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Abstract Marine phytoplankton biomass and chlorophyll-a concentration are often estimated from pigment fluorescence measurements, which have become routine despite known variability in the fluorescent response for a given amount of chlorophyll-a. Here, we present a near-global, monthly climatology of chlorophyll-a fluorescence measurements from profiling floats combined with ocean color satellite estimates of chlorophyll-a concentration to illuminate seasonal biases in the fluorescent response and expand upon previously observed regional patterns in this bias. Global biases span over an order of magnitude, and can vary seasonally by a factor of 10. An independent estimate of chlorophyll-a from light attenuation shows similar global patterns in the chlorophyll-fluorescence bias when compared to biases derived from satellite estimates. Without accounting for these biases, studies or models using fluorescence-estimated chlorophyll-a will inherit the seasonal and regional biases described here. 

Abstract As global ocean monitoring programs and marine carbon dioxide removal methods expand, so does the need for scalable biogeochemical sensors. Currently, pH sensors are widely used to measure the ocean carbonate system on a variety of autonomous platforms. This paper assesses a commercially available optical pH sensor (optode) distributed by PyroScience GmbH for oceanographic applications. Results from this study show that the small, solid‐state pH optode demonstrates a precision of 0.001 pH and relative accuracy of 0.01 pH using an improved calibration routine outlined in the manuscript. A consistent pressure coefficient of 0.029 pH/1000 dbar is observed across multiple pH optodes tested in this study. The response time is investigated for standard and fast‐response versions over a range of temperatures and flow rates. Field deployments include direct comparison to ISFET‐based pH sensor packages for both moored and profiling platforms where the pH optodes experience sensor‐specific drift rates up to 0.006 pH d⁻¹. In its current state, the pH optode potentially offers a viable and scalable option for short‐term field deployments and laboratory mesocosm studies, but not for long term deployments with no possibility for recalibration like on profiling floats. 

Abstract Net community production (NCP) was estimated from nitrate profiles measured via biogeochemical Argo floats drifting in the Argentine Basin. Two criteria were tested for defining hydrographic fronts used to separate the study area into five zones: potential density anomaly at 450 m and potential temperature at 100 m. The latter definition was preferred as it minimized overlapping among zones. Float profiles within each zone were used to construct monthly median profiles of nitrate. Monthly nitrate inventories were calculated for each zone by integrating the median profiles between the surface and a depth of 100 or 200 m. Three methods were utilized to estimate NCP from the nitrate drawdown. The resulting mean NCP estimates indicated a decline in NCP from 3 to 4 mol C m⁻² yr⁻¹south of ∼40°S to ≤1 mol C m⁻² yr⁻¹north of ∼40°S. The monthly median profiles suggested 20%–100% of drawdown occurred by the end of December; however, chlorophyll fluorescence indicated phytoplankton activity persisted through austral summer. We speculate that primary production during these summer months was supported by regenerated nitrogen sources (not nitrate), despite replete concentrations, likely due to the relative scarcity of bioavailable iron known to persist in the region. While a northward advective flux of nitrate was strongly suggested by meridional nitrate gradients over the upper 0–300 m, vertical mixing was apparently necessary to stimulate new production, indicating both processes are important for NCP in the Argentine Basin. This work highlights the potential for floats in studying biogeochemical cycles in hydrographically complex regions. 

Abstract The Argo array provides nearly 4000 temperature and salinity profiles of the top 2000 m of the ocean every 10 days. Still, Argo floats will never be able to measure the ocean at all times, everywhere. Optimized Argo float distributions should match the spatial and temporal variability of the many societally important ocean features that they observe. Determining these distributions is challenging because float advection is difficult to predict. Using no external models, transition matrices based on existing Argo trajectories provide statistical inferences about Argo float motion. We use the 24 years of Argo locations to construct an optimal transition matrix that minimizes estimation bias and uncertainty. The optimal array is determined to have a 2° × 2° spatial resolution with a 90-day time step. We then use the transition matrix to predict the probability of future float locations of the core Argo array, the Global Biogeochemical Array, and the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) array. A comparison of transition matrices derived from floats using Argos system and Iridium communication methods shows the impact of surface displacements, which is most apparent near the equator. Additionally, we demonstrate the utility of transition matrices for validating models by comparing the matrix derived from Argo floats with that derived from a particle release experiment in the Southern Ocean State Estimate (SOSE). 

