Significant Diurnal Warming Events Observed by Saildrone at High Latitudes
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.
Jia, C., Minnett, P. J., & Luo, B. (2023). Significant diurnal warming events observed by Saildrone at high latitudes. Journal of Geophysical Research: Oceans, 128, e2022JC019368. https://doi.org/10.1029/2022JC019368Download Paper (PDF)
High Latitude Sea Surface Skin Temperatures Derived From Saildrone Infrared Measurements
From May 15 to October 11, 2019, six Saildrone uncrewed surface vehicles (USVs) were deployed for 150-day cruises collecting a suite of atmospheric and oceanographic measurements from Dutch Harbor, Alaska, transiting the Bering Strait into the Chukchi Sea and the Arctic Ocean. Two Saildrones funded by the National Aeronautics and Space Administration (NASA), SD-1036 and SD-1037, were equipped with infrared (IR) radiation pyrometers in a “unicorn” structure on the deck for the determination of the ocean sea surface skin temperature (SST skin ). We present an algorithm to derive SSTskin from the downward- and upward-looking radiometers and estimate the main contributions to the inaccuracy of SSTskin. After stringent quality control of data and eliminating measurements influenced by sea ice and precipitation, and restricting the acceptable tilt angles of the USV based on radiative transfer simulations, SSTskin can be derived to an accuracy of approximately 0.12 K. The error budget of the derived SSTskin is developed, and the largest component comes from the instrumental uncertainties, assuming that the viewing geometry is adequately determined. Thus, Saildrones equipped with these sensors could provide sufficiently accurate SSTskin retrievals for studying the physics of the thermal skin effect, in conjunction with accurate subsurface thermometer measurements, and for validating satellite-derived SSTskin fields at high latitudes.
C. Jia, P. J. Minnett, M. Szczodrak and M. Izaguirre, "High Latitude Sea Surface Skin Temperatures Derived From Saildrone Infrared Measurements," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-14, 2023, Art no. 4200214, doi: 10.1109/TGRS.2022.3231519.Download Paper (PDF)
Using Saildrones to Assess the SMAP Sea Surface Salinity Retrieval in the Coastal Regions
Remote sensing of sea surface salinity (SSS) near land is difficult due to land contamination. In this article, we assess SSS retrieved from the soil moisture active passive (SMAP) mission in coastal region. SMAP SSS products from the Jet Propulsion Laboratory (JPL), and from the remote sensing systems (RSS) are collocated with in situ data collected by saildrones during the North American West Coast Survey. Satellite and saildrone salinity measurements reveal consistent large-scale features: the fresh water (low SSS) assocciated with the Columbia River discharge, and the relatively salty water (high SSS) near Baja California associated with regional upwelling. The standard deviation of the difference for collocations with SMAP Level 3 (eight days average) between 40 and 100 km from land is 0.51 (0.56) psu for JPL V5 (RSS V4 70 km). This is encouraging for the potential application of SMAP SSS in monitoring coastal zone freshwater particularly where there exists large freshwater variance. We analyze the different land correction approaches independently developed at JPL and RSS using SMAP level 2 matchups. We found that JPL's land correction method is more promising in pushing SMAP SSS retrieval towards land. For future improvement, we suggest implementing dynamic land correction versus the current climatology-based static land correction to reduce uncertainty in estimating land contribution. In level 2 to level 3 processing, a more rigorous quality control may help to eliminate outliers and deliver reliable level 3 products without over-smoothing, which is important in resolving coastal processes such as fronts or upwelling.
W. Tang et al., "Using Saildrones to Assess the SMAP Sea Surface Salinity Retrieval in the Coastal Regions," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 7042-7051, 2022, doi: 10.1109/JSTARS.2022.3200305.Download Paper (PDF)
Validating Salinity from SMAP and HYCOM Data with Saildrone Data during EUREC4A-OA/ATOMIC
The 2020 ‘Elucidating the role of clouds-circulation coupling in climate-Ocean-Atmosphere’ (EUREC4A-OA) and the ‘Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign’ (ATOMIC) campaigns focused on improving our understanding of the interaction between clouds, convection and circulation and their function in our changing climate. The campaign utilized many data collection technologies, some of which are relatively new. In this study, we used saildrone uncrewed surface vehicles, one of the newer cutting edge technologies available for marine data collection, to validate Level 2 and Level 3 Soil Moisture Active Passive (SMAP) satellite and Hybrid Coordinate Ocean Model (HYCOM) sea surface salinity (SSS) products in the Western Tropical Atlantic. The saildrones observed fine-scale salinity variability not present in the lower-spatial resolution satellite and model products. In regions that lacked significant small-scale salinity variability, the satellite and model salinities performed well. However, SMAP Remote Sensing Systems (RSS) 70 km generally outperformed its counterparts outside of areas with submesoscale SSS variation, whereas RSS 40 km performed better within freshening events such as a fresh tongue. HYCOM failed to detect the fresh tongue. These results will allow researchers to make informed decisions regarding the most ideal product and its drawbacks for their applications in this region and aid in the improvement of mesoscale and submesoscale SSS products, which can lead to the refinement of numerical weather prediction (NWP) and climate models.
