Articles | Volume 26, issue 7
https://doi.org/10.5194/nhess-26-3291-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-3291-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Jul 2026
Research article |
| 17 Jul 2026
| 17 Jul 2026
Coupled simulation of landslide, tsunami, and ground displacement for the 2017 Nuugaatsiaq event in Greenland
Hideo Aochi
CORRESPONDING AUTHOR
Direction of Energy, BRGM, Orléans 45060, France
Masumi Yamada
Disaster Prevention Research Institute, Kyoto University, Uji, 611-0011, Japan
Tung-Cheng Ho
Disaster Prevention Research Institute, Kyoto University, Uji, 611-0011, Japan
Gonéri Le Cozannet
Direction of Risks, BRGM, Orléans 45060, France
Arno Christian Hammann
Asiaq Greenland Survey, Nuuk, 3900, Greenland
Ruth Mottram
Danish Meteorological Institute, Copenhagen, 2100, Denmark
Related authors
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Elke Schlager, Sebastian Scher, Ruth H. Mottram, and Peter L. Langen
The Cryosphere, 20, 3313–3343, https://doi.org/10.5194/tc-20-3313-2026, https://doi.org/10.5194/tc-20-3313-2026, 2026
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Predicting Greenland's surface melt is critical for understanding sea-level rise, but traditional firn models are too slow for exploring many climate scenarios. We developed a neural network optimized through systematic input selection and network tuning to identify the necessary information to accurately emulate surface melt. This approach cuts computation costs by orders of magnitude and can be retrained for different climate forcings or extended to other surface mass balance properties.
Ian Simpson, Edward Hanna, Ryan S. Williams, Linh Luu, Andrew Orr, Julie Jones, Xavier Fettweis, Jose Abraham Torres Alavez, Ole Bøssing Christensen, Ella Gilbert, Sid Gumber, Christoph Kittel, Sihan Li, Damien Maure, Ruth Mottram, Tony Phillips, Willem Jan van de Berg, and Kristiina Verro
EGUsphere, https://doi.org/10.5194/egusphere-2026-2270, https://doi.org/10.5194/egusphere-2026-2270, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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The warming trend in global temperatures has potential to result in accelerated break up of the Antarctic ice shelves, which would contribute to rising sea levels. Here we present a novel database of Antarctic extreme weather events over a selection of the Antarctic ice shelves. We examine air temperature, precipitation, wind and surface pressure. In addition, we examine trends in the frequency of extreme events and the links with weather patterns around Antarctica.
Fredrik Boberg, Xavier Fettweis, Nicolaj Hansen, Ruth Mottram, and Michiel R. van den Broeke
The Cryosphere, 20, 2947–2960, https://doi.org/10.5194/tc-20-2947-2026, https://doi.org/10.5194/tc-20-2947-2026, 2026
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An ensemble of regional climate model simulations is used to estimate the 21st century change in precipitation on the Greenland ice sheet. For the end of the century, the change is in the range 40 to 170 Gt per year, depending on the emission scenario. Using annual values of 2 m air temperature and precipitation, we estimate an increase in precipitation of 35 Gt per year for every degree of warming.
Ella Gilbert, José Abraham Torres-Alavez, Marte G. Hofsteenge, Willem Jan van de Berg, Fredrik Boberg, Ole Bøssing Christensen, Christiaan Timo van Dalum, Xavier Fettweis, Siddharth Gumber, Nicolaj Hansen, Christoph Kittel, Clara Lambin, Damien Maure, Ruth Mottram, Martin Olesen, Andrew Orr, Tony Phillips, Maurice van Tiggelen, Kristiina Verro, and Priscilla A. Mooney
The Cryosphere, 20, 2629–2658, https://doi.org/10.5194/tc-20-2629-2026, https://doi.org/10.5194/tc-20-2629-2026, 2026
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Here we present a new dataset – the PolarRES ensemble – of four state-of-the-art regional climate models, which capture the full complexity of Antarctica's climate. The ensemble out-performs other available tools, advancing our ability to explore Antarctic climate. While it still has limitations, the PolarRES ensemble offers a novel and exciting way of evaluating climate processes and features, and we encourage researchers to use the data, which are freely available.
Susan E. Hanson, Robert J. Nicholls, Floris R. Calkoen, Gonéri Le Cozannet, and Arjen P. Luijendijk
Nat. Hazards Earth Syst. Sci., 26, 1685–1703, https://doi.org/10.5194/nhess-26-1685-2026, https://doi.org/10.5194/nhess-26-1685-2026, 2026
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The CoasTER geographic database provides erosion relevant characteristics (sediment type, land use, geomorphology, historical shoreline movement trend) for Europe’s coastal floodplains. Building on earlier erosion research, it also includes a coastal geomorphological typology incorporating hard engineering and other infrastructure. Analysis demonstrates that episodic/long-term erosion and coastal flooding interactions are widespread and an important aspect within any management strategy.
Benjamin Heurgue, Charles Amory, Christoph Kittel, Fredrik Boberg, Gaël Durand, Vincent Favier, Xavier Fettweis, Quentin Glaude, Heiko Goelzer, Nicolaj Hansen, Nicolas C. Jourdain, Ruth Mottram, Martin Olesen, Willem Jan Van de Berg, Michiel R. Van den Broeke, and René R. Wijngaard
EGUsphere, https://doi.org/10.5194/egusphere-2026-624, https://doi.org/10.5194/egusphere-2026-624, 2026
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We studied how the Antarctic ice sheet surface mass balance may change by 2100 using three high-resolution climate models forced by the same future climate scenario. While the models agree for present-day conditions, they project very different futures, especially over floating ice shelves. These differences mainly come from how melting, refreezing, and temperature are represented. Our results show that future sea level projections strongly depend on how well models simulate today’s climate.
