Séminaire
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 precipitation and temperature series at two locations in North America from a multi-member regional climate ensemble. The preservation of the internal variability is assessed in terms of monthly mean and hourly quantiles. Besides, the preservation of the internal variability in a changing climate is evaluated.
Results show that, contrary to the usual approach, the proposed ensemble bias correction approaches adequately preserve the internal variability even in changing climate. Moreover, the climate change signal given by the original ensemble is also conserved by both approaches.
Pradeebane Vaittinada Ayar (1,2),* , Mathieu Vrac (3), and Alain Mailhot (1)
(1) Institut national de la recherche scientifique, Centre Eau Terre Environnement, Québec, G1K 9A9 Canada
(2) NORCE Norwegian Research Centre, Bjerknes Centre for Climate Research, Bergen, Norway
(3) Laboratoire des Sciences du Climat et l’Environnement (LSCE-IPSL) CNRS/CEA/UVSQ, UMR8212, Université Paris-Saclay, Gif-sur-Yvette, 91190 France
The talk will be retransmitted online: https://bbb.lsce.ipsl.fr/b/dav-7ax-yma
If the conditions are favorable, the presentation will be done at the same time at LSCE, building 714, in room 1129.
Soulivanh Thao, LSCE-IPSL soulivanh.thao@lsce.ipsl.fr