Investigating Small-Scale Processes from an Abundance of Autonomous Observations

Sylvia T. Cole

Abstract

Small-scale processes, those with spatial and/or temporal scales less than a few hundred kilometers and a few weeks, vary on global and decadal scales. Such large-scale variations in small-scale processes have been difficult to observe. Within the last decade, global and regional-scale autonomous observations have begun to fill this observational gap. The specific processes that can be investigated from autonomous platforms are determined by the minimum scale in space and time sampled by each platform. Recent examples are highlighted, and the future potential is discussed.

 

Introduction

Autonomous platforms sample a range of horizontal and temporal scales regardless of whether they are utilized for short-term localized studies, regional studies, or decadal-scale global studies. Spatially, observations span the submesoscale or mesoscale in the horizontal to regional or global scales of interest. Temporally, observations span hours to weeks at a minimum, to several months or, increasingly, more than a decade at a maximum. Data collection is often motivated by larger-scale phenomena, whether regional or global in nature, and the smaller-scale phenomena that are also observed are frequently removed or smoothed. Increasingly, smaller spatial or temporal scale phenomena are being investigated, and the potential to investigate such processes on the regional to global scale or seasonal to decadal scale should not be overlooked in the future.

Investigating global- or decadal-scale variations in smaller-​scale processes requires a large amount of data. Global-scale programs have collected enough data through operations over multiple years (e.g., surface drifters, Argo floats). Such data sets are appealing for this purpose, as data coverage is somewhat uniform in space and time. Regional-scale programs will continue to build up sufficient data through the combined data set of a particular platform (e.g., all glider or autonomous underwater vehicle data). While coverage is certainly not uniform in space or time, and is often biased toward dynamically interesting regions, investigating dependence on parameters of interest (e.g., latitude, background stratification) is feasible.

 

Mining Small-Scale Processes

The specific small-scale processes that can be investigated are determined by the minimum scale at which platforms sample. This minimum scale varies by platform, for example, one hour for drifters (Lumpkin and Pazos, 2007), a few hours and a few kilometers for gliders (Rudnick et al., 2004; Rudnick 2016) and Ice-Tethered Profilers (Toole et al., 2011); 10 days and typically tens of kilometers for Argo floats (Roemmich et al., 2009). The minimum scale is often variable, with higher temporal or spatial resolution for some deployments compared with their standard operation. With profiling platforms, the vertical resolution can also be a determining factor, with minimum vertical resolutions ranging from 0.25 m for Ice-Tethered Profilers to 10 m or more for the standard operation of Argo floats.

On the global scale, the Argo and drifter data sets have been utilized to investigate several small-scale processes. Near-inertial and tidal surface currents have been quantified from the global drifter data set (Poulain and Centurioni, 2015; Elipot et al., 2016). Internal wave energy and parameterizations of vertical diffusivity have been investigated from Argo floats utilizing vertical strain of the density field (Whalen et al., 2012; Figure 1a,b). Mesoscale processes have also been studied using Argo float profiles or drifter data, resulting in parameterizations of horizontal diffusivity with global coverage (Zhurbas et al., 2014; Cole et al., 2015; Figure 1c,d). Such studies have shown significant variability with depth and geographic location of for example, horizontal and vertical diffusivity (Figure 1). This variability is not captured by other platforms with global coverage, as such platforms are limited in either depth resolution (satellites) or spatial and temporal resolution (e.g., ship-based hydrographic surveys). Autonomous platforms have advanced our knowledge about the larger-scale variations of such small-scale processes.

Figure 1. (a) Map, and (b) globally averaged profile of vertical diffusivity from Argo float temperature and salinity profiles (adapted from Whalen et al., 2012, with data updated through June 2016). (c) Map at 500 m depth, and (d) globally averaged profile of horizontal eddy diffusivity derived from Argo float temperature and salinity profiles and ECCO-2 eddy kinetic energy (adapted from Cole et al., 2015, with data updated to cover 2005–2015). Global averages are shown at depths with sufficient data.

