Monitoring ocean biogeochemistry with autonomous platforms

Chai, Fei; Johnson, Kenneth S.; Claustre, Hervé; Xing, Xiaogang; Wang, Yuntao; Boss, Emmanuel; Riser, Stephen C.; Fennel, Katja; Schofield, Oscar; Sutton, Adrienne J.

doi:10.1038/s43017-020-0053-y

Cited by 168 publications

(101 citation statements)

References 168 publications

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Section: Introductionmentioning

confidence: 99%

“…Historically, measurements have come from oceanographic cruises and from continuous measurements at fixed stations (buoys and moorings) (Chai et al, 2020). However, the low spatial and temporal resolutions of these sampling platforms have resulted in chronic under-sampling of biogeochemical variables, creating "observational gaps" (Tanhua et al, 2019;Weller et al, 2019;Chai et al, 2020). Today, technological advances (miniaturization of sensors, automation of measurements) have made it possible to develop a network of autonomous platforms such as profiling floats (Riser et al, 2016;Roemmich et al, 2019;Claustre et al, 2020) and ocean gliders (Testor et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Some variables are presently almost systematically measured regardless of the acquisition platform, for instance physical data (temperature, salinity, pressure) and dissolved oxygen concentration (O 2 ). However, measurements from these autonomous platforms remain limited to a small type of biogeochemical variables, owing to the high cost of some sensors and technological limitations (Bittig et al, 2019;Chai et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…They use O 2 , temperature, salinity, pressure, longitude and latitude, day of the year and year as input variables to predict the concentrations of three macronutrients [nitrates (NO − 3 ), phosphates (PO 3− 4 ), silicates (SiOH 4 )] as well as carbonate system variables [total alkalinity (A T ), total dissolved inorganic carbon (C T ), pH on the total scale (pH T ), and CO 2 partial pressure (pCO 2 )]. This method referred to as CANYON for "CArbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural network" predicts these key oceanographic variables, which cannot be measured independently or with sufficient accuracy, with much improved temporal and spatial resolution (Chai et al, 2020). This method as well as its subsequent improvement, CANYON-B (Bittig et al, 2018), were trained on high-quality data collected over the past thirty years (the GLODAPv2 for Global Ocean Data Analysis Project version 2 database; Olsen et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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A Regional Neural Network Approach to Estimate Water-Column Nutrient Concentrations and Carbonate System Variables in the Mediterranean Sea: CANYON-MED

et al. 2020

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A regional neural network-based method, "CANYON-MED" is developed to estimate nutrients and carbonate system variables specifically in the Mediterranean Sea over the water column from pressure, temperature, salinity, and oxygen together with geolocation and date of sampling. Six neural network ensembles were developed, one for each variable (i.e., three macronutrients: nitrates (NO − 3), phosphates (PO 3− 4) and silicates (SiOH 4), and three carbonate system variables: pH on the total scale (pH T), total alkalinity (A T), and dissolved inorganic carbon or total carbon (C T), trained using a specific quality-controlled dataset of reference "bottle" data in the Mediterranean Sea. This dataset is representative of the peculiar conditions of this semi-enclosed sea, as opposed to the global ocean. For each variable, the neural networks were trained on 80% of the data chosen randomly and validated using the remaining 20%. CANYON-MED retrieved the variables with good accuracies (Root Mean Squared Error): 0.73 µmol.kg −1 for NO − 3 , 0.045 µmol.kg −1 for PO 3− 4 and 0.70 µmol.kg −1 for Si(OH) 4 , 0.016 units for pH T , 11 µmol.kg −1 for A T and 10 µmol.kg −1 for C T. A second validation on the ANTARES independent time series confirmed the method's applicability in the Mediterranean Sea. After comparison to other existing methods to estimate nutrients and carbonate system variables, CANYON-MED stood out as the most robust, using the aforementioned inputs. The application of CANYON-MED on the Mediterranean Sea data from autonomous observing systems (integrated network of Biogeochemical-Argo floats, Eulerian moorings and ocean gliders measuring hydrological properties together with oxygen concentration) could have a wide range of applications. These include data quality control or filling gaps in time series, as well as biogeochemical data assimilation and/or the initialization and validation of regional biogeochemical models still lacking crucial reference data. Matlab and R code are available at https:// github.com/MarineFou/CANYON-MED/.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Regional Neural Network Approach to Estimate Water-Column Nutrient Concentrations and Carbonate System Variables in the Mediterranean Sea: CANYON-MED

