Scientific Papers

The collection of environmental DNA (eDNA) samples is often laborious, costly, and logistically difficult to accomplish at high frequency in remote locations and over large geographic areas. Here, we addressed those challenges by combining two robotic technologies: an uncrewed surface vessel (USV) fitted with an automated eDNA sample collection device to survey surface waters in the eastern North Pacific Ocean from Alameda, CA to Honolulu, HI. USV Surveyor SD 1200 (Saildrone) carrying the Environmental Sample Processor (ESP) collected 2-L water samples by filtration followed by RNAlater preservation at regular intervals over a 4200-km, 29-day transit. Sixty samples (52 field and 8 controls) were acquired and used to estimate the concentration of specific genes and assess eukaryotic diversity via targeted qPCR and metabarcoding of the cytochrome oxidase subunit I (COI) gene, respectively. Comparisons of control samples revealed important considerations for interpreting results. Samples stored at ambient temperatures onboard Surveyor over the length of the voyage had less total recoverable DNA and specific target gene concentrations compared to the same material immediately flash-frozen after collection and stored in a laboratory. In contrast, the biodiversity of the COI genes in those samples was similar regardless of sample age and storage condition. COI genes affiliated with 40 eukaryotic phyla were found in native samples collected during the voyage. The distribution and dominance of those phyla varied across different regions, with some taxa spanning large continuous stretches >2000 km, while others were only detected in a single sample. This work highlights the utility and potential of using USVs fitted with autonomous eDNA sample collection devices to improve ocean exploration and support large, basin-scale, systematic biodiversity surveys. Results of this study also inform future technical considerations for using automated eDNA samplers to acquire material and store it over prolonged periods under prevailing environmental conditions.

There is growing recognition that key to addressing critical issues like climate change, global sea level rise, and the long-term sustainability of humankind is a more complete understanding of our oceans and processes within them that account for the distribution of global heat, CO, and provide sustenance to so many. Yet, despite years of effort, less than 25% of the global ocean seafloor has been mapped, and less than 5% of the ocean volume explored, likely due to the cost and inefficiency of traditional ocean mapping and exploration techniques using large, very expensive, crewed research vessels. Recent advances in the development of uncrewed surface vessels offer the possibility to reduce costs and increase efficiency of ocean mapping and exploration. Such efficiencies can be gained by using small mother ship-deployed uncrewed vessels acting as relatively inexpensive mapping and sampling force multipliers or the use of small uncrewed vessels launched to from a mother ship to monitor and control autonomous underwater vehicles, allowing multiple operations simultaneously and “verified, directed sampling,” all while freeing the mother ship for independent operations. We are also seeing the development of larger uncrewed vessels launched from shore with long endurance and range, capable of carrying a full suite of deep ocean mapping and exploration tools. All of these systems and approaches offer great hope, but it is very early in our understanding of their full capabilities, costs, and limitations, and we must be careful not to overpromise, leading to disappointments and early abandonment of a potentially innovative approach, while at the same time maintaining the patience required to continue the research, investment, and innovation that will hopefully bring us to a new world of efficient and effective ocean mapping and exploration that will allow us to meet our goal of complete coverage of the ocean.

High-frequency wind measurements from Saildrone autonomous surface vehicles are used to calculate wind stress in the tropical east Pacific. Comparison between direct covariance (DC) and bulk wind stress estimates demonstrates very good agreement. Building on previous work that showed the bulk input data were reliable, our results lend credibility to the DC estimates. Wind flow distortion by Saildrones is comparable to or smaller than other platforms. Motion correction results in realistic wind spectra, albeit with signatures of swell-coherent wind fluctuations that may be unrealistically strong. Fractional differences between DC and bulk wind stress magnitude are largest at wind speeds below 4 m s−1. The size of this effect, however, depends on choice of stress direction assumptions. Past work has shown the importance of using current-relative (instead of Earth-relative) winds to achieve accurate wind stress magnitude. We show that it is also important for wind stress direction.

High-frequency wind measurements from Saildrone autonomous surface vehicles are used to calculate wind stress in the tropical east Pacific. Comparison between direct covariance (DC) and bulk wind stress estimates demonstrates very good agreement. Building on previous work that showed the bulk input data were reliable, our results lend credibility to the DC estimates. Wind flow distortion by Saildrones is comparable to or smaller than other platforms. Motion correction results in realistic wind spectra, albeit with signatures of swell-coherent wind fluctuations that may be unrealistically strong. Fractional differences between DC and bulk wind stress magnitude are largest at wind speeds below 4 m s−1. The size of this effect, however, depends on choice of stress direction assumptions. Past work has shown the importance of using current-relative (instead of Earth-relative) winds to achieve accurate wind stress magnitude. We show that it is also important for wind stress direction.

