Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991–2013

• Published in: Ocean Science
• Main Authors: Xie, Jiping, Bertino, Laurent, Counillon, François, Lisæter, Knut A., Sakov, Pavel
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2017
• Subjects: article; Verlagsveröffentlichung; Arctic; International Polar Year; North Atlantic; Sea ice
• Online Access: https://doi.org/10.5194/os-13-123-2017; https://noa.gwlb.de/receive/cop_mods_00042707; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00042327/os-13-123-2017.pdf; https://os.copernicus.org/articles/13/123/2017/os-13-123-2017.pdf

Long dynamical atmospheric reanalyses are widely used for climate studies, but data-assimilative reanalyses of ocean and sea ice in the Arctic are less common. TOPAZ4 is a coupled ocean and sea ice data assimilation system for the North Atlantic and the Arctic that is based on the HYCOM ocean model and the ensemble Kalman filter data assimilation method using 100 dynamical members. A 23-year reanalysis has been completed for the period 1991–2013 and is the multi-year physical product in the Copernicus Marine Environment Monitoring Service (CMEMS) Arctic Marine Forecasting Center (ARC MFC). This study presents its quantitative quality assessment, compared to both assimilated and unassimilated observations available in the whole Arctic region, in order to document the strengths and weaknesses of the system for potential users. It is found that TOPAZ4 performs well with respect to near-surface ocean variables, but some limitations appear in the interior of the ocean and for ice thickness, where observations are sparse. In the course of the reanalysis, the skills of the system are improving as the observation network becomes denser, in particular during the International Polar Year. The online bias estimation successfully maintains a low bias in our system. In addition, statistics of the reduced centered random variables (RCRVs) confirm the reliability of the ensemble for most of the assimilated variables. Occasional discontinuities of these statistics are caused by the changes of the input data sets or the data assimilation settings, but the statistics remain otherwise stable throughout the reanalysis, regardless of the density of observations. Furthermore, no data type is severely less dispersed than the others, even though the lack of consistently reprocessed observation time series at the beginning of the reanalysis has proven challenging.