José Abraham Torres Alavez, Ruth Mottram, Thomas Lavergne, Rasmus Pedersen, and Ole Bøssing Christensen
EGUsphere, https://doi.org/10.5194/egusphere-2025-6183, https://doi.org/10.5194/egusphere-2025-6183, 2026
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Coastal polynyas shape sea ice production and polar climate. This study analyses Greenland and Antarctic polynyas using two satellite sea-ice products and HCLIM simulations. The high-resolution CCI dataset improves polynya representation, boundary-layer structure, and near-surface conditions. Results show that integrating high-resolution sea-ice data enhances regional climate modelling and supports better reanalysis systems.
Heiko Goelzer, Constantijn J. Berends, Fredrik Boberg, Gael Durand, Tamsin L. Edwards, Xavier Fettweis, Fabien Gillet-Chaulet, Quentin Glaude, Philippe Huybrechts, Sébastien Le clec'h, Ruth Mottram, Brice Noël, Martin Olesen, Charlotte Rahlves, Jeremy Rohmer, Michiel van den Broeke, and Roderik S. W. van de Wal
The Cryosphere, 19, 6887–6906, https://doi.org/10.5194/tc-19-6887-2025, https://doi.org/10.5194/tc-19-6887-2025, 2025
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We present an ensemble of ice sheet model projections for the Greenland ice sheet. The focus is on providing projections that improve our understanding of the range future sea-level rise and the inherent uncertainties over the next 100 to 300 years. Compared to earlier work we more fully account for some of the uncertainties in sea-level projections. We include a wider range of climate model output, more climate change scenarios and we extend projections schematically up to year 2300.
Jeremy Rohmer, Heiko Goelzer, Tamsin L. Edwards, Goneri Le Cozannet, and Gael Durand
The Cryosphere, 19, 6421–6444, https://doi.org/10.5194/tc-19-6421-2025, https://doi.org/10.5194/tc-19-6421-2025, 2025
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Developing robust protocols to design multi-model ensembles is of primary importance for the uncertainty quantification of sea level projections. Here, we set up a series of computer experiments to reflect design decisions for the prediction of future sea level contribution of the Greenland ice sheet in 2100. We show the importance of including the most extreme climate scenario and the implications of using a single type of numerical model for ice sheets or regional climate.
Mirna Badillo-Interiano, Jérémy Rohmer, Gonéri Le Cozannet, and Virginie Duvat
Nat. Hazards Earth Syst. Sci., 25, 4527–4543, https://doi.org/10.5194/nhess-25-4527-2025, https://doi.org/10.5194/nhess-25-4527-2025, 2025
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Small islands face increasing threats from climate change. In this context, exploring new modeling approaches is needed to improve climate risk assessments. We applied Bayesian Networks to assess the risk to future habitability on four atoll islands. The findings show that Bayesian Networks are powerful tools for efficiently assessing climate-related risks by combining expert judgments and confidence levels, providing a comprehensive framework to assess risks in data-limited island settings.
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.
Anna Puggaard, Nicolaj Hansen, Ruth Mottram, Thomas Nagler, Stefan Scheiblauer, Sebastian B. Simonsen, Louise S. Sørensen, Jan Wuite, and Anne M. Solgaard
The Cryosphere, 19, 2963–2981, https://doi.org/10.5194/tc-19-2963-2025, https://doi.org/10.5194/tc-19-2963-2025, 2025
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Regional climate models are currently the only source for assessing the melt volume of the Greenland Ice Sheet on a global scale. This study compares the modeled melt volume with observations from weather stations and melt extent observed from the Advanced SCATterometer (ASCAT) to assess the performance of the models. It highlights the importance of critically evaluating model outputs with high-quality satellite measurements to improve the understanding of variability among models.
Sofie Hedetoft, Olivia Bang Brinck, Ruth Mottram, Andrea M. U. Gierisch, Steffen Malskær Olsen, Martin Olesen, Nicolaj Hansen, Anders Anker Bjørk, Erik Loebel, Anne Solgaard, and Peter Thejll
EGUsphere, https://doi.org/10.5194/egusphere-2025-1907, https://doi.org/10.5194/egusphere-2025-1907, 2025
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Iceberg mélange is the jumble of icebergs in front of some glaciers that calve into the sea. Some studies suggest mélange might help to control the retreat of glaciers. We studied 3 glaciers in NW Greenland where we used GPS sensors and satellites to track ice movement. We found that glaciers push forward and calve all year, including when mélange and landfast sea ice are present, suggesting mélange is not important in supporting glaciers, but may influence the seasonal calving cycle.
Alexander Bisaro, Giulia Galluccio, Elisa Fiorini Beckhauser, Fulvio Biddau, Ruben David, Floortje d'Hont, Antonio Góngora Zurro, Gonéri Le Cozannet, Sadie McEvoy, Begoña Pérez Gómez, Claudia Romagnoli, Eugenio Sini, and Jill Slinger
State Planet, 3-slre1, 7, https://doi.org/10.5194/sp-3-slre1-7-2024, https://doi.org/10.5194/sp-3-slre1-7-2024, 2024
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This paper assesses coastal adaptation governance by examining socio-economic and political contexts, reviewing policy frameworks, and identifying challenges. Results show that regional and basin-scale frameworks lack sea level rise provisions, but significant national progress is observed. The main governance challenges are time horizons and uncertainty, coordination, and social vulnerability. These, however, can be addressed if flexible planning and nature-based solutions are implemented.
Nicolaj Hansen, Andrew Orr, Xun Zou, Fredrik Boberg, Thomas J. Bracegirdle, Ella Gilbert, Peter L. Langen, Matthew A. Lazzara, Ruth Mottram, Tony Phillips, Ruth Price, Sebastian B. Simonsen, and Stuart Webster
The Cryosphere, 18, 2897–2916, https://doi.org/10.5194/tc-18-2897-2024, https://doi.org/10.5194/tc-18-2897-2024, 2024
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We investigated a melt event over the Ross Ice Shelf. We use regional climate models and a firn model to simulate the melt and compare the results with satellite data. We find that the firn model aligned well with observed melt days in certain parts of the ice shelf. The firn model had challenges accurately simulating the melt extent in the western sector. We identified potential reasons for these discrepancies, pointing to limitations in the models related to representing the cloud properties.
