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We review the characteristics of sea level variability at the coast focussing on how it differs from the variability in the nearby deep ocean. Sea level variability occurs on all timescales, with processes at higher frequencies tending to have a larger magnitude at the coast due to resonance and other dynamics. In the case of some processes, such as the tides, the presence of the coast and the shallow waters of the shelves results in the processes being considerably more complex than offshore. However, ‘coastal variability’ should not always be considered as ‘short spatial scale variability’ but can be the result of signals transmitted along the coast from 1000s km away. Fortunately, thanks to tide gauges being necessarily located at the coast, many aspects of coastal sea level variability can be claimed to be better understood than those in the deep ocean. Nevertheless, certain aspects of coastal variability remain under-researched, including how changes in some processes (e.g., wave setup, river runoff) may have contributed to the historical mean sea level records obtained from tide gauges which are now used routinely in large-scale climate research.

Changes in ocean properties and circulation lead to a spatially non-uniform pattern of ocean dynamic sea-level change (DSLC). The projections of ocean dynamic sea level presented in the IPCC AR5 were constructed with global climate models (GCMs) from the Coupled Model Intercomparison Project 5 (CMIP5). Since CMIP5 GCMs have a relatively coarse resolution and exclude tides and surges it is unclear whether they are suitable for providing DSLC projections in shallow coastal regions such as the Northwestern European Shelf (NWES). One approach to addressing these shortcomings is dynamical downscaling – i.e. using a high-resolution regional model forced with output from GCMs. Here we use the regional shelf seas model AMM7 to show that, depending on the driving CMIP5 GCM, dynamical downscaling can have a large impact on DSLC simulations in the NWES region. For a business-as-usual greenhouse gas concentration scenario, we find that downscaled simulations of twenty-first century DSLC can be up to 15.5 cm smaller than DSLC in the GCM simulations along the North Sea coastline owing to unresolved processes in the GCM. Furthermore, dynamical downscaling affects the simulated time of emergence of sea-level change (SLC) above sea-level variability, and can result in differences in the projected change of the amplitude of the seasonal cycle of sea level of over 0.3 mm/yr. We find that the difference between GCM and downscaled results is of similar magnitude to the uncertainty of CMIP5 ensembles used for previous DSLC projections. Our results support a role for dynamical downscaling in future regional sea-level projections to aid coastal decision makers.

Variability of Sea-Surface Height (SSH) from ocean dynamic processes is an important component of sea-level change. In this study we dynamically downscale a present-day control simulation of a climate model to replicate sea-level variability in the Northwest European shelf seas. The simulation can reproduce many characteristics of sea-level variability exhibited in tide gauge and satellite altimeter observations. We examine the roles of lateral ocean boundary conditions and surface atmospheric forcings in determining the sea-level variability in the model interior using sensitivity experiments. Variability in the oceanic boundary conditions leads to uniform sea-level variations across the shelf. Atmospheric variability leads to spatial SSH variability with a greater mean amplitude. We separate the SSH variability into a uniform loading term (change in shelf volume with no change in distribution), and a spatial redistribution term (with no volume change). The shelf loading variance accounted for 80% of the shelf mean total variance, but this drops to ~ 60% around Scotland and in the southeast North Sea. We analyse our modelled variability to provide a useful context to coastal planners and managers. Our 200-year simulation allows the distribution of the unforced trends (over 4–21 year) of sea-level changes to be quantified. We found that the 95th percentile change over a 4-year period can lead to coastal sea-level changes of ~ 58 mm, which must be considered when using smooth sea level projections. We also found that simulated coastal SSH variations have long correlation length-scales, suggesting that observations of interannual sea-level variability from tide gauges are typically representative of > 200 km of the adjacent coast. This helps guide the use of tide gauge variability estimates.

