Drift Correction and Sub-Ensemble Predictive Skill Evaluation of the Decadal Prediction Large Ensemble With Application to Regional Studies
Metadatos
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Rosa-Cánovas, Juan José; García Valdecasas Ojeda, Matilde María del Valle; Romero-Jiménez, Emilio; Yeste Donaire, Patricio; Gámiz Fortís, Sonia Raquel; Castro Díez, Yolanda; Esteban Parra, María JesúsEditorial
AGU Advancing Earth and Space Science
Fecha
2023-11-13Referencia bibliográfica
Rosa-Cánovas, J. J., García-Valdecasas Ojeda, M., Romero-Jiménez, E., Yeste, P., Gámiz-Fortis, S. R., Castro-Díez, Y., & Esteban-Parra, M. J. (2023). Drift correction and sub-ensemble predictive skill evaluation of the decadal prediction large ensemble with application to regional studies. Journal of Geophysical Research: Atmospheres, 128, e2023JD039709. https://doi.org/10.1029/2023JD039709
Patrocinador
PID2021-126401OB-I00, funded by MCIN/AEI/10.13039/501100011033/ FEDER Una manera de hacer Europa; CGL2017-89836-R, funded by the Spanish Ministry of Economy and Competitiveness with additional FEDER funds; P20_00035, funded by FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades; LifeWatch-2019- 10-UGR-01, funded by FEDER/Ministerio de Ciencia e Innovación; Funding for open access charge: Universidad de Granada/CBUAResumen
A large ensemble of experiments is required to reveal the predictable climate signal masked
by the background noise in the decadal climate prediction (DCP). This is one of the main obstacles which
complicates the generation of high-resolution decadal climate information at regional scale, given the
computing cost of the task. In this study, a set of representative sub-ensembles of three members (ENS3) from
the Decadal Prediction Large Ensemble has been selected to produce dynamically downscaled DCPs in future
studies, minimizing the amount of computing resources required to conduct the regionalization while reducing
as much as possible the loss of predictive skill with respect to the full ensemble (ENS40). The procedure
to follow comprises two steps: first, an analysis to choose the most appropriate method of drift correction
to remove the model drift; second, the selection of three members to build ENS3 and the evaluation of its
performance and the impact of ensemble size on sub-ensemble performance. The study has been focused on
sea surface temperature (SST), near-surface temperature anomaly and sea level pressure over some Coordinated
Regional Climate Downscaling Experiment regions: Europe, South America and North America. The initial
condition-based approach has been shown to be the most suitable method in the three domains. Although there
is an inevitable loss of predictive skill when reducing the ensemble size, ENS3 has shown to be a relatively
good alternative to ENS10 and ENS40 when facing computing constraints and the analysis is focused on SST.