Scientific Papers

Characterizing submesoscale ocean processes requires high-resolution observations in both space O(1) km and time O(1) h. Resolving their velocity gradients requires velocity accuracies of O(1) cm s⁻¹. In the present analysis, we utilize multiple mobile platforms, including Saildrones (SDs), to achieve high-resolution measurements of submesoscale features. We assess Saildrone acoustic Doppler current profiler (ADCP) measurements against shipboard ADCP data, both collected during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE). The results show that the standard 5-min average Saildrone ADCP along-track velocity difference variability (3 cm s⁻¹) is consistent with shipboard ADCP, considered in the present study as a reference. However, direct ADCP comparisons between a Saildrone and the R/V Oceanus give a small mean difference (∼1 cm s⁻¹). The mean difference could stem from spatial inhomogeneities rather than surface waves, whose influence is expected to be negligible at most sampled depths. Based on 1-Hz Saildrone ADCP data, we found that averaging over 3 min of ADCP-derived currents (250 m in space) provides minimal unwanted signal. We investigate the uncertainty of submesoscale current gradients derived from Saildrone ADCP measurements and find that the velocity gradient at a 2-km scale can be obtained with a 0.1f uncertainty using four Saildrones. The methodologies we developed to ascertain the optimal averaging window are versatile and applicable to other uncrewed surface vehicles (USVs) or multiple-ship arrays.

Satellite observations can reveal chlorophyll blooms in the wake of hurricane disturbances but their subsurface biogeochemical anomalies remain poorly described due to limited in situ observations. Here, we quantify the biogeochemical response across the ocean water column to Hurricane Idalia (2023) in the Gulf of America (also known as the Gulf of Mexico). We compile observations across the eastern Gulf using satellite data and two autonomous platforms: a profiling Biogeochemical-Argo (BGC-Argo) float and saildrone. Prior to the formation of Hurricane Idalia, an anomalously large extension of the Mississippi River plume spanned much of the eastern Gulf, contributing low-salinity and high-chlorophyll conditions. Following Idalia’s passage, the saildrone observed surface chlorophyll increases in the river plume extension, while the BGC-Argo float observed subsurface nitrate depletion and oxygen enrichment. These changes occurred as the float measured background ocean conditions evolving from the edge of the Loop Current to a cyclonic eddy, influenced by the river plume extension. Increases in chlorophyll concentration, decreases in nitrate, and elevated dissolved oxygen levels suggested increased primary production. BGC-Argo float observations revealed enhanced upwelling below the surface layer (~22 m) that shoaled the nitracline, fueling the increase in subsurface primary production (20–50 m depth). Our study provides a glimpse on the surface and subsurface ocean-biogeochemical changes associated with the Hurricane Idalia passage, highlighting the importance of the background mesoscale seascape on shaping the phytoplankton response to hurricane-induced disturbances. The combination of observations underscores the value of continuous in situ monitoring to better understand hurricane-driven impacts on the full ocean water column and the impacts these dynamics have on the base of the marine food web.

Air-sea fluxes have rarely or never been estimated from in situ observations in many parts of the global oceans, especially in the Arctic, despite their critical roles in weather and climate. In consequence, their reproductions by numerical models have seldomly been validated against observations. In this study, observations from Saildrone Explorer uncrewed surface vehicles are used to validate surface sensible and latent heat fluxes from GFS deterministic forecasts and GEFS ensemble forecasts in the Arctic during May – October 2019. The most striking result from this study is the low biases in sea surface temperature (SST) in the initial conditions of both the deterministic and ensemble forecasts. Excessively cold predictions of SST lead to reversed signs in air-sea differences in temperature and humidity in comparison to the observations. Consequently, surface sensible and latent heat fluxes in the forecast can be negative (from air into the water), while observed fluxes are positive. The larger SST biases at the initial time og the GEFS ensemble forecasts is the main reason for their underperformance in comparison to the GFS deterministic forecasts. The results clearly demonstrate the vital step of improving forecasts in the Arctic is to prepare for accurate initial conditions of SST.

The ATL2MED mission, conducted between October 2019 and July 2020, investigated the variability of air–sea CO₂ exchange in the Eastern Atlantic and the Mediterranean Sea. The main objectives were to assess the spatial and temporal variability of the seawater partial pressure of CO₂ (pCO_2sw), identify its controlling physical and biogeochemical processes, estimate the CO₂ fluxes across the sea–air interface, and evaluate the performance of neural network-based predictions (CANYON-MED) in contrasting oceanographic regions. High-resolution autonomous measurements were collected using Saildrone Unmanned Surface Vehicles (USVs), complemented by fixed ocean stations, gliders, and research vessels. Data quality was ensured through cross-validation among platforms, despite challenges such as sensor drift caused by biofouling.

In 2024, Meta and Saildrone collaborated to evaluate the capabilities of the 20-meter Saildrone Surveyor Uncrewed Surface Vehicle (USV) during a 26-day demonstration in the North Atlantic, covering 4,500 km (plus 2,000 km of transit) without any port calls or outside assistance. Conducted in June and July, the demonstration took place along previously surveyed cable routes, thus allowing for direct comparisons in terms of progress rates, line keeping, data quality, and coverage. Water depths ranged from 900 m to 5,500 m, with the latest Kongsberg EM304 MKII multibeam sonar used to capture seabed data over a swathe of up to 10 km in width. Metrics on fuel consumption and environmental impact demonstrated greenhouse gas emissions were more than 50 times lower than that of a conventional survey vessel over the same distance1. A Starlink satellite link was used for real-time data analysis and communications. This paper will discuss the advantages of Saildrone’s wind-assisted, unmanned platform, which could potentially revolutionize deep-water route surveying. It will also discuss the future developments necessary to meet the demands of subsea telecommunications projects.

