An anatomy of Arctic sea ice forecast biases in the seasonal prediction system with EC-Earth

• Published in: Climate Dynamics
• Main Authors: Cruz-García, Rubén, Ortega, Pablo, Guemas, V., Acosta Navarro, Juan, Massonnet, François, Doblas-Reyes, Francisco
• Other Authors: Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Université Catholique de Louvain = Catholic University of Louvain (UCL), Institució Catalana de Recerca i Estudis Avançats (ICREA)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2021
• Subjects: Arctic; Sea ice; Bias; Forecast; Shock; Initialization; [SDU.OCEAN]Sciences of the Universe [physics]/Ocean; Atmosphere; Greenland; Greenland Sea
• Online Access: https://hal.science/hal-03413052; https://hal.science/hal-03413052/document; https://hal.science/hal-03413052/file/Cruz-Garc%C3%ADa_et_al_2021.pdf; https://doi.org/10.1007/s00382-020-05560-4

International audience The quality of initial conditions (ICs) in climate predictions controls the level of skill. Both the use of the latest high-quality observations and of the most efficient assimilation method are of paramount importance. Technical challenges make it frequent to assimilate observational information independently in the various model components. Inconsistencies between the ICs obtained for the different model components can cause initialization shocks. In this study, we identify and quantify the contribution of the ICs inconsistency relative to the model inherent bias (in which the Arctic is generally too warm) to the development of sea ice concentration forecast biases in a seasonal prediction system with the EC-Earth general circulation model. We estimate that the ICs inconsistency dominates the development of forecast biases for as long as the first 24 (19) days of the forecasts initialized in May (November), while the development of model inherent bias dominates afterwards. The effect of ICs inconsistency is stronger in the Greenland Sea, in particular in November, and mostly associated to a mismatch between the sea ice and ocean ICs. In both May and November, the ICs inconsistency between the ocean and sea ice leads to sea ice melting, but it happens in November (May) in a context of sea ice expansion (shrinking). The ICs inconsistency tend to postpone (accelerate) the November (May) sea ice freezing (melting). Our findings suggest that the ICs inconsistency might have a larger impact than previously suspected. Detecting and filtering out this signal requires the use of high frequency data.