MicroSoar: A New Instrument for Measuring Microscale Turbulence from Rapidly Moving Submerged Platforms

by

T. Dillon, J. Barth, A. Erofeev, and G. May

Turbulent stirring and mixing processes are important mechanisms for transporting heat, salt, and mass across density surfaces in the ocean. Distributions of chemical, biological, geological, and optical properties are also influenced by turbulence. Mixing of dissolved scalar properties must occur at very small scales, typically less than a centimeter, because molecular diffusion of properties across property gradients is very slow at large scale, but is rapid when the gradients occur over small scales. Consequently, observations of scalar property concentration gradients at centimeter and sub-centimeter length scales is necessary for direct measurements of property transport. Modeling and understanding global-scale processes, such as climactic change, depend on our knowledge of heat transport across density surfaces in the stratified ocean. The problem of predicting climate change and temperature distributions on global length scales therefore depends on our ability to describe, in a statistical sense, the temperature distribution on length scales 10 ⁹ times smaller than global length scales themselves.

Accurate description of oceanic diapycnal transport has, consequently, been a major goal of oceanographic research. A reliable theoretical description of oceanic turbulence is not available. Most efforts to understand turbulent mixing have been semi-empirical, based on observations of the intensity and distribution of mixing events. Turbulent mixing events in the ocean are extremely intermittent, and basin-scale averages of transport are dominated by rare, energetic events. Samples of mixing must be intensively collected over very large space and time scales to obtain a meaningful average. The ability to accurately determine turbulent transport from small-scale measurements is therefore highly dependant on the ease and rapidity with which measurements can be made, and on the total volume of fluid which can be sampled in a brief period.

Measurements of oceanic turbulence have been occurring for at least the last four decades. The "first generation" instruments were free-fall, internally recording, vertical profilers (see discussion in Gregg, 1998). These devices were launched from a ship, fell freely to a pre-determined depth, released a ballast weight, and then buoyantly ascended for recovery and data downloading. The main turbulence sensor was a fast-responding thermistor (a modern incarnation of which is the Thermometrics FP-07), which limited the fall speed to O(0.1 m/s). Total elapsed time for a cast to, say, 500 m, was roughly 4-6 hours, and only a few profiles per day could be collected. The need to recover the instrument also limited operations to reasonably good weather conditions, and, usually, to daylight hours. On a 28 day research cruise, it would be possible in principle to sample 40 to 80 km of water; in practice it was often less. Though the statistics obtained with them was not good by modern standards, these profilers provided scientists with their first good view of subsurface stratified turbulence.

The currently used "second generation" microstructure profilers are tethered with a slack cable, and fall semi-freely (Caldwell, Dillon and Moum, 1982; Gregg, 198_). These instruments typically fall at speeds O(0.75 m/s), and send signals through their umbilical tether for recording aboard the mother ship. The usual sensor suite includes a fast-responding thermistor (e.g., Thermometrics FP07), one or more airfoil shear sensors (Osborn, 1980), a pressure (or depth) sensor, and a conductivity sensor. A variety of additional sensors may also be used. The most significant advantages of tethered dropped instruments lies in their ability to make many profiles per day, and in the ability to provide real-time data to the operator. They may also be used in more severe weather than true free-fall profilers; the tether allows operation (ideally) whenever it is safe to be on the deck of a research ship. Although their umbilical may (rarely) foul screws or other parts of a ship, tethered instruments remain the conventional "work-horse" of modern oceanic turbulence research. On a 28 day research cruise, it is possible in principle to sample of order 100 km of fluid, and make 1,000-2500 casts, depending on the maximum depth sampled and the actual drop speed used. For example, Moum and Caldwell (1985) made 2,200 casts with average depth 120 m in the Equatorial Pacific, and sampled about 300 km of fluid. Padman and Dillon (1991) made over 1,500 casts to an average depth of 350 m while camped on an ice floe over the Yermak Plateau, sampling approximately 450 km of fluid. This is about an order of magnitude more observational information per expedition, for roughly equivalent expense and effort in terms of ship-days and man-years, than could be obtained with "first generation" recovered free-fall instruments.

