Open AccessA comparison of hydrographically and optically derived mixed layer depths
Author(s) -
Zawada David G.,
Zaneveld J. Ronald V.,
Boss Emmanuel,
Gardner Wilford D.,
Richardson Mary Jo,
Mishonov Alexey V.
Publication year - 2005
Publication title -
journal of geophysical research: oceans
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2004jc002417
Subject(s) - mixed layer , attenuation , scattering , computational physics , algorithm , computer science , environmental science , physics , optics , meteorology
Efforts to understand and model the dynamics of the upper ocean would be significantly advanced given the ability to rapidly determine mixed layer depths (MLDs) over large regions. Remote sensing technologies are an ideal choice for achieving this goal. This study addresses the feasibility of estimating MLDs from optical properties. These properties are strongly influenced by suspended particle concentrations, which generally reach a maximum at pycnoclines. The premise therefore is to use a gradient in beam attenuation at 660 nm ( c 660) as a proxy for the depth of a particle‐scattering layer. Using a global data set collected during World Ocean Circulation Experiment cruises from 1988–1997, six algorithms were employed to compute MLDs from either density or temperature profiles. Given the absence of published optically based MLD algorithms, two new methods were developed that use c 660 profiles to estimate the MLD. Intercomparison of the six hydrographically based algorithms revealed some significant disparities among the resulting MLD values. Comparisons between the hydrographical and optical approaches indicated a first‐order agreement between the MLDs based on the depths of gradient maxima for density and c 660. When comparing various hydrographically based algorithms, other investigators reported that inherent fluctuations of the mixed layer depth limit the accuracy of its determination to 20 m. Using this benchmark, we found a ∼70% agreement between the best hydrographical‐optical algorithm pairings.