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
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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

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Latest update: 16 Jun 2024
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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.