Sea-level changes on multiple spatial scales: estimates and contributing processes

As a leading indicator of global climate change, contemporary global mean sea level (GMSL) change is mainly driven by thermosteric (thermal expansion) and barystatic (ocean mass increase) contributions. GMSL change has been continuously measured by satellite altimetry since 1993, while thermosteric sea level change can be inferred from in-situ hydrographic measurements dating back to the 1970s. However, direct observations of barystatic sea level change were generally lacking until the launch of the Gravity Recovery and Climate Experiment (GRACE) in 2002. In the absence of GRACE, barystatic sea level estimation relies primarily on the so-called mass budget approach by summing individual surface mass change estimates (e.g. ice sheets, glaciers, and terrestrial water storage) obtained from different remote sensing or geophysical modelling techniques, providing an indirect observation due to the lack of global constraints. In this study, we use low-degree gravity fields obtained from satellite laser ranging (SLR), a traditional space geodetic technique over decades, to directly estimate barystatic sea level changes since 1993. To this end, we effectively address the issues of signal leakage and missing geocenter motion for SLR gravity fields using the forward modelling technique. Our SLR-based barystatic sea level estimates allow the direct observation-based assessment of the GMSL budget over the satellite altimetry era, and also provide an independent dataset for cross-validation and gap-filling between GRACE and its successor GRACE-FO. Using reprocessed altimetry data from NASA's Goddard Space Flight Center and updated thermosteric sea level ensembles, we reconcile the GMSL rise budget from 1993 to 2022. Our results show that the sum of thermosteric and SLR-based barystatic contributions (3.16 ± 0.64 mm/yr) agrees well with the altimetry-observed GMSL rate (3.22 ± 0.28 mm/yr), suggesting that the GMSL budget can be closed within uncertainties over the last three decades. Nevertheless, we observe increased budget residuals when using different altimetry datasets, especially in recent years, highlighting the ongoing challenges in accurately observing GMSL change and robustly closing the GMSL budget.

Here, we describe a new national tide gauge‐based sea level dataset for monitoring sea level changes around Australia, and the novel homogenization methodology used to develop it. Homogenization is a two‐step process that involves the detection of jumps or steps in the data (inhomogeneities) followed by a correction applied to remove the change. This new dataset is called the Australian National Collection of Homogenized Observations of Relative Sea Level (ANCHORS). ANCHORS provides coastal sea levels at hourly resolution with homogenization performed on annual means. ANCHORS is intended to be a national tide gauge‐based sea level dataset to monitor changes in mean sea level and resultant coastal flood frequency changes around Australia. Whilst sea levels are rising around Australia in response to global mean sea level changes, coastal water levels represent a notable gap in the suite of in situ high‐quality datasets available for researchers, governments and industry. Tide gauge datasets are well suited to impact‐based assessments of changes in flood frequency, duration and risk. In this regard, ANCHORS will sit alongside existing satellite‐ and numerical modelling‐based sea level datasets to provide enhanced insights into the physical impacts of sea level rise on Australian coastlines. We have adapted established homogenization techniques to develop a national collection of homogenous long tide gauge records. The dataset comprises 38 tide gauge records from around Australia, varying in length from 34 to 123 years, with an average of 57 years. Finally, we discuss limitations and potential applications for ANCHORS and how these have informed the design of this coastal sea level dataset.

Vertical land movements can cause regional relative sea level changes to differ substantially from climate-driven absolute sea level changes. While absolute sea level has been accurately monitored by satellite altimetry since 1992, there are limited observations of vertical land motion. Vertical land motion is generally modeled as a linear process, despite some evidence of non-linear motion associated with tectonic activity, changes in surface loading, or groundwater extraction. As a result, the temporal evolution of vertical land motion, and its contribution to projected sea level rise and its uncertainty, remains unresolved. Here, we present a probabilistic vertical land motion reconstruction from 1995-2020 and determine the impact of regional scale and non-linear vertical land motion on relative sea level projections up to 2150. We show that regional variations in projected coastal sea level changes are equally influenced by vertical land motion and climate-driven processes, with vertical land motion causing relative sea level changes of up to 50 cm by 2150. Accounting for non-linear vertical land motion increases the uncertainty in projections by up to 1 m on a regional scale. Our results highlight the uncertainty in future coastal impacts and demonstrate the importance of including non-linear vertical land motions in sea level change projections. In addition to its application to regional sea level projections, the vertical land motion estimate is an important source of information for various sea level studies focusing on the analysis of tide gauge or satellite altimetry observations in coastal areas.