At the regional scale, the use of autonomous platforms to gather multiyear data sets is of interest here (e.g., Toole et al., 2011; Rudnick et al., 2017), as opposed to short-lived process studies that are designed specifically to capture smaller-scale features (e.g., Martin et al., 2009). Similar themes to the global scale emerge, with investigations of submesoscale and mesoscale dynamics and vertical mixing and diffusivity. Glider data have been used to investigate internal wave energy at a regional scales, illustrating its enhancement near topography (e.g., Johnston et al. 2013; Johnston and Rudnick, 2015). Ice-Tethered Profiler data have demonstrated decadal and latitudinal trends within the Arctic Ocean (Dosser and Rainville, 2016). Ice-Tethered Profiler data have also been used to quantify spatial modulations in double-diffusive staircases at the shortest vertical scales (Shibley et al., 2017). Mesoscale and submesoscale processes are also routinely investigated in regional data sets (e.g., Cole and Rudnick, 2012; Pelland et al., 2013; Zhao et al., 2016). While investigations of such processes are not exclusive to autonomous platforms, they are growing increasingly common and feasible. Even at the regional scale, autonomous platforms show larger spatial- or temporal-scale modulations in smaller-scale processes then are practical from other platforms (e.g., ship-based observations or moorings).

Regional platforms also often permit a more thorough investigation of processes of interest, such as the internal wave energy flux and energy density (that requires velocity measurements; Johnston et al., 2013) and not simply the parameterized vertical diffusivity (via Argo float density profiles; Whalen et al., 2012). Global analysis of regional-scale observations will provide key advances in the future.

 

Future Potential

Several factors influence the future potential of autonomous platforms to advance our knowledge of small-scale processes on regional to global scales. The amount, resolution, and types of data collected are the main factors, though availability of the data sets is also important. Regardless of what specific advances are made, many different studies have already advanced our knowledge of smaller-scale processes by combining multiple years of autonomous observations, and that will continue into the future.

Increasing amounts, resolution, and types of data collected will permit more detailed investigations of many processes. Improvements in technology will allow for finer temporal, horizontal, or vertical resolution via cheaper platforms that increase the number of platforms deployed, increase battery life, and/or increased ease or decrease cost of data telemetry. Additional sensors on autonomous platforms will also expand and enhance the study of smaller-scale phenomena. For example, as biogeochemical observations become more routine, they permit studies of biogeochemical-specific processes, as well as physical processes for which such observations serve as a maker (e.g., eddy stirring). Turbulent-scale processes are already directly observed from autonomous platforms (e.g., gliders, autonomous underwater vehicles, Wave Gliders), and the growing collection will lead to its study on larger spatial and temporal scales. The range of temporal scales will also expand beyond decadal, providing a more detailed look at interannual variability of small-scale phenomena. Access to autonomous observations is a key component of such future studies, especially for those platforms that are used in numerous regional studies throughout the global ocean.

Autonomous platforms provide a tool for studying the ocean as a system, and the interactions between processes at different scales. The geography and seasonal to decadal variations in such processes are still being explored. The next decade of autonomous observations will significantly improve our ability to understand the link between smaller-scale processes and larger-scale or longer-time dynamics within the ocean.

 

References

Cole, S.T., and D.L. Rudnick. 2012. The spatial distribution and annual cycle of upper ocean thermohaline structure. Journal of Geophysical Research 117, C02027, https://doi.org/10.1029/2011JC007033.

Cole, S.T., C. Wortham, E. Kunze, and W.B. Owens. 2015. Eddy stirring and horizontal diffusivity from Argo float observations: Geographic and depth variability. Geophysical Research Letters 42:3,989–3,997, https://doi.org/​10.1002/2015GL063827.

Dosser, H.V., and L. Rainville. 2016. Dynamics of the changing near-inertial internal wave field in the Arctic Ocean. Journal of Physical Oceanography 46:395–415, https://doi.org/10.1175/JPO-D-15-0056.1.

Elipot, S., R. Lumpkin, R.C. Perez, J.M. Lilly, J.J. Early, and A.M. Sykulski. 2016. A global surface drifter data set at hourly resolution. Journal of Geophysical Research 121:2,937–2,966, https://doi.org/10.1002/2016JC011716.

Johnston, T.M.S., and D.L. Rudnick. 2015. Trapped diurnal internal tides, propagating semidiurnal internal tides, and mixing estimates in the California Current System from sustained glider observations, 2006–2012. Deep Sea Research Part II 112:61–78, https://doi.org/10.1016/j.dsr2.2014.03.009.

