4.6 Article

Intercomparison and validation of the mixed layer depth fields of global ocean syntheses

Journal

CLIMATE DYNAMICS
Volume 49, Issue 3, Pages 753-773

Publisher

SPRINGER
DOI: 10.1007/s00382-015-2637-7

Keywords

Ocean reanalysis; Mixed layer depth; Ocean Reanalyses Intercomparison Project (ORA-IP); Data assimilation; Ocean general circulation model; Isothermal layer depth

Funding

  1. Research Program on Climate Change Adaptation (RECCA) of Ministry of Education, Culture, Sports, Science and Technology of the Japanese government (MEXT)
  2. Data Integration and Analysis System (DIAS) of the MEXT
  3. UK DECC/Defra Met Office Hadley Centre Climate Programme [GA01101]
  4. UK Public Weather Service Research Programme
  5. European Commission
  6. Directorate For Geosciences
  7. Division Of Ocean Sciences [0961713] Funding Source: National Science Foundation
  8. Natural Environment Research Council [nceo020004] Funding Source: researchfish
  9. NERC [nceo020004] Funding Source: UKRI

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Intercomparison and evaluation of the global ocean surface mixed layer depth (MLD) fields estimated from a suite of major ocean syntheses are conducted. Compared with the reference MLDs calculated from individual profiles, MLDs calculated from monthly mean and gridded profiles show negative biases of 10-20 m in early spring related to the re-stratification process of relatively deep mixed layers. Vertical resolution of profiles also influences the MLD estimation. MLDs are underestimated by approximately 5-7 (14-16) m with the vertical resolution of 25 (50) m when the criterion of potential density exceeding the 10-m value by 0.03 kg m(-3) is used for the MLD estimation. Using the larger criterion (0.125 kg m(-3)) generally reduces the underestimations. In addition, positive biases greater than 100 m are found in wintertime subpolar regions when MLD criteria based on temperature are used. Biases of the reanalyses are due to both model errors and errors related to differences between the assimilation methods. The result shows that these errors are partially cancelled out through the ensemble averaging. Moreover, the bias in the ensemble mean field of the reanalyses is smaller than in the observation-only analyses. This is largely attributed to comparably higher resolutions of the reanalyses. The robust reproduction of both the seasonal cycle and interannual variability by the ensemble mean of the reanalyses indicates a great potential of the ensemble mean MLD field for investigating and monitoring upper ocean processes.

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