Autonomous Tracking and Sampling of the Deep Chlorophyll Maximum Layer in an Open-Ocean Eddy by a Long-Range Autonomous Underwater Vehicle

Abstract

Phytoplankton communities residing in the open ocean, the largest habitat on Earth, play a key role in global primary production. Through their influence on nutrient supply to the euphotic zone, open-ocean eddies impact the magnitude of primary production and its spatial and temporal distributions. It is important to gain a deeper understanding of the microbial ecology of marine ecosystems under the influence of eddy physics with the aid of advanced technologies. In March and April 2018, we deployed autonomous underwater and surface vehicles in a cyclonic eddy in the North Pacific Subtropical Gyre to investigate the variability of the microbial community in the deep chlorophyll maximum (DCM) layer. One long-range autonomous underwater vehicle (LRAUV) carrying a third-generation Environmental Sample Processor (3G-ESP) autonomously tracked and sampled the DCM layer for four days without surfacing. The sampling LRAUV's vertical position in the DCM layer was maintained by locking onto the isotherm corresponding to the chlorophyll peak. The vehicle ran on tight circles while drifting with the eddy current. This mode of operation enabled a quasi-Lagrangian time series focused on sampling the temporal variation of the DCM population. A companion LRAUV surveyed a cylindrical volume around the sampling LRAUV to monitor Manuscript

Targeted Sampling by Autonomous Underwater Vehicles

“…This speed was consistent with the drift speed (0.27 m/s) of a GPS-tracked drifter (comprising a surface float and a drogue at 120 m depth) deployed near Aku, and R/V Falkor (near Aku's route) shipboard ADCPmeasured Earth-referenced current velocity (0.25 m/s) at the 103-m depth bin (nearest DCM's mean depth of 105 m). The closeness between Aku's drift speed and that of the drifter as well as the ship ADCP-measured eddy current velocity shows that Aku followed the DCM water mass in a quasi-Lagrangian mode (Zhang et al, 2019a).…”

“…When density variation is dominated by temperature variation, an isopycnal can be effectively tracked by tracking an isotherm. We developed an algorithm to enable a Tethys-class LRAUV to autonomously track the DCM layer by locking onto the isotherm corresponding to the chlorophyll peak (Zhang et al, 2019a), and to sample the DCM layer using an autonomous robotic sampler designed as a payload in the LRAUV, the 3rd-generation ESP (3G-ESP) (Pargett et al, 2015;Scholin et al, 2017).…”

“…In the March-April 2018 SCOPE (Simons Collaboration on Ocean Processes and Ecology) Hawaiian Eddy Experiment, the LRAUV Aku (carrying a 3G-ESP) (bottom photos in Figure 8) ran the algorithm to track and sample the DCM microbial community for 4 days in a cyclonic eddy to the northeast of Moloka'i (Zhang et al, 2019a). The vehicle ran on tight circles (circle radius ∼10 m) at 1 m/s speed while drifting with the eddy current.…”

Bridging the Spatiotemporal Gap in Diazotroph Activity and Diversity With High-Resolution Measurements

“…Deployed nutrient sensors with nanomolar limits of detection (Beaton et al, 2012) can facilitate the observation of episodic or small-scale events in oligotrophic mixed layers. Autonomous vehicles capable of Lagrangian drift can follow specific water masses, sampling the microbial community in its environmental context (Birch et al, 2019) and autonomous orientation using chlorophyll concentrations allows targeted sampling of structures within the water column (Zhang et al, 2019). Deployments of combined sensors will enable a more thorough understanding of diazotroph -environmental interactions on fine scales, to be integrated across water masses for larger scale understanding.…”

Robotic oceanography: Revealing ocean-scale biochemical structure with a deep-diving autonomous vehicle.

“…AUVs have also been adapted to the study of surface plankton processes (47,48) . AUVs have been equipped with dedicated sample-return systems to support these studies, including the GULPER (49) and the Environmental Sample Processor (50) . AUV volume and payload constraints require a significantly higher degree of instrument customization and accommodation than for ROVs.…”