Spatiotemporal variations of extreme sea levels around the South China Sea: assessing the influence of tropical cyclones, monsoons and major climate modes

Abstract

With sea levels projected to rise as a result of climate change, it is imperative to understand not only long-term average trends, but also the spatial and temporal patterns of extreme sea level. In this study, we use a comprehensive set of 30 tide gauges spanning 1954–2014 to characterize the spatial and temporal variations of extreme sea level around the low-lying and densely populated margins of the South China Sea. We also explore the long-term evolution of extreme sea level by applying a dynamic linear model for the generalized extreme value distribution (DLM-GEV), which can be used for assessing the changes in extreme sea levels with time. Our results show that the sea-level maxima distributions range from ~ 90 to 400 cm and occur seasonally across the South China Sea. In general, the sea-level maxima at northern tide gauges are approximately 25–30% higher than those in the south and are highest in summer as tropical cyclone-induced surges dominate the northern signal. In contrast, the smaller signal in the south is dominated by monsoonal winds in the winter. The trends of extreme high percentiles of sea-level values are broadly consistent with the changes in mean sea level. The DLM-GEV model characterizes the interannual variability of extreme sea level, and hence, the 50-year return levels at most tide gauges. We find small but statistically significant correlations between extreme sea level and both the Pacific Decadal Oscillation and El Niño/Southern Oscillation. Our study provides new insight into the dynamic relationships between extreme sea level, mean sea level and the tidal cycle in the South China Sea, which can contribute to preparing for coastal risks at multi-decadal timescales.

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References

  1. Amiruddin AM (2017) Sea-level changes over the last six decades in the South China Sea. University of Southampton

  2. Amiruddin AM, Haigh ID, Tsimplis MN, Calafat FM, Dangendorf S (2015) The seasonal cycle and variability of sea level in the South China Sea. J Geophys Res Oceans 120:5490–5513. https://doi.org/10.1002/2015JC010923

    Article  Google Scholar 

  3. Chen H, Tkalich P, Malanotte-Rizzoli P, Wei J (2012) The forced and free response of the South China Sea to the large-scale monsoon system. Ocean Dyn 62:377–393. https://doi.org/10.1007/s10236-011-0511-7

    Article  Google Scholar 

  4. Church JA, White NJ (2006) A 20th century acceleration in global sea-level rise. Geophys Res Lett. https://doi.org/10.1029/2005gl024826

    Article  Google Scholar 

  5. Church JA, White NJ (2011) Sea-level rise from the late 19th to the early 21st century. Surv Geophys 32:585–602. https://doi.org/10.1007/s10712-011-9119-1

    Article  Google Scholar 

  6. Codiga DL (2011) Unified tidal analysis and prediction using the UTide Matlab functions. Graduate School of Oceanography, University of Rhode Island, Narragansett, RI. ftp://www.po.gso.uri.edu/pub/downloads/codiga/pubs/2011Codiga-UTide-Report.pdf

  7. Coles S (2001) An introduction to statistical modeling of extreme values. Springer series in statistics. Springer, London

    Google Scholar 

  8. Dangendorf S, Müller-Navarra S, Jensen J, Schenk F, Wahl T, Weisse R (2014) North sea storminess from a novel storm surge record since AD 1843. J Clim 27:3582–3595. https://doi.org/10.1175/JCLI-D-13-00427.1

    Article  Google Scholar 

  9. de Vries H et al (1995) A comparison of 2D storm surge models applied to three shallow European seas. Environ Softw 10:23–42. https://doi.org/10.1016/0266-9838(95)00003-4

    Article  Google Scholar 

  10. Devlin AT, Jay DA, Talke SA, Zaron ED, Pan J, Lin H (2017) Coupling of sea level and tidal range changes, with implications for future water levels. Sci Rep 7:17021. https://doi.org/10.1038/s41598-017-17056-z

    Article  Google Scholar 

  11. Ding X, Zheng D, Chen Y, Chao J, Li Z (2001) Sea level change in Hong Kong from tide gauge measurements of 1954–1999. J Geod 74:683–689. https://doi.org/10.1007/s001900000128

