Enhanced Turbulence in the Upper Mixed Layer under Light Winds and Heating: Implications for Gas Fluxes

Seasonal variability is a powerful component of the spatio-temporal dynamics of plankton communities, especially in the regions with oxygen-depleted waters. The Arabian Sea and the Black Sea are typical representatives of these regions. In both, the dinoflagellate Noctiluca scintillans (Macartney) Kofoid & Swezy, 1921, is one of the abundant plankton species which forms algal blooms. Sampling on coastal stations in the upper mixed layer by the plankton nets with the 120-140 &#181;m mesh size was carried out in 2004-2010. Monthly data were averaged over years. A comparison of seasonal patterns of Noctiluca abundance pointed to the persistence of a bimodal seasonal cycle in both regions. The major peak was observed during spring in the Black Sea and during the winter (Northeast) monsoon in the Arabian Sea. The timing of the second (minor) peak was different over regions as well. This peak was modulated by advection of seasonally fluctuating velocity of coastal currents which transport waters enriched by nutrients by coastal upwelling. The abundance of Noctiluca of the major peak (with the concentration around 1.5*106 cells m-3) was from one to two orders as much high in the western Arabian Sea compared to the northern Black Sea. The remotely sensed chlorophyll-a concentration during the time of the major seasonal peak exhibited a fivefold difference over these regions. In terms of nutrientconcentration in the upper mixed layer (in particular, nitrates and silicates), a difference of about one order of magnitude was observed.

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On the numerical study of thermohydrodynamics and biochemistry of inland water bodies

10.5194/egusphere-egu21-13335 ◽

2021 ◽

Author(s):

Daria Gladskikh ◽

Evgeny Mortikov ◽

Victor Stepanenko

Keyword(s):

Mixed Layer ◽

Numerical Study ◽

Three Dimensional ◽

Water Bodies ◽

Dimensional Model ◽

Three Dimensional Model ◽

One Dimensional ◽

Lake Model ◽

Upper Mixed Layer ◽

The One

The study of thermodynamic and biochemical processes of inland water objects using one- and three-dimensional RANS numerical models was carried out both for idealized water bodies and using measurements data. The need to take into account seiche oscillations to correctly reproduce the deepening of the upper mixed layer in one-dimensional (vertical) models is demonstrated. We considered the one-dimensional LAKE model [1] and the three-dimensional model [2, 3, 4] developed at the Research Computing Center of Moscow State University on the basis of a hydrodynamic code combining DNS/LES/RANS approaches for calculating geophysical turbulent flows. The three-dimensional model was supplemented by the equations for calculating biochemical substances by analogy with the one-dimensional biochemistry equations used in the LAKE model. The effect of mixing processes on the distribution of concentration of greenhouse gases, in particular, methane and oxygen, was studied.The work was supported by grants of the RF President&#8217;s Grant for Young Scientists (MK-1867.2020.5, MD-1850.2020.5) and by the RFBR (19-05-00249, 20-05-00776).&#160;1. Stepanenko V., Mammarella I., Ojala A., Miettinen H., Lykosov V., Timo V. LAKE 2.0: a model for temperature, methane, carbon dioxide and oxygen dynamics in lakes // Geoscientific Model Development. 2016. V. 9(5). P. 1977&#8211;2006. 2. Mortikov E.V., Glazunov A.V., Lykosov V.N. Numerical study of plane Couette flow: turbulence statistics and the structure of pressure-strain correlations // Russian Journal of Numerical Analysis and Mathematical Modelling. 2019. 34(2). P. 119-132. 3. Mortikov, E.V. Numerical simulation of the motion of an ice keel in stratified flow // Izv. Atmos. Ocean. Phys. 2016. V. 52. P. 108-115. 4. Gladskikh D.S., Stepanenko V.M., Mortikov E.V. On the influence of the horizontal dimensions of inland waters on the thickness of the upper mixed layer // Water Resourses. 2021.V. 45, 9 pages. (in press)&#160;

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Reexamining the Gradient and Countergradient Representation of the Local and Nonlocal Heat Fluxes in the Convective Boundary Layer

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-17-0198.1 ◽

2018 ◽

Vol 75 (7) ◽

pp. 2317-2336 ◽

Cited By ~ 5

Author(s):

Bowen Zhou ◽

Shiwei Sun ◽

Kai Yao ◽

Kefeng Zhu

Keyword(s):

Boundary Layer ◽

Temperature Gradient ◽

Mixed Layer ◽

Convective Boundary Layer ◽

Correction Term ◽

Potential Temperature ◽

Heat Fluxes ◽

Gradient Term ◽

Convective Boundary ◽

Upper Mixed Layer

Abstract Turbulent mixing in the daytime convective boundary layer (CBL) is carried out by organized nonlocal updrafts and smaller local eddies. In the upper mixed layer of the CBL, heat fluxes associated with nonlocal updrafts are directed up the local potential temperature gradient. To reproduce such countergradient behavior in parameterizations, a class of planetary boundary layer schemes adopts a countergradient correction term in addition to the classic downgradient eddy-diffusion term. Such schemes are popular because of their simple formulation and effective performance. This study reexamines those schemes to investigate the physical representations of the gradient and countergradient (GCG) terms, and to rebut the often-implied association of the GCG terms with heat fluxes due to local and nonlocal (LNL) eddies. To do so, large-eddy simulations (LESs) of six idealized CBL cases are performed. The GCG fluxes are computed a priori with horizontally averaged LES data, while the LNL fluxes are diagnosed through conditional sampling and Fourier decomposition of the LES flow field. It is found that in the upper mixed layer, the gradient term predicts downward fluxes in the presence of positive mean potential temperature gradient but is compensated by the upward countergradient correction flux, which is larger than the total heat flux. However, neither downward local fluxes nor larger-than-total nonlocal fluxes are diagnosed from LES. The difference reflects reduced turbulence efficiency for GCG fluxes and, in terms of physics, conceptual deficiencies in the GCG representation of CBL heat fluxes.

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