Aslak Grinsted, Nicholas Mossor Rathmann, Ruth Mottram, Anne Munck Solgaard, Joachim Mathiesen, and Christine Schøtt Hvidberg
The Cryosphere, 18, 1947–1957, https://doi.org/10.5194/tc-18-1947-2024, https://doi.org/10.5194/tc-18-1947-2024, 2024
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Ice fracture can cause glacier crevassing and calving. These natural hazards can also modulate the flow and evolution of ice sheets. In a new study, we use a new high-resolution dataset to determine a new failure criterion for glacier ice. Surprisingly, the strength of ice depends on the mode of deformation, and this has potential implications for the currently used flow law of ice.
Baptiste Vandecrux, Robert S. Fausto, Jason E. Box, Federico Covi, Regine Hock, Åsa K. Rennermalm, Achim Heilig, Jakob Abermann, Dirk van As, Elisa Bjerre, Xavier Fettweis, Paul C. J. P. Smeets, Peter Kuipers Munneke, Michiel R. van den Broeke, Max Brils, Peter L. Langen, Ruth Mottram, and Andreas P. Ahlstrøm
The Cryosphere, 18, 609–631, https://doi.org/10.5194/tc-18-609-2024, https://doi.org/10.5194/tc-18-609-2024, 2024
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How fast is the Greenland ice sheet warming? In this study, we compiled 4500+ temperature measurements at 10 m below the ice sheet surface (T10m) from 1912 to 2022. We trained a machine learning model on these data and reconstructed T10m for the ice sheet during 1950–2022. After a slight cooling during 1950–1985, the ice sheet warmed at a rate of 0.7 °C per decade until 2022. Climate models showed mixed results compared to our observations and underestimated the warming in key regions.
Nicolaj Hansen, Louise Sandberg Sørensen, Giorgio Spada, Daniele Melini, Rene Forsberg, Ruth Mottram, and Sebastian B. Simonsen
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-104, https://doi.org/10.5194/tc-2023-104, 2023
Preprint withdrawn
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We use ICESat-2 to estimate the surface elevation change over Greenland and Antarctica in the period of 2018 to 2021. Numerical models have been used the compute the firn compaction and the vertical bedrock movement so non-mass-related elevation changes can be taken into account. We have made a parameterization of the surface density so we can convert the volume change to mass change. We find that Antarctica has lost 135.7±27.3 Gt per year, and the Greenland ice sheet 237.5±14.0 Gt per year.
Inès N. Otosaka, Andrew Shepherd, Erik R. Ivins, Nicole-Jeanne Schlegel, Charles Amory, Michiel R. van den Broeke, Martin Horwath, Ian Joughin, Michalea D. King, Gerhard Krinner, Sophie Nowicki, Anthony J. Payne, Eric Rignot, Ted Scambos, Karen M. Simon, Benjamin E. Smith, Louise S. Sørensen, Isabella Velicogna, Pippa L. Whitehouse, Geruo A, Cécile Agosta, Andreas P. Ahlstrøm, Alejandro Blazquez, William Colgan, Marcus E. Engdahl, Xavier Fettweis, Rene Forsberg, Hubert Gallée, Alex Gardner, Lin Gilbert, Noel Gourmelen, Andreas Groh, Brian C. Gunter, Christopher Harig, Veit Helm, Shfaqat Abbas Khan, Christoph Kittel, Hannes Konrad, Peter L. Langen, Benoit S. Lecavalier, Chia-Chun Liang, Bryant D. Loomis, Malcolm McMillan, Daniele Melini, Sebastian H. Mernild, Ruth Mottram, Jeremie Mouginot, Johan Nilsson, Brice Noël, Mark E. Pattle, William R. Peltier, Nadege Pie, Mònica Roca, Ingo Sasgen, Himanshu V. Save, Ki-Weon Seo, Bernd Scheuchl, Ernst J. O. Schrama, Ludwig Schröder, Sebastian B. Simonsen, Thomas Slater, Giorgio Spada, Tyler C. Sutterley, Bramha Dutt Vishwakarma, Jan Melchior van Wessem, David Wiese, Wouter van der Wal, and Bert Wouters
Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, https://doi.org/10.5194/essd-15-1597-2023, 2023
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By measuring changes in the volume, gravitational attraction, and ice flow of Greenland and Antarctica from space, we can monitor their mass gain and loss over time. Here, we present a new record of the Earth’s polar ice sheet mass balance produced by aggregating 50 satellite-based estimates of ice sheet mass change. This new assessment shows that the ice sheets have lost (7.5 x 1012) t of ice between 1992 and 2020, contributing 21 mm to sea level rise.
Jeremy Rohmer, Remi Thieblemont, Goneri Le Cozannet, Heiko Goelzer, and Gael Durand
The Cryosphere, 16, 4637–4657, https://doi.org/10.5194/tc-16-4637-2022, https://doi.org/10.5194/tc-16-4637-2022, 2022
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To improve the interpretability of process-based projections of the sea-level contribution from land ice components, we apply the machine-learning-based
SHapley Additive exPlanationsapproach to a subset of a multi-model ensemble study for the Greenland ice sheet. This allows us to quantify the influence of particular modelling decisions (related to numerical implementation, initial conditions, or parametrisation of ice-sheet processes) directly in terms of sea-level change contribution.
Jeremy Rohmer, Deborah Idier, Remi Thieblemont, Goneri Le Cozannet, and François Bachoc
Nat. Hazards Earth Syst. Sci., 22, 3167–3182, https://doi.org/10.5194/nhess-22-3167-2022, https://doi.org/10.5194/nhess-22-3167-2022, 2022
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We quantify the influence of wave–wind characteristics, offshore water level and sea level rise (projected up to 2200) on the occurrence of flooding events at Gâvres, French Atlantic coast. Our results outline the overwhelming influence of sea level rise over time compared to the others. By showing the robustness of our conclusions to the errors in the estimation procedure, our approach proves to be valuable for exploring and characterizing uncertainties in assessments of future flooding.
