Contemporary sea-level changes from global to local scales: a review

Cazenave, Anny; Moreira, Lorena

doi:10.1098/rspa.2022.0049

Cited by 41 publications

(29 citation statements)

References 188 publications

(317 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sea-level budget assessments are important tools for understanding the processes driving sea-level change, for detecting temporal changes in sea-level and its components, for identifying missing contributions to the budget, and for validating and constraining climate models used in sea-level projections (Cazenave and Moreira, 2022). In particular, understanding the processes on a finer spatial scale is essential for local sea-level projections and coastal management planning.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, instrumental problems (e.g. in Argo salinity data and satellite drifts) have raised questions about the performance and closure of the global mean sea-level budget after 2016 (Chen et al, 2020;Barnoud et al, 2021;Cazenave and Moreira, 2022). We hypothesize that by investigating the budget in covariant and physically coherent regions, we can resolve the discrepancies (i.e.…”

mentioning

confidence: 93%

See 1 more Smart Citation

Regionalizing the sea-level budget with machine learning techniques

Camargo

Riva²,

Hermans³

et al. 2023

Ocean Sci.

View full text Add to dashboard Cite

Abstract. Attribution of sea-level change to its different drivers is typically done using a sea-level budget approach. While the global mean sea-level budget is considered closed, closing the budget on a finer spatial scale is more complicated due to, for instance, limitations in our observational system and the spatial processes contributing to regional sea-level change. Consequently, the regional budget has been mainly analysed on a basin-wide scale. Here we investigate the sea-level budget at sub-basin scales, using two machine learning techniques to extract domains of coherent sea-level variability: a neural network approach (self-organizing map, SOM) and a network detection approach (δ-MAPS). The extracted domains provide more spatial detail within the ocean basins and indicate how sea-level variability is connected among different regions. Using these domains we can close, within 1σ uncertainty, the sub-basin regional sea-level budget from 1993–2016 in 100 % and 76 % of the SOM and δ-MAPS regions, respectively. Steric variations dominate the temporal sea-level variability and determine a significant part of the total regional change. Sea-level change due to mass exchange between ocean and land has a relatively homogeneous contribution to all regions. In highly dynamic regions (e.g. the Gulf Stream region) the dynamic mass redistribution is significant. Regions where the budget cannot be closed highlight processes that are affecting sea level but are not well captured by the observations, such as the influence of western boundary currents. The use of the budget approach in combination with machine learning techniques leads to new insights into regional sea-level variability and its drivers.

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 93%

Regionalizing the sea-level budget with machine learning techniques

Camargo

Riva²,

Hermans³

et al. 2023

Ocean Sci.

View full text Add to dashboard Cite

show abstract

“…5 Discussion & Conclusions SLB assessments are important tools for understanding the processes driving SLC, for detecting temporal changes in sea-level and its components, for identifying missing contributions to the budget, and for validating and constraining climate models used in sea-level projections (Cazenave and Moreira, 2022). In particular, understanding the processes on a finer spatial scale is essential for local sea-level projections and coastal management planning.…”

Section: Sea-level Budget Sensitivitymentioning

confidence: 99%

Regionalizing the Sea-level Budget With Machine Learning Techniques

Camargo

Riva

Hermans

et al. 2022

Preprint

View full text Add to dashboard Cite

Abstract. Attribution of sea-level change to its different drivers is typically done using a sea-level budget (SLB) approach. While the global mean SLB is considered closed, closing the SLB on a finer spatial scale is more complicated due to, for instance, limitations in our observational system and the spatial processes contributing to regional sea-level change. Consequently, the regional SLB has been mainly analysed on a basin-wide scale. Here we investigate the SLB at sub-basin scales, using two machine learning techniques to extract domains of coherent sea-level variability: a neural network approach (Self-Organising Maps) and a network detection approach (δ-MAPS). The extracted domains provide a higher level of spatial detail than entire ocean basins and besides indicating how sea-level variability is connected among different regions. Using these domains we can close the regional SLB world-wide on different spatial scales. Steric variations dominate the temporal sea-level variability and determine a significant part of the total regional change. Sea-level change due to mass transport between ocean and land has a relatively homogeneous contribution to all regions. In highly dynamic regions (e.g., Gulf Stream region) the dynamic mass redistribution is significant. Regions where the SLB cannot be closed highlight processes that are affecting sea level but are not well captured by the observations, such as the influence of western boundary currents. Hence, the use of the SLB approach in combination with machine learning techniques leads to new insights into regional sea-level variability and its drivers.

show abstract

“…The TOPEX‐Jason missions are equipped with pulse‐limited radar and cover global mountain glaciers at latitudes between ±66°. The TOPEX‐Jason missions will be continued for decades to come because of the need of global sea‐level change monitoring (Cazenave & Moreira, 2022). In addition to sea level change, the data from the TOPEX‐Jason missions have been used for long‐term monitoring of lake level changes (Hwang, Cheng, et al., 2016; Hwang et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

The Rise and Fall of Alaska and Yukon Glaciers Detected by TOPEX/Poseidon and Jason‐2 Altimeters Using a Novel Glacier‐Threshold Method

et al. 2023

View full text Add to dashboard Cite

There are about 27,000 glaciers in Alaska (Mcgrath et al., 2017;Pfeffer et al., 2014). The glaciers here began to retreat as early as the 18th century, and some small glaciers have disappeared in the 20th century (Molnia, 2008). Recent studies show that in the past half century, the rapidly melting glaciers in Alaska have contributed to a third of the global sea level rise (Zemp et al., 2019). This ice loss has been accelerating in the last decade (Hugonnet et al., 2021;Jakob et al., 2020). Mass losses associated with Alaska glacier melting have been detected by the Gravity Recovery and Climate Experiment (GRACE) data. Luthcke et al. (2008Luthcke et al. ( , 2013 first used GRACE mascon solutions to confirm significant mass losses in Alaska, similar to the results from the solutions using spherical harmonic expansions (Gardner et al., 2013;Jacob et al., 2012;Wouters et al., 2019). The latest time-varying gravity observations of GRACE and GRACE Follow-On show that the glacier-induced mass losses are intensifying

show abstract

Contemporary sea-level changes from global to local scales: a review

Cited by 41 publications

References 188 publications

Regionalizing the sea-level budget with machine learning techniques

Regionalizing the sea-level budget with machine learning techniques

Regionalizing the Sea-level Budget With Machine Learning Techniques

The Rise and Fall of Alaska and Yukon Glaciers Detected by TOPEX/Poseidon and Jason‐2 Altimeters Using a Novel Glacier‐Threshold Method

Contact Info

Product

Resources

About