Quantifying observational errors in Biogeochemical‐Argo oxygen, nitrate and chlorophyll a concentrations

Type Article
Date 2019-04
Language English
Author(s) Mignot A1, D'Ortenzio F1, Taillandier V1, Cossarini G2, Salon S2
Affiliation(s) 1 : Sorbonne Universités, UPMC Univ. Paris 06, CNRS, UMR 7093, Laboratoire d’Océanographie de Villefranche (LOV), 181 Chemin du Lazaret, 06230 Villefranche-sur-mer, France.
2 : Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, Borgo Grotta Gigante 42/c, 34010 Sgonico (TS), Italy
Source Geophysical Research Letters (0094-8276) (American Geophysical Union), 2019-04 , Vol. 46 , N. 8 , P. 4330-4337
DOI 10.1029/2018GL080541
WOS© Times Cited 15

BGC‐Argo floats observations are becoming a major data source for assimilation into and constraining of ocean biogeochemical models. An important prerequisite for a successful synthesis between models and observations is the characterization of the observational errors in BGC‐Argo float data. The root‐mean‐squared error, multiplicative and additive biases in quality‐controlled data sets of oxygen, nitrate and chlorophyll a concentrations collected with 17 BGC‐Argo floats in the Mediterranean Sea between 2013 and 2017 are assessed using the triple collocation analysis. The analysis suggests that BGC‐Argo float oxygen, nitrate and chlorophyll a data suffer from an additive bias of 2.9± 5.5 μmol kg‐1, 0.46± 0.07 μmol kg‐1 and ‐0.06 ± 0.02 mg m‐3, respectively. The root‐mean‐squared error is evaluated at 5.1 ± 0.8 μmol kg‐1, 0.25 ± 0.07 μmol kg‐1 and 0.03 ± 0.01 mg m‐3. Additional studies should determine whether these values are applicable to the global ocean.

Plain Language Summary

The Biogeochemical‐Argo program is a network of ocean robots whose sensors monitor, oxygen, nitrate and chlorophyll a concentration, information which is needed to detect decadal changes in biological carbon production, ocean acidification, ocean carbon uptake, and hypoxia in the world ocean. One of the goals of the Biogeochemical‐Argo program is to incorporate these observations into ocean models to understand and forecast the changing state of the carbon cycle. The successful integration of the float data into numerical models, however, requires the specification of the observational errors. This study provides, for the first time, the biases and errors of the three cores variables of the BGC‐Argo floats network: oxygen, nitrate and chlorophyll a concentrations.

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