Scientific Papers

Current carbon measurement strategies leave spatiotemporal gaps that hinder the scientific understanding of the oceanic carbon biogeochemical cycle. Data products and models are subject to bias because they rely on data that inadequately capture mesoscale spatiotemporal (kilometers and days to weeks) changes. High-resolution measurement strategies need to be implemented to adequately evaluate the global ocean carbon cycle. To augment the spatial and temporal coverage of ocean–atmosphere carbon measurements, an Autonomous Surface Vehicle CO2 (ASVCO2) system was developed. From 2011 to 2018, ASVCO2 systems were deployed on seven Wave Glider and Saildrone missions along the U.S. Pacific and Australia’s Tasmanian coastlines and in the tropical Pacific Ocean to evaluate the viability of the sensors and their applicability to carbon cycle research. Here we illustrate that the ASVCO2 systems are capable of long-term oceanic deployment and robust collection of air and seawater pCO2 within ±2 μatm based on comparisons with established shipboard underway systems, with previously described Moored Autonomous pCO2 (MAPCO2) systems, and with companion ASVCO2 systems deployed side by side.

From 11 April to 11 June 2018 a new type of ocean observing platform, the Saildrone surface vehicle, collected data on a round-trip, 60-day cruise from San Francisco Bay, down the U.S. and Mexican coast to Guadalupe Island. The cruise track was selected to optimize the science team’s validation and science objectives. The validation objectives include establishing the accuracy of these new measurements. The scientific objectives include validation of satellite-derived fluxes, sea surface temperatures, and wind vectors and studies of upwelling dynamics, river plumes, air–sea interactions including frontal regions, and diurnal warming regions. On this deployment, the Saildrone carried 16 atmospheric and oceanographic sensors. Future planned cruises (with open data policies) are focused on improving our understanding of air–sea fluxes in the Arctic Ocean and around North Brazil Current rings. The California coastal waters are important for the economy, society (this is the coast of the most populous state in the union), national security (they are the home waters of the Navy’s Pacific fleet), and environment (it is along an eastern boundary current with biologically important upwelling). In the California Current region, the air–land–sea interface is complex, characterized by coastal promontories, upwelling jets and shadows, river plumes, and a narrow continental shelf that affects coastal dynamics producing highly variable sea surface temperature (SST) and concentration of the photosynthetic pigment chlorophyll a (Chl) (Checkley and Barth 2009; Strub and James 1995; Kelly et al. 1998; Brink et al. 2000). Along the U.S. and Mexican west coast, upwelling induces a flux of cold, nutrient-rich, dense, low-in-oxygen, and acidic waters to the surface ocean layers, leading to important air–sea and coastal–open ocean interactions (Sverdrup et al. 1942). Due to its economic importance, the California Current System is one of the most studied and well-monitored upwelling systems in the world, including high-frequency (HF) radar for surface currents, regular oceanographic research cruises, and moored buoys for near-surface meteorological measurements and ocean temperature. Yet, even in this heavily sampled region, there are substantial gaps not filled by the current sampling strategy. Geostationary and polar-orbiting satellites provide discrete glimpses of the spatial structuring at the air–sea interface for a limited subset of environmental parameters. Temporal evolution of features can be provided by moored buoys but the fixed locations limit their use in understanding spatiotemporal structures and spatial scales of dynamical interactions. Other in situ platforms, such as subsurface gliders, floats, and drifters, provide valuable vertical and subsurface oceanographic measurements critical for measuring ocean heat content and transport, ocean velocities, thermohaline circulation, and other oceanographic applications. Wave Gliders provide both surface atmospheric (wind speed and direction, atmospheric pressure, and air temperature) and subsurface oceanographic observations and are able to travel at velocities of typically 0.8 m s‒1. The Saildrone measurements provide significant value to certain types of scientific studies through their design as a solar-powered, movable, steerable platform that samples a wide variety of air–sea-interface and upper-ocean parameters, especially in regions where it is difficult to deploy and maintain other types of assets. Wave Gliders and Saildrones both provide air–sea measurements that address the need for flexible, deployable, movable in situ observational assets, with each vehicle providing different capabilities for different types of scientific investigations. Wave Gliders can provide subsurface observations while Saildrones provide interfacial observations. The Saildrone vehicle’s advantage is for science applications needing rapid spatial sampling (it can travel at up to 4 m s‒1), with additional atmospheric and oceanographic measurements needed to advance research into upwelling dynamics, submesoscale variability, and air–sea fluxes in the vicinity of ocean fronts, diurnal warming modeling, carbon cycling, and biophysical interactions and coupled atmosphere–ocean modeling and data assimilation. It is important to assess the accuracy of Saildrone observations for science. We believe that such an assessment is important for two reasons: first, the Saildrone business model is different from the way research has been previously accomplished. Instead of purchasing equipment, which scientists then maintain, calibrate, and deploy, Saildrone owns and operates the vehicles and sensors, it is the data that are purchased. Second, there may be deployment issues associated with some of the instruments because of the nature of the vehicle. In the following we touch briefly on the former with a bit more discussion devoted to the latter.

