14:I[71133,["4507","static/chunks/e2799680-446c489506865058.js","7665","static/chunks/7665-81c5407bc4489ff9.js","8500","static/chunks/8500-e4dd1cb1bb626827.js","9753","static/chunks/9753-028c0a26aa71f2d4.js","8437","static/chunks/8437-a09d0b83fcec76af.js","4557","static/chunks/4557-64c5efa221bc528f.js","947","static/chunks/947-410627998426400f.js","5357","static/chunks/5357-6142894e88cb7da2.js","8382","static/chunks/app/dataset/%5Bid%5D/data/view/page-177ea8adea7aed86.js"],"default"] 15:T498,

Data were collected and made freely available by the Global Ocean Biogeochemistry Array (GO-BGC) Project funded by the National Science Foundation, Division of Ocean Sciences (NSF OCE-1946578), and by the International Argo Program and the national programs that contribute to it. (http://www.argo.ucsd.edu, http://ocean-ops.org). The Argo Program is part of the Global Ocean Observing System.

Biogeochemical Argo particulate backscatter (BBP700) profiles were downloaded from the Argo Global Data Assembly Center (GDAC) (Argo, 2025). BBP700 profiles are provided by the Argo data management teams directly. Only profiles from global open ocean basins were included in this study. Profiles in marginal seas, like the Mediterranean, were excluded. Data from a total of 1,045 floats yielding 102,666 profiles are included in this dataset.

There are a variety of different BGC-Argo float models and manufacturers. All floats used in this study were equipped with particulate backscatter sensors, but the exact sensor model depends on the float design.

16:T772,

At the time of data processing, the real-time quality control (RTQC) protocols for BGC-Argo backscatter were limited (Dall'Olmo et al., 2023). As a result, all profiles were quality controlled following the methods of Dall'Olmo et al., 2023 using the EuroArgo RTQC toolbox, which is available on GitHub (https://github.com/euroargodev/BBP_RTQC; DOI: 10.5281/zenodo.7934400). This toolbox processes and assigns QC flags to B-profiles. Only profiles with QC flags of 1 ("Good data") or 2 ("Probably good data") were used in this study.

After data QC, every profile backscatter (b_bp) profile was decomposed into its large (b_bl) and small particle (b_bs) backscatter components following a modified version of Briggs et al., 2020. This decomposition was done on a per-profile basis, and each profile was decomposed into 4 components--a large (b_bl), small (b_bs), refractory (b_br), and instrument noise component (b_noise):

b_bp = b_bl + b_bs + b_br + b_noise

Code to decompose backscatter profile is available on GitHub (https://github.com/ellenrpark/spike_decomp; doi: 10.5281/zenodo.19486817).

Profiles were then assigned to a Fay and McKinley biome based on year (Fay and McKinley, 2014). The biomes recreated from 2010-2024 (https://doi.org/10.5281/zenodo.14989532) were used for this study. Profiles were assigned a biome based on year and using nearest-neighbor to the closest grid point.

