Rate Of Sea-level Change Research Articles

Effect of Argo Salinity Drift since 2016 on the Estimation of Regional Steric Sea Level Change Rates

Since 2016, the Argo (Array for Real-Time Geostrophic Oceanography) ocean salinity data has exhibited significant drift, directly affecting the accurate quantification of the global steric sea level (SSL) rates. To further investigate how salinity drift affects the estimation of SSL rates in different depths and regions, we divide the 0–2000 m into three layers (0–300 m, 300–1000 m and 1000–2000 m) and select five open oceans (the South and North Pacific, the South and North Atlantic, and the Indian Ocean) for discussion. By comparing the SSL rates between the periods of 2005–2015 and 2005–2019, we can evaluate the impact of salinity drift. Taking the estimated results from the IPRC (provided by the International Pacific Research Center at the University of Hawaii) and BOA (provided by the Second Institute of Oceanography, China) data as examples, we find that the effect of salinity drift is the largest at the depth of 1000–2000 m, about 29% for IPRC data and about 18% for BOA data. Moreover, the South Atlantic is susceptible to the effects of salinity drift, with an approximately 13% impact for IPRC data and 21% for BOA data.

SDUST2020MGCR: a global marine gravity change rate model determined from multi-satellite altimeter data

Abstract. Investigating the global time-varying gravity field mainly depends on GRACE/GRACE-FO gravity data. However, satellite gravity data exhibit low spatial resolution and signal distortion. Satellite altimetry is an important technique for observing the global ocean and provides many consecutive years of data, which enables the study of high-resolution marine gravity variations. This study aims to construct a high-resolution marine gravity change rate (MGCR) model using multi-satellite altimetry data. Initially, multi-satellite altimetry data and ocean temperature–salinity data from 1993 to 2019 are utilized to estimate the altimetry sea level change rate (SLCR) and steric SLCR, respectively. Subsequently, the mass-term SLCR is calculated. Finally, based on the mass-term SLCR, the global MGCR model on 5′ × 5′ grids (SDUST2020MGCR) is constructed by applying the spherical harmonic function method and mass load theory. Comparisons and analyses are conducted between SDUST2020MGCR and GRACE2020MGCR resolved from GRACE/GRACE-FO gravity data. The spatial distribution characteristics of SDUST2020MGCR and GRACE2020MGCR are similar in the sea areas where gravity changes significantly, such as the eastern seas of Japan, the western seas of the Nicobar Islands, and the southern seas of Greenland. The statistical mean values of SDUST2020MGCR and GRACE2020MGCR in global and local oceans are all positive, indicating that MGCR is rising. Nonetheless, differences in spatial distribution and statistical results exist between SDUST2020MGCR and GRACE2020MGCR, primarily attributable to spatial resolution disparities among altimetry data, ocean temperature–salinity data, and GRACE/GRACE-FO data. Compared with GRACE2020MGCR, SDUST2020MGCR has higher spatial resolution and excludes stripe noise and leakage errors. The high-resolution MGCR model constructed using altimetry data can reflect the long-term marine gravity change in more detail, which is helpful in studying seawater mass migration and its associated geophysical processes. The SDUST2020MGCR model data are available at https://doi.org/10.5281/zenodo.10701641 (Zhu et al., 2024).

Coastal Management: A Review of Key Elements for Vulnerability Assessment

Damaging and accelerated anthropization in coastal areas, as well as the need to adapt to climate change, means we must concentrate on improving management plans based on the diagnoses provided by coastal studies. Among these studies is the vulnerability assessment, obtained from evaluating a set of variables or indicators, which contribute to sustainable development. Since there is no single list of variables to consider in determining coastal vulnerability, 60 vulnerability studies from a period of 29 years (1994–2023), from across the globe, were consulted, and through a statistical mode method, the variables most used by multidisciplinary authors were identified. These studies were organized into groups: ecological, geomorphological, maritime climate, socioeconomic and legislative; creating sets categorized as the minimum indispensable, acceptable, and ideal variables. The results showed that most studies use between six and seven variables from only the maritime climate and geomorphological information groups. The number of variables used by individual studies, on the other hand, was not directly related to the scales (global, national, regional, local), but to the risks, such as flooding and erosion, it resolved. Only two studies included the minimum essential information for the legislative group, which is the presence of protected natural areas. Coastline displacements was the variable most used (43 studies), followed by the geoform type and the rate of sea level change (36), the wave regime (35) and the tidal range (33). The DSSs (Decision Support Systems) for coastal management were also reviewed, showing that these systems focus on a topic with a greater number of variables.