Estimates of regional and global average sea level change remain a focus of climate change research. One complication in obtaining coherent estimates is that geodetic datasets measure different aspects of the sea level field. Satellite altimetry constrains changes in the sea surface height (SSH; or absolute sea level), whereas tide gauge data provide a measure of changes in SSH relative to the crust (i.e., relative sea level). The latter is a direct measure of changes in ocean volume (and the combined impacts of ice sheet melt and steric effects), but the former is not since it does not account for crustal deformation. Nevertheless, the literature commonly conflates the two estimates by directly comparing them. We demonstrate that using satellite altimetry records to estimate global ocean volume changes can lead to biases that can exceed 15%. The level of bias will depend on the relative contributions to sea level changes from the Antarctic and Greenland Ice Sheets. The bias is also more sensitive to the detailed geometry of mass flux from the Antarctic Ice Sheet than the Greenland Ice Sheet due to rotational effects on sea level. Finally, in a regional sense, altimetry estimates should not be compared to relative sea level changes because radial crustal motions driven by polar ice mass flux are nonnegligible globally.

<p>The effective climate sensitivity (EffCS) of models in the Coupled Model Intercomparison Project 6 (CMIP6) has increased relative to CMIP5. Consequently, using CMIP6 models tends to lead to larger projections of global mean surface air temperature (GSAT) increase for a given emissions scenario. The effect of increased EffCS on projections of global mean sea-level (GMSL) change however, has so far only been studied using a reduced complexity model. Here, we explore the implications of increased EffCS in CMIP6 models for GMSL change projections in 2100 for three emissions scenarios: SSP5-8.5, SSP2-4.5 and SSP1-2.6.</p><p>Whereas CMIP6 projections of GSAT change are substantially higher than in CMIP5, projections of global mean thermal expansion (GTE) are only slightly higher. We use these projections as input to construct projections of GMSL change, using the Monte Carlo approach of IPCC AR5. Isolating the impact of the CMIP6 simulations using consistent methods is an important step to ensure traceability to past IPCC projections of global and regional sea-level change. The resulting 95<sup>th</sup> percentile of projected GMSL change at 2100 is only 3-7 cm higher for CMIP6 than for CMIP5, depending on the emissions scenario. Projected rates of GMSL rise around 2100 increase more strongly from CMIP5 to CMIP6, though, implying more pronounced differences beyond 2100 and greater committed sea-level rise. GMSL change in 2100 is accurately predicted by time-integrated temperature change and therefore mitigation requires early reduction of emissions.</p><p>We also find that the 95<sup>th</sup> percentile projections based on individual CMIP6 models can differ as much as 51 cm and that the 5-95% range of projected GMSL change for individual CMIP6 models can be substantially outside of the 5-95% range of the CMIP6 multi-model ensemble. Thus, through subsetting the CMIP6 ensemble using EffCS, a choice can be made between characterizing the central part of the probability distribution and more comprehensively sampling the high end of the GMSL projection space, which is relevant to risk-averse stakeholders. Our results show this may substantially alter ensemble projections, underlining the need to constrain EffCS in global climate models in order to reduce uncertainty in sea-level projections.</p>

Global sea-level rise has been drawing increasingly greater attention in recent years, as it directly impacts the livelihood and sustainable development of humankind. Our research focuses on identifying causal factors and pathways on sea level changes (both global and regional) and subsequently predicting the magnitude of such changes. To this end, we have designed a novel analysis pipeline including three sequential steps: (1) a dynamic structural equation model (dSEM) to identify pathways between the global mean sea level (GMSL) and various predictors, (2) a vector autoregression model (VAR) to quantify the GMSL changes due to the significant relations identified in the first step, and (3) a generalized additive model (GAM) to model the relationship between regional sea level and GMSL. Historical records of GMSL and other variables from 1992 to 2020 were used to calibrate the analysis pipeline. Our results indicate that greenhouse gases, water, and air temperatures, change in Antarctic and Greenland Ice Sheet mass, sea ice, and historical sea level all play a significant role in future sea-level rise. The resulting 95% upper bound of the sea-level projections was combined with a threshold for extreme flooding to map out the extent of sea-level rise in coastal communities using a digital coastal tracker.

