scholarly journals The influence of temperature and salinity variability on the upper ocean density and mixed layer

2010 ◽  
Vol 7 (4) ◽  
pp. 1469-1495 ◽  
Author(s):  
R. W. Helber ◽  
J. G. Richman ◽  
C. N. Barron
Keyword(s):  
Mixed Layer ◽  
Relative Influence ◽  
Global Ocean ◽  
Dominant Factor ◽  
Upper Ocean ◽  
Layer Depth ◽  
Density Compensation ◽  
Influence Of Temperature ◽  
Vertical Density ◽  
Turner Angle

Abstract. The relative influence of both temperature and salinity on the mixed layer depth (MLD) is evaluated using a relationship of binned regressions of MLD on vertical density compensation and isothermal layer depth (ILD) from a global set of in situ profile observations. Our approach is inspired by the observations of the difference between the MLD and the sonic layer depth (SLD) that evolve seasonally around the global ocean. In this article, we hypothesize that vertical density compensation governs SLD-MLD differences and can be used for mapping the relative influence of temperature and salinity on upper ocean structure. The Turner angle, computed between the surface and 200 m (bulk Turner angle, BTA), serves as a measure of vertical density compensation that quantifies times and areas where either temperature or salinity is destabilizing. For temperature destabilization the ocean exhibits cool/fresh overlying hot/salty water. For salinity destabilization the ocean exhibits hot/salty overlying cool/fresh water. These two classes of density compensation have seasonal variability with different geographical characteristics. Profiles with salinity controlled stable density and destabilizing temperature gradient are found most often at high latitudes. Profiles with temperature controlled stable density and destabilizing salinity gradient are found in the tropics and subtropics of all oceans. Results indicate that about half of the ocean has vertical density compensation that is a necessary condition for SLD-MLD differences. While density compensation is necessary, it is not a sufficient condition for predicting the dependence of MLD on BTA. Density compensation is the dominant factor in MLD variability in heavy river input and subduction regions that cover only ~14% of the ocean.

scholarly journals Summertime increases in upper-ocean stratification and mixed-layer depth

Nature ◽  
2021 ◽  
Vol 591 (7851) ◽  
pp. 592-598
Author(s):  
Jean-Baptiste Sallée ◽  
Violaine Pellichero ◽  
Camille Akhoudas ◽  
Etienne Pauthenet ◽  
Lucie Vignes ◽  
...  
Keyword(s):  
Mixed Layer ◽  
Mixed Layer Depth ◽  
Upper Ocean ◽  
Layer Depth ◽  
Ocean Stratification

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.

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.

scholarly journals Interannual Variability of the Mixed Layer Winter Convection and Spice Injection in the Eastern Subtropical North Atlantic

2015 ◽  
Vol 45 (2) ◽  
pp. 504-525 ◽  
Author(s):  
Nicolas Kolodziejczyk ◽  
Gilles Reverdin ◽  
Alban Lazar
Keyword(s):  
Interannual Variability ◽  
Mixed Layer ◽  
North Atlantic ◽  
Mixed Layer Depth ◽  
Buoyancy Flux ◽  
Upper Ocean ◽  
Late Winter ◽  
Layer Depth ◽  
Ocean Layer ◽  
Bulk Model

AbstractThe Argo dataset is used to study the winter upper-ocean conditions in the northeastern subtropical (NEA) Atlantic during 2006–12. During late winter 2010, the mixed layer depth is abnormally shallow and a negative anomaly of density-compensated salinity, the so-called spiciness, is generated in the permanent pycnocline. This is primarily explained by unusual weak air–sea buoyancy flux during the late winter 2010, in contrast with the five other studied winters. Particularly deep mixed layers and strong spiciness anomalies are observed during late winter 2012. The 2010 winter conditions appear to be related to historically low North Atlantic Oscillation (NAO) and high tropical North Atlantic index (TNA). Interannual variability of the eastern subtropical mixed layer is further investigated using a simple 1D bulk model of mean temperature and salinity linear profiles, based on turbulent kinetic energy conservation in the upper-ocean layer, and forced only with seasonal air–sea buoyancy forcing corresponding to fall–winter 2006–12. It suggests that year-to-year variability of the winter convective mixing driven by atmospheric buoyancy flux is able to generate interannual variability of both late winter mixed layer depth and spiciness in a strongly compensated layer at the base of the mixed layer and in the permanent pycnocline.

A synthesis of upper ocean geostrophic kinetic energy spectra from a global submesoscale permitting simulation

2021 ◽  
Author(s):  
Hemant Khatri ◽  
Stephen Griffies ◽  
Takaya Uchida ◽  
Han Wang ◽  
Dimitris Menemenlis
Keyword(s):  
Kinetic Energy ◽  
Mixed Layer ◽  
Seasonal Variability ◽  
Ocean Model ◽  
Global Ocean ◽  
Upper Ocean ◽  
Late Winter ◽  
Second Phase ◽  
Ocean Turbulence ◽  
Two Phases

<p>In the upper ocean, submesoscale turbulence shows seasonal variability and is pronounced in winter. We analyze geostrophic KE spectra in a submesoscale-permitting global ocean model to study the seasonal variability in the upper ocean turbulence. Submesoscale processes peak in winter and, consequently, geostrophic kinetic energy (KE) spectra tend to be relatively shallow in winter (<em>k</em><sup>-2</sup>) with steeper spectra in summer (<em>k</em><sup>-3</sup>). The roles of frontogenesis processes and mixed-layer instabilities in submesoscale turbulence and their effects on the evolution of KE spectra over an annual cycle are discussed. It is shown that this transition in KE spectral scaling has two phases. In the first phase (late autumn), KE spectra show a presence of two spectral regimes: <em>k</em><sup>-3</sup> scaling in mesoscales (100-300 km) and <em>k</em><sup>-2</sup> scaling in submesoscales (< 50 km), indicating the coexistence of QG, surface-QG, and frontal dynamics. In the second phase (late winter), mixed-layer instabilities convert available potential energy into KE, which cascades upscale leading to flattening of the KE spectra at larger scales, and <em>k</em><sup>-2</sup> power-law develops in mesoscales too.</p>

scholarly journals Intercomparison and validation of the mixed layer depth fields of global ocean syntheses

2015 ◽  
Vol 49 (3) ◽  
pp. 753-773 ◽  
Author(s):  
Takahiro Toyoda ◽  
Yosuke Fujii ◽  
Tsurane Kuragano ◽  
Masafumi Kamachi ◽  
Yoichi Ishikawa ◽  
...  
Keyword(s):  
Mixed Layer ◽  
Mixed Layer Depth ◽  
Global Ocean ◽  
Layer Depth

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.

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