The development and deployment of autonomous instrumentation in the aquatic sciences, which has rapidly expanded during the last decade, has led to fantastic new insights into unexplored waters, increased understanding of temporal and spatial dynamics, and amazing discoveries across the aquatic sciences. These tools have allowed us to acquire more data, at higher frequency in time and space, and at a lower cost. Sensors deployed on gliders, autonomous underwater vehicles, and buoyed and float-based profilers have permitted near-real time, high-temporal and -spatial resolution observation of water physics (e.g., temperature, turbulence, currents, optics), chemistry (e.g., dissolved oxygen, pH, major and trace nutrients), and biology (e.g., plankton, environmental DNA, algal toxins). These technologies have led to unprecedented opportunities and understanding of numerous and complex properties of aquatic systems, including oceanic circulation patterns and interactions with the atmosphere, temporal, vertical and horizontal dynamics of plankton and associated biogeochemistry, and physical-chemical-biological interactions in both pelagic and benthic environments. In this special session, we welcome the submission of studies from all fields of oceanography and limnology, from physics to biology, which rely on the use of autonomous instrumentation and/or Big Data to make significant advancement in the understanding of key aquatic processes. Contributions may include studies at global to local scales with broad implications across the aquatic sciences and beyond. A special issue on this topic is expected for Fall 2024 in Limnology and Oceanography .
Lead Organizer: Steeve Comeau, Sorbonne Université - CNRS (steeve.comeau@obs-vlfr.fr)
Co-organizers:
David Hambright, The University of Oklahoma (dhambright@ou.edu)
Julia Mullarney, University of Waikato (julia.mullarney@waikato.ac.nz)
Elisa Schaum, University of Hamburg (elisa.schaum@uni-hamburg.de)
Presentations
10:30 AM
ECOSTRESS high resolution space-based thermal radiometer provides spatial context of trajectory data from autonomous sensor platforms (7014)
Primary Presenter: David Wethey, University of South Carolina (wethey@biol.sc.edu)
The 70m pixel scale ECOSTRESS thermal radiometer on the International Space Station serves as a precursor for future high resolution thermal missions including TRISHNA (ISRO/CNES), SBG (NASA), and LSTM (ESA). The ECOSTRESS spatial sampling scale matches 1 to 5 minute observations from autonomous Saildrone platforms. We compared Saildrone SST (IR and thermistor) to ECOSTRESS skin temperatures in upwelling regions off California. In clear sky conditions, bias-corrected ECOSTRESS skin temperature is highly correlated with in-situ observations from Saildrones (R2>0.80, RMSE < 0.35 C). ECOSTRESS 370 × 420 km scenes provide much needed spatial context for the in-situ trajectory observations, including temperature, salinity, chlorophyll and oxygen, by linking them to the 2-D spatial locations of thermal fronts, filaments and eddies that are not resolvable by other space-based radiometers.
10:45 AM
KEEPING TABS ON THE OCEAN CO2 CYCLE USING NEW AUTONOMOUS SENSING TECHNOLOGIES (6833)
Primary Presenter: Socratis Loucaides, National Oceanography Centre (s.loucaides@noc.ac.uk)
The ocean carbon cycle is undergoing unprecedented changes and, therefore, carbonate system parameters have been ranked by the Global Ocean Observing System (GOOS) as the highest impact biogeochemical Essential Ocean Variables (EOVs). Accurate carbonate chemistry characterization is required to understand the oceanic carbon cycle and monitor ocean acidification and its consequences on marine organisms. Although efforts have been made to increase the availability of high-resolution carbonate chemistry measurements in the ocean, the lack of automated high-performance low-cost carbonate sensors continues to hinder continuous and spatially extensive observations using autonomous platforms. Developing sensors capable of fulfilling future observing goals is therefore key for deciphering knowledge gaps and uncertainties in our understanding of the global ocean carbon cycle and optimisation of global models of ocean acidification and its impacts. The capability of Lab-On-a-Chip (LOC)-based sensors has been demonstrated on moving and fixed observing platforms around the globe from surface to the deep ocean (6000 m). LOC technology enables miniaturization and automation of high-performance reagent-based analytical techniques offering high-quality autonomous observations even on small autonomous platforms. Here we present recent developments in autonomous LOC-based sensors for measurements of seawater pH, Total Alkalinity (TA) and Dissolved Inorganic Carbon (DIC), highlighting some recent applications and successes and finishing with a glimpse of future carbon observing capabilities.