Ioanna Karagali, Magnus Barfod Suhr, Ruth Mottram, Pia Nielsen-Englyst, Gorm Dybkjær, Darren Ghent, and Jacob L. Høyer
The Cryosphere, 16, 3703–3721, https://doi.org/10.5194/tc-16-3703-2022, https://doi.org/10.5194/tc-16-3703-2022, 2022
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Ice surface temperature (IST) products were used to develop the first multi-sensor, gap-free Level 4 (L4) IST product of the Greenland Ice Sheet (GIS) for 2012, when a significant melt event occurred. For the melt season, mean IST was −15 to −1 °C, and almost the entire GIS experienced at least 1 to 5 melt days. Inclusion of the L4 IST to a surface mass budget (SMB) model improved simulated surface temperatures during the key onset of the melt season, where biases are typically large.
Nicolaj Hansen, Sebastian B. Simonsen, Fredrik Boberg, Christoph Kittel, Andrew Orr, Niels Souverijns, J. Melchior van Wessem, and Ruth Mottram
The Cryosphere, 16, 711–718, https://doi.org/10.5194/tc-16-711-2022, https://doi.org/10.5194/tc-16-711-2022, 2022
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We investigate the impact of different ice masks when modelling surface mass balance over Antarctica. We used ice masks and data from five of the most used regional climate models and a common mask. We see large disagreement between the ice masks, which has a large impact on the surface mass balance, especially around the Antarctic Peninsula and some of the largest glaciers. We suggest a solution for creating a new, up-to-date, high-resolution ice mask that can be used in Antarctic modelling.
Fredrik Boberg, Ruth Mottram, Nicolaj Hansen, Shuting Yang, and Peter L. Langen
The Cryosphere, 16, 17–33, https://doi.org/10.5194/tc-16-17-2022, https://doi.org/10.5194/tc-16-17-2022, 2022
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Using the regional climate model HIRHAM5, we compare two versions (v2 and v3) of the global climate model EC-Earth for the Greenland and Antarctica ice sheets. We are interested in the surface mass balance of the ice sheets due to its importance when making estimates of future sea level rise. We find that the end-of-century change in the surface mass balance for Antarctica is 420 Gt yr−1 (v2) and 80 Gt yr−1 (v3), and for Greenland it is −290 Gt yr−1 (v2) and −1640 Gt yr−1 (v3).
Nicolaj Hansen, Peter L. Langen, Fredrik Boberg, Rene Forsberg, Sebastian B. Simonsen, Peter Thejll, Baptiste Vandecrux, and Ruth Mottram
The Cryosphere, 15, 4315–4333, https://doi.org/10.5194/tc-15-4315-2021, https://doi.org/10.5194/tc-15-4315-2021, 2021
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We have used computer models to estimate the Antarctic surface mass balance (SMB) from 1980 to 2017. Our estimates lies between 2473.5 ± 114.4 Gt per year and 2564.8 ± 113.7 Gt per year. To evaluate our models, we compared the modelled snow temperatures and densities to in situ measurements. We also investigated the spatial distribution of the SMB. It is very important to have estimates of the Antarctic SMB because then it is easier to understand global sea level changes.
Ruth Mottram, Nicolaj Hansen, Christoph Kittel, J. Melchior van Wessem, Cécile Agosta, Charles Amory, Fredrik Boberg, Willem Jan van de Berg, Xavier Fettweis, Alexandra Gossart, Nicole P. M. van Lipzig, Erik van Meijgaard, Andrew Orr, Tony Phillips, Stuart Webster, Sebastian B. Simonsen, and Niels Souverijns
The Cryosphere, 15, 3751–3784, https://doi.org/10.5194/tc-15-3751-2021, https://doi.org/10.5194/tc-15-3751-2021, 2021
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We compare the calculated surface mass budget (SMB) of Antarctica in five different regional climate models. On average ~ 2000 Gt of snow accumulates annually, but different models vary by ~ 10 %, a difference equivalent to ± 0.5 mm of global sea level rise. All models reproduce observed weather, but there are large differences in regional patterns of snowfall, especially in areas with very few observations, giving greater uncertainty in Antarctic mass budget than previously identified.
Amy Solomon, Céline Heuzé, Benjamin Rabe, Sheldon Bacon, Laurent Bertino, Patrick Heimbach, Jun Inoue, Doroteaciro Iovino, Ruth Mottram, Xiangdong Zhang, Yevgeny Aksenov, Ronan McAdam, An Nguyen, Roshin P. Raj, and Han Tang
Ocean Sci., 17, 1081–1102, https://doi.org/10.5194/os-17-1081-2021, https://doi.org/10.5194/os-17-1081-2021, 2021
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Freshwater in the Arctic Ocean plays a critical role in the global climate system by impacting ocean circulations, stratification, mixing, and emergent regimes. In this review paper we assess how Arctic Ocean freshwater changed in the 2010s relative to the 2000s. Estimates from observations and reanalyses show a qualitative stabilization in the 2010s due to a compensation between a freshening of the Beaufort Gyre and a reduction in freshwater in the Amerasian and Eurasian basins.
Rémi Thiéblemont, Gonéri Le Cozannet, Jérémy Rohmer, Alexandra Toimil, Moisés Álvarez-Cuesta, and Iñigo J. Losada
Nat. Hazards Earth Syst. Sci., 21, 2257–2276, https://doi.org/10.5194/nhess-21-2257-2021, https://doi.org/10.5194/nhess-21-2257-2021, 2021
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Sea level rise and its acceleration are projected to aggravate coastal erosion over the 21st century. Resulting shoreline projections are deeply uncertain, however, which constitutes a major challenge for coastal planning and management. Our work presents a new extra-probabilistic framework to develop future shoreline projections and shows that deep uncertainties could be drastically reduced by better constraining sea level projections and improving coastal impact models.
