The rate at which global mean sea level (GMSL) rose during the 20th century is uncertain, with little consensus between various reconstructions that indicate rates of rise ranging from 1.3 to 2 mm⋅y⁻¹. Here we present a 20th-century GMSL reconstruction computed using an area-weighting technique for averaging tide gauge records that both incorporates up-to-date observations of vertical land motion (VLM) and corrections for local geoid changes resulting from ice melting and terrestrial freshwater storage and allows for the identification of possible differences compared with earlier attempts. Our reconstructed GMSL trend of 1.1 ± 0.3 mm⋅y⁻¹ (1σ) before 1990 falls below previous estimates, whereas our estimate of 3.1 ± 1.4 mm⋅y⁻¹ from 1993 to 2012 is consistent with independent estimates from satellite altimetry, leading to overall acceleration larger than previously suggested. This feature is geographically dominated by the Indian Ocean–Southern Pacific region, marking a transition from lower-than-average rates before 1990 toward unprecedented high rates in recent decades. We demonstrate that VLM corrections, area weighting, and our use of a common reference datum for tide gauges may explain the lower rates compared with earlier GMSL estimates in approximately equal proportion. The trends and multidecadal variability of our GMSL curve also compare well to the sum of individual contributions obtained from historical outputs of the Coupled Model Intercomparison Project Phase 5. This, in turn, increases our confidence in process-based projections presented in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

There is observational evidence that global sea level is rising and there is concern that the rate of rise will increase, significantly threatening coastal communities. However, considerable debate remains as to whether the rate of sea level rise is currently increasing and, if so, by how much. Here we provide new insights into sea level accelerations by applying the main methods that have been used previously to search for accelerations in historical data, to identify the timings (with uncertainties) at which accelerations might first be recognized in a statistically significant manner (if not apparent already) in sea level records that we have artificially extended to 2100. We find that the most important approach to earliest possible detection of a significant sea level acceleration lies in improved understanding (and subsequent removal) of interannual to multidecadal variability in sea level records.

Mean sea level (MSL) variations across a range of time scales are examined for the North Sea under the consideration of different forcing factors since the late 19th century. We use multiple linear regression models, which are validated for the second half of the 20th century against the output of a tide+surge model, to determine the barotropic response of the ocean to fluctuations in atmospheric forcing. We find that local atmospheric forcing mainly initiates MSL variability on time scales up to a few years, with the inverted barometric effect dominating the variability along the UK and Norwegian coastlines and wind controlling the MSL variability in the south from Belgium up to Denmark. On decadal time scales, MSL variability mainly reflects steric changes, which are largely forced remotely. A spatial correlation analysis of altimetry observations and gridded steric heights suggests evidence for a coherent signal extending from the Norwegian shelf down to the Canary Islands. This fits with the theory of longshore wind forcing along the eastern boundary of the North Atlantic causing coastally trapped waves to propagate over thousands of kilometers along the continental slope. Implications of these findings are assessed with statistical Monte‐Carlo experiments. It is demonstrated that the removal of known variability increases the signal to noise ratio with the result that: (i) linear trends can be estimated more accurately; (ii) possible accelerations (as expected, e.g., due to anthropogenic climate change) can be detected much earlier. Such information is of crucial importance for anticipatory coastal management, engineering, and planning.