Observed Spatiotemporal Variability in the Annual Sea Level Cycle Along the Global Coast

被引:4
作者
Barroso, A. [1 ,2 ]
Wahl, T. [1 ,2 ]
Li, S. [3 ]
Enriquez, A. [1 ,2 ,4 ]
Morim, J. [1 ,2 ]
Dangendorf, S. [5 ]
Piecuch, C. [6 ]
Thompson, P. [7 ]
机构
[1] Univ Cent Florida, Dept Civil Environm & Construct Engn, Orlando, FL 32816 USA
[2] Univ Cent Florida, Natl Ctr Integrated Coastal Res, Orlando, FL 32816 USA
[3] Chinese Acad Sci, Innovat Acad Precis Measurement Sci & Technol, State Key Lab Geodesy & Earths Dynam, Wuhan, Peoples R China
[4] Vrije Univ Amsterdam, Inst Environm Studies IVM, Amsterdam, Netherlands
[5] Tulane Univ, Dept River Coastal Sci & Engn, New Orleans, LA USA
[6] Woods Hole Oceanog Inst, Dept Phys Oceanog, Woods Hole, MA USA
[7] Univ Hawaii, Dept Oceanog, Honolulu, HI USA
关键词
mean sea level; seasonal cycle; clustering; seasonality; CHANGING SEASONALITY; BALTIC SEA; OSCILLATION; PACIFIC; TRENDS;
D O I
10.1029/2023JC020300
中图分类号
P7 [海洋学];
学科分类号
0707 ;
摘要
Changes in the seasonal sea level cycle can modulate the flooding risk along coastlines. Here, we use harmonic analysis to quantify changes in the amplitude and phase of the annual component of the sea level cycle at 798 tide gauge locations along the global coastline where long records are available. We identify coastal hotspots by applying clustering methods revealing coherent regions with similar patterns of variability in the annual sea level cycle. Results show that for most tide gauges the annual amplitude reached its maximum after 1970 and its peak typically occurs during the fall season of the respective hemisphere. Many tide gauges exhibit non-stationarity in the annual cycle in terms of amplitude and/or phase. For example, at 226 tide gauges we find significant trends in the amplitude (either increasing or decreasing) for the time period after 1970; while several sites (50 in total), mostly in the Mediterranean and around Pacific islands, experienced phase changes leading to shifts in the timing of the peak of the annual cycle by more than a month over their entire record. Our results highlight the importance of accounting for potential non-stationarity in seasonal mean sea level cycles along coastlines. The seasonal sea level cycle is the pattern of high and low mean sea level (MSL) observed throughout the year at a location. Shifts in the timing of the maximum sea level impact coastlines with increased risk of flooding if the higher MSL occurs at a time of year when factors like storms or high tides are also at their peak. The seasonal cycle consists of semi-annual and annual components; here we focus on the dominating annual sea level cycle (ASLC) after decomposing the MSL at 798 locations into amplitude and phase components which describe the variability and timing of the MSL yearly peak, respectively. We find that MSL amplitude typically reaches its maximum during the fall in each hemisphere. Coastal hotspots in the North Atlantic have been identified with coherent regions showing similar patterns in their ASLC variability. We find 226 locations with significant trends in amplitude and 50 locations where the yearly MSL peak has changed by more than a month throughout the record. Additionally, we find relationships between the MSL annual amplitudes and climate indices characterizing regional-scale climate phenomena. Our results provide a comprehensive description of changes in the ASLC along portions of the global coastline. The annual sea level cycle varied in amplitude and phase since the mid-20th century at many tide gauge locations There are well-defined regional clusters of tide gauges where the annual sea level cycle exhibits coherent variability Changes in the annual sea level cycle are linked to dominant modes of climate variability such as: AMO/NAO (Baltic), and ENSO/PDO (Pacific)
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页数:16
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共 51 条
  • [1] Time-series clustering - A decade review
    Aghabozorgi, Saeed
    Shirkhorshidi, Ali Seyed
    Teh Ying Wah
    [J]. INFORMATION SYSTEMS, 2015, 53 : 16 - 38
  • [2] The seasonal cycle and variability of sea level in the South China Sea
    Amiruddin, A. M.
    Haigh, I. D.
    Tsimplis, M. N.
    Calafat, F. M.
    Dangendorf, S.
    [J]. JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2015, 120 (08) : 5490 - 5513
  • [3] Changing seasonality in North Atlantic coastal sea level from the analysis of long tide gauge records
    Barbosa, S. M.
    Silva, M. E.
    Fernandes, M. J.
    [J]. TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY, 2008, 60 (01) : 165 - 177
  • [4] Low-frequency sea-level change in Chesapeake Bay: Changing seasonality and long-term trends
    Barbosa, S. M.
    Silva, M. E.
    [J]. ESTUARINE COASTAL AND SHELF SCIENCE, 2009, 83 (01) : 30 - 38
  • [5] Barnard PL, 2015, NAT GEOSCI, V8, P801, DOI [10.1038/ngeo2539, 10.1038/NGEO2539]
  • [6] CircStat: A MATLAB Toolbox for Circular Statistics
    Berens, Philipp
    [J]. JOURNAL OF STATISTICAL SOFTWARE, 2009, 31 (10): : 1 - 21
  • [7] Directional statistics of the wind and waves
    Bowers, JA
    Morton, ID
    Mould, GI
    [J]. APPLIED OCEAN RESEARCH, 2000, 22 (01) : 13 - 30
  • [8] Coherent modulation of the sea-level annual cycle in the United States by Atlantic Rossby waves
    Calafat, Francisco M.
    Wahl, Thomas
    Lindsten, Fredrik
    Williams, Joanne
    Frajka-Williams, Eleanor
    [J]. NATURE COMMUNICATIONS, 2018, 9
  • [9] Analysis of clustering and selection algorithms for the study of multivariate wave climate
    Camus, Paula
    Mendez, Fernando J.
    Medina, Raul
    Cofino, Antonio S.
    [J]. COASTAL ENGINEERING, 2011, 58 (06) : 453 - 462
  • [10] Migrating songbirds recalibrate their magnetic compass daily from twilight cues
    Cochran, WW
    Mouritsen, H
    Wikelski, M
    [J]. SCIENCE, 2004, 304 (5669) : 405 - 408