Articles | Volume 16, issue 11
https://doi.org/10.5194/tc-16-4637-2022
https://doi.org/10.5194/tc-16-4637-2022
Research article
 | Highlight paper
 | 
04 Nov 2022
Research article | Highlight paper |  | 04 Nov 2022

Improving interpretation of sea-level projections through a machine-learning-based local explanation approach

Jeremy Rohmer, Remi Thieblemont, Goneri Le Cozannet, Heiko Goelzer, and Gael Durand

Related authors

Insights into the prediction uncertainty of machine-learning-based digital soil mapping through a local attribution approach
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
Short summary
Partitioning the contributions of dependent offshore forcing conditions in the probabilistic assessment of future coastal flooding
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
Short summary
Statistical estimation of spatial wave extremes for tropical cyclones from small data samples: validation of the STM-E approach using long-term synthetic cyclone data for the Caribbean Sea
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
Short summary
Deep uncertainties in shoreline change projections: an extra-probabilistic approach applied to sandy beaches
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
Short summary
Non-stationary extreme value analysis applied to seismic fragility assessment for nuclear safety analysis
Jeremy Rohmer, Pierre Gehl, Marine Marcilhac-Fradin, Yves Guigueno, Nadia Rahni, and Julien Clément
Nat. Hazards Earth Syst. Sci., 20, 1267–1285, https://doi.org/10.5194/nhess-20-1267-2020,https://doi.org/10.5194/nhess-20-1267-2020, 2020
Short summary

Related subject area

Discipline: Ice sheets | Subject: Numerical Modelling
Antarctic sensitivity to oceanic melting parameterizations
Antonio Juarez-Martinez, Javier Blasco, Alexander Robinson, Marisa Montoya, and Jorge Alvarez-Solas
The Cryosphere, 18, 4257–4283, https://doi.org/10.5194/tc-18-4257-2024,https://doi.org/10.5194/tc-18-4257-2024, 2024
Short summary
Analytical solutions for the advective–diffusive ice column in the presence of strain heating
Daniel Moreno-Parada, Alexander Robinson, Marisa Montoya, and Jorge Alvarez-Solas
The Cryosphere, 18, 4215–4232, https://doi.org/10.5194/tc-18-4215-2024,https://doi.org/10.5194/tc-18-4215-2024, 2024
Short summary
Ice viscosity governs hydraulic fracture that causes rapid drainage of supraglacial lakes
Tim Hageman, Jessica Mejía, Ravindra Duddu, and Emilio Martínez-Pañeda
The Cryosphere, 18, 3991–4009, https://doi.org/10.5194/tc-18-3991-2024,https://doi.org/10.5194/tc-18-3991-2024, 2024
Short summary
Biases in ice sheet models from missing noise-induced drift
Alexander A. Robel, Vincent Verjans, and Aminat A. Ambelorun
The Cryosphere, 18, 2613–2623, https://doi.org/10.5194/tc-18-2613-2024,https://doi.org/10.5194/tc-18-2613-2024, 2024
Short summary
Sensitivity of Future Projections of the Wilkes Subglacial Basin Ice Sheet to Grounding Line Melt Parameterizations
Yu Wang, Chen Zhao, Rupert Gladstone, Thomas Zwinger, Ben Galton-Fenzi, and Poul Christoffersen
EGUsphere, https://doi.org/10.5194/egusphere-2024-1005,https://doi.org/10.5194/egusphere-2024-1005, 2024
Short summary

Cited articles

Aas, K., Jullum, M., and Løland, A.: Explaining individual predictions when features are dependent: More accurate approximations to Shapley values, Artif. Intell., 298, 103502, https://doi.org/10.1016/j.artint.2021.103502, 2021. 
Achen, C. H.: Intepreting and Using Regression, Sage Publications, Thousand Oaks, https://doi.org/10.4135/9781412984560, 1982. 
Aschwanden, A., Bartholomaus, T. C., Brinkerhoff, D. J., and Truffer, M.: Brief communication: A roadmap towards credible projections of ice sheet contribution to sea level, The Cryosphere, 15, 5705–5715, https://doi.org/10.5194/tc-15-5705-2021, 2021. 
Bamber, J. L., Griggs, J. A., Hurkmans, R. T. W. L., Dowdeswell, J. A., Gogineni, S. P., Howat, I., Mouginot, J., Paden, J., Palmer, S., Rignot, E., and Steinhage, D.: A new bed elevation dataset for Greenland, The Cryosphere, 7, 499–510, https://doi.org/10.5194/tc-7-499-2013, 2013. 
Barthel, A., Agosta, C., Little, C. M., Hattermann, T., Jourdain, N. C., Goelzer, H., Nowicki, S., Seroussi, H., Straneo, F., and Bracegirdle, T. J.: CMIP5 model selection for ISMIP6 ice sheet model forcing: Greenland and Antarctica, The Cryosphere, 14, 855–879, https://doi.org/10.5194/tc-14-855-2020, 2020. 
Download
Co-editor-in-chief
This manuscript addresses an urgent problem: the proper quantification and attribution of uncertainties relating to sea-level rise. The authors show how a machine-learning approach may show the way towards a more rigorous treatment of these uncertainties, and how this might be used for policy making.
Short summary
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 exPlanations approach 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.