Global climate models are too coarsely-spaced to resolve turbulent mixing processes, such that turbulent buoyancy fluxes and corresponding diffusivity coefficients must be parameterized. The substantial effect of imposing imprecise diffusivities on climate predictions was illustrated in a recent coupled global climate model analysis in which global average diapycnal diffusivity was varied between two realistic values (i.e., 0.9×10⁻⁴ m² s⁻¹ and 1.7×10⁻⁴ m² s⁻¹, Hieronymus et al., 2019). The difference between the least and most diffusive runs leads to a 3.6°C difference in volume-mean ocean temperature, a 2.4°C difference in sea surface temperature, and a 3°C difference in global-mean air temperature 2m above the ocean surface. In models too coarse to resolve the oceanic mesoscale eddy field (such as comprehensive Earth System Models), isopycnal diffusivities must also be parameterized (Redi, 1982) and these have a leading-order effect on the ventilation and structure of oceanic water masses (Jones and Abernathey, 2019).

Ocean mixing influences the efficiencies of heat and carbon uptake from the atmosphere to the ocean through its impact on the stratification (e.g., Tatebe et al., 2018). Enhanced diapycnal mixing erodes the stratification in the upper ocean leading to a weaker and more diffuse thermocline, which enables more downward heat transport (e.g., Kuhlbrodt and Gregory, 2012; Melet et al., 2016) and more carbon uptake (Schmittner et al., 2009; Ehlert et al., 2017).

Using an eddy-resolving ocean model that assimilates ocean observations, Ellison et al. (2023) compared the effects of imposing two different background diffusivities. They showed that by altering the prescribed mixing rates from 10⁻⁴ m² s⁻¹ to 10⁻⁵ m² s⁻¹ (a common range of diapycnal diffusivities used in models), they could create a 40% change in Southern Ocean air–sea fluxes in only a few years. The different diffusivities led to an altered distribution of dissolved inorganic carbon, alkalinity, temperature and salinity, all of which affect the surface flux of CO₂ ( Figure 2 ). Furthermore, direct turbulence observations – in regions where outgassing occurs in the subpolar Southern Ocean – showed strong episodic CO₂ outgassing events driven by storms (Nicholson et al., 2022).

A remarkable emergent property of Earth system models is that, due to compensation between the effects of anthropogenic heat and carbon uptake, the peak global-mean surface warming is proportional to cumulative anthropogenic carbon emissions (Matthews et al., 2009). While this result underlies global climate mitigation policy (Drake and Henderson, 2022), the all-important constant of proportionality— known as the Transient Climate Response (TCR)—remains frustratingly uncertain (Matthews et al., 2020), with about 50% of the geophysical uncertainty attributable to oceanic tracer uptake processes (Lutsko and Popp, 2019) and the remainder due to radiative feedbacks. Using a simple climate-economic model, Hope (2015) estimates that the benefit of halving uncertainty in the TCR is valued at $10 trillion (USD), suggesting that the potential societal value of ocean mixing research could easily be in the many billions of dollars due to climate considerations alone.

These results illustrate the fundamental role that ocean mixing has in driving ocean warming, sea level rise, and ocean acidification, directly impacting coastal communities where 40% of the world’s population lives and indirectly impacting the rest of the global population through changes in regional climate.

Stratification inhibits the vertical exchanges of nutrients and dissolved gases. Turbulent mixing processes supply nutrients to the biologically productive upper ocean, which helps regulate the net primary productivity of the open ocean and coastal environments. Turbulent vertical exchanges may also be the main source of dissolved oxygen replenishment in the deeper layers of the water column. In some environments, the lack of mixing precludes dissolved oxygen replenishment at depth (Bourgault et al., 2012), leading to hypoxic conditions that are detrimental to the ecosystem. Turbulence measurements are required regularly to document the physical processes that enhance these exchanges before they can be adequately linked with biogeochemical processes over seasonal timescales (e.g., Rippeth et al., 2009; Bluteau et al., 2021) and over time as the climate changes.

Mixing has an especially important role in maintaining the concentrations of dissolved oxygen, nitrate, and other nutrients in coastal and estuarine environments. A lack of mixing in the deepest parts of inlets and estuaries causes them to become hypoxic (Bourgault et al., 2012), while elevated mixing at sills may generate biodiversity hotspots by re-oxygenating bottom waters and transporting nutrients upwards into the euphotic (well-lit) surface layers. Estuarine circulations and tidal flow over topography cause mixing that brings nitrate up into the surface layer. The relative strength of this type of tidally-induced mixing varies depending on other biogeochemical processes at play, like the seasonal strength of riverine nitrate influx to the system, but it can be as important as river discharge (Bluteau et al., 2021).

