Hotspot of accelerated sea-level rise on the Atlantic coast of North America

Transcription

1 Nature Clim. Change 2, (2012) Hotspot of accelerated sea-level rise on the Atlantic coast of North America Asbury H. Sallenger Jr, Kara S. Doran and Peter A. Howd The authors have updated the Supplementary Information to include the numerical results of the rate difference calculations for total window lengths of 60 years (Table S4), 50 years (Table S5) and 40 years (Table S6). These data are in spreadsheet form and provide the numerical values represented in Figs 1 and 2 in the main text and Fig. S3. These changes have been made in this file 26 April 2013.

2 SUPPLEMENTARY INFORMATION DOI: /NCLIMATE1597 Hotspot of accelerated sea-level rise on the Atlantic coast of North America Asbury H. Sallenger, Jr. 1 *, Kara S. Doran 1, and Peter A. Howd 1 1. U. S. Geological Survey, St. Petersburg Coastal and Marine Science Center, th Street South, St. Petersburg, FL, USA, *To whom correspondence should be addressed. asallenger@usgs.gov This document contains: Supplementary Methods Supplementary References 1 to 14 Supplementary Figures S1 to S9 Supplementary Tables S1 to S6 NATURE CLIMATE CHANGE 1

3 Supplementary Methods Data sources The tide gauge data used in this study are annual mean sea-level time series (RLR) downloaded from the Permanent Service for Mean Sea Level (PSMSL) web site ( in March By December 2011, some of the PSMSL tide stations used here had not been updated with data through Hence, for internal consistency, we use data for all gauges only up to, but not including, 2010; the last annual mean value corresponds to calendar year North American gauge selection criteria were: 1. The gauge had to be operational beginning no later than 1970; 2. The gauge record had to extend through 2007 (for example Grand Isle, Louisiana was not included because the PSMSL annual record ended in 2006 and was not re-started until after 2009); 3. In the case of gaps exceeding 10 percent of the total record length, we used only the data after gap, subject to the post-gap record meeting the start- and end-year requirements. This last criterion had the effect of shifting start years for the New York City gauge to 1893 and the Providence, Rhode Island gauge to Supplementary Table S1 provides the PSMSL geographic station names and station identification numbers of those gauges used in this work. Within this list, if a particular analysis required records longer than 40 years (longer than 1970 through 2009 for instance), listed gauges not meeting the more stringent duration requirement were dropped from that analysis phase. Greenland ice melt index (GIM) was calculated using the equation 1 GIM t ( ) = Temp ( t) ( ) ( NAO( t) ) (S1) where 4Temp is comprised of the June, July, August average temperatures from Ilullisat, Nuuk and Qaqortoq for 1784 through 1895 and Ilullisat, Nuuk, Qaqortoq and Tasiilaq for 1895 through NAO is the December, January and February averaged north Atlantic oscillation index. The merged Greenland averaged monthly (June, July, August) temperatures from Ilulissat, Nuuk and Qaqortoq used for were downloaded from

4 The merged Greenland averaged monthly (June, July, August) temperatures from Ilulissat, Nuuk and Tasiilaq for were downloaded from Danish Meteorological Institute (DMI) ( Qaqortoq data were obtained from and the NAO time series from The global and northern hemisphere combined land-surface air and sea-surface water temperature anomalies data (Land- Ocean Temperature Index, GLOTI and NHLOTI respectively) were downloaded from links found at The unsmoothed Atlantic Multidecadal Oscillation (AMO) data were obtained from and are described at Regression analysis Regression coefficients were determined using the standard least squares criterion to fit a set of basis functions to the relative sea-level elevation time series, RSL(t). Regression models take the form: RSL t n ( ) = β i t i i=0 + ε t n ( ) (S2) where β i are the regression coefficients to be found, t is time in years relative to some t = 0, ε i (t) are the model errors (residuals), and n=1 for basis functions yielding coefficients for the mean and linear slope and n=2 for basis functions yielding mean, linear and quadratic-term coefficients. Standard errors on the regression coefficients are given by:

