Small scale mixing in coastal areas

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Spice in coastal areas 1, Jody Klymak 1, Igor Yashayaev 2 University of Victoria 1 Bedford Institute of Oceanography 2 19 October, 2015

Spice Lateral stirring on scales less than the Rossby radius are rarely observed. Labrador Sea has large salinity contracts between the Labrador Current and the main basin. Goal1: Improving understanding of upper ocean by examining transects in the Labrador Sea (Part A).

Spice Lateral stirring on scales less than the Rossby radius are rarely observed. Labrador Sea has large salinity contracts between the Labrador Current and the main basin. Goal1: Improving understanding of upper ocean by examining transects in the Labrador Sea (Part A). Goal2: Understanding the mechanisms of the enhanced submesoscale activity in the winter mixed layer using ADCP data (Part B).

Moving Vessel Profiler Moving Vessel Profiler Collecting data sets from both shallow (100m) and deep water( Maximum-800m) Primary function: Without the need to stop the vessel Spice Figure: The MVP deployed from a typical vessel collecting three sets of profile data Figure: It shows the path of the Free Fall Fish for a typical cast.

Spice Figure: Locations of data with Survey A section noted by the black arrow MVP can capture both lateral and vertical information; Downcasts were used for the ; Vertical and horizontal resolution are 0.5m and 6.5km, respectively; The ship s speed is 6m/s on average.

Spice: γ = sgn(t T )(α 2 (T T ) 2 + β 2 (S S) 2 ) 1 2 Spice Figure: Temperature-salinity plot from SurveyA. Color dots indicate distance along the line, with red indicating south and gray shows north. Black curves is the mean for the section used to calculate spice along isopycnals. Figure: a) Salinity and b) temperature along Survey A plotted at the mean depth of the isopycnals with isopycnals contoured in black lines c) Spice,, plotted on isopycnal depths.

Spice statistics: Lateral decorrelation length scale The length scales reflect the size of eddies stirring the fluid Spice Figure: Lateral lag correlations for high-passed (λ less than 50km ) spice along isopycnals. Individual traces are in grey, and the average for the depth bin is colored. The short black line represents the decorrelation scale, which is defined as when the mean trace first drop below a lag correlation of 0.1.

Spice statistics: Spice spectra Spice Indicate what physical processes are stirring the tracer along isopycnals Figure: Spectra of lateral spice variation based on different depth bins, normalized by large-scale spice gradient. The dashed lines are power law fit over wavenumber range 0.005 cpkm to 0.04 cpkm. The deep spectra is redder with depth, which indicates that more dissipation of lateral variance with depth The redder spectra in depth is the opposite of the trend predicted by QG/SQG theory What is happened here?

Spice statistics: PDF of lateral spice gradient Spice Peaked pdf compared with normal distribution Fontogenesis is acting sharpen small scale gradients. Other spice statistics: Figure: PDF of normalized spice gradient with two depth bins of 50-120m and 120-170m.

Spice statistics: PDF of lateral spice gradient Spice Peaked pdf compared with normal distribution Fontogenesis is acting sharpen small scale gradients. Other spice statistics: Lateral displacement; Figure: PDF of normalized spice gradient with two depth bins of 50-120m and 120-170m.

Spice statistics: PDF of lateral spice gradient Spice Peaked pdf compared with normal distribution Fontogenesis is acting sharpen small scale gradients. Other spice statistics: Lateral displacement; Vertical de-coherence length scales Figure: PDF of normalized spice gradient with two depth bins of 50-120m and 120-170m.

Part B : ADCP Spice

Part B: Observational transection Spice 50.38 N 49.66 N 48.94 N 48.22 N 47.5 N 142.483 W 138.767 W 135.05 W 131.333 W 127.617 W Figure: Maps showing LineP line.

Part B: Observational results Spice Depth[m] 100 200 300 400 U (Along track) 0 200 400 600 800 10001200 Distance from LineP26 0.30 0.24 0.18 0.12 0.06 0.00-0.06-0.12-0.18-0.24-0.30 Depth[m] 100 200 300 400 V (Across track) 0 200 400 600 800 10001200 Distance from LineP26 0.30 0.24 0.18 0.12 0.06 0.00-0.06-0.12-0.18-0.24-0.30 Figure: Along track (U) and across track (V) velocities in the upper 500m.

Part B: Observational results Spice Power spectrum density (m 3 s 2 ) 10 1 10 0 10-1 10-2 10-3 Cu: Along track power spectra k 2 Cu 10-4 10-3 10-2 10-1 10 0 Wavenumber k x [cpkm] 10 1 10 0 10-1 10-2 10-3 Cv: Across track power spectra k 2 Cv 10-4 10-3 10-2 10-1 10 0 Wavenumber k x [cpkm] Figure: Power spectrum of observed transverse (a) and longitudinal (b) kinetic energy at depth of 152m.

Part B: Observational results Helmholtz decomposition: Spice PSD 10 2 10 1 10 0 10-1 10-2 10-3 10-4 72-104m (a) k 2 10 2 10 1 10 0 10-1 10-2 10-3 10-4 104-152m (b) k 2 10 2 10 1 10 0 10-1 10-2 10-3 10-4 152-232m (c) k 2 10-5 10-6 K ψ : non-divergent k 3 K φ : divergent 10-7 10-3 10-2 10-1 10 0 wavenumber[cpkm 1 ] 10-5 10-6 K ψ : non-divergent k 3 K φ : divergent 10-7 10-3 10-2 10-1 10 0 wavenumber[cpkm 1 ] 10-5 10-6 K ψ : non-divergent k 3 K φ : divergent 10-7 10-3 10-2 10-1 10 0 wavenumber[cpkm 1 ] Figure: ADCP KE spectrum for three layers: (a) 76-104 m, (b) 104-152 m, and (c) 152-232 m. The figure shows along-track (blue line) and across-track (green line) KE spectra, and the spectral decomposition into horizontally rotational (red line) and divergent components (cyan line).

Whether there is a seasonal cycle for the submesoscale in the upper mixed layer? Spice

Spice Whether there is a seasonal cycle for the submesoscale in the upper mixed layer? How the seasonality in the submesoscale turbulence affects the exchanges of the nutrients between the nutrient-depleted mixed layer and the nutrient-rich thermocline?

Spice Whether there is a seasonal cycle for the submesoscale in the upper mixed layer? How the seasonality in the submesoscale turbulence affects the exchanges of the nutrients between the nutrient-depleted mixed layer and the nutrient-rich thermocline? Applying these methodology for modelling data (Youyu Lu).

Spice