The atmospheric marine boundary layer (AMBL) plays a crucial role in regulating air-sea interactions and influencing the climate system, particularly in the Arctic due to its high sensitivity to global warming. This study utilizes five years (2018–2022) of saildrone data from the Bering-Chukchi-Beaufort Seas to analyze atmospheric near-surface stability and air-sea turbulent heat fluxes during the Arctic summer. By applying Monin-Obukhov similarity theory, we investigate the temporal variability and mechanisms that influence AMBL stability. Our findings reveal two distinct regimes of stable and unstable conditions in two contrasting years of 2020 and 2022. In 2020, cold air advection driven by northerly winds consistently destabilizes the AMBL, leading to increased oceanic heat loss. In 2022, however, southerly winds and warm air advection stabilize the AMBL, suppressing air-sea heat exchanges. The temporal variation of turbulent heat fluxes is primarily driven by air-sea temperature differences, with the magnitude of wind speed and its temperature covariance serving as secondary factors. We also show the importance of skin temperature measurements from saildrones for improving estimates of turbulent heat fluxes. These insights enhance our understanding of Arctic air-sea interactions and inform climate models, underscoring the need for high-resolution observations in polar regions and the improvement of bulk flux parameterization for stable AMBL.

Observing air-sea interactions on a global scale is essential for improving Earth system forecasts. Yet these exchanges are challenging to quantify for a range of reasons, including extreme conditions, vast and remote under-sampled locations, requirements for a multitude of co-located variables, and the high variability of fluxes in space and time. Uncrewed Surface Vehicles (USVs) present a novel solution for measuring these crucial air-sea interactions at a global scale. Powered by renewable energy (e.g., wind and waves for propulsion, solar power for electronics), USVs have provided navigable and persistent observing capabilities over the past decade and a half.

Survey time series are used to track species- and ecosystem-level trends over time to support ecosystem-based fishery management. However, these recurring survey efforts are subject to unpredictable cancellations and reductions in effort. This occurred in 2020 when the COVID-19 pandemic forced the cancellation of the research vessel-based 2020 eastern Bering Sea (EBS) acoustic-trawl survey, which has provided an estimate of euphausiid abundance and distribution since 2004. As a partial replacement for this lost effort, three uncrewed surface vehicles (USVs) were used to collect acoustic data. In contrast to the standard vessel-based survey, which provides 4-frequency acoustic data, the USVs collected acoustic data at only two frequencies. This presented a challenge given that four frequencies are currently used to identify euphausiids in this time series. Here, we first evaluated two methods to provide comparable euphausiid abundance estimates using fewer acoustic frequencies. We found that a random forest classifier was able to produce abundance estimates comparable to those obtained in the vessel-based time series. This method was then used to estimate euphausiid abundance and distribution from the 2020 USV survey. We additionally estimated the increase in survey uncertainty due to the use of the random forest classifier and changes in the acoustic instruments. Together, this allowed for the EBS euphausiid abundance time series to be extended with fewer acoustic frequencies.

Over the long term, it would be wise to consider introducing an additional source of in situ calibration ground truth for surface ocean observations, such as from Saildrones. The Saildrones carry sonic anemometers that are calibrated in wind tunnels, independently from the buoy anemometers. Some of the Saildrone observations have already been cross-calibrated or verified in proximity of the buoy locations in the tropical Atlantic and many missions have been successfully deployed in other ocean basins; they are planned to continue and possibly expand in the future. According to a recent article, NDBC Director Dr. William Burnett highlighted an urgent need for “a system of systems to resolve ocean observation gaps,” such as in the event of buoy “outages.” One example is a recent Saildrone long-term mission funded by NOAA Office of Marine and Aviation Operations (OMAO), to replace a buoy off the coast of Half Moon Bay, California, beginning in September 2023 and still ongoing in 2024. Using Saildrones in conjunction with the buoys, for shorter cross-calibration periods, would add confidence in the accuracy of both observational methods and provide robust, continuous monitoring and more stability to the integrated in situ and satellite observing system.

Lesser sandeel (Ammodytes marinus) exhibits a peculiar diel vertical migration (DVM) during the feeding season, burying into the seabed at night and emerging during daytime to form schools that feed on zooplankton. Large schools may consist of a pelagic component searching for prey and a bottom component connected by collective bridge-like formations. However, the temporal variation in the schools’ vertical distribution is poorly understood. In this study, 38 and 200 kHz acoustic data recorded with Saildrones were used to examine the schooling dynamics during their main feeding season in May–June. A total of 1497 sandeel schools that were identified by linear discriminant analysis displayed two distinct vertical components throughout the season: one in the pelagic zone and one near the seabed. The pelagic component was distributed deepest at noon and had a similar pattern to zooplankton DVM, suggesting that sandeel followed the vertical distribution of their prey. Their diurnal ascension was greater in both distance and hours in May than June, suggesting a decline in feeding motivation towards the end of the feeding season. These findings were made possible with the long-term monitoring by silent Saildrones, which did not seem to affect the natural behaviour of sandeel schools.