Validation of satellite-based retrieval of ocean parameters like Sea Surface Temperature (SST) and Sea Surface Salinity (SSS) is commonly done via statistical comparison with in situ measurements. Because in situ observations derived from coastal/tropical moored buoys and Argo floats are only representatives of one specific geographical point, they cannot be used to measure spatial gradients of ocean parameters (i.e., two-dimensional vectors). In this study, we exploit the high temporal sampling of the unmanned surface vehicle (USV) Saildrone (i.e., one measurement per minute) and describe a methodology to compare the magnitude of SST and SSS gradients derived from satellite-based products with those captured by Saildrone. Using two Saildrone campaigns conducted in the California/Baja region in 2018 and in the North Atlantic Gulf Stream in 2019, we compare the magnitude of gradients derived from six different GHRSST Level 4 SST (MUR, OSTIA, CMC, K10, REMSS, and DMI) and two SSS (JPLSMAP, RSS40km) datasets. While results indicate strong consistency between Saildrone- and satellite-based observations of SST and SSS, this is not the case for derived gradients with correlations lower than 0.4 for SST and 0.1 for SSS products.

We tested the feasibility of using Saildrone unmanned wind- and solar-powered surface vehicles to conduct remote focal follow studies of northern fur seals Callorhinus ursinus. Using Argos satellite and transmitted GPS locations, the Saildrones followed a fur seal while recording oceanographic conditions and mapping prey abundance and depth distribution using a scientific echosounder. The Saildrones successfully followed 6 fur seals over 2.4 ± 0.2 d (mean ± SE) and 149.7 ± 16.3 km of the foraging path. Median separation distance between the Saildrone and fur seal path was 0.65 ± 0.1 km and average time separation was 9.9 ± 1.4 h, with minimum time separations ranging from 1.9-4.9 h. Time and distance separation were a function of both animal behavior and study design. Our results show that Saildrones can approach satellite tracked marine predators from long distances and follow them over extended periods while collecting oceanographic and prey data. These successful focal follows demonstrate that unmanned surface vehicles are a valuable tool for collecting data on fine-scale relationships between marine predators, their prey, and the environment.

Abstract: In vivo chlorophyll fluorescence (ChlF) can serve as a reasonable estimator of in situ phytoplankton biomass with the benefits of efficiently and affordably extending the global chlorophyll (Chl) data set in time and space to remote oceanic regions where routine sampling by other vessels is uncommon. However, in vivo ChlF measurements require correction for known, spurious biases relative to other measures of Chl concentration, including satellite ocean color retrievals. Spurious biases affecting in vivo ChlF measurements include biofouling, colored dissolved organic matter (CDOM) fluorescence, calibration offsets, and non-photochemical quenching (NPQ). A more evenly distributed global sampling of in vivo ChlF would provide additional confidence in estimates of uncertainty for satellite ocean color retrievals. A Saildrone semi-autonomous, ocean-going, solar- and wind-powered surface drone recently measured a variety of ocean and atmospheric parameters, including ChlF, during a 60-day deployment in mid-2018 in the California Current region. Correcting the Saildrone ChlF data for known biases, including deriving an NPQ-correction, greatly improved the agreement between the drone measurements and satellite ocean color retrievals from MODIS-Aqua and VIIRS-SNPP, highlighting that once these considerations are made, Saildrone semi-autonomous surface vehicles are a valuable, emerging data source for ocean and ecosystem monitoring.

Two Saildrone unmanned surface vehicles (USVs) were instrumented with echosounders and deployed in the Bering Sea to make acoustic observations of walleye pollock for 103 days. The Saildrones proved to be a suitable platform for measurement of fish backscatter: they produced high-quality measurements at wind speeds of <10 m s−1. Pollock backscatter measured from the Saildrones was compared to backscatter measured by a noise-reduced research vessel during two “follow-the-leader” comparisons. In a location where pollock were shallowly distributed (30–100 m), there was evidence of depth-dependent avoidance reactions to the ship. This behaviour was not evident in a second comparison, where the fish were primarily deeper than 90 m. Opportunistic comparisons indicate that backscatter where the ship and USVs crossed paths was similar. However, the Saildrones observed higher densities of shallow fish, which is consistent with the diving response inferred in the first follow-the-leader comparison. USVs equipped with echosounders, like all platforms, have inherent strengths (endurance) and limitations (species identification) that should be carefully considered for a given application. USVs can complement traditional ship-based surveys by increasing the spatial and temporal extent of acoustic observations, and their use is likely to become more widespread.

ECMWF has started assimilating data from wind-powered ocean drones, called saildrones, that have the potential to improve Earth system observation coverage in remote areas. Despite the rapid growth of satellite observations, in-situ data remain vital to numerical weather prediction. Direct measurements of key atmospheric parameters often provide useful adjustments to the analysis in sensitive areas. The impact of such observations is larger in less-observed regions. An article in the spring 2019 issue of the ECMWF Newsletter described the successful launch of 32 drifting buoys with pressure sensors in the northeast Pacific. Saildrone technology is another emerging platform well positioned to improve the coverage in remote areas and to perform targeted observation campaigns in regions of interest.