Coastal Vulnerability Assessment of Kanthi Coast, India by the Geospatial Technology

Since ancient times, most of the world’s civilization flourished along the banks of rivers and the coastal region. So the coastal region plays a vital role for human economic activities as well as their livelihood. The Kanthi coast, the northernmost part of the North Circus coast of India stretches in West Bengal and northern Odisha. The 45 km stretched coast land is associated with a dense population and faces the tropical cyclone emerging from the Bay of Bengal. The prime objective of the paper is to assess the coastal vulnerability of the study area. With the help of several indicators, viz. shoreline change rate, rate of sea level change, slope of the beach, wave height, tidal range, regional elevation, geomorphic features, sediment properties, coastal regulation zone (CRZ) violation ratio, the research work assess the Coastal Vulnerability Zone (CVZ) of the Kanthi Coastal region. The weightage sum method and Coastal Vulnerability Index (CVI) are being used. From this research work, it has been revealed that the western segment especially, Digha and Shankarpur are experiencing a high vulnerability situation.

Holocene geomorphological evolution of a sediment-starved coastal embayment in response to sea level change: Insights from the Qing'ao Embayment, southern China

Aiming to gain a better understanding of the response of sediment-starved coastal systems to the climate-driven sea level change, this study examines the Holocene evolution history of Qing'ao Embayment, southern China, as a case study. Results suggest three distinct stages of the evolution history: Stage 1 (8400–6000 cal yr BP) records an initial sedimentation phase associated with the early Holocene marine transgression into the embayment. Stage 2 (6000–3000 cal yr BP) records early sediment deposition under relatively strong hydrodynamic inshore/open-bay environment during early marine regression. Stage 3 (3000 cal yr BP to present) records two substages: Stage 3a relates to the semi-enclosed bay, intertidal and lagoonal salt marsh facies between 3000 and 1300 cal yr BP, and Stage 3b records the final infilling of the embayment into a lagoonal plain. This evolution model suggests that the rates of sea level change and sediment accretion are the two major controlling factors for the Holocene geomorphological evolution of sediment-starving coastal systems.Comparison of the evolution of Qing'ao embayment with sediment-supply rich large-river estuary/deltas reveals unique features in the response to Holocene sea level change of this sediment-starved embayment: During the early Holocene marine transgression, sediment starts to accumulate in the small embayment around 8400 cal yr BP, hundreds of years later than in large river estuaries. During 8400–6000 cal yr BP, sediment accretion rate remains dramatically lower than sea level rise rate, which results in the quick growth of sediment accommodation space during this stage. Around 6000 cal yr BP, though sea level has remained relatively stable and large-river deltas have been growing for about 1000 years, the sediment-starved embayment was still under shallow marine and/or open bay conditions. At 3000 cal yr BP, shoreline started to prograde in sediment-starved embayment, which is about 3000 years later than large river deltas. Finally, agricultural activity started around 1300 cal yr BP in the embayment, thousands of years later than large river deltas. Limited or minimal fluvial sediment supply to the small embayment has constrained the land expansion during the sea level stabilization since mid-Holocene, and the limited living space might result in the much later agricultural activity in such coastal system.Due to the different balances between sea level change and sediment flux, sediment-starved coasts are expected to be under higher risks than large river deltas. However, earlier and more extensive human activities in large river basins have resulted in the reduction of sediment discharge that heighten the risks of large river deltas against future sea level rise. Thus, more attention should be paid to the coastal sediment supply budgets in the context of the background of rising sea level.

