Displays

Extreme sea levels are a significant threat to life, property, and the environment. These threats are managed by coastal planers through the implementation of risk mitigation strategies. Central to such strategies is knowledge of extreme event probabilities. Typically, these probabilities are estimated by fitting a suitable distribution to the observed extreme data. Estimates, however, are often uncertain due to the small number of extreme events in the tide gauge record and are only available at gauged locations. This restricts our ability to implement cost-effective mitigation. A remarkable fact about sea-level extremes is the existence of spatial dependences, yet the vast majority of studies to date have analyzed extremes on a site-by-site basis. Here we demonstrate that spatial dependences can be exploited to address the limitations posed by the spatiotemporal sparseness of the observational record. We achieve this by pooling all the tide gauge data together through a Bayesian hierarchical model that describes how the distribution of surge extremes varies in time and space. Our new approach has two highly desirable advantages: 1) it enables sharing of information across data sites, with a consequent drastic reduction in estimation uncertainty; 2) it permits interpolation of both the extreme values and the extreme distribution parameters at any arbitrary ungauged location. Using our model, we produce the first, to our knowledge, observation-based probabilistic reanalysis of surge extremes covering the entire Atlantic and North Sea coasts of Europe for the period 1960-2013.

How to cite: Mir Calafat, F. and Marcos, M.: Probabilistic reanalysis of storm surge extremes in Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4888, https://doi.org/10.5194/egusphere-egu2020-4888, 2020.

A rise in global mean sea level is a robust feature of projected anthropogenic climate change using state-of-the-art atmosphere-ocean general circulation models (AOGCMs). However, there is considerable disagreement over the more policy-relevant regional patterns of sea level rise. The Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) aims to improve our understanding of the mechanisms controlling regional and dynamic sea level change. In FAFMIP, identical air-sea buoyancy and momentum flux perturbations are applied to an ensemble of different AOGCMs, to sample the uncertainty associated with model structure and physical processes. Our novel implementation applies FAFMIP perturbations to an ensemble of OGCMs. This framework enables an estimate of the unknown atmosphere-ocean feedbacks, by comparing the coupled and ocean-only response to surface flux perturbations.

Comparing the response to idealised FAFMIP forcing with more realistic, increasing CO2 forcing, much of the spread in regional sea level projections for the North Atlantic and Southern Ocean arises from ocean model structural differences. Ocean-only simulations indicate that only a small proportion of this spread is due to differences in the atmosphere-ocean feedback. Novel tendency diagnostics indicate the relative effect of resolved advection, parametrised eddies, and dianeutral mixing on regional and dynamic sea level change. This study helps to reduce uncertainty in regional sea level projections by refining our estimates of atmosphere-ocean feedbacks and developing our understanding of the physical processes controlling sea level change.

How to cite: Todd, A., Zanna, L., and Gregory, J.: Ocean-only FAFMIP: Understanding Regional Patterns of Ocean Heat Content and Dynamic Sea Level Change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8563, https://doi.org/10.5194/egusphere-egu2020-8563, 2020.

We are warming our planet, and sea levels are rising as oceans expand and ice on land melts. This instigates a threat to coastal communities and ecosystems, and there is an urgent need for sea level predictions encompassing all known uncertainties to plan for it. Comprehensive assessments have concluded that sea level is unlikely to rise by more than about 1.1m this century but with further increase beyond 2100. However, some studies conclude that considerably greater sea level rise could be realised and an expert elicitation assign a substantially higher likelihood to this scenario. Here, we show that models used to assess future sea level in AR5 & SROCC have a lower sea level sensitivity than inferred from observations. By analyzing mean rate of change in sea level (not sea level itself), we identify a near linear relationship with global mean surface temperature in both model projections, and in observations. The model projections fall below expectations from the more recent observational period. This comparison suggests that the likely range of sea level projections in IPCC AR5 and SROCC would be too low.

How to cite: Grinsted, A. and Christensen, J. H.: The transient sensitivity of sea level rise, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7084, https://doi.org/10.5194/egusphere-egu2020-7084, 2020.

Global-mean sea level (GMSL) has been rising unsteadily by about 1.5 mm/yr since 1900, but the underlying causes of this trend and the multi-decadal variations are still poorly understood. Over the last few years, updated estimates of the underlying contributing processes have become available, notably for the contributions from glaciers, terrestrial water storage, the Greenland Ice Sheet, and thermal expansion. In parallel, 20th-century GMSL estimates have been revised downward as a result of improved reconstruction approaches, spatial bias correction schemes, and the inclusion of estimates of local vertical land motion at tide-gauge locations. Together, both developments now necessitate the re-evaluation of the GMSL budget to determine whether the observed sea-level rise since 1900 can be reconciled with the estimated sum of contributing processes. 

