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Increasing interest in the deployment of optical oxygen sensors, or optodes, on oceanographic moorings reflects the value of dissolved oxygen (DO) measurements in studies of physical and biogeochemical processes. Optodes are well-suited for moored applications but require careful, multi-step calibrations in the field to ensure data accuracy. Without a standardized set of protocols, this can be an obstacle for science teams lacking expertise in optode data processing and calibration. Here, we provide a set of recommendations for the deployment andin situcalibration of data from moored optodes, developed from our experience working with a set of 60 optodes deployed as part of the Gases in the Overturning and Horizontal circulation of the Subpolar North Atlantic Program (GOHSNAP). In particular, we detail the correction of drift in moored optodes, which occurs in two forms: (i) an irreversible, time-dependent drift that occurs during both optode storage and deployment and (ii) a reversible and pressure-and-time-dependent drift that is detectable in some optodes deployed at depths greater than 1,000 m. The latter is virtually unidentified in the literature yet appears to cause a low-bias in measured DO on the order of 1 to 3µmol kg⁻¹per 1,000 m of depth, appearing as an exponential decay over the first days to months of deployment. Comparisons of our calibrated DO time series against serendipitous mid-deployment conductivity-temperature-depth (CTD)-DO profiles, as well as biogeochemical (BGC)-ARGO float profiles, suggest the protocols described here yield an accuracy in optode-DO of ∼1%, or approximately 2.5 to 3µmol kg⁻¹. We intend this paper to serve as both documentation of the current best practices in the deployment of moored optodes as well as a guide for science teams seeking to collect high-quality moored oxygen data, regardless of expertise. 

Abstract. This paper provides an overview and demonstration of emerging float-based methods for quantifying gross primary production (GPP) and net community production (NCP) using Biogeochemical-Argo (BGC-Argo) float data. Recent publications have described GPP methods that are based on the detection of diurnal oscillations in upper-ocean oxygen or particulate organic carbon concentrations using single profilers or a composite of BGC-Argo floats. NCP methods rely on budget calculations to partition observed tracer variations into physical or biological processes occurring over timescales greater than 1 d. Presently, multi-year NCP time series are feasible at near-weekly resolution, using consecutive or simultaneous float deployments at local scales. Results, however, are sensitive to the choice of tracer used in the budget calculations and uncertainties in the budget parameterizations employed across different NCP approaches. Decadal, basin-wide GPP calculations are currently achievable using data compiled from the entire BGC-Argo array, but finer spatial and temporal resolution requires more float deployments to construct diurnal tracer curves. A projected, global BGC-Argo array of 1000 floats should be sufficient to attain annual GPP estimates at 10∘ latitudinal resolution if floats profile at off-integer intervals (e.g., 5.2 or 10.2 d). Addressing the current limitations of float-based methods should enable enhanced spatial and temporal coverage of marine GPP and NCP measurements, facilitating global-scale determinations of the carbon export potential, training of satellite primary production algorithms, and evaluations of biogeochemical numerical models. This paper aims to facilitate broader uptake of float GPP and NCP methods, as singular or combined tools, by the oceanographic community and to promote their continued development. 

Background: Biogeochemical-Argo floats are collecting an unprecedented number of profiles of optical backscattering measurements in the global ocean. Backscattering (BBP) data are crucial to understanding ocean particle dynamics and the biological carbon pump. Yet, so far, no procedures have been agreed upon to quality control BBP data in real time. Methods: Here, we present a new suite of real-time quality-control tests and apply them to the current global BBP Argo dataset. The tests were developed by expert BBP users and Argo data managers and have been implemented on a snapshot of the entire Argo dataset. Results: The new tests are able to automatically flag most of the “bad” BBP profiles from the raw dataset. Conclusions: The proposed tests have been approved by the Biogeochemical-Argo Data Management Team and will be implemented by the Argo Data Assembly Centres to deliver real-time quality-controlled profiles of optical backscattering. Provided they reach a pressure of about 1000 dbar, these tests could also be applied to BBP profiles collected by other platforms.