One of the robust oceanic signals of climate change is an increase in upper ocean stratification (Bindoff et al., 2019; Melet et al., 2022), both from increased thermal stratification and increased salinity stratification in high latitudes. Projections suggest that the net primary productivity of the ocean will very likely decrease by between 4% and 11% by 2100 under an unmitigated climate change scenario, mostly because the enhanced upper ocean stratification reduces the supply of nutrients via turbulent mixing across the pycnocline (Bindoff et al., 2019; Melet et al., 2022).

The upper boundary layer of the ocean and the processes occurring in the surface mixed layer figure prominently in the global climate system because it mediates momentum, heat, and gas fluxes between the ocean and the atmosphere (Frankignoul and Hasselmann, 1977; Bopp et al., 2015; Pörtner et al., 2019). Ultimately, these fluxes are governed by the dynamics of turbulent vertical mixing in this boundary layer (D’Asaro, 2014).

As an example, the sea surface temperature (SST) is a critical control on the atmosphere (Xie, 2004) and impacts equatorial climate by controlling precipitation (Xie et al., 2015). Over the African Sahel, precipitation changes with the SST difference between the neighboring subtropical North Atlantic and global tropics (Giannini et al., 2013). In the tropics, the SST rises by 2°C during boreal spring, when heating of the upper ocean by the atmosphere exceeds cooling by mixing from below. In boreal summer, SST decreases because cooling from below exceeds heating from above (Moum et al., 2013). Such a quantitative assessment of how mixing (computed from χ measurements) varies on timescales longer than a few weeks clearly shows its controlling influence on the seasonal cooling of SST in a critical oceanic regime. This case study is but one example of the indirect impacts of ocean mixing through coupled Earth system interactions and teleconnections.

In the coming decades, there will be greatly increased attention on anthropogenic use of the ocean. One example is deep-sea mining of abundant critical mineral resources (e.g., nickel, cobalt) in the abyssal ocean (Peacock and Alford, 2018). Such activities will generate benthic plumes of sediment, and other biogeochemical factors (e.g., dissolved heavy metals), that have the potential to negatively impact the ocean environment (Peacock and Ouillon, 2023). Recent modeling studies identified turbulent vertical mixing as a critical parameter that influences model predictions of plume extent, which will be the basis of decision-making by regulators (Chen et al., 2023).

While deep-sea mining is projected to be a multi-billion dollar industry, marine Carbon Dioxide Removal (mCDR) is projected to become an even larger trillion-dollar industry (National Academies of Sciences, Engineering, and Medicine, 2021). Several different mCDR technologies are being proposed (e.g., alkalinity enhancement, artificial upwelling, seaweed cultivation). For all proposed technologies, turbulent vertical mixing in the vicinity of the upper mixed layer is the critical governing physical process by which enhanced uptake of atmospheric carbon dioxide occurs, setting the efficacy of any mCDR technology. Critically, this nascent global industry will rely on model predictions informed by field data to identify potentially promising mCDR locations and to create the so-called MRV (Measurement, Reporting & Verification) value chain that will be the basis for a global market of mCDR carbon credits. Without correct parameterization of turbulent vertical mixing in mCDR modeling systems, there will be no way to achieve confidence in the mCDR MRV process – or to reliably assess the potentially negative impacts of proposed mCDR approaches. Global standards for turbulent diapycnal fluxes as an EOV will be essential.

Another technology that utilizes the ocean and is growing rapidly is offshore wind farms. There is, for example, a major expansion of activities in the United States (Musial et al., 2021), that lags behind Europe in this space (Fernández, 2023), which itself is scaling up activities in the North Sea. The introduction of fixed installations in shallow waters and floating structures in offshore waters creates strong turbulence in the wake of the installations (Dorrell et al., 2022). These enhanced turbulence levels can impact the surrounding marine environment and this needs to be well characterized in order to inform environmental assessments (Dorrell et al., 2022).

