Multimodel combination by a bayesian hierarchical model: Assessment of ice accumulation over the Oceanic Arctic Region
International audience The performance of general circulation models (GCMs) varies across regions and periods. When projecting into the future, it is therefore not obvious whether to reject or to prefer a certain GCM. Combining the outputs of several GCMs may enhance results. This paper presents a m...
Published in: | Journal of Climate |
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Main Authors: | , , , |
Other Authors: | , , , , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
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HAL CCSD
2010
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Online Access: | https://hal.science/hal-03200934 https://hal.science/hal-03200934/document https://hal.science/hal-03200934/file/%5B15200442%20-%20Journal%20of%20Climate%5D%20Multimodel%20Combination%20by%20a%20Bayesian%20Hierarchical%20Model%20Assessment%20of%20Ice%20Accumulation%20over%20the%20Oceanic%20Arctic%20Region.pdf https://doi.org/10.1175/2010JCLI3107.1 |
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Open Polar |
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Institut national des sciences de l'Univers: HAL-INSU |
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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 Kallache, Malaak Maksimovich, Elena Michelangeli, Paul-Antoine Naveau, Philippe Multimodel combination by a bayesian hierarchical model: Assessment of ice accumulation over the Oceanic Arctic Region |
topic_facet |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment |
description |
International audience The performance of general circulation models (GCMs) varies across regions and periods. When projecting into the future, it is therefore not obvious whether to reject or to prefer a certain GCM. Combining the outputs of several GCMs may enhance results. This paper presents a method to combine multimodel GCM projections by means of a Bayesian model combination (BMC). Here the influence of each GCM is weighted according to its performance in a training period, with regard to observations, as outcome BMC predictive distributions for yet unobserved observations are obtained. Technically, GCM outputs and observations are assumed to vary randomly around common means, which are interpreted as the actual target values under consideration. Posterior parameter distributions of the authors' Bayesian hierarchical model are obtained by a Markov chain Monte Carlo (MCMC) method. Advantageously, all parameters-such as bias and precision of the GCM models-are estimated together. Potential time dependence is accounted for by integrating a Kalman filter. The significance of trend slopes of the common means is evaluated by analyzing the posterior distribution of the parameters. The method is applied to assess the evolution of ice accumulation over the oceanic Arctic region in cold seasons. The observed ice index is created out of NCEP reanalysis data. Outputs of seven GCMs are combined by using the training period 1962-99 and prediction periods 2046-65 and 2082-99 with Special Report on Emissions Scenarios (SRES) A2 and B1. A continuing decrease of ice accumulation is visible for the A2 scenario, whereas the index stabilizes for the B1 scenario in the second prediction period. |
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) Climpact Data Science Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN) Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)) École normale supérieure - Paris (ENS-PSL) Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL) Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS) ANR-09-RISK-0007,MOPERA,MOdélisation Probabiliste pour l'Evaluation du Risque Avalanche(2009) European Project: 36305,NICE European Project: 212250,EC:FP7:ENV,FP7-ENV-2007-1,ACQWA(2008) European Project: 32567,DAMOCLES |
format |
Article in Journal/Newspaper |
author |
Kallache, Malaak Maksimovich, Elena Michelangeli, Paul-Antoine Naveau, Philippe |
author_facet |
Kallache, Malaak Maksimovich, Elena Michelangeli, Paul-Antoine Naveau, Philippe |
author_sort |
Kallache, Malaak |
title |
Multimodel combination by a bayesian hierarchical model: Assessment of ice accumulation over the Oceanic Arctic Region |
title_short |
Multimodel combination by a bayesian hierarchical model: Assessment of ice accumulation over the Oceanic Arctic Region |
title_full |
Multimodel combination by a bayesian hierarchical model: Assessment of ice accumulation over the Oceanic Arctic Region |
title_fullStr |
Multimodel combination by a bayesian hierarchical model: Assessment of ice accumulation over the Oceanic Arctic Region |
title_full_unstemmed |
Multimodel combination by a bayesian hierarchical model: Assessment of ice accumulation over the Oceanic Arctic Region |
title_sort |
multimodel combination by a bayesian hierarchical model: assessment of ice accumulation over the oceanic arctic region |
publisher |
HAL CCSD |
publishDate |
2010 |