In this report, we describe MicroSoar, a new high-speed data acquisition and turbulence measuring system, which is designed to improve turbulence sampling ability by another order of magnitude. MicroSoar is attached to the undercarriage of the towed profiling platform SeaSoar (Pollard, 1986), and is towed by a research vessel at approximately 3.5 m s ^-1 . The angle of attack of wings on the SeaSoar body can be controlled through a conducting cable, enabling SeaSoar to be cycled through a chosen depth range. In August 1996 and April-May 1997, MicroSoar was used to measure microscale turbulence in the Middle Atlantic Bight during the Coastal Mixing and Optics (CMO) experiment (O’Malley et al. , 1998). The typical depth cycle of surface to 75 m was repeated every two minutes. For comparison, a 28 day cruise could in principle sample a path of length 8600 km; in practice, ½ day out of every 3 days is needed for downloading raw data, yielding an expected maximum sampling path length of 7100 km. During the August 1996 CMO cruise, over 11 Gbytes (1 Gbyte = 10 ¹² bytes) of raw data were collected over 11 towing days, and the travel path of MicroSoar was approximately 3400 km; on the April-May CMO cruise, sampling was done on a path length of approximately 5,000 km, and 16 Gbytes of raw data was collected. The total time spent towing MicroSoar during these two experiments was approximately 25 days, and the total fluid path length traversed was approximately 8,400 km. MicroSoar had no malfunctions during these cruises, although one sensor set was destroyed during recovery in moderately high seas, and was replaced. We consider that our design objective, an order of magnitude increase in microstructure sampling capability, has been achieved.

Design Characteristics

MicroSoar is contained in an anodized aluminum (alloy 7075 T-6) pressure case with an inside diameter of 15.2 cm (6"), outside diameter of 17.8 cm (7"), and a length of 67.3 cm (26.5"). Sensors are mounted on the forward end cap, and connectors for power, control, and data transmission are located on the rear end cap. The pressure limit for the case is approximately 8,900 psi, equivalent to 6,000 m depth, where the failure mode is thick-wall crush. The recommended safe operating limit is 1,000 m depth, and is determined by sensor O-ring pressure limitations.

MicroSoar is mounted on the undercarriage of SeaSoar, in place of SeaSoar's usual ballast weight (Fig. 1) . MicroSoar's nosecone contains approximately 30 kg of lead ballast (because it is otherwise almost neutrally buoyant). Power is supplied through a Wet Labs Modaps communications unit, which is also used for powering and communicating with other SeaSoar sensor systems (CTD, etc). A subset of data (typically, 1 second averages of all signals) is sent to Modaps, which incorporates it into its data stream, and then sends it to the surface vessel through a 7-conductor armored towing cable. All raw data could be sent to the surface if sufficient bandwidth was available through the towing cable.

All computational and control functions are carried out by stackable system components with a PC/104 form-factor (3.8" x 3.6" x 5/8" board dimensions). MicroSoar is controlled with a 486 DX2-66 single-board computer. Two 16-bit analog-to-digital converters (ADCs) are used for data acquisition. They each have 8 differential channels and operate at a maximum speed of 100,000 conversions per second. One ADC samples 8 "slow" channels (at 256 samples per channel per second), and the other samples three "fast" channels (at 2048 samples per channel per second). Two hard disk drives are used for data storage, giving 3.8 Gbytes of formatted data storage availability. A 4-channel I/O card is used for serial communications, an Ethernet board is used for networking in the laboratory and data downloading in the field, and a video controller card is added for bench-top diagnostics and software development (Fig. 2) .

The sensors on MicroSoar (Table 1) include a Thermometrics FastTip FP07 thermistor, a pressure (depth) sensor, a Capillary MicroConductivity Sensor (CMCS; see Paka, Nabatov, Lozovatsky and Dillon, 1997), and a 3-axis accelerometer. Analog sensor drivers, filters, and amplifier systems are mounted on an aluminum plate parallel to the pressure case axis, and are shielded from the digital system by a metal housing (May, 1997; Erofeev et al. , 1998). The analog systems occupy approximately 1/3 of the pressure case volume, and are connected to the digital compartment with EMUS rejection filters.  