The Global Ocean Observing System (GOOS) – a program co-sponsored by four organizations: three United Nations agencies, Intergovernmental Oceanographic Commission of UNESCO, the World Meteorological Organization, United Nations Environment Programme, along with a non-governmental organization, the International Council of Science – has the mission to support an international community of ocean observing organizations and set global standards for maintaining sustained observations and outputting data. Since 2011, GOOS has implemented a Framework for Ocean Observing that serves as a road map for supporting the ocean observing system (UNESCO, 2012). The Framework includes a list of Essential Ocean Variables (EOVs), which are measurable parameters considered vital to inform the three GOOS delivery areas: climate, ocean health, and forecasts and warnings. The selection of EOVs relies on two criteria: the impact (e.g., scientific, ocean services and health) and the feasibility (e.g., technological, political, economical) of making sustained measurements.

Once an EOV is selected, GOOS facilitates and coordinates the sustained operation of observation programs of the EOVs at global scales. It achieves this by encouraging funding from international or national agencies for observation programs and technological developments that would improve the sustainability of EOV measurements. The current list of EOVs contains variables linked to ocean circulation and the distribution and transport of heat, salt, and other water properties ( Table 2 ). These include temperature, salinity, ocean currents, and sea-surface fluxes–variables that are continuously sampled through the global ocean today.

Our proposed variables (see Table 1 ) can be derived from the turbulent dissipation rate of kinetic energy (ϵ) and the turbulent dissipation rate of temperature (or another scalar) variance (χ), which are more easily measurable than direct covariance estimates from co-located velocity and tracer variance measurements (i.e., “eddy correlation” methods). Turbulent dissipation rates are obtained from measuring perturbations – ranging from a few centimeters to a few meters – of velocities and scalars such as temperature, conductivity (salinity), or dissolved oxygen. These variables are already part of the EOV list ( Table 2 ), and ϵ is mentioned as a derived variable of Subsurface Currents in its EOV Specification Sheet. We argue, however, that GOOS should consider establishing an EOV specific to turbulent diapycnal fluxes for the following reasons:

The first in-situ observations of ocean turbulence were made by researchers at the Pacific Naval Laboratory of Canada’s Defence Research Board using hot film anemometers (Grant et al., 1962). Since then, the ocean mixing community has continued to design and improve instruments that are increasingly resilient and easy to deploy. The rapid evolution of instrumentation has enabled more autonomous measurements and the mobile electronics industry has progressed tremendously over the past decade, reducing the size and power of electronic components while increasing data storage capacity. The types of platforms used to measure turbulence have expanded from profilers and towed vehicles to moorings, gliders, AUVs, autonomous profilers, and Argo floats.

The basic sensor technology available to make direct mixing observations in the microstructure range has been in place since the 1970s (see exhaustive review by Lueck et al., 2002). Airfoil shear probes and FP07 thermistors sample small-scale gradients of velocity and temperature, respectively, at frequencies of 100-512 Hz, which equates to a spatial resolution of centimeters. FP07 thermistors and shear probes each have advantages and disadvantages for measuring turbulence. Shear probes measure small-scale velocity fluctuations, directly measuring the loss of kinetic energy associated with turbulent motions. FP07 thermistors measure small-scale temperature fluctuations and thus provide an indirect measurement of how turbulent motions affect background temperature gradients. Shear probes are more sensitive to platform speeds and vibrations than FP07 thermistors. Conversely, thermistors have a slower response time, limiting their ability to resolve the smallest eddies in areas of strong turbulence at reasonable fall speeds. Both types of sensors are very delicate and can be damaged if they encounter large zooplankton in the water column. A new type of plastic membrane (e.g., polyvinylidene fluoride, PVDF) with similar piezo-electric properties to the currently used ceramic materials has the potential to greatly improve the resilience of shear probes over the next five years. These plastic membranes are flexible and, therefore, more resilient to shocks and pressure forces.

Since the early turbulence measurements, the sensors themselves have not changed significantly, but the electronics and data acquisition systems have improved resulting in higher signal-to-noise ratios and reduced power consumption. Onboard data storage has also increased significantly, allowing the on-board processing of the turbulent dissipation rates (ϵ,χ). Autonomous platforms (e.g., gliders and floats) already use on-board processing schemes (e.g., Hughes et al., 2023). Using vibration sensors and data processing algorithms has also reduced signal contamination from sources such as platform vibrations (Goodman et al., 2006). One can also rely on the larger scales of the microstructure subrange to estimate ϵ (Bluteau et al., 2016b) and χ (Bluteau et al., 2018) to avoid vibration issues by sampling less rapidly. Furthermore, progress has been made in estimating the statistical uncertainty of a dissipation estimate and the quality of a spectrum objectively (Lueck, 2022a,b), which allows automated and reliable quality control of dissipation estimates. These improvements, combined with the recent progress in real-time data processing, have 1) allowed more compact instruments to be built, 2) enabled the transmission of data over satellite connections, and 3) allowed for instruments to be deployed for longer periods (e.g., Rainville et al., 2017). They also enable more data to be recovered from autonomous platforms deployed in high-traffic areas where platforms are sometimes damaged or lost.

