Research ArticleCLIMATE CHANGE

Future extreme sea level seesaws in the tropical Pacific

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Science Advances  25 Sep 2015:
Vol. 1, no. 8, e1500560
DOI: 10.1126/sciadv.1500560
  • Fig. 1 Observed sea level anomalies (1979–2013; see Materials and Methods) from Ocean Reanalysis System 4 (ORA-S4) (blue) and available tide gauge records (orange) around Guam (Apra Harbor), Samoa (Pago Pago), central Line Islands (Kiritimati), and the Galápagos Islands (Santa Cruz), respectively.

    Locations are shown in Fig. 2A (4° latitude × 4° longitude averaging regions for ORA-S4). Here, any long-term trends are retained and there is no filtering to remove stochastic noise. The strong El Niño events during 1982–1983 and 1997–1998 are highlighted. There is close correspondence between ORA-S4 and tide gauges, except around Samoa since the 2009 earthquake (dashed line).

  • Fig. 2 Observed sea surface height variability on interannual (top) and annual cycle (bottom) time scales and the corresponding CMIP5 21st-century projections.

    (A and C) Observed (1979–2013) SD of sea surface height (cm). The red boxes in (A) represent island averaging regions used in Fig. 5. Orange contours in (C) enclose large annual cycle variations in wind stress curl (SD exceeding 3.5 × 10−8 s−1). (B and D) Multimodel projection (22 models that simulate the observed nonlinear relationship between El Niño and the SPCZ) (18) for RCP8.5 (2006–2100) with respect to the historical experiment (1911–2005) for the SD of interannual and annual cycle variability (% change). Stippling denotes regions with no significant change in the multimodel variance according to a two-sample F test (P < 95%) with degrees of freedom specified by either the number of model-years (B, 22 models × 95 years) or model-months (D, 22 models × 12 months), respectively, for interannual and annual cycle changes.

  • Fig. 3 Two principal modes of sea surface height variability observed (1979–2013; black lines) in the tropical Pacific and the CMIP5-simulated sea surface height anomalies (dots) for the historical (1911–2005) and RCP8.5 (2006–2100) experiments projected onto the observed EOF1 and EOF2 patterns (maps).

    (A and B) EOF1 (EOF2) explains 43% (14%) of the observed variance, and the SDs of these modes are projected to increase by 32 and 35%, respectively (RCP8.5 minus historical; blue and red vertical bars indicate the ±2 SD range of the historical and RCP8.5 periods, respectively). Green lines show the 20-year running mean and ±2 SD range of the principal modes. Strong and extreme events are shaded (orange and red, respectively, exceeding the ±2 or ±3 running SD range).

  • Fig. 4 Observed nonlinear relationship of sea surface height variability and its future change under greenhouse warming.

    (A) Nonlinear and lagged (8-month) relationship between the first and second EOF projection time series of sea surface height anomalies. The inset shows the correlation coefficient between the first and second EOF projections as a function of lag in months (EOF2 lags EOF1 by 8 months, r = 0.57). The critical value (P > 95%) for the correlation coefficient is 0.39 based on the autocorrelation decay time scales of the projections. The solid green box encloses the prolonged sea level drops for the tropical southwestern Pacific (Taimasa; 13 months observed when both EOF1 and EOF2 projections exceed 2 SDs). Prolonged interannual coastal inundation of similar magnitude and duration has not been observed (dashed green box). (B) Projected future change (RCP8.5 minus historical; color scale) and 20th-century (historical; dashed and solid black contours) multimodel probability of occurrence (%) for the principal variability patterns of sea surface height (EOF2 lags EOF1 by 8 months). Contour intervals are nonlinear: 10−3, 10−2, 10−1 (dashed), and 1 (solid). A kernel smoothing function (normal distribution; bin width of 1 SD) is applied to the bivariate distribution. An increase in Taimasa events (380 versus 875 simulated-months) and prolonged coastal inundation (18 versus 98 simulated-months) from the historical to future period is projected (months when both EOF1 and EOF2 projections exceed ±2 SDs; blue circles, historical; red dots, RCP8.5).

