Articles | Volume 12, issue 1
https://doi.org/10.5194/ascmo-12-123-2026
© 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/ascmo-12-123-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Simulation of extreme functionals in meteoceanic data: application to surge evolution over tidal cycles
UMR CNRS 5219, Institut de Mathématiques de Toulouse, INSA, Université de Toulouse, Toulouse, France
Olivier Roustant
UMR CNRS 5219, Institut de Mathématiques de Toulouse, INSA, Université de Toulouse, Toulouse, France
Jérémy Rohmer
BRGM, 45060 Orléans, France
Déborah Idier
BRGM, 45060 Orléans, France
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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.
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EGUsphere, https://doi.org/10.5194/egusphere-2024-3615, https://doi.org/10.5194/egusphere-2024-3615, 2025
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This study comparer three data-driven methodologies to overcome the computational burden of numerical simulations for early warning purpose. They are all based on the statistical analysis of pre-calculated databases, to downscale total sea levels and predict marine flooding maps from offshore metocean forecasts. Conclusions highlight the relevance of metamodel-based approaches for fast prediction and the added value of precalculated databases during the prepardness phase.
Jeremy Rohmer, Stephane Belbeze, and Dominique Guyonnet
SOIL, 10, 679–697, https://doi.org/10.5194/soil-10-679-2024, https://doi.org/10.5194/soil-10-679-2024, 2024
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Machine learning (ML) models have become key ingredients for digital soil mapping. To explain why the ML model is reliable, we apply a popular method from explainable artificial intelligence to the uncertainty prediction, with an application to the mapping of hydrocarbon pollutants on urban soil. We show the benefit of a joint analysis of the influence on the best estimate and the uncertainty to improve communication with end users and support decisions regarding covariates’ characterisation.
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.
Ryota Wada, Jeremy Rohmer, Yann Krien, and Philip Jonathan
Nat. Hazards Earth Syst. Sci., 22, 431–444, https://doi.org/10.5194/nhess-22-431-2022, https://doi.org/10.5194/nhess-22-431-2022, 2022
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Characterizing extreme wave environments caused by tropical cyclones in the Caribbean Sea near Guadeloupe is difficult because cyclones rarely pass near the location of interest. STM-E (space-time maxima and exposure) model utilizes wave data during cyclones on a spatial neighbourhood. Long-duration wave data generated from a database of synthetic tropical cyclones are used to evaluate the performance of STM-E. Results indicate STM-E provides estimates with small bias and realistic uncertainty.
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.
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Short summary
The analysis of the effect of extreme meteoceanic conditions is usually based on physical simulators, which rely on simulated extreme inputs consistent with the observations. However, surge measurements often fail to meet the theoretical assumptions. To address this, we propose a new simulation method which makes it possible to adjust the desired level of extremes after retrieving standard hypotheses. The consistency of simulations with the observations is then validated by using several tools.
The analysis of the effect of extreme meteoceanic conditions is usually based on physical...