Present and LGM permafrost from climate simulations: contribution of statistical downscaling
International audience We quantify the agreement between permafrost distributions from PMIP2 (Paleoclimate Modeling Intercomparison Project) climate models and permafrost data. We evaluate the ability of several climate models to represent permafrost and assess the variability between their results....
Published in: | Climate of the Past |
---|---|
Main Authors: | , , , , , |
Other Authors: | , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
HAL CCSD
2011
|
Subjects: | |
Online Access: | https://hal.science/hal-03202529 https://hal.science/hal-03202529/document https://hal.science/hal-03202529/file/cp-7-1225-2011.pdf https://doi.org/10.5194/cp-7-1225-2011 |
id |
ftinsu:oai:HAL:hal-03202529v1 |
---|---|
record_format |
openpolar |
institution |
Open Polar |
collection |
Institut national des sciences de l'Univers: HAL-INSU |
op_collection_id |
ftinsu |
language |
English |
topic |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment |
spellingShingle |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment Levavasseur, G. Vrac, M. Roche, Didier M. Paillard, D. Martin, A. Vandenberghe, J. Present and LGM permafrost from climate simulations: contribution of statistical downscaling |
topic_facet |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment |
description |
International audience We quantify the agreement between permafrost distributions from PMIP2 (Paleoclimate Modeling Intercomparison Project) climate models and permafrost data. We evaluate the ability of several climate models to represent permafrost and assess the variability between their results. Studying a heterogeneous variable such as permafrost implies conducting analysis at a smaller spatial scale compared with climate models resolution. Our approach consists of applying statistical downscaling methods (SDMs) on large- or regional-scale atmospheric variables provided by climate models, leading to local-scale permafrost modelling. Among the SDMs, we first choose a transfer function approach based on Generalized Additive Models (GAMs) to produce high-resolution climatology of air temperature at the surface. Then we define permafrost distribution over Eurasia by air temperature conditions. In a first validation step on present climate (CTRL period), this method shows some limitations with non-systematic improvements in comparison with the large-scale fields. So, we develop an alternative method of statistical downscaling based on a Multinomial Logistic GAM (ML-GAM), which directly predicts the occurrence probabilities of local-scale permafrost. The obtained permafrost distributions appear in a better agreement with CTRL data. In average for the nine PMIP2 models, we measure a global agreement with CTRL permafrost data that is better when using ML-GAM than when applying the GAM method with air temperature conditions. In both cases, the provided local information reduces the variability between climate models results. This also confirms that a simple relationship between permafrost and the air temperature only is not always sufficient to represent local-scale permafrost. Finally, we apply each method on a very different climate, the Last Glacial Maximum (LGM) time period, in order to quantify the ability of climate models to represent LGM permafrost. The prediction of the SDMs (GAM and ML-GAM) is not ... |
author2 |
Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette (LSCE) Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)) Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA) Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR) Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)) Modélisation du climat (CLIM) |
format |
Article in Journal/Newspaper |
author |
Levavasseur, G. Vrac, M. Roche, Didier M. Paillard, D. Martin, A. Vandenberghe, J. |
author_facet |
Levavasseur, G. Vrac, M. Roche, Didier M. Paillard, D. Martin, A. Vandenberghe, J. |
author_sort |
Levavasseur, G. |
title |
Present and LGM permafrost from climate simulations: contribution of statistical downscaling |
title_short |
Present and LGM permafrost from climate simulations: contribution of statistical downscaling |
title_full |
Present and LGM permafrost from climate simulations: contribution of statistical downscaling |
title_fullStr |
Present and LGM permafrost from climate simulations: contribution of statistical downscaling |
title_full_unstemmed |
Present and LGM permafrost from climate simulations: contribution of statistical downscaling |
title_sort |