    Article  Google Scholar 

  12. Durbin J, Koopman SJ (2012) Time series analysis by state space methods. Oxford statistical science series: 38, 2nd edn. Oxford University Press, Oxford

    Google Scholar 

  13. Durbin J, Watson GS (1950) Testing for serial correlation in least squares regression: I. Biometrika 37:409–428. https://doi.org/10.2307/2332391

    Article  Google Scholar 

  14. Durbin J, Watson GS (1951) testing for serial correlation in least squares regression. II. Biometrika 38:159–177. https://doi.org/10.2307/2332325

    Article  Google Scholar 

  15. Durbin J, Watson GS (1971) Testing for serial correlation in least squares regression. III. Biometrika 58:1–19. https://doi.org/10.1093/biomet/58.1.1

    Article  Google Scholar 

  16. Fang G, Chen H, Wei Z, Wang Y, Wang X, Li C (2006) Trends and interannual variability of the South China Sea surface winds, surface height, and surface temperature in the recent decade. J Geophys Res Oceans. https://doi.org/10.1029/2005jc003276

    Article  Google Scholar 

  17. Feng X, Tsimplis MN (2014) Sea level extremes at the coasts of China. J Geophys Res Oceans 119:1593–1608. https://doi.org/10.1002/2013JC009607

    Article  Google Scholar 

  18. Firing YL, Merrifield MA (2004) Extreme sea level events at Hawaii: influence of mesoscale eddies. Geophys Res Lett. https://doi.org/10.1029/2004gl021539

    Article  Google Scholar 

  19. Gönnert G, Dube SK, Murty T, Siefert W (2001) Global storm surges: theory, observations and applications. German Coastal Engineering Council, vol 63, 623 pp

  20. Haigh ID, Eliot M, Pattiaratchi C (2011) Global influences of the 18.61 year nodal cycle and 8.85 year cycle of lunar perigee on high tidal levels. J Geophys Res. https://doi.org/10.1029/2010jc006645

    Article  Google Scholar 

  21. Haigh ID, MacPherson LR, Mason MS, Wijeratne EMS, Pattiaratchi CB, Crompton RP, George S (2014a) Estimating present day extreme water level exceedance probabilities around the coastline of Australia: tropical cyclone-induced storm surges. Clim Dyn 42:139–157. https://doi.org/10.1007/s00382-012-1653-0

    Article  Google Scholar 

  22. Haigh ID, Wijeratne EMS, MacPherson LR, Pattiaratchi CB, Mason MS, Crompton RP, George S (2014b) Estimating present day extreme water level exceedance probabilities around the coastline of Australia: tides, extra-tropical storm surges and mean sea level. Clim Dyn 42:121–138. https://doi.org/10.1007/s00382-012-1652-1

    Article  Google Scholar 

  23. Haigh ID et al (2015) A user-friendly database of coastal flooding in the United Kingdom from 1915–2014. Sci Data 2:150021. https://doi.org/10.1038/sdata.2015.21

    Article  Google Scholar 

  24. Han G, Huang W (2008) Pacific decadal oscillation and sea level variability in the Bohai, Yellow, and East China Seas. J Phys Oceanogr 38:2772–2783. https://doi.org/10.1175/2008JPO3885.1

    Article  Google Scholar 

  25. Hong Kong Royal Observatory (1980) Meteorological results 1980. Part III—tropical cyclone summaries. Royal Observatory, Hong Kong. http://www.hko.gov.hk/publica/tc/tc1980.pdf

  26. Horsburgh KJ, Wilson C (2007) Tide–surge interaction and its role in the distribution of surge residuals in the North Sea. J Geophys Res Oceans. https://doi.org/10.1029/2006jc004033

    Article  Google Scholar 

  27. Hu Z, Tan Y, Song X, Zhou L, Lian X, Huang L, He Y (2014) Influence of mesoscale eddies on primary production in the South China Sea during spring inter-monsoon period. Acta Oceanol Sin 33:118–128. https://doi.org/10.1007/s13131-014-0431-8

    Article  Google Scholar 

  28. Huang NE et al (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A Math Phys Eng Sci 454:903–995. https://doi.org/10.1098/rspa.1998.0193

