Multivariate bias corrections of climate simulations seen through impact model | Multivariate bias corrections of climate simulations seen through impact model: Results of the COMPROMISE project
2023
Allard, Denis | François, Bastien | García de Cortázar-Atauri, I. | Vrac, Mathieu | Biostatistique et Processus Spatiaux (BioSP) ; Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Royal Netherlands Meteorological Institute (KNMI) | Agroclim (AGROCLIM) ; Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | 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) | Meta-programme Adaptation au Changement Climatique de l'Agriculture et de la Forêt (ACCAF), INRAE
Atmospheric variables simulated from climate models often present biases relative to the same variables calculated by reanalysis in the past (SAFRAN reanalysis for example). In order to use these models to assess the impact of climate change on processes of interest, it is necessary to correct these biases. Currently, the bias correction methods used operationally correct one-dimensional time series and are therefore applied separately, physical variable by physical variable and site by site. Multivariate bias correction methods have been developed to better take into account dependencies between variables and in space. In this work, we propose a comparison between two multivariate bias correction methods (R2D2 and dOTC) and a univariate correction (CDF-t) through several highly multivariate impact models (phenological stage, reference evapo-transpiration, soil water content, forest weather index) integrating the climatic signal throughout a season. The data, the impact models and the statistical methods are first presented. The experimental design is then described. Extensive results are illustrated but not commented.
اظهر المزيد [+] اقل [-]الكلمات المفتاحية الخاصة بالمكنز الزراعي (أجروفوك)
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تم تزويد هذا السجل من قبل Institut national de la recherche agronomique