Hall, Kashawn, Alton Daley, Shanice Whitehall, Sanola Sandiford, and Chelle L. Gentemann. 2022. "Validating Salinity from SMAP and HYCOM Data with Saildrone Data during EUREC4A-OA/ATOMIC" Remote Sensing 14, no. 14: 3375. https://doi.org/10.3390/rs14143375Download Paper (PDF)
Assessment of Saildrone Extreme Wind Measurements in Hurricane Sam Using MW Satellite Sensors
In 2021, a novel NOAA-Saildrone project deployed five uncrewed surface vehicle Saildrones (SDs) to monitor regions of the Atlantic Ocean and Caribbean Sea frequented by tropical cyclones. One of the SDs, SD-1045, crossed Hurricane Sam (Category 4) on September 30, providing the first-ever surface-ocean videos of conditions in the core of a major hurricane and reporting near-surface winds as high as 40 m/s. Here, we present a comprehensive analysis and interpretation of the Saildrone ocean surface wind measurements in Hurricane Sam, using the following datasets for direct and indirect comparisons: an NDBC buoy in the path of the storm, radiometer tropical cyclone (TC) winds from SMAP and AMSR2, wind retrievals from the ASCAT scatterometers and SAR (RadarSat2), and HWRF model winds. The SD winds show excellent consistency with the satellite observations and a remarkable ability to detect the strength of the winds at the SD location. We use the HWRF model and satellite data to perform cross-comparisons of the SD with the buoy, which sampled different relative locations within the storm. Finally, we review the collective consistency among these measurements by describing the uncertainty of each wind dataset and discussing potential sources of systematic errors, such as the impact of extreme conditions on the SD measurements and uncertainties in the methodology.
Ricciardulli, Lucrezia, Gregory R. Foltz, Andrew Manaster, and Thomas Meissner. 2022. "Assessment of Saildrone Extreme Wind Measurements in Hurricane Sam Using MW Satellite Sensors" Remote Sensing 14, no. 12: 2726. https://doi.org/10.3390/rs14122726Download Paper (PDF)
Evaluation of Surface Conditions from Operational Forecasts Using In Situ Saildrone Observations in the Pacific Arctic
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.
Zhang, Chidong, Aaron F. Levine, Muyin Wang, Chelle Gentemann, Calvin W. Mordy, Edward D. Cokelet, Philip A. Browne, Qiong Yang, Noah Lawrence-Slavas, Christian Meinig, Gregory Smith, Andy Chiodi, Dongxiao Zhang, Phyllis Stabeno, Wanqiu Wang, Hong-Li Ren, K. Andrew Peterson, Silvio N. Figueroa, Michael Steele, Neil P. Barton, Andrew Huang, and Hyun-Cheol Shin. "Evaluation of Surface Conditions from Operational Forecasts Using In Situ Saildrone Observations in the Pacific Arctic", Monthly Weather Review 150, 6 (2022): 1437-1455, accessed Sep 13, 2022, https://doi.org/10.1175/MWR-D-20-0379.1Download Paper (PDF)
Autonomous Wintertime Observations of Air-Sea Exchange in the Gulf Stream Reveal a Perfect Storm for Ocean CO2 Uptake
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.
Nickford, S., Palter, J. B., Donohue, K., Fassbender, A. J., Gray, A. R., Long, J., et al. (2022). Autonomous wintertime observations of air-sea exchange in the Gulf Stream reveal a perfect storm for ocean CO2 uptake. Geophysical Research Letters, 49, e2021GL096805. https://doi.org/10.1029/2021GL096805Download Paper (PDF)
Comparison of GHRSST SST Analysis in the Arctic Ocean and Alaskan Coastal Waters Using Saildrones
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).
Vazquez-Cuervo, Jorge, Sandra L. Castro, Michael Steele, Chelle Gentemann, Jose Gomez-Valdes, and Wenqing Tang. 2022. "Comparison of GHRSST SST Analysis in the Arctic Ocean and Alaskan Coastal Waters Using Saildrones" Remote Sensing 14, no. 3: 692. https://doi.org/10.3390/rs14030692Download Paper (PDF)
Polar Region Bathymetry: Critical Knowledge for the Prediction of Global Sea Level Rise
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.
Jakobsson M and Mayer LA (2022) Polar Region Bathymetry: Critical Knowledge for the Prediction of Global Sea Level Rise. Front. Mar. Sci. 8:788724. doi: 10.3389/fmars.2021.788724Download Paper (PDF)
Uncrewed Ocean Gliders and Saildrones Support Hurricane Forecasting and Research
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.
Miles, T.N., D. Zhang, G.R. Foltz, J. Zhang, C. Meinig, F. Bringas, J. Triñanes, M. Le Hénaff, M.F. Aristizabal Vargas, S. Coakley, C.R. Edwards, D. Gong, R.E. Todd, M.J. Oliver, W.D. Wilson, K. Whilden, B. Kirkpatrick, P. Chardon-Maldonado, J.M. Morell, D. Hernandez, G. Kuska, C.D. Stienbarger, K. Bailey, C. Zhang, S.M. Glenn, and G.J. Goni. 2021. Uncrewed ocean gliders and saildrones support hurricane forecasting and research. Pp. 78–81 in Frontiers in Ocean Observing: Documenting Ecosystems, Understanding Environmental Changes, Forecasting Hazards. E.S. Kappel, S.K. Juniper, S. Seeyave, E. Smith, and M. Visbeck, eds, A Supplement to Oceanography 34(4), https://doi.org/10.5670/oceanog.2021.supplement.02-28.Download Paper (PDF)