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SUPPLEMENTARY METHODS
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Flow velocity data were collected using a 1200 kHz acoustic Doppler current profiler
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(ADCP) (RD Instruments Rio Grande 600 kHz) housed within the autonomous
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underwater vehicle (AUV) Autosub3. The velocity data were collected on Autosub3
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mission M436 conducted from 22nd -24th May 2010. The data presented in this paper
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are a subset of the data collected during this deployment.
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The velocity data were processed using the following steps: i) position data
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from the AUV’s inertial navigation system was corrected for drift using GPS fixes; ii)
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vector data was rotated from a coordinate system relative to the AUV to a global
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coordinate system; iii) the component of velocity resulting from movement of the
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AUV was removed from the ADCP velocity iv) depth measurements were corrected
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for vertical offset of the ADCP relative to the AUV’s pressure sensor; v) all data
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below the depth of maximum ADCP backscatter intensity and within the acoustic
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side-lobe blanking distance (B) of the seafloor were removed, using B = asin2θ, where
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a is the altitude of Autosub3 and θ is the angle that the acoustic beam is emitted from
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the ADCP (20°); vi) the magnitude and direction of the resultant velocity vector was
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calculated from the three velocity components; vii) depth data were converted from
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being relative to the submarine to relative to the sea surface.
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Conductivity-temperature-depth (CTD) profiles were acquired from the Koca
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Piri Reis. The two profiles presented in this study (stations 51 and 98) were collected
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on the 19th May 2010 and 23rd May 2010, respectively. Fluid density was calculated
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from the CTD data using UNESCO (1983).
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As noted in processing step five, there is a blanking area near the seafloor
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where the ADCP cannot measure velocities. We tried several methods for
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interpolating the data in this region, assuming a no slip condition at the seafloor. As
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the size of the blanking distance varies along the section and we do not know the
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shape of the profile in this region we ultimately chose to use a linear interpolation for
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its simplicity. It should be noted that regardless of how this data is interpolated the
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Froude numbers calculated using this data will always be less than the Froude number
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calculated using the maximum velocity rather than depth averaging the data.
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The ADCP provides an estimate of the standard deviation in each velocity
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measurements by evaluating the difference in values of velocity calculated using
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different combinations of three of the four ADCP beams. For each vertical velocity
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profile in the velocity matrix the mean error was calculated. Matrices of maximum
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and minimum velocity measurements were then created by adding and subtracting
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these errors respectively. The manufacturer’s resolution estimates for salinity (0.4
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ppm) and temperature (0.0001C) were used to estimate the error in the density
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measurement by propagating them through the equations presented in UNESCO
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[1983], resulting in uncertainty in the density measurements of 0.08 kg/m3. These
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errors were the propagated through the Froude equation to provide estimates of
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maximum and minimum Froude number for each vertical velocity profile.
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SUPPLEMENTARY REFERENCES
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UNESCO (1983) Algorithms for computation of fundamental properties of seawater:
Paris, UNESCO Division of Marine Science Technical Papers in Marine Science 44,
53p.
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