17:T40d,This section documents curation actions performed prior to publication review with the submitter, and additional information relevant to understanding and reusing this dataset. It distinguishes changes made to the submitted (meta)data from unresolved issues and/or enhancements that improve future reuse and interoperability. CURATION ACTIONS PERFORMED ON DATA - Loaded original file "processed_backscatter.csv" into the BCO-DMO system. - Converted JULD column (format "%Y-%m-%d %H:%M:%S", UTC) to new ISO_DateTime_UTC column in ISO 8601 datetime format ("%Y-%m-%dT%H:%M:%SZ", UTC). - Saved the final file as "1001948_v1_global_bgc-argo_bbp.csv". CURATION ACTIONS PERFORMED ON METADATA - BCO-DMO's standard metadata entry and text formatting steps were performed. See: https://www.bco-dmo.org/how-to/standard-curation-edits ISSUES POTENTIALLY IMPACTING REUSE - This dataset has been published "as is". Final review by the data submitter was not received after the data were imported into the BCO-DMO data system.18:Tf9e,NSF Award Abstract: Approximately one quarter of carbon emissions each year is taken up by the worlds’ oceans, which play a critical role in the global carbon cycle. As the oceans continue to respond to climate change, it is important to understand the carbon cycle and how it will change as well. In the surface waters of the ocean, phytoplankton take up carbon through photosynthesis, and a small fraction of this material then sinks to the deeper ocean in a process referred to as the “biological pump.” Current estimates of the global biological pump are similar in size to the amount of global carbon emissions, but uncertainties in these estimates are very large. This project will focus on understanding the role that marine phytoplankton in the North Atlantic Ocean play in the carbon cycle. The project will employ coordinated use of state-of-the-art, remote observational tools, namely biogeochemical profiling floats and ocean color observations from satellites. New techniques will be developed to maximize the information that can be glean from these observations and constrain biological carbon fluxes out of the sunlit upper ocean and through its deeper interior layers over basin-wide, seasonal, and annual scales. Finally, the performance of existing, satellite-based models of these biological fluxes will be evaluated in comparison with analogous estimates made from biogeochemical profiling float data, which can help to fill in satellite observing gaps below the surface and during periods of heavy cloud cover. The proposed effort will provide training and mentorship to a graduate student and a postdoctoral researcher. Outreach will be conducted through the 2021 Education And Research: Testing Hypotheses (EARTH) Workshop: A successful, ~15-year collaborative effort between MBARI and the Monterey Bay Aquarium to assist educators with incorporating ocean science information and real-time data into the classroom. To enhance diversity and inclusion in ocean science, a first-generation college student will be selected to participate in the 2021 MBARI Summer Intern Program. Persistent and spatially-distributed in situ observations are needed to develop, and continuously train, more accurate models and remote-sensing algorithms to reduce the uncertainty (currently >100%) in estimates of the biological pump magnitude. The goals of the proposed effort are to develop, implement, and compare robust methods for quantifying biological carbon sequestration by leveraging the complementary information provided by bio-optical and chemical sensors on profiling floats deployed in the North Atlantic Ocean. This project will dovetail with several larger observational programs (EXPORTS, PACE, and pending Global Biogeochemical Argo) and leverage committed NOAA and NASA funding to deploy four biogeochemical profiling floats. Results from the analysis of float data will also be compared with estimates derived from satellite observations to assess the influences of 1) subpolar spring bloom timing and magnitude; and 2) more efficient subtropical carbon export processes, on the amount of carbon annually sequestered by the biological pump. Evaluating carbon production, recycling, and export from different perspectives (e.g., chemical, optical, and remote sensing), will capitalize on the strengths of different sensors and platforms using a combination of standard and novel methods. Quality-control of the data from ~300 existing and four newly deployed profiling floats in the North Atlantic will result in a valuable resource for other investigators. Data handling and processing tools developed during this project will be made publicly available to reduce barriers to entry for other scientists and students interested in analyzing large volumes of BGC float and ocean color data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.6:["$","$L14",null,{"dataset":{"_id":"1001948","description":"","problem":"","acquisition":"$15","processing":"$16","bcodmoProcessing":"$17","title":"Processed particulate backscatter data from the global Biogeochemical Argo array from 2010-2025","nickname":"Global BGC-Argo processed particulate backscatter","dataUrl":"","currentState":"Final","currentStateId":"7","validated":false,"restricted":false,"version":"1","version_date":"2026-06-26T04:00:00.000Z","abstract":"This dataset is processed particulate backscatter (bbp) data from the global, open-ocean Biogeochemical Argo array. 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\n2009-08-30T14:05:00[.xx]-05:00
\nThe dashes and the colons can be dropped.
\nThe T can also be dropped \"by mutual agreement\", but one needs the trailing Z if the time is UTC.

Sample ISO_datetime_utc:
\n2009-08-30T14:05:00[.xx]Z (UTC time)

Format: YYYY-mm-ddTHH:MM:SS[.xx]Z (UTC time)

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N., Poteau, A., Sauzède, R., Schallenberg, C., Schmechtig, C., Schmid, C., & Xing, X. (2023). BGC Argo quality control manual for particles backscattering (Version 1.0). Ifremer. https://doi.org/10.13155/60262","relation_type":["Methods"],"relation_desc":""},{"id":"1001963","citation":"Ellen Park. (2026). ellenrpark/faymckinleybiomes: v1.0.0 (Version v1.0.0) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.19486879","relation_type":["Software"],"relation_desc":""},{"id":"1001962","citation":"Fay, A. R., & McKinley, G. A. (2014). Global open-ocean biomes: mean and temporal variability. Earth System Science Data, 6(2), 273–284. https://doi.org/10.5194/essd-6-273-2014\r\n","relation_type":["Methods"],"relation_desc":""},{"id":"1001961","citation":"ellenrpark. (2026). ellenrpark/spike_decomp: v1.0.0 (Version v1.0.0) [Computer software]. 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