Cited articles
Abadie, S., Paris, A., Ata, R., Le Roy, S., Arnaud, G., Poupardin, A., Clous, L., Heinrich, P., Harris, J., Pedreros, R., and Krien, Y.: La Palma landslide tsunami: calibrated wave source and assessment of impact on French territories, Nat. Hazards Earth Syst. Sci., 20, 3019–3038, https://doi.org/10.5194/nhess-20-3019-2020, 2020.
Abadie, S. M., Harris, J. C., Grilli, S. T., and Fabre, R.: Numerical modeling of tsunami waves generated by the flank collapse of the Cumbre Vieja Volcano (La Palma, Canary Islands): Tsunami source and near field effects, J. Geophys. Res., 117, C05030, https://doi.org/10.1029/2011JC007646, 2012.
Aochi, H.: Finite difference method code in C for 3D seismic wave propagation by Aochi and Madariaga (2003) (Version v1), Zenodo [code], https://doi.org/10.5281/zenodo.10225172, 2020.
Aochi, H. and Madariaga, R.: The 1999 Izmit, Turkey, earthquake: Non-planar fault structure, dynamic rupture process and strong ground motion, Bull. Seism. Soc. Am., 93, 1249–1266, https://doi.org/10.1785/0120020167, 2003.
Batchelor, C. L., Dowdesell, J. A., Rignot, E., and Millan, R.: Submarine moraines in Southeast Greenland fjords reveal contrasting outlet-glacier behaviour since the last glacial maximum, Geophys. Res. Lett., 46, 3279–3286, https://doi.org/10.1029/2019GL082556, 2019.
Bessette-Kirton, E., Allstadtn K., Pursley, J., and Godt, J.: Preliminary analysis of satellite imagery and seismic observations of the Nuugaatsiaq landslide and tsunami, Greenland, USGS on-line report, https://www.usgs.gov/programs/landslide-hazards/science/preliminary-analysis-satellite-imagery-and-seismic-observations (last access: 18 June 2026), 2017.
Bessette-Kirton, E., Allstadt, K., Pursley, J., and Godt, J.: Preliminary analysis of satellite imagery and seismic observations of the Nuugaatsiaq landslide and tsunami, USGS Landslide Hazards Program, Preliminary Assessments, https://www.usgs.gov/natural-hazards/landslide-hazards/science/preliminary-analysis-satellite-imagery-and-seismic (last accessed: 26 March 2025), 2018.
Bettinelli, P., Avouac, J.-P., Flouzat, M., Bollinger, L., Ramillien, G., Rajeure, S., and Sapkota, S.: Seasonal variations of seismicity and geodetic strain in Himalaya induced by surface hydrology, Earth Planet Sci. Lett., 266, 332–344, https://doi.org/10.1016/j.epsl.2007.11.021, 2008.
Boudin, F., Allgeyer, S., Bernard, P., Hébert, H., Olcay, M., Madariaga, R., El-Madani, M., Vilotte, J.-P., Peyrat, S., Nercessian, A., Schurr, B., Esnoult, M.-F., Asch, G., Nunez, I., and Kammenthaler, M.: Analysis and modelling of tsunami-induced tilt for the 2007, M = 7.6, Tocopilla and the 2010, M = 8.8 Maule earthquakes, Chile, from long-base tiltmeter and broadband seismometer records, Geophys. J. Int., 194, 269–288, https://doi.org/10.1093/gji/ggt123, 2013.
Boussinesq, M. J.: Application des potentiels à l'etude de l'equilibre et du mouvement des solides elastiques – Gauthier-Villars, Paris, 721 pp., 1885.
Carrillo-Ponce, A., Heimann, S., Peterse, G. M., Walter, T. R., Cesca, S., and Dahm, T.: The 16 September 2023 Greenland Megatsunami: Analysis and modeling of the source and a week-long monochromatic seismic signal, Seismic Record, 4, 172–183, https://doi.org/10.1785/0320240013, 2024.
Chao, W.-A., Wu, T. R., Ma, K.-F., Kuo, Y.-T., Wu, Y.-M., Zhao, L., Chung, M. J., Wu, H., and Tsai, Y. L.: The Large Greenland Landslide of 2017: Was a Tsunami Warning Possible?, Seismol. Res. Lett., 89, 1335–1344, https://doi.org/10.1785/0220170160, 2018.
Clinton, J. F., Nettles, M., Walter, F., Anderson, K., Dahl-Jensen, T., Giardini, D., Govoni, A., Hanka, W., Lasocki, S., Lee, W. S., McCormack, D., Mykkeltveit, S., Stutzmann, E., and Tsuboi, S.: Seismic Network in Greenland Monitors Earth and Ice System, Eos, 95, 13–14, https://doi.org/10.1002/2014EO020001, 2014.
Constable, A. J., Harper, S., Dawson, J., Holsman, K., Mustonen, T., Piepenburg, D., and Rost, B.: Cross-Chapter Paper 6: Polar Regions, in: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Pörtner, H.-O., Roberts, D. C., Tignor, M., Poloczanska, E. S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V., Okem, A., and Rama, B., Cambridge University Press, Cambridge, UK and New York, NY, USA, 2319–2368, https://doi.org/10.1017/9781009325844.023, 2022.
Dahl-Jensen, T., Larsen, T. B., and Voss, P. H.: Greenland ice sheet monitoring network (GLISN): a seismological approach, GEUS Bulletin, 20, 55–58, https://doi.org/10.34194/geusb.v20.4956, 2010.
Doi, I. and Maeda, T.: Landslide Characteristics Revealed by High-Frequency Seismic Waves from the 2017 Landslide in Central Japan, Seismol. Res. Lett., 91, 2719–2729, https://doi.org/10.1785/0220200032, 2020.
Ekström, G. and Stark, C. P.: Simple scaling of catastrophic landslide dynamics, Science, 339, 1416–1419, https://doi.org/10.1126/science.1232887, 2013.
Gauthier, D., Anderson, S. A., Fritz, H. M., and Giachetti, T.: Karrat Fjord (Greenland) tsunamigenic landslide of 17 June 2017: initial 3D observations, Landslides, 15, 327–332, https://doi.org/10.1007/s10346-017-0926-4, 2018.
