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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 The Southern Ocean is rich in highly dynamic mesoscale eddies and substantially modulates global biogeochemical cycles. However, the overall surface and subsurface effects of eddies on the Southern Ocean biogeochemistry have not been quantified observationally at a large scale. Here, we co‐locate eddies, identified in the Meta3.2DT satellite altimeter‐based product, with biogeochemical Argo floats to determine the effects of eddies on the dissolved inorganic carbon (DIC), nitrate, and dissolved oxygen concentrations in the upper 1,500 m of the ice‐free Southern Ocean, as well as the eddy effects on the carbon fluxes in this region. DIC and nitrate concentrations are lower in anticyclonic eddies (AEs) and increased in cyclonic eddies (CEs), while dissolved oxygen anomalies switch signs above (CEs: positive, AEs: negative) and below the mixed layer (CEs: negative, AEs: positive). We attribute these anomalies primarily to eddy pumping (isopycnal heave), as well as eddy trapping for oxygen. Maximum anomalies in all tracers occur at greater depths in the subduction zone north of the Antarctic Circumpolar Current (ACC) compared to the upwelling region in the ACC, reflecting differences in background vertical structures. Eddy effects on air–sea exchange have significant seasonal variability, with additional outgassing in CEs in fall (physical process) and additional oceanic uptake in AEs and CEs in spring (biological and physical process). Integrated over the Southern Ocean, AEs contribute 0.01 Pg C (7 ) to the Southern Ocean carbon uptake, and CEs offset this by 0.01 Pg C (2 ). These findings underscore the importance of considering eddy impacts in observing networks and climate models. 

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 Proposals from multiple nations to deploy air–sea flux moorings in the Southern Ocean have raised the question of how to optimize the placement of these moorings in order to maximize their utility, both as contributors to the network of observations assimilated in numerical weather prediction and also as a means to study a broad range of processes driving air–sea fluxes. This study, developed as a contribution to the Southern Ocean Observing System (SOOS), proposes criteria that can be used to determine mooring siting to obtain best estimates of net air–sea heat flux ( Q net ). Flux moorings are envisioned as one component of a multiplatform observing system, providing valuable in situ point time series measurements to be used alongside satellite data and observations from autonomous platforms and ships. Assimilating models (e.g., numerical weather prediction and reanalysis products) then offer the ability to synthesize the observing system and map properties between observations. This paper develops a framework for designing mooring array configurations to maximize the independence and utility of observations. As a test case, within the meridional band from 35° to 65°S we select eight mooring sites optimized to explain the largest fraction of the total variance (and thus to ensure the least variance of residual components) in the area south of 20°S. Results yield different optimal mooring sites for low-frequency interannual heat fluxes compared with higher-frequency subseasonal fluxes. With eight moorings, we could explain a maximum of 24.6% of high-frequency Q net variability or 44.7% of low-frequency Q net variability. 

Wintertime surface ocean heat loss is the key process driving the formation of Subantarctic Mode Water (SAMW), but there are few direct observations of heat fluxes, particularly during winter. The Ocean Observatories Initiative (OOI) Southern Ocean mooring in the southeast Pacific Ocean and the Southern Ocean Flux Station (SOFS) in the southeast Indian Ocean provide the first concurrent, multiyear time series of air–sea fluxes in the Southern Ocean from two key SAMW formation regions. In this work we compare drivers of wintertime heat loss and SAMW formation by comparing air–sea fluxes and mixed layers at these two mooring locations. A gridded Argo product and the ERA5 reanalysis product provide temporal and spatial context for the mooring observations. Turbulent ocean heat loss is on average 1.5 times larger in the southeast Indian (SOFS) than in the southeast Pacific (OOI), with stronger extreme heat flux events in the southeast Indian leading to larger cumulative winter ocean heat loss. Turbulent heat loss events in the southeast Indian (SOFS) occur in two atmospheric regimes (cold air from the south or dry air circulating via the north), while heat loss events in the southeast Pacific (OOI) occur in a single atmospheric regime (cold air from the south). On interannual time scales, wintertime anomalies in net heat flux and mixed layer depth (MLD) are often correlated at the two sites, particularly when wintertime MLDs are anomalously deep. This relationship is part of a larger basin-scale zonal dipole in heat flux and MLD anomalies present in both the Indian and Pacific basins, associated with anomalous meridional atmospheric circulation. 

The top 2000 m of the Southern Ocean has freshened and warmed over recent decades. However, the high-latitude (south of 50°S) southeast Pacific was observed to be cooler and fresher in the years 2008–10 compared to 2005–07 over a wide depth range including surface, mode, and intermediate waters. The causes and impacts of this event are analyzed using the ocean–sea ice data-assimilating Southern Ocean State Estimate (SOSE) and observationally based products. In 2008–10, a strong positive southern annular mode coincided with a negative El Niño–Southern Oscillation and a deep Amundsen Sea low. Enhanced meridional winds drove strong sea ice export from the eastern Ross Sea, bringing large amounts of ice to the Amundsen Sea ice edge. In 2008, together with increased precipitation, this introduced a strong freshwater anomaly that was advected eastward by the Antarctic Circumpolar Current (ACC), mixing along the way. This anomaly entered the ocean interior not only as Antarctic Intermediate Water, but also as lighter Southeast Pacific Subantarctic Mode Water (SEPSAMW). A numerical particle release experiment carried out in SOSE showed that the Ross Sea sector was the dominant source of particles reaching the SEPSAMW formation region. This suggests that large-scale climate fluctuations can induce strong interannual variability of volume and properties of SEPSAMW. These fluctuations act at different time scales: instantaneously via direct forcing and also lagged over advective time scales of several years from upstream regions.