基于SSA和MGF的海面变化长期预测及对比

doi:10.11821/yj2008020008

地理研究 ›› 2008, Vol. 27 ›› Issue (2): 305-313.doi: 10.11821/yj2008020008

基于SSA和MGF的海面变化长期预测及对比

袁林旺, 谢志仁, 俞肇元

南京师范大学地理科学学院,南京 210097

收稿日期:2007-07-17 修回日期:2007-11-15 出版日期:2008-03-25 发布日期:2008-03-25
作者简介:袁林旺 (1973- ),男,江苏海安人,博士,副教授。主要从事海面变化研究。Email :yuanlinwang@njnu.edu.cn
基金资助:
973计划前期研究专项课题(2007CB416602);江苏省普通高校自然科学基金项目(06KJD170102)资助

Long-term prediction and comparison of sea-level change based on the SSA and MGF model

YUAN Lin-wang, XIE Zhi-ren, YU Zhao-yuan

Geo-Science School of Nanjing Normal University, Nanjing 210097, China

Received:2007-07-17 Revised:2007-11-15 Online:2008-03-25 Published:2008-03-25
Supported by:
973计划前期研究专项课题(2007CB416602);江苏省普通高校自然科学基金项目(06KJD170102)资助

摘要/Abstract

摘要：

海面变化预测受到建模思路、方法选择、数据长度及数据质量等因素的影响,导致了海面变化预测的不确定性。本文以国内6个验潮站自20世纪50年代以来的月平均潮位序列为基础,采用奇异谱分析(SSA)与均值生成函数(MGF)模型相结合的方案,以各站位最初20余年数据为基础建立预测模型,以后续年份的实测数据进行了多方案对比验证及检验。预测试验显示MGF模型具有较高的预测精度,并表现出较好的长期预测的稳定性特点。以SSA去噪序列为基础,应用MGF模型预测了各站位至2050年的月尺度海面值,年均值计算结果表明至2050年海面波动上升的幅度不超过20cm,海面变化速率同样表现出阶段性和波动性。与前人相关研究成果对比表明,本文所采用的SSA与MGF相结合的预测结果具有可比性,在方法原理和验证结果上看具有较好的长期预测潜力。

关键词: 海面变化, 预测, 奇异谱分析(SSA), 均生函数预测模型(MGF)

Abstract:

Tide gauges and satellite altimetry are the two measurements techniques of present-day sea level change, and tide gauges provide sea-level variations with respect to the land on which they lie. The predictions of sea-level changes are affected by modeling, methods, data length, data quality and other factors, which cause the uncertainties of prediction. Based on the monthly average tidal records of six tidal gauge stations in East China since the 1950s, Mean Generation Function(MGF) and Singular Spectrum Analysis(SSA) are employed to discuss the stability of long-term prediction.MGF model is built with each station's initial data of over 20 years, and the subsequent data are used to undertake comparative multi-experiments and tests. As a result, these prediction experiments testify that MGF exhibits more favorable and steady long-term prediction.Therefore, based on SSA denoised series, the MGF model is used to predict the sea-level changes of each station on the monthly scale till the year 2050. All stations take on obvious fluctuated rising trend. The calculated annual mean series indicate that the upper limit of the fluctuated sea-level changes can be no more than 20 cm. The velocity of the sea-level changes show periodity and fluctuant with prominent differences in the ascending and degressive segments accompanying with obviously spatial variation. Compared with the previous research findings, whose results are primarily done under linear hypothesis and may show limitation to some extent. Owing to the fluctuations and irregularities of sea-level changes, the prediction conclusion adopted by SSA and MGF methods are relatively comparable, and have favorable long-term prediction potential in terms of methodology and experimental results. Natural forcing, which is a combination with anthropogenic forcing plays an important role in the sea-level change, and there are still enormous uncertainties about sea-level change and its prediction.The integrated prediction system should be constructed with the consideration of multiple factors. Furthermore, comprehensive and comparative research is also needed.

Key words: sea-level change, prediction, singular spectrum analysis (SSA), mean generation function model (MGF)

袁林旺, 谢志仁, 俞肇元. 基于SSA和MGF的海面变化长期预测及对比[J]. 地理研究, 2008, 27(2): 305-313.

YUAN Lin-wang, XIE Zhi-ren, YU Zhao-yuan. Long-term prediction and comparison of sea-level change based on the SSA and MGF model[J]. GEOGRAPHICAL RESEARCH, 2008, 27(2): 305-313.