Global Sea Level Change Rate, Acceleration and Its Components from 1993 to 2016

Investigating the global sea level budget is essential to quantify the total sea level change (altimetry) and its components, including the steric sea level change and the ocean mass change (gravity), where the latter is mainly attributed to four mass-driven components (Greenland, Antarctica, glaciers and land water storage). In this study, a 24-year global ocean mass change is derived by the joint use of Tongji-LEO2021 and Tongji-Grace2018 monthly gravity field models over 1993–2016, with which the sea level budgets in terms of rate and acceleration are investigated over global oceans within the latitudes 66oN to 66oS together with the IGG-SLR-HYBRID gravity field models, altimetry, steric and four mass-driven components. The statistical results show that the global mean ocean mass change rate accounts for ∼54% of 2.85 ± 0.30 mm/year of global mean total sea level change. The accelerations of global mean total sea level change and its components are 0.145 ± 0.025 mm/year2 (altimetry), 0.003 ± 0.021 mm/year2 (steric), 0.139 ± 0.047 mm/year2 (ocean mass from Tongji), and 0.137 ± 0.010 mm/year2 (the sum of mass-driven components) respectively, indicating that the global sea level budget in terms of acceleration can be closed and nearly no acceleration exists in the global mean steric sea level change for the period 1993–2016.

The 95 per cent confidence interval for the mean sea-level change rate derived from tide gauge data

SUMMARYTide gauge (TG) data are crucial for assessing global sea-level and climate changes, coastal subsidence and inundation. Mean sea-level (MSL) time-series derived from TG data are autocorrelated. The conventional ordinary least-squares regression method provides reasonable estimates of relative sea-level (RSL) change rates (linear trends) but underestimates their uncertainties. In order to cope with the autocorrelation issue, we propose an approach that uses an ‘effective sample size’ (${N}_{\mathrm{ eff}}$) to estimate the uncertainties (±95 per cent confidence interval, or 95 per cent CI for short). The method involves decomposing the monthly MSL time-series into three components: a linear trend, a periodic component and a noise time-series. The ${N}_{\mathrm{ eff}}$ is calculated according to the autocorrelation function (ACF) of the noise time-series. We present an empirical model that fits an inverse power-law relationship between 95 per cent CI and time span (T) based on 1160 TG data sets globally distributed, where $95\ \mathrm{ per}\ \mathrm{ cent}\,\mathrm{ CI} = 179.8{T}^{ - 1.29}$. This model provides a valuable tool for projecting the optimal observational time span needed for the desired uncertainty in sea-level rise rate or coastal subsidence rate from TG data. It suggests that a 20-yr TG time-series may result in a 3–5 mm yr−1 uncertainty (95 per cent CI) for the RSL change rate, while a 30-yr data set may achieve about 2 mm yr−1 uncertainty. To achieve a submillimetre per year (< 1 mm yr−1) uncertainty, approximately 60 yr of TG observations are needed. We also present a Python module (TG_Rate_95CI.py) for implementing the methodology. The Python module and the empirical model have broad applications in global sea-level rise and climate change studies, and coastal environmental and infrastructure planning, as well as Earth science education.

Numerical modeling on global-scale mantle water cycle and its impact on the sea-level change

Using numerical modeling of the global-scale mantle water cycle, I have clarified the mechanisms governing the deep Earth water content variations and evaluated their effects on sea-level changes. The following points are found: 1. The temporal variations of water content in the deep Earth are mainly determined by a balance between the ingassing flux from the ocean and the dehydration flux in the mantle wedge. 2. The primary control of ingassing flux comes from the water content of oceanic crust, rather than the style of plate subduction promoted by the free surface boundary condition. 3. The water content of oceanic crust ranges from 0.1 to 1 wt. percent to match the ingassing flux with the inferred one from the geological analysis.Based on the best-fit scenario for the ingassing flux from the ocean to the deep interior, I obtain an average rate of sea-level change of −1 to −1.5 m/Myrs over 1 billion year, which is nearly consistent with the long-term trend of sea-level change estimated from geological and paleontological data (∼−1 m/Myr). Note, however, that the detailed profile of sea-level change in the numerical modeling is difficult to explain the observation one. This underlines the importance of dehydration process in the mantle wedge in understanding the sea-level change resulting from the water budget in the Earth's deep interior over the Earth's history.

