Search for: All records

This dataset collects a record of physical and chemical observations made by two uncrewed sailing vehicles (Saildrone 1038 and Saildrone 1039) in the South Indian Ocean during 2022 and 2023. SD1038 collected data near 15°E and between latitudes 35°S and 50°S during July 19-26, 2022. SD1039 made observations within the Subantarctic Zone (37-47°S, 20-45°E) between September 1, 2022 until February 24, 2023. The local atmospheric and surface ocean parameters measured are listed below: -- Atmospheric measurements: temperature, pressure, humidity. -- Seawater measurements: temperature, pressure, conductivity, salinity. -- Carbon measurements: fCO2, xCO2, and pCO2 in atmosphere and seawater. -- Chlorophyll measurements: concentration. -- Oxygen measurements: concentration, saturation, ratio of O2 in water to air. -- Wind measurements: eastward, northward, and downward speed, plus gusts and direction. -- Wave measurements: significant wave height and dominant wave period. -- Irradiation measurements (SD1039 only): longwave, shortwave, and PAR. -- Current velocity measurements (SD1039 only): eastwards, northwards, and upwards, down to 102m. -- CCMP wind estimates (hourly only): collocated to Saildrone time and location. -- Directions of large eddies transitted (SD1039 hourly only): based on AVISO eddy database. Four files included: -- SD1038_1min.nc (SD1038’s raw data at 1-minute timesteps with frequent gaps) -- SD1039_1min.nc (SD1039’s raw data at 1-minute timesteps with frequent gaps) -- SD1038_hrly.nc (SD1038’s hourly-averaged data, plus eddies and CCMP winds) -- SD1039_hrly.nc (SD1039’s hourly-averaged data, plus eddies and CCMP winds) 1-minute files contain the “raw” data, at all times it was collected. Because many variables were only sampled once or a few times an hour, these files include frequent gaps. Hourly files were made from the 1-minute data, averaging whatever data exists within each hour, such that few gaps in the data exist for most variables. 

This dataset includes surface underway chemical, meteorological and physical data collected from Autonomous Surface Vehicle (ASV) Saildrone 1038 (EXPOCODE 316420220616) in the Indian Ocean, Southern Ocean from 2022-06-16 to 2022-07-26. These data include xCO2 SW (wet) - mole fraction of CO2 in air in equilibrium with the seawater at sea surface temperature and measured humidity; H2O SW - Mole fraction of H2O in air from equilibrator; xCO2 Air (wet) - Mole fraction of CO2 in air from airblock, 0.67m (26") above the sea surface at measured humidity; H2O Air - Mole fraction of H2O in air from airblock, 0.67m (26") above the sea surface; Atmospheric pressure at the airblock, 0.67m (26") above the sea surface; Atmospheric pressure at the airblock, 0.67m (26") above the sea surface; Temperature of the Infrared Licor 820 in degrees Celsius; MAPCO2 %O2 - The percent oxygen of the surface seawater divided by the percent oxygen of the atmosphere at 0.67m (26") above the sea surface; Sea Surface Temperature; Sea Surface Salinity; xCO2 SW (dry) - Mole fraction of CO2 in air in equilibrium with the seawater at sea surface temperature (dry air); xCO2 Air (dry) - Mole fraction of CO2 in air at the airblock, 0.67m (26") above the sea surface (dry air); fCO2 SW (sat) - Fugacity of CO2 in air in equilibrium with the seawater at sea surface temperature (100% humidity); fCO2 Air (sat) - Fugacity of CO2 in air at the airblock, 0.67m (26") above the sea surface (100% humidity); dfCO2 - Difference of the fugacity of the CO2 in seawater and the fugacity of the CO2 in air (fCO2 SW - fCO2 Air); pCO2 SW (wet) - Partial Pressure of CO2 in air in equilibrium with the seawater at sea surface temperature (100% humidity); pCO2 Air (wet) - Partial Pressure of CO2 in air at the airblock, 0.67m (26") above the sea surface (100% humidity); dpCO2 - Difference of the partial pressure of CO2 in seawater and air (pCO2 SW - pCO2 Air; pH of Seawater (total scale). The Autonomous Surface Vehicle CO2 (ASVCO2) instruments used to collect these data include Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer, Humidity Sensor, and oxygen meter. 