    Article  Google Scholar 

  29. Huerta G, Sansó B (2007) Time-varying models for extreme values. Environ Ecol Stat 14:285–299. https://doi.org/10.1007/s10651-007-0014-3

    Article  Google Scholar 

  30. Huerta G, Stark GA (2013) Dynamic and spatial modelling of block maxima extremes. Oxford University Press, Oxford. https://doi.org/10.1093/acprof:oso/9780199695607.001.0001

    Google Scholar 

  31. Intergovernmental Panel on Climate Change (2014) Climate change 2013—the physical science basis: working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge. https://doi.org/10.1017/cbo9781107415324

    Google Scholar 

  32. Jevrejeva S, Grinsted A, Moore JC, Holgate S (2006) Nonlinear trends and multiyear cycles in sea level records. J Geophys Res 111:C09012. https://doi.org/10.1029/2005JC003229

    Article  Google Scholar 

  33. Joint Typhoon Warning Center (2018) Western North Pacific Ocean Best Track Data. http://www.metoc.navy.mil/jtwc/jtwc.html?western-pacific

  34. Lee HS (2013) Estimation of extreme sea levels along the Bangladesh coast due to storm surge and sea level rise using EEMD and EVA. J Geophys Res Oceans 118:4273–4285. https://doi.org/10.1002/jgrc.20310

    Article  Google Scholar 

  35. Leffler KE, Jay DA (2009) Enhancing tidal harmonic analysis: robust (hybrid) solutions. Cont Shelf Res 29:78–88. https://doi.org/10.1016/j.csr.2008.04.011

    Article  Google Scholar 

  36. Li J, Zeng Q (2002) A unified monsoon index. Geophys Res Lett 29:115-111–115-114. https://doi.org/10.1029/2001gl013874

    Article  Google Scholar 

  37. Lowe JA et al (2010) Past and future changes in extreme sea levels and waves. In: Understanding sea-level rise and variability. Wiley-Blackwell, pp 326–375. https://doi.org/10.1002/9781444323276.ch11

    Google Scholar 

  38. Lunn D, Spiegelhalter D, Thomas A, Best N (2009) The BUGS project: evolution, critique and future directions. Stat Med 28:3049–3067. https://doi.org/10.1002/sim.3680

    Article  Google Scholar 

  39. Luu QH, Tkalich P, Tay TW (2015) Sea level trend and variability around Peninsular Malaysia. Ocean Sci 11:617–628. https://doi.org/10.5194/os-11-617-2015

    Article  Google Scholar 

  40. Mantua NJ, Hare SR, Zhang Y, Wallace JM, Francis RC (1997) A Pacific interdecadal climate oscillation with impacts on salmon production. Bull Am Meteorol Soc 78:1069–1080

    Article  Google Scholar 

  41. Marcos M, Tsimplis MN, Shaw AGP (2009) Sea level extremes in southern Europe. J Geophys Res Oceans. https://doi.org/10.1029/2008jc004912

    Article  Google Scholar 

  42. Marcos M, Calafat FM, Berihuete Á, Dangendorf S (2015) Long-term variations in global sea level extremes. J Geophys Res Oceans 120:8115–8134. https://doi.org/10.1002/2015JC011173

    Article  Google Scholar 

  43. Mawdsley R, Haigh ID (2016) Spatial and temporal variability and long-term trends in skew surges globally. Front Mar Sci 3:29. https://doi.org/10.3389/fmars.2016.00029

    Article  Google Scholar 

  44. Mawdsley RJ, Haigh ID, Wells NC (2015) Global secular changes in different tidal high water, low water and range levels. Earth’s Fut 3:66–81. https://doi.org/10.1002/2014ef000282

    Article  Google Scholar 

  45. Menéndez M, Woodworth PL (2010) Changes in extreme high water levels based on a quasi-global tide-gauge data set. J Geophys Res 115:C10011. https://doi.org/10.1029/2009JC005997

    Article  Google Scholar 

  46. Neumann B, Vafeidis AT, Zimmermann J, Nicholls RJ (2015) Future coastal population growth and exposure to sea-level rise and coastal flooding—a global assessment. PLoS ONE 10:1–34. https://doi.org/10.1371/journal.pone.0118571

