030741.
[14]	CHEN H Q, YONG B, KIRSTETTER P E, et al. Global component analysis of errors in three satellite-only global precipitation estimates[J]. Hydrology and Earth System Sciences, 2021, 25(6): 3087-3104. DOI: 10.5194/hess-25-3087-2021.
[15]	XIANG Y H, CHEN J, LI L, et al. Evaluation of eight global precipitation datasets in hydrological modeling[J]. Remote Sensing, 2021, 13(14): 2831. DOI: 10.3390/rs13142831.
[16]	TRAN A P, TRAN B C, CAMPBELL S B, et al. Spatio-temporal characterization of drought variability in data-scarce regions using global precipitation data: a case study in Cauto River Basin, Cuba[J]. Scientific Reports, 2024, 14: 11659. DOI: 10.1038/s41598-024-61709-9. pmid: 38778092
[17]	LI X, ZHANG K, GU P R, et al. Changes in precipitation extremes in the Yangtze River Basin during 1960—2019 and the association with global warming, ENSO, and local effects[J]. The Science of the Total Environment, 2021, 760: 144244. DOI: 10.1016/j.scitotenv.2020.144244.
[18]	QI Z S, CUI C F, JIANG Y T, et al. Changes in the spatial and temporal characteristics of China’s arid region in the background of ENSO[J]. Scientific Reports, 2022, 12(1): 17826. DOI: 10.1038/s41598-022-21712-4.
[19]	王冠楠. 钟贻森. 周朦, 等. 运用CSEOF方法分析南海表面温度季节与年际变化[J]. 中国海洋大学学报:自然科学版, 2019(6):007-019.DOI:10.16441/j.cnki.hdxb.20180065.
[20]	魏凤英. 现代气候统计诊断与预测技术[M]. 2版. 北京: 气象出版社, 2007.
[21]	KIM K Y, NORTH G R. EOFs of harmonizable cyclostationary processes[J]. Journal of the Atmospheric Sciences, 1997, 54(19): 2416-2427. DOI: 10.1175/1520-0469(1997)054<2416:eohcp>2.0.co;2.
[22]	KIM K Y, WU Q G. A comparison study of EOF techniques: analysis of nonstationary data with periodic statistics[J]. Journal of Climate, 1999, 12(1): 185-199. DOI: 10.1175/1520-0442-12.1.185.
[23]	KIM K Y, CHUNG C. On the evolution of the annual cycle in the tropical Pacific[J]. Journal of Climate, 2001, 14(5): 991-994. DOI: 10.1175/1520-0442(2001)014<0991:oteota>2.0.co;2.
[24]	徐建军, 朱乾根. ENSO及其年代际异常对全球及亚洲季风降水影响的数值研究[J]. 气象学报, 1999(3): 46-60.
[25]	张春玲, 许建平. 基于Argo观测的太平洋温、盐度分布与变化(Ⅱ):盐度[J]. 海洋通报, 2015, 34(1):21-31.
[26]	袁媛, 高辉, 贾小龙. 2014—2016年超强厄尔尼诺事件的气候影响[J]. 气象, 2016, 42(5): 532-539.
[27]	高弋斌, 路春燕, 钟连秀, 等. 1951—2016年中国沿海地区气温与降水量的时空特征[J]. 森林与环境学报, 2019, 39(5):530-539.
[28]	杨小欣, 吴晓芬, 刘增宏. 西太平洋暖池海域上层海洋热盐含量初步研究[J]. 中国海洋大学学报:自然科学版, 2016, 46(6):1-12.
[29]	郝钰茜. 北半球夏季风降水年际协同变化的主要特征及与热带海温变化的联系[D]. 北京: 中国气象科学研究院, 2016.
[30]	袁良, 何金海. 两类ENSO对我国华南地区冬季降水的不同影响[J]. 干旱气象, 2013, 31(1):24-31. doi: 10． 11755 /j． issn． 1006 － 7639( 2013) － 01 － 0024
[31]	ASHOK K, BEHERAS RAOS, et al. El Niño Modoki and its possible teleconnection[J]. Journal of Geophysical Research, 2007, 112:C11007. DOI:10.1029/2006JC003798.
[32]	LI G, REN B H, YANG C Y, et al. Indices of El Niño and El Niño Modoki: an improved El Niño Modoki index[J]. Advances in Atmospheric Sciences, 2010, 27(5):1210-1220.