  • Fig. 5 Probability distribution of observed and simulated sea surface height anomalies (cm) for island regions in the tropical Pacific.

    (A to D) Monthly anomalies around Guam, Samoa, Kiritimati in the central Line Islands, and the Galápagos Islands, respectively. Locations are shown in Fig. 2A (4° latitude × 4° longitude averaging regions). A kernel smoothing function (normal distribution; bin width of 1 cm) is applied to each distribution of sea surface heights: black (observations), blue (historical), and red (RCP8.5). The orange line shows the ratio Embedded Image (expressed in %) for future values exceeding 0.1% (right axis). For reference, the global mean sea level (GMSL) rise projected for midcentury (2046–2065) is indicated by vertical lines (median and likely range for the RCP8.5 scenario) (31).

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/1/8/e1500560/DC1

    Fig. S1. Drift of global average sea surface height anomalies in CMIP5 (zos variable name) for the 20th century (historical) and 21st century (RCP8.5).

    Fig. S2. CMIP5 21st-century projections of sea surface height variability on interannual (top) and annual cycle (bottom) time scales for the nine models that do not simulate the observed nonlinear relationship between El Niño and the SPCZ.

    Fig. S3. EOF1 and EOF2 patterns of sea surface height variability for the historical (1911–2005) and RCP8.5 (2006–2100) experiments for the 22 models that simulate the observed nonlinear relationship between El Niño and the SPCZ.

    Fig. S4. Two principal modes of wind stress variability observed (1979–2013; black lines) in the tropical Pacific and the CMIP5-simulated wind stress anomalies (dots) for the historical (1911–2005) and RCP8.5 (2006–2100) experiments projected onto the observed EOF1 and EOF2 patterns (maps).

    Fig. S5. Observed nonlinear relationship of wind stress variability and its future change under greenhouse warming.

    Fig. S6. Observed and changing sea surface height projections (y axes) as a function of wind stress projections (x axes).

    Fig. S7. Probability of prolonged interannual sea level drops.

    Fig. S8. Probability of prolonged interannual sea level rise.

    Fig. S9. Changing sea surface height and wind stress projections with linear trends removed.

    Fig. S10. Future change of sea surface height variability under greenhouse warming for the nine models that do not simulate the observed nonlinear relationship between El Niño and the SPCZ.

    Table S1. Statistics of CMIP5 models.

    Table S2. SDs of observed and simulated sea surface height anomalies (cm) for island regions in the tropical Pacific.

  • Supplementary Materials

    This PDF file includes:

    • Fig. S1. Drift of global average sea surface height anomalies in CMIP5 (zos variable name) for the 20th century (historical) and 21st century (RCP8.5).
    • Fig. S2. CMIP5 21st-century projections of sea surface height variability on interannual (top) and annual cycle (bottom) time scales for the nine models that do not simulate the observed nonlinear relationship between El Niño and the SPCZ.
    • Fig. S3. EOF1 and EOF2 patterns of sea surface height variability for the historical (1911–2005) and RCP8.5 (2006–2100) experiments for the 22 models that simulate the observed nonlinear relationship between El Niño and the SPCZ.
    • Fig. S4. Two principal modes of wind stress variability observed (1979–2013; black lines) in the tropical Pacific and the CMIP5-simulated wind stress anomalies (dots) for the historical (1911–2005) and RCP8.5 (2006–2100) experiments projected onto the observed EOF1 and EOF2 patterns (maps).
    • Fig. S5. Observed nonlinear relationship of wind stress variability and its future change under greenhouse warming.
    • Fig. S6. Observed and changing sea surface height projections (y axes) as a function of wind stress projections (x axes).
    • Fig. S7. Probability of prolonged interannual sea level drops.
    • Fig. S8. Probability of prolonged interannual sea level rise.
    • Fig. S9. Changing sea surface height and wind stress projections with linear trends removed.
    • Fig. S10. Future change of sea surface height variability under greenhouse warming for the nine models that do not simulate the observed nonlinear relationship between El Niño and the SPCZ.
    • Table S1. Statistics of CMIP5 models.
    • Table S2. SDs of observed and simulated sea surface height anomalies (cm) for island regions in the tropical Pacific.

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