Multimodel combination by a bayesian hierarchical model: Assessment of ice accumulation over the Oceanic Arctic Region

• Published in: Journal of Climate
• Main Authors: Kallache, Malaak, Maksimovich, Elena, Michelangeli, Paul-Antoine, Naveau, Philippe
• Other Authors: 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
• Language: English
• Published: HAL CCSD 2010
• Subjects: [SDU.OCEAN]Sciences of the Universe [physics]/Ocean; Atmosphere; [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces; environment; Arctic
• 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

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.