Objective determination of global ocean surface mixed layer depth

2010 ◽  
Author(s):  
P C Chu ◽  
Chenwu Fan
Keyword(s):  
Mixed Layer ◽  
Mixed Layer Depth ◽  
Ocean Surface ◽  
Global Ocean ◽  
Surface Mixed Layer ◽  
Layer Depth ◽  
Objective Determination ◽  
Ocean Surface Mixed Layer

scholarly journals Influence of Pycnocline Smoothing and Subgrid-Scale Variability of Density Profiles on the Determination of Mixed Layer Depth

2017 ◽  
Vol 34 (9) ◽  
pp. 2083-2101 ◽  
Author(s):  
Hyejin Ok ◽  
Yign Noh ◽  
Yeonju Choi
Keyword(s):  
Mixed Layer ◽  
General Circulation ◽  
Mixed Layer Depth ◽  
Global Ocean ◽  
Observation Data ◽  
Subgrid Scale ◽  
Layer Depth ◽  
Profile Error ◽  
Density Profiles

AbstractThis study investigates how pycnocline smoothing and subgrid-scale variability of density profiles influence the determination of the mixed layer depth (MLD) in the global ocean, and applies the results of analysis to assess the ability of ocean general circulation models (OGCM) to simulate the MLD. For this purpose, individual, monthly mean, and climatological profiles are analyzed over a horizontal resolution of 1° × 1° for both observation data (Argo) and eddy-resolving OGCM (OFES) results. It is found that the MLDs from averaged profiles are generally smaller than those from individual profiles because of pycnocline smoothing induced by the averaging process. A correlation is found between the decrease in MLD Δh and the increase in pycnocline thickness Δδ of averaged profiles, except during winter in the high-latitude ocean. The relation is estimated as Δh = −αΔδ − β, where α ≃ 0.7 in all cases, but β increases with the subgrid-scale variability of density profiles. A correlation is also found between Δh and the standard deviation of the MLD within a grid. The results are applied to estimate how much of the MLD bias of OFES is due to prediction error and how much is due to profile error, induced by different pycnocline smoothing and the subgrid-scale variability of density profiles. The study also shows how profile error varies with the threshold density difference criterion.

scholarly journals Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network

2020 ◽  
Author(s):  
Wei-Lei Wang ◽  
Guisheng Song ◽  
François Primeau ◽  
Eric S. Saltzman ◽  
Thomas G. Bell ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Mixed Layer ◽  
Dimethyl Sulfide ◽  
Mixed Layer Depth ◽  
Environmental Parameters ◽  
Global Scale ◽  
Global Ocean ◽  
Layer Depth

Abstract. Marine dimethyl sulfide (DMS) is important to climate due to the ability of DMS to alter Earth's radiation budget. However, a knowledge of the global-scale distribution, seasonal variability, and sea-to-air flux of DMS is needed in order to understand the factors controlling surface ocean DMS and its impact on climate. Here we examine the use of an artificial neural network (ANN) to extrapolate available DMS measurements to the global ocean and produce a global climatology with monthly temporal resolution. A global database of 57 810 ship-based DMS measurements in surface waters was used along with a suite of environmental parameters consisting of lat-lon coordinates, time-of-day, time-of-year, solar radiation, mixed layer depth, sea surface temperature, salinity, nitrate, phosphate, silicate, and oxygen. Linear regressions of DMS against the environmental parameters show that on a global scale mixed layer depth and solar radiation are the strongest predictors of DMS, however, they capture 14 % and 12 % of the raw DMS data variance, respectively. The multi-linear regression can capture more (∼29 %) of the raw data variance, but strongly underestimates high DMS concentrations. In contrast, the ANN captures ~61 % of the raw data variance in our database. Like prior climatologies our results show a strong seasonal cycle in DMS concentration and sea-to-air flux. The highest concentrations (fluxes) occur in the high-latitude oceans during the summer. We estimate a lower global sea-to-air DMS flux (17.90 ± 0.34 Tg S yr−1) than the prior estimate based on a map interpolation method when the same gas transfer velocity parameterization is used.

scholarly journals Observations of the Transition Layer

2009 ◽  
Vol 39 (3) ◽  
pp. 780-797 ◽  
Author(s):  
T. M. Shaun Johnston ◽  
Daniel L. Rudnick
Keyword(s):  
Layer Thickness ◽  
Mixed Layer ◽  
Potential Vorticity ◽  
Transition Layer ◽  
Mixed Layer Depth ◽  
Density Ratio ◽  
Horizontal Resolution ◽  
Surface Mixed Layer ◽  
Layer Depth ◽  
Vertical Displacements