Multiscale Analysis and Prediction of Sea Level in the Northern South China Sea Based on Tide Gauge and Satellite Data

Under the influence of global warming, the problem of sea-level rise is becoming increasingly prominent. The northern part of the South China Sea (SCS) is low lying, with intense economic development, and densely populated. These characteristics make the region extremely sensitive to the consequences of rising sea levels. This study aims to reveal the trends of sea-level changes in the northern SCS and provide scientific insights into the potential flooding risks in low-lying areas. To achieve this, the Ensemble Empirical Mode Decomposition (EEMD) method is used to analyze the water level time series data from three tide gauges along the coast of Hong Kong. This analysis reveals the multidimensional change characteristics and response mechanisms of the sea level in the SCS. The findings reveal distinct seasonal, interannual, decadal, and interdecadal variations in sea-level changes. Furthermore, we explore the impact of the El Niño-Southern Oscillation (ENSO) on sea-level changes in the study area, finding a 6-month lagged correlation between the sea level and ENSO. Spatially, the rate of sea-level change is faster in nearshore areas than in the open ocean and higher in the northern regions than in the southern regions. The Multifractal Detrended Fluctuation Analysis (MF-DFA) method is employed to analyze the sea-level change time series, revealing long-range correlations and multifractal characteristics. In addition, we propose a sea-level prediction method that combines EEMD with Long Short-Term Memory (LSTM) neural networks and conducts empirical research on sea-level changes in the northern South China Sea. The results indicate that the EEMD-LSTM model outperforms the standalone LSTM model in terms of predictive accuracy, effectively eliminating noise from signals and providing a valuable reference. In summary, this research delves into the multiscale characteristics and influencing factors of sea-level changes in the northern SCS, proposing an improved sea-level prediction method that integrates EEMD and LSTM. The findings lay the groundwork for evaluating the risks of sea-level rise in low-lying regions of the northern SCS and inform future response strategies.

Deglacial perspectives of future sea level for Singapore

Low elevation equatorial and tropical coastal regions are highly vulnerable to sea level rise. Here we provide probability perspectives of future sea level for Singapore using regional geological reconstructions and instrumental records since the last glacial maximum ~21.5 thousand years ago. We quantify magnitudes and rates of sea-level change showing deglacial sea level rose from ~121 m below present level and increased at averaged rates up to ~15 mm/yr, which reduced the paleogeographic landscape by ~2.3 million km2. Projections under a moderate emissions scenario show sea level rising 0.95 m at a rate of 7.3 mm/yr by 2150 which has only been exceeded (at least 99% probability) during rapid ice mass loss events ~14.5 and ~9 thousand years ago. Projections under a high emissions scenario incorporating low confidence ice-sheet processes, however, have no precedent during the last deglaciation.

On Modelling Sea State Bias of Jason-2 Altimeter Data Based on Significant Wave Heights and Wind Speeds

Altimeter data processing is very important to improve the quality of sea surface height (SSH) measurements. Sea state bias (SSB) correction is a relatively uncertain error correction due to the lack of a clear theoretical model. At present, the commonly used methods for SSB correction are polynomial models (parametric models) and non-parametric models. The non-parametric model usually was constructed by collinear data. However, the amount of collinear data was enormous, and it contained redundant information. In this study, the non-parametric regression estimation model was optimized by using the parameter replacement method of ascending and descending tracks based on the crossover data. In this method, significant wave heights from the Jason-2 altimeter data during cycles 200–301 and wind speed from the ERA5 reanalysis data were used. The non-parametric regression estimation model of Jason-2 was constructed by combining it with local linear regression, Epanechnikov kernel function and local window width. At the same time, based on the significant wave height and wind speed at the crossover points, the SSB polynomial model containing six parameters was constructed by using the Taylor series expansion, and the model was optimized. By comparing polynomial model construction with different parameters, the optimized model was obtained. The SSH of the crossover points and the tide gauge records were used to validate these results derived from two models and GDR. Compared with the crossover discrepancies of SSH corrected by the polynomial model, the RMS of the crossover discrepancies of SSH corrected by the non-parametric regression estimation model was reduced by 7.9%. Compared with the crossover discrepancies of SSH corrected by the conventional non-parametric model from GDR, the RMS of the crossover discrepancies of SSH corrected by the non-parametric regression estimation model was reduced by 4.1%. This shows that the precision of the SSHs derived by after the SSB correction, as calculated by the non-parametric regression estimation model, was better than that of the polynomial model and the SSB correction from GDR. Using the Jason-2 altimeter data, the along-track geoid gradient and the sea level change rate of the global ocean were determined by using two models to correct the SSB. By comparing the results of the two models, the accuracy of the geoid gradient along the orbit that was obtained by the non-parametric regression estimation model was better than that of the polynomial model and GDR. The global average sea level change rate after the non-parametric regression estimation model correction was 3.47 ± 0.09 mm/y, which was the closest to the average sea level change rate that has been published in the international literature within this field.