Sea level variations prior to the launch of satellite altimeters are estimated by analyzing historic tide gauge records. Recently, a number of groups have reconstructed sea level by applying EOF techniques to fill missing observations. We complement this study with alternative methods. In a first step gaps in 178 records of sea level change are filled using the pattern recognition capabilities of artificial neural networks. Afterward satellite altimetry is used to extrapolate local sea level change to global fields. Patterns of sea level change are compared to prior studies. Global mean sea level change since 1900 is found to be 1.77±0.38 mm yr−1 on average. Local trends are essentially positive with the highest values found in the western tropical Pacific and in the Indian Ocean east of Madagascar where it reaches about +6 mm yr−1. Regions with negative trends are spotty with a minimum value of about −2 mm yr−1 south of the Aleutian Islands. Although the acceleration found for the global mean, +0.0042 ± 0.0092 mm yr−2, is not significant, local values range from −0.1 mm yr−2 in the central Indian Ocean to +0.1 mm yr−2 in the western tropical Pacific and east of Japan. These extrema are associated with patterns of sea level change that differ significantly from the first half of the analyzed period (i.e., 1900–1950) to the second half (1950–2000). We take this as an indication of long period oceanic processes that are superimposed to the general sea level rise.

Modeled steric and mass-driven sea level change caused by Greenland Ice Sheet melting

Reef response to sea-level and environmental changes in the Central South Pacific over the past 6000 years

Regional sea level change is driven by multiple physical processes, resulting in complex dynamics and pronounced spatio–temporal heterogeneity. This study proposes a hybrid framework that integrates the physical fingerprints with deep learning to achieve both sea level budget closure and temporal prediction of regional sea level variations. Total sea level changes are firstly decomposed into the steric and barystatic components. By further considering the mass redistribution of ice sheets, glaciers, and terrestrial water storage and their associated sea level fingerprints, the cryo–hydrological contribution (CHC) sea level, is introduced to replace the traditional barystatic term. This substitutes direct observations of local mass change with the sea level response to mass redistributions occurring elsewhere, thereby enhancing the physical interpretability of the decomposition. Subsequently, a convolutional neural network and bidirectional long short-term memory hybrid model is employed to jointly predict the total, steric, barystatic, and CHC sea level components.We quantify the impacts of mass variations in cryo–hydrological domain on sea level changes across 20 oceanic regions, achieving a quantitative projection from mass redistribution to sea level response. Results demonstrate excellent budget closure within the analyzed regions, with mean correlation coefficients exceeding 0.9 and root mean square difference of approximately 15 mm. In the temporal domain, the deep learning network effectively reproduces both long-term trends and seasonal oscillations (correlation ≥ 0.8 in most prediction windows). From a physical perspective, the presented study establishes the regional sea level response to cryo–hydrological mass redistribution and demonstrates strong practical relevance.

Chapter 13 - Sequence Stratigraphy and Sea-Level Change

Southwest Fujian area has experienced a large-scale transgression-regression cycle in Late Triassic-Middle Jurassic and the maximum transgression has taken place in Early Jurassic. The migration and enrichment of geochemical element in the continuous fine-grained sediments in the basin recorded the paleosalinity and the paleodepth. The changes of paleosalinity and paleodepth indicate the sea (lake) level relative change in every period of Late Triassic-Middle Jurassic in southwestern Fujian. The relative change curve of sea (lake) level in southwestern Fujian is established based on the m value (m=100×w(MgO)/w(Al2O3)) and the ratios of w(B)/w(Ga), w(Sr)/w(Ba) and w(Ca)/w(Mg). The curve indicates that level I sea-level relative change in southwestern Fujian is composed of the transgression in Late Triassic-Early Jurassic and the regression in the late period of Early Jurassic-Middle Jurassic. The level III sea-level relative change is frequent, which is composed by the lake level descent-lake level rise-lake level descent of Wenbin Shan formation in Late Triassic, the regression-transgression-regression of Lishan formation in Early Jurassic and the lake-level rise-lake level descent-lake level rise-lake level descent of Zhangping formation in Middle Jurassic. The transgression-regression cycle in southwestern Fujian is significantly controlled by the sea-level change in the north of South China Sea. The relative change curve trends of the level I sea-level in the north of South China Sea and the one in southwestern Fujian are the same. The maximum transgressions both occur in Early Jurassic. The level III sea-level curve reflects the fluctuation of a transgression and two regressions in the early period of Early Jurassic.