The CMCS (Fig. 3) concentrates an electric field between two concentric stainless steel electrodes, separated by an insulating material (similar to an epoxy) in such a manner that all conducting flux lines between the electrodes must pass through a small hole (1.5 mm dia.) at the sensor tip. The inner electrode is a long, cylindrical, stainless steel capillary tube, similar to a hypodermic needle. It is exposed to seawater through the sensor tip, and is recessed approximately 1 mm from the tip. The non-conducting recessed region insures that all electric field lines exiting the tip are parallel to the sensor body. The outer electrode is a cylinder (14.4 mm dia, 20 cm long) with a tapered section 7.5 mm long ending 10 mm from the insulated tip. Shielded wire connects the electrodes to their analog driver circuitry. The resistance of the sensor varies between 200 and 600 ohms over typical oceanic conductivities. The CMCS was designed to minimize polarization effects by maximizing electrode surface area, while retaining a very small sensitive volume (Paka, Nabatov, Lozovatsky, and Dillon, 1997; May, 1997).

The impedance inside the sensor’s recessed tip ( R _INT ) is 27% of the total CMCS impedance ( R _CMCS ), and 73% of the impedance occurs in the free stream flow. Since there is no flow through the CMCS, changes in R _INT occur mainly as a result of molecular diffusion of heat from the CMCS aperture, and, to a much lesser degree, through the CMCS body. We can approximate this response by solving the heat equation for diffusion of temperature fluctuations in a long tube whose temperature at one end is impulsively changed and thereafter held constant. At any given time, the conductivity (or resistivity) can be found as a function of temperature; integrating the resistivity through the first 1 mm of the tube determines R _INT as a function of time. The response is small for times less than 1 s (Fig. 4) , indicating that internal impedance fluctuations will be rather small for 1 Hz fluctuations, and will be negligible at frequencies above 10 Hz. Since MicroSoar was towed at approximately 3.5 m s ^-1 , a 1 Hz fluctuation in R _INT would appear as a fluctuation in conductivity at wavelength 3.5 m, and is a negligible source of error because structures contributing to the temperature variance dissipation rate occur in the wavenumber band of 3 cpm k 200 cpm ( k = U/f, where U is the platform speed, f is frequency, and 1 cpm = 1 cycles per meter), corresponding to frequencies 10 Hz f 750 Hz.

The CMCS impedance controlled by conductivity fluctuations in the free-stream flow amounts to 73% of the total sensor impedance. Unlike the internal impedance response function, which fundamentally depends on frequency, the external impedance depends fundamentally on wavenumber. The spatial response of the CMCS was determined by finding the electric field with a finite-element numerical integration method. We used a "shareware" version of QuickField, available at Internet address http://archives.math.utk.edu/ The CMCS potential field is nearly inversely proportional to distance from the sensor tip (Fig. 5) . At the tip, the impedance has dropped to by 27%. A further factor-of-two drop, to 36.5%, occurs at 2 mm from the tip. Less than 10% of the response remains affected by fluctuations occurring farther than 1 cm from the tip. The off-axis potential distribution is quite similar to the axial distribution, but tends to experience attenuation somewhat more rapidly than in the axial direction. We conclude that the CMCS can easily resolve 200 cpm fluctuations, and could be response-corrected to resolve as high as 500 cpm fluctuations, as long as they occur at frequencies above 1 Hz.

The circuitry used to drive the CMCS supplied a 2.0 v peak-to-peak square wave to the electrodes in a driven-bridge configuration (May, 1997). The bridge voltage was rectified, amplified, and electronically filtered at 1 kHz. Laboratory tests showed improved stability when the driving frequency was large (May, 1997), and that even at 10 kHz, small polarization effects were apparent. A driving frequency of 100 kHz was chosen to minimize polarization error, subject to the constraint of using standard, readily-obtainable circuit components (May, 1997).