Coastal Vulnerability Assessment of Bali Province, Indonesia Using Remote Sensing and GIS Approaches

Coastal zones are considered to be highly vulnerable to the effects of climate change, such as erosion, flooding, and storms, including sea level rise (SLR). The effects of rising sea levels endanger several nations, including Indonesia, and it potentially affects the coastal population and natural environment. Quantification is needed to determine the degree of vulnerability experienced by a coast since measuring vulnerability is a fundamental phase towards effective risk reduction. Therefore, the main objective of this research is to identify how vulnerable the coastal zone of Bali Province by develop a Coastal Vulnerability Index (CVI) of areas exposed to the sea-level rise on regional scales using remote sensing and Geographic Information System (GIS) approaches. This study was conducted in Bali Province, Indonesia, which has a beach length of ~640 km, and six parameters were considered in the creation to measure the degree of coastal vulnerability by CVI: geomorphology, shoreline change rate, coastal elevation, sea-level change rate, tidal range, and significant wave height. The different vulnerability parameters were assigned ranks ranging from 1 to 5, with 1 indicating the lowest and 5 indicating the highest vulnerabilities. The study revealed that about 138 km (22%) of the mapped shoreline is classified as being at very high vulnerability and 164 km (26%) of shoreline is at high vulnerability. Of remaining shoreline, 168 km (26%) and 169 km (26%) are at moderate and low risk of coastal vulnerability, respectively. This study outcomes can provide an updated vulnerability map and valuable information for the Bali Province coast, aimed at increasing awareness among decision-makers and related stakeholders for development in mitigation and adaptation strategies. Additionally, the result may be utilized as basic data to build and implement appropriate coastal zone management.

Revisiting sea-level budget by considering all potential impact factors for global mean sea-level change estimation

Accurate estimates of global sea-level change from the observations of Altimetry, Argo and Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (GRACE-FO) are of great value for investigating the global sea-level budget. In this study, we analyzed the global sea-level change over the period from January 2005 to December 2019 by considering all potential impact factors, i.e. three factors for Altimetry observations (two Altimetry products, ocean bottom deformation (OBD) and glacial isostatic adjustment (GIA)), three factors for Argo observations (four Argo products, salinity product error and deep-ocean steric sea-level change), and seven factors for GRACE/GRACE-FO observations including three official RL06 solutions, five spatial filtering methods, three GIA models, two C20 (degree 2 order 0) products, Geocenter motion, GAD field and global mass conservation. The seven impact factors of GRACE/GRACE-FO observations lead to ninety combinations for the post-procession of global mean barystatic sea-level change estimation, whose rates range from 2.00 to 2.45 mm/year. The total uncertainty of global barystatic sea-level change rate is ± 0.27 mm/year at the 95% confidence level, estimated as the standard deviation of the differences between the different datasets constituting the ensembles. The statistical results show that the preferred GIA model developed by Caron et al. in 2018 can improve the closure of the global sea-level budget by 0.20–0.30 mm/year, which is comparable with that of neglecting the halosteric component. About 30.8% of total combinations (GRACE/GRACE-FO plus Argo) can close the global sea-level budget within 1-sigma (0.23 mm/year) of Altimetry observations, 88.9% within 2-sigma. Once the adopted factors including GRACE/GRACE-FO solutions from Center for Space Research (CSR), Caron18 GIA model, SWENSON filtering and Argo product from China Second Institute of Oceanography, the linear trend of global sterodynamic sea-level change derived from GRACE/GRACE-FO plus Argo observations is 3.85 ± 0.14 mm/year, nearly closed to 3.90 ± 0.23 mm/year of Altimetry observations.

Coastal Vulnerability Assessment: A case study of the Ratnagiri coast, Maharashtra, India

The coastal zone is a vulnerable habitat that needs extra caution to protect ecosystems. Coastal systems are increasingly threatened by possible climate change consequences, as evidenced by the Intergovernmental Panel on Climate Change’s consecutive assessments. Increased tropical storm occurrences in recent years, in addition to the devastation caused by the tsunami in December 2004, have highlighted the necessity of analyzing the coast’s susceptibility to flooding-induced hazards to get a better knowledge of the factors that generate various hazards and, as a result, reduce the after-effects of future occurrences. The Ratnagiri coast in Maharashtra is prone to erosional hazards, periodic land rehabilitation, and sudden rises in sea level. The main objective is to calculate the CVI (Coastal Vulnerability Index) for the Ratnagiri coast. To analyze the vulnerability of the coastal region, eight risk parameters were used, viz. shoreline change rate, coastal elevation, sea level change rate, coastal slope, tide range, significant wave height, coastal geomorphology, and tsunami arrival height. The coastal vulnerability map was created by categorizing the numerous coastal portions into three vulnerability groups: high, medium, and low, which will aid coastal residents in risk mitigation in the future.

