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The added value of the multi-system spread information for ocean heat content and steric sea level investigations in the CMEMS GREP ensemble reanalysis product

  • Andrea StortoEmail author
  • Simona Masina
  • Simona Simoncelli
  • Doroteaciro Iovino
  • Andrea Cipollone
  • Marie Drevillon
  • Yann Drillet
  • Karina von Schuckman
  • Laurent Parent
  • Gilles Garric
  • Eric Greiner
  • Charles Desportes
  • Hao Zuo
  • Magdalena A. Balmaseda
  • K. Andrew Peterson
Article

Abstract

Since 2016, the Copernicus Marine Environment Monitoring Service (CMEMS) has produced and disseminated an ensemble of four global ocean reanalyses produced at eddy-permitting resolution for the period from 1993 to present, called GREP (Global ocean Reanalysis Ensemble Product). This dataset offers the possibility to investigate the potential benefits of a multi-system approach for ocean reanalyses, since the four reanalyses span by construction the same spatial and temporal scales. In particular, our investigations focus on the added value of the information on the ensemble spread, implicitly contained in the GREP ensemble, for temperature, salinity, and steric sea level studies. It is shown that in spite of the small ensemble size, the spread is capable of estimating the flow-dependent uncertainty in the ensemble mean, although proper re-scaling is needed to achieve reliability. The GREP members also exhibit larger consistency (smaller spread) than their predecessors, suggesting advancement with time of the reanalysis vintage. The uncertainty information is crucial for monitoring the climate of the ocean, even at regional level, as GREP shows consistency with CMEMS high-resolution regional products and complement the regional estimates with uncertainty estimates. Further applications of the spread include the monitoring of the impact of changes in ocean observing networks; the use of multi-model ensemble anomalies in hybrid ensemble-variational retrospective analysis systems, which outperform static covariances and represent a promising application of GREP. Overall, the spread information of the GREP product is found to significantly contribute to the crucial requirement of uncertainty estimates for climatic datasets.

Keywords

Ocean synthesis, reanalysis accuracy Uncertainty Hybrid data assimilation Observation impact 

Notes

Acknowledgements

Data from the reanalyses presented in this work are available from the Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/). Part of this work was supported by the EOS COST Action (“Evaluation of Ocean Synthesis”, http://eos-cost.eu/) through its Short Term Scientific Missions program. The full C-GLORS dataset is available at http://c-glors.cmcc.it. This work has received funding from the Copernicus Marine Environment Monitoring Service (CMEMS). The EN4 subsurface ocean temperature and salinity data were quality-controlled and distributed by the U.K. Met Office. The authors declare no conflicts of interest. We are grateful to four anonymous reviewers for their help in improving the quality of the manuscript.

Supplementary material

382_2018_4585_MOESM1_ESM.docx (1.4 mb)
Supplementary material 1 (DOCX 1409 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Andrea Storto
    • 1
    • 7
    Email author
  • Simona Masina
    • 1
  • Simona Simoncelli
    • 2
  • Doroteaciro Iovino
    • 1
  • Andrea Cipollone
    • 1
  • Marie Drevillon
    • 3
  • Yann Drillet
    • 3
  • Karina von Schuckman
    • 3
  • Laurent Parent
    • 3
  • Gilles Garric
    • 3
  • Eric Greiner
    • 4
  • Charles Desportes
    • 3
  • Hao Zuo
    • 5
  • Magdalena A. Balmaseda
    • 5
  • K. Andrew Peterson
    • 6
  1. 1.Ocean Modeling and Data Assimilation Division, Centro Euro-Mediterraneo sui Cambiamenti ClimaticiBolognaItaly
  2. 2.National Institute for Geophysics and Volcanology (INGV)BolognaItaly
  3. 3.Mercator OceanRamonville Saint-AgneFrance
  4. 4.Collecte Localisation Satellites (CLS)Ramonville Saint-AgneFrance
  5. 5.European Center for Medium Range Weather Forecasts (ECMWF)ReadingUK
  6. 6.Met OfficeExeterUK
  7. 7.Andrea Storto, Centre for Maritime Research and Experimentation (CMRE)La SpeziaItaly

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