Analysis of spatiotemporal characteristics of global precipitation at middle and low latitudes

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 32

Related Articles 15

Metrics

Comments

Recommended 0

[1]	JIANG Xiaodong, WANG Leilei, SUN Peng, YANG Guang, GENG Junqi, WANG Jiawen, HUANG Sheng, QU Shuai, WANG Chen, SHANG Ying. Fault detection of an on-load tap changer based on generative adversarial network [J]. Shandong Science, 2023, 36(6): 68-73.
[2]	DONG Guanlei, JIANG Xiaodong, SUN Peng, YANG Guang, GENG Junqi, WANG Jiawen, QU Shuai, HUANG Sheng, WANG Chen, SHANG Ying. On-load transformer fault detection based on distributed optical fiber sensing system [J]. Shandong Science, 2023, 36(5): 52-59.
[3]	ZHANG Hua, HU Binxin, ZHU Feng, WANG Jiqiang, SONG Guangdong. Optical fiber microseismic monitoring system and its application research in Wuyang Coal Mine [J]. Shandong Science, 2023, 36(5): 60-66.
[4]	TAI Peng, SONG Miaomiao, WANG Bo, CHEN Shizhe, FU Xiao, HU Wei, GAO Saiyu, CHENG Kaiyu, ZHENG Shanshan, JIAO Zixuan, WANG Longfei. Wave sensor fault diagnosis method based on t-SNE reduction and KNN algorithm [J]. Shandong Science, 2023, 36(4): 1-9.
[5]	QIU Shi, YANG Maoshui, SUN Yan, REN Yan, WU Juxiu. Research on measurement error variation of a weighing precipitation sensor [J]. Shandong Science, 2023, 36(1): 124-130.
[6]	MA Long, ZHANG Fa-xiang, LIU Xiao-hui, WANG Ying-ying, WANG Chang, LI Hui. Design of flow-velocity sensor based on double fiber Bragg gratings [J]. Shandong Science, 2022, 35(6): 109-115.
[7]	WANG Ji-Jun,LÜ Bing-Xi,LIU Li-Bin. Antifreezing organic hydrogel electrolyte with high mechanical strength and ionic conductivity [J]. Shandong Science, 2022, 35(2): 60-69.
[8]	ZHU Si-rong, BI Chun-yuan, DU Yi, ZHANG Jin-ling, GAO Guang-heng, ZHANG Li-qun, ZHAO Xiao-hua, YANG Yan. An analysis method for suppressing interference from the reversible inhibitors of amperometric enzyme electrode biosensors [J]. Shandong Science, 2021, 34(4): 87-94.
[9]	YUAN Da, FENG Xian-dong, ZHANG Yun-yan, WU Bing-wei. Development of marine in situ dissolved oxygen sensor based on fluorescence quenching and its quantitative calibration algorithm [J]. Shandong Science, 2021, 34(2): 1-10.
[10]	LI Rong, ZHENG Wen. Characteristics of the analog of electromagnetically induced transparency in case of a polarization-insensitive metamaterial [J]. Shandong Science, 2021, 34(1): 28-34.
[11]	HE Ya-nan, ZHU Hong-hai. Libmodbus-based bus protocol design for navigational sea surface temperature sensors [J]. Shandong Science, 2020, 33(6): 1-7.
[12]	SUN Hui-nan, GAO Guang-heng , LIU Qing-ai , MA Yao-hong, LI Jing , SHI Jian-guo. Application of blood lactic acid biosensor in the diagnosis and treatment of COVID-19 [J]. Shandong Science, 2020, 33(3): 1-6.
[13]	YU Peng-feia, WANG Xiao-shana, YU Zi-qiang, WEI Yu-bin, ZHANG Ting-ting, HU Jie, LIU Tong-yu, WANG Zhao-wei. Research on laser online detection technology application of precision fermentation of CO₂ tail gas [J]. Shandong Science, 2020, 33(1): 68-74.
[14]	LIU Feng-qing, MA Hai-kuan, WU Ning, MA Ran, CAO Xuan. Application of wireless sensor network based on ZigBee in the monitoring of the marine ecological environment [J]. Shandong Science, 2019, 32(6): 1-8.
[15]	DU Yi, ZHANG Jin-ling, ZHU Si-rong, ZHANG Li-qun, ZHAO Xiao-hua, SHI Jian-guo, BI Chun-yuan. Determination of milk lactose content using composite enzyme membrane biosensor based on difference method [J]. Shandong Science, 2019, 32(6): 43-48.