Articles | Volume 18, issue 4
https://doi.org/10.5194/os-18-1093-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-18-1093-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Jul 2022
Research article |
| 27 Jul 2022
| 27 Jul 2022
Attributing decadal climate variability in coastal sea-level trends
School of Geographical Sciences, University of Bristol, Bristol, UK
Rory J. Bingham
School of Geographical Sciences, University of Bristol, Bristol, UK
Jonathan L. Bamber
School of Geographical Sciences, University of Bristol, Bristol, UK
AI4EO, Technical University Munich, Munich, Germany
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Alejandro Blazquez, Benoit Meyssignac, Robin Fraudeau, Michael Ablain, Jonathan Bamber, Antonio Bonaduce, Marie Bouih, Anny Cazenave, Thorben Döhne, Ines Dussaillant, Ramiro Ferrari, Martin Horwath, Nicolas Kolodziejczyk, Hugo Lecomte, Stephanie Leroux, William Llovel, Daniele Melini, Erwan Oulhen, Thierry Penduff, Roshin P. Raj, Giorgio Spada, Marius Schlaak, Papasarafianou Stamatia, Andrea Storto, Chunxue Yang, and Sarah Connors
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-627, https://doi.org/10.5194/essd-2026-627, 2026
Preprint under review for ESSD
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Closing the sea‑level budget on annual and longer time scales is a cornerstone of physical oceanography because sea‑level rise is one of the best indicators of climate change, and a closed budget shows we have identified and quantified all major drivers. We examined it from 1993 to 2022, finding an accelerated rise that matched ice melt and warm water until 2015. Afterwards an unexplained gap appears. Better deep‑ocean observations and refined gravity processing are needed to close the budget.
Julia Pfeffer, Benoît Meyssignac, Rory Bingham, Alejandro Blazquez, Marie Bouih, Carla Braitenberg, Luca Brocca, Henryk Dobslaw, Ramiro Ferrari, Ehsan Forootan, Helena Gerdener, Muhammad Tahir Javed, Laura Jensen, Volker Klemann, Anna Kremer, Jürgen Kusche, Gilles Larnicol, Muhammad Usman Liaqat, Francesco Leopardi, Elisavet-Maria Mamagiannou, Gerardo Maurizio, Roland Pail, Isabelle Panet, Thomas Papanikolaou, Peyman Saemian, Ingo Sasgen, Maike Schumacher, Marius Schlaak, Linus Shihora, Alireza Sobouti, Nico Sneeuw, Mohammad Javad Tourian, Dimitrios Tsoulis, Georgios Vergos, Bert Wouters, Fan Yang, and Ilias Daras
EGUsphere, https://doi.org/10.5194/egusphere-2026-4642, https://doi.org/10.5194/egusphere-2026-4642, 2026
This preprint is open for discussion and under review for Earth Observation (EO).
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The ESA SING project demonstrates how the future satellite gravity missions NGGM and MAGIC will improve observations of continental water storage, oceans, glaciers, sea level, earthquakes, and climate change. Their more accurate, higher-resolution gravity measurements will enhance Earth system monitoring, improve climate and hazard assessments, and strengthen operational services for water management, disaster preparedness, and environmental decision-making.
Xiaojun Ma, Matt King, Jonathan Bamber, Bin Liu, Wenke Sun, and Qiuyu Wang
EGUsphere, https://doi.org/10.5194/egusphere-2026-3461, https://doi.org/10.5194/egusphere-2026-3461, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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Satellite records of glacier change are only about two decades long, so short-term climate swings can strongly affect estimated trends. Using satellite gravity data, we show that the largest year-to-year changes in global glacier mass come from the Arctic and are linked to the North Atlantic Oscillation and Pacific Decadal Oscillation. Accounting for these effects makes Alaska’s estimated ice loss about one-third larger, improving interpretation of glacier change and future sea-level rise.