Johnston, T.M.S., D.L. Rudnick, M.H. Alford, A. Pickering, and H.L. Simmons. 2013. Internal tidal energy fluxes in the South China Sea from density and velocity measurements by gliders. Journal of Geophysical Research 118:3,939–3,949, https://doi.org/10.1002/jgrc.20311.

Lumpkin, R., and M. Pazos. 2007. Measuring surface currents with Surface Velocity Program drifters: The instrument, its data, and some recent results. Chapter 2 in Lagrangian Analysis and Prediction of Coastal and Ocean Dynamics. A. Griffa, A.D. Kirwan, A. Mariano, T. Ozgokmen, and T. Rossby, eds, Cambridge University Press.

Martin, J.P., C.M. Lee, C.C. Eriksen, C. Ladd, and N.B. Kachel. 2009. Glider observations of kinematics in a Gulf of Alaska eddy. Journal of Geophysical Research 114, C12021, https://doi.org/10.1029/2008JC005231.

Pelland, N.A., C.C. Eriksen, and C.M. Lee. 2013. Subthermocline eddies over the Washington continental slope as observed by Seagliders, 2003–09. Journal of Physical Oceanography 43:2,025–2,053, https://doi.org/10.1175/JPO-D-12-086.1.

Poulain, P.-M., and L. Centurioni. 2015. Direct measurements of world ocean tidal currents with surface drifters. Journal of Geophysical Research 120:6,986–7,003, https://doi.org/10.1002/2015JC010818.

Roemmich, D., G.C. Johnson, S. Riser, R. Davis, J. Gilson, W.B. Owens, S.L. Garzoli, C. Schmid, and M. Ignaszewski. 2009. The Argo program: Observing the global ocean with profiling floats. Oceanography 22(2):34–43, https://doi.org/10.5670/oceanog.2009.36.

Rudnick, D.L. 2016. Ocean research enabled by underwater gliders. Annual Review of Marine Science 8:519–541, https://doi.org/10.1146/annurev-marine-122414-033913.

Rudnick, D.L., R.E. Davis, C.C. Eriksen, D.M. Fratantoni, and M.J. Perry. 2004. Underwater gliders for ocean research. Marine Technology Society Journal 38:73–84, https://doi.org/10.4031/002533204787522703.

Rudnick, D.L., K.D. Zaba, R.E. Todd, and R.E. Davis. 2017. A climatology of the Califronia Current System from a network of underwater gliders. Progress in Oceanography 154:64–106, https://doi.org/10.1016/j.pocean.2017.03.002.

Shibley, N., M.-L. Timmermans, J.R. Carpenter, and J. Toole. 2017. Spatial variability of the Arctic Ocean’s double-diffusive staircase. Journal of Geophysical Research 122:980–994, https://doi.org/10.1002/2016JC012419.

Toole, J.M., R.A. Krishfield, M.-L. Timmermans, and A. Proshutinsky. 2011. The Ice-Tethered Profiler: Argo of the Arctic. Oceanography 24(3):126–135, https://doi.org/​10.5670/oceanog.2011.64.

Whalen, C.B., L.D. Talley, and J.A. MacKinnon. 2012. Spatial and temporal variability of global ocean mixing inferred from Argo profiles. Geophysical Research Letters 39, L18612, https://doi.org/10.1029/2012GL053196.

Zhao, M., M.-L. Timmermans, S. Cole, R. Krishfield, and J. Toole. 2016. Evolution of the eddy field in the Arctic Ocean’s Canada Basin, 2005–2015. Geophysical Research Letters 43:8,106–8,114, https://doi.org/10.1002/2016GL069671.

Zhurbas, V., D. Lyzhkov, and N. Kuzmina. 2014. Drifter-derived estimates of lateral eddy diffusivity in the world ocean with emphasis on the Indian Ocean and problems of parameterization. Deep Sea Research Part I 83:1–11, https://doi.org/​10.1016/j.dsr.2013.09.001.

 

Author

Sylvia T. Cole, Woods Hole Oceanographic Institution, Woods Hole, MA, USA, scole@whoi.edu