Journal metrics

Journal metrics

  • IF value: 2.289 IF 2.289
  • IF 5-year value: 2.756 IF 5-year 2.756
  • CiteScore value: 2.76 CiteScore 2.76
  • SNIP value: 1.050 SNIP 1.050
  • SJR value: 1.554 SJR 1.554
  • IPP value: 2.65 IPP 2.65
  • h5-index value: 30 h5-index 30
  • Scimago H index value: 41 Scimago H index 41
Volume 13, issue 1 | Copyright
Ocean Sci., 13, 123-144, 2017
https://doi.org/10.5194/os-13-123-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 13 Feb 2017

Research article | 13 Feb 2017

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

Jiping Xie1, Laurent Bertino1, François Counillon1, Knut A. Lisæter1, and Pavel Sakov2 Jiping Xie et al.
  • 1Nansen Environmental and Remote Sensing Center, Bergen 5006, Norway
  • 2Bureau of Meteorology, Melbourne VIC3001, Australia

Abstract. 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.

Download & links
Publications Copernicus
Download
Short summary
The Arctic Ocean plays an important role in the global climate system, but the concerned interpretation about its changes is severely hampered by the sparseness of the observations of sea ice and ocean. The focus of this study is to provide a quantitative assessment of the performance of the TOPAZ4 reanalysis for ocean and sea ice variables in the pan-Arctic region (north of 63 °N) in order to guide the user through its skills and limitations.
The Arctic Ocean plays an important role in the global climate system, but the concerned...
Citation
Share