5 σ ( β i ) = RSS N r X T 1 ( X ) jj (S3) where σ(β i ) is the standard error of the i th parameter estimate (i = j-1), RSS is the sum of squares of the residuals, N r is the number of degrees of freedom appropriate for the regression model, and X is the matrix of basis functions used in the regression model. σ(β i ) is multiplied by 2.0 to obtain the 95% confidence interval about the regression coefficient. Time series were tested for serial correlation using the Durbin-Watson statistic 2 and the results indicate significant autocorrelation for the data used here. We are interested in the low frequency components and correct our error estimates for the influence of serial correlation by modifying the degrees of freedom rather than removing low-frequency variability with a filtering methodology 2. We estimate an effective number of data points (N eff ) to replace N in the typical representation of N r = N-j (N = number of data points used in the regression model and j is the number of estimated coefficients). A method of calculating N eff for annual mean sea level measurements using the lag-1 autocorrelation (r 1 ) is 3 : N eff = N 1 r 1 1+ r 1 (S4) We can calculate the lag-1 autocorrelation directly, but it is not a stable estimate for noisy time series. Instead, we fit an AR(1) model to the residuals and use the AR(1) coefficient for r 1. A typical value for the AR(1) coefficient is ~0.40 and the effect of serial correlation is to amplify the standard error of the parameter by a factor of ~1.5. Of course, this amplification varies on a model-to-model basis and this value is only meant as general guidance. We also tested several other commonly used methods of estimating the effect of serial correlation, including fitting a Gaussian function to the lagged autocorrelation sequence to estimate the lag-1 value 3 and using the lag-1 autocorrelation directly

6 computed from the autocorrelation sequence. We also tested spectral methods of estimating effective degrees of freedom, which involve fitting AR(1) or power law noise models to the spectrum and integrating to obtain the autocorrelation sequence 4,5. Differences in the estimated parameter uncertainties using these different approaches were small, on the order of ± 10 percent of the value computed via the AR(1)-modeled lagged autocorrelation. Quantifying rate changes in sea-level rise Quantification of rate change (or alternatively the acceleration) in observed sea-level elevation time series has been approached with two fundamentally different but equally simplistic models of how sea level responds to external forcing. One common approach has been to examine the magnitude and statistical significance of regression model of the form: β 2 in a quadratic RSL( t) = β 0 + β 1 t + β 2 t 2 + ε 2 ( t) (S5) This model assumes both the linear and quadratic coefficients are constants over the duration of the model. In addition, this model requires a priori specification of time t = 0 when the physical conditions driving the change in RSL originated. While the solution for β 2 is insensitive to the choice of indexing for a fixed set of RSL values, the coefficient β 1 is highly sensitive and should not be blindly interpreted as representative of the long-term linear trend. In spite of this statistical sensitivity, there is no impact on our spatial definition of the hotspot if the quadratic model, with a uniform definition of the temporal limits for the regression on all included gauges, is selected (Supplementary Figures S2, S4). A common alternative approach is to assume linear rates of SLR, allowing the rate to change through the historical record as changes occur in the dynamics driving total SLR. Under this assumption the regression model we fit for the mean and linear trend becomes:

7 RSL( t,τ ) = β 0 ( τ ) + β 1 ( τ )t + ε ( t,τ ) (S6) where τ represents the window length over which the regression is calculated. If this regression window is shifted through the observed record at intervals of Δτ (set here at the 1 year sample interval), the regression coefficients can be estimated as a function of both the midpoint-year of the regression, t c, and window duration τ, giving β i = β i (t c, τ). If the window is further sub-divided in two halves and regression coefficients calculated for each, the difference in SLR rates between the two segments can be found: SLRD(t c,τ ) = β HR2 t c2, τ 2 β t, τ HR1 c1 2 (S7) where SLRD is the sea-level rate difference between the two half-window series, t c is the central year of the total window width τ (or, on the right-hand side, of either half record window, HR1 and HR2), and the β values represent the linear rates of sea level change calculated for the first and second halves of the windowed record. For plotting purposes with even values of τ, t c is taken as the first year of the most recent half-record. The average acceleration over the interval defined by t c and τ is simply 2SLRD(t c, τ)/τ. The standard error of SLRD is calculated as: σ SLRD t c,τ 2 ( ) = σ HR1 t c1, τ σ HR2 t c2, τ 2 (S8) where the standard errors of the half-records (HR1 and HR2) are calculated as given by equations S3 and S4. σ SLRD is multiplied by a factor of 2.0 to obtain 95 percent confidence intervals. We assume each tide gauge provides an independent estimate of sea level elevation as forced by oceanographic and other geophysical processes; thus, regionally averaging the individual SLRD estimates reduces the error of the mean by a factor of 1/ N 1/2 where N is the number of stations included in the average (e.g. the number of hotspot gauges, Figures 2, 4, Supplementary Figure S3).

