{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Vaittinada Ayar P"],"funding":["Natural Sciences and Engineering Research Council of Canada","ERA4CS","Mitacs"],"pagination":["3098"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7862270"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(1)"],"pubmed_abstract":["Climate simulations often need to be adjusted (i.e., corrected) before any climate change impacts studies. However usual bias correction approaches do not differentiate the bias from the different uncertainties of the climate simulations: scenario uncertainty, model uncertainty and internal variability. In particular, in the case of a multi-run ensemble of simulations (i.e., multiple runs of one model), correcting, as usual, each member separately, would mix up the model biases with its internal variability. In this study, two ensemble bias correction approaches preserving the internal variability of the initial ensemble are proposed. These \"Ensemble bias correction\" (EnsBC) approaches are assessed and compared to the approach where each ensemble member is corrected separately, using preci"],"journal":["Scientific reports"],"pubmed_title":["Ensemble bias correction of climate simulations: preserving internal variability."],"pmcid":["PMC7862270"],"funding_grant_id":["690462"],"pubmed_authors":["Vaittinada Ayar P","Mailhot A","Vrac M"],"additional_accession":[]},"is_claimable":false,"name":"Ensemble bias correction of climate simulations: preserving internal variability.","description":"Climate simulations often need to be adjusted (i.e., corrected) before any climate change impacts studies. However usual bias correction approaches do not differentiate the bias from the different uncertainties of the climate simulations: scenario uncertainty, model uncertainty and internal variability. In particular, in the case of a multi-run ensemble of simulations (i.e., multiple runs of one model), correcting, as usual, each member separately, would mix up the model biases with its internal variability. In this study, two ensemble bias correction approaches preserving the internal variability of the initial ensemble are proposed. These \"Ensemble bias correction\" (EnsBC) approaches are assessed and compared to the approach where each ensemble member is corrected separately, using preci","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Feb","modification":"2025-04-04T22:04:08.156Z","creation":"2025-04-04T22:04:08.156Z"},"accession":"S-EPMC7862270","cross_references":{"pubmed":["33542411"],"doi":["10.1038/s41598-021-82715-1"]}}