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Acta Geographica Sinica >

2011 , Vol. 66 >Issue 1: 111 - 122

DOI: https://doi.org/10.11821/xb201101011

The Prediction of Sea-level Change Based on the Tide Data in Northwest Pacific

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  • Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210046, China

Received date: 2010-08-09

  Revised date: 2010-10-08

  Online published: 2011-01-25

Supported by

Key Project of National Natural Science Foundation of China, No.40730527; National High Technology R&D Program of China, No.2009AA12Z205

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Abstract

Due to the impacts of the inconsistent data timespan, the heterogeneous spatial distribution, and the complexity and uncertainty of sea level change etc., it is difficult to conduct regional sea-level change researches.. Based on the 22 tidal gauge station records in Northwest Pacific ocean marginal sea, the regularized EM algorithm (RegEM) and the Multi-Taper spectral Method (MTM) are adopted to discuss their multi-scale fluctuation processes and their spatial-temporal variations. Integrating orderly cluster and the space adjacent relation of gauge stations, the whole research area is generally divided into five sub-regions, there are Bohai Sea-the coast of the northern Yellow Sea, Yellow-East China Sea near the coast of China, the East China Sea near the coast of Japan, the southern East China Sea and the northwestern South China Sea. Then Mean Generation Function (MGF) is used to predict the medium and long-term trends of each tide station, and Principal Component Analysis (PCA) is employed to obtain regional-scale sea level change trends. The result shows that the sea-level rise rates of five sub-regions from 2001 to 2030 are 1.23-1.27 mm/a, 3.30-3.34 mm/a, 2.72-2.76 mm/a, 1.43-1.47 mm/a and 1.13-1.15 mm/a respectively, and the whole region sea-level rise rate is changed between 2.01 mm/a and 2.11 mm/a. By introducing spatial-temporal variation and multi-scale pedigree characteristic analysis, our work is an attempt of the integrated research on sea-level change characteristic and its prediction.

Key words： sea-level prediction; temporal-spatial variation; RegEM; MGF; MTM; Northwest Pacific

Cite this article

LUOWen, YUAN Linwang, YI Lin, YU Zhaoyuan, XIE Zhiren . The Prediction of Sea-level Change Based on the Tide Data in Northwest Pacific[J]. Acta Geographica Sinica, 2011 , 66(1) : 111 -122 . DOI: 10.11821/xb201101011

References

[1] Wu Zhongding, Li Zhanqiao, Zhao Mingcai. The process and prediction of sea level change of China offshore waters in 50years. Hydrographic Surveying and Charting, 2003, 23(2): 17-19. [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.]



[2] Zhang Jinwen. Estimate model of MSL change along the coast of China. Marine Science Bulletin, 1997, 16(4): 3-11. [张锦文. 中国沿海海平面的上升预测模型. 海洋通报. 1997, 16(4): 3-11.]



[3] Li Yongping, Qin Zenghao, Duan Yihong. Research and prediction of mean sea level of Shanghai area. Acta GeographicaSinica, 1998, 53(5): 393-403. [李永平, 秦曾灏, 端义宏. 上海地区海平面上升趋势的预测和研究. 地理学报, 1998, 53(5): 393-403.]



[4] Solomon S, Qin D, Manning M et al. Intergovernmental Panel on Climate Change (IPCC)//Climate Change 2007: ThePhysical Science Basis. Cambridge: Intergovernmental Panel on Climate Change, 2007: 1-18.



[5] Raper S C B, Braithwaite R J. Low sea level rise projections from mountain glaciers and ice caps under global warming.Nature, 2006, 439: 311-213.



[6] Rahmstorf S. A semi-empirical approach to projecting future sea-level rise. Science, 2007, 315: 368-370.



[7] Knutti R, Stocker T F. Influence of the thermohaline circulation on projected sea level rise. Journal of Climate, 2000, 13:1997-2001.



[8] Collins M, Allen M R. Assessing the relative roles of initial and boundary conditions in interannual to decadal climatepredictability. Journal of Climate, 2002, 15: 3104-3109.



[9] Rahmstorf S, Cazenave A, Church J A. Recent climate observations compared to projections. Science, 2007, 316: 709.



[10] Rahmstorf S. Response to comments on "A semi-empirical approach to projecting future sea-level Rise". Science, 2007,317: 1866.



[11] Grinsted A, Moore J C, Jevrejeva S. Reconstructing sea level from paleo and projected temperatures 2000 to 2100AD.Climate Dynamics, 2009, 34(4): 461-472.



[12] Douglas B C. Global sea rise: A redetermination. Surveys in Geophysics, 1997, 18(2/3): 279-292.



[13] Jevrejeva S, Grinsted A, Moore J C et al. Nonlinear trends and multiyear cycles in sea level records. Journal ofGeophysical Research, 2006, 111: C09012.



[14] Siddall M, Stocker T F, Clark, P U. Constraints on future sea-level rise from past sea-level change. Nature Geoscience,2009, 2(8): 571-575.



[15] Chambers D P, Mehlhaff C A, Urban T J et al. Low-frequency variations in global mean sea level: 1950-2000. Journal ofGeophysical Research, 2002, 107(C4): 3026.