The ecological problems of the Caspian Sea and its coast are related to both long-term natural changes in the sea level and climate and present-day socioeconomic problems. The destruction of the coastal landscapes and infrastructure caused by contemporary changes in the sea level, as well as the extraction of mineral deposits, leads to additional water pollution by oil products. The study of the causes of changes in the sea level, outcomes, and forecast of its multiannual oscillations are of primary importance. Geological models that make it possible to explain the causes of short-term oscillations in the level with application of the seismogeodynamic model of compression-tension of a sedimentary sequence of the South Caspian depression are considered.

<p>Vertical land motion (VLM) is a major contributor to relative sea level change (RSLC). Hence, understanding and estimating VLM is critical for the investigation of contemporary and projected coastal RSLC and the allocation of its uncertainties. However, there are several challenges involved in the determination of the linear component of VLM. Firstly, the sparse and inhomogeneous distribution of point-wise VLM observations hinder the direct analysis of VLM continuously in space along the coastline. Secondly, the commonly applied working-hypothesis that VLM can be generally assumed as ‘linear’, is not entirely valid for regions, which are affected by nonlinear processes such as earthquakes, surface mass loading changes or other phenomena. Thus, in order to overcome the limitations of data-availability and to account for time-variable VLM, we develop a new approach to estimate continuous time- and space-resolving (3D) VLM over the period 1995-2020.</p><p>We apply a Bayesian Principal Component Analysis to a global network of quality controlled VLM observations (GNSS data and differences of satellite altimetry and tide gauge observations) to extract common modes of variability and to cope with the incomplete VLM data. The estimated modes represent a superposition of large scale VLM fingerprints. These include linear motion signatures, e.g., associated with the Glacial Isostatic Adjustment (GIA), as well as regional patterns of coherent responses to earthquakes or terrestrial water storage changes, which exhibit inter-annual to decadal variability. To generate the 3D VLM reconstruction, the VLM fingerprints are interpolated in space with a Bayesian transdimensional regression, which automatically infers the spatial resolution based on the distribution of the data.</p><p>Our approach not only provides an essential observation-based alternative to previously employed VLM estimates from GIA models or interpolated VLM maps, but also allows to directly attribute VLM trend uncertainties to the temporal variability estimated over the period of observation. We combine the VLM dataset with century-long tide gauge RSLC observations to demonstrate the limitations of extrapolating nonlinear VLM back in time and to identify regional differences (in the order of mm/year) of contemporary absolute sea level (ASL) change (1900-2015) w.r.t. a recent sea level reconstruction, which employs a GIA-VLM signature only. Using the present-day VLM estimates, we disentangle the contributions of VLM and projected ASL change (from CMIP6 models) and uncertainties to RSLC (2020-2150). The regional RSLC error budget analysis enables the identification of regions where robust assessments of future RSLC are feasible and where VLM uncertainties dominate the projected ASL uncertainties, while explaining up to 75% of the combined uncertainties. Besides these applications, the VLM estimate represents a vital source of information for various sea level studies focused on the analysis of tide gauge or satellite altimetry observations in coastal areas.</p>

The relative elevation of sea and land has been changing through time in response to two fundamentally different groups of factors. Global factors include changes in the volume of the ocean basins owing to tectonic processes and changes in the total amount of ocean water due to glaciation. Local factors include subsidence of continental margins and compaction of recent sediments. Over this century, global sea level (eustatic) appears to have been rising at a rate of 1.2 mm per year. Along the south-central Louisiana coast the land surface appears to be sinking at a rate of about 8 mm per year. Recent global climatic modeling suggests strongly that we are about to enter a period of rapidly accelerating warming due to increased amounts of carbon dioxide in the atmosphere. As a consequence, eustatic sea level rise is predicted to accelerate because of both steric expansion of the ocean water and continued melting of polar ice caps. For the next 40 years the eustatic sea level rise may average 10 mm per year. The local relative sea level in coastal Louisiana would therefore rise at about twice its present rate over this time period. At this rate, local sea level will, in the year 2020, stand some 70 to 75 cm (2.3 to 2.5 ft) higher than at present. The numbers presented above are average values for the Louisiana coastal plain. Local variability in subsidence rate appears to be related to the thickness of Holocene sediments. The highest rates of subsidence are found in the modern Mississippi (birdfoot) delta and in coastal Terrebonne Parish above the late Pleistocene Mississippi trench; in both areas the Holocene section is in excess of 200 m (650 ft) thick. The high rate of local sea level rise along the Louisiana coast makes it imperative that plans for coastal development and protection consider the long-term consequences of sea level change. End_of_Article - Last_Page 1469------------