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.

Sgubin, G., Swingedouw, D., Borchert, L.F., Menary, M.B., Noël, T., Loukos, H., et al. (2021). Systematic investigation of skill opportunities in decadal prediction of air temperature over Europe. CLIMATE DYNAMICS, 57(11-12), 3245-3263 [10.1007/s00382-021-05863-0].

Systematic investigation of skill opportunities in decadal prediction of air temperature over Europe

Sgubin, Giovanni
Primo
;
2021-01-01

Abstract

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.
2021
Sgubin, G., Swingedouw, D., Borchert, L.F., Menary, M.B., Noël, T., Loukos, H., et al. (2021). Systematic investigation of skill opportunities in decadal prediction of air temperature over Europe. CLIMATE DYNAMICS, 57(11-12), 3245-3263 [10.1007/s00382-021-05863-0].
File in questo prodotto:
File Dimensione Formato  
Sgubin2021_Article_SystematicInvestigationOfSkill-compresso.pdf

Solo gestori archvio

Tipologia: Versione Editoriale
Dimensione 1.26 MB
Formato Adobe PDF
1.26 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/638055
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 4
social impact