11:00 AM
High-frequency, year-round time series of the carbonate chemistry in a high-Arctic fjord (Svalbard) (4842)
Primary Presenter: Jean-Pierre Gattuso, CNRS-Sorbonne University-Iddri (jean-pierre.gattuso@imev-mer.fr)
The Arctic Ocean is subject to high rates of ocean warming and acidification, with critical implications for marine organisms as well as ecosystems and the services they provide. Carbonate system data in the Arctic realm are spotty in space and time and, until recently, there was no time-series station measuring the carbonate chemistry at high frequency in this region, particularly in coastal waters. We report here on the first high-frequency (1 h), multi-year (6 years) dataset of salinity, temperature, dissolved inorganic carbon, total alkalinity, CO2 partial pressure (pCO2) and pH at a coastal site (12 m) in Kongsfjorden, Svalbard. We show that the choice of formulations for calculating the dissociation constants of the carbonic acid remains unsettled, (2) the water column is generally somewhat stratified despite the shallow depth, (3) the saturation state of calcium carbonate is subject to large seasonal changes but never reaches undersaturation (OmegaAragonite ranges between 1.4 and 3.0) and (4) pCO2 is lower than atmospheric CO2 at all seasons, making this site a sink for atmospheric CO2 (20 mol m-2 yr-1).
11:15 AM
GLIDER CHARACTERIZATION OF RAPID CHANGES IN THE WATER COLUMN INDUCED BY AN INTENSE STORM IN THE BAY OF BISCAY (5330)
Primary Presenter: Ivan Manso-Narvarte, AZTI (imanso@azti.es)
A glider equipped with a Conductivity Temperature Depth (CTD) and an echosounder traveled meridionally through a transect perpendicular to the coastline in the SE Bay of Biscay from September 23 to October 13, 2022. It surveyed the shelf-break, slope and open ocean areas from the surface to 200 m depth. The survey coincided with a strong 5-day storm, enabling the study of its effects on the upper water column hydrographic and hydrodynamic conditions, as well as on the small pelagics community. During the storm, surface currents were oriented towards the coast and this transport yielded the subduction of surface coastal waters in a process referred to as downwelling. After the storm, the hydrographic profiles depicted a gradual relaxation of the downwelling event, recovering the initial state after five days. This event was also monitored through a coastal upwelling index based on gap-filled high-frequency radar surface current data. The storm markedly decreased the salinity and temperature values in the upper water column, indicating the beginning of the transition regime from summer (stratified) to winter (mixed). Several of the transects covered by the glider coincided with those monitored (using trawls and acoustics) by a research vessel during the JUVENA survey, enabling comparisons for the validation of the glider observations. Moreover, the glider data before, during and after the storm are used to study the impact of the storm on small pelagic fish and vertebrate species observed at different depths in the area.
11:30 AM
Performance Evaluation of the Undersee FerryBox ocean sensor system (7156)
Primary Presenter: Tiago Cristóvão, Undersee (tiagocristovao@undersee.io)
The Undersee FerryBox is a state-of-the-art oceanographic instrument used for measurement of various water quality parameters such as temperature, salinity, dissolved oxygen,chlorophyll-a and turbidity. Recently, the Norwegian Institute for Water Research (NIVA) installed the Undersee FerryBox system at their Solbergstrand research facility to monitor the marine environment in the Oslofjord. A NIVA Norwegian Ships Of Opportunity Program (NorSOOP) FerryBox was operated alongside and validation samples and reference instruments were used as a benchmark. This study aimed to evaluate the performance of the Undersee FerryBox system at the NIVA research facility and compare it with other oceanographic instruments with similar functions. To achieve this, we conducted multiple trials and analyzed the data obtained from the Undersee FerryBox system to determine its accuracy and reliability. Our results showed that the Undersee FerryBox system performed well and delivered reliable measurements when compared to the reference data. The calibration of the individual sensor showed the importance of regular calibration with proper reference material. Such tests will document the instrument performance and detect where to improve the system. The system worked continuously for extended periods, collecting and analyzing data from the trials. In conclusion, our study highlights the reliability and user-friendliness of the Undersee FerryBox system and graphical interface for monitoring water quality parameters in the marine environment. The system accuracy and portability make it a valuable tool for researchers and environmental managers seeking to understand and maintain the health and sustainability of the marine environment.
11:45 AM
Fish-Inspired Navigation via Flow Sensing in an Autonomous Robotic Swimmer (6160)
Primary Presenter: Peter Gunnarson, Caltech (pjgunnar@caltech.edu)
Autonomous ocean-exploring vehicles promise to transform the rate at which we can explore ocean environments. Such robots, however, face the challenge of navigating through unknown ocean currents and seeking out areas of interest without prior knowledge of their surrounding environment. Inspired by the ability of aquatic animals to navigate via flow sensing, we constructed a palm-sized robotic swimmer platform to test flow-based navigation strategies in a 6 by 6 by 16-foot water tank. The robot is equipped with distributed pressure sensors to mimic the function of canal neuromasts found in the lateral lines of fishes. A deep reinforcement learning algorithm runs onboard a high-speed microcontroller, which trains a neural network in real-time to control the robot’s thrusters based on sensor inputs. As an analogy for tracking hydrothermal vent plumes in the ocean, the robot is tasked with locating a turbulent jet flow using data from the pressure sensors and insights from the flow physics of the turbulent plume.
SS094A Autonomous Instrumentation and Big Data: New Windows, Knowledge, and Breakthroughs in the Aquatic Sciences
Description
Time: 10:30 AM
Date: 5/6/2023
Room: Sala Santa Catalina