Scientific Papers

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.

In 2020, the developing COVID-19 pandemic disrupted fisheries surveys to an unprecedented extent. Many surveys were cancelled, including those for walleye pollock (Gadus chalcogrammus) in the eastern Bering Sea (EBS), the largest fishery in the United States. To partially mitigate the loss of survey information, we deployed three uncrewed surface vehicles (USVs) equipped with echosounders to extend the ship-based acoustic-trawl time series of pollock abundance. Trawling was not possible from USVs, so an empirical relationship between pollock backscatter and biomass established from previous surveys was developed to convert USV backscatter observations into pollock abundance. The EBS is well suited for this approach since pollock dominate midwater fishes in the survey area. Acoustic data from the USVs were combined with historical surveys to provide a consistent fishery-independent index in 2020. This application demonstrates the unique capabilities of USVs and how they could be rapidly deployed to collect information on pollock abundance and distribution when a ship-based survey was not feasible. We note the limitations of this approach (e.g. higher uncertainty relative to previous ship-based surveys), but found the USV survey to be useful in informing the stock assessment in a situation where ship-based surveys were not possible.

With accelerating global warming and human activities, the North Sea is one of the marine ecosystems undergoing rapid change. The need for spatially-temporally extendable survey platforms for assisting well-established vessel-based surveys are increasing. In this thesis, short-term variation in spatial structure of plankton and lesser sandeel (Ammodytes marinus) were investigated in the North Sea by using unmanned surface vehicle (USVs) Saildrones equipped with dual-frequency (38, 200 kHz) echo sounder. The data was collected in two areas, a part of the standard Aberdeen-Hanstholm transect and English Klondyke, an important sandeel fishing ground. These areas were repeatedly covered by two Saildrones in May-June 2019. Repeated surveys witnessed high plankton density in the western part of the Aberdeen-Hanstholm transect constantly during the survey period. Salinity seemed to be one possible factor explaining the heterogeneity of plankton density in both vertical and horizontal structure. Sandeel appeared diurnally at various depths from 2 m to near the sea bottom. There was only a weak tendency that the schools were distributed deeper around midday. However, their diverse vertical distribution indicated underlying drivers of their behavior other than light. Despite the existing uncertainty of species identification due to lack of ground-truthing and limited frequency availability, this saildrone survey conveyed little but purposeful information of the dynamics in spatial utilization of plankton and sandeel over a short period of time.

New in situ observations collected by Saildrones, a novel uncrewed surface vehicle (USV), are used to investigate atmospheric cold pools during three 6-month missions to the central and eastern (∼140°W–125°W) tropical Pacific. Cold pool fronts in the atmospheric boundary layer are identified by a −1.5°C air temperature drop occurring in 10 min or less. While cold pool events were observed in the central Pacific as far north as 30°N and within the equatorial band, the majority of observed cold pools occurred within the convective, low-wind shear environment of the Intertropical Convergence Zone. Composite time series analysis of measurements during the 382 cold pool events reveals new insights on high-frequency variations in air temperature, wind speed, humidity, pressure, and sea surface temperature and salinity associated with cold pool fronts. The results highlight the unique capabilities of Saildrone USVs to resolve small spatial and temporal scales of variability over observationally sparse ocean regions.

More high-quality, in situ observations of essential marine variables are needed over the seasonal ice zone to better understand Arctic (or Antarctic) weather, climate, and ecosystems. To better assess the potential for arrays of uncrewed surface vehicles (USVs) to provide such observations, five wind-driven and solar-powered saildrones were sailed into the Chukchi and Beaufort Seas following the 2019 seasonal retreat of sea ice. They were equipped to observe the surface oceanic and atmospheric variables required to estimate air-sea fluxes of heat, momentum and carbon dioxide. Some of these variables were made available to weather forecast centers in real time. Our objective here is to analyze the effectiveness of existing remote ice navigation products and highlight the challenges and opportunities for improving remote ice navigation strategies with USVs. We examine the sources of navigational sea-ice distribution information based on post-mission tabulation of the sea-ice conditions encountered by the vehicles. The satellite-based ice-concentration analyses consulted during the mission exhibited large disagreements when the sea ice was retreating fastest (e.g., the 10% concentration contours differed between analyses by up to ∼175 km). Attempts to use saildrone observations to detect the ice edge revealed that in situ temperature and salinity measurements varied sufficiently in ice bands and open water that it is difficult to use these variables alone as a reliable ice-edge indicator. Devising robust strategies for remote ice zone navigation may depend on developing the capability to recognize sea ice and initiate navigational maneuvers with cameras and processing capability onboard the vehicles.

The Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC) took place from 7 January to 11 July 2020 in the tropical North Atlantic between the eastern edge of Barbados and 51∘ W, the longitude of the Northwest Tropical Atlantic Station (NTAS) mooring. Measurements were made to gather information on shallow atmospheric convection, the effects of aerosols and clouds on the ocean surface energy budget, and mesoscale oceanic processes. Multiple platforms were deployed during ATOMIC including the NOAA RV Ronald H. Brown (RHB) (7 January to 13 February) and WP-3D Orion (P-3) aircraft (17 January to 10 February), the University of Colorado's Robust Autonomous Aerial Vehicle-Endurant Nimble (RAAVEN) uncrewed aerial system (UAS) (24 January to 15 February), NOAA- and NASA-sponsored Saildrones (12 January to 11 July), and Surface Velocity Program Salinity (SVPS) surface ocean drifters (23 January to 29 April). The RV Ronald H. Brown conducted in situ and remote sensing measurements of oceanic and atmospheric properties with an emphasis on mesoscale oceanic–atmospheric coupling and aerosol–cloud interactions. In addition, the ship served as a launching pad for Wave Gliders, Surface Wave Instrument Floats with Tracking (SWIFTs), and radiosondes. Details of measurements made from the RV Ronald H. Brown, ship-deployed assets, and other platforms closely coordinated with the ship during ATOMIC are provided here. These platforms include Saildrone 1064 and the RAAVEN UAS as well as the Barbados Cloud Observatory (BCO) and Barbados Atmospheric Chemistry Observatory (BACO). Inter-platform comparisons are presented to assess consistency in the data sets. Data sets from the RV Ronald H. Brown and deployed assets have been quality controlled and are publicly available at NOAA's National Centers for Environmental Information (NCEI) data archive (https://www.ncei.noaa.gov/archive/accession/ATOMIC-2020, last access: 2 April 2021). Point-of-contact information and links to individual data sets with digital object identifiers (DOIs) are provided herein.