Climate change has profound impacts on the ocean, a critical component of the Earth's climate system. In marine ecosystems, phytoplankton are central to the cycling of key elements and compounds, making them an important component in understanding oceanic responses to climate change. Monitoring phytoplankton dynamics, in particular chlorophyll-a concentration (Chl-a), a proxy for phytoplankton biomass, has long been a major focus of marine science. Satellite remote sensing of ocean colour remains the only method capable of observing phytoplankton across the entire surface ocean, with extensive spatial coverage and high temporal resolution.

However, standard algorithms for estimating Chl-a from ocean colour satellite data often face the problem of ambiguity - different types of phytoplankton, due to their specific optical properties and the influence of other water constituents within their ecological niches, can produce different ocean colours even at the same Chl-a concentration. This leads to uncertainties in Chl-a estimates using standard algorithms, which are often based on empirical relationships, making them less reliable in regions with distinct optical characteristics, such as polar regions. Additionally, as the need to understand phytoplankton community composition and its functional roles in marine ecosystems grows, there is an increasing demand for advanced methods to extract this information from satellite data.

To address these challenges, we developed a new Ocean Colour Modelling Framework (OCMF) that incorporates ecological concepts, specifically the phytoplankton size structure, to improve the understanding of the relationship between ocean colour and Chl-a. The model incorporates sea surface temperature, a key factor influencing phytoplankton size composition both directly and indirectly. Developed using the largest available global in-situ dataset and validated with both in-situ and satellite data, the OCMF demonstrates improved accuracy across diverse marine environments. By applying the OCMF to long-term satellite data, we have observed its ability to simultaneously assess the effects of climate change on phytoplankton biomass and size composition, where standard algorithms may fall short. Our model provides a tool for advancing research on phytoplankton diversity, adaptability, and critical tipping points, thereby providing new insights to address existing gaps in understanding the responses of marine ecosystems to climate change.

phytoplankton,climate change,phytoplankton size structure,ocean colour remote sensing