Here we present a probabilistic framework to reconstruct and budget sea level with independent observations considering their inherent uncertainties. We find that the sum of thermal expansion, ice-mass loss and terrestrial water storage changes is consistent with the trends and multi-decadal variability in observed sea level on both global and basin scales, which we reconstruct from tide-gauge records. 

Glacier-dominated cryospheric mass loss has caused twice as much sea-level rise as thermal expansion since 1900. Glacier and Greenland Ice Sheet mass loss well explains the high rates typically seen in global sea-level reconstructions during the 1930s, while a sharp increase in water impoundment by artificial reservoirs has been the dominant contributor to lower-than-average rates during the 1970s. The acceleration since the 1970s is caused by both thermal expansion and increased Greenland mass loss. No additional large-scale deep ocean warming or additional mass loss from Antarctica are needed to explain 20th-century changes in global-mean sea level. This assessment reconciles the magnitude of observed global-mean sea-level rise since 1900 with estimates of underlying processes.

How to cite: Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., and Wu, Y.-C.: The causes of sea-level rise since 1900, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7907, https://doi.org/10.5194/egusphere-egu2020-7907, 2020.

Sea-level rise projections and knowledge of their uncertainties are vital to make informed mitigation and adaptation decisions. To elicit expert judgments from members of the scientific community regarding future global mean sea-level (GMSL) rise and its uncertainties, we repeated a survey originally conducted five years ago. Under Representative Concentration Pathway (RCP) 2.6, 106 experts projected a likely (at least 66% probability) GMSL rise of 0.30–0.65 m by 2100, and 0.54–2.15 m by 2300, relative to 1986–2005. Under RCP 8.5, the same experts projected a likely GMSL rise of 0.63–1.32 m by 2100, and 1.67–5.61 m by 2300. Expert projections for 2100 are similar to those from the original survey, although the projection for 2300 has extended tails and is higher than the original survey. Experts give a likelihood of 42% (original survey) and 45% (current survey) that under the high emissions scenario GMSL rise will exceed the upper bound (0.98 m) of the likely (i.e. an exceedance probability of 17%) range estimated by the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Responses to open-ended questions suggest that the increases in upper-end estimates and uncertainties arose from recent influential studies about the impact of marine ice cliff instability on the meltwater contribution to GMSL rise from the Antarctic Ice Sheet.

How to cite: Horton, B., Khan, N., Cahill, N., Lee, J., Shaw, T., Garner, A., Kemp, A., Engelhart, S., and Rahmstorf, S.: Estimating global mean sea-level rise and its uncertainties by 2100 and 2300 from expert assessment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7302, https://doi.org/10.5194/egusphere-egu2020-7302, 2020.

The sea level budget (SLB) equates changes in sea surface height (SSH) to the sum of various geo-physical processes that contribute to sea level change. Currently, it is a common practice to explain a change in SSH as a sum of ocean mass and steric change, assuming that solid-Earth motion is corrected for and completely explained by secular visco-elastic relaxation of mantle, due to the process of glacial isostatic adjustment. Yet, since the Solid Earth also responds elastically to changes in present day mass load near the surface of the Earth, we can expect the ocean bottom to respond to ongoing ocean mass changes. This elastic ocean bottom deformation (OBD) has been ignored until very recently because the contribution of ocean mass to sea level rise was thought to be smaller than the steric contribution and the resulting OBD was within observation system uncertainties. However, ocean mass change has increased rapidly in the last 2 decades. Therefore, OBD is no longer negligible and recent studies have shown that its magnitude is similar to that of the deep steric sea level contribution: a global mean of about 0.1 mm/yr but regional changes at some places can be more than 10 times the global mean. Although now an important part of the SLB, especially for regional sea level, OBD is considered by only a few budget studies and they treat it as a spatially uniform correction. This is due to lack of a mathematical framework that defines the contribution of OBD to the SLB. Here, we use a mass-volume framework to derive, for the first time, a SLB equation that partitions SSH change into its component parts accurately and it includes OBD as a physical response of the Earth system. This updated SLB equation is important for various disciplines of Earth Sciences that use the SLB equation: as a constraint to assess the quality of observational time-series; as a means to quantify the importance of each component of sea level change; and, to adequately include all processes in global and regional sea level projections. We recommend using the updated SLB equation for sea level budget studies. We also revisit the contemporary SLB with the updated SLB equation using satellite altimetry data, GRACE data, and ARGO data.