Abstract Sensors that use ultraviolet (UV) light absorption to measure nitrate in seawater at in situ temperatures require a correction to the calibration coefficients if the calibration and sample temperatures are not identical. This is mostly due to the bromide molecule, which absorbs more UV light as temperature increases. The current correction applied to in situ ultraviolet spectrophotometer (ISUS) and submersible ultraviolet nitrate analyzer (SUNA) nitrate sensors generally follows Sakamoto et al. (2009, Limnol. Oceanogr. Methods 7, 132–143). For waters warmer than the calibration temperature, this correction model can lead to a 1–2 μmol kg⁻¹positive bias in nitrate concentration. Here we present an updated correction model, which reduces this small but noticeable bias by at least 50%. This improved model is based on additional laboratory data and describes the temperature correction as an exponential function of wavelength and temperature difference from the calibration temperature. It is a better fit to the experimental data than the current model and the improvement is validated using two populations of nitrate profiles from Biogeochemical Argo floats navigating through tropical waters. One population is from floats equipped with ISUS sensors while the other arises from floats with SUNA sensors on board. Although this model can be applied to both ISUS and SUNA nitrate sensors, it should not be used for OPUS UV nitrate sensors at this time. This new approach is similar to that used for OPUS sensors (Nehir et al., 2021, Front. Mar. Sci. 8, 663800) with differing model coefficients. This difference suggests that there is an instrumental component to the temperature correction or that there are slight differences in experimental methodologies. 

Abstract Acoustically-tracked subsurface floats provide insights into ocean complexity and were first deployed over 60 years ago. A standard tracking method uses a Least-Squares algorithm to estimate float trajectories based on acoustic ranging from moored sound sources. However, infrequent or imperfect data challenge such estimates, and Least-Squares algorithms are vulnerable to non-Gaussian errors. Acoustic tracking is currently the only feasible strategy for recovering float positions in the sea ice region, a focus of this study. Acoustic records recovered from under-ice floats frequently lack continuous sound source coverage. This is because environmental factors such as surface sound channels and rough sea ice attenuate acoustic signals, while operational considerations make polar sound sources expensive and difficult to deploy. Here we present a Kalman Smoother approach that, by including some estimates of float behavior, extends tracking to situations with more challenging data sets. The Kalman Smoother constructs dynamically constrained, error-minimized float tracks and variance ellipses using all possible position data. This algorithm outperforms the Least-Squares approach and a Kalman Filter in numerical experiments. The Kalman Smoother is applied to previously-tracked floats from the southeast Pacific (DIMES experiment), and the results are compared with existing trajectories constructed using the Least- Squares algorithm. The Kalman Smoother is also used to reconstruct the trajectories of a set of previously untracked, acoustically-enabled Argo floats in the Weddell Sea. 

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. 

Abstract Global estimates of absolute velocities can be derived from Argo float trajectories during drift at parking depth. A new velocity dataset developed and maintained at Scripps Institution of Oceanography is presented based on all Core, Biogeochemical, and Deep Argo float trajectories collected between 2001 and 2020. Discrepancies between velocity estimates from the Scripps dataset and other existing products including YoMaHa and ANDRO are associated with quality control criteria, as well as selected parking depth and cycle time. In the Scripps product, over 1.3 million velocity estimates are used to reconstruct a time-mean velocity field for the 800–1200 dbar layer at 1° horizontal resolution. This dataset provides a benchmark to evaluate the veracity of the BRAN2020 reanalysis in representing the observed variability of absolute velocities and offers a compelling opportunity for improved characterization and representation in forecast and reanalysis systems. Significance Statement The aim of this study is to provide observation-based estimates of the large-scale, subsurface ocean circulation. We exploit the drift of autonomous profiling floats to carefully isolate the inferred circulation at the parking depth, and combine observations from over 11 000 floats, sampling between 2001 and 2020, to deliver a new dataset with unprecedented accuracy. The new estimates of subsurface currents are suitable for assessing global models, reanalyses, and forecasts, and for constraining ocean circulation in data-assimilating models.