This dataset includes surface underway chemical, meteorological and physical data collected from Autonomous Surface Vehicle (ASV) Saildrone 1039 (EXPOCODE 316420220901) in the Indian Ocean, Southern Ocean from 2022-09-01 to 2023-04-27. These data include xCO2 SW (wet) - mole fraction of CO2 in air in equilibrium with the seawater at sea surface temperature and measured humidity; H2O SW - Mole fraction of H2O in air from equilibrator; xCO2 Air (wet) - Mole fraction of CO2 in air from airblock, 0.67m (26") above the sea surface at measured humidity; H2O Air - Mole fraction of H2O in air from airblock, 0.67m (26") above the sea surface; Atmospheric pressure at the airblock, 0.67m (26") above the sea surface; Atmospheric pressure at the airblock, 0.67m (26") above the sea surface; Temperature of the Infrared Licor 820 in degrees Celsius; MAPCO2 %O2 - The percent oxygen of the surface seawater divided by the percent oxygen of the atmosphere at 0.67m (26") above the sea surface; Sea Surface Temperature; Sea Surface Salinity; xCO2 SW (dry) - Mole fraction of CO2 in air in equilibrium with the seawater at sea surface temperature (dry air); xCO2 Air (dry) - Mole fraction of CO2 in air at the airblock, 0.67m (26") above the sea surface (dry air); fCO2 SW (sat) - Fugacity of CO2 in air in equilibrium with the seawater at sea surface temperature (100% humidity); fCO2 Air (sat) - Fugacity of CO2 in air at the airblock, 0.67m (26") above the sea surface (100% humidity); dfCO2 - Difference of the fugacity of the CO2 in seawater and the fugacity of the CO2 in air (fCO2 SW - fCO2 Air); pCO2 SW (wet) - Partial Pressure of CO2 in air in equilibrium with the seawater at sea surface temperature (100% humidity); pCO2 Air (wet) - Partial Pressure of CO2 in air at the airblock, 0.67m (26") above the sea surface (100% humidity); dpCO2 - Difference of the partial pressure of CO2 in seawater and air (pCO2 SW - pCO2 Air; pH of Seawater (total scale). The Autonomous Surface Vehicle CO2 (ASVCO2) instruments used to collect these data include Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer, Humidity Sensor, and oxygen meter. 

Abstract Profiles of oxygen measurements from Argo profiling floats now vastly outnumber shipboard profiles. To correct for drift, float oxygen data are often initially adjusted to deployment casts, ship‐based climatologies, or, recently, measurements of atmospheric oxygen for in situ calibration. Air calibration enables accurate measurements in the upper ocean but may not provide similar accuracy at depth. Using a quality controlled shipboard data set, we find that the entire Argo oxygen data set is offset relative to shipboard measurements (float minus ship) at pressures of 1,450–2,000 db by a median of −1.9 μmol kg⁻¹(mean ± SD of −1.9 ± 3.9, 95% confidence interval around the mean of {−2.2, −1.6}) and air‐calibrated floats are offset by −2.7 μmol kg⁻¹(−3.0 ± 3.4 (CI_95%{−3.7, −2.4}). The difference between float and shipboard oxygen is likely due to offsets in the float oxygen data and not oxygen changes at depth or biases in the shipboard data set. In addition to complicating the calculation of long‐term ocean oxygen changes, these float oxygen offsets impact the adjustment of float nitrate and pH measurements, therefore biasing important derived quantities such as the partial pressure of CO₂(pCO₂) and dissolved inorganic carbon. Correcting floats with air‐calibrated oxygen sensors for the float‐ship oxygen offsets alters float pH by a median of 3.0 mpH (3.1 ± 3.7) and float‐derived surfacepCO₂by −3.2 μatm (−3.2 ± 3.9). This adjustment to floatpCO₂represents half, or more, of the bias in float‐derivedpCO₂reported in studies comparing floatpCO₂to shipboardpCO₂measurements. 

Abstract. The Southern Ocean is highly under-sampled for the purpose of assessing total carbon uptake and its variability. Since this region dominates the mean global ocean sink for anthropogenic carbon, understanding temporal change is critical. Underway measurements of pCO2 collected as part of the Drake Passage Time-series (DPT) program that began in 2002 inform our understanding of seasonally changing air–sea gradients in pCO2, and by inference the carbon flux in this region. Here, we utilize available pCO2 observations to evaluate how the seasonal cycle, interannual variability, and long-term trends in surface ocean pCO2 in the Drake Passage region compare to that of the broader subpolar Southern Ocean. Our results indicate that the Drake Passage is representative of the broader region in both seasonality and long-term pCO2 trends, as evident through the agreement of timing and amplitude of seasonal cycles as well as trend magnitudes both seasonally and annually. The high temporal density of sampling by the DPT is critical to constraining estimates of the seasonal cycle of surface pCO2 in this region, as winter data remain sparse in areas outside of the Drake Passage. An increase in winter data would aid in reduction of uncertainty levels. On average over the period 2002–2016, data show that carbon uptake has strengthened with annual surface ocean pCO2 trends in the Drake Passage and the broader subpolar Southern Ocean less than the global atmospheric trend. Analysis of spatial correlation shows Drake Passage pCO2 to be representative of pCO2 and its variability up to several hundred kilometers away from the region. We also compare DPT data from 2016 and 2017 to contemporaneous pCO2 estimates from autonomous biogeochemical floats deployed as part of the Southern Ocean Carbon and Climate Observations and Modeling project (SOCCOM) so as to highlight the opportunity for evaluating data collected on autonomous observational platforms. Though SOCCOM floats sparsely sample the Drake Passage region for 2016–2017 compared to the Drake Passage Time-series, their pCO2 estimates fall within the range of underway observations given the uncertainty on the estimates. Going forward, continuation of the Drake Passage Time-series will reduce uncertainties in Southern Ocean carbon uptake seasonality, variability, and trends, and provide an invaluable independent dataset for post-deployment assessment of sensors on autonomous floats. Together, these datasets will vastly increase our ability to monitor change in the ocean carbon sink.