Improving interpretation of sea-level projections through a machine-learning-based local explanation approach
Process-based projections of the sea-level contribution from land ice components are often obtained from simulations using a complex chain of numerical models. Because of their importance in supporting the decision-making process for coastal risk assessment and adaptation, improving the interpretabi...
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Copernicus Publications
2022
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00063336 2023-05-15T16:27:52+02:00 Improving interpretation of sea-level projections through a machine-learning-based local explanation approach Rohmer, Jeremy Thieblemont, Remi Le Cozannet, Goneri Goelzer, Heiko Durand, Gael 2022-11 electronic https://doi.org/10.5194/tc-16-4637-2022 https://noa.gwlb.de/receive/cop_mods_00063336 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00062406/tc-16-4637-2022.pdf https://tc.copernicus.org/articles/16/4637/2022/tc-16-4637-2022.pdf eng eng Copernicus Publications The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-16-4637-2022 https://noa.gwlb.de/receive/cop_mods_00063336 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00062406/tc-16-4637-2022.pdf https://tc.copernicus.org/articles/16/4637/2022/tc-16-4637-2022.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess CC-BY article Verlagsveröffentlichung article Text doc-type:article 2022 ftnonlinearchiv https://doi.org/10.5194/tc-16-4637-2022 2022-11-07T00:12:05Z Process-based projections of the sea-level contribution from land ice components are often obtained from simulations using a complex chain of numerical models. Because of their importance in supporting the decision-making process for coastal risk assessment and adaptation, improving the interpretability of these projections is of great interest. To this end, we adopt the local attribution approach developed in the machine learning community known as “SHAP” (SHapley Additive exPlanations). We apply our methodology to a subset of the multi-model ensemble study of the future contribution of the Greenland ice sheet to sea level, taking into account different modelling choices related to (1) numerical implementation, (2) initial conditions, (3) modelling of ice-sheet processes, and (4) environmental forcing. This allows us to quantify the influence of particular modelling decisions, which is directly expressed in terms of sea-level change contribution. This type of diagnosis can be performed on any member of the ensemble, and we show in the Greenland case how the aggregation of the local attribution analyses can help guide future model development as well as scientific interpretation, particularly with regard to spatial model resolution and to retreat parametrisation. Article in Journal/Newspaper Greenland Ice Sheet The Cryosphere Niedersächsisches Online-Archiv NOA Greenland The Cryosphere 16 11 4637 4657 |
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Open Polar |
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Niedersächsisches Online-Archiv NOA |
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ftnonlinearchiv |
language |
English |
topic |
article Verlagsveröffentlichung |
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article Verlagsveröffentlichung Rohmer, Jeremy Thieblemont, Remi Le Cozannet, Goneri Goelzer, Heiko Durand, Gael Improving interpretation of sea-level projections through a machine-learning-based local explanation approach |
topic_facet |
article Verlagsveröffentlichung |
description |
Process-based projections of the sea-level contribution from land ice components are often obtained from simulations using a complex chain of numerical models. Because of their importance in supporting the decision-making process for coastal risk assessment and adaptation, improving the interpretability of these projections is of great interest. To this end, we adopt the local attribution approach developed in the machine learning community known as “SHAP” (SHapley Additive exPlanations). We apply our methodology to a subset of the multi-model ensemble study of the future contribution of the Greenland ice sheet to sea level, taking into account different modelling choices related to (1) numerical implementation, (2) initial conditions, (3) modelling of ice-sheet processes, and (4) environmental forcing. This allows us to quantify the influence of particular modelling decisions, which is directly expressed in terms of sea-level change contribution. This type of diagnosis can be performed on any member of the ensemble, and we show in the Greenland case how the aggregation of the local attribution analyses can help guide future model development as well as scientific interpretation, particularly with regard to spatial model resolution and to retreat parametrisation. |
format |
Article in Journal/Newspaper |
author |
Rohmer, Jeremy Thieblemont, Remi Le Cozannet, Goneri Goelzer, Heiko Durand, Gael |
author_facet |
Rohmer, Jeremy Thieblemont, Remi Le Cozannet, Goneri Goelzer, Heiko Durand, Gael |
author_sort |
Rohmer, Jeremy |
title |
Improving interpretation of sea-level projections through a machine-learning-based local explanation approach |
title_short |
Improving interpretation of sea-level projections through a machine-learning-based local explanation approach |
title_full |
Improving interpretation of sea-level projections through a machine-learning-based local explanation approach |
title_fullStr |
Improving interpretation of sea-level projections through a machine-learning-based local explanation approach |
title_full_unstemmed |
Improving interpretation of sea-level projections through a machine-learning-based local explanation approach |
title_sort |
improving interpretation of sea-level projections through a machine-learning-based local explanation approach |
publisher |
Copernicus Publications |
publishDate |
2022 |
url |
https://doi.org/10.5194/tc-16-4637-2022 https://noa.gwlb.de/receive/cop_mods_00063336 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00062406/tc-16-4637-2022.pdf https://tc.copernicus.org/articles/16/4637/2022/tc-16-4637-2022.pdf |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland Ice Sheet The Cryosphere |
genre_facet |
Greenland Ice Sheet The Cryosphere |
op_relation |
The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-16-4637-2022 https://noa.gwlb.de/receive/cop_mods_00063336 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00062406/tc-16-4637-2022.pdf https://tc.copernicus.org/articles/16/4637/2022/tc-16-4637-2022.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5194/tc-16-4637-2022 |
container_title |
The Cryosphere |
container_volume |
16 |
container_issue |
11 |
container_start_page |
4637 |
op_container_end_page |
4657 |
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1766017429710831616 |