We investigate the impact of the Indian Ocean Dipole (IOD) and El Niño and the Southern Oscillation (ENSO) on sea level variations in the North Indian Ocean during 1957–2008. Using tide‐gauge and altimeter data, we show that IOD and ENSO leave characteristic signatures in the sea level anomalies (SLAs) in the Bay of Bengal. During a positive IOD event, negative SLAs are observed during April–December, with the SLAs decreasing continuously to a peak during September–November. During El Niño, negative SLAs are observed twice (April–December and November–July), with a relaxation between the two peaks. SLA signatures during negative IOD and La Niña events are much weaker. We use a linear, continuously stratified model of the Indian Ocean to simulate their sea level patterns of IOD and ENSO events. We then separate solutions into parts that correspond to specific processes: coastal alongshore winds, remote forcing from the equator via reflected Rossby waves, and direct forcing by interior winds within the bay. During pure IOD events, the SLAs are forced both from the equator and by direct wind forcing. During ENSO events, they are primarily equatorially forced, with only a minor contribution from direct wind forcing. Using a lead/lag covariance analysis between the Niño‐3.4 SST index and Indian Ocean wind stress, we derive a composite wind field for a typical El Niño event: the resulting solution has two negative SLA peaks. The IOD and ENSO signatures are not evident off the west coast of India. Key Points We identify the signatures of IOD and ENSO in sea level variability Distinguish the signatures of IOD with signatures of ENSO Identify the forcing mechanisms of the sea‐level variability

Arctic absolute sea level variations were analyzed based on multi-mission satellite altimetry data and tide gauge observations for the period of 1993–2018. The range of linear absolute sea level trends were found −2.00 mm/a to 6.88 mm/a excluding the central Arctic, positive trend rates were predominantly located in shallow water and coastal areas, and negative rates were located in high-latitude areas and Baffin Bay. Satellite-derived results show that the average secular absolute sea level trend was (2.53±0.42) mm/a in the Arctic region. Large differences were presented between satellite-derived and tide gauge results, which are mainly due to low satellite data coverage, uncertainties in tidal height processing and vertical land movement (VLM). The VLM rates at 11 global navigation satellite system stations around the Arctic Ocean were analyzed, among which 6 stations were tide gauge co-located, the results indicate that the absolute sea level trends after VLM corrected were of the same magnitude as satellite altimetry results. Accurately calculating VLM is the primary uncertainty in interpreting tide gauge measurements such that differences between tide gauge and satellite altimetry data are attributable generally to VLM.

Atmospheric forcing and climate modes of variability on various timescales are important drivers of sea level variability. However, the influence of such drivers on sea level variability along the South African east and south coast has not yet been adequately investigated. Here, we determine the timescales of sea level variability and their relationships with various drivers. Empirical Mode Decomposition (EMD) was applied to seven tide gauge records and potential forcing data for this purpose. The oscillatory modes identified by the EMD were summed to obtain physically more meaningful timescales—specifically, the sub-annual (less than 18 months) and interannual (greater than two years) scales. On the sub-annual scale, sea level responds to regional zonal and meridional winds associated with mesoscale and synoptic weather disturbances. Ekman dynamics resulting from variability in sea level pressure and alongshore winds are important for the coastal sea level on this timescale. On interannual timescales, there were connections with ENSO, the Indian Ocean Dipole (IOD) and the Southern Annular Mode (SAM), although the results are not consistent across all the tide gauge stations and are not particularly strong. In general, El Niño and positive IOD events are coincident with high coastal sea levels and vice versa, whereas there appears to be an inverse relationship between SAM phase and sea level.