The noise spectrum of the CMCS and associated circuitry is nearly proportional to frequency (Fig. 6) , and is best described by _N (f) = ₀ (f/f ₀ ) ⁿ , where f is frequency, f ₀ = 1 Hz, n 1.3, and ₀ = 5.71 x 10 ^-10 [(siemens/m ² ) ² (cycles/ sec) ^-1 ]. In an environment of constant salinity (for example, 34 psu at 10 ^o C), this noise level would be observed, in temperature units, as T/ C | _{S=34 psu, T=10} o _C )x ₀ = 3.8x10 ^-8 ( ^o C/m) ² (cycles/sec) ^-1 . For comparison with other devices, the temperature-equivalent rms noise in the 1 Hz - 100 Hz band is 4.2 x 10 ^{-4 o} C. At our vehicle speed of 3.5 m s ^-1 , the equivalent noise in the 1 cycle/m to 100 cycle/m wavenumber band, which encompasses most of the microstructure bandwidth, is 5.1 x 10 ^{-4 o} C. This performance compares favorably with typical noise levels from a thermistor.

A narrow-band peak was sometimes seen at 70 Hz in very low signal level spectra ( Fig. 6 , solid line, open triangles). Had this peak been observable while towing through non-turbulent, highly stratified regions, it would be a cause for concern, as it might indicate contamination from sensor displacements caused by vehicular motion; however, since see no correlation between mean stratification, we conclude another effect is responsible. We believe the peak is caused by a slight velocity sensitivity, or "anemometer effect", due to ohmic heating of the fluid, because the peak is observable only when the signal level is very low. In addition, the peak occurs predominantly when SeaSoar is near the bottom of its dive and the tow cable is at high tension. This suggests that cable strumming is the responsible driving mechanism.

The CMCS was calibrated by comparing it with the SeaBird CTD normally carried by SeaSoar. The comparison was done every five minutes during the CMO cruise, by performing a least-squares fit to MicroSoar and CTD conductivity measurements. Some drift over periods of many hours to days was seen, presumably due to chemical changes on the CMCS interior sensor. Post-cruise inspection of the CMCS revealed some thin, irregular rust-colored deposits on the interior electrode surface, possibly caused by corrosion of the electrode’s stainless-steel alloy. The alloy used in the Russian-manufactured CMCS is unknown. Emerson and soaking tests in a salt-water bath have show similar colored deposits accumulate on stainless-steel alloy 304. No corrosion, and only a small amount of flocculation within the salt water bath, was observed while performing a similar test with stainless-steel alloy 316. Because of the potential for CMCS calibration drift, we think it unwise to use the CMCS as the primary sensor for determining salinity, unless some calibration standard, such as a CTD, is simultaneously used for comparison.

The temperature probe, built at OSU, utilizes a Thermometrics series FP07 thin glass-coated bead thermistor. The response of the thermistor is limited to frequencies less than about 20 Hz. A vented stainless steel sleeve protects the thermistor tip during handling and use, and does not disturb the flow experienced by the thermistor, as long as the angle of attack is 30 degrees or less, which is satisfied for all of our SeaSoar operations. The pressure sensor, used to monitor depth, is an Endevco model 8510B 500 PSIG piezoresistive pressure transducer with a sensitivity of 0.5mv/psi.

MicroSoar uses a three-axis accelerometer (IC Sensors model 3140-002) to monitor platform vibrations. The accelerometers have a response bandwidth of 0 to 500 Hz, and a 2-gravity full-scale range. Since SeaSoar vibrates energetically when being towed, acceleration was measured to determine the extent of vibrational noise. The vibrational contamination of small-scale, high-frequency measurements of scalar variables arises chiefly from sensor displacements. If typical displacement amplitudes are smaller than the sensor’s averaging length, no displacement errors can occur, for it will not matter how the sensor moves, as long as it remains within its averaging volume. MicroSoar’s typical sensor displacement, within a one second interval, is O(1 mm) or less (Fig. 7) , compared to the CMCS’s O(3 mm) averaging length (Fig. 5) , and vibrational displacements cannot be an important factor. This criterion is, in fact, much too restrictive in the general case, because displacement errors also depend on frequency. A more accurate assessment of vibrational displacement effects at any frequency f must refer to the total movement that occurs at all higher frequencies:

              (1)

where ( f ) is the displacement spectrum, and is a reference length scale for the measurement. In the case of towed measurements, = U/f . The criterion for ignoring displacements becomes

                                          (2)

Vibrational velocity fluctuations, in themselves, do not complicate scalar measurements, as long as (2) is satisfied. It is conceivable, however, that Doppler shifting of a scalar spectrum may occur, if the vibrational velocity is comparable to the tow velocity. In our case, the ratio of vibrational velocity to tow speed is O(1000), and we conclude that vibrations of our platform are not a significant factor (Fig. 8) .

