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.
Increasing coastal inundation risk in a warming climate will require accurate and reliable seasonal forecasts of sea level anomalies at fine spatial scales. In this study, we explore statistical ...downscaling of monthly hindcasts from six current seasonal prediction systems to provide a high‐resolution prediction of sea level anomalies along the North American coast, including at several tide gauge stations. This involves applying a seasonally invariant downscaling operator, constructing by linearly regressing high‐resolution (1/12°) ocean reanalysis data against its coarse‐grained (1°) counterpart, to each hindcast ensemble member for the period 1982–2011. The resulting high‐resolution coastal hindcasts have significantly more deterministic skill than the original hindcasts interpolated onto the high‐resolution grid. Most of this improvement occurs during summer and fall, without impacting the seasonality of skill noted in previous studies. Analysis of the downscaling operator reveals that it boosts skill by amplifying the most predictable patterns while damping the less predictable patterns.
Plain Language Summary
Currently, the large computer models that form the basis of seasonal climate prediction systems produce coastal sea level forecasts spaced about 100 km apart. This is too coarse to meet the needs of U.S. coastal ocean management and services, which are becoming increasingly important as sea levels rise in a warming climate. In this study, we explored a method to provide such information on much smaller spatial scales, which better correspond to local coastal sea level measurements by tide gauges. We developed an efficient way to generate monthly sea level predictions on distances as small as 10 km apart, by applying the observed statistical relationship between sea level variations on scales of 100–1,000 km and finer‐scale coastal ocean observations to the original coarser model predictions. By testing our approach on past forecasts (“hindcasts”) from existing climate forecast systems, we found that we could improve forecasts for different local regions along both the U.S. West and East Coasts.
Key Points
Sea level prediction from relatively coarse operational forecasts can be enhanced to finer coastal scales using statistical downscaling
Downscaling can be determined by multivariate linear regression trained from high‐resolution reanalysis and its coarse‐grained counterpart
This downscaling method significantly improves skill compared to bilinearly interpolated hindcasts at several U.S. tide gauge locations
Barystatic sea level rise (SLR) caused by the addition of freshwater to the ocean from melting ice can in principle be recorded by a reduction in seawater salinity, but detection of this signal has ...been hindered by sparse data coverage and the small trends compared to natural variability. Here, we develop an autoregressive machine learning method to estimate salinity changes in the global ocean from 2001 to 2019 that reduces uncertainties in ocean freshening trends by a factor of four compared to previous estimates. We find that the ocean mass rose by 13,000 ± 3,000 Gt from 2001 to 2019, implying a barystatic SLR of 2.0 ± 0.5 mm/yr. Combined with SLR of 1.3 ± 0.1 mm/yr due to ocean thermal expansion, these results suggest that global mean sea level rose by 3.4 ± 0.6 mm/yr from 2001 to 2019. These results provide an important validation of remote‐sensing measurements of ocean mass changes, global SLR, and global ice budgets.
Plain Language Summary
Global sea level rise (SLR) is caused by heating of the ocean, and by the input of freshwater from the melting of glaciers and ice caps. Global freshwater input to the oceans from melting ice during the 21st century has primarily been tracked by satellites that measure changes in the mass of the ocean. Here, we show that trends in global SLR can also be accurately tracked by global observations of ocean salinity changes, as freshwater runoff from melting ice enters the ocean and dilutes ocean salinity. These results show that ocean salinity measurements are critical for monitoring global sea level changes, particularly as polar warming intensifies and the melting of ice sheets accelerates.
Key Points
A new full‐depth ocean salinity product yields robust global freshening trend of (35 ± 10) × 10−6 yr−1 from 2001 to 2019
Combined with estimates of sea ice loss, this freshening implies that ocean mass rose by 13,000 ± 3,000 Gt from 2001 to 2019
Sea level rise derived from ocean temperature and salinity measurements is 3.4 ± 0.6 mm/yr, confirming the satellite altimetry trend
The causes of sea-level rise since 1900 Frederikse, Thomas; Landerer, Felix; Caron, Lambert ...
Nature (London),
08/2020, Volume:
584, Issue:
7821
Journal Article
Peer reviewed
The rate of global-mean sea-level rise since 1900 has varied over time, but the contributing factors are still poorly understood
. Previous assessments found that the summed contributions of ice-mass ...loss, terrestrial water storage and thermal expansion of the ocean could not be reconciled with observed changes in global-mean sea level, implying that changes in sea level or some contributions to those changes were poorly constrained
. Recent improvements to observational data, our understanding of the main contributing processes to sea-level change and methods for estimating the individual contributions, mean another attempt at reconciliation is warranted. Here we present a probabilistic framework to reconstruct sea level since 1900 using independent observations and their inherent uncertainties. The sum of the contributions to sea-level change from thermal expansion of the ocean, ice-mass loss and changes in terrestrial water storage is consistent with the trends and multidecadal variability in observed sea level on both global and basin scales, which we reconstruct from tide-gauge records. Ice-mass loss-predominantly from glaciers-has caused twice as much sea-level rise since 1900 as has thermal expansion. Mass loss from glaciers and the Greenland Ice Sheet explains the high rates of global sea-level rise during the 1940s, while a sharp increase in water impoundment by artificial reservoirs is the main cause of the lower-than-average rates during the 1970s. The acceleration in sea-level rise since the 1970s is caused by the combination of thermal expansion of the ocean and increased ice-mass loss from Greenland. Our results reconcile the magnitude of observed global-mean sea-level rise since 1900 with estimates based on the underlying processes, implying that no additional processes are required to explain the observed changes in sea level since 1900.