Acoustic-Doppler Current Profilers (ADCPs) and point-velocity measurements are instruments routinely deployed on bottom landers and moorings. Estimating ϵ from these sensors requires measuring velocities at a sufficiently high rate to sample within the inertial subrange (seconds and several cms). The point-velocity measurements are more mature than using ADCPs to obtain ϵ as it relies on fitting the inertial subrange of spectra derived from time-series over a small sampling volume of a few centimeters. The first estimates of ϵ were from electro-magnetic sensors (Bowden and Fairbairn, 1956) or drag spheres (Lumley and Terray, 1983) until acoustic-Doppler velocimeters with sufficiently low measurement noise became common in the late 1990s (e.g., Voulgaris and Trowbridge, 1998; Kim et al., 2000). Initially, turbulence estimates were only possible from fixed platforms since high-frequency platform motion (e.g., vibrations) could not be easily removed from the velocity signals. Over time, techniques to handle the changes in expected spectral forms from surface waves were generalized (Lumley and Terray, 1983; Feddersen et al., 2007), while reducing the impact of motion contamination on moored point-velocity measurements (e.g., Bluteau et al., 2016a).

The use of ADCPs to derive ϵ, can provide estimates over a wider range of depths than one point-velocity meters. The technique was first introduced by Wiles et al. (2006) and relies on the structure-function first employed in meteorology in the late 1960s (Sauvageot, 1992). Deriving ϵ requires differencing instantaneous turbulent velocities at different separation distances along an ADCP beam. As with the point-velocities, the sampling rate must be sufficiently fast to measure within the inertial subrange (1-2 Hz typically). The noise and measurement quality must be much higher than for measuring mean currents, and so the ADCPs must be programmed in pulse-to-pulse coherent mode. This mode results in higher resolution measurements but over a small spatial range of typically less than 10m. The method also requires that the bin separation is sufficiently small so that a few bins are fully contained within the inertial subrange. For relatively weak turbulence levels of ϵ ∼ 10⁻⁸ Wkg⁻¹, a bin size of 20cm is sufficiently small. In more energetic flows, the inertial subrange extends to smaller scales but also to larger scales such that the bin size does not need to be reduced at the expense of the ADCP’s vertical sampling range.

Several commercial offerings currently exist to measure ϵ over a wide range of turbulence levels and spatial scales with the structure-function. For example, the structure-function has been applied to ADCPs available from Nortek and RDI Teledyne (see Guerra and Thomson, 2017, for comparisons between instruments). The structure-function method was been applied to moored ADCPs using off-the-shelf instruments (Lucas et al., 2014). Velocity profiles of sufficient quality for turbulence analysis are also possible from off-the-shelf instruments mounted on Lagrangian (drifting) floats (Shcherbina et al., 2018).

Another type of acoustic measurement uses travel-time velocimeter technology (TTV, Polzin et al., 2021). Similar to the point velocities, TTV instruments measure a small volume of water using a pair of transducers “pinging” at each other. This technology allows for a very fast sampling in low-scattering environments and can be used for direct observation of turbulent fluxes (eddy-correlation methods). The eddy-correlation method is relatively new in the deep ocean science community and requires a time-scale separation that could lead to bias in the turbulent fluxes computation.

Microstructure measurements have been made using several different instruments mounted on a variety of platforms ( Figure 5 ). The first measurements were made using horizontal profilers that were towed by a ship (e.g., Grant et al., 1962). By the late 1970s, most microstructure measurements were made using vertical profilers developed by over 10 different research groups around the world (Lueck et al., 2002). Currently, microstructure instruments are made commercially by Rockland Scientific International (Canada) and Sea and Sun Technology (Germany), and by research groups at Oregon State University (OSU), Scripps Institution of Oceanography (SIO), and Woods Hole Oceanographic Institution (WHOI).

