全球中低纬度降水量时空特征分析

doi:10.3976/j.issn.1002-4026.20240013

山东科学 ›› 2024, Vol. 37 ›› Issue (6): 125-134.doi: 10.3976/j.issn.1002-4026.20240013

• 环境与生态 • 上一篇

全球中低纬度降水量时空特征分析

王秋妍¹(), 宋秀瑜²^,^*(), 杨帆³, 李耀如³

1.唐山市自然资源和规划局,河北唐山 063000
2.大连市气象装备保障中心,辽宁大连 116001
3.自然资源部北海生态中心自然资源部渤海生态预警与保护修复重点实验室,山东青岛 266000

收稿日期:2024-01-18 出版日期:2024-12-20 发布日期:2024-12-05
通信作者: *宋秀瑜(1992—),女,工程师,研究方向为综合气象探测研究。E-mail:1148814266@qq.com, Tel:13940959125
作者简介:王秋妍(1987—),女,硕士,研究方向为海洋调查与监测。E-mail:445346435@qq.com
基金资助:
中国海油海洋环境与生态保护公益基金会基金(CF-MEEC/TR/2024-6)

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

WANG Qiuyan¹(), SONG Xiuyu²^,^*(), YANG Fan³, LI Yaoru³

1. Bureau of Nature Resources and Planning of Tangshan, Tangshan 063000, China
2. Dalian Meteorological Equipment Support Center, Dalian 116001, China
3. North China Sea Ecological Center of the Ministry of Natural Resources,Key Laboratory of Ecological Prewarning and Protection of Bohai Sea, Ministry of Natural Resources, Qingdao 266000, China

Received:2024-01-18 Online:2024-12-20 Published:2024-12-05

摘要/Abstract

摘要：

基于1982年1月至2022年4月全球降水量的月平均数据资料,利用循环平稳经验正交函数(CSEOF)方法,讨论了第一、二模态的空间分布和时间序列,分析了全球中低纬度海洋降水量的空间分布、季节变化特征和年际变化特征,讨论了可能引起该变化的原因。结果表明,降水量的年际变化呈现周期性变化特征,其主要变化区域分布在热带太平洋地区,第一模态空间场呈现东-西反相位振荡特征,冬季强于夏季;第二模态空间场季节变化相对于第一模态空间场更为复杂,冬季正负变异高值区主要表现为东-西反相位振荡特征,而夏季以负变异区为主,变异区强度较弱。第一模态时空变化特征主要受到厄尔尼诺-南方涛动(ENSO)现象影响,而第二模态主要受到中部型厄尔尼诺(El Niño Modoki)现象影响,降水量的主要变化特征受到两种现象的叠加影响。

关键词: 循环平稳经验正交函数, 全球降水, 厄尔尼诺-南方涛动, CMAP数据集

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

中图分类号:

P426.611

王秋妍, 宋秀瑜, 杨帆, 李耀如. 全球中低纬度降水量时空特征分析[J]. 山东科学, 2024, 37(6): 125-134.

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.

图/表 6

图1

图2

图3

图4

图5

图6

参考文献 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.

全球中低纬度降水量时空特征分析

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

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献 32

相关文章 0

Metrics

本文评价

推荐阅读 0