How to cite: Vishwakarma, B. D., Royston, S., Riva, R. E. M., Westaway, R. M., and Bamber, J. L.: A revised sea level budget equation to accurately represent physical processes driving sea level rise, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8808, https://doi.org/10.5194/egusphere-egu2020-8808, 2020.

Science-based policy for coastal protection requires accurate estimates of the uncertainty in regional sea-level rise. These estimates are strongly influenced by the codependence of individual contributors: thermosteric expansion, ocean dynamics, and mass loss from glaciers and ice sheets. In this study, we use model output and parameterisations to quantify the projected total sea-level rise from a set of 15 Earth System Models from the Coupled Model Intercomparison Project (CMIP) 5. We use these model-based estimates of total sea-level rise to quantify the codependence of individual contributors, determined by the full climate response. We find that assumptions on codependence made in recent reports of the Intergovernmental Panel on Climate Change (IPCC) lead to an overestimation in the uncertainty in regional sea-level rise by 20 to 60%. We further conclude that global mean surface temperature rise is a poor indicator for the inter-model difference in regional sea-level rise as it does not account for inter-model differences in atmospheric and oceanic heat distribution and precipitation patterns. The codependencies derived in this study are suitable for application to new projections, allowing for accurate and consistent estimates of the uncertainty in global and regional sea-level rise.

How to cite: Lambert, E., Le Bars, D., and van de Wal, R.: The codependence of contributors to regional sea-level rise, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13312, https://doi.org/10.5194/egusphere-egu2020-13312, 2020.

    Global warming-related SLR (sea level rise) constitutes a substantial threat to Macau, due to its low elevation, small size and ongoing land reclamation. This study was devised to determine the long-term variation of sea level change in Macau, as well as to develop future projections based on tide gauge and satellite data and GCM simulations, aiming to provide knowledge for SLR mitigation and adaptation.

    Based on local tide gauge records, sea level in Macau is now rising at an accelerated rate: 1.35 mm yr⁻¹ over 1925–2010 and jumping to 4.2 mm yr⁻¹ over 1970–2010, reflecting an apparent acceleration of SLR. Furthermore, the sea level near Macau rose 10% faster than the global mean during the period from 1993 to 2012. In addition, the rate of VLM (vertical land movement) at Macau is estimated at -0.153mm yr^-1, contributing little to local sea level change.

    In the future, as projected by a suite of climate models, the rate of SLR in Macau will be about 20% higher than the global average. This is induced primarily by a greater-than-average rate of oceanic thermal expansion in Macau, together with enhanced southerly anomalies that lead to a piling up of sea water. Specifically, the sea level is projected to rise 8–12, 22–51 and 35–118 cm by 2020, 2060 and 2100 with respect to the 1986–2005 baseline climatology, respectively, depending on the emissions scenario and climate sensitivity. If we consider the medium emissions scenario RCP4.5 along with medium climate sensitivity, Macau can expect to experience an SLR of 10, 34 and 65 cm by 2020, 2060 and 2100. If the worst case happens (RCP8.5 plus high climate sensitivity), the SLR will be far higher than that in the medium case; namely, 12, 51 and 118 cm by 2020, 2060, and 2100, respectively. The SLR under the lower emissions scenario is expected to be less severe than that under the higher emissions scenarios: by 2100, an SLR of 65–118 cm in Macau under RCP8.5, almost twice as fast as that under RCP2.6. The key source of uncertainty stems from the emissions scenario and poor knowledge of climate sensitivity. By 2020, the uncertainty range is only 4 cm, yet by 2100 the range will be increased to 83 cm.

How to cite: Wang, L., Huang, G., Zhou, W., and Chen, W.: Sea Level Rise in Macau and Adjacent Southern China Coast: Historical Change and Future Projections, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1909, https://doi.org/10.5194/egusphere-egu2020-1909, 2020.

The steric component of sea-level change comprises variations in the temperature (thermosteric) and salinity (halosteric) of the oceans, which alter the water’s density, leading to volumetric variations of the water column. Although its importance is unarguable, throughout the literature there is a disagreement on how much the steric component actually contributes to sea-level change.