The sea surface temperature (SST) is one of the essential parameters needed to understand the climate change in the Arctic. Saildrone, an advanced autonomous surface vehicle, has proven to be a useful tool for providing accurate SST data at high latitudes. Here, data from two Saildrones, deployed in the Arctic in the summer of 2019, are used to investigate the diurnal variability of upper ocean thermal structure. An empirical cool skin effect model with dependence on the wind speed with new coefficients was generated. Several local large diurnal warming events were observed, the amplitudes of warming in the skin layer >5 K, rarely reported in previous studies. Furthermore, the warming signals could persist beyond 1 day. For those cases, it was found surface warm air suppressed the surface turbulent heat loss to maintain the persistence of diurnal warming under low wind conditions. Salinity also plays an important role in the formation of upper ocean density stratification during diurnal warming at high latitudes. A less salty and hence less dense surface layer was likely created by precipitation or melting sea ice, providing favorable conditions for the formation of upper ocean stratification. Comparisons with two prognostic diurnal warming models showed the simulations match reasonably well with Saildrone measurements for moderate wind speeds but exhibit large differences at low winds. Both schemes show significant negative biases in the early morning and late afternoon. It is necessary to improve the model schemes when applied at high latitudes.

Observations from uncrewed surface vehicles (saildrones) in the Bering, Chukchi, and Beaufort Seas during June–September 2019 were used to evaluate initial conditions and forecasts with lead times up to 10 days produced by eight operational numerical weather prediction centers. Prediction error behaviors in pressure and wind are found to be different from those in temperature and humidity. For example, errors in surface pressure were small in short-range (<6 days) forecasts, but they grew rapidly with increasing lead time beyond 6 days. Non-weighted multimodel means outperformed all individual models approaching a 10-day forecast lead time. In contrast, errors in surface air temperature and relative humidity could be large in initial conditions and remained large through 10-day forecasts without much growth, and non-weighted multimodel means did not outperform all individual models. These results following the tracks of the mobile platforms are consistent with those at a fixed location. Large errors in initial condition of sea surface temperature (SST) resulted in part from the unusual Arctic surface warming in 2019 not captured by data assimilation systems used for model initialization. These errors in SST led to large initial and prediction errors in surface air temperature. Our results suggest that improving predictions of surface conditions over the Arctic Ocean requires enhanced in situ observations and better data assimilation capability for more accurate initial conditions as well as better model physics. Numerical predictions of Arctic atmospheric conditions may continue to suffer from large errors if they do not fully capture the large SST anomalies related to Arctic warming.

A scarcity of wintertime observations of surface ocean carbon dioxide partial pressure (pCO2) in and near the Gulf Stream creates uncertainty in the magnitude of the regional carbon sink and its controlling mechanisms. Recent observations from an Uncrewed Surface Vehicle (USV), outfitted with a payload to measure surface ocean and lower atmosphere pCO2, revealed sharp gradients in ocean pCO2 across the Gulf Stream. Surface ocean pCO2 was lower by ∼50 μatm relative to the atmosphere in the subtropical mode water (STMW) formation region. This undersaturation combined with strong wintertime winds allowed for rapid ocean uptake of CO2, averaging −11.5 mmol m−2 day−1 during the February 2019 USV mission. The unique timing of this mission revealed active STMW formation. The USV proved to be a useful tool for CO2 flux quantification in the poorly observed, dynamic western boundary current environment.