Sea-level rise and warming mediate coastal groundwater discharge in the Arctic

Groundwater discharge is an important mechanism through which fresh water and associated solutes are delivered to the ocean. Permafrost environments have traditionally been considered hydrogeologically inactive, yet with accelerated climate change and permafrost thaw, groundwater flow paths are activating and opening subsurface connections to the coastal zone. While warming has the potential to increase land-sea connectivity, sea-level change has the potential to alter land-sea hydraulic gradients and enhance coastal permafrost thaw, resulting in a complex interplay that will govern future groundwater discharge dynamics along Arctic coastlines. Here, we use a recently developed permafrost hydrological model that simulates variable-density groundwater flow and salinity-dependent freeze-thaw to investigate the impacts of sea-level change and land and ocean warming on the magnitude, spatial distribution, and salinity of coastal groundwater discharge. Results project both an increase and decrease in discharge with climate change depending on the rate of warming and sea-level change. Under high warming and low sea-level rise scenarios, results show up to a 58% increase in coastal groundwater discharge by 2100 due to the formation of a supra-permafrost aquifer that enhances freshwater delivery to the coastal zone. With higher rates of sea-level rise, the increase in discharge due to warming is reduced to 21% as sea-level rise decreased land-sea hydraulic gradients. Under lower warming scenarios for which supra-permafrost groundwater flow was not established, discharge decreased by up to 26% between 1980 and 2100 for high sea-level rise scenarios and increased only 8% under low sea-level rise scenarios. Thus, regions with higher warming rates and lower rates of sea-level change (e.g. northern Nunavut, Canada) will experience a greater increase in discharge than regions with lower warming rates and higher rates of sea-level change. The magnitude, location and salinity of discharge have important implications for ecosystem function, water quality, and carbon dynamics in coastal zones.

A Study on Analysis Method of Sea Level Change in Korean Coastal Zone

In this study, the analysis of sea level change using Lanczos filtering, a filtering method of Fourier transform, was performed. The high frequency(short period) of tide was excluded by low-pass filter of Lanczos filtering, the analysis of sea level change was carried out considering only low frequency(long period). First, all tide data provided by the Korea Hydrographic and Oceanographic Administration(KHOA) were preprocessed to delete the error value, the astronomical tide and the meteorological tide repeated in short period were excluded by Lanczos filtering. The Mean Sea Level(MSL) for each tidal station was calculated using these results, the MSL tended to rise at most point in the Korean coastal zone. And the rate of sea level change, which means annual sea level change(mm/year), was estimated and compared with previous study results. The rate of sea level change was higher than values in past study, this means that future sea level change may be greater than previous expected. The analysis method applied to this study can be effectively used to improve the accuracy of prediction of sea level change.

Bayhead delta evolution in the context of late Quaternary and Holocene sea-level change, Richards Bay, South Africa.

