@article{fdi:010082274,
  title = {{S}ystematic investigation of skill opportunities in decadal prediction of air temperature over {E}urope},
  author = {{S}gubin, {G}. and {S}wingedouw, {D}. and {B}orchert, {L}. {F}. and {M}enary, {M}. {B}. and {N}oel, {T}. and {L}oukos, {H}. and {M}ignot, {J}uliette},
  editor = {},
  language = {{ENG}},
  abstract = {{D}ecadal {C}limate {P}redictions ({DCP}) have gained considerable attention for their potential utility in promoting optimised plans of adaptation to climate change and variability. {T}heir 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. {H}ere we explore the performance of the {IPSL}-{CM}5{A}-{LR} {DCP} system in predicting air temperature over {E}urope, by proposing a systematic assessessment of the prediction skill for different time windows (periods of the calendar time, forecast years and months/seasons). {I}n this framework, we also compare raw and de-biased hindcasts, in which the temperature outputs have been corrected using a quantile matching method. {T}he systematic analysis allows to discern certain conditions conferring larger predictability, which we find to be intermittent in time. {T}he predictions appear more skilful around the 1960s and after the 1980s, in coincidence with large shifts of the {A}tlantic {M}ultidecadal {V}ariability, which are well reproduced in the hindcasts. {A}verages 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. {M}oreover, we find an overall added value due to initialisation, while de-biased predictions significantly outperform raw predictions only for a few specific time windows. {F}inally, we discuss the potential implications of the proposed systematic exploration of skill opportunities in {DCP}s for integrated applications in climate sensitive sectors.},
  keywords = {{C}limate variability ; {D}ecadal climate predictions ; {D}e-biasing ; {A}tlantic ; multidecadal variability ; {C}limate service ; {EUROPE} ; {ATLANTIQUE}},
  booktitle = {},
  journal = {{C}limate {D}ynamics},
  volume = {[{E}arly access]},
  numero = {},
  pages = {[19 ]},
  ISSN = {0930-7575},
  year = {2021},
  DOI = {10.1007/s00382-021-05863-0},
  URL = {https://www.documentation.ird.fr/hor/fdi:010082274},
}