8 Simple Illustrative Examples Examples representing the two simple conceptualizations of SLR serve to demonstrate the performance of this technique. First, assume a RSL time series (TS1) with a rate increase from 2 mm yr -1 to 4 mm yr -1 in the middle of a long (relative to τ) record (mean removed; Supplementary Figure S5). The magnitude of the rate change (2 mm yr -1 ) and the year of the step increase in rate (t s = 1950, the year of the kink in RSL elevation) are recovered at the local maximum in the SLRD time series (SLRD(t c = 1950, τ = 60); Supplementary Figure S5). The shifting regression window acts as a weighted moving average filter on the true instantaneous rate change time series. The true maximum rate difference is approached as the central year of the regression approaches the year of the rate increase. Next, assume a RSL time series (TS2) with a quadratic increase of mm yr -2 (no linear trend, mean removed, t = 0 set at year 1850). In this case the instantaneous rate of SLR over the entire time series is simply the derivative, 0.03t. As observed, SLRD(t c, 60) for any two rate estimates separated by τ/2 = 30 yr would be constant at 0.9 mm yr -1 (Supplementary Figure S5). Analyses of 10 4 time series realizations using TS1 with added AR(1) noise with ρ = 0.4 are presented (Supplementary Figure S6). Noise variance was set to match that observed in the New York City annual tide data. Three immediately relevant characteristics emerge. First, the averaged estimates of the maximum rate difference are unbiased both in magnitude and with respect to the year when the rate is known to change. Second, the width of the rate difference peak increases at the interval length increases due to the inclusion of the rate increase in earlier years as the duration of the window increases. Third, the statistical confidence in the estimated rate difference decreases with decreasing regression window duration. As could be expected, Monte Carlo-based estimates of confidence for rate difference calculations depend on the spectral characteristics of the noise. Confidence decreases with increasing importance of low-frequency noise (red spectral noise) while confidence increases as the noise becomes dominated by high frequencies (white into blue spectral noise).

9 An example of quadratic and rate-differenced estimates of acceleration (or alternately SLRD) for New York City sea level data from is shown in Supplementary Figure S7. All three regressions are the least-square error estimates based on their respective subsamples of points. The linear regression segments are not constrained to intersect at the center point of the interval, thus the regression solutions for the two segments are independent estimates of the local rates of SLR. In this interval, the 60-year average acceleration estimate from the quadratic fit is ± mm yr -2 (1σ error) and the estimate derived from the SLRD is ± mm yr -2 (1σ error). Sea Level Projections for New York City An obvious goal is to compare a range of existing model predictions to observations within the hotspot. Many model scenarios have resulted in the prediction of a NE hotspot with magnitude dependent on the details of both the model and the forcing conditions. An obvious extension of predicting the geographic extent is to compare the SLR magnitudes of the observed hotspot to different model scenarios. As a first step we make two extrapolations based on our analysis of the annual mean sea level data from New York City. A dynamic SLR is based on extrapolation of the SLRD estimate. At a level of sophistication suitable for qualitative comparison, the SLRD estimates are used to predict the excess SLR resulting from a shift in ocean conditions, including changes to both dynamic and steric rates, assumed to be initiated near the midpoint of the calculation interval (Supplementary Figure S8). It could be argued 6-8 that this excess SLR in the hotspot may be due to two primary changes, the slowing of AMOC and the resulting relaxation of a cross-shelf pressure gradient, and the steric response to warming temperatures. Both processes are assumed to continue over the prediction period. In fact, the steric increase may be slower to appear than dynamic response 7 and thus we may not fully capture its contribution with available data. If so, we expect this extrapolated value to be slightly larger than the modeled dynamic SLR, but less than the modeled total SLR. The second extrapolation based on observations is the total expected SLR over the interval Here we assume that the most recent rate of SLR can simply be applied into the future, i.e. that there is no future rate change. This estimate presumably