present and lgm permafrost from climate simulations: contribution of statistical downscaling |
publisher |
HAL CCSD |
publishDate |
2011 |
url |
https://hal.science/hal-03202529 https://hal.science/hal-03202529/document https://hal.science/hal-03202529/file/cp-7-1225-2011.pdf https://doi.org/10.5194/cp-7-1225-2011 |
genre |
permafrost |
genre_facet |
permafrost |
op_source |
ISSN: 1814-9324 EISSN: 1814-9332 Climate of the Past https://hal.science/hal-03202529 Climate of the Past, 2011, 7 (4), pp.1225-1246. ⟨10.5194/cp-7-1225-2011⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.5194/cp-7-1225-2011 hal-03202529 https://hal.science/hal-03202529 https://hal.science/hal-03202529/document https://hal.science/hal-03202529/file/cp-7-1225-2011.pdf doi:10.5194/cp-7-1225-2011 |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.5194/cp-7-1225-2011 |
container_title |
Climate of the Past |
container_volume |
7 |
container_issue |
4 |
container_start_page |
1225 |
op_container_end_page |
1246 |
_version_ |
1797567550808129536 |
spelling |
ftinsu:oai:HAL:hal-03202529v1 2024-04-28T08:35:27+00:00 Present and LGM permafrost from climate simulations: contribution of statistical downscaling Levavasseur, G. Vrac, M. Roche, Didier M. Paillard, D. Martin, A. Vandenberghe, J. Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette (LSCE) Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)) Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA) Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR) Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)) Modélisation du climat (CLIM) 2011 https://hal.science/hal-03202529 https://hal.science/hal-03202529/document https://hal.science/hal-03202529/file/cp-7-1225-2011.pdf https://doi.org/10.5194/cp-7-1225-2011 en eng HAL CCSD European Geosciences Union (EGU) info:eu-repo/semantics/altIdentifier/doi/10.5194/cp-7-1225-2011 hal-03202529 https://hal.science/hal-03202529 https://hal.science/hal-03202529/document https://hal.science/hal-03202529/file/cp-7-1225-2011.pdf doi:10.5194/cp-7-1225-2011 info:eu-repo/semantics/OpenAccess ISSN: 1814-9324 EISSN: 1814-9332 Climate of the Past https://hal.science/hal-03202529 Climate of the Past, 2011, 7 (4), pp.1225-1246. ⟨10.5194/cp-7-1225-2011⟩ [SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment info:eu-repo/semantics/article Journal articles 2011 ftinsu https://doi.org/10.5194/cp-7-1225-2011 2024-04-05T00:37:31Z International audience We quantify the agreement between permafrost distributions from PMIP2 (Paleoclimate Modeling Intercomparison Project) climate models and permafrost data. We evaluate the ability of several climate models to represent permafrost and assess the variability between their results. Studying a heterogeneous variable such as permafrost implies conducting analysis at a smaller spatial scale compared with climate models resolution. Our approach consists of applying statistical downscaling methods (SDMs) on large- or regional-scale atmospheric variables provided by climate models, leading to local-scale permafrost modelling. Among the SDMs, we first choose a transfer function approach based on Generalized Additive Models (GAMs) to produce high-resolution climatology of air temperature at the surface. Then we define permafrost distribution over Eurasia by air temperature conditions. In a first validation step on present climate (CTRL period), this method shows some limitations with non-systematic improvements in comparison with the large-scale fields. So, we develop an alternative method of statistical downscaling based on a Multinomial Logistic GAM (ML-GAM), which directly predicts the occurrence probabilities of local-scale permafrost. The obtained permafrost distributions appear in a better agreement with CTRL data. In average for the nine PMIP2 models, we measure a global agreement with CTRL permafrost data that is better when using ML-GAM than when applying the GAM method with air temperature conditions. In both cases, the provided local information reduces the variability between climate models results. This also confirms that a simple relationship between permafrost and the air temperature only is not always sufficient to represent local-scale permafrost. Finally, we apply each method on a very different climate, the Last Glacial Maximum (LGM) time period, in order to quantify the ability of climate models to represent LGM permafrost. The prediction of the SDMs (GAM and ML-GAM) is not ... Article in Journal/Newspaper permafrost Institut national des sciences de l'Univers: HAL-INSU Climate of the Past 7 4 1225 1246 |