How uncertain and observable are marine ecosystem indicators in shelf seas?

Skákala, J., Ford, D., Fowler, A. ORCID: https://orcid.org/0000-0003-3650-3948, Lea, D., Martin, M. J. and Ciavatta, S. (2024) How uncertain and observable are marine ecosystem indicators in shelf seas? Progress in Oceanography, 224. 103249. ISSN 1873-4472 doi: 10.1016/j.pocean.2024.103249

Abstract/Summary

Operational analysis and forecast products of shelf sea biogeochemistry often lack reliable information on uncertainty. This is problematic, as good quality uncertainty information is both requested by the product end-users and essential for data assimilation. To address this problem we developed a quality-assessed ensemble representation of many leading sources of uncertainty in a coupled marine physical-biogeochemical model of the North-West European Shelf. Based on these ensembles we have estimated the uncertainty of several marine ecosystem health indicators (MEHIs), acting as proxies for biological productivity, phytoplankton community structure, trophic fluxes, deoxygenation, acidification and carbon export. We have also evaluated how observable these MEHIs are from the most widely available observations of total chlorophyll (mostly from the surface), highlighting those MEHIs and locations that need to be better monitored. Our results show that the most uncertain and the least observable MEHI is the phytoplankton community composition, highlighting the value of its observations (and their assimilation) particularly in the UK regional waters. We demonstrate that daily operational estimates of the other MEHIs, produced by the Met Office, are fairly well constrained. We also quantify how much MEHI uncertainties are reduced when one substantially coarsens the MEHI spatial and temporal resolution, as in the global and/or climate applications.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/116232
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO) Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Elsevier
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