Here, we investigate two sources of uncertainty to steric trends, both at global and regional scale. First, we look at how the use of different temperature and salinity datasets influences the estimated steric height. For that, we analyzed 15 datasets, combining different techniques (hydrographic profiles, Argo floats and ocean reanalyses). Second, since the estimation of uncertainties for linear and quadratic trends requires the adoption of a noise model, we compared the performance of several different noise models.  

We find that by varying both the dataset and noise-model, the global mean trend and uncertainty from 2005 to 2015 can vary from 0.566 to 2.334 mm/yr and 0.022 to 1.646 mm/yr, respectively. This range becomes even larger at regional scales. At a global scale, the selection of datasets has a larger influence on the trend, while at a regional scale the choice of the noise model dominates the spread in steric sea-level trends. Our results emphasize the need to use an ensemble of datasets to infer steric changes, and to carefully choose a noise model.

How to cite: Camargo, C., Riva, R., Hermans, T., and Slangen, A.: Revisiting the Global and Regional Steric Sea-level Trends in the Satellite Era, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3009, https://doi.org/10.5194/egusphere-egu2020-3009, 2020.

How to cite: van Westen, R. and Dijkstra, H.: Resolution Dependency of Future Caribbean Sea Level Response, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3381, https://doi.org/10.5194/egusphere-egu2020-3381, 2020.

Understanding present day sea level changes and their drivers requires the separation of the total sea level change into individual mass and steric related contributions. Total sea level rise has been observed continuously since 1993 providing a more than 25 year long time series of global and regional sea level variations. However, direct monitoring of ocean mass change has only been done since the start of the Gravity Recovery And Climate Experiment (GRACE) mission in 2002. It ended in 2017 and was succeeded by the follow-on mission (GRACE-FO) in 2018 leaving a gap of about 1 year. In the same time period of GRACE, since the early 2000s, a global array of freely drifting Argo floats samples temperature and salinity profiles of up to 2000m depth which can be converted to steric sea level change.

By combining altimetry, GRACE(-FO) and Argo data sets it is possible to derive global and regional sea level budgets. The conventional approach is to analyze at least two of the data sets and derive the residual, or compare with the third one. A more recent approach is the global joint inversion method (Rietbroek et al., 2016) which fits forward-modeled spatial fingerprints to a combination of GRACE gravity data and Jason-1/-2 satellite altimetry data. This enables us, additionally, to separate altimetric sea level change into mass contributions from terrestrial hydrology, the melting of land glaciers and the ice-sheets in Greenland and Antarctica as well as contributions from steric sea level changes due to variations in ocean temperature and salinity. It also allows to include a data weighting scheme in the analysis.

Here, we present global and regional sea level budget results from an updated inversion based on multi-mission altimetry (Jason-1/-2/-3, Envisat, Cryosat-2, Sentinel-3, …) providing better spatial coverage as well as new RL06 GRACE and GRACE-FO data which enables us to extend the time series of individual components of the sea level budget beyond the GRACE era from 2002-04 till 2019-06. The presented sea level budget is closed on global scale with a residual (unexplained) contribution of about 0.1 mm/yr, globally, originating in eddy-active regions. We provide consistent validation of our results against conventionally analyzed altimetry and GRACE data sets where we find agreement on global scales to be better than 0.1 mm/yr but a larger disagreement at regional scales as well as the implications of our results for deriving ocean heat content. We will also provide first results for filling the gap in the sea level budget estimates due to the gap between the GRACE and GRACE-FO missions by additionally incorporating time-variable gravity information from the Swarm mission as well as from Satellite Laser Ranging (SLR) to 5 satellites (Lageos-1/-2, Stella, Starlette, Ajisai).

How to cite: Uebbing, B., Lück, C., Rietbroek, R., Vielberg, K., and Kusche, J.: Closing the global and regional sea level budgets by combining multi-mission altimetry and GRACE(-FO) data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3507, https://doi.org/10.5194/egusphere-egu2020-3507, 2020.

Regional sea level change is strongly societal relevant. The knowledge about the forcing mechanisms for contemporary regional sea level variation is important to the evaluation of climate models and the fidelity of projected sea level changes using these models. Dynamic sea level variation off the northeast US coast has been a subject of significant interest of late. However, there is inadequate understanding about the forcing mechanisms and the underlying oceanic processes. The Estimating the Circulation and Climate of the Ocean (ECCO) state estimate reproduced well the observed interannual-to-decadal variation of dynamic sea level in the region during the satellite altimeter era. Here we use the ECCO adjoint sensitivity tools to quantify the relative contributions of local and remote winds, surface heat flux, and surface freshwater flux on dynamic sea level variation in this region. We further characterize the salient oceanic processes associated with different surface forcings on different time scales.

