Preprints
https://doi.org/10.5194/essd-2026-311
https://doi.org/10.5194/essd-2026-311
12 May 2026
 | 12 May 2026
Status: this preprint is currently under review for the journal ESSD.

BGC-Argo+: Global biogeochemical Argo float data with secondary quality control and derived parameters

Seth M. Bushinsky, Zachary Nachod, Mathilde Jutras, Daniela König, Shannon McClish, and Charles Addey

Abstract. Biogeochemical Argo floats have quickly become the largest single source of ocean biogeochemical measurements. The vast amount of data collected and the long life-time, typically over 5 years, during which floats measure the ocean makes it challenging to provide consistently quality controlled data. We developed a set of automatic and manual outlier detection methods with a focus on oxygen, nitrate, and pH data that we applied to the 2,429 biogeochemical Argo floats with data available through the global data assembly centers that were deployed as of January 2025. The full biogeochemical dataset, named BGC-Argo+, is a uniform repository of profiles and gridded data to enable easy use of the global dataset for direct observational, mapping, or modeling uses. Data are available through an archived repository or at www.bgc-argo-plus.info, along with information on the outliers removed that should enable future improvements to the global dataset of biogeochemical float measurements.

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Seth M. Bushinsky, Zachary Nachod, Mathilde Jutras, Daniela König, Shannon McClish, and Charles Addey

Status: open (until 19 Jun 2026)

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Seth M. Bushinsky, Zachary Nachod, Mathilde Jutras, Daniela König, Shannon McClish, and Charles Addey

Data sets

BGC Argo+, v0.1 2026_04 Seth Bushinsky, Zachary Nachod, Mathilde Jutras, Daniela König, Shannon McClish, and Charles Addey https://doi.org/10.5281/zenodo.19709012

Model code and software

BGC-Argo+ Processing and Outlier Detection Code Seth Bushinsky, Zachary Nachod, Mathilde Jutras, Daniela König, Shannon McClish, and Charles Addey https://doi.org/10.5281/zenodo.19705310

Seth M. Bushinsky, Zachary Nachod, Mathilde Jutras, Daniela König, Shannon McClish, and Charles Addey
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Short summary
In this work we describe the creation of a secondary quality-controlled dataset for profiling float data. We focus on oxygen, nitrate, and pH measurements, using a mix of automated and manual detection methods to identify data points and profiles that are likely to be bad data. This work grew out of our individual recognition that spurious data exist in these widely used data and the need to remove these data before many types of oceanographic analyses can take place.
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