Most of these instruments are designed to make simultaneous measurements of both shear and temperature microstructure from vertical profilers (autonomous or tethered) for a variety of depth ratings (up to 11,000m) and modular instruments that can be mounted on autonomous platforms such as gliders, AUVs, and drifting or moored profilers (e.g., Argo floats or Wirewalkers). Vertical profilers remained the dominant method of obtaining microstructure measurements until the 2010s, when instruments began to be mounted on moorings, gliders, AUVs, and surface-following platforms (e.g., Moum and Nash, 2009; Fer et al., 2014; Hughes et al., 2020; Iyer et al., 2021; Zippel et al., 2021). These new platforms, combined with more versatile instruments, have led to a significant increase in the volume of data being collected and the duration of deployments. Deployments on autonomous platforms are power-limited and typically last up to 45 days. This equates to 300 glider profiles to 1000m.

Longer turbulence deployments are possible from moored platforms, although measurements are limited to discrete depths rather than the full water column. χpods, which contain FP07 sensors mounted horizontally, have provided near-continuous mixing measurements at the equator on several of the Tropical Atmosphere–Ocean (TAO) array moorings since late 2005 (Moum et al., 2013) in the Pacific Ocean, and later in the Atlantic Ocean (PIRATA array) and Indian Ocean (RAMA array). The TAO/TRITON (Triangle Trans-Ocean Buoy Network) program evolved from the original TAO project started in 1985 and was sponsored by CLIVAR, GOOS, and SCOR (https://www.pmel.noaa.gov/gtmba/, McPhaden et al. (2010)). PIRATA launched in 1997 (Bourlès et al., 2008), RAMA evolved from the Indian Ocean Observing System (IndOOS) with original deployment in 2006 (McPhaden et al., 2009; Beal et al., 2020). Only a few sensors allowing for direct measurements of turbulent fluxes (e.g., MAVS Figure 5 using TTV technology) can be mounted on mooring and record velocity-tracers fluctuations over a wide range of frequencies.

CTD-χpods – an adaptation of χ-pods suitable for mounting on a standard rosette ( Figure 6 ) – have been deployed on cruises that are part of the international GO-SHIP Repeat Hydrography Program since 2014. Through this effort, CTD-χpods have provided χ and turbulence measurements across sections in the Pacific, Atlantic, Indian, and Arctic Oceans with the intent to obtain repeated full-depth measurements on a global scale.

One of the most relevant programs to our discussion is the international Argo program, which collects ocean data using a fleet of autonomous instruments that drift with the ocean currents and move up and down between the surface and a mid-water level. The Argo floats are part of a global coordinated effort of over 30 nations with more than 3500 autonomous ocean-observing profilers currently active in the network (http://www.jcommops.org/). The floats provide the scientific community with near-real-time ocean measurements including temperature and salinity profiles, so they are already an important platform for physics EOVs. The ongoing integration of turbulence sensors on Argo floats has the potential to provide turbulent dissipation rate measurements on a daily basis at a global scale. The present discussion recognizes the need for the ocean mixing community to further coordinate its efforts to measure and distribute data, taking advantage of the means provided by the GOOS.

Mixing measurements from Argo floats (Argo-mix) are one of the emerging branches of the Argo program (Roemmich et al., 2019). This new branch, will provide the scientific community repeated microstructure measurements at a global scale. These measurements would offer insights into the impact of ocean mixing on water mass transformations, air-sea interactions and a plethora of other processes (Naveira Garabato and Meredith, 2022). Technological improvement needs to provide resilient and low-power new sensors so their integration does not modify the life expectancy or the core mission of a float– namely long-term deployments profiling the upper 2000 m.

An early effort obtained more than 1000 profiles over two weeks in the Bay of Bengal from two χSOLO floats, which are Argo floats equipped with fast thermistors (Shroyer et al., 2016). This instrument motivated development of the Flippin’ χSOLO (FCS), which has shear probes and fast thermistors at one end of the float and a communication antenna at the other (Moum et al., 2023). By flipping around at the top and bottom of a cast, turbulence profiling of undisturbed fluid is achieved on both down- and up-casts all the way through the sea surface. Similar to glider performances, FCS missions can last 45 days when profiling to 120m at a rate of roughly 100 profiles per day. Missions are controlled from a shore station by remote communications.