    Article  Google Scholar 

  47. Newey WK, West KD (1987) A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica 55:703–708. https://doi.org/10.2307/1913610

    Article  Google Scholar 

  48. Ozsoy O, Haigh ID, Wadey MP, Nicholls RJ, Wells NC (2016) High-frequency sea level variations and implications for coastal flooding: a case study of the Solent. UK Cont Shelf Res 122:1–13. https://doi.org/10.1016/j.csr.2016.03.021

    Article  Google Scholar 

  49. Pawlowicz R, Beardsley B, Lentz S (2002) Classical tidal harmonic analysis including error estimates in MATLAB using T_TIDE. Comput Geosci 28:929–937. https://doi.org/10.1016/S0098-3004(02)00013-4

    Article  Google Scholar 

  50. Pugh D, Woodworth PL (2014) Sea-level science: understanding tides, surges, tsunami and mean sea-level changes. Cambridge University Press, New York

    Google Scholar 

  51. Rong Z, Liu Y, Zong H, Cheng Y (2007) Interannual sea level variability in the South China Sea and its response to ENSO. Glob Planet Change 55:257–272. https://doi.org/10.1016/j.gloplacha.2006.08.001

    Article  Google Scholar 

  52. Saji NH, Goswami BN, Vinayachandran PN, Yamagata T (1999) A dipole mode in the tropical Indian Ocean. Nature 401:360–363

    Google Scholar 

  53. Slangen ABA, Adloff F, Jevrejeva S, Leclercq PW, Marzeion B, Wada Y, Winkelmann R (2016) A review of recent updates of sea-level projections at global and regional scales. Surv Geophys. https://doi.org/10.1007/s10712-016-9374-2

    Article  Google Scholar 

  54. Soumya M, Vethamony P, Tkalich P (2015) Inter-annual sea level variability in the southern South China Sea. Glob Planet Change 133:17–26. https://doi.org/10.1016/j.gloplacha.2015.07.003

    Article  Google Scholar 

  55. Sturtz S, Ligges U, Gelman A (2005) R2WinBUGS: a package for running WinBUGS from R. J Stat Softw 12:1–16

    Article  Google Scholar 

  56. Talke SA, Orton P, Jay DA (2014) Increasing storm tides in New York Harbor, 1844–2013. Geophys Res Lett 41:3149–3155. https://doi.org/10.1002/2014gl059574

    Article  Google Scholar 

  57. Tan CK, Ishizaka J, Matsumura S, Yusoff FM, Mohamed MIH (2006) Seasonal variability of SeaWiFS chlorophyll a in the Malacca Straits in relation to Asian monsoon. Cont Shelf Res 26:168–178. https://doi.org/10.1016/j.csr.2005.09.008

    Article  Google Scholar 

  58. Tkalich P, Vethamony P, Babu MT, Malanotte-Rizzoli P (2013) Storm surges in the Singapore Strait due to winds in the South China Sea. Nat Hazards 66:1345–1362. https://doi.org/10.1007/s11069-012-0211-8

    Article  Google Scholar 

  59. Torres RR, Tsimplis MN (2014) Sea level extremes in the Caribbean Sea. J Geophys Res Oceans 119:4714–4731. https://doi.org/10.1002/2014JC009929

    Article  Google Scholar 

  60. Tsimplis MN, Woodworth PL (1994) The global distribution of the seasonal sea level cycle calculated from coastal tide gauge data. J Geophys Res 99:16031–16039. https://doi.org/10.1029/94JC01115

    Article  Google Scholar 

  61. Wahl T, Chambers DP (2015) Evidence for multidecadal variability in US extreme sea level records. J Geophys Res Oceans 120:1527–1544. https://doi.org/10.1002/2014JC010443

    Article  Google Scholar 

  62. Wahl T, Chambers DP (2016) Climate controls multidecadal variability in U.S. extreme sea level records. J Geophys Res Oceans 121:1274–1290. https://doi.org/10.1002/2015jc011057

