TG records

Sea level along the East Coast of the United States and Canada exhibits interannual fluctuations superimposed on multidecadal variations and a long-term upward trend5,7,18,19,20. Modelling and long-term TG data indicate that the behaviour of sea level is similar and highly correlated north or south of Cape Hatteras4,5,7,18,20. By taking the long-term rate and especially the 2009 SLR rate into account (Supplementary Fig. 1), we further divide the East Coast of North America into three SLR regimes: NE (North of New York City), Mid-Atlantic (New York City to Cape Hatteras) and Southeast (south of Cape Hatteras; Fig. 1a). To minimize the effect of local factors and reveal regionally coherent behaviour, we calculate the time series of the sea-level composite for the three SLR regimes (see the Methods section). From the noisy background, we separate an extreme SLR event having occurred between 2009 and 2010 along the NE Coast of North America.

Figure 1: Three SLR regimes along the East Coast of North America and the corresponding sea-level composite. (a) Three SLR regimes and locations of the 40 TG stations used by this study. Red, blue and purple colours indicate the Northeast, Mid-Atlantic and Southeast region, respectively. (b–d) Time series of sea-level composite (line) in the three regimes and counts of TG stations used in the composite calculation as a function of time (squares). Full size image

The NE sea-level composite is calculated as the mean of maximum 18 TG stations from Montauk, New York, to Rimouski, Canada (Fig. 1b and Supplementary Table 1). Next, we calculate the yearly SLR rate by differentiating the annual mean time series of the sea-level composite (Fig. 2a) (see the Methods section). The results indicate that the s.d. (σ) of the yearly SLR rate is 14.5 mm per year since 1920, much larger than the secular trend of 2.5 mm per year. However, the composite SLR rate in 2009 is a remarkable outlier and reaches 46.9 mm per year (>3σ) (Fig. 2b). Between 2009 and 2010, the sea level in this region jumped by nearly 100 mm on average (Figs 1b and 3a), contributing significantly to the identified SLR acceleration during the recent decades5,7,18. To calculate the return period, we fit a Gaussian distribution to the yearly SLR rates from 1920 to 2011. The result shows that the 2009 SLR rate is a 1-in-850 year event (Fig. 2c).

Figure 2: Yearly SLR rates along the East Coast of North America. (a,d,g) Yearly SLR rates (mm per year) of the Northeast, Mid-Atlantic and Southeast composite, respectively. The absolute and relative (to 1σ) rates are shown by the right and left y axis, respectively. (b,e,h) Box and whisker plots indicate the position of the 2009 SLR rates for the three sea-level composites, respectively. The 2009 SLR rates are depicted by the green bars and dots. (c,f,i) Return period of the yearly SLR rates. Dots show the TG data during 1920–2011. Solid lines indicate the exceedance probability of the Gaussian distribution and the corresponding return period. Dotted lines represent the 5%–95% confidence interval of the Gaussian distribution fit. The 2009 box marks the NE 2009 SLR rate. See Methods section for the calculation of the return period. Full size image

Figure 3: Mechanisms of the 2009–2010 extreme SLR event. (a) Sea-level increase (mm) between 2008 and 2010 from the Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO; shading) and TG stations (colour dots). The black line indicates the shelf break—500 m depth. (b) Steric sea-level anomalies (mm) in 2009 for the upper 2,000 m. (c) Correlation between the monthly AVISO and RAPID AMOC data for 2004–2012. (d) Correlation between the annual mean DSL and AMOC index (45°N) in the long-term control runs of the 10 GFDL and 14 CMIP5 models. The values show multi-model ensemble mean. See Supplementary Figs 7 and 8 for individual models. (e) Anomalies of sea-level pressure (shading; hPa) and wind stress (vector; N m−2) in 2009–2010 from the GFDL reanalysis. (f) Difference in sea-level pressure (shading; hPa) and wind stress (vector; N m−2) between the years of extreme positive and negative SLR at Boston. The results show the ensemble mean of the GFDL and CMIP5 models for 100-year control runs. See Supplementary Figs 10 and 11 for more details. Full size image

To test the robustness of the result and its sensitivity to different methods, we repeat the yearly SLR calculation based on the linear fit to the monthly data on interannual time scales (Supplementary Fig. 2). We also identify record-breaking years of sea level and compare the sea-level increase over the previous records (Supplementary Fig. 3). The 2009–10 event stands out in all three methods (Table 1).

