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Phytoplankton play a central role in the planetary cycling of important elements and compounds. Understanding how phytoplankton are responding to climate change is consequently a major question in Earth Sciences. Monitoring phytoplankton is key to answering this question. Satellite remote sensing of ocean colour is our only means of monitoring phytoplankton in the entire surface ocean at high temporal and large spatial scales, and the continuous ocean-colour data record is now approaching a length suitable for addressing questions around climate change, at least in some regions. Yet, developing ocean-colour algorithms for climate change studies requires addressing issues of ambiguity in the ocean-colour signal. For example, for the same chlorophyll-a concentration (Chl-a) of phytoplankton, the colour of the ocean can be different depending on the type of phytoplankton present. One route to tackle the issue of ambiguity is by enriching the ocean-colour data with information on sea surface temperature (SST), a good proxy of changes in three phytoplankton size classes (PSCs) independent of changes in total Chl-a, a measure of phytoplankton biomass. Using a global surface in-situ dataset of HPLC (high performance liquid chromatography) pigments, size-fractionated filtration data, and concurrent satellite SST spanning from 1991 to 2021, we re-tuned, validated and advanced an SST-dependent three-component model that quantifies the relationship between total Chl-a and Chl-a associated with the three PSCs (pico-, nano- and microplankton). Similar to previous studies, striking dependencies between model parameters and SST were captured, which were found to improve model performance significantly. These relationships were applied to 40 years of monthly composites of satellite SST, and significant trends in model parameters were observed globally, in response to climate warming. Changes in these parameters highlight issues in estimating long-term trends in phytoplankton biomass (Chl-a) from ocean colour using standard empirical algorithms, which implicitly assume a fixed relationship between total Chl-a and Chl-a of the three size classes. The proposed ecological model will be at the centre of a new ocean-colour modelling framework, designed for investigating the response of phytoplankton to climate change, described in subsequent parts of this series of papers., This work is supported primarily by a UKRI Future Leader Fellowship (MR/V022792/1). Additional supports from the UK National Centre for Earth Observation (NCEO), the Simons Foundation Project Collaboration on Computational Biogeochemical Modeling of Marine Ecosystems (CBIOMES, 549947, Shubha Sathyendranath), and Royal Society International Exchanges 2021 Cost Share (NSFC) grant (IEC NSFC 211058) are acknowledged. Astrid Bracher and Vanda Brotas are funded by the European Union’s Horizon 2020 Research and Innovation Programme (N810139): Project Portugal Twinning for Innovation and Excellence in Marine Science and Earth Observation (PORTWIMS). Astrid Bracher is also funded by the ESA 656 S5P+Innovation Theme 7 Ocean Colour (S5POC) project (No. 4000127533/19/I-NS). The AWI in-situ data are supported by the Helmholtz Infrastructure Initiative FRAM. Tarron Lamont and Ray Barlow acknowledge funding, logistical, and administrative support from the South African National Department of Forestry, Fisheries and the Environment (DFFE), Bayworld Centre for Research and Education (BCRE), and the South African National Research Foundation (NRF grants: 129229 and 132073). Fang Shen is funded by National Natural Science Foundation of China (No. 42076187 and No. 41771378) for the sampling of in-situ data in eastern China seas. The Atlantic Meridional Transect (AMT) is funded by the UK Natural Environment Research Council (NERC) through its National Capability Long-term Single Centre Science Programme, Climate Linked Atlantic Sector Science (grant number NE/R015953/1) to Plymouth Marine Laboratory. This work contributes to the international IMBeR project and is contribution number 386 of the AMT programme. The authors would like to acknowledge all the contributors who have shared in-situ data to the public domains, including the Western Channel Observatory, TARA Ocean, Rothera Research Station, NASA SeaBASS, ADON, Government of Canada, DATAONE, BCODMO, EDI Data Portal, PANGAEA, and BODC, and all the scientists and crew who were involved in the collection of in-situ data are sincerely appreciated. We thank NOAA for providing daily and monthly OISST (version 2) SST data; ESA for providing monthly SST-CCI (version 2.1) SST data; ESA for providing monthly OC-CCI (version 5.0) Chl-a climatology data; GEBCO for providing bathymetric data (GEBCO2021 Grid). The authors also thank Hongyan Xi for providing comments and suggestions on the early version of the manuscript.

The established procedure to access the quality of atmospheric correction processors and their underlying algorithms is the comparison of satellite data products with related in-situ measurements. Although this approach addresses the accuracy of derived geophysical properties in a straight forward fashion, it is also limited in its ability to catch systematic sensor and processor dependent behaviour of satellite products along the scan-line, which might impair the usefulness of the data in spatial analyses. The Ocean Colour Climate Change Initiative (OC-CCI) aims to create an ocean colour dataset on a global scale to meet the demands of the ecosystem modelling community. The need for products with increasing spatial and temporal resolution that also show as little systematic and random errors as possible, increases. Due to cloud cover, even temporal means can be influenced by along-scanline artefacts if the observations are not balanced and effects cannot be cancelled out mutually. These effects can arise from a multitude of results which are not easily separated, if at all. Among the sources of artefacts, there are some sensor-specific calibration issues which should lead to similar responses in all processors, as well as processor-specific features which correspond with the individual choices in the algorithms. A set of methods is proposed and applied to MERIS data over two regions of interest in the North Atlantic and the South Pacific Gyre. The normalised water leaving reflectance products of four atmospheric correction processors, which have also been evaluated in match-up analysis, is analysed in order to find and interpret systematic effects across track. These results are summed up with a semi-objective ranking and are used as a complement to the match-up analysis in the decision for the best Atmospheric Correction (AC) processor. Although the need for discussion remains concerning the absolutes by which to judge an AC processor, this example demonstrates clearly, that relying on the match-up analysis alone can lead to misjudgement.