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An Objective Method for Determining Ocean Mixed Layer Depth with Applications to WOCE Data

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  • 1 State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, and University of Chinese Academy of Sciences, Beijing, China
  • 2 State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China
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Abstract

A new method is developed to identify the mixed layer depth (MLD) from individual temperature or density profiles. A relative variance profile is obtained that is the ratio between the standard deviation and the maximum variation of the temperature (density) from the sea surface, and the depth of the minimum relative variance is defined as the MLD. The new method is robust in finding the MLD under the influence of random noise (noise level ≤ 5%). A comparison with other available methods, which include the threshold (difference, difference interpolation, gradient, and hybrid methods) and objective (curvature and maximum angle methods) methods, is carried out using the World Ocean Circulation Experiment (WOCE) data. It is found that for a variety of depth sampling resolutions ranging from 0.04 to 25 dbar, the new method and the difference-interpolation method predict MLD values that are closer to the visually inspected ones than those by other methods. Moreover, the quality index (QI) of the MLD that is determined by the new method is the highest when compared with those of the available methods. Also, the application of the new method on the WOCE global dataset yields 94% of MLD values with , substantially higher than those (≤86%) of other methods. Ultimately, it is found that the new method determines very similar MLD values when applied to temperature or density profiles globally because it identifies the base of the mixed layer rather than the uppermost depth of the thermocline. This unique advantage makes the new method applicable in many cases, especially when the density profile is unavailable.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JTECH-D-17-0104.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Sheng-Qi Zhou, sqzhou@scsio.ac.cn

Abstract

A new method is developed to identify the mixed layer depth (MLD) from individual temperature or density profiles. A relative variance profile is obtained that is the ratio between the standard deviation and the maximum variation of the temperature (density) from the sea surface, and the depth of the minimum relative variance is defined as the MLD. The new method is robust in finding the MLD under the influence of random noise (noise level ≤ 5%). A comparison with other available methods, which include the threshold (difference, difference interpolation, gradient, and hybrid methods) and objective (curvature and maximum angle methods) methods, is carried out using the World Ocean Circulation Experiment (WOCE) data. It is found that for a variety of depth sampling resolutions ranging from 0.04 to 25 dbar, the new method and the difference-interpolation method predict MLD values that are closer to the visually inspected ones than those by other methods. Moreover, the quality index (QI) of the MLD that is determined by the new method is the highest when compared with those of the available methods. Also, the application of the new method on the WOCE global dataset yields 94% of MLD values with , substantially higher than those (≤86%) of other methods. Ultimately, it is found that the new method determines very similar MLD values when applied to temperature or density profiles globally because it identifies the base of the mixed layer rather than the uppermost depth of the thermocline. This unique advantage makes the new method applicable in many cases, especially when the density profile is unavailable.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JTECH-D-17-0104.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Sheng-Qi Zhou, sqzhou@scsio.ac.cn
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