Partnership between the private sector and the ocean observing community brings exciting opportunities to address observing challenges through leveraging the unique strengths of each sector. Here, we discuss a case study of a successful relationship between the National Oceanic and Atmospheric Administration (NOAA) Pacific Marine Environmental Laboratory (PMEL) and Saildrone to instrument an Unmanned Surface Vehicle (USV) in order to serve shared goals. This case study demonstrates that a private company working with a federal laboratory has provided innovative ocean observing solutions deployed at regional scale in only a few years, and we project that this model will be sustainable over the long-term. An alignment of long-term goals with practical deliverables during the development process and integrating group cultures were key to success. To date, this effort has expanded NOAA’s interdisciplinary observing capabilities, improved public access to ocean data, and paved the way for a growing range of USV applications in every ocean. By emphasizing shared needs, complementary strengths, and a clear vision for a sustainable future observing system, we believe that this case study can serve as a blueprint for public and private partners who wish to improve observational capacity. We recommend that the international scientific community continue to foster collaborations between the private sector and regional ocean observing networks. This effort could include regional workshops that build community confidence through independent oversight of data quality. We also recommend that an international framework should be created to organize public and private partners in the atmospheric and oceanographic fields. This body would coordinate development of observational technologies that adhere to best practices and standards for sensor integration, verification, data quality control and delivery, and provide guidance for unmanned vehicle providers. Last, we also recommend building bridges between the private sector, ocean observing community, and the operational forecast community to consider the future of this new private sector, with goals to determine targeted ocean observing needs; assess the appropriateness of USVs as science platforms, sensors, and data format standards; and establish usage and data quality control and distribution protocols for ocean observing and operational forecasting.

Turbulent and radiative exchanges of heat between the ocean and atmosphere (hereafter heat fluxes), ocean surface wind stress, and state variables used to estimate them, are Essential Ocean Variables (EOVs) and Essential Climate Variables (ECVs) influencing weather and climate. This paper describes an observational strategy for producing 3-hourly, 25-km (and an aspirational goal of hourly at 10-km) heat flux and wind stress fields over the global, ice-free ocean with breakthrough 1-day random uncertainty of 15 W m–2 and a bias of less than 5 W m–2. At present this accuracy target is met only for OceanSITES reference station moorings and research vessels (RVs) that follow best practices. To meet these targets globally, in the next decade, satellite-based observations must be optimized for boundary layer measurements of air temperature, humidity, sea surface temperature, and ocean wind stress. In order to tune and validate these satellite measurements, a complementary global in situ flux array, built around an expanded OceanSITES network of time series reference station moorings, is also needed. The array would include 500–1000 measurement platforms, including autonomous surface vehicles, moored and drifting buoys, RVs, the existing OceanSITES network of 22 flux sites, and new OceanSITES expanded in 19 key regions. This array would be globally distributed, with 1–3 measurement platforms in each nominal 10° by 10° box. These improved moisture and temperature profiles and surface data, if assimilated into Numerical Weather Prediction (NWP) models, would lead to better representation of cloud formation processes, improving state variables and surface radiative and turbulent fluxes from these models. The in situ flux array provides globally distributed measurements and metrics for satellite algorithm development, product validation, and for improving satellite-based, NWP and blended flux products. In addition, some of these flux platforms will also measure direct turbulent fluxes, which can be used to improve algorithms for computation of air-sea exchange of heat and momentum in flux products and models. With these improved air-sea fluxes, the ocean’s influence on the atmosphere will be better quantified and lead to improved long-term weather forecasts, seasonal-interannual-decadal climate predictions, and regional climate projections.

A successful integrated ocean acidification (OA) observing network must include (1) scientists and technicians from a range of disciplines from physics to chemistry to biology to technology development; (2) government, private, and intergovernmental support; (3) regional cohorts working together on regionally specific issues; (4) publicly accessible data from the open ocean to coastal to estuarine systems; (5) close integration with other networks focusing on related measurements or issues including the social and economic consequences of OA; and (6) observation-based informational products useful for decision making such as management of fisheries and aquaculture. The Global Ocean Acidification Observing Network (GOA-ON), a key player in this vision, seeks to expand and enhance geographic extent and availability of coastal and open ocean observing data to ultimately inform adaptive measures and policy action, especially in support of the United Nations 2030 Agenda for Sustainable Development. GOA-ON works to empower and support regional collaborative networks such as the Latin American Ocean Acidification Network, supports new scientists entering the field with training, mentorship, and equipment, refines approaches for tracking biological impacts, and stimulates development of lower-cost methodology and technologies allowing for wider participation of scientists. GOA-ON seeks to collaborate with and complement work done by other observing networks such as those focused on carbon flux into the ocean, tracking of carbon and oxygen in the ocean, observing biological diversity, and determining short- and long-term variability in these and other ocean parameters through space and time.