et al. 2020

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“…The synergy and joint use of BGC-Argo and satellite remote sensing data contribute to studies using both observing assets [55]. BGC-Argo floats provide the largest dataset for validation and evaluation of satellite products in the global ocean, extend the satellite ocean color observations from surface to depth, and fill missing data in satellite coverage due to low sun angle, high latitude winters and clouds; remote sensing is helpful in guiding the deployment of BGC-Argo floats (e.g., in the subtropical gyres or seasonal bloom regions), identifying the spatial scale of float-observed phenomena (basin-, meso-or submeso-scale), extending the BGC-Argo observation from discrete locations to continuous temporal and spatial distributions, and even as a method for calibration of chlorophyll fluorometers deployed on floats (e.g., [23]).…”

Section: Final Remarks and Conclusionmentioning

confidence: 99%

Evaluation of Ocean Color Remote Sensing Algorithms for Diffuse Attenuation Coefficients and Optical Depths with Data Collected on BGC-Argo Floats

et al. 2020

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The vertical distribution of irradiance in the ocean is a key input to quantify processes spanning from radiative warming, photosynthesis to photo-oxidation. Here we use a novel dataset of thousands local-noon downwelling irradiance at 490 nm (Ed(490)) and photosynthetically available radiation (PAR) profiles captured by 103 BGC-Argo floats spanning three years (from October 2012 to January 2016) in the world’s ocean, to evaluate several published algorithms and satellite products related to diffuse attenuation coefficient (Kd). Our results show: (1) MODIS-Aqua Kd(490) products derived from a blue-to-green algorithm and two semi-analytical algorithms show good consistency with the float-observed values, but the Chla-based one has overestimation in oligotrophic waters; (2) The Kd(PAR) model based on the Inherent Optical Properties (IOPs) performs well not only at sea-surface but also at depth, except for the oligotrophic waters where Kd(PAR) is underestimated below two penetration depth (2zpd), due to the model’s assumption of a homogeneous distribution of IOPs in the water column which is not true in most oligotrophic waters with deep chlorophyll-a maxima; (3) In addition, published algorithms for the 1% euphotic-layer depth and the depth of 0.415 mol photons m−2 d−1 isolume are evaluated. Algorithms based on Chla generally work well while IOPs-based ones exhibit an overestimation issue in stratified and oligotrophic waters, due to the underestimation of Kd(PAR) at depth.

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Biogeochemical‐Argo data suggest significant contributions of small particles to the vertical carbon flux in the subpolar North Atlantic

Wang

Fennel

2022

Limnology & Oceanography

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The biological carbon pump exerts a strong control on atmospheric CO2 levels. It includes a range of processes that generate organic carbon in the surface ocean and transport this organic matter from the surface to the deep ocean where it is remineralized and sequestered as inorganic carbon for decades to millennia. While ocean productivity is relatively well observed through a combination of approaches including remote sensing, the magnitude of vertical carbon transport remains poorly constrained by observations and the detailed processes involved are insufficiently understood. In particular, attention to the contribution of small particles has increased in recent years, but previous estimates of the associated vertical carbon flux have ignored remineralization and particle fragmentation. The resulting estimates are likely biased. In this study, we present a method for estimating (1) vertical carbon flux of two different size classes of organic particles and (2) the effect of remineralization and particle fragmentation on mesopelagic flux attenuation using Biogeochemical‐Argo profiles of backscattering and dissolved oxygen. We applied this method to observations from the subpolar North Atlantic and found that, on annual timescales, gravitational settling of large organic particles is dominating the vertical flux through the lower mesopelagic zone. However, small particles contribute significantly to the vertical carbon flux at 100 m (around 36% but can be up to 63%), via different mechanisms, and at 600 m (0–25%) since they can be produced by the fragmentation within the mesopelagic zone.

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Monitoring ocean biogeochemistry with autonomous platforms

Cited by 168 publications

References 168 publications

A Regional Neural Network Approach to Estimate Water-Column Nutrient Concentrations and Carbonate System Variables in the Mediterranean Sea: CANYON-MED

A Regional Neural Network Approach to Estimate Water-Column Nutrient Concentrations and Carbonate System Variables in the Mediterranean Sea: CANYON-MED

Evaluation of Ocean Color Remote Sensing Algorithms for Diffuse Attenuation Coefficients and Optical Depths with Data Collected on BGC-Argo Floats

Biogeochemical‐Argo data suggest significant contributions of small particles to the vertical carbon flux in the subpolar North Atlantic

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