ABSTRACT 1149 | POSTER 245

IMPROVING CROSS-PLATFORM COMPARABILITY OF SEAL-BORNE FLUORESCENCE MEASUREMENTS WITHIN THE GLOBAL OCEAN OBSERVING SYSTEM

Miniaturised chlorophyll fluorescence sensors integrated into Satellite Relay Data Loggers (SRDLs) deployed on southern elephant seals (Mirounga leonina) provide essential bio-optical observations in remote Southern Ocean regions where shipboard and autonomous sampling is sparse. Particularly in coastal Antarctica, seal-borne fluorescence measurements often provide the only available in situ chlorophyll data source, enabling studies into biological productivity across multiple trophic levels in these climate-critical regions. Despite their scientific value, the field accuracy of the miniaturised Valeport Hyperion fluorometers currently used on SRDLs, and their comparability with other bio-optical sensor platforms within the Global Ocean Observing System (GOOS), remain poorly constrained. Here, we present the first systematic field calibration of nine SRDL Valeport Hyperion sensors against a shipboard SeaBird ECO FLBB sensor (routinely mounted on BGC-Argo floats), both operating at 470/695 nm excitation/emission wavelength. Hyperion fluorometers consistently underestimated chlorophyll relative to the ECO sensors by a factor of 0.59±0.14. We recommend correcting for this difference using a 1.71±0.61 Hyperion-to-ECO gain factor, which was independently validated using a regional seal-borne/BGC-Argo comparison dataset. This gain-adjustment enables interoperability between SRDL Hyperion and ECO-based fluorescence from autonomous and ship-based platforms, effectively integrating seal-borne data into the GOOS. Established BGC-Argo physiological correction factors can then be applied to obtain more accurate chlorophyll estimates, further improving cross-platform comparability. By resolving calibration biases between biologging and autonomous platforms, this work directly supports the long-term vision of globally consistent observations from profiling floats and animal-borne sensors. This work has been submitted for publication to L&O:Methods.

Jakob Weis, UTAS, ACEAS, [email protected], https://orcid.org/0000-0002-9493-4888

Christina Schallenberg, CSIRO, [email protected], https://orcid.org/0000-0002-3073-7500

Esmee van Wijk, CSIRO, AAPP, [email protected], https://orcid.org/0000-0002-9375-4383

Christophe Guinet, CEBC, [email protected], https://orcid.org/0000-0003-2481-6947

Laura Harraiz-Borreguero, CSIRO, AAPP, [email protected], https://orcid.org/0000-0002-4455-801X

Fabien Roquet, University of Gothenburg, [email protected], https://orcid.org/0000-0003-1124-4564

Clive R McMahon, IMOS, [email protected], https://orcid.org/0000-0001-5241-8917

Ian Jonsen, StochasticQC, [email protected], https://orcid.org/0000-0001-5423-6076

David Green, UTAS, ACEAS, [email protected], https://orcid.org/0000-0002-0346-3129

Mark Hindell, UTAS, [email protected], https://orcid.org/0000-0002-7823-7185

Sophie Bestley, UTAS, ACEAS, [email protected], https://orcid.org/0000-0001-9342-669X

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