10 includes contributions from added volume, any vertical land motion, as well as the dynamic and steric components captured (or not) by our analysis of existing data. A range of extrapolated values is given by varying the number of years (20, 25, 30) used to determine the rate. Model studies provide two types of predictions for New York City SLR over the 21 st century for varying emissions scenarios 7,8. The first, in both studies, is referred to as dynamic SLR, the local SLR resulting from changes in the dynamical processes of the ocean, that is, from changes in flow. The second estimate, total SLR, is the sum of the dynamic SLR and the predicted global steric SLR. Vertical ground motion is not included in either of these model predictions. We caution that even if we are comparing apples to apples, one could still pick up two varieties (Red Delicious and a Granny Smith) and find them very different, and in the spirit of that analogy we urge that the comparison between numbers in Supplementary Table S2 be qualitative in nature. A much more detailed analysis, beyond the scope of this paper, is needed. It is noteworthy, however, that there is overlap between appropriate estimates, and that differences are in the direction of and do not exceed what might be expected given what each prediction or extrapolation assumes and/or excludes. The model-data comparison is encouraging but far from complete. Lagged cross-correlations and regressions To maximize statistical independence of SLRD values within the NEH, we use only the SLRD estimate(s) that are statistically independent (i.e. SLRD estimates from regression intervals that have no points in common, Δτ τ ). A composite record (cslrd, Figure 4 in main) is constructed from these independent points as follows. The start years for the independent tide gauge records are assumed to be random and we select the first available SLRD estimate for each gauge. Working alphabetically through the gauge names, if the start year for a gauge is not unique with respect to other gauges, the start date for the fixed-length SLRD regression window is shifted forward in time until it becomes unique. No shifts exceeded 1 year. If the total record duration is sufficient to allow a second independent estimate, the second start date is taken as the next unique year, following the same procedure. No additional shifting has been done to

11 optimize number or the temporal distribution of points. For τ = 60 years, there are N = 15 independent SLRD estimates pooled from tide gauges within the NEH to form a composite SLRD time series, cslrd, with 20 and 28 independent points in the τ = 50 and τ = 40-year cslrd series, respectively. The lagged cross-correlation values between shifted versions of the rate-differenced GIM (RDGI, or any of the other rate-differenced climate indices as indicated by RD prefix) and the composite SLRD series were calculated using r RDGI,cSLRD τ,m ( ) = C τ,m RDGI,cSLRD ( ) ( τ )s cslrd ( τ ) s RDGI (S9) where C RDGI,cSLRD τ,m N ( ) = RDGI t ci ( k) m,τ k=1 ( ) csldr t ci k ( ) ( ( )) N N RDGI t ci ( k) m,τ cslrd t ci ( k),τ k=1 k=1 ( ) (S10) is the covariance at lag m, s RDGI and s cslrd are the respective standard deviations, and t ci are the times of the N independent csldr estimates (main text Fig. 4c). The lag space (range of m) is limited to the range that includes all independent SLRD estimates (-30 m 56 for τ = 60). Positive lags correspond to an event in RDGI preceding the corresponding event in cslrd. The N-2 degrees of freedom for the lagged crosscorrelation estimates are based solely on the number of independent cslrd estimates. The lag with the maximum cross-correlation (m MAX ) is chosen and a regression model of the form cslrd( t,τ ) = a + b RDGI ( t + m,τ ) + ε ( t,τ ) (S11) is solved for a and b (Supplementary Figure S9).

12 Influence of other potential signals We compare means of rate-differences from groups of hotspot gauges that reflect different susceptibilities to recent (nonlinear) subsidence resulting from e.g. groundwater extraction. If the means of the high susceptibility locations are statistically higher than the means of the low susceptibility locations, we conclude that subsidence may be contributing nonlinearity to the sea-level time series and biasing our rate differences, or accelerations, high. If the means are statistically the same, we conclude subsidence is not affecting our calculations. (These conclusions assume rate differences, in the absence of subsidence, are approximately uniform across the hotspot.) We use a previous assessment of eight tide gauge locations to establish a relative scale of low and high susceptibility to subsidence within the SLR hotspot 9 (Supplementary Table S3). For example, low refers to gauges on or near crystalline rocks and hence they are considered relatively stable and high refers to gauges on relatively thick sequences of unconsolidated sediments that are close to known groundwater-related subsidence problems 9. Supplementary Table S3 shows that for each time series length, the means of susceptibility groups are statistically the same, suggesting that subsidence is not a major factor in hotspot development. Further, examination of Global Positioning System (GPS) time series near tide gauges along the northeast coast, although relatively short (~10 years), found they were essentially linear 10, supporting the conclusion that nonlinear land motions did not significantly affect our results. It remains a possibility that what we see as acceleration at the upper limit of time scales resolved by this analysis (40-80 years, Figure 4 in main paper) are part of a natural oscillation in sea level, rather than the forced response to the processes described in the main text and shown in models 6-8, Should this prove to be the case as future observations are made, it is clear from existing data that the signal is expected have larger amplitude in the hotspot than along other sections of the North American coastline and the period is at least 60 yrs, which is long enough to be of societal importance. It is also clear that any interannual to decadal variability 14 is effectively damped by our analysis