Georgia Institute of Technology: After recon trip, researchers say Greenland tsunami in June reached 300 feet high, https://www.ce.gatech.edu/news/after-recon-trip-researchers-say-greenland-tsunami-june-reached-300-feet-high (last access: 26 March 2025), 2017.
Giachetti, T., Paris, R., Kelfoun, K., and Pérez-Torrado, F. J.: Numerical modelling of the tsunami triggered by the Güimar debris avalance, Tenerife (Canary Islands): Comparison with field-based data, Mar. Geol., 284, 189–202, https://doi.org/10.1016/j.margeo.2011.03.018, 2011.
Giachetti, T., Paris, R., Kelfoun, K., and Ontowirjo, B.: Tsunami hazard related to a flank collapse of Anak Krakatau volcano, Sunda Strait, Indonesia, Geol. Soc. London., 361, 79–90, https://doi.org/10.1144/SP361.7, 2012.
Grilli, S. T., Harris, J. C., Tajalli Bakhsh, T., Masterlark, T. L., Kyriakopoulos, C., Kirby, J. T., and Shi, F.: Numerical simulation of the 2011 Tohoku tsunami based on a new transient FEM co-seismic source: Comparison to far- and near-field observations, Pure Appl. Geophys., 170, 1333–1359, https://doi.org/10.1007/s00024-012-0528-y, 2013.
Higman, B., Shugar, D. H., Stark, C. P., Ekström, G., Koppes, M. N., Lynett, P., Dufresne, A., Haeussler, P. J., Geertsema, M., Gulick, S., and Mattox, A.: The 2015 landslide and tsunami in Taan Fiord, Alaska, Sci. Rep., 8, 12993, https://doi.org/10.1038/s41598-018-30475-w, 2015.
Ho, T. C., Mori, N., and Yamada, M.: Ocean gravity waves generated by the meteotsunami at the Japan Trench following the 2022 Tonga volcanic eruption, Earth Planets Space, 75, 25, https://doi.org/10.1186/s40623-023-01775-x, 2023.
Joughin, I., Howat, I., Alley, R. B., Ekstrom, G., Fahnestock, M., Moon, T., Nettles, M., Truffer, M., and Tsai, V. C.: Ice-front variation and tidewater behavior on Helheim and Kangerdlugssuaq Glaciers, Greenland, J. Geophys. Res., 113, https://doi.org/10.1029/2007JF000837, 2008.
Kelfoun, K. and Druitt, T. H.: Numerical modeling of the emplacement of Socompa rock avalanche, Chile, J. Geophys. Res., 110, B12202, https://doi.org/10.1029/2005JB003758, 2005.
Kelfoun, K., Giachetti, T., and Labazuy, P.: Landslide-generated tsunamis at Réunion Island, J. Geophys. Res., 115, F04012, https://doi.org/10.1029/2009JF001381, 2010.
Kennett, B. L. N., Engdahl, E. R., and Buland, R.: Constraints on seismic velocities in the Earth from traveltimes, Geophys. J. Int., 122, 108–124, https://doi.org/10.1111/j.1365-246X.1995.tb03540.x, 1995.
Kimura, T., Tanaka, S., and Saito, T.: Ground tilt changes in Japan caused by the 2010 Maule, Chile, earthquake tsunami, J. Geophys. Res., 118, https://doi.org/10.1029/2012JB009657, 2013.
Kirby, J. T., Wei, G., Chen, Q., Kennedy, A. B., and Dalrymple, R. A.: FUNWAVE 1.0: fully nonlinear Boussinesq wave model-Documentation and user's manual. CACR-98-06, University of Delaware Center for Applied Coastal Research, Newark, DE, https://csdms.colorado.edu/w/images/Cacr-98-06.pdf (last access: 24 June 2026), 1998.
Kirby, J. T., Shi, F., Tehranirad, B., Harris, J. C., and Grilli, S. T.: Dispersive tsunami waves in the ocean: Model equations and sensitivity to dispersion and Coriolis effects, Ocean Model., 62, 39–55, https://doi.org/10.1016/j.ocemod.2012.11.009, 2013.
Kirby, J. T., Shi, F., Nicolsky, D., and Misra, S.: The 27 April 1975 Kitimat, British Columbia, submarine landslide tsunami: a comparison of modeling approaches, Landslides, 13, 1421–1434, https://doi.org/10.1007/s10346-016-0682-x, 2016.
Korsgaard, N. J., Svennevig, K., Søndergaard, A. S., Luetzenburg, G., Oksman, M., and Larsen, N. K.: Evidence of Middle Holocene landslide-generated tsunamis recorded in lake sediments from Saqqaq, West Greenland, Nat. Hazards Earth Syst. Sci., 24, 757–772, https://doi.org/10.5194/nhess-24-757-2024, 2024.
Kubota, T., Saito, T., Chikasada, N. Y., and Suzuki, W.: Ultrabroadband seismic and tsunami wave observation of high-sampling ocean-bottom pressure gauge covering periods from seconds to hours, Earth Space Science, 7, e2020EA001197, https://doi.org/10.1029/2020EA001197, 2020.
Laske, G., Masters, G., Ma, Z., and Pasyanos, M.: Update on CRUST1.0 – A 1-degree Global Model of Earth's Crust, Geophys. Res. Abstracts, 15, Abstract EGU2013-2658, https://meetingorganizer.copernicus.org/EGU2013/EGU2013-2658.pdf (last access: 24 June 2026), 2013.
Matti, S., Cullen, M., Reichardt, U., and Vigfúsdóttir, A.: Planned relocation due to landslide-triggered tsunami risk in recently deglaciated areas, Int. J. Disast. Risk Re., 86, 103536, https://doi.org/10.1016/j.ijdrr.2023.103536, 2023.
Meredith, M., Sommerkorn, M., Cassotta, S., Derksen, C., Ekaykin, A., Hollowed, A., Kofinas, G., Mackintosh, A., Melbourne-Thomas, J., Muelbert, M. M. C., Ottersen, G., Pritchard, H., and Schuur, E. A. G.: Polar Regions, in: 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., Weyer, N. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, 203–320, https://doi.org/10.1017/9781009157964.005, 2019.
