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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2022
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Online Access: | https://doi.org/10.5194/tc-16-4637-2022 https://doaj.org/article/f55168a8b86f495b9b90ea928d0f0c04 |
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ftdoajarticles:oai:doaj.org/article:f55168a8b86f495b9b90ea928d0f0c04 2023-05-15T16:27:52+02:00 Improving interpretation of sea-level projections through a machine-learning-based local explanation approach J. Rohmer R. Thieblemont G. Le Cozannet H. Goelzer G. Durand 2022-11-01T00:00:00Z https://doi.org/10.5194/tc-16-4637-2022 https://doaj.org/article/f55168a8b86f495b9b90ea928d0f0c04 EN eng Copernicus Publications https://tc.copernicus.org/articles/16/4637/2022/tc-16-4637-2022.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-16-4637-2022 1994-0416 1994-0424 https://doaj.org/article/f55168a8b86f495b9b90ea928d0f0c04 The Cryosphere, Vol 16, Pp 4637-4657 (2022) Environmental sciences GE1-350 Geology QE1-996.5 article 2022 ftdoajarticles https://doi.org/10.5194/tc-16-4637-2022 2022-12-30T20:48:09Z 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 Directory of Open Access Journals: DOAJ Articles Greenland The Cryosphere 16 11 4637 4657 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
spellingShingle |
Environmental sciences GE1-350 Geology QE1-996.5 J. Rohmer R. Thieblemont G. Le Cozannet H. Goelzer G. Durand Improving interpretation of sea-level projections through a machine-learning-based local explanation approach |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
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 |
J. Rohmer R. Thieblemont G. Le Cozannet H. Goelzer G. Durand |
author_facet |
J. Rohmer R. Thieblemont G. Le Cozannet H. Goelzer G. Durand |
author_sort |
J. Rohmer |
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://doaj.org/article/f55168a8b86f495b9b90ea928d0f0c04 |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland Ice Sheet The Cryosphere |
genre_facet |
Greenland Ice Sheet The Cryosphere |
op_source |
The Cryosphere, Vol 16, Pp 4637-4657 (2022) |
op_relation |
https://tc.copernicus.org/articles/16/4637/2022/tc-16-4637-2022.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-16-4637-2022 1994-0416 1994-0424 https://doaj.org/article/f55168a8b86f495b9b90ea928d0f0c04 |
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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1766017435846049792 |