13 technique (using an appropriate choice of τ) and does not contaminate our rate difference results. Supplementary References 1. Frauenfeld, O. W., Knappenberger, P. C., & Michaels, P. J. A reconstruction of annual Greenland ice melt extent, J. Geophys. Res., 116, D08104 (7 pp.), doi: /2010jd014918, (2011). 2. Boon, J. D., Brubaker, J. M. & Forrest, D. R. Chesapeake Bay Land Subsidence And Sea Level Change: An Evaluation Of Past And Present Trends And Future Outlook, Special Report No. 425 in Applied Marine Science and Ocean Engineering, Virginia Institute of Marine Science (2010). 3. Nerem, R. S. & Mitchum, G. Estimates of vertical crustal motion derived from differences of TOPEX/POSEIDON and tide gauge sea level measurements. Geophys. Res. Lett., 29, , doi: /2002gl015037, (2002). 4. Mann, M. E. & Lees, J. M. Robust estimation of background noise and signal detection in climatic time series. Climatic Change, 33, , doi: /BF , (1996). 5. Mao, A., Harrison, G. A., & Dixon, T. H. Noise in GPS coordinate time series. J. Geophys. Res., 104(B2) , doi: /1998jb (1999). 6. Yin, J., Schlesinger, M. E., & Stouffer, R. J., Model projections of rapid sea-level rise on the northeast coast of the United States. Nature Geoscience 2, , doi: /ngeo462, (2009). 7. Yin, J., Griffies, S. M., & Stouffer, R. J. Spatial variability of sea level rise in twenty-first century projections. J. of Climate, 23, , doi: /2010JCLI (2010). 8. Schleussner, C. F., Frieler, K., Meinshausen, M., Yin, J. & Levermann, A. Emulating Atlantic overturning strength for low emission scenarios: consequences for sea-level rise along the North American east coast. Earth Syst. Dynam., 2, , doi: /esd (2011). 9. Davis, G. H. Land subsidence and sea level rise on the Atlantic coastal plain of the United States. Environ. Geol. Water Sci., 10, 67-80, doi: /BF , (1987) 10. Doran, K. J. Addressing the problem of land motion at tide gauges, M. S. Thesis 1616, University of South Florida, St. Petersburg, FL, (2010). 11. Levermann, A., Griesel, A., Hofmann, M., Montoya, M. & Rahmstorf, S. Dynamic sea level changes following changes in the thermohaline circulation. Clim. Dyn. 24, , doi: /s y, (2005). 12. Hu, A., Meehl, G., Han, W., & Yin, J. Effect of the potential melting of the Greenland Ice Sheet on the meridional overturning circulation and global climate in the future. Deep-Sea Research II, 58, , doi: /j.dsr , (2011). 13. Kopp, R. E., Mitrovica, J. X., Griffies, S. M., Yin, J., Hay, C. C., & Stouffer, R. J. The impact of Greenland melt on regional sea level: a partially coupled analysis

14 of dynamic and static equilibrium effects in idealized water-hosing experiments. Climatic Change, 103, , DOI: /s , (2010). 14. Frankcombe, L., & Dijkstra, H., Coherent multidecadal variability in North Atlantic sea level. Geophys. Res. Lett., 36, L15604 (5 pp.), doi: /2009gl039455, (2009).

15 Supplementary Figure S1. SLRD versus latitude for tide gauges between Key West, Florida, USA and St. John s, Newfoundland, CA with 1σ error bars on the individual gauge values for regression window lengths of 60 (a), 50 (b), and 40 (c) years.

16 Supplementary Figure S2. Maps of the east coast of North America showing colorcoded accelerations found from quadratic fits for 60 (a), 50 (b) and 40-year (c) time series, all ending with 2009.

17 Supplementary Figure S3. SLRD around North America for: (a) 50 year time series window; (b) 40-year time series window.

18 Supplementary Figure S4. Hotspot-averaged acceleration (from quadratic fits of tide gauge data) versus variable time series lengths. Note that the x axis is the start year for the regression calculations, not the midpoint date associated with the average acceleration estimates elsewhere in this work. All regressions end with year Color coding indicates the number of hotspot gauges included in the average, ±2σ confidence intervals.