Measuring microscale velocity from SeaSoar would be a much more daunting task measuring scalar variables, for here, the platform velocity itself is of vital importance. The rms vibrational velocity over a one second interval is O(10 ^-3 m/s) (Fig. 7) . The scale necessary to measure velocity microstructure is O( ) ^1/4 , where is the kinetic energy dissipation rate and is the kinematic viscosity. Over a typical range of Oceanic dissipation rates, the microscale velocity ranges from 10 ^-3 to 10 ^-4 m s ^-1 . We conclude that, while accurately measuring microscale scalars from SeaSoar is not very difficult, the outlook for measuring microscale velocity is, at best, problematical.

Temperature ( T ) can be found from conductivity ( C ) if salinity ( S ) is known, by using a Taylor series expansion of the thermodynamic equation for T(C,S) :

              (3)

Here, _TC (or  _TS ) is calculated from the equation of state, while holding S (or C ) constant. When the measured T-S relation is locally linear, S = _ST T , where _ST = T(x,y,z)/ S(x,y,z) is the measured local slope of the T-S curve, averaged over a few meters. We assume that the local meter-scale T-S relation also holds at small scales, and find temperature fluctuations from conductivity fluctuations using (1b).

Practical use of (3) requires that ( _TS × _ST ) be sufficiently different from unity. We define a "figure of merit", _M (x,y,z) , as the local slope of the T-C diagram:

p;              (4)

When _M = O( _TC ) , temperature fluctuations can be well-defined by conductivity fluctuations. If, however, when _{TS ST} = O(1), then _M >> _TC , and salinity controls conductivity more strongly than does temperature. Since _TS -1 ^o C psu ^-1 , salinity’s influence on microscale conductivity can never be ignored in cases where _TS = O(-1 ^o C psu ^-1 ). A pathological case arises when _{TS ST} >> 0, _M << 0 , because fluctuations of temperature and salinity may actually compensate each other’s effects on conductivity (Nash and Moum, 1998), unless T and S are uncorrelated. Since measurements show that T- S correlations may be high even when _TS is negative (Nash and Moum, 1998), we think it unadvisable to use (3) whenever _M << 0.

The smallest scales to which fluctuations of a scalar property P will persist is determined by the Batchelor wavenumber (Batchelor, 1959; Dillon and Caldwell, 1980):

                                                                      (5)

where D _P is the molecular diffusivity of property P , is the kinematic viscosity, and is the turbulent kinetic energy dissipation rate. Since the diffusivity of temperature ( D _T ) is O(100) times larger than the diffusivity of salinity ( D _S ), the Batchelor wavenumber for salinity ( K _BS, ) is O(10) times larger than the Batchelor wavenumber for temperature ( K _BT ). It is therefore possible for the conductivity gradient variance to be dominated by salinity, rather than by temperature, in some situations.

Spectra of microscale conductivity gradient can be used to determine the temperature spectrum when it is appropriate to use (3b). Typically, we find the conductivity gradient spectral peak slightly broader than would be expected from an ideal Batchelor spectrum (Batchelor, 1959; Dillon and Caldwell, 1980) because of conductivity’s sensitivity to salinity as well as to temperature (Washburn, Duda, and Jacobs, 1996). The saline sensitivity can be quantified, and corrections can be applied (Washburn et al, 1996; Nash and Moum, 1998), as long as the T-S relation is relatively smooth, and _M = O( _TC ) . We assume hereafter that these condition apply, and that temperature fluctuations can be accurately obtained from (3).