How to cite: Lee, T., Wang, O., Zhang, H., Fenty, I., and fukumori, I.: Deciphering forcing mechanisms for dynamic sea level variations off the northeast US coast, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6393, https://doi.org/10.5194/egusphere-egu2020-6393, 2020.

The Caribbean islands encompass some of the most vulnerable coastlines in terms of sea level rise, exposure to tropical cyclones, changes in waves and storm surges. Climate in the Caribbean is already changing and sea level rise impacts are already being felt. Considerable local and regional variations in the rate, magnitude, and direction of sea-level change can be expected as a result of thermal expansion, tectonic movements, and changes in ocean circulation. Governments in the Caribbean recognise that climate change and sea level rise are serious threats to the sustainable development and economic growth of the Caribbean islands and urgent actions are required to increase the resilience and make decisions about how to adapt to future climate change (Caribbean Marine Climate Change Report Card 2017; IPCC 2014).

As part of the UK Commonwealth Marine Economies (CME) Programme and through collaboration with local stakeholders in St Vincent, we have identified particular areas at risk from changing water level and wave conditions. The Caribbean Sea, particularly the Lesser Antilles, suffers from limited observational data due to a lack of coastal monitoring, making numerical models even more important to fill this gap. The current projects brings together improved access to tide gauge observations, as well as global, regional and local water level and wave modelling to provide useful tools for coastal planners.

We present our initial design of a coastal data hub with sea level information for stakeholder access in St. Vincent and Grenadines, Grenada and St Lucia, with potential development of the hub for the Caribbean region. The work presented here is a contribution to the wide range of ongoing activities under the Commonwealth Marine Economies (CME) Programme in the Caribbean, falling within the work package “Development of a coastal data hub for stakeholder access in the Caribbean region”, under the NOC led projects “Climate Change Impact Assessment: Ocean Modelling and Monitoring for the Caribbean CME states”.

How to cite: Jevrejeva, S., Wolf, J., Matthews, A., Williams, J., Byrne, D., Bradshaw, E., Williams, S., Hibbert, A., Gordon, K., Rickards, L., De Dominicis, M., and Bricheno, L.: Developing future sea level services for Small Island Developing States, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6495, https://doi.org/10.5194/egusphere-egu2020-6495, 2020.

A set of historical tide-gauge sea-level records from two locations in Santander (Northern Spain) and Alicante (Spanish Mediterranean coast) have been recovered from logbooks stored in national archives. Sea-level measurements have been digitised, quality-controlled and merged into three consistent sea-level time series (two in Alicante and one in Santander) using high precision levelling information. The historical sea-level record in Santander consists of a daily time series spanning the period 1876-1924 and it is further connected to the modern tide-gauge station nearby, ensuring datum continuity up to the present. The sea-level record in Alicante starts in 1870 with daily averaged values until the 1920s and hourly afterwards, and is still in operation, thus representing the longest tide-gauge sea-level time series in the Mediterranean Sea. The long-term consistency and reliability of the new records is discussed based on the comparison with nearby tide gauge time series.

How to cite: Marcos, M., Puyol, B., Amores, A., Pérez Gómez, B., Fraile, M. Á., and Talke, S. A.: Historical tide-gauge sea-level observations in Alicante and Santander (Spain) since the 19th century, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7596, https://doi.org/10.5194/egusphere-egu2020-7596, 2020.

Sea level rise is one of the most indisputable effects of global warming with important consequences for current decisions concerning mitigation and adaptation. A few evolutions in the climate and decision making fields have recently increased the concerns of sea level information users about the potential impacts of future sea level. We identify four main evolutions:

- Most countries are not on track to reach their Paris agreement emission pledges, making the goal of staying well bellow 2ºC less and less attainable.

- New climate model simulations from the Coupled Model Inter-comparison Project 6 (CMIP6) have both a higher average climate sensitivity and a larger spread between models compared to CMIP5.

- The Greenland and Antarctic ice sheets are melting faster than expected in previous IPCC reports and future projections from a recent structured expert judgment show larger expected melt and more uncertainty than both current numerical models and a previous expert judgment.

