Scientific Papers

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.

Uncrewed surface vehicles (USVs) equipped with echosounders have the potential to replace or enhance acoustic observations from conventional research vessels (RVs), increase spatial and temporal coverage, and reduce cost and carbon emission. We discuss the objectives, system requirements, infrastructure, and regulations for using USVs with echosounders to conduct ecological experiments, acoustic-trawl surveys, and long-term monitoring. We present four example applications of USVs with lengths <8 m, and highlight some advantages and disadvantages relative to RV-based data acquisitions. Sail-driven USVs operate continuously for months and are more mature than motorized USVs, but they are slower. To maintain the pace of an RV, multiple sail-powered USVs sample in coordination. In comparison, motorized USVs can travel as fast as RVs and therefore may facilitate a combined survey, interleaving USV and RV transects, with RV-based biological sampling. Important considerations for all USVs include platform design, noise and transducer motion mitigation, communications and operations infrastructure, onboard data processing, biological sampling approach, and legal requirements. This technology is evolving and applied in multiple disciplines, but further development and institutional commitment are needed to allow USVs equipped with echosounders to become ubiquitous and useful components of a worldwide network of autonomous ocean observation platforms.

These data were derived from hydroacoustic data collected by uncrewed surface vehicles (USVs) and powered research vessels in Lake Superior in 2022. The powered vessels overtook the USVs to study fish avoidance of survey vessels during traditional acoustic surveys. The water column was divided into four depth groups for analysis. Each USV transect was binned into 30-sec intervals and measured hydroacoustic values were averaged in this region. To compare vessels and USVs, parallel overtakes (overtakes where vessel and drone followed the same path) which were ~2 km long, were measured by both platforms and the differences between acoustic measures compared.

The Arctic is one of the most important regions in the world’s oceans for understanding the impacts of a changing climate. Yet, it is also difficult to measure because of extreme weather and ice conditions. In this work, we directly compare four datasets from the Group for High-Resolution Sea Surface Temperature (GHRSST) with a NASA Saildrone deployment along the Alaskan Coast and the Bering Sea and Bering Strait. The four datasets used are the Remote Sensing Systems Microwave Infrared Optimally Interpolated (MWIR) product, the Canadian Meteorological Center (CMC) product, the Daily Optimally Interpolated Product (DOISST), and the Operational Sea Surface Temperature and Ice Analysis (OSTIA) product. Spatial sea surface temperature (SST) gradients were derived for both the Saildrone deployment and GHRSST products, with the GHRSST products collocated with the Saildrone deployment. Overall, statistics indicate that the OSTIA product had a correlation of 0.79 and a root mean square difference of 0.11 °C/km when compared with Saildrone. CMC had the highest correlation of 0.81. Scatter plots indicate that OSTIA had the slope closest to one, thus best reproducing the magnitudes of the Saildrone gradients. Differences increased at latitudes > 65°N where sea ice would have a greater impact. A trend analysis was then performed on the gradient fields. Overall, positive trends in gradients occurred in areas along the coastal regions. A negative trend occurred at approximately 60°N. A major finding of this study is that future work needs to revolve around the impact of changing ice conditions on SST gradients. Another major finding is that a northward shift in the southern ice edge occurred after 2010 with a maxima at approximately 2019. This indicates that the shift of the southern ice edge is not gradual but has dramatically increased over the last decade. Future work needs to revolve around examining the possible causes for this northward shift.