Abstract The transition layer is the poorly understood interface between the stratified, weakly turbulent interior and the strongly turbulent surface mixed layer. The transition layer displays elevated thermohaline variance compared to the interior and maxima in current shear, vertical stratification, and potential vorticity. A database of 91 916 km or 25 426 vertical profiles of temperature and salinity from SeaSoar, a towed vehicle, is used to define the transition layer thickness. Acoustic Doppler current measurements are also used, when available. Statistics of the transition layer thickness are compared for 232 straight SeaSoar sections, which range in length from 65 to 1129 km with typical horizontal resolution of ∼4 km and vertical resolution of 8 m. Transition layer thicknesses are calculated in three groups from 1) vertical displacements of the mixed layer base and of interior isopycnals into the mixed layer; 2) the depths below the mixed layer depth of peaks in shear, stratification, and potential vorticity and their widths; and 3) the depths below or above the mixed layer depth of extrema in thermohaline variance, density ratio, and isopycnal slope. From each SeaSoar section, the authors compile either a single value or a median value for each of the above measures. Each definition yields a median transition layer thickness from 8 to 24 m below the mixed layer depth. The only exception is the median depth of the maximum isopycnal slope, which is 37 m above the mixed layer base, but its mode is 15–25 m above the mixed layer base. Although the depths of the stratification, shear, and potential vorticity peaks below the mixed layer are not correlated with the mixed layer depth, the widths of the shear and potential vorticity peaks are. Transition layer thicknesses from displacements and the full width at half maximum of the shear and potential vorticity peak give transition layer thicknesses from 0.11× to 0.22× the mean depth of the mixed layer. From individual profiles, the depth of the shear peak below the stratification peak has a median value of 6 m, which shows that momentum fluxes penetrate farther than buoyancy fluxes. A typical horizontal scale of 5–10 km for the transition layer comes from the product of the isopycnal slope and a transition layer thickness suggesting the importance of submesoscale processes in forming the transition layer. Two possible parameterizations for transition layer thickness are 1) a constant of 11–24 m below the mixed layer depth as found for the shear, stratification, potential vorticity, and thermohaline variance maxima and the density ratio extrema; and 2) a linear function of mixed layer depth as found for isopycnal displacements and the widths of the shear and potential vorticity peaks.

Variability of the Oceanic Mixed Layer, 1960–2004

2008 ◽  
Vol 21 (5) ◽  
pp. 1029-1047 ◽  
Author(s):  
James A. Carton ◽  
Semyon A. Grodsky ◽  
Hailong Liu
Keyword(s):  
Mixed Layer ◽  
North Atlantic ◽  
Southern Oscillation ◽  
Mixed Layer Depth ◽  
Global Ocean ◽  
Boreal Summer ◽  
Layer Depth ◽  
The North ◽  
The North Atlantic ◽  
Mixed Layers

Abstract A new monthly uniformly gridded analysis of mixed layer properties based on the World Ocean Atlas 2005 global ocean dataset is used to examine interannual and longer changes in mixed layer properties during the 45-yr period 1960–2004. The analysis reveals substantial variability in the winter–spring depth of the mixed layer in the subtropics and midlatitudes. In the North Pacific an empirical orthogonal function analysis shows a pattern of mixed layer depth variability peaking in the central subtropics. This pattern occurs coincident with intensification of local surface winds and may be responsible for the SST changes associated with the Pacific decadal oscillation. Years with deep winter–spring mixed layers coincide with years in which winter–spring SST is low. In the North Atlantic a pattern of winter–spring mixed layer depth variability occurs that is not so obviously connected to local changes in winds or SST, suggesting that other processes such as advection are more important. Interestingly, at decadal periods the winter–spring mixed layers of both basins show trends, deepening by 10–40 m over the 45-yr period of this analysis. The long-term mixed layer deepening is even stronger (50–100 m) in the North Atlantic subpolar gyre. At tropical latitudes the boreal winter mixed layer varies in phase with the Southern Oscillation index, deepening in the eastern Pacific and shallowing in the western Pacific and eastern Indian Oceans during El Niños. In boreal summer the mixed layer in the Arabian Sea region of the western Indian Ocean varies in response to changes in the strength of the southwest monsoon.

Sign in / Sign up

Export Citation Format

Share Document