Mulia, I. E., Watada, S., Ho, T.-C., Satake, K., Wang, Y., and Aditiya, A.: Simulation of the 2018 tsunami due to the flank failure of Anak Krakatau volcano and implication for future observing systems, Geophys. Res. Lett., 47, e2020GL087334, https://doi.org/10.1029/2020GL087334, 2020.
Nakano, M., Kumagai, H., and Inoue, H.: Waveform inversion in the frequency domain for the simultaneous determination of earthquake source mechanism and moment function, Geophys. J. Int., 173, 1000–1011, https://doi.org/10.1111/j.1365-246X.2008.03783.x, 2008.
Nettles, M., Larsen, T. B., Elósegui, Hamilton, G. S., Steams, L. A., Ahlstrøm, A. P., Davis, J. L., Andersen, M. L., de Juan, J. Khan, S. A., Stenseng, L., Ekström, G., and Forsberg, R.: Step-wise changes in glacier flow speed coincide with calving and glacial earthquakes at Helheim Glacier, Greenland, Geophys. Res. Lett., 35, L24503, https://doi.org/10.1029/2008GL036127, 2008.
NGI: Hazard scenario simulations and 2017 event hindcast, project report for GEUS, No. 20200823-01-R, https://www.geus.dk/Media/638140441113026078/NGI_20200823-01-R_Tsunami_Karrat.pdf (last access: 26 March 2025), 2021.
Nishida, K., Maeda, T., and Fukao, Y.: Seismic observation of tsunami at Island broadband stations, J. Geophys. Res., 124, 1910–1928, https://doi.org/10.1029/2018JB016833, 2019.
Otosaka, I. N., Shepherd, A., Ivins, E. R., Schlegel, N.-J., Amory, C., van den Broeke, M. R., Horwath, M., Joughin, I., King, M. D., Krinner, G., Nowicki, S., Payne, A. J., Rignot, E., Scambos, T., Simon, K. M., Smith, B. E., Sørensen, L. S., Velicogna, I., Whitehouse, P. L., A, G., Agosta, C., Ahlstrøm, A. P., Blazquez, A., Colgan, W., Engdahl, M. E., Fettweis, X., Forsberg, R., Gallée, H., Gardner, A., Gilbert, L., Gourmelen, N., Groh, A., Gunter, B. C., Harig, C., Helm, V., Khan, S. A., Kittel, C., Konrad, H., Langen, P. L., Lecavalier, B. S., Liang, C.-C., Loomis, B. D., McMillan, M., Melini, D., Mernild, S. H., Mottram, R., Mouginot, J., Nilsson, J., Noël, B., Pattle, M. E., Peltier, W. R., Pie, N., Roca, M., Sasgen, I., Save, H. V., Seo, K.-W., Scheuchl, B., Schrama, E. J. O., Schröder, L., Simonsen, S. B., Slater, T., Spada, G., Sutterley, T. C., Vishwakarma, B. D., van Wessem, J. M., Wiese, D., van der Wal, W., and Wouters, B.: Mass balance of the Greenland and Antarctic ice sheets from 1992 to 2020, Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, 2023.
Paris, A., Okal, E. A., Guérin, C., Heinrich, Ph., Schindelé, F., and Hébert, H.: Numerical modeling of the June 17, 2017 Landslide and Tsunami Events in Karrat Fjord, West Greenland, Pageoph, 176, 3035–3057, https://doi.org/10.1007/s00024-019-02123-5, 2019.
Paris, R. and Ulvrova, M.: Tsunamis generated by subaqueous volcanic explosions in Taal Caldera Lake, Philippines, B. Volcanol., https://doi.org/10.1007/s00445-019-1272-2, 2019.
Paris, R., Giachetti, T., Chevalier, J., Guillou, H., and Frank, N.: Tsunami deposits in Santiago Island (Cape Verde archipelago) as possible evidence of a massive flank failure of Fogos volcano, Sediment. Geol., 239, 129–145, https://doi.org/10.1016/j.sedgeo.2011.06.006, 2011.
Pinel, V., Sigmundsson, F., Sturkell, E., Geirsson, H., Einarsson, P., Gudmundsson, M. T., and Högnadóttir, T.: Discriminating volcano deformation due to magma movements and variable surface loads: Application to Katla subglacial volcano, Iceland, Geophys. J. Int., 169, 325–338, https://doi.org/10.1111/j.1365-246X.2006.03267.x, 2007.
Poli, P: Creep and slip: Seismic precursors to the Nuugaatsiaq landslide (Greenland), Geophys. Res. Lett., 44, 8832–8836, https://doi.org/10.1002/2017GL075039, 2017.
Röösli, C., Walter, F., Husen, S., Andrews, L. C., Lüthi, M. P., Catania, G. A., and Kissling, E.: Sustained seismic tremors and icequakes detected in the ablation zone of the Greenland ice sheet, J. Glaciol., 60, 563–575, https://doi.org/10.3189/2014JoG13J210, 2014.
Schambach, L., Grilli, S. T., Kirby, J. T., and Shi, F.: Landslide Tsunami Hazard Along the Upper US East Coast: Effects of Slide Deformation, Bottom Friction, and Frequency Dispersion, Pure Appl. Geophys, 176, 3059–3098, https://doi.org/10.1007/s00024-018-1978-7, 2019.
Schiermeier, Q.: Huge landslide triggered rare Greenland mega-tsunami, Nature, https://doi.org/10.1038/nature.2017.22374, 2017.
Sergeant, A., Mageney, A., Stutzmann, E., Montagner, J.-P., Walter, F., Moretti, L., and Castelnau O.: Complex force history of a calving-generated glacial earthquake derived from broadband seismic inversion, Geophys. Res. Lett., 43, https://doi.org/10.1002/2015GL066785, 2016.
