Link between autumnal Arctic Sea ice and Northern Hemisphere winter forecast skill

• Published in: Geophysical Research Letters
• Main Authors: Acosta Navarro, Juan Camilo, Ortega, Pablo, Batté, Lauriane, Smith, Doug, Bretonnière, Pierre-Antoine, Guemas, Virginie, Massonnet, François, Sicardi, Valentina, Torralba, Veronica, Tourigny, Etienne, Doblas-Reyes, Francisco
• Other Authors: Barcelona Supercomputing Center
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Geophysical Union (AGU) 2020
• Subjects: Àrees temàtiques de la UPC::Desenvolupament humà i sostenible::Prospectiva; sistèmica i modelització; Forecasting; Precipitation (Meteorology); Climatology; Sea ice > Arctic Ocean; Atmospheric circulation; Winter climate predictions; Artic Sea ice; Eurasian climate; Atmospheric teleconnections; Large ensembles; Climatologia; Previsió; Circulació atmosfèrica; Temperatura; Precipitacions (Meteorologia); MODELS; Arctic; Arctic Ocean; Kara Sea; Cierva; Sea ice
• Online Access: http://hdl.handle.net/2117/185324; https://doi.org/10.1029/2019GL086753

Dynamical forecast systems have low to moderate skill in continental winter predictions in the extratropics. Here we assess the multimodel predictive skill over Northern Hemisphere high latitudes and midlatitudes using four state‐of‐the‐art forecast systems. Our main goal was to quantify the impact of the Arctic sea ice state during November on the sea level pressure (SLP), surface temperature, and precipitation skill during the following winter. Interannual variability of the November Barents and Kara Sea ice is associated with an important fraction of December to February (DJF) prediction skill in regions of Eurasia. We further show that skill related to sea ice in these regions is accompanied with enhanced skill of DJF SLP in western Russia, established by a sea ice‐atmosphere teleconnection mechanism. The teleconnection is strongest when atmospheric blocking conditions in Scandinavia/western Russia in November reduce a systematic SLP bias that is present in all systems. This work was funded by the European Union projects APPLICATE (Grant 727862), PRIMAVERA (Grant 641727), INTAROS (Grant 727890), and ESA/CMUG‐CCI3. We acknowledge PRACE for awarding us access to MareNostrum IV at Barcelona Supercomputing Center (BSC), Spain. J. C. A. N. received financial support from the Spanish Ministerio de Ciencia,Innovación y Universidades through a Juan de la Cierva personal grant (FJCI‐2017‐34027). E. T. received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska‐Curie grant Agreement 748750 (SPFireSD project). V. G. received funding from the Agence Nationale de la Recherche through the Make Our Planet Great Again Grant ANR‐17‐MPGA‐003. The data from EC‐Earth3.2 and CNRM‐CM6‐1 are publicly available (at https://applicate.eu/data/data‐portal). GloSea5 (v13) and SEAS5 data are publicly available (at https://cds.climate.copernicus.eu/cdsapp#!/dataset/seasonal‐ monthly‐single‐levels?tab=overview). All the data were downloaded from their original source, converted to NetCDF in a format designed for efficient analysis with the tools mentioned before, and quality checked at several levels. Peer Reviewed Postprint (published version)