An Assessment of Vertical Carbon Flux Parameterizations Using Backscatter Data From BGC Argo

Wang, Bin; Fennel, Katja

doi:10.1029/2022gl101220

Cited by 3 publications

(2 citation statements)

References 66 publications

(77 reference statements)

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“…We acknowledge, however, that further validation of algorithm performance during various time frames, seasons, and depths outside the algorithm development dataset is desirable. Currently, estimates of POC with BGC-Argo floats rely almost exclusively on univariate b bp algorithms (e.g., Dall'Olmo and Mork, 2014;Johnson et al, 2017;Xing et al, 2020;Wang and Fennel, 2023), or approaches which rely on adjustments to account for vertical and regional variability in the relationship between b bp and POC (Bol et al, 2018;Galı́et al, 2022). We also believe that multivariable Model B can be particularly useful for assessments of POC with optical measurements from autonomous gliders in nearshore or coastal waters which are often not surveyed by BGC-Argo floats and are optically-complex containing nonnegligible contributions of non-phytoplankton material.…”

Section: Discussionmentioning

confidence: 99%

“…It is therefore sensible to correlate the measured b bp associated with all particles with the best possible estimate of total POC. This distinction is important to consider when comparing results of Model A or Model B with other estimates of POC, either from measurement results with GF/F filters from other studies or from biogeochemical modeling results (e.g., Galı́et al, 2022;Wang and Fennel, 2023), as we consider our algorithms to provide optimal estimates of total POC with the currently available methodology of POC determinations on GF/F filters.…”

Section: Uncertainties and Implications To Applicationsmentioning

confidence: 99%

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Improved multivariable algorithms for estimating oceanic particulate organic carbon concentration from optical backscattering and chlorophyll-a measurements

Koestner,

Stramski,

Reynolds

2024

Front. Mar. Sci.

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The capability to estimate the oceanic particulate organic carbon concentration (POC) from optical measurements is crucial for assessing the dynamics of this carbon reservoir and the capacity of the biological pump to sequester atmospheric carbon dioxide in the deep ocean. Optical approaches are routinely used to estimate oceanic POC from the spectral particulate backscattering coefficient bbp, either directly (e.g., with backscattering sensors on underwater platforms like BGC-Argo floats) or indirectly (e.g., with satellite remote sensing). However, the reliability of algorithms which relate POC to bbp is typically limited due to the complexity of interactions between light and natural assemblages of marine particles, which depend on variations in particle concentration, composition, and size distribution. This study expands on our previous work by analysis of an extended field dataset created with judicious data inclusion criteria with the aim to provide POC algorithms for multiple light wavelengths of measured bbp, which can be useful for applications with in situ optical sensors as well as above-water active or passive measurement systems. We describe an improved empirical multivariable approach to estimate POC from simultaneous measurements of bbp and chlorophyll-a concentration (Chla) to better account for the effects of variable particle composition on the relationship between POC and bbp. The multivariable regression models are formulated using a relatively large dataset of coincident measurements of POC, bbp, and Chla, including surface and subsurface data from the Atlantic, Pacific, Arctic, and Southern Oceans. We show that the multivariable algorithm provides reduced uncertainty of estimated POC across diverse marine environments when compared with a traditional univariate algorithm based on only bbp. We also propose an improved formulation of univariate algorithm based on bbp alone. Finally, we examine performance of several algorithms to estimate POC using our dataset as well as a dataset consisting of optical measurements from BGC-Argo floats and traditional POC measurements collected during a coincident research cruise in the Atlantic Ocean.

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Section: Discussionmentioning

confidence: 99%

Section: Uncertainties and Implications To Applicationsmentioning

confidence: 99%

Improved multivariable algorithms for estimating oceanic particulate organic carbon concentration from optical backscattering and chlorophyll-a measurements

Koestner,

Stramski,

Reynolds

2024

Front. Mar. Sci.

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Distinct sources of uncertainty in simulations of the ocean biological carbon pump at different depths

Wang,

Fennel

2024

Commun Earth Environ

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Model uncertainty in simulating the biological carbon pump was quantified and partitioned using 14 models from the Coupled Model Intercomparison Project Phase 6. Uncertainty increases with depth. On the global scale, uncertainty in carbon export dominates above 900 m and uncertainty in transfer efficiency below. Reducing model uncertainty in carbon export and transfer efficiency offers similar benefits for understanding century-scale carbon sequestration and climate. These models produce three different qualitative patterns in transfer efficiency: one where it is globally homogenous and two opposite latitudinal patterns due to different model structures and parameters. The exponent b of the Martin curve, which has long been used to compare different representations of transfer efficiency, is shown here to underestimate uncertainty in transfer efficiency. This highlights the importance of using vertical profiles of carbon flux rather than the single exponent b in model validation and intercomparison exercises.

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Combining neural networks and data assimilation to enhance the spatial impact of Argo floats in the Copernicus Mediterranean biogeochemical model

Amadio,

Teruzzi,

Pietropolli

et al. 2024

Ocean Sci.

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Abstract. Biogeochemical-Argo (BGC-Argo) float profiles provide substantial information on key vertical biogeochemical dynamics and have been successfully integrated in biogeochemical models via data assimilation approaches. Although BGC-Argo assimilation results have been encouraging, data scarcity remains a limitation with respect to their effective use in operational oceanography. To address availability gaps in the BGC-Argo profiles, an observing system experiment (OSE) that combines a neural network (NN) and data assimilation (DA) was performed here. A NN was used to reconstruct nitrate profiles, starting from oxygen profiles and associated Argo variables (pressure, temperature, and salinity), while a variational data assimilation scheme (3DVarBio) was upgraded to integrate BGC-Argo and reconstructed observations in the Copernicus Mediterranean operational forecast system (MedBFM). To ensure the high quality of oxygen data, a post-deployment quality control method was developed with the aim of detecting and eventually correcting potential sensors drift. The Mediterranean OSE features three different set-ups: a control run without assimilation; a multivariate run with assimilation of BGC-Argo chlorophyll, nitrate, and oxygen; and a multivariate run that also assimilates reconstructed observations. The general improvement in the skill performance metrics demonstrated the feasibility of integrating new variables (oxygen and reconstructed nitrate). Major benefits have been observed with respect to reproducing specific biogeochemical-process-based dynamics such as the nitracline dynamics, primary production, and oxygen vertical dynamics. The assimilation of BGC-Argo nitrate corrects a generally positive bias of the model in most of the Mediterranean areas, and the addition of reconstructed profiles makes the corrections even stronger. The impact of enlarged nitrate assimilation propagates to ecosystem processes (e.g. primary production) at a basin-wide scale, demonstrating the importance of the assimilation of BGC-Argo profiles in forecasting the biogeochemical ocean state.

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An Assessment of Vertical Carbon Flux Parameterizations Using Backscatter Data From BGC Argo

Cited by 3 publications

References 66 publications

Improved multivariable algorithms for estimating oceanic particulate organic carbon concentration from optical backscattering and chlorophyll-a measurements

Improved multivariable algorithms for estimating oceanic particulate organic carbon concentration from optical backscattering and chlorophyll-a measurements

Distinct sources of uncertainty in simulations of the ocean biological carbon pump at different depths

Combining neural networks and data assimilation to enhance the spatial impact of Argo floats in the Copernicus Mediterranean biogeochemical model

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