参考文献

[1] Munk W. Ocean freshening, sea level rising.Science,2003,300:2041~2043.

[2] Cazenave A, Nerem R S.Present-day sea level change: Observation and causes.Reviews of Geophysics, 2004,42:1~20.

[3] Levitus S, Antonov J I,Boyer T P, et al.Warming of the world ocean. Science,2000 287,2225~2229.

[4] Clark J,Primus J A.Sea-level change resulting from future retreat of ice sheets: An effect of CO₂ warming of the climate.In:Tootey M J,Shennan I(eds.):Sea-level Change.New York Basil Blackwell,1987.333~356.

[5] Vaughan D G.How Does the Antarctic ice sheet affect sea level rise?Science,2005,308:1877~1878.

[6] Dowdeswell J A.The Greenland ice sheet and global sea-level rise.Science,2006,311:963~964.

[7] Van der Veen C J.Polar ice sheets and global sea levelto:How well can we predict the future?Global and Planetary Change, 2002, 32 (2-3):165~194.

[8] Miller L,Douglas B C.Mass and volume contributions to twentieth-century global sea level rise.Nature,2004, 428:406~409.

[9] Gregory J M,Oerlemans J.Simulated future sea level rise due to glacier melt based on regionally and seasonally resolved temperature changes. Nature, 1998,391:474~6.

[10] Huybrechts P, Wolde D. The dynamic response of the Greenland and Antarctic ice sheets to multiple-century climatic warming. Journal of Climate,1999,12:2169~2188.

[11] Raper S C B ,Braithwaite R J.Low sea level rise projections from mountain glaciers and icecaps under global warming.Nature, 2006,439(7074):311~313.

[12] 郑大伟,虞南华,上海地区海平面上升趋势的长期预测研究.中国科学院上海天文台年刊,1996,17:36~45.

[13] 赵明才,何嘉重.中国近海海平面的上升及其预测.测绘科学,1995,(1):32~33.

[14] 于道永.中国沿岸现代海平面变化及未来趋势分析.海洋预报,1996,13(2),43~49.

[15] 张锦文,王喜亭,王惠.未来中国沿海海平面上升趋势估计.测绘通报,2001,(4):4~5.

[16] 张锦文,杜碧兰.中国黄海沿岸潮差的显著增大趋势.海洋通报,2000,19(1):1~9

[17] 杜碧兰,刘法孔,张锦文.威胁中国沿海脆弱区的海平面上升及预测.见:杜碧兰主编.海平面上升对中国沿海主要脆弱区的影响及对策.北京. 海洋出版社,1997.

[18] 张锦文.中国沿海海平面的上升预测模型.海洋通报,1997,16(4):1~9.

[19] 郑文振.我国海平面年速率的分布和长周期分潮的变化.海洋通报,1999,18(4):1~10.

[20] 郑文振.全球和我国近海验潮站及任意地点(区)的21世纪海平面预测,海洋通报,1996,15(6):1~7.

[21] 左军成,陈宗镛,周天华.中国沿岸相对海面变化的本征分析和预测.海洋与湖沼,1995,26(3):331~337.

[22] 羊天柱,应仁方,张俊彪,等,浙江沿岸海平面研究和变化预测.东海海洋,1999,17 (4):1~11.

[23] 林继华,刘序俨,郭逢英,等,福建沿海的海面变化及其影响.地壳形变与地震,1997,17(4):26~31.

[24] 蒋国俊,王宗涛.浙江海面变化的灰色模型分析与预测.海洋科学,1994,(3):61~65.

[25] 秦曾灏,李永平.上海海平面变化规律及其长期预测方法的初探.海洋学报, 1997, 19(1):1~7.

[26] 田晖,周天华,陈宗镛.平均海平面变化的一种随机动态预测模型.青岛海洋大学学报,1993,23(1):33~42.

[27] 吴中鼎,李占桥,赵明才.中国近海近 50年海平面变化速度及预测.海洋测绘,2003,23(2):17~19.

[28] 马继瑞,田素珍.太平洋水位站相对海平面升降趋势分析.海洋学报,1996,18(5):14~20.

[29] 陈宗镛.中国近海平均海面变化.中国科学院院士咨询报告(总第1号).北京:科学出版社,1994.40~44.

[30] 田素珍,马继瑞.海平面上升趋势数据的诊断及中国沿海海平面的上升趋势.海洋通报,1995,14(6):13~18.

[31] Spencer N E,Woodworth P L.Data holdings of the permanent service of mean sea level. PSMSL Data Holdings, 1993,5:1~81.