Earth is heading towards a climate that last existed more than three million years ago (Ma) during the ‘mid-Pliocene warm period’ 1 , when atmospheric carbon dioxide concentrations were about 400 parts per million, global sea level oscillated in response to orbital forcing 2 , 3 and peak global-mean sea level (GMSL) may have reached about 20 metres above the present-day value 4 , 5 . For sea-level rise of this magnitude, extensive retreat or collapse of the Greenland, West Antarctic and marine-based sectors of the East Antarctic ice sheets is required. Yet the relative amplitude of sea-level variations within glacial–interglacial cycles remains poorly constrained. To address this, we calibrate a theoretical relationship between modern sediment transport by waves and water depth, and then apply the technique to grain size in a continuous 800-metre-thick Pliocene sequence of shallow-marine sediments from Whanganui Basin, New Zealand. Water-depth variations obtained in this way, after corrections for tectonic subsidence, yield cyclic relative sea-level (RSL) variations. Here we show that sea level varied on average by 13 ± 5 metres over glacial–interglacial cycles during the middle-to-late Pliocene (about 3.3–2.5 Ma). The resulting record is independent of the global ice volume proxy 3 (as derived from the deep-ocean oxygen isotope record) and sea-level cycles are in phase with 20-thousand-year (kyr) periodic changes in insolation over Antarctica, paced by eccentricity-modulated orbital precession 6 between 3.3 and 2.7 Ma. Thereafter, sea-level fluctuations are paced by the 41-kyr period of cycles in Earth’s axial tilt as ice sheets stabilize on Antarctica and intensify in the Northern Hemisphere 3 , 6 . Strictly, we provide the amplitude of RSL change, rather than absolute GMSL change. However, simulations of RSL change based on glacio-isostatic adjustment show that our record approximates eustatic sea level, defined here as GMSL unregistered to the centre of the Earth. Nonetheless, under conservative assumptions, our estimates limit maximum Pliocene sea-level rise to less than 25 metres and provide new constraints on polar ice-volume variability under the climate conditions predicted for this century. Sea level varied by 13 ± 5 metres on average, but up to 25 metres, over glacial–interglacial cycles during the Pliocene epoch, due to partial collapses of Antarctic Ice Sheets.

The Norwegian Climate Prediction Model version 1 (NorCPM1) is a new research tool for performing climate reanalyses and seasonal-to-decadal climate predictions. It combines the Norwegian Earth System Model version 1 (NorESM1) – which features interactive aerosol–cloud schemes and an isopycnic-coordinate ocean component with biogeochemistry – with anomaly assimilation of sea surface temperature (SST) and T/S-profile observations using the ensemble Kalman filter (EnKF).We describe the Earth system component and the data assimilation (DA) scheme, highlighting implementation of new forcings, bug fixes, retuning and DA innovations. Notably, NorCPM1 uses two anomaly assimilation variants to assess the impact of sea ice initialization and climatological reference period: the first (i1) uses a 1980–2010 reference climatology for computing anomalies and the DA only updates the physical ocean state; the second (i2) uses a 1950–2010 reference climatology and additionally updates the sea ice state via strongly coupled DA of ocean observations.We assess the baseline, reanalysis and prediction performance with output contributed to the Decadal Climate Prediction Project (DCPP) as part of the sixth Coupled Model Intercomparison Project (CMIP6). The NorESM1 simulations exhibit a moderate historical global surface temperature evolution and tropical climate variability characteristics that compare favourably with observations. The climate biases of NorESM1 using CMIP6 external forcings are comparable to, or slightly larger than those of, the original NorESM1 CMIP5 model, with positive biases in Atlantic meridional overturning circulation (AMOC) strength and Arctic sea ice thickness, too-cold subtropical oceans and northern continents, and a too-warm North Atlantic and Southern Ocean. The biases in the assimilation experiments are mostly unchanged, except for a reduced sea ice thickness bias in i2 caused by the assimilation update of sea ice, generally confirming that the anomaly assimilation synchronizes variability without changing the climatology. The i1 and i2 reanalysis/hindcast products overall show comparable performance. The benefits of DA-assisted initialization are seen globally in the first year of the prediction over a range of variables, also in the atmosphere and over land. External forcings are the primary source of multiyear skills, while added benefit from initialization is demonstrated for the subpolar North Atlantic (SPNA) and its extension to the Arctic, and also for temperature over land if the forced signal is removed. Both products show limited success in constraining and predicting unforced surface ocean biogeochemistry variability. However, observational uncertainties and short temporal coverage make biogeochemistry evaluation uncertain, and potential predictability is found to be high. For physical climate prediction, i2 performs marginally better than i1 for a range of variables, especially in the SPNA and in the vicinity of sea ice, with notably improved sea level variability of the Southern Ocean. Despite similar skills, i1 and i2 feature very different drift behaviours, mainly due to their use of different climatologies in DA; i2 exhibits an anomalously strong AMOC that leads to forecast drift with unrealistic warming in the SPNA, whereas i1 exhibits a weaker AMOC that leads to unrealistic cooling. In polar regions, the reduction in climatological ice thickness in i2 causes additional forecast drift as the ice grows back. Posteriori lead-dependent drift correction removes most hindcast differences; applications should therefore benefit from combining the two products.The results confirm that the large-scale ocean circulation exerts strong control on North Atlantic temperature variability, implying predictive potential from better synchronization of circulation variability. Future development will therefore focus on improving the representation of mean state and variability of AMOC and its initialization, in addition to upgrades of the atmospheric component. Other efforts will be directed to refining the anomaly assimilation scheme – to better separate internal and forced signals, to include land and atmosphere initialization and new observational types – and improving biogeochemistry prediction capability. Combined with other systems, NorCPM1 may already contribute to skilful multiyear climate prediction that benefits society.