Table 1 Summary of the rise rates in the three SLR regimes. Full size table

The 2009–10 extreme SLR event is also evident in individual TG data (Supplementary Fig. 4). The range of the 2009 SLR rate in the NE region varies from 32.5 (Rimouski) to 64 (Portland) mm per year. Four NE stations (Montauk, Woods Hole, North Sydney and Charlottetown) did not provide data for the calculation of the 2009 SLR rate. Of the remaining 14 stations in the NE region, 11 stations show that the 2009 SLR rate was the highest on record, with the other 3 being the second highest. In addition, the 2009 SLR rate was above 2σ at all NE stations except Rimouski. Five stations (Port-Aux-Basques, Eastport, Halifax, Portland and Boston) show the 2009 SLR rate >3σ (Supplementary Fig. 4). The spatial coherency and extreme SLR rate suggest that large-scale ocean climate dynamics (for example, the AMOC4,21, Gulf Stream6,22 and wind effect20,23) rather than local mechanisms (for example, land subsidence24) is the main cause of the 2009 SLR event. Land subsidence is at least one order of magnitude smaller than the yearly SLR rates.

The signal of the 2009–10 extreme SLR event attenuates towards the south. Compared with the NE region, the amplitude of the yearly sea-level fluctuation is larger (σ=19.9 mm per year) in the Mid-Atlantic region, while the 2009 SLR rate is less extreme (Fig. 2d,e and Supplementary Fig. 4). The composite SLR rate is 31.2 mm per year (>1σ) in 2009, with a range of 22.0–41.5 mm per year at individual stations. In the Mid-Atlantic region, especially the Chesapeake Bay, land subsidence induced by glacial isostatic adjustment contributes to the long-term SLR24. The pronounced SLR rate during 1982–83 (>2.5σ and with a return period of 150 years) is likely related to the strong El Niño in the Pacific and the resulting more coastal storms in the Mid-Atlantic region23 (Fig. 2d,f).

South of Cape Hatteras, the 2009 SLR rate further reduces to 3.0 mm per year and falls within ±1σ (Fig. 2g,h). The coastal sea level in this region is influenced by the North Atlantic subtropical gyre25. An extreme sea-level fall (~−3σ) occurred in 1949, following a rapid and continuous SLR during much of the 1940s. This decade was characterized by faster global SLR26. The detailed investigation about this extreme event is beyond the scope of this study.

The satellite altimetry data indicate that the most significant interannual variability of the dynamic sea level (DSL) occurs in the ocean interior, especially along the Gulf Stream and its extension (Fig. 3a). Along the East Coast of North America, the altimetry data is generally consistent with the TG data regarding the 2009–10 SLR event, but the exact magnitude differs.

Role of the AMOC

The 2009–10 sea-level spike in the NE region coincides with two significant ocean and climate events: a 30% downturn of the AMOC9 (Fig. 4a) and an extreme negative NAO index27. Both winds20,23,25 and the AMOC4,5,18,21 can cause sea-level variability and change along the NE coast of North America on various time scales. After removing the seasonal cycle, the monthly NE sea-level composite shows a good correlation (R=0.78; 2-month lag) with the AMOC index (Fig. 4b). Especially, the two sea-level spikes during the winters of 2009–10 and 2010–11 coincide with the two AMOC minima, respectively. Regression based on all monthly data from 2004 to 2012 further reveals a 13.2-mm SLR along the NE coast in response to 1 Sv AMOC slowdown. After excluding the 30% downturn period of the AMOC, the AMOC-SLR ratio increases to 16.6 mm Sv−1, reflecting the sea-level response to the gradual decline of the AMOC during 2004–2012. In the altimetry data, the AMOC–DSL correlation extends far offshore, especially from the NE coast of North America (Fig. 3c). It changes sign across the Gulf Stream, implying that a decrease in the AMOC transport reduces the cross-current sea-level gradient and thus lifts sea level along the NE coast5,6.

Figure 4: Correlation between the AMOC and the sea-level composite along the NE coast. (a) The time series of the NE sea-level composite (monthly grey, filtered blue) and the AMOC strength at 26.5°N (Sv, monthly grey, filtered red). The seasonal cycle has been removed with a 6-month filter applied. (b) The monthly correlation and regression between the AMOC and NE sea-level composite (mm per Sv, 2-month lag with the AMOC leading the SLR). The blue dots highlight the data during the period of the 30% AMOC downturn. The linear fit in black is based on all monthly data during April 2004–September 2012. The linear fit in red is based on the red dots only and excluding the period of the 30% AMOC downtown. The data are from PSMSL and the RAPID-WATCH MOC project. Full size image

Interestingly, the 30% downturn of the AMOC in 2009–2010 followed a brief return to deep convection in the Labrador Sea during the winter of 2007–2008 (ref. 28). A data assimilation product by GFDL29,30 (see the Methods section) shows that strong deep downwelling in 2007–2008 was mainly induced by a reduction in the freshwater input into the Labrador Sea (Supplementary Fig. 5). However, a record low of oceanic heat and buoyancy loss, dominated by the sensible heat flux (Supplementary Fig. 6), occurred in the subsequent years 2008–2010. Associated with these changes is a significant positive steric sea-level anomaly in 2009 (>100 mm), southeastward of the shelf break (Fig. 3b). The gradient across the shelf break can drive more water mass towards the shelf, thereby causing SLR along the NE coast of North America4,31.

