Estimating Mixed Layer Depth from Oceanic Profile Data

Abstract

Estimates of mixed layer depth are important to a wide variety of oceanic investigations including upper-ocean productivity, air?sea exchange processes, and long-term climate change. In the absence of direct turbulent dissipation measurements, mixed layer depth is commonly derived from oceanic profile data using threshold, integral, least squares regression, or other proxy variables. The different methodologies often yield different values for mixed layer depth. In this paper, a new method?the split-and-merge (SM) method?is introduced for determining the depth of the surface mixed layer and associated upper-ocean structure from digital conductivity?temperature?depth (CTD) profiles. Two decades of CTD observations for the continental margin of British Columbia are used to validate the SM method and to examine differences in mixed layer depth estimates for the various computational techniques. On a profile-by-profile basis, close agreement is found between the SM and de facto standard threshold methods. However, depth estimates from these two methods can differ significantly from those obtained using the integral and least squares regression methods. The SM and threshold methods are found to approximate the ?true? mixed layer depth whereas the integral and regression methods typically compute the depth of the underlying pycnocline. On a statistical basis, the marginally smaller standard errors for spatially averaged mixed layer depths for the SM method suggest a slight improvement in depth determination over threshold methods. This improvement, combined with the added ability of the SM method to delineate simultaneously ancillary features of the upper ocean (such as the depth and gradient of the permanent pycnocline), make it a valuable computational tool for characterizing the structure of the upper ocean.