- Decision makers are more and more interested in events with a large impact and a small probability to build robust infrastructure and design robust long term strategies concerning relocation of coastal communities.

While these four evolutions are fundamentally deeply uncertain, to explore their combined effect we build a subjective probabilistic framework that allows to propagate the uncertainty through the different components and obtain sea level rise projections. In this presentation we present this framework, the results and their sensitivity to multiple hypothesis and we discuss implications for different uses and users of sea level rise information.

How to cite: Le Bars, D., Drijfhout, S., and Haasnoot, M.: The future of sea level: More knowledge, more uncertainty, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7675, https://doi.org/10.5194/egusphere-egu2020-7675, 2020.

More than 28 years of high precision satellite altimetry enables analysis of recent global sea level changes. Several studies have determined the trend and acceleration of global mean sea level (GMSL). This is however done almost exclusively with data from the TOPEX/Poseidon, Jason-1, Jason-2 and Jason-3 satellites (TPJ data). In this study we extend the altimetry record in both time and space by including independent data from the ERS-1, ERS-2, Envisat and CryoSat-2 satellites (ESA data). This increases the time-series to span more than 28 years (1991.7-2020.0) and the spatial coverage is extended from ± 66⁰ to ± 82⁰ latitude. Another advantage of the ESA data is that it is independent of the Cal-1 mode issues which introduces a significant uncertainty to the first 6 years of data from the TOPEX altimeter. Resulting GMSL accelerations of 0.080 ± 0.008 mm/yr² (TPJ) and 0.095 ± 0.009 mm/yr² (ESA).The distribution of sea level acceleration across the global ocean are highly similar between the ESA and TPJ dataset. 

The Pinatubo eruption in 1991 and El-Nino Southern Ocean Oscillation will both affect GMSL. Particularly so as Pinatubo erupted right before the launch of the first ERS-1 satellite. The decrease in GMSL during the first years is seen in the ERS-1 data. We conclude that the effect of the Pinatubo as well as the ENSO effect on GMSL acceleration estimates are below the noise level with the extended time series.

How to cite: Andersen, O. B. and Veng, T.: Consolidating Sea Level Acceleration Estimates from Altimetry for the 1991-2019 Period, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8254, https://doi.org/10.5194/egusphere-egu2020-8254, 2020.

Tide gauges are the main source of information about sea-level changes in the Industrial Age. When looking at global mean values, century-long reconstructions produce rates between 1-2 mm/yr, while estimates over the last three decades reveal a much faster rise of about 3 mm/yr, as also indicated by satellite altimetry observations. In spite of this evidence for a recent acceleration, its quantification remains a challenging and relevant task, because results are highly dependent on the length of the record and on the reconstruction technique, whereas decision makers require clear proof to legitimise action.

While global mean results are very important to understand climate change, regional to local variations are more relevant for the purpose of planning mitigation and adaptation measures. However, mainly due to natural variability, looking at individual tide gauge stations hampers the accurate determination of linear and non-linear trends.

We analyse tide gauge records along the Dutch coast by means of advanced statistical techniques, with the main objective of determining whether and under which conditions it is possible to detect departures from secular trends. We particularly focus on how to handle noise in the natural system, which for the Dutch coast is mainly represented by local atmospheric effects and by variability in ocean dynamics in the NE Atlantic.

How to cite: Riva, R., Steffelbauer, D., Kwakkel, J., Timmermans, J., and Bakker, M.: Detecting non-linear sea-level variations in tide gauge records: a study case along the Dutch coast., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8848, https://doi.org/10.5194/egusphere-egu2020-8848, 2020.

The Permanent Service for Mean Sea Level (PSMSL) is the internationally recognised global sea level data bank for long-term sea level change information from tide gauges, responsible for the collection, publication, analysis and interpretation of sea level data. The primary aim of PSMSL is to collate, archive and distribute long-term sea level information from tide gauges. There is a need both for more records in data sparse regions such as Antarctica, the Arctic and Africa, and for a low cost method for monitoring climate change through sea level.

Recent studies have demonstrated the utility of ground-based GNSS Interferometric Reflectometry (GNSS-IR) for the observation of sea level. GNSS receivers suffer from multipath, but if the physical and geometric effects multipath has on the measured signals are understood then this knowledge can be used to measure other environmental parameters such as the sea surface reflection. The GNSS receiver can also determine vertical land motion.