Richards Bay is part of a back-barrier lagoon fronted by high coastal dunes on the NE, Indian Ocean coast of South Africa. In the early 1970s, a berm was constructed, dividing the original Mhlathuze Estuary into two separate systems; the Richards Bay Harbour and the new Mhlathuze Estuary. This study investigates the stratigraphic evolution of the incised valley system and bayhead delta in the Richards Bay Harbour segment. Seven seismic units (Units 1–7) were imaged. A single regionally developed sequence boundary (SB) along with two tidal ravinement surfaces (tRS1 and tRS2) were identified. Surface SB is associated with the LGM lowstand which developed when sea levels were ~ 130 m below present, until ~18,000 year BP. Cretaceous age siltstones (Unit 1) form the basement. Transgressive material overlying SB (Unit 2) reflects the filling of an incised valley located in the middle segment of a wave-dominated back-barrier system. It is overlain by a bayhead delta (Unit 3), the geometry and seismic signature of which indicate alternating periods of aggradation/progradation and backstepping. The behaviour is attributed to episodic jumps in sea-level, and is tentatively (on the basis of elevations in relation to the regional sea-level curve) linked to periods of rapidly rising sea-level (8.2 ka event and Meltwater Pulse (MWP)-1d). These intervals of rapidly rising sea-level, combined with relatively low gradient settings facilitated backstepping of the delta. Fills (Unit 4) occur within minor incisions along the delta top. These are interpreted as distributary channels that fed sediment to the seaward edge of the bayhead delta system. Elongated mounds on the seafloor (Unit 5) are interpreted as spoil from contemporary port dredging. Slump deposits (Unit 6) along the delta front are attributed to a combination of oversteepening of the delta by dredging, as well as deposition of modern sediments brought into the system by tidal currents. The system is capped by fine-grained, tidally redistributed and deposited sediments (Unit 7) which were possibly sourced from older organic material of an indeterminate source. This site is especially sensitive to episodic rates of sea-level change due to the relatively small Glacial Isostatic Adjustments (GIA) during the postglacial transgression and the flat antecedent gradients of both the subaerial unconformity and the overlying tidal ravinement.

Comparing satellite altimetry and tide gauges observations in the Mediterranean Sea within the framework of singular spectrum analysis

By considering time series from satellite altimetry and tide gauges that extend back to 1993, Singular Spectrum Analysis (SSA) is applied to investigate and compare the non linear trends of the sea level along the Mediterranean coasts. The major issue of this comparison is to show if the satellite altimetry data could be representative of the local sea level as observed by tide gauges.
 
 The results indicate that the local trends estimated from an in-situ tide gauge and satellite altimetry data show nearly identical positive rates over the period from 1993 to 2017. The differences between the estimated rates of sea level change from in-situ tide gauge and satellite measurements vary, in absolute value, from 0.18 to 4.29 mm/year with an average of 1.55 mm/year.
 
 This result is sufficient to admit, if necessary, on the one hand, the complementarily of the two measurement techniques (satellite altimetry and tide gauges) and, on the other hand, the rise in sea level near the Mediterranean coastal areas.

High-resolution sea level change around China seas revealed through multi-satellite altimeter data

Sea level change is not uniform across the oceans and high-resolution sea level trend (SLT) models reveal that the rate of sea level change differs between different waters. In this study, we developed a new method for obtaining a high-resolution, 1′×1′-grid SLT model, using the time-varying mean sea surface (MSS) of several MSSs calculated from averaged multi-satellite-derived sea surface heights (TOPEX/Poseidon, Jason-1/2/3, ERS-1/2, Envisat, GFO, Cryosat-2, SARAL/AltiKa, Sentinel-3A, HY-2A) over different periods. We applied this model to estimate the mean sea level change in the China seas and their adjacent ocean (0°–41°N, 100°–140°E). This new model revealed the patterning of sea level change within the study region in detail, and detected a sea level change dipole south of Japan. A zonal SLT pattern was identified in three regions: the region of the North Pacific Subtropical Counter Current (an eastward current located in the band of 19.5°–22.5°N, populated with eddies), and the areas east of Taiwan and east of Luzon Island. Moreover, the rate of sea level rise in the offshore zone was found to be ~ 0.67 mm/yr (20%) higher than that in the open ocean. This finding has important implications for coastal protection.

Climate pacing of millennial sea-level change variability in the central and western Mediterranean

Future warming in the Mediterranean is expected to significantly exceed global values with unpredictable implications on the sea-level rise rates in the coming decades. Here, we apply an empirical-Bayesian spatio-temporal statistical model to a dataset of 401 sea-level index points from the central and western Mediterranean and reconstruct rates of sea-level change for the past 10,000 years. We demonstrate that the mean rates of Mediterranean industrial-era sea-level rise have been significantly faster than any other period since ~4000 years ago. We further highlight a previously unrecognized variability in Mediterranean sea-level change rates. In the Common Era, this variability correlates with the occurrence of major regional-scale cooling/warming episodes. Our data show a sea-level stabilization during the Late Antique Little Ice Age cold event, which interrupted a general rising trend of ~0.45 mm a−1 that characterized the warming episodes of the Common Era. By contrast, the Little Ice Age cold event had only minor regional effects on Mediterranean sea-level change rates.