To better understand extreme SLR events in this region, we use two sets of state-of-the-art climate models: an ensemble of ten models developed at GFDL, including high-resolution models with eddying oceans32 (Table 2), and the other set including 14 CMIP5 models with the AMOC and sea-level data available at the CMIP5 archive33 (Table 3; also see the Methods section). The GFDL ensemble represents different model generations with progressive improvement and a systematic model development effort at one modelling centre. On the other hand, the CMIP5 ensemble represents similar generation models from different institutes, which bear significant difference in many aspects. Therefore, these models cover a wide range in the model formulation, parameterization and uncertainty space.

Table 2 Ten global climate/Earth system models developed at GFDL and used in this study. Full size table

Table 3 14 CMIP5 global climate/Earth system models used in this study. Full size table

In the long-term control runs of these models without changing external forcing, the DSL along the NE coast of North America shows an instantaneous correlation with the AMOC on the interannual time scale (Fig. 3d and Supplementary Figs 7 and 8). This correlation suggests that a 30% weakening of the AMOC in 2009–2010 may have contributed to and increased the chance of extreme coastal SLR events. The two models with eddying oceans (GFDL CM2.6 and CM2.5) show weaker correlation, probably due to the relatively weak AMOC in these two models32 (Supplementary Fig. 9).

Role of the NAO and associated winds

The significant negative NAO index during December 2009 through February 2010 (ref. 27) contributes to the extreme SLR event both remotely and locally. First, the negative NAO can cause an anomalous heat flux into the Labrador Sea, thereby influencing the AMOC and SLR along the NE coast of North America (Supplementary Fig. 5). Second, the NAO-induced wind stress anomalies can pile up waters directly against the NE coast or generate onshore Ekman transport.

The control runs with both the GFDL and other CMIP5 models indicate that extreme SLR events along the NE coast typically occur when the nearby wind stress shows an onshore (easterly) or alongshore (northeasterly) anomaly pattern (Fig. 3f and Supplementary Figs 10 and 11). Indeed, the lower sea-level pressure east of North America in 2009 results in northeasterly wind stress anomalies near the NE coastal regions (Fig. 3e). The anomalous Ekman transport contributes to the 2009–2010 SLR extreme. The negative NAO can also influence storminess, which contributes to higher sea levels due to more frequent storm surges34.

The NE sea-level composite shows correlation (R=0.6) with the NAO index during 2004–2012 (Supplementary Fig. 12). However, the correlation reduces significantly for the entire period of 1920–2012. In addition to 2009–2010, extreme negative NAO index also occurred in 1969 and other years (Supplementary Fig. 13). The lack of extreme SLR signal on the NE coast during these years indicates that the NAO is not the sole mechanism of the 2009–2010 SLR event. Finally, the lower sea-level pressure (by ~1.6 hPa) in 2009 could account for ~15% of the 2009–10 SLR event through the inverse barometer effect (Fig. 3e).

Future projections of extreme SLR events

Similar to extreme temperature and precipitation events, extreme SLR events on the interannual time scale may be also linked to human-induced climate change35. Increased greenhouse gas concentrations are likely to shift the probability density function towards more extremes. To study future changes of extreme SLR events, we consider the ten GFDL climate models and their long-term control runs, and idealized 1% per year CO 2 increase experiments for 100 years. Along the NE coast of North America, most of these models (CM2.6, CM2.5, CM2.5 FLORa6, CM2.5 FLOR, ESM2M and ESM2preG) suggest an increase in the magnitude and frequency of the extreme SLR events in response to the CO 2 increase (Fig. 5 and Supplementary Fig. 14). In five of these models (CM2.6, CM2.5 FLORa6, CM2.5 FLOR, ESM2M and ESM2preG), the increase in the yearly SLR extrema is unproportionately larger than the increase in mean sea level (Supplementary Fig. 14).

Figure 5: Return periods of the yearly SLR rates in the control runs and 1% per year CO 2 increase experiments with the ten GFDL models. The values show the composite SLR rates along the NE Coast of North America and are calculated based on both the DSL changes and the global ocean thermal expansion. Both the control (blue colour) and CO 2 runs (red colour) are 100-year long. Dots show the model simulation results. Solid lines indicate the exceedance probability of the Gaussian distribution and the corresponding return period. Dotted lines represent the 5%–95% confidence interval of the Gaussian distribution fit. The red line above the blue line indicates an increase in the return level or a decrease in the return period. Full size image