[16] Church J A, White N J. Estimates of the regional distribution of sea level rise over the 1950-2000 period. Journal ofClimate, 2004, 17: 2609-2625.



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



[18] Mann M E, Bradley R S, Hughes M K. Global-scale temperature patterns and climate forcing over the past six centuries.Nature, 1998, 392: 779-787.



[19] Moberg A, Sonechkin D, Holmgren K et al. Highly variable Northern Hemisphere temperatures reconstructed from lowandhigh-resolution proxy data. Nature, 2005, 433: 613-617.



[20] Schneider T. Analysis of incomplete climate data: Estimation of mean values and covariance matrices and imputation ofmissing values. Journal of Climate, 2001, 14: 853-871.



[21] Mann M E, Zhang Z, Rutherford S et al. Global signatures and dynamical origins of the Little Ice Age and medievalclimate anomaly. Science, 2009, 326: 1256-1260.



[22] Thomson D J. Spectrum estimation and harmonic analysis. Proceeding of the IEEE, 1982, 70(9): 1055-1096.



[23] Lanzerotti L J, Thomson D J, Medford L V et al. Electromagnetic study of the Atlantic continental margin using a sectionof a transatlantic cable. Journal of Geophysical Research, 1986, 91: 7417-7428.



[24] Riedel K S, Sidorenko A. Smoothed multiple taper spectral analysis and adaptive implementation. IEEE Transactions onSignal Processing, 1994, 44: 464-467.



[25] Percival D B, Walden A T. Spectral Analysis for Physical Applications: Multitaper and Conventional UnivariateTechniques. Cambridge: Cambridge University Press, 1993.



[26] Luo Dejiang, Wang Xingwang, Liu Wei. Automatic separate layer model based on the phase-space reconstruction andimproved optimum division method. Progress in Geophysics, 2009, 24(2): 675-679. [罗德江, 汪兴旺, 刘伟. 基于相空间重构和改进最优分割法的自动分层模型. 地球物理学进展, 2009, 24(2): 675-679.]



[27] Zhou Jin, Pan Yuqi, Zhang Ping. Application on solving classified problem of vector by enhanced fisher analysis.Computer Engineering and Design, 2006, 27(4): 719-721. [周劲, 潘玉奇, 张平. 扩展的最优分割法在有序样本分类中的应用. 计算机工程与设计, 2006, 27(4): 719-721.]



[28] Cao Hongxing, Wei Fengying, Wang Yongzhong. Time series model of long-range prediction and its applications. ActaMet. Sic., 1990, 4(1): 120-127.



[29] Cao Hongxing, Wei Fengying. A method of time series analysis based on a mean generating function. Journal onNumerical Methods and Computer Applications，1991, 12(2): 82-89. [曹鸿兴, 魏风英. 基于均值生成函数的时间序列分析. 数值计算与计算机应用, 1991, 12(2): 82-89.]



[30] Tang Qiyi, Feng Mingguang. DPS Data Processing System: Experimental Design, Statistical Analysis and ModelOptimization. Beijing: Science Press, 2006: 880-882. [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.]



[31] Yuan Linwang, Xie Zhiren, Yu Zhaoyuan. Long-term prediction and comparison of sea-level change based on the SSA andMGF model. Geographical Research, 2008, 27(2): 305-313. [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. 地理研究, 2008, 27(2): 305-313.]



[32] Huang N E, Shen Z, Long S R et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear andnon-stationary series analysis. Proceedings Series A: Matehematical, Physical and Engineeering Sciences, 1998, 454:903-995.



[33] Wu Z, Huang N E, Long S R et al. On the trend, detrending, and variability of nonlinear and nonstationary time series.Proceedings of the National Academy of Sciences, 2007, 104(38): 14889-14894.



[34] Kang S K, Chemiawsky J Y, Foreman M G et al. Spatial variability in annual sea level variations around the Koreanpeninsula. Geophysical Research Letters, 2008, 35: L03603.



[35] Woodworth P L, White N J, Jevrejeva S et al. Evidence for the accelerations of sea level on multi-decade and centurytimescales. International Journal of Climatology, 2009, 29: 777-789.



[36] Prandi P, Cazenave A, Becker M. Is coastal mean sea level rising faster than the global mean? A comparison between tidegauges and satellite altimetry over 1993-2007. Geophysical Research Letters, 2009, 36: L05602.



[37] Ho C B, Hoon K D Jin C Y et al. Projections of ocean climate for northwestern Pacific Ocean. Acta Oceanologica Sinica,2005, 24(1): 134-145.



[38] Morner N, Estimating future sea level changes from past records. Global and Planetary Change, 2004, 40 (1/2): 49-54.



[39] Morner N. Comment on comment by Nerem et al. (2007) on“Estimating future sea level changes from past records”byNils-Axel M?rner (2004). Global and Planetary Change, 2008, 62(3/4): 219-220.



[40] Milly P C D, Cazenave A, Gennero C. Contribution of climate-driven change in continental water storage to recentsea-level rise. PNAS, 2003, 100(23): 13158-13161.



[41] Miller L, Douglas B C. Mass and volume contributions to twentieth-century global sea level rise. Nature, 2004, 428(6981): 406-409.
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