The infrared (IR) satellite remote sensing of sea surface skin temperature (SST_skin) is challenging in the northern high-latitude region, especially in the Arctic because of its extreme environmental conditions, and thus the accuracy of SST_skin retrievals is questionable. Several Saildrone uncrewed surface vehicles were deployed at the Pacific side of the Arctic in 2019, and two of them, SD-1036 and SD-1037, were equipped with a pair of IR pyrometers on the deck, whose measurements have been shown to be useful in the derivation of SST_skin with sufficient accuracy for scientific applications, providing an opportunity to validate satellite SST_skin retrievals. This study aims to assess the accuracy of MODIS-retrieved SST_skin from both Aqua and Terra satellites by comparisons with collocated Saildrone-derived SST_skin data. The mean difference in SST_skin from the SD-1036 and SD-1037 measurements is ~0.4 K, largely resulting from differences in the atmospheric conditions experienced by the two Saildrones. The performance of MODIS on Aqua and Terra in retrieving SST_skin is comparable. Negative brightness temperature (BT) differences between 11 μm and 12 μm channels are identified as being physically based, but are removed from the analyses as they present anomalous conditions for which the atmospheric correction algorithm is not suited. Overall, the MODIS SST_skin retrievals show negative mean biases, −0.234 K for Aqua and −0.295 K for Terra. The variations in the retrieval inaccuracies show an association with diurnal warming events in the upper ocean from long periods of sunlight in the Arctic. Also contributing to inaccuracies in the retrieval is the surface emissivity effect in BT differences characterized by the Emissivity-introduced BT difference (EΔBT) index. This study demonstrates the characteristics of MODIS-retrieved SST_skin in the Arctic, at least at the Pacific side, and underscores that more in situ SST_skin data at high latitudes are needed for further error identification and algorithm development of IR SST_skin.

Few observational platforms are able to sustain direct measurements of all the key variables needed in the bulk calculation of air-sea carbon dioxide (CO₂) exchange, a capability newly established for some Uncrewed Surface Vehicles (USVs). Western boundary currents are particularly challenging observational regions due to strong variability and dangerous sea states but are also known hot spots for CO₂ uptake, making air-sea exchange quantification in this region both difficult and important. Here, we present new observations collected by Saildrone USVs in the Gulf Stream during the winters of 2019 and 2022. We compared Saildrone data across co-located vehicles and against the Pioneer Array moorings to validate the data quality. We explored how CO₂ flux estimates differ when all variables needed to calculate fluxes from the bulk formulas are simultaneously measured on the same platform, relative to the situation where in situ observations must be combined with publicly-available data products. We systematically replaced variables in the bulk formula with those often used for local and regional flux estimates. The analysis revealed that when using the ERA-5 reanalysis wind speed in place of in situ observations, the ocean uptake of CO₂ is underestimated by 8%; this underestimate grows to 9% if the NOAA Marine Boundary Layer atmospheric CO₂ product and ERA-5 significant wave height are also used in place of in situ observations. Overall our findings point to the importance of collecting contemporaneous observations of wind speed and ocean pCO₂ to reduce biases in estimates of regional CO₂ flux, especially during high wind events.

The strength and variability of the Southern Ocean carbon sink is a significant source of uncertainty in the global carbon budget. One barrier to reconciling observations and models is understanding how synoptic weather patterns modulate air-sea carbon exchange. Here, we identify and track storms using atmospheric sea level pressure fields from reanalysis data to assess the role that storms play in driving air-sea CO₂ exchange. We examine the main drivers of CO₂ fluxes under storm forcing and quantify their contribution to Southern Ocean annual air-sea CO₂ fluxes. Our analysis relies on a forced ocean-ice simulation from the Community Earth System Model, as well as CO₂ fluxes estimated from Biogeochemical Argo floats. We find that extratropical storms in the Southern Hemisphere induce CO₂ outgassing, driven by CO₂ disequilibrium. However, this effect is an order of magnitude larger in observations compared to the model and caused by different reasons. Despite large uncertainties in CO₂ fluxes and storm statistics, observations suggest a pivotal role of storms in driving Southern Ocean air-sea CO₂ outgassing that remains to be well represented in climate models, and needs to be further investigated in observations.

Characterizing temperature and salinity (T-S) conditions is a standard framework in oceanography to identify and describe deep water masses and their dynamics. At the surface, this practice is hindered by multiple air–sea–land processes impacting T-S properties at shorter time scales than can easily be monitored. Now, however, the unsurpassed spatial and temporal coverage and resolution achieved with satellite sea surface temperature (SST) and salinity (SSS) allow us to use these variables to investigate the variability of surface processes at climate-relevant scales. In this work, we use SSS and SST data, aggregated into domains using a cluster algorithm over a T-S diagram, to describe the surface characteristics of the California Current System (CCS), validating them with in situ data from uncrewed Saildrone vessels. Despite biases and uncertainties in SSS and SST values in highly dynamic coastal areas, this T-S framework has proven useful in describing CCS regional surface properties and their variability in the past and in real time, at novel scales. This analysis also shows the capacity of remote sensing data for investigating variability in land–air–sea interactions not previously possible due to limited in situ data.