The temperature variance dissipation rate ( _T ), Cox number ( C _x ), turbulent temperature diffusivity ( K _T ) and the diathermal heat flux ( F _H ) can be calculated from temperature measurements along any linear path   , as long as the smallest-scale structures are isotropic:

                                             (6)

                                          (7)

                                      (8)

                                          (9)

It is usually inadvisable to estimate _T directly from the arithmetic variance of T/   because electronic noise will sometimes dominate at high-frequency. More accuracy is obtained by integrating the temperature gradient spectrum over a signal-dominated spectral band, and discarding variance due to noise. It is also necessary to consider salinity’s effect on high-wavenumber conductivity, even when _M = O( _TC ) . Using the Batchelor as a first-order, or "benchmark" approximation of an arbitrary scalar gradient spectrum, it is seen that most of the variance occurs for k < 0.1K _BP (Dillon and Caldwell, 1980; Nash and Moum, 1998)

Nash and Moum (1998) found a high correlation between S and T for k < 0.1K _BT . They also pointed out that it is often possible to neglect salinity fluctuations, because salinity’s contribution to the conductivity gradient variance occurs at wavenumbers a decade larger than does temperature’s contribution. They verified that reasonable estimates of _T could be obtained by integrating the conductivity spectrum up to k = 0.1 K _BT , neglecting all higher wavenumbers. Our estimates of _T are obtained by integrating the conductivity gradient spectrum, _C’ (f) , in the range 1 Hz < f < 500 Hz, subject to the condition that _C’ (f) > _N (f) . This integration bandwidth corresponds to the wavenumber band 0.3 cpm < k < 150 cpm, and will resolve _T whenever 10 ^-10 m ² s ^-3 < < 10 ^-7 m ² s ^-3 . A higher limit, say to = 10 ^-5 m ² s ^-3 , could be used by adjusting the upper integration limit and correcting for the CMCS spatial response; however, it would be risky to raise the limit without insuring that salinity fluctuations are not interpreted as temperature fluctuations. A better procedure would be to integrate out to k = 0.1K _BT , but that choice is not available to us because is not measured from MicroSoar. We are investigating the possibility of roughly estimating from the Thorpe Scale (Thorpe, 1977; Dillon, 1980, 1984; Dillon and Park, 1987); if this proves possible, an adequate estimate of K _BT could be made.

On the shelf of the Mid-Atlantic Bight, we observed three distinct _M regimes (Fig. 9) . The salinity was nearly constant in the upper 50 m, with 32.25 psu < S < 32.4 (Fig. 10) ; here, _M = 11.8 ^o C siemens ^-1 m. Below 70 - 75 m depth, _ST = 0.4 psu ^o C ^-1 and, _M = 7.9 ^o C siemens ^-1 m. In the intermediate water, between 50 m and 75 m, _ST = 0.67 psu ^o C ^-1 , _M = 6.5 ^o C siemens ^-1 m. A significant feature occurs at the transition between the surface and intermediate layers. Because the C-T slope abruptly decreases (by nearly a factor of 2) at the surface and intermediate water intersection, an inflection point exists in the T-C and T-S diagrams ( Fig. 9 , Fig. 10 ). The occurrence of an inflection point, at which _M changes sign, is inevitable whenever _ST (x,y,z) becomes less than -1 while passing from one water mass to another.

No information about _T can be obtained from microconductivity measurements in a volume of water containing an inflection point, or "knee", in the T-C relation (Fig. 9) . In the CMO observations, the volume of water containing an inflection point is small, and in most cases has a vertical extent O(1 m). Processes occurring in these volumes may be significant, because mixing of water masses with distinctly different T-S properties occurs here first. The microconductivity method of estimating temperature variance dissipation rate, Cox number, diffusivity, and fluxes of heat, salt, and mass, fails at precisely these extremely interesting inflection points. However, knowledge of mixing processes very near these points can be obtained, as long as _M is sufficiently large.

The applicability of the microconductivity method for estimating turbulence parameters in the CMO data can be seen by examining _TS (Fig. 11) and _M ( Fig. 12 , Fig. 13 ). A clear separation of surface layer (shelf water) and deeper water masses (slope water and an intermediate or transition layer) is apparent when _TS is viewed as a function of density (Fig. 11) . Near the intermediate density (approximately 25.55 kg m ^-3 ), _TS becomes less than -1, and (in principle) must continue to decrease until reaching - , whereupon it changes to + . It then rapidly decreases within the intermediate transition layer.

