The Ocean Colour Climate Change Initiative (OC-CCI) project has spent a decade working to create an ever-improving climate data record (CDR) of the ocean colour Essential Climate Variable (ECV). The cyclical process of improvement, release and feedback has led to 6 versions of the dataset being created to-date with incorporation of data from additional sensors, incremental improvements at all stages of the processing chain, from top-of-atmosphere derived pixel flagging to inter-sensor bias correction and blended in-water algorithms. The OC-CCI dataset is the highest volume dataset of the ESA ECV catalogue, containing over 90 variables, spanning 23 years and now being provided for testing purposes at 1km resolution. The OC-CCI dataset has been used in over 180 publications including a number of prominent climate related studies such as the upcoming IPCC working group 2 report and publications in eminent journals such as Nature (Tang et al. 2021, Dutkiewicz et al. 2019). The data has been used to study the impacts of climate from the local scale, such as climate impacts on Sea Urchin Settlement (Okamoto et al. 2019), to the global scale, such as computation of oceanic primary production (Kulk et al. 2020). The data have also been used to investigate a variety of cross-disciplinary research areas such as the connections between ocean physics and biology (Balaguru et al. 2018), the synergy between ocean models and remote-sensing observations (Baird et al. 2020), and the links between ocean and human health (Campbell et al. 2020). This uptake of the data by the scientific community has been aided by the standardised formatting and multiple channels for access to the data (from bulk downloads to web-based interactive browsers). The processing chains that were developed in a research phase under OC-CCI are now used for operational data processing for services such as CMEMS and C3S. Here, we reflect on the lessons learned over the last decade of ECV development and consider the current maturity and future of the OC-CCI dataset in terms of 1) transforming research mode data into operational products, 2) data accessibility and interface with toolboxes, 3) the percolation of the data into decision-making spheres, and 4) the use of the data in cross-disciplinary science.
Baird, M; Chai, F; Ciavatta, S; Dutkiewicz, S; Edwards, C; Evers-King, H; Friedrichs, M; Frolov, S; Gehlen, Ma; Henson, S; Hickman, A; Jahn, O; Jones, E; Kaufman, D; Mélin, F; Mouw, C; Muhling, B; Rousseaux, C; Shulman, I; Wiggert, J. Synergy between Ocean Colour and Biogeochemical/Ecosystem Models. IOCCG Report Number 19 (2020).
Balaguru K, Doney SC, Bianucci L, Rasch PJ, Leung LR, Yoon J-H, et al. (2018) Linking deep convection and phytoplankton blooms in the northern Labrador Sea in a changing climate. PLoS ONE 13(1): e0191509. https://doi.org/10.1371/journal.pone.0191509
Campbell AM, Racault MF, Goult S, Laurenson A. Cholera Risk: A Machine Learning Approach Applied to Essential Climate Variables. Int J Environ Res Public Health. 2020;17(24):9