Shi, F., Kirby, J. T., Harris, J. C., Geiman, J. D., and Grilli, S. T.: A high-order adaptive time-stepping TVD solver for Boussinesq modeling of breaking waves and coastal inundation, Ocean Model., 43, 36–51, https://doi.org/10.1016/j.ocemod.2011.12.004, 2012.
Strzelecki, M. C. and Jaskólski, M. W.: Arctic tsunamis threaten coastal landscapes and communities – survey of Karrat Isfjord 2017 tsunami effects in Nuugaatsiaq, western Greenland, Nat. Hazards Earth Syst. Sci., 20, 2521–2534, https://doi.org/10.5194/nhess-20-2521-2020, 2020.
Svennevig, K.: Preliminary landslide mapping in Greenland, GEUS Bulletin, 43, https://doi.org/10.34194/GEUSB-201943-02-07, 2019.
Svennevig, K., Dahl-Jensen, T., Keiding, M., Merryman Boncori, J. P., Larsen, T. B., Salehi, S., Munck Solgaard, A., and Voss, P. H.: Evolution of events before and after the 17 June 2017 rock avalanche at Karrat Fjord, West Greenland – a multidisciplinary approach to detecting and locating unstable rock slopes in a remote Arctic area, Earth Surf. Dynam., 8, 1021–1038, https://doi.org/10.5194/esurf-8-1021-2020, 2020.
Svennevig, K., Keiding, M., Korsgaard, N. J., Lucas, A., Owen, M., Poulsen, M. D., Priebe, J., Sørensen, E. V., and Morino, C.: Uncovering a 70-year-old permafrost degradation induced disaster in the Arctic, the 1952 Niiortuut landslide-tsunami in central West Greenland, Sci. Total Environ., 859, 160110, https://doi.org/10.1016/j.scitotenv.2022.160110, 2023.
Svennevig, K., Hicks, S. P., Forbriger, T., Lecocq, T., Widmer-Schnidrig, R., Mangeney, A., Hibert, C., Korsgaard, N. J., Lucas, A., Satriano, C., Anthony, R. E., Mordret, A., Schippkus, S., Rysgaard, S., Boone, W., Gibbons, S. J., Cook, K. L., Glimsdal, S., Løvholt, F., Van Noten, K., Assink, J. D., Marboeuf, A., Lomax, A., Vanneste, K., Taira, T., Spagnolo, M., De Plaen, R., Koelemeijer, P., Ebeling, C., Cannata, A., Harcourt, W. D., Cornwell, D. G., Caudron, C., Poli, P., Bernard, P., Larose, E., Stutzmann, E., Voss, P. H., Lund, B., Cannavo, F., Castro-Díaz, M. J., Chaves, E., Dahl-Jensen, T., De Pinho Dias, N., Déprez, A., Develter, R., Dreger, D., Evers, L. G., Fernández-Nieto, E. D., Ferreira, A. M. G., Funning, G., Gabriel, A. A., Hendrickx, M., Kafka, A. L., Keiding, M., Kerby, J., Khan, S. A., Dideriksen, A. K., Lamb, O. D., Larsen, T. B., Lipovsky, B., Magdalena, I., Malet, J. P., Myrup, M., Rivera, L., Ruiz-Castillo, E., Wetter, S., and Wirtz, B: A rockslide-generated tsunami in a Greenland fjord rang Earth for 9 days, Science, 385, 1196–1205, https://doi.org/10.1126/science.adm9247, 2024.
Tanguy, R., Bartsch, A., Nitze, I., Irrgang, A., Petzold, P., Widhalm, B., von Baeckmann, C., Boike, J., Martin, J., Efimova, A. Vieira, G., Whalen, D., Heim, B., Wieczorek, M., and Grosse, G.: Pan-Arctic assessment of coastal settlements and infrastructure vulnerable to coastal erosion, sea-level rise, and permafrost thaw, Earths Future, 12, e2024EF005013, https://doi.org/10.1029/2024EF005013, 2024.
Underwood, E.: What precursors foretold Greenland's recent 100-meter tsunami?, Eos, 98, https://doi.org/10.1029/2017EO085499, 2017.
Veitch, S. A. and Nettles, M.: Spatial and temporal variations in Greenland glacial-earthquake activity, 1993–2010, J. Geophys. Res.-Earth, 117, https://doi.org/10.1029/2012JF002412, 2012.
Walter, F., Olivieri, M., and Clinton, J. F.: Calving event detection by observation of seiche effects on the Greenland fjords, J. Glaciol., 59, 162–178, https://doi.org/10.3189/2013JoG12J118, 2013.
Wei, G., Kirby, J. T., Grilli, S. T., and Subramanya, R.: A fully nonlinear Boussinesq model for surface waves. part 1. Highly nonlinear unsteady waves, J. Fluid Mech., 294, 71–92, 1995.
Xie, H. J., Chu, R., and Ni, S.: Relocation of the 17 June 2017 Nuugaatsiaq (Greenland) Landslide based on Green's functions from ambient seismic noises, J. Geophys. Res., 125, e2019JB018947, https://doi.org/10.1029/2019JB018947, 2020.
Yamada, M., Kumagai, H., Matsushi, Y., and Matsuzawa, T.: Dynamic Landslide Processes Revealed by Broadband Seismic Records, Geophys. Res. Lett., 40, 2998–3002, https://doi.org/10.1002/grl.50437, 2013.
Yamada, M., Matsushi, Y., and Matsuzawa, T.: Dynamic movement history of the Iiyama slope failure revealed from seismic data, Eng. Geol., 274, 105696, https://doi.org/10.1016/j.enggeo.2020.105696, 2020.
Short summary
The 2017 Landslide-made tsunami in Greenland occurred in a context of global warming and heavily impacted local communities. We analyze this event using seismic data to reconstruct the whole chain of processes from the landslide to the tsunami. Our results validate a new approach to analyze crustal deformations caused by tsunami propagation in fjords, suggesting that alert systems based on seismic data are feasible, potentially allowing to reduce tsunami risks in polar regions.
The 2017 Landslide-made tsunami in Greenland occurred in a context of global warming and heavily...
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