While χSOLOs have reached a mature level of operation, additional microstructure floats are under development ( Figure 7 ). The microstructure sensors developed by Rockland Scientific International and SIO are currently being integrated in the MRV ALTO and APEX floats, respectively. Both of these floats are part of the Argo fleet. The deployments of these floats began in 2022 during a few pilot experiments that profiled to 2000 m. The first results show a good sensitivity range despite the slow motion of the floats. The integration of these turbulence sensors on Deep-SOLO floats – Argo float profiling down to 6000 m depth – is also underway. The probes used on the sensors are expected to survive the challenging pressure at such a depth since they show normal sensitivity and behavior after multiple bench-tested pressure cycles up to 10000 PSI (>7000 m depth).

A new project from the National Oceanographic Partnership Program (NOPP) aims to improve the global simulation of internal waves and their impacts on ocean mixing. As part of this effort, the SQUID (“Sampling QUantitative Internal-wave Distributions”) sub-project sponsored by NSF will deploy 50 EMAPEX microstructure floats measuring velocity, temperature, salinity, and χ from cruises of opportunity throughout the world in 2023–2025 ( Figure 8 ). Sampling will differ from the Argo paradigm by making bursts of 4-6 profiles to 2000 meters over two days, allowing the estimation of a statistical average and high-frequency perturbations in shear, strain, and dissipation, as well as energy flux by dominant internal wave components through low-mode fitting and harmonic analysis. Bursts will be separated by multi-day drifting at 1000m, with a target duration of one year per float. The overall SQUID array, while not covering the globe by any means, will aim to span the range of major internal wave forcing parameters (including internal tide amplitude as estimated from altimetry, bathymetric roughness, high-frequency wind forcing, mesoscale eddies, and latitude).

Practical measurements of ϵ (and fluxes derived from these) have unavoidable uncertainties. Even an instrument with multiple (redundant) sensors will not necessarily produce the same values. Further, turbulence has short spatial and temporal scales and therefore point measurements are not necessarily representative of that part of the ocean on longer (e.g., >weekly) or larger (e.g., >10km) scales and requires multiple realizations to address the time and space variability turbulent diapycnal fluxes.

Lueck (2022a) showed that ϵ values measured by individual shear sensors on the same instrument agree in an average sense but that there is typically variability up to a factor of two. Specifically, for 90-cm segments, agreement between sensors was worse than a factor of two in 10% of cases. Shorter segments have larger variability and longer segments have smaller variability. Similar levels of agreement are described by Oakey (1982) who compared ϵ derived from a shear probe to that derived from a thin film thermometer, and by Kolås et al. (2022) who compared outputs from two shear probes installed on an AUV. (Further examples are given in Section 3.1 of the turbulence methodology review by Burchard et al. (2008)).

Averaged over hours to days, turbulence measurements from independent instruments—but from the same nominal part of the ocean—should give the same results. Moum et al. (1995) tested this with two free-falling profilers from two ships spaced within 11km of each other on the equator (0°, 140°W). At most depths, the agreement between the full 3.5-day means were within a factor of two. Agreement between individual data pairs was typically within a factor of three but sometimes beyond a factor of 10. In other words, the natural variability of geophysical turbulence over kilometer scales is comparable to, or larger than, the uncertainty associated with each point measurement of ϵ.

Clearly, if turbulence measurements at a given spot are to be considered representative, averaging is needed and/or uncertainty levels need to be quantified. As with any measurement, but especially for turbulence, more samples are better for achieving accurate statistics. Observed distributions of ϵ are often approximately lognormal. Hence, distributions of log(ϵ) are approximately Gaussian, which makes standard statistical quantities such as mean and standard deviation easy to calculate (e.g., Baker and Gibson, 1987). However, one can avoid assuming a Gaussian distribution by using bootstrapping, a statistical method that is agnostic to distribution shape. Indeed, bootstrapping is a standard procedure in turbulence analysis (e.g., Shay and Gregg, 1986; Lu et al., 2000; Greenan et al., 2001; Nash and Moum, 2001; Inall and Rippeth, 2002; Klymak and Moum, 2007; St Laurent and Thurnherr, 2007; Perlin and Moum, 2012; Whalen et al., 2012; Sutherland et al., 2013; Waterhouse et al., 2014; Wenegrat and McPhaden, 2015).

