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

Author(s)
Toyoda, Takahiro  
Fujii, Yosuke  
Kuragano, Tsurane  
Kamachi, Masafumi  
Ishikawa, Yoichi  
Masuda, Shuhei  
Sato, Kanako  
Awaji, Toshiyuki  
Hernandez, Fabrice  
Ferry, Nicolas  
Guinehut, Stéphanie  
Martin, Matthew J.  
Peterson, Andrew K.  
Good, Simon  
Valdivieso, Maria  
Haines, Keith  
Storto, Andrea  
Masina, Simona  
Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Bologna, Bologna, Italia  
Köhl, Armin  
Zuo, Hao  
Balmaseda, Magdalena  
Yin, Yonghong  
Shi, Li  
Alves, Oscar  
Smith, Gregory  
Chang, You-Soon  
Vernieres, Guillaume  
Wang, Xiaochun  
Forget, Gael  
Heimbach, Patrick  
Wang, Ou  
Fukumori, Ichiro  
Lee, Tong  
Language
English
Obiettivo Specifico
4A. Oceanografia e clima
Status
Published
JCR Journal
JCR Journal
Peer review journal
Yes
Journal
Climate Dynamics  
Issue/vol(year)
/49 (2017)
Pages (printed)
753–773
Date Issued
August 2017
DOI
10.1007/s00382-015-2637-7
URI
https://www.earth-prints.org/handle/2122/11769
Abstract
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.
Type
article
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