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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
Title of the book
Climate Dynamics 
Issue/vol(year)
/49 (2017)
Pages (printed)
753–773
Issued date
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.
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