PSMSL has received funding to create an international archive to preserve and deliver GNSS-IR data and to integrate these data with existing sea level observing networks. We aim to create an efficient data delivery mechanism to allow the sea level community to access these new data and incorporate them into existing records. We will develop a data format and create and/or populate controlled vocabularies with the new parameters, site identifiers and other discovery metadata required.

Currently, we have processed records from over 250 GNSS receivers across the globe: each will be made available alongside information detailing how the records were processed; which GNSS constellations, satellites and frequencies were used; and visual diagnostics of each site. In this presentation we will give a brief overview of the theory behind GNSS-IR, and present some of the content that we plan to include in the completed portal.

How to cite: Matthews, A., Williams, S., Bradshaw, E., Gordon, K., Hibbert, A., Jevrejeva, S., Rickards, L., and Woodworth, P.: An International Data Centre for GNSS Interferometric Reflectometry Data for Observing Sea Level Change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9706, https://doi.org/10.5194/egusphere-egu2020-9706, 2020.

The European Space Agency, in the framework of the Sea Level Climate Change Initiative (SL_CCI), is developing consistent and long-term satellite-based data-sets to study climate-scale variations of sea level globally and in the coastal zone. Two altimetry data-sets were recently produced. The first product is generated over a grid of 0.25x0.25 degrees, merging and homogenizing the various satellite altimetry missions. The second product that is still experimental is along track over a grid of 0.35 km. An operational production of climate-oriented altimeter sea level products has just started in the framework of the European Copernicus Climate Change Service (C3S) and a daily-mean product is now available over a grid of 0.125x0.125 degrees covering the global ocean since 1993 to present.

We made a comparison of the SL_CCI satellite altimetry dataset with sea level time series at selected tide gauges in the Mediterranean Sea, focusing on Venice and Trieste. There, the coast is densely covered by civil settlements and industrial areas with a strongly rooted seaside tourism, and tides and storm-related surges reach higher levels than in most of the Mediterranean Sea, causing damages and casualties as in the recent storm of November 12th, 2019: the second higher water registered in Venice since 1872. Moreover, in the Venice area the ground displacements exhibit clear negative trends which deepen the effects of the absolute sea level rise.

Several authors have pointed out the synergy between satellite altimetry and tide gauges to corroborate evidences of ground displacements. Our contribution aims at understanding the role played by subsidence, estimated by the diffence between coastal altimetry and in situ measurements, on the local sea level rise. A partial validation of these estimates has been made against GPS-derived values, in order to distinguish the contributions of subsidence and eustatism. This work will contribute to identify problems and challenges to extend the sea level climate record to the coastal zone with quality comparable to the open ocean, and also to assess the suitability of altimeter-derived absolute sea levels as a tool to estimate subsidence from tide gauge measurement in places where permanent GPS receivers are not available.

How to cite: De Biasio, F., Vignudelli, S., and Baldin, G.: Estimating Vertical Land Motion in Northern Adriatic Sea with Coastal Altimetry and In Situ Observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9754, https://doi.org/10.5194/egusphere-egu2020-9754, 2020.

The coastlines of the Baltic Sea and Indonesia are both relatively complex, so that the estimation of extreme sea levels caused by the atmospheric forcing becomes complex with conventional methods. Here, we explore whether Machine Learning methods can provide a model surrogate to compute more rapidly daily extremes in sea level from large-scale atmosphere-ocean fields. We investigate the connections between the atmospheric and ocean drivers of local extreme sea level in South East Asia and along the Baltic Sea based on statistical analysis by Random Forest Models, driven by large-scale meteorological predictors and daily extreme sea level measured by tide-gauge records over the last few decades.

First results show that in some Indonesian areas extremes are driven by large-scale climate fields; in other areas they are incoherently driven by local processes. An area where random forest predicted extremes show good correspondence to observed extremes is found to be the Malaysian coastline. For the Indonesian coasts, the Random Forest Algorithm was unable to predict extreme sea levels in line with observations. Along the Baltic Sea, in contrast, the Random Forest model is able to produce reasonable estimations of extreme sea levels based on the large-scale atmospheric fields. An analysis of the interrelations of extreme sea levels in the South Asia regions suggests that either the data quality may be compromised in some regions or that other forcing factors, distinct from the large-scale atmospheric fields, may also be involved.