The conversion of water masses due to turbulent mixing, and possibly double-diffusive processes, must be occurring within this transition layer. It is important to note that, in almost all cases, the microconductivity method proves useful within this layer. The behavior of _M reveals a well-defined "gap" near the transition depth ( Fig. 12 , upper panel); the separation of properties is even more apparent when viewing _M as a function of temperature ( Fig. 12 , lower panel). The existence of the transition layer is also apparent, and can be described by the conditions { _M < 9 ^o C siemens ^-1 m; T < 7.5 ^o C} ( Fig. 12 , lower panel).

We choose a limit (somewhat arbitrarily) of _M > 4 ^o C siemens ^-1 m (i.e., _TC ^-1 _M 0.4) for useful microconductivity measurements. It must also be kept in mind that this criterion applies only when there is enough change in temperature and conductivity to obtain stable estimates of _M . For example, within well-mixed layers, T , C , and S variations over a few meters may be insufficiently large to meaningfully calculate _M . Excluding surface and bottom mixed layers, where _M must be averaged over many meters to become stable, we find few cases of _M < 4 ^o C siemens ^-1 m (Fig. 13) . Of all samples reported here, 1.5% satisfy { _TC < 4 ^o C siemens ^-1 m, 30 dBar < P < 50 dBar}. These samples lie near the inflection point ( Fig. 9 , Fig. 10 ), and mixed layers at the surface or bottom are eliminated by the depth constraint. For these 1.5% of samples, _TC (and _M ) are too small to determine temperature spectra from microconductivity spectra. The remaining 98% of samples either have _TC < 4 ^o C siemens ^-1 m, or lie in well-mixed surface and bottom boundary layers. We believe that for 98% of all our samples, accurate estimates of temperature microstructure can be made from microconductivity measurements, as long as care is taken to insure that integration of the conductivity spectrum is limited to wavenumbers below 0.7 cm.

While it is not our goal in this work to fully discuss the meaning and implications of our turbulence measurements, it is useful to present some preliminary results. We have been able to collect, for the first time, large-scale synoptic measurements of full-resolved small-scale scalar turbulence variables. As an example, Fig. 14 shows one cross-shelf section of temperature, salinity, _T , Cox number, and heat flux, measured during the Spring 1997 CMO experiment (O’Malley et. Al. , 1998; Erofeev et al. , 1998). The ability to make surveys of scalar turbulence over large spatial provides a unique opportunity to view oceanic mixing as a function of large-scale hydrography.

The temperature, salinity, and _t sections (Fig. 14) show a cold wedge of shelf water (identifiable by S < 32.6 psu), which was, presumably, once well-mixed. Springtime surface heating has partially re-stratified the surface water, and a well-mixed layer ( T > 7.5 ^o C, depth < 20 m) above a weak diurnal thermocline. A deeper mixed layer, presumably a relic of prior surface heating and wind-driven mixing, is separated from the interior by a strong developing seasonal thermocline (~20 m - 40 m depth; 6.5 ^o C < T < 7.5 ^o C). The near-shore water is well-mixed from the seasonal thermocline to the bottom shoreward of 40.45 ^o N. Seaward of 40.45 ^o N a warm, salty, wedge-shaped volume of water can be seen, apparently intruding from near the continental shelf-slope water mass ( S > 32.6 psu, T > 7 ^o C). Farther seaward, the slope water mass shallows, until it surfaces (not shown in Fig. 14 , but known from other observations to surface at approximately 39.8 ^o N latitude). The stratification separating shelf and slope water masses is strong, and isosurfaces of temperature, salinity, and density are extremely convoluted.

