Enhancing OneArgo Chlorophyll-a Data Quality and Uniformity

Phytoplankton biomass, the foundation of the oceanic food web, is predominantly estimated from chlorophyll-a (Chla) concentration. In vivo chlorophyll-a fluorescence (fluo), a key proxy for Chla, has become one of the most widely measured biogeochemical parameters in the ocean. This advancement is largely due to the integration of fluorometers onto BioGeoChemical-Argo (BGC-Argo) profiling floats, a key component of the multidisciplinary OneArgo array. By significantly expanding the number of fluo profiles compared to historical ship-based observations, this development has solidified OneArgo's role as a cornerstone of the global biogeochemical observing system.

However, converting fluo into Chla is not straightforward, as it is influenced by various factors, including the composition and physiological state of phytoplankton communities. Accurate calibration of fluo into Chla is therefore both challenging and essential for fully utilizing the rapidly growing volume of fluo data. The Argo Data Management Team (ADMT) has made significant efforts to calibrate and validate fluo measurements from OneArgo floats, aiming to deliver Chla estimates with the highest possible accuracy. Despite these efforts, the current OneArgo Chla dataset still exhibits substantial regional biases in real-time (RT), particularly in high-latitude regions such as the Southern Ocean.

Recent advances in observation-based products have introduced innovative solutions to address these challenges, including new delayed-mode (DM) correction methods that significantly reduce regional biases in Chla estimates. However, a key issue persists: DM and real-time (RT) datasets often differ considerably depending on the location, resulting in inconsistencies that compromise the homogeneity and interoperability of the OneArgo database. To address this, we propose a new RT correction method, based on observation-based products, to improve Chla accuracy and better align RT data with DM-calibrated values. This advancement is expected to be implemented soon, enabling a more seamless integration of RT and DM datasets and ultimately enhancing the overall quality and utility of the OneArgo Chla dataset.

This study underscores the potential of new observation-based products to enhance the accuracy and coherence of the OneArgo Chla dataset. High-quality OneArgo data are critical for both scientific research and operational oceanography, including the assimilation of data into biogeochemical models.