Small stress changes such as those from sea level fluctuations can be large enough to trigger earthquakes. If small and large earthquakes initiate similarly, high‐resolution catalogs with low ...detection thresholds are best suited to illuminate such processes. Below the Sea of Marmara section of the North Anatolian Fault, a segment of ≈ $\approx $150 km is late in its seismic cycle. We generated high‐resolution seismicity catalogs for a hydrothermal region in the eastern Sea of Marmara employing AI‐based and template matching techniques to investigate the link between sea level fluctuations and seismicity over 6 months. All high resolution catalogs show that local seismicity rates are larger during time periods shortly after local minima of sea level, when it is already rising. Local strainmeters indicate that seismicity is promoted when the ratio of differential to areal strain is the largest. The strain changes from sea level variations, on the order of 30–300 nstrain, are sufficient to promote seismicity.
Plain Language Summary
Quasi‐periodic phenomena are a natural probe to test how the Earth's responses to a certain stress perturbation. High‐resolution catalogs with low detection thresholds may provide a new opportunity to look for this type of earthquake triggering. A segment of 150 km below the Sea of Marmara section of the North Anatolian Fault is late in its seismic cycle. Here, we generated high‐resolution seismicity catalogs for 6 months covering a hydrothermal region south of Istanbul in the eastern Sea of Marmara including seismicity up to MW 4.5. For first time in this region, we document a strong effect of the Sea of Marmara water level changes on the local seismicity. Both high‐resolution catalogs show that local seismicity rates are significantly larger during time periods shortly after local minima on sea level, when the sea level is rising. The available local instrumentation provided an estimate of the strain changes that were sufficient to promote seismicity. If such small stress perturbations from sea level changes are enough to trigger seismicity, it may suggest that the region is very close to failure.
Key Points
We generated enhanced seismicity catalogs to investigate the potential link between sea level change and seismicity in a hydrothermal region
Higher seismicity rates from the entire and declustered catalogs are observed during time periods when sea level is rising
Strain estimates from local strainmeters show that seismicity was promoted during reduced normal and enhanced shear strain conditions
Sea level anomaly extremes impact tropical Pacific Ocean islands, often with too little warning to mitigate risks. With El Niño, such as the strong 2015/16 event, comes weaker trade winds and mean ...sea level drops exceeding 30 cm in the western Pacific that expose shallow-water ecosystems at low tides. Nearly opposite climate conditions accompany La Niña events, which cause sea level high stands (10–20 cm) and result in more frequent tide- and storm-related inundations that threaten coastlines. In the past, these effects have been exacerbated by decadal sea level variability, as well as continuing global sea level rise. Climate models, which are increasingly better able to simulate past and future evolutions of phenomena responsible for these extremes (i.e., El Niño–Southern Oscillation, Pacific decadal oscillation, and greenhouse warming), are also able to describe, or even directly simulate, associated sea level fluctuations. By compiling monthly sea level anomaly predictions from multiple statistical and dynamical (coupled ocean–atmosphere) models, which are typically skillful out to at least six months in the tropical Pacific, improved future outlooks are achieved. From this multimodel ensemble comes forecasts that are less prone to individual model errors and also uncertainty measurements achieved by comparing retrospective forecasts with the observed sea level. This framework delivers online a new real-time forecasting product of monthly mean sea level anomalies and will provide to the Pacific island community information that can be used to reduce impacts associated with sea level extremes.
Sea level variations at the coast can have drastic environmental and socio-economic impacts in particular in the context of an ever-increasing coastal population and anthropogenic climate change. ...Regional to global climate variability influences all these factors and exerts a strong control on the coastal sea level over a wide range of time scales. Here, we focus on understanding interannual changes which is paramount to improve interannual forecasting systems as well as to constrain and reduce uncertainties on the secular trend in global mean sea level. We consider the coastal total water level (TWL) as the compound effect of three main components: the wave setup, mean regional sea level anomaly (
i.e.,
steric and ocean circulation influences) and atmospheric surge (
i.e.,
influence of local wind and surface atmospheric pressure). To understand their variability at a global scale, we focus on the effect of four climate modes that affect the major oceanic basins: the El Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO), the North Atlantic Oscillation (NAO) and the Southern Annual Mode (SAM). The contrasted regional influence of these different climate modes on the interannual variations of TWL components are quantified. Results suggest that even if the regional mean sea level is overall the main contributor to the interannual variations of TWL variations at the coast and mostly related to ENSO, the contributions from wave setup and atmospheric surge are not negligible in particular at high latitudes and mostly related to the NAO in the Northern Atlantic and to the SAM in the Southern Hemisphere. Such influences from the NAO and SAM can be seen far away from their extratropical regions of action due to their atmospheric forcing of ocean waves that can significantly propagate their imprint towards tropical areas. Implications for interannual to decadal forecasts of the coastal TWL and related hazards are discussed in the light of regression statistical models and the climate modes own predictability.
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