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We contend that ocean turbulent fluxes should be included in the list of Essential Ocean Variables (EOVs) created by the Global Ocean Observing System. This list aims to identify variables that are essential to observe to inform policy and maintain a healthy and resilient ocean. Diapycnal turbulent fluxes quantify the rates of exchange of tracers (such as temperature, salinity, density or nutrients, all of which are already EOVs) across a density layer. Measuring them is necessary to close the tracer concentration budgets of these quantities. Measuring turbulent fluxes of buoyancy (Jb), heat (Jq), salinity (JS) or any other tracer requires either synchronous microscale (a few centimeters) measurements of both the vector velocity and the scalar (e.g., temperature) to produce time series of the highly correlated perturbations of the two variables, or microscale measurements of turbulent dissipation rates of kinetic energy (e) and of thermal/salinity/tracer variance (c), from which fluxes can be derived. Unlike isopycnal turbulent fluxes, which are dominated by the mesoscale (tens of kilometers), microscale diapycnal fluxes cannot be derived as the product of existing EOVs, but rather require observations at the appropriate scales. The instrumentation, standardization of measurement practices, and data coordination of turbulence observations have advanced greatly in the past decade and are becoming increasingly robust. With more routine measurements, we can begin to unravel the relationships between physical mixing processes and ecosystem health. In addition to laying out the scientific relevance of the turbulent diapycnal fluxes, this review also compiles the current developments steering the community toward such routine measurements, strengthening the case for registering the turbulent diapycnal fluxes as an pilot Essential Ocean Variable. [ABSTRACT FROM AUTHOR]

We contend that ocean turbulent fluxes should be included in the list of Essential Ocean Variables (EOVs) created by the Global Ocean Observing System. This list aims to identify variables that are essential to observe to inform policy and maintain a healthy and resilient ocean. Diapycnal turbulent fluxes quantify the rates of exchange of tracers (such as temperature, salinity, density or nutrients, all of which are already EOVs) across a density layer. Measuring them is necessary to close the tracer concentration budgets of these quantities. Measuring turbulent fluxes of buoyancy (Jb), heat (Jq), salinity (JS) or any other tracer requires either synchronous microscale (a few centimeters) measurements of both the vector velocity and the scalar (e.g., temperature) to produce time series of the highly correlated perturbations of the two variables, or microscale measurements of turbulent dissipation rates of kinetic energy (ϵ) and of thermal/salinity/tracer variance (χ), from which fluxes can be derived. Unlike isopycnal turbulent fluxes, which are dominated by the mesoscale (tens of kilometers), microscale diapycnal fluxes cannot be derived as the product of existing EOVs, but rather require observations at the appropriate scales. The instrumentation, standardization of measurement practices, and data coordination of turbulence observations have advanced greatly in the past decade and are becoming increasingly robust. With more routine measurements, we can begin to unravel the relationships between physical mixing processes and ecosystem health. In addition to laying out the scientific relevance of the turbulent diapycnal fluxes, this review also compiles the current developments steering the community toward such routine measurements, strengthening the case for registering the turbulent diapycnal fluxes as an pilot Essential Ocean Variable. [ABSTRACT FROM AUTHOR]

Autonomous and expendable profiling-float arrays such as those deployed in the Argo Program require the transmission of reliable data from remote sites. However, existing satellite data transfer rates preclude complete transmission of rapidly sampled turbulence measurements. It is therefore necessary to reduce turbulence data on board. Here we propose a scheme for onboard data reduction and test it with existing turbulence data obtained with a modified SOLO-II profiling float. First, voltage spectra are derived from shear probe and fast-thermistor signals. Then, we focus on a fixed-frequency band that we know to be unaffected by vibrations and that approximately corresponds to a wavenumber band of 5–25 cpm. Over the fixed-frequency band, we make simple power law fits that – after calibration and correction in post-processing – yield values for the turbulent kinetic energy dissipation rate ϵ and thermal-variance dissipation rate χ. With roughly 1 m vertical segments, this scheme reduces the necessary data transfer volume 300-fold to approximately 2.5 kB for every 100 m of a profile (when profiling at 0.2 m s -1). As a test, we apply our scheme to a dataset comprising 650 profiles and compare its output to that from our standard turbulence-processing algorithm. For ϵ , values from the two approaches agree within a factor of 2 87 % of the time; for χ , they agree 78 % of the time. These levels of agreement are greater than or comparable to that between the ϵ and χ values derived from two shear probes and two fast thermistors, respectively, on the same profiler. [ABSTRACT FROM AUTHOR]

Autonomous and expendable profiling float arrays such as deployed in the Argo Program require the transmission of reliable data from remote sites. However, existing satellite data transfer rates preclude complete transmission of rapidly sampled turbulence measurements. It is therefore necessary to reduce turbulence data onboard. Here we propose a scheme for onboard data reduction and test it with existing turbulence data obtained with a newly developed version of a SOLO-II profiling float. The scheme invokes simple power law fits to (i) shear probe voltage spectra and (ii) fast thermistor voltage spectra that yield a fit value plus a quality control metric. At roughly 1m vertical interval resolution, this scheme reduces the necessary data transfer volume 240-fold to approximately 3 kB for every 100m of a profile (when profiling at 0.2ms-1). Turbulent kinetic energy dissipation rate ε and thermal variance dissipation rate χ are recovered in post-processing. As a test, we apply our scheme to a dataset comprising 650 profiles and compare its output to that from our standard turbulence processing algorithm. For ε, values from the two approaches agree within a factor of two 87% of the time; for χ, 78%. These levels of agreement are greater than or comparable to that between the ε and χ values derived from two shear probes and two fast thermistors, respectively, on the same profiler. [ABSTRACT FROM AUTHOR]