Investigation of Mixed Layer Depth in the Southern Ocean by using a 1-D mixed layer model

Chin-Ying Chien & Kevin SpeerGeophysical Fluid Dynamics Institute, Florida State University

Abstract: The atmosphere exchanges mass, energy, and momentum with the ocean through atmospheric forcing in the upper few hundred meters. The forced motions include wind stresses, heat fluxes, evaporation, and precipitation. The dynamics of energy and momentum transfer from wind to water and through the surface mixed layer to the deep ocean are still not well understood. In addition, air-sea and fresh water heat fluxes play an essential role to estimate the heat budget. Generally, sensible heat and latent heat fluxes can effect both ocean and atmospheric temperature. In the atmospheric boundary layer, wind-driven shear and turbulence cause oceanic mixed layer deepening. The mixed layer temperature is largely attributed to the air-sea heat fluxes. The one-dimensional Kantha-Clayson Mixed Layer (KCML) model appears to predict ocean mixed layer well on the various time scales, from hours to seasonal scale from their studies. In this study, our goal is to validate KCML model in the region of the Southern Ocean by combination of ARGO data, COAPS wind stresses, NCEP/NCAR fluxes and rain rate. We compare the mixed layer depth from in-situ data and KCML modeling output. The relation of mixed layer depth and air-sea fluxes transfer and base of mixed layer statistics to Argo is also our interest to study in the future.

Introduction to Mixed Layer Model:Generally, there are 2 types of upper ocean models. One is the bulk mixed layer model which suggests the mixed layer is a well-mixed box. Within this box, physical and chemical mechanisms/properties are uniformly distributed. The other one is turbulence closure model which involves more about the turbulence processes occurring in the mixed layer. Herein, consider the regions of the Southern Ocean with frontal areas which vertical turbulent mixing is necessary to be considered. The 1 dimensional KCML model is the latter. In this study, use the KCML model as a start. The model accepts a profile and a time-series of the following inputs:

(1) Profile for inputs: u, v, T, S(2) Surface forcing with time series:

• wind stress (zonal & meridional stress): [N/m^2]• incoming solar radiation: [W/m^2]• all other fluxes (long wave radiation, sensible heat flux, latent heat flux): [W/m^2]• evaporation rate: [m/s]• precipitation rate: [m/s]

Output data from model:

(1) MLD based on: temperature, salinity, and turbulence(2) Temperature profile with time series(3) Salinity profile with time series(4) density profile with time series

Case (1): ARGO float 5900698Following ARGO float # 5900698 (100 profiles)

start ->2004/12/17/11Z; last-> 2007/10/27/00Z

Data (2): Forcing• Data from NCEP/NCAR reanalysis-1:

– Taux: momentum flux (zonal) – Tauy: momentum flux (meridional)– Incoming solar radiation: Downward

solar radiation flux – All other fluxes: Qbr=longwv+shf*-

1.0+lhf% QBR: positiveindicates heat loss by ocean

% check: To>Ta, so heat from ocean to air, but shf<0. Hence change sign of shf

• long wave radiation: Net longwave radiation

• sensible heat flux: Sensible heat net flux • latent heat flux: Latent heat net flux

– evaporation rate: Ev(m/s)=LHF/Lv(J/kg)/Qw(kg/m3); Lv=2.45*10^6(J/kg), Qw=1000kg/m^3

– precipitation rate: precipitation rate

Data & method (1):• ARGO: temperature (T), salinity (S), depth (D)• Yomaha: velocity data (u & v) at parking level

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Calculation of current (U,V) profiles:– U=Uek+Ug (Ekman current + Geostrophic current). – Use Yomaha velocity data as I.C. and argo data to obtain

geostrophic current velocity by applying thermal wind equation.

– Use theorem of surface Ekman layer and NCEP wind stress data to get Ekman current velocity.

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Results:

Discussions / Work in Progress: In this study, we assume that water mass is the same in the same ARGO float from the T-S plot. Since the tracks of floats are between 40S and 50S, the seasonal cycle of forcing and mixing in the upper ocean is observed from ARGO float data. However, the modeling output of mixed layer depth is not consistent with ARGO data. There are many possible factors to result in this questionable results, such as forces, proper input data, simulating a long time period, and frontal regions with complicated T/S structures, etc. The work in progress is to check the possible factors to impact the modeling results and use a simple bulk mixed layer model (Price, 1986) to compare with the results from KCML model.

Reference:• Kantha, L. H., and C.A. Clayson, 1994. An improved mixed layer model for geophysical applications. J.

Geophys. Res., 99, 25,235-25,266.

• Price, J. F., R. A. Weller, and R. Pinkel, 1986. Diurnal cycling: Observations and model of the upper ocean response of diurnal heating, cooling, and wind mixing, J. Geophys. Res., 91(C7), 8411–8427.