    Article  Google Scholar 

  63. Wang L, Chen W (2013) An intensity index for the east Asian winter monsoon. J Climate 27:2361–2374. https://doi.org/10.1175/JCLI-D-13-00086.1

    Article  Google Scholar 

  64. Wang G, Su J, Ding Y, Chen D (2007) Tropical cyclone genesis over the south China sea. J Mar Syst 68:318–326. https://doi.org/10.1016/j.jmarsys.2006.12.002

    Article  Google Scholar 

  65. Wang B, Huang F, Wu Z, Yang J, Fu X, Kikuchi K (2009) Multi-scale climate variability of the South China Sea monsoon: a review. Dyn Atmos Oceans 47:15–37. https://doi.org/10.1016/j.dynatmoce.2008.09.004

    Article  Google Scholar 

  66. West M, Harrison J (1997) Bayesian forecasting and dynamic models, 2nd edn. Springer series in statistics. Springer, New York. https://doi.org/10.1007/b98971

    Google Scholar 

  67. White NJ et al (2014) Australian sea levels-Trends, regional variability and influencing factors. Earth Sci Rev 136:155–174. https://doi.org/10.1016/j.earscirev.2014.05.011

    Article  Google Scholar 

  68. Wolter K, Timlin MS (1993) Monitoring ENSO in COADS with a seasonally adjusted principal component index. Paper presented at the 17th climate diagnostics workshop, Norman, OK

  69. Wolter K, Timlin MS (1998) Measuring the strength of ENSO events: how does 1997/98 rank? Weather 53:315–324. https://doi.org/10.1002/j.1477-8696.1998.tb06408.x

    Article  Google Scholar 

  70. Woodworth PL, Blackman DL (2004) Evidence for systematic changes in extreme high waters since the mid-1970s. J Climate 17:1190–1197

    Article  Google Scholar 

  71. Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 01:1–41. https://doi.org/10.1142/S1793536909000047

    Article  Google Scholar 

  72. Wyrtki K (1961) Physical oceanography of the Southeast Asian waters. Scripps Institution of Oceanography, UC San Diego. https://escholarship.org/uc/item/49n9x3t4

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Acknowledgements

This research was supported by grants from National Research Foundation Singapore (National Research Fellow Award No. NRF-RF2010-04) and the Singapore Ministry of Education under the Research Centres of Excellence initiative Academic Research Fund (AcRF) Complexity Tier 1 Project RGC4/14 “Preparing Asian mega cities for changing climate and the potential Increase in extreme sea levels and storm surges”. E. Hill was supported by NRF Award No. NRF-NRFF2010-064. H. Nguyen was supported by QGTD 13.09/2014 project (Vietnam National University). We acknowledge the University of Hawaii Sea Level Center (UHSLC) for hourly tide-gauge data at most sites; Hoang Trung Thanh (Vietnam Marine Hydrometeorological Centre) for providing hourly data for Hon Dau, Da Nang, Quy Nhon and Vung Tau tide gauges. Support for the Twentieth Century Reanalysis Project data set is provided by the US Department of Energy, Office of Science Innovative and Novel Computational Impact on Theory and Experiment (DOE INCITE) program, and Office of Biological and Environmental Research (BER), and by the National Oceanic and Atmospheric Administration Climate Program Office. We appreciate Francisco M. Calafat for sharing the training data set so that we could test our model; Wang Lin for his East Asian Winter Monsoon index data; Xiangbo Feng, Marcos Marta and Robert Mawdsley for discussions on methods and data processing. We would like to express our thanks to Pavel Adamek and Constance Chua for their linguistic advice that significantly improved this manuscript. This work comprises Earth Observatory of Singapore contribution 152.

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Pham, D.T., Switzer, A.D., Huerta, G. et al. Spatiotemporal variations of extreme sea levels around the South China Sea: assessing the influence of tropical cyclones, monsoons and major climate modes. Nat Hazards 98, 969–1001 (2019). https://doi.org/10.1007/s11069-019-03596-2

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Keywords

  • Extreme sea level
  • Tide gauge
  • South China Sea
  • Tropical cyclones
  • Monsoons
  • Dynamic linear model