19 Supplementary Figure S5. SLRD technique applied to simple, noise-free time series. (a) Time series with rate increase from 2 mm/yr to 4 mm/yr in SLR rate at year 1950; (b) SLRD calculated for time series in (a) with a sliding regression window of 60 years; (c) Time series with constant quadratic coefficient of 0.015; (d) SLRD calculated for time series in (c) with a sliding regression window of 60 years.

20 Supplementary Figure S6. Results of Monte Carlo simulation (N = 10 4 ) for influence of the length of the regression window on the prediction of the rate change: (a) Example replicate of TS1 + AR(1) noise; (b) Mean SLRD(t c, τ=40 yr); (c) Mean SLRD(t c, τ=50 yr); (d) Mean SLRD(t c, τ = 60 yr); (e) PDF for estimated SLRD(t c =1950, τ=40 yr); (f) PDF for estimated SLRD(t c = 1950, τ = 50 yr); (g) PDF for estimated SLRD(t c = 1950, τ = 60 yr).

21 Supplementary Figure S7. New York City annual average sea level data with three regression results used in this study for the most recent 60-year subsample ( ). The mean elevation of the annual NYC sea level data ( ) was removed and time t = 0 was set at year 1950 (t = year 1950) for these regressions.

22 Supplementary Figure S8. Schematic representation of the data-based extrapolations used to predict SL changes from 2000 to SLRD can be used to predict the excess rise in SL due to the rate change in the late 20 th century, while the most recent estimate of the rate of SLR itself can be used to extrapolate to a total SLR over the interval.

23 Supplementary Figure S9. Results for regression of the independent cslrd points on lagged climate indices. Lags determined by maximum values of the cross-correlation function between cslrd and index. Errors on the regression coefficients (a, b) are 1σ. (a) cslrd regressed on GIRD. (b) cslrd regressed on rate-differenced LOTI. (c) cslrd regressed on rate-differenced AMO. (d) cslrd regressed on rate-differenced NAO.

24 Supplementary Table S1. North American tide station identification for those gauges used in this study. Names and station ID correspond to those assigned on the PSMSL website ( Those locations included in the hotspot-averaged time series of SLRD are indicated by *. Dates are inclusive of the ending year. PSMSL Station Name PSMSL ID Dates Used Alameda (Naval Air Station), CA *Annapolis (Naval Academy), MD Astoria (Tongue Point), OR *Atlantic City, NJ *Baltimore, MD Bar Harbor, Frenchman Bay, ME *Boston, MA *Bridgeport, CT *Cape May, NJ Cedar Key II, FL Charleston I, SC Charlottetown, PE Crescent City, CA Eastport, ME Fernandina, FL Fort Myers, FL Fort Pulaski, GA Friday Harbor (Ocean. Labs.), WA Galveston II, Pier 21, TX Halifax, NS *Hampton Roads, VA Key West, FL *Kiptopeke Beach, VA La Jolla (Scripps Pier), CA *Lewes, DE Los Angeles, CA *Montauk, NY *Nantucket, MA Naples, FL Neah Bay, WA *New London, CT *New York, NY *Newport, RI Pensacola, FL *Philadelphia (Pier 9N), PA Point Atkinson, BC Port Isabel, TX Port San Luis, CA Port-Aux-Basques, NL Portland, ME

25 *Providence, RI Rockport, TX San Diego (Quarantine Station), CA San Francisco, CA *Sandy Hook, NJ Santa Monica, CA Seattle, WA *Solomon's Island (Biol. Lab), MD St. John's, NL St. Petersburg, FL Tofino, BC Vancouver, BC Victoria, BC *Washington, D.C Wilmington, N.C *Woods Hole (Ocean. Inst.), MA

26 Supplementary Table S2. Sea level rise estimates for New York City ( ). Dynamic Dynamic+Global Steric Low Emissions cm cm IPCC Emissions cm cm SLRD Extrapolation Rate Extrapolation Observation cm cm Supplementary Table S3. Comparison of hotspot rate differences from groups of gauges reflecting different susceptibilities to subsidence due to fluid withdrawl. Time Series Length (yrs) Low Susceptibility a (mm/yr) High Susceptibility b (mm/yr) ± ± ± ± ± ± 0.63 a. Gauges that Davis 9 argues have relatively low susceptibility to subsidence from fluid withdrawal are New York City (Battery), Philadelphia, Baltimore, Washington. b. Gauges that Davis 9 argues have relatively high susceptibility to subsidence from fluid withdrawal are Atlantic City, Hampton Roads (Sewells Point), Annapolis, and Solomon s, MD.