Application of a new net primary production methodology: a daily to annual-scale data set for the North Sea, derived from autonomous underwater gliders and satellite Earth observation

Loveday, Benjamin R.; Smyth, Timothy; Akpinar, Anil; Hull, Tom; Inall, Mark E.; Kaiser, Jan; Queste, Bastien Y.; Tobermann, Matt; Williams, Charlotte A. J.; Palmer, Matthew R.. 2022 Application of a new net primary production methodology: a daily to annual-scale data set for the North Sea, derived from autonomous underwater gliders and satellite Earth observation. Earth System Science Data, 14 (9). 3997-4016. 10.5194/essd-14-3997-2022

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Official URL: http://dx.doi.org/10.5194/essd-14-3997-2022

Abstract/Summary

Shelf seas play a key role in both the global carbon cycle and coastal marine ecosystems through the draw-down and fixing of carbon, as measured through phytoplankton net primary production (NPP). Measuring NPP in situ and extrapolating this to the local, regional, and global scale presents challenges however because of limitations with the techniques utilised (e.g. radiocarbon isotopes), data sparsity, and the inherent biogeochemical heterogeneity of coastal and open-shelf waters. Here, we introduce a new data set generated using a technique based on the synergistic use of in situ glider profiles and satellite Earth observation measurements which can be implemented in a real-time or delayed-mode system (https://doi.org/10.5285/e6974644-2026-0f94-e053-6c86abc00109; Loveday and Smyth, 2022). We apply this system to a fleet of gliders successively deployed over a 19-month time frame in the North Sea, generating an unprecedented fine-scale time series of NPP in the region. At a large scale, this time series gives close agreement with existing satellite-based estimates of NPP for the region and previous in situ estimates. What has not been elucidated before is the high-frequency, small-scale, depth-resolved variability associated with bloom phenology, mesoscale phenomena, and mixed layer dynamics.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.5194/essd-14-3997-2022
ISSN:	1866-3516
Date made live:	06 Dec 2022 19:55 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/533693

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.5194/essd-14-3997-2022

ISSN:

1866-3516

Date made live:

06 Dec 2022 19:55 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/533693