There is high demand for complete satellite SST maps (or L4 SST analyses) of the Arctic regions to monitor the rapid environmental changes occurring at high latitudes. Although there are a plethora of L4 SST products to choose from, satellite-based products evolve constantly with the advent of new satellites and frequent changes in SST algorithms, with the intent of improving absolute accuracies. The constant change of these products, as reflected by the version product, make it necessary to do periodic validations against in situ data. Eight of these L4 products are compared here against saildrone data from two 2019 campaigns in the western Arctic, as part of the MISST project. The accuracy of the different products is estimated using different statistical methods, from standard and robust statistics to Taylor diagrams. Results are also examined in terms of spatial scales of variability using auto- and cross-spectral analysis. The three products with the best performance, at this point and time, are used in a case study of the thermal features of the Yukon–Kuskokwim delta. The statistical analyses show that two L4 SST products had consistently better relative accuracy when compared to the saildrone subsurface temperatures. Those are the NOAA/NCEI DOISST and the RSS MWOI SSTs. In terms of the spectral variance and feature resolution, the UK Met Office OSTIA product appears to outperform all others at reproducing the fine scale features, especially in areas of high spatial variability, such as the Alaska coast. It is known that L4 analyses generate small-scale features that get smoothed out as the SSTs are interpolated onto spatially complete grids. However, when the high-resolution satellite coverage is sparse, which is the case in the Arctic regions, the analyses tend to produce more spurious small-scale features. The analyses here indicate that the high-resolution coverage, attainable with current satellite infrared technology, is too sparse, due to cloud cover to support very high resolution L4 SST products in high latitudinal regions. Only for grid resolutions of ~9–10 km or greater does the smoothing of the gridding process balance out the small-scale noise resulting from the lack of high-resolution infrared data. This scale, incidentally, agrees with the Rossby deformation radius in the Arctic Ocean (~10 km).

The ocean and the marine parts of the cryosphere interact directly with, and are affected by, the seafloor and its primary properties of depth (bathymetry) and shape (morphology) in many ways. Bottom currents are largely constrained by undersea terrain with consequences for both regional and global heat transport. Deep ocean mixing is controlled by seafloor roughness, and the bathymetry directly influences where marine outlet glaciers are susceptible to the inflow relatively warm subsurface waters - an issue of great importance for ice-sheet discharge, i.e., the loss of mass from calving and undersea melting. Mass loss from glaciers and the Greenland and Antarctic ice sheets, is among the primary drivers of global sea-level rise, together now contributing more to sea-level rise than the thermal expansion of the ocean. Recent research suggests that the upper bounds of predicted sea-level rise by the year 2100 under the scenarios presented in IPCC’s Special Report on the Ocean and Cryosphere in a Changing Climate (SROCCC) likely are conservative because of the many unknowns regarding ice dynamics. In this paper we highlight the poorly mapped seafloor in the Polar regions as a critical knowledge gap that needs to be filled to move marine cryosphere science forward and produce improved understanding of the factors impacting ice-discharge and, with that, improved predictions of, among other things, global sea-level. We analyze the bathymetric data coverage in the Arctic Ocean specifically and use the results to discuss challenges that must be overcome to map the most remotely located areas in the Polar regions in general.

In the United States alone, hurricanes have been responsible for thousands of deaths and over US$1 trillion in damages since 1980 (https://www.ncdc.noaa.gov/billions/). These impacts are significantly greater globally, particularly in regions with limited hurricane early warning systems and where large portions of the population live at or near sea level. The high socioeconomic impacts of tropical cyclones will increase with a changing climate, rising sea level, and increasing coastal populations. To mitigate these impacts, efforts are underway to improve hurricane track and intensity forecasts, which drive storm surge models and evacuation orders and guide coastal preparations. Hurricane track forecasts have improved steadily over past decades, while intensity forecasts have lagged until recently (Cangialosi et al., 2020). Hurricane intensity changes are influenced by a combination of large-scale atmospheric circulation, internal storm dynamics, and air-sea interactions (Wadler et al., 2021, and references therein). Components of the sustained ocean observing system (e.g., profiling floats, expendable bathythermographs, drifters, moorings) are useful for understanding the role of the ocean in hurricane intensity changes. However, gaps in the ocean observing system, particularly collection of data near the air-sea interface and in coastal regions, boundary currents (e.g., the Gulf Stream, Kuroshio, among others), and areas with complex currents and seafloor topography (e.g., the Caribbean Sea), have led to difficulties in accurately representing upper ocean features and processes in numerical ocean models. Employment of uncrewed ocean observing platforms has begun to fill these gaps by offering rapid relocation and adaptive sampling of regions and ocean features of interest. These platforms include autonomous underwater gliders (Figure 1; Testor et al., 2019) and surface vehicles (Meinig et al., 2019). Uncrewed surface vehicles (USVs), such as saildrones and wave gliders, are systems designed for data collection in hazardous conditions. Data collected by these platforms have improved our understanding of upper ocean temperature and salinity stratification and mixing processes and are becoming critical in improving operational ocean and coupled air-sea hurricane forecast models (Domingues et al., 2021). This paper provides a broad overview of the ongoing US hurricane glider project and details of a new effort with the Saildrone USV during the 2021 hurricane season. While this article focuses on the US East Coast, Gulf of Mexico, and Caribbean Sea, similar efforts are underway in Korea, the Philippines, Japan, and China, among other countries.