%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Sgubin, G.
%A Swingedouw, D.
%A Borchert, L. F.
%A Menary, M. B.
%A Noel, T.
%A Loukos, H.
%A Mignot, Juliette
%T Systematic investigation of skill opportunities in decadal prediction of air temperature over Europe
%D 2021
%L fdi:010082274
%G ENG
%J Climate Dynamics
%@ 0930-7575
%K Climate variability ; Decadal climate predictions ; De-biasing ; Atlantic ; multidecadal variability ; Climate service
%K EUROPE ; ATLANTIQUE
%M ISI:000669296900001
%P [19 ]
%R 10.1007/s00382-021-05863-0
%U https://www.documentation.ird.fr/hor/fdi:010082274
%> https://www.documentation.ird.fr/intranet/publi/2021-08/010082274.pdf
%V [Early access]
%W Horizon (IRD)
%X Decadal Climate Predictions (DCP) have gained considerable attention for their potential utility in promoting optimised plans of adaptation to climate change and variability. Their effective applicability to a targeted problem is nevertheless conditional on a detailed evaluation of their ability to simulate the near-term climate evolution under specific conditions. Here we explore the performance of the IPSL-CM5A-LR DCP system in predicting air temperature over Europe, by proposing a systematic assessessment of the prediction skill for different time windows (periods of the calendar time, forecast years and months/seasons). In this framework, we also compare raw and de-biased hindcasts, in which the temperature outputs have been corrected using a quantile matching method. The systematic analysis allows to discern certain conditions conferring larger predictability, which we find to be intermittent in time. The predictions appear more skilful around the 1960s and after the 1980s, in coincidence with large shifts of the Atlantic Multidecadal Variability, which are well reproduced in the hindcasts. Averages on longer forecast periods also generally imply better prediction skill, while the best predicted months appear to be mainly those between late spring and early autumn. Moreover, we find an overall added value due to initialisation, while de-biased predictions significantly outperform raw predictions only for a few specific time windows. Finally, we discuss the potential implications of the proposed systematic exploration of skill opportunities in DCPs for integrated applications in climate sensitive sectors.
%$ 021 ; 032 ; 020