url |
https://hal.science/hal-03200934 https://hal.science/hal-03200934/document https://hal.science/hal-03200934/file/%5B15200442%20-%20Journal%20of%20Climate%5D%20Multimodel%20Combination%20by%20a%20Bayesian%20Hierarchical%20Model%20Assessment%20of%20Ice%20Accumulation%20over%20the%20Oceanic%20Arctic%20Region.pdf https://doi.org/10.1175/2010JCLI3107.1 |
genre |
Arctic Arctic |
genre_facet |
Arctic Arctic |
op_source |
ISSN: 0894-8755 EISSN: 1520-0442 Journal of Climate https://hal.science/hal-03200934 Journal of Climate, 2010, 23 (20), pp.5421-5436. ⟨10.1175/2010JCLI3107.1⟩ |
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op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1175/2010JCLI3107.1 |
container_title |
Journal of Climate |
container_volume |
23 |
container_issue |
20 |
container_start_page |
5421 |
op_container_end_page |
5436 |
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ftinsu:oai:HAL:hal-03200934v1 2024-04-28T08:05:05+00:00 Multimodel combination by a bayesian hierarchical model: Assessment of ice accumulation over the Oceanic Arctic Region Kallache, Malaak Maksimovich, Elena Michelangeli, Paul-Antoine Naveau, Philippe 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) Climpact Data Science Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN) Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)) École normale supérieure - Paris (ENS-PSL) Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL) Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS) ANR-09-RISK-0007,MOPERA,MOdélisation Probabiliste pour l'Evaluation du Risque Avalanche(2009) European Project: 36305,NICE European Project: 212250,EC:FP7:ENV,FP7-ENV-2007-1,ACQWA(2008) European Project: 32567,DAMOCLES 2010-10-15 https://hal.science/hal-03200934 https://hal.science/hal-03200934/document https://hal.science/hal-03200934/file/%5B15200442%20-%20Journal%20of%20Climate%5D%20Multimodel%20Combination%20by%20a%20Bayesian%20Hierarchical%20Model%20Assessment%20of%20Ice%20Accumulation%20over%20the%20Oceanic%20Arctic%20Region.pdf https://doi.org/10.1175/2010JCLI3107.1 en eng HAL CCSD American Meteorological Society info:eu-repo/semantics/altIdentifier/doi/10.1175/2010JCLI3107.1 info:eu-repo/grantAgreement//36305/EU/Network for Ice sheet and Climate Evolution/NICE info:eu-repo/grantAgreement/EC/FP7/212250/EU/Assessment of Climatic change and impacts on the Quantity and quality of Water/ACQWA info:eu-repo/grantAgreement//32567/EU/Developing Arctic Modelling and Observing Capabilities for Long-term Environmental Studies/DAMOCLES hal-03200934 https://hal.science/hal-03200934 https://hal.science/hal-03200934/document https://hal.science/hal-03200934/file/%5B15200442%20-%20Journal%20of%20Climate%5D%20Multimodel%20Combination%20by%20a%20Bayesian%20Hierarchical%20Model%20Assessment%20of%20Ice%20Accumulation%20over%20the%20Oceanic%20Arctic%20Region.pdf doi:10.1175/2010JCLI3107.1 info:eu-repo/semantics/OpenAccess ISSN: 0894-8755 EISSN: 1520-0442 Journal of Climate https://hal.science/hal-03200934 Journal of Climate, 2010, 23 (20), pp.5421-5436. ⟨10.1175/2010JCLI3107.1⟩ [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 2010 ftinsu https://doi.org/10.1175/2010JCLI3107.1 2024-04-05T00:37:31Z International audience The performance of general circulation models (GCMs) varies across regions and periods. When projecting into the future, it is therefore not obvious whether to reject or to prefer a certain GCM. Combining the outputs of several GCMs may enhance results. This paper presents a method to combine multimodel GCM projections by means of a Bayesian model combination (BMC). Here the influence of each GCM is weighted according to its performance in a training period, with regard to observations, as outcome BMC predictive distributions for yet unobserved observations are obtained. Technically, GCM outputs and observations are assumed to vary randomly around common means, which are interpreted as the actual target values under consideration. Posterior parameter distributions of the authors' Bayesian hierarchical model are obtained by a Markov chain Monte Carlo (MCMC) method. Advantageously, all parameters-such as bias and precision of the GCM models-are estimated together. Potential time dependence is accounted for by integrating a Kalman filter. The significance of trend slopes of the common means is evaluated by analyzing the posterior distribution of the parameters. The method is applied to assess the evolution of ice accumulation over the oceanic Arctic region in cold seasons. The observed ice index is created out of NCEP reanalysis data. Outputs of seven GCMs are combined by using the training period 1962-99 and prediction periods 2046-65 and 2082-99 with Special Report on Emissions Scenarios (SRES) A2 and B1. A continuing decrease of ice accumulation is visible for the A2 scenario, whereas the index stabilizes for the B1 scenario in the second prediction period. Article in Journal/Newspaper Arctic Arctic Institut national des sciences de l'Univers: HAL-INSU Journal of Climate 23 20 5421 5436 |