During the altimeter era, the sea level in the South China Sea (SCS) and western tropical Pacific (WTP) experienced significant decadal variability. The sea level rose during 1993–2009 and fell during 2010–2019. The decadal variability of Walker Circulation associated with the Pacific Decadal Oscillation can explain the sea level variability in the WTP to a great extent. The wind forced westward propagating Rossby waves increased (decreased) the sea level in the WTP during 1993–2009 (2010–2019). However, the interior wind forcing has a negligible contribution to the decadal variability of the sea level in the SCS. The remote forcing from WTP through the oceanic bridge was supposed to play a dominant role. The sensitive experiments of a 1½‐layer model and Regional Oceanic Modeling System suggested that the sea level signals via the Sibutu Passage and Mindoro Strait accounted for the decadal variability of sea level in the central basin of SCS. Plain Language Summary During the altimeter era, the global mean sea level rose continuously, while the regional sea level trends are not uniform. The atmospheric and oceanic processes associated with climate modes played an important role. The Walker Circulation anomalies, connected closely with the “atmospheric bridge” of the Pacific Decadal Oscillation (PDO), forced a reverse sea level trend in the western and eastern tropical Pacific via propagations of Kelvin/Rossby waves. The sea level signals propagated into the South China Sea (SCS) via the Sibutu Passage and Mindoro Strait, which dominated the sea level variations in the central SCS. Unlike in the tropical Pacific, the decadal variability of sea level in the SCS was associated with PDO through “oceanic bridge.” The results in this study shed light on our understanding on the dynamics of sea level and upper‐layer circulation changes in the SCS and adjacent regions. Key Points Sea level trends in the South China Sea (SCS) and western tropical Pacific experienced regime shift during the altimeter era Remote forcing originated from tropical Pacific dominated the decadal variability of sea level in the SCS Tropical Pacific conveys its impact on the sea level in the central SCS mainly via the Sibutu Passage and Mindoro Strait

Coastal areas epitomize the notion of ‘at-risk’ territory in the context of climate change and sea level rise (SLR). Knowledge of the water level changes at the coast resulting from the mean sea level variability, tide, atmospheric surge and wave setup is critical for coastal flooding assessment. This study investigates how coastal water level can be altered by interactions between SLR, tides, storm surges, waves and flooding. The main mechanisms of interaction are identified, mainly by analyzing the shallow water equations. Based on a literature review, the orders of magnitude of these interactions are estimated in different environments. The investigated interactions exhibit a strong spatiotemporal variability. Depending on the type of environments (e.g., morphology, hydrometeorological context), they can reach several tens of centimeters (positive or negative). As a consequence, probabilistic projections of future coastal water levels and flooding should identify whether interaction processes are of leading order, and, where appropriate, projections should account for these interactions through modeling or statistical methods.