This discussion of uncertainties, which so far only considers ϵ, can be extended to turbulent fluxes, albeit with caution. As Moum (1997) emphasizes, microstructure measurements are not flux measurements, and the assumptions linking the two are ‘fraught with uncertainty’. That said, direct numerical simulations show that the Osborn and Cox (1972) and Osborn (1980) relations outperform their underlying assumptions in predicting diffusivity and hence the irreversible buoyancy flux (Taylor et al., 2019). This is consistent with the widely held notion from observations that Γ = 0.2 (see Gregg et al., 2018, and elsewhere in this paper).

The TAO and CTD-χpods projects mentioned earlier have demonstrated the feasibility of collecting basinwide datasets of microstructure observations ( Figure 6 ). These projects integrated turbulence measurements into the structure and culture of repeat hydropgraphy cruises and developed a methodology for processing and interpreting the acquired ocean mixing data. CTD-χpods are also being tested in coastal environments to possibly widen the scope of their usage. Expanding to global observations is a matter of putting more instruments in the ocean.

Measurements of ϵ are reaching a similar level of maturity within the Argo-mix context and the ongoing integration of turbulence packages measuring ϵ and χ inside Argo floats. The float controls a unique low-power electronic board that can collect data from analog channels and has the required memory space to compute the dissipation rates and a few quality flags. Such packages, as well as the use of easy-toreplace shear and FP07 probes, simplify the integration of microstructure sensors greatly. Once they are an integrated part of the Argo fleet, microstructure measurements at a global scale will automatically be available to a wide community. Before their full integration, a few regional pilot experiments (e.g., the SQUID project mentioned in section 5.4) will be performed to validate and prove that measurements from autonomous Argo floats are fit-for-purpose.

The recent success of the biogeochemical community in incorporating sensors onto global observation platforms like Argo floats can be used as a roadmap for the ocean mixing community. Autonomous floats or vehicles able to sample the turbulent fluxes and diffusivities during the lifetime of coherent oceanic structures (e.g., mesoscale eddies or equatorial cold tongue) can provide insights about local energy transfers inside structures impacting the global circulation. The scales (time and space) of these structures makes them ideal candidates for pilot experiments that will demonstrate that Argomix floats are also fit-for-purpose of the core Argo mission.

Argomix floats, ϵ measurements from already existing platforms (e.g., gliders, profilers, high-resolution ADCPs or ADVs), and current efforts to develop a consistent methodology for processing and interpreting measurements (e.g., ATOMIX SCOR group) all converge toward integrating turbulent flux observations into the structure and culture of repeated hydrography cruises and observing systems.

The cost-effectiveness of repeated microstructure measurements represents a balance between 1) the price of the turbulence sensors, 2) the cost of the platforms used for these measurements and 3) the funds dedicated to coordinate observing systems and ship opportunities in charge of maintaining these measurements. Over the past 40 years, many regional research projects have been funded by national research agencies in attempts to quantify turbulent dissipation (Waterhouse et al., 2014). Regional scale experiments provide high-resolution time series of ocean turbulence that shed light on its variability and the multi-scale interaction between ocean energy sources and energy dissipation. Understanding these interactions is of primary importance for future human activities (e.g., deep sea mining, energy harvesting, sea level rise). As the connection between ocean mixing and human activities has become more apparent, private organizations have begun supporting studies that include microstructure measurements (e.g., Schmidt Family Foundation and the Benioff Foundation, Muñoz-Royo et al., 2021). In 2018, the physical oceanography program at NASA identified ocean mixing as one of its three priority areas and funded several projects, including efforts associated with the χ measurements made on the international GO-SHIP hydrography program, described above.

The cost of autonomous platforms like Argo floats spans from $20k for a core (Temperature-SalinityPressure) float to about $100k for a biogeochemical Argo float with six additional sensors. As of today, the microstructure packages considered for the Argo float integration range around $30k–$40k in their current form. Progress in the electronics industry is triggering the development of new generations of turbulence sensors and new fabrication techniques are lowering the fabrication cost of these packages by a factor of three (Le Boyer et al., 2021). This will bring the cost of an “Argomix” float well within the price range of a core Argo float and a biogeochemical float. The necessary simplification of turbulence packages to fit into the tight space of the float, as well as any production scaling, will further reduce the price of Argo-mix floats.

Consequently, the ocean mixing community will be able to design regional scale experiments with sampling strategies using a small fleet of ∼10 microstructure sensors measuring both ϵ and χ from a variety of easy-to-deploy platforms with a<$500k budget within the next five years (e.g., SQUIDD project mentioned above).