How to cite: Bierstedt, S., Zorita, E., and Hünicke, B.: Statistical Downscaling of daily extreme Sea Level with Random Forest: Examples from South-East Asia and the Baltic Sea, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10082, https://doi.org/10.5194/egusphere-egu2020-10082, 2020.

The satellite altimeter record has provided an unprecedented climate data record for understanding sea level rise and has recently exceeded 27 years in length. This record of sea level change is becoming sufficiently long that we can begin to infer how sea level will change in the future. Results from Large Ensembles (LEs) of climate model simulations reveal that (a) the trend pattern of the Forced Response (FR) of sea level due to aerosols and Greenhouse gases (GHGs) is beginning to emerge from the altimeter record, (b) this pattern is likely to continue similarly for decades into the future, (c) the altimeter record falls during an interesting period when we are transitioning from an aerosol-dominated FR to a GHG dominated FR. All of these results provide clues into the causes of regional variations in the altimeter-observed regional trend pattern. In addition, these results suggest a possible path forward for performing short-term data-driven extrapolations of the satellite altimeter record to better understand future sea level change. We will review all of these results, show initial attempts at extrapolating the measurements, and discuss potential societal implications of the results.

How to cite: Nerem, R. S. and Fasullo, J.: Observed Regional Sea Level Trends: Climate Drivers and Implications for Projecting Future Change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12644, https://doi.org/10.5194/egusphere-egu2020-12644, 2020.

Studies of the sea-level budget are a means of assessing our ability to quantify and understand sea-level changes and their causes. ESA's Climate Change Initiative (CCI) projects include Sea Level CCI, Greenland Ice Sheet CCI, Antarctic Ice Sheet CCI, Glaciers CCI and the Sea Surface Temperature CCI, all addressing Essential Climate Variables (ECVs) related to sea level. The cross-ECV project CCI Sea Level Budget Closure used different products for the sea level and its components, based on the above CCI projects in conjunction with in situ data for ocean thermal expansion (e.g., Argo), GRACE-based assessments of ocean mass change, land water and land ice mass change, and model-based data for glaciers and land hydrology. The involvement of the authors of the individual data products facilitated consistency and enabled a unified treatment of uncertainties and their propagation to the overall budget closure. 

After conclusion of the project, the developed data products are now available for science users and the public. This poster summarizes the project results with a focus on presenting these data products. They include time series (for the periods 1993-2016 and 2003-2016) of global mean sea level changes and global mean sea level contributions from the steric component, from the ocean mass component and from the individual mass contributions by glaciers, the Greenland Ice Sheet, the Antarctic Ice Sheet and changes in land water storage. They are designed and documented in the consistent framework of ESA SLBC_cci and include uncertainty measures per datum. Additional more comprehensive information, such as geographic grids underlying the global means, are available for some components.

For the long-term trend, the budget is closed within uncertainties on the order of 0.3 mm/yr (1 sigma). Moreover, the budget is also closed within uncertainties for interannual variations.

How to cite: Horwath, M. and the Sea Level Budget Closure CCI Team: Data products from the ESA CCI Sea Level Budget Closure project, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12811, https://doi.org/10.5194/egusphere-egu2020-12811, 2020.

Observational altimetry data and data of 18 phase 5 of the Coupled Model Intercomparison Project (CMIP5) are investigated to analyze decadal sea level variability for the subtropical South Pacific. The altimetry data covers the period 1993 to 2017. In order to analyze decadal variability yearly means of detrended data are considered. An Empirical Orthogonal Function (EOF) analysis of the Region 20°S to 60°S is performed in to analyze sea level variablility in the subtropics. The tropical region has been omitted in order to avoid the strong El Niño Southern Oscillation (ENSO) signal masking other subtropical variability in the analysis. The first EOF of the altimetry data shows a clear pattern with a North-South dipole explaining 30% of the variance and the corresponding time series shows a decadal periodicity. The decadal variability of this pattern is reproduced by the CMIP5 models. Analyzing model ocean circulation data show consistent decadal variability in the North-South velocity. As a possible forcing zonal (westerly) surface winds are analyzed. Their pattern confirm Ekman transport to the North (South) in the lower (higher) latitudes, leading to a convergence zone and therefore explaining the sea level rise as seen in the EOF pattern, consistently with the Ekman transport a deep compensatory poleward flow is observed.

How to cite: Albrecht, F., Pizarro, O., and Zorita, E.: Decadal Sea level Variability in the subtropical South Pacific, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20007, https://doi.org/10.5194/egusphere-egu2020-20007, 2020.