[32] Cao Hongxing,Wei Fengying,Wang Yongzhong. Time series model of long-range prediction and its application. Acta Met.Sic.,1990,4(1):120~127.

[33] 刘长生,曹鸿兴,魏凤英.隐含趋势气候序列的均生函数建模.南京气象学院学报,1994,17(3):297~302.

[34] 唐启义,冯明光.DPS数据处理系统——实验设计、统计分析及模型优化.北京:科学出版社,2006.880~882.

[35] Ghil M, Allen M R, Dettinger M D, et al, Advanced spectral methods for climatic time series.Rev. Geophys. ,2002,40(1):3.1~3.41.

[36] Broomhead D S, King G P.Extracting qualitative dynamics from experimental data. Phys.D,1986, 20:217~236.

[37] Solomon S, Qin D, Manning M, et al.(eds.).IPCC, Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). Cambridge, UK and New York, NY, USA:Cambridge University Press, 2007, 996.

[38] 谢志仁,中国海平面变化的预估.见:秦大河主编.中国气候与环境演变.上卷:气候与环境的演变及预测.北京:科学出版社,2005.536~542.

[39] Cabanes C, Cazenave A, LeProvost C. Sea level rise during past 40 years determined from satellite and in situ observations. Science,2001,294:840~842.

[40] Church J A.How fast are sea levels rising?Science,2001, 294:802~803.

基于SSA和MGF的海面变化长期预测及对比

Long-term prediction and comparison of sea-level change based on the SSA and MGF model

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	黄金川, 林浩曦. 京津冀城市群多情景空间演化模拟[J]. 地理研究, 2017, 36(3): 506-517.
[2]	刘璇, 张永战, 夏非, 王嵘, 任珊, WÜNNEMANN Bernd. 北海南部Lunden湾全新世沉积环境演变[J]. 地理研究, 2017, 36(11): 2261-2276.
[3]	向秀容, 潘韬, 吴绍洪, 刘卫东, 马丽, 王晓峰, 尹云鹤, 李静. 基于生态足迹的天山北坡经济带生态承载力评价与预测[J]. 地理研究, 2016, 35(5): 875-884.
[4]	孙鹏, 张强, 白云岗, 张江辉, 邓晓宇, 刘剑宇. 基于马尔科夫模型的新疆水文气象干旱研究[J]. 地理研究, 2014, 33(9): 1647-1657.
[5]	宋马林, 徐龙, 王舒鸿, 张琳玲. 小样本数据下安徽省经济—环境系统协调发展的实证分析[J]. 地理研究, 2013, 32(8): 1402-1410.
[6]	吴洪颜, 高苹, 刘梅. 基于太平洋海温的冬小麦春季湿渍害预测模型[J]. 地理研究, 2013, 32(8): 1421-1429.
[7]	刘云刚, 王丰龙. 快速城市化过程中的城市建设用地规模预测方法[J]. 地理研究, 2011, 30(7): 1187-1197.
[8]	马林兵, 曹小曙, 牟少杰. 一种融合地理空间指标的土地需求量预测方法——以佛山市南海区为例[J]. 地理研究, 2011, 30(5): 854-860.
[9]	方恺, 沈万斌, 董德明. 能源足迹核算的改进与预测——以吉林省为例[J]. 地理研究, 2011, 30(10): 1835-1846.
[10]	张文佳,柴彦威. 基于家庭的购物行为时、空间决策模型及其应用[J]. 地理研究, 2010, 29(2): 338-350.
[11]	彭令, 牛瑞卿, 陈丽霞. GIS支持下三峡库区秭归县滑坡灾害空间预测[J]. 地理研究, 2010, 29(10): 1889-1898.
[12]	俞肇元, 袁林旺, 闾国年, 谢志仁, 张季一, 梅伟长. 西北太平洋边缘海区海面变化多尺度解析及空间分异[J]. 地理研究, 2009, 28(6): 1644-1655.
[13]	张怀清, 唐晓旭, 刘锐, 周金星, 凌成星. 盐城湿地类型演化预测分析[J]. 地理研究, 2009, 28(6): 1713-1721.
[14]	李双成, 赵志强, 高阳, 王仰麟. 城市植被覆盖变化可预测性及其空间格局的定量递归分析——以深圳市为例[J]. 地理研究, 2008, 27(6): 1243-1252.
[15]	孙根年, 马丽君. 基于本底线的2008年北京奥运会客流量预测[J]. 地理研究, 2008, 27(1): 65-74.