Leyue Tang, Jonathan L. Bamber, Tian Li, and Gang Qiao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-414, https://doi.org/10.5194/essd-2026-414, 2026
Preprint under review for ESSD
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Many Antarctic ice shelves are thinning and retreating, threatening future sea-level rise. Surface damage indicates structural weakening, but long-term records are scarce. We created a multi-decadal surface damage record for nine representative Antarctic ice shelves using satellite imagery and deep learning. The dataset shows that Pine Island, Thwaites and Larsen B have experienced the most significant damage changes since 1999, providing valuable insights into ice shelf stability.
Nils Lehmann, Cesar Aybar, Ando Shah, Marcello Passaro, Jonathan L. Bamber, and Xiao Xiang Zhu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-232, https://doi.org/10.5194/essd-2026-232, 2026
Revised manuscript accepted for ESSD
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We created a global ocean dataset that brings together satellite measurements, model outputs, and observations into one consistent system. We did this to reduce the time and effort needed to combine different data sources, to improve reproducibility, and enable new analyses. The result makes it easier to study ocean changes, compare methods, and support better understanding of climate processes and extreme events.
Gavin A. Schmidt, Kenneth D. Mankoff, Jonathan L. Bamber, Clara Burgard, Dustin Carroll, David M. Chandler, Violaine Coulon, Benjamin J. Davison, Matthew H. England, Paul R. Holland, Nicolas C. Jourdain, Qian Li, Juliana M. Marson, Pierre Mathiot, Clive R. McMahon, Twila A. Moon, Ruth Mottram, Sophie Nowicki, Anna Olivé Abelló, Andrew G. Pauling, Thomas Rackow, and Damien Ringeisen
Geosci. Model Dev., 18, 8333–8361, https://doi.org/10.5194/gmd-18-8333-2025, https://doi.org/10.5194/gmd-18-8333-2025, 2025
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The impact of increasing mass loss from the Greenland and Antarctic ice sheets has not so far been included in historical climate model simulations. This paper describes the protocols and data available for modeling groups to add this anomalous freshwater to their ocean modules to better represent the impacts of these fluxes on ocean circulation, sea ice, salinity and sea level.
Adam Igneczi and Jonathan Louis Bamber
Earth Syst. Sci. Data, 17, 3203–3218, https://doi.org/10.5194/essd-17-3203-2025, https://doi.org/10.5194/essd-17-3203-2025, 2025
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Freshwater from Arctic land ice loss strongly affects the Arctic and North Atlantic oceans. Datasets describing this freshwater discharge have low resolution and do not cover the entire Arctic. We statistically enhanced coarse-resolution climate model data – from approximately 6 km to 250 m – and routed meltwater towards the coastlines to provide high-resolution data covering all Arctic regions. This approach has far fewer computational requirements than running climate models at high resolution.
Viola Steidl, Jonathan Louis Bamber, and Xiao Xiang Zhu
The Cryosphere, 19, 645–661, https://doi.org/10.5194/tc-19-645-2025, https://doi.org/10.5194/tc-19-645-2025, 2025
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Glacier ice thickness is difficult to measure directly but is essential for glacier evolution modelling. In this work, we employ a novel approach combining physical knowledge and data-driven machine learning to estimate the ice thickness of multiple glaciers in Spitsbergen, Barentsøya, and Edgeøya in Svalbard. We identify challenges for the physics-aware machine learning model and opportunities for improving the accuracy and physical consistency that would also apply to other geophysical tasks.
Tian Li, Konrad Heidler, Lichao Mou, Ádám Ignéczi, Xiao Xiang Zhu, and Jonathan L. Bamber
Earth Syst. Sci. Data, 16, 919–939, https://doi.org/10.5194/essd-16-919-2024, https://doi.org/10.5194/essd-16-919-2024, 2024
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Our study uses deep learning to produce a new high-resolution calving front dataset for 149 marine-terminating glaciers in Svalbard from 1985 to 2023, containing 124 919 terminus traces. This dataset offers insights into understanding calving mechanisms and can help improve glacier frontal ablation estimates as a component of the integrated mass balance assessment.
Benoit S. Lecavalier, Lev Tarasov, Greg Balco, Perry Spector, Claus-Dieter Hillenbrand, Christo Buizert, Catherine Ritz, Marion Leduc-Leballeur, Robert Mulvaney, Pippa L. Whitehouse, Michael J. Bentley, and Jonathan Bamber
Earth Syst. Sci. Data, 15, 3573–3596, https://doi.org/10.5194/essd-15-3573-2023, https://doi.org/10.5194/essd-15-3573-2023, 2023
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The Antarctic Ice Sheet Evolution constraint database version 2 (AntICE2) consists of a large variety of observations that constrain the evolution of the Antarctic Ice Sheet over the last glacial cycle. This includes observations of past ice sheet extent, past ice thickness, past relative sea level, borehole temperature profiles, and present-day bedrock displacement rates. The database is intended to improve our understanding of past Antarctic changes and for ice sheet model calibrations.
Alice C. Frémand, Peter Fretwell, Julien A. Bodart, Hamish D. Pritchard, Alan Aitken, Jonathan L. Bamber, Robin Bell, Cesidio Bianchi, Robert G. Bingham, Donald D. Blankenship, Gino Casassa, Ginny Catania, Knut Christianson, Howard Conway, Hugh F. J. Corr, Xiangbin Cui, Detlef Damaske, Volkmar Damm, Reinhard Drews, Graeme Eagles, Olaf Eisen, Hannes Eisermann, Fausto Ferraccioli, Elena Field, René Forsberg, Steven Franke, Shuji Fujita, Yonggyu Gim, Vikram Goel, Siva Prasad Gogineni, Jamin Greenbaum, Benjamin Hills, Richard C. A. Hindmarsh, Andrew O. Hoffman, Per Holmlund, Nicholas Holschuh, John W. Holt, Annika N. Horlings, Angelika Humbert, Robert W. Jacobel, Daniela Jansen, Adrian Jenkins, Wilfried Jokat, Tom Jordan, Edward King, Jack Kohler, William Krabill, Mette Kusk Gillespie, Kirsty Langley, Joohan Lee, German Leitchenkov, Carlton Leuschen, Bruce Luyendyk, Joseph MacGregor, Emma MacKie, Kenichi Matsuoka, Mathieu Morlighem, Jérémie Mouginot, Frank O. Nitsche, Yoshifumi Nogi, Ole A. Nost, John Paden, Frank Pattyn, Sergey V. Popov, Eric Rignot, David M. Rippin, Andrés Rivera, Jason Roberts, Neil Ross, Anotonia Ruppel, Dustin M. Schroeder, Martin J. Siegert, Andrew M. Smith, Daniel Steinhage, Michael Studinger, Bo Sun, Ignazio Tabacco, Kirsty Tinto, Stefano Urbini, David Vaughan, Brian C. Welch, Douglas S. Wilson, Duncan A. Young, and Achille Zirizzotti
Earth Syst. Sci. Data, 15, 2695–2710, https://doi.org/10.5194/essd-15-2695-2023, https://doi.org/10.5194/essd-15-2695-2023, 2023
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This paper presents the release of over 60 years of ice thickness, bed elevation, and surface elevation data acquired over Antarctica by the international community. These data are a crucial component of the Antarctic Bedmap initiative which aims to produce a new map and datasets of Antarctic ice thickness and bed topography for the international glaciology and geophysical community.
Tian Li, Geoffrey J. Dawson, Stephen J. Chuter, and Jonathan L. Bamber
The Cryosphere, 17, 1003–1022, https://doi.org/10.5194/tc-17-1003-2023, https://doi.org/10.5194/tc-17-1003-2023, 2023
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The Totten and Moscow University glaciers in East Antarctica have the potential to make a significant contribution to future sea-level rise. We used a combination of different satellite measurements to show that the grounding lines have been retreating along the fast-flowing ice streams across these two glaciers. We also found two tide-modulated ocean channels that might open new pathways for the warm ocean water to enter the ice shelf cavity.
Stephen J. Chuter, Andrew Zammit-Mangion, Jonathan Rougier, Geoffrey Dawson, and Jonathan L. Bamber
The Cryosphere, 16, 1349–1367, https://doi.org/10.5194/tc-16-1349-2022, https://doi.org/10.5194/tc-16-1349-2022, 2022
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We find the Antarctic Peninsula to have a mean mass loss of 19 ± 1.1 Gt yr−1 over the 2003–2019 period, driven predominantly by changes in ice dynamic flow like due to changes in ocean forcing. This long-term record is crucial to ascertaining the region’s present-day contribution to sea level rise, with the understanding of driving processes enabling better future predictions. Our statistical approach enables us to estimate this previously poorly surveyed regions mass balance more accurately.
Tom Mitcham, G. Hilmar Gudmundsson, and Jonathan L. Bamber
The Cryosphere, 16, 883–901, https://doi.org/10.5194/tc-16-883-2022, https://doi.org/10.5194/tc-16-883-2022, 2022
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We modelled the response of the Larsen C Ice Shelf (LCIS) and its tributary glaciers to the calving of the A68 iceberg and validated our results with observations. We found that the impact was limited, confirming that mostly passive ice was calved. Through further calving experiments we quantified the total buttressing provided by the LCIS and found that over 80 % of the buttressing capacity is generated in the first 5 km of the ice shelf downstream of the grounding line.
Tian Li, Geoffrey J. Dawson, Stephen J. Chuter, and Jonathan L. Bamber
Earth Syst. Sci. Data, 14, 535–557, https://doi.org/10.5194/essd-14-535-2022, https://doi.org/10.5194/essd-14-535-2022, 2022
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Accurate knowledge of the Antarctic grounding zone is important for mass balance calculation, ice sheet stability assessment, and ice sheet model projections. Here we present the first ICESat-2-derived high-resolution grounding zone product of the Antarctic Ice Sheet, including three important boundaries. This new data product will provide more comprehensive insights into ice sheet instability, which is valuable for both the cryosphere and sea level science communities.
Fanny Lehmann, Bramha Dutt Vishwakarma, and Jonathan Bamber
Hydrol. Earth Syst. Sci., 26, 35–54, https://doi.org/10.5194/hess-26-35-2022, https://doi.org/10.5194/hess-26-35-2022, 2022
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Many data sources are available to evaluate components of the water cycle (precipitation, evapotranspiration, runoff, and terrestrial water storage). Despite this variety, it remains unclear how different combinations of datasets satisfy the conservation of mass. We conducted the most comprehensive analysis of water budget closure on a global scale to date. Our results can serve as a basis to select appropriate datasets for regional hydrological studies.
Cited articles
Adloff, F., Jordà, G., Somot, S., Sevault, F., Arsouze, T., Meyssignac, B.,
Li, L., and Serge, P.: Improving sea level simulation in Mediterranean
regional climate models, Clim. Dynam., 51, 1167–1178,
https://doi.org/10.1007/s00382-017-3842-3, 2018. a
Becker, M., Karpytchev, M., Marcos, M., Jevrejeva, S., and Lennartz-Sassinek,
S.: Do climate models reproduce complexity of observed sea level changes?,
Geophys. Res. Lett., 43, 5176–5184, https://doi.org/10.1002/2016GL068971,
2016. a
Bingham, R. J. and Hughes, C. W.: Local diagnostics to estimate density-induced
sea level variations over topography and along coastlines, J.
Geophys. Res.-Ocean., 117, C01013, https://doi.org/10.1029/2011JC007276, 2012. a
Bos, M. S., Fernandes, R. M. S., Williams, S. D. P., and Bastos, L.: Fast error
analysis of continuous GNSS observations with missing data, J.
Geodesy, 87, 351–360, https://doi.org/10.1007/s00190-012-0605-0, 2013. a
Carson, M., Köhl, A., and Stammer, D.: The Impact of Regional Multidecadal and
Century-Scale Internal Climate Variability on Sea Level Trends in CMIP5
Models, J. Clim., 28, 853–861, https://doi.org/10.1175/JCLI-D-14-00359.1,
2015. a, b
Carson, M., Lyu, K., Richter, K., Becker, M., Domingues, C. M., Han, W., and
Zanna, L.: Climate Model Uncertainty and Trend Detection in Regional Sea
Level Projections: A Review, Surv. Geophys., 40, 1631–1653,
https://doi.org/10.1007/s10712-019-09559-3, 2019. a, b
Coward, A. C.: Archive data from run 6 of the NEMO
Dangendorf, S., Rybski, D., Mudersbach, C., Müller, A., Kaufmann, E.,
Zorita, E., and Jensen, J.: Evidence for long-term memory in sea level,
Geophys. Res. Lett., 41, 5530–5537, https://doi.org/10.1002/2014GL060538,
2014. a
Dussin, R., Barnier, B., and Brodeau, L.: The making of Drakkar forcing set
DFS5, Tech. rep., LGGE, DRAKKAR/MyOcean Report 01-04-16, 2016. a
Emery, W. J. and Thomson, R. E.: Data Analysis Methods in Physical
Oceanography, Elsevier Science, Elsevier, https://doi.org/10.1016/C2010-0-66362-0, 2001. a
ESA: Time series of gridded Sea Level Anomalies, CCI Open Data Portal [data set],
https://doi.org/10.5270/esa-sea_level_cci-MSLA-1993_2015-v_2.0-201612, 2018. a, b
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer,
R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison
Project Phase 6 (CMIP6) experimental design and organization, Geosci.
Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Fasullo, J. T. and Nerem, R. S.: Altimeter-era emergence of the patterns of
forced sea-level rise in climate models and implications for the future,
P. Natl. Acad. Sci. USA, 115, 12944–12949,
https://doi.org/10.1073/pnas.1813233115, 2018. a, b
Fasullo, J. T., Gent, P. R., and Nerem, R. S.: Forced Patterns of Sea Level
Rise in the Community Earth System Model Large Ensemble From 1920 to 2100,
J. Geophys. Res.-Ocean., 125, e2019JC016030,
https://doi.org/10.1029/2019JC016030, 2020. a
Frankcombe, L. M., McGregor, S., and England, M. H.: Robustness of the modes of
Indo-Pacific sea level variability, Clim. Dynam., 45, 1281–1298,
https://doi.org/10.1007/s00382-014-2377-0, 2015. a
Frederikse, T., Riva, R. E. M., and King, M. A.: Ocean Bottom Deformation Due
To Present-Day Mass Redistribution and Its Impact on Sea Level Observations,
Geophys. Res. Lett., 44, 12306–12314, https://doi.org/10.1002/2017GL075419,
2017. a
Frederikse, T., Jevrejeva, S., Riva, R. E. M., and Dangendorf, S.: A Consistent
Sea-Level Reconstruction and Its Budget on Basin and Global Scales over
1958–2014, J. Clim., 31, 1267–1280,
https://doi.org/10.1175/JCLI-D-17-0502.1, 2018. a, b, c
Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey,
V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., and Wu, Y.-H.: The
causes of sea-level rise since 1900, Zenodo [data set],
https://doi.org/10.5281/zenodo.3862995,
2020a. a, b, c
Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrye,
V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., and Wu, Y.-H.: The
causes of sea-level rise since 1900, Nature, 54, 393–397,
https://doi.org/10.1038/s41586-020-2591-3, 2020b. a, b, c
GCOS: GCOS Dipole Mode Index (DMI), NOAA [data set],
https://psl.noaa.gov/gcos_wgsp/Timeseries/DMI/, last access: 2 November 2020. a
Greatbatch, R. J.: A note on the representation of steric sea level in models
that conserve volume rather than mass, J. Geophys. Res., 99, 12 767–12 771,
1994. a
Gregory, J. M., Griffies, S. M., Hughes, C. W., Lowe, J. A., Church, J. A.,
Fukumori, I., Gomez, N., Kopp, R. E., Landerer, F., Cozannet, G. L., Ponte,
R. M., Stammer, D., Tamisiea, M. E., and van de Wal, R. S. W.: Concepts and
Terminology for Sea Level: Mean, Variability and Change, Both Local and
Global, Surv. Geophys., 40, 1251–1289, https://doi.org/10.1007/s10712-019-09525-z, 2019. a
Griffies, S. M. and Greatbatch, R. J.: Physical processes that impact the
evolution of global mean sea level in ocean climate models, Ocean Modell.,
51, 37–72, https://doi.org/10.1016/j.ocemod.2012.04.003, 2012. a
Haigh, I. D., Wahl, T., Rohling, E. J., Price, R. M., Pattiaratchi, C. B.,
Calafat, F. M., and Dangendorf, S.: Timescales for detecting a significant
acceleration in sea level rise, Nat. Commun., 5, 3635,
https://doi.org/10.1038/ncomms4635, 2014. a
Hamlington, B. D., Leben, R. R., Strassburg, M. W., Nerem, R. S., and Kim,
K.-Y.: Contribution of the Pacific Decadal Oscillation to global mean sea
level trends, Geophys. Res. Lett., 40, 5171–5175,
https://doi.org/10.1002/grl.50950, 2013. a, b, c
Hamlington, B. D., Strassburg, M. W., Leben, R. R., Han, W., Nerem, R. S., and
K-Y., K.: Uncovering the anthropogenic sea-level rise signal in the Pacific
Ocean, Nat. Clim. Change, 4, 782–785, https://doi.org/10.1038/nclimate2307,
2014. a
Hamlington, B. D., Fasullo, J. T., Nerem, R. S., Kim, K.-Y., and Landerer,
F. W.: Uncovering the Pattern of Forced Sea Level Rise in the Satellite
Altimeter Record, Geophys. Res. Lett., 46, 4844–4853,
https://doi.org/10.1029/2018GL081386, 2019. a, b, c, d
Hamlington, B. D., Frederikse, T., Thompson, P., Willis, J., Nerem, R., and
Fasullo, J.: Past, Present and Future Pacific Sea Level-Change, Earth's
Future, 8, 2020EF001839, https://doi.org/10.1029/2020EF001839, 2020a. a, b, c
Hamlington, B. D., Frederikse, T., Nerem, R. S., Fasullo, J. T., and Adhikari,
S.: Investigating the Acceleration of Regional Sea Level Rise During the
Satellite Altimeter Era, Geophys. Res. Lett., 47, e2019GL086528,
https://doi.org/10.1029/2019GL086528, 2020b. a, b
Hamlington, B. D., Piecuch, C. G., Reager, J. T., Chandanpurkar, H.,
Frederikse, T., Nerem, R. S., Fasullo, J. T., and Cheon, S.-H.: Origin of
interannual variability in global mean sea level, P. Natl.
Acad. Sci. USA, 117, 13983–13990, https://doi.org/10.1073/pnas.1922190117,
2020c. a, b, c
Holgate, S. J., Matthews, A., Woodworth, P. L., Rickards, L. J., Tamisiea,
M. E., Bradshaw, E., Foden, P. R., Gordon, K. M., Jevrejeva, S., and Pugh,
J.: New Data Systems and Products at the Permanent Service for Mean
Sea Level, J. Coast. Res., 29, 493–504,
https://doi.org/10.2112/JCOASTRES-D-12-00175.1, 2013. a
IPCC: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, 755 pp., https://doi.org/10.1017/9781009157964, 2019. a
Kleinherenbrink, M., Riva, R., and Sun, Y.: Sub-basin-scale sea level budgets from satellite altimetry, Argo floats and satellite gravimetry: a case study in the North Atlantic Ocean, Ocean Sci., 12, 1179–1203, https://doi.org/10.5194/os-12-1179-2016, 2016. a
Landerer, F. W., Jungclaus, J. H., and Marotzke, J.: Regional Dynamic and
Steric Sea Level Change in Response to the IPCC-A1B Scenario, J.
Phys. Oceanogr., 37, 296–312, https://doi.org/10.1175/JPO3013.1, 2007. a
Legeais, J.-F., Ablain, M., Zawadzki, L., Zuo, H., Johannessen, J. A., Scharffenberg, M. G., Fenoglio-Marc, L., Fernandes, M. J., Andersen, O. B., Rudenko, S., Cipollini, P., Quartly, G. D., Passaro, M., Cazenave, A., and Benveniste, J.: An improved and homogeneous altimeter sea level record from the ESA Climate Change Initiative, Earth Syst. Sci. Data, 10, 281–301, https://doi.org/10.5194/essd-10-281-2018, 2018. a
Llovel, W., Penduff, T., Meyssignac, B., Molines, J.-M., Terray, L.,
Bessières, L., and Barnier, B.: Contributions of Atmospheric Forcing and
Chaotic Ocean Variability to Regional Sea Level Trends Over 1993–2015,
Geophys. Res. Lett., 45, 13405–13413, https://doi.org/10.1029/2018GL080838,
2018. a, b, c
Marshall, G. J.: An observation-based Southern Hemisphere Annular Mode
Index, British Antarctic Survey [data set], https://legacy.bas.ac.uk/met/gjma/sam.html, last access: 31 August 2020. a
Marzocchi, A.-M., Hirschi, J. J., Holliday, N. P., Cunningham, S. A., Blaker,
A. T., and Coward, A. C.: The North Atlantic subpolar circulation in an
eddy-resolving global ocean model, J. Mar. Syst.,
142, 126–143,
https://doi.org/10.1016/j.jmarsys.2014.10.007, 2015. a, b, c
Meyssignac, B., Slangen, A. B. A., Melet, A., Church, J. A., Fettweis, X.,
Marzeion, B., Agosta, C., Ligtenberg, S. R. M., Spada, G., Richter, K.,
Palmer, M. D., Roberts, C. D., and Champollion, N.: Evaluating Model
Simulations of Twentieth-Century Sea-Level Rise, Part II: Regional Sea-Level
Changes, J. Clim., 30, 8565–8593,
https://doi.org/10.1175/JCLI-D-17-0112.1, 2017. a, b, c, d
Moat, B. I., Josey, S. A., Sinha, B., Blaker, A. T., Smeed, D. A., McCarthy,
G. D., Johns, W. E., Hirschi, J. J.-M., Frajka-Williams, E., Rayner, D., Duchez, A., and Coward, A. C.: Major variations in subtropical North Atlantic heat
transport at short (5 day) timescales and their causes, J.
Geophys. Res.-Ocean., 121, 3237–3249, https://doi.org/10.1002/2016JC011660,
2016. a, b
Nidheesh, A., Lengaigne, M., and Vialard, J.: Natural decadal sea-level
variability in the Indian Ocean: lessons from CMIP models, Clim.
Dynam., 53, 5653–5673, https://doi.org/10.1007/s00382-019-04885-z, 2019. a, b
NOAA-CPC: NOAA-CPC Arctic Oscillation Index (AO), NOAA [data set],
https://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/ao_index.html, last access: 2 November 2020. a
NOAA-NCEI: NOAA-ESRL PSL Multivariate ENSO Index (MEI), NOAA [data set], https://www.psl.noaa.gov/enso/mei.old/, last access: 31 August
2020a. a
NOAA-NCEI: NCEI North Atlantic Oscillation (NAO) index, NOAA [data set], https://www.ncdc.noaa.gov/teleconnections/nao/, last access: 31 August
2020b. a
NOAA-NCEI: NCEI Pacific Decadal Oscillation (PDO)index, NOAA [data set], https://www.ncdc.noaa.gov/teleconnections/pdo/, last access 27 February
2020c. a
Peltier, W. R., Argus, D. F., and Drummond, R.: Space geodesy constrains ice
age terminal deglaciation: The global ICE-6G_C (VM5a) model, J.
Geophys. Res.-Sol. Ear., 120, 450–487, https://doi.org/10.1002/2014JB011176,
2015. a, b
Penduff, T., Llovel, W., Close, S., Garcia-Gomez, I., and Leroux, S.:
Processing Choices Affect Ocean Mass Estimates From GRACE, J.
Geophys. Res.-Ocean., 124, 1029–1044, https://doi.org/10.1029/2018JC014341,
2019. a, b, c, d
Peyser, C. E., Yin, J., Landerer, F. W., and Cole, J. E.: Pacific sea level
rise patterns and global surface temperature variability, Geophys. Res. Lett., 43, 8662–8669, https://doi.org/10.1002/2016GL069401, 2016. a
Pfeffer, J., Cazenave, A., and Barnoud, A.: Analysis of the interannual
variability in satellite gravity solutions: detection of climate modes
fingerprints in water mass displacements across continents and oceans, Clim.
Dynam., 58, 1065–1084, https://doi.org/10.1007/s00382-021-05953-z, 2022. a, b, c, d, e, f
Richter, K., Meyssignac, B., Slangen, A. B. A., Melet, A., Church, J. A.,
Fettweis, X., Marzeion, B., Agosta, C., Ligtenberg, S. R. M., Spada, G.,
Palmer, M. D., Roberts, C. D., and Champollion, N.: Detecting a forced signal
in satellite-era sea-level change, Environ. Res. Lett., 15,
094079, https://doi.org/10.1088/1748-9326/ab986e, 2020. a, b, c
Royston, S., Bingham, R. J., and, Bamber, J. L.: Attributing decadal climate variability in coastal sea-level trends, Zenodo [data set], https://doi.org/10.5281/zenodo.5849268, 2022. a
Sérazin, G., Penduff, T., Grégorio, S., Barnier, B., Molines, J.-M.,
and Terray, L.: Intrinsic Variability of Sea Level from Global Ocean
Simulations: Spatiotemporal Scales, J. Clim., 28, 4279–4292,
https://doi.org/10.1175/JCLI-D-14-00554.1, 2015. a
Sérazin, G., Meyssignac, B., Penduff, T., Terray, L., Barnier, B., and
Molines, J.-M.: Quantifying uncertainties on regional sea level change
induced by multidecadal intrinsic oceanic variability, Geophys. Res. Lett., 43, 8151–8159, https://doi.org/10.1002/2016GL069273, 2016. a, b
Tamisiea, M. E.: Ongoing glacial isostatic contributions to observations of sea
level change, Geophys. J. Int., 186, 1036–1044,
https://doi.org/10.1111/j.1365-246X.2011.05116.x, 2011. a, b
TEOS-10: Release on the IAPWS Formulation 2008 for the Thermodynamic
Properties of Seawater, IAPWS, Tech. Rep., R13-08,
http://www.teos-10.org (last access: 16 April 2019), 2008. a
Wada, Y., Reager, J., Chao, B., Wang, J., Lo, M.-H., Song, C., Li, Y., and
Gardner, A. S.: Recent Changes in Land Water Storage and its Contribution to
Sea Level Variations, Surv. Geophys., 38, 131–152,
https://doi.org/10.1007/s10712-016-9399-6, 2017.
a
Woodworth, P. L., Melet, A., Marcos, M., Ray, R. D., Wöppelmann, G.,
Sasaki, Y. N., Cirano, M., Hibbert, A., Huthnance, J. M., Monserrat, S., and
Merrifield, M. A.: Forcing Factors Affecting Sea Level Changes at the Coast,
Surv. Geophys., 40, 1351–1397, https://doi.org/10.1007/s10712-019-09531-1,
2019. a
Yin, J., Griffies, S. M., and Stouffer, R. J.: Spatial Variability of Sea Level
Rise in Twenty-First Century Projections, J. Clim., 23, 4585–4607, https://doi.org/10.1175/2010JCLI3533.1, 2010. a
Short summary
Decadal sea-level variability masks longer-term changes and increases uncertainty in observed trend and acceleration estimates. We use numerical ocean models to determine the magnitude of decadal variability we might expect in sea-level trends at coastal locations around the world, resulting from natural, internal variability. A proportion of that variability can be replicated from known climate modes, giving a range to add to short- to mid-term projections of regional sea-level trends.
Decadal sea-level variability masks longer-term changes and increases uncertainty in observed...