The _T section (Fig. 14) shows strong mixing occurring at the base of the surface mixed layer, and at the seasonal thermocline. It also shows a "branching" structure at ~40.6 ^o N, where the seasonal and the diurnal thermoclines appear to diverge. Solely by looking at the stratification, one might expect that mixing is confined to the diurnal thermocline, but this is clearly not the case, because _T is also large within the seasonal thermocline seaward of 40.6 ^o N. It is also a surprise to find another branch of high _T separate from the seasonal thermocline (40.5 ^o N , ~40 m depth), deepen, and meet with a very high _T layer near the bottom (~40.45 ^o N), near the intersection of shelf water with the deep slope water intrusion. On and within the intrusion (~50 m < z < ~75 m; ~40.45 ^o N - ~40.2 ^o N), we also see intense mixing. Within the slope water mass ( latitude < 40.2 ^o N; S > 32.6 psu) can be seen large numbers of irregularly distributed intensely mixing patches. The Cox number (i.e., the don-dimensional diffusivity) section ( Fig. 14 , bottom panel) displays the same features, with large C _x in the surface layer, the bottom layer of shelf water (latitude < 40.45 ^o N), and the slope water at the shelf-slope break (i.e., S > 32.6 psu, Latitude < 40.45 ^o N). The diffusivity in these locations often falls in the range of O(0.7 x 10 ^-4 m ² s ^-1 ) to O(14 x 10 ^-4 m ² s ^-1 ).

The large C _x found near the shelf-slope break is especially significant. Diffusivities here are often as large 10 x 10 ^-4 m ² s ^-1 , and sometimes much larger. As a comparison, typical mid-ocean thermocline diffusivity values are O(0.1 - 0.05 x 10 ^-4 m ² s ^-1 ) (Ledwell, Watson, and Law, 1993; Toole, Polzen, and Schmitt, 1994). Our observations near the Middle Atlantic shelf-slope break are a factor O(100) larger than the mid-ocean thermocline diffusivity. Other observations of microstructure near topographic irregularities have also shown very large mixing rates (Padman and Dillon, 1991; Padman, Dillon, Wijesekera, Levine, Paulson, and Pinkel, 1991; also, Gregg, 1998, presents a recent review of other examples).

We have designed, developed, and used a robust, versatile, high-speed underwater data acquisition system, configured initially with a capillary microconductivity sensor, fine-scale temperature sensor, pressure (depth) sensor, and a 3-axis accelerometer. We have been able to resolve conductivity fluctuations as small the diffusive temperature cut-off. We have demonstrated that MicroSoar, configured with a capillary microconductivity sensor, is hardened enough, and robust enough, to make routine measurements of scalar turbulence in coastal water which contains large amounts of particulate matter, zooplankton, and larger organisms. MicroSoar can be towed at 3-4 m/s (or even higher than is possible with SeaSoar towing operations) without loss of small-scale resolution.

The ability to make large-scale synoptic surveys without loss of small-scale resolution allows a unique opportunity see the behavior of oceanic mixing as it relates to large-scale hydrography and local forcing. Synoptic measurement of mixing on regional scales is critical for understanding of variations of transport processes, and for diagnostic studies of "hot spots", where much of the global diapycnal heat transport is thought to occur (Gregg, 1998). We found the mixing of shelf and slope water near the shelf-slope break is O(100) times larger than in the mid-gyre Atlantic ocean. While we have no direct evidence that similar mixing occurs on other shelf-slope regions, we have no reason to suspect that the Middle Atlantic Bight is unique. Such mixing of interior ocean water near the continental slope could prove important to global mixing processes, and cannot be neglected. We believe insights such as this can be obtained only from synoptic two-dimensional, or even three-dimensional, representations of mixing. The ability to routinely make large-scale synoptic surveys of mixing will be vital to the next generation of regional and global scale modeling efforts.

We thank Mark Willis and Linda Fayler, OSU Marine Technicians, who were responsible for the highly successful SeaSoar operations, and Robert O’Malley, who processed the SeaSoar SeaBird CTD data for use in calibrating the MicroSoar sensors. The officers, mates, and crew of the R/V ENDEAVOR performed superbly, allowing us to tow SeaSoar through a region with considerable shipping traffic and fishing activity. We also thank Kieran O’Driscoll Andy Dale, who were in charge of assembling and mounting MicroSoar on SeaSoar. This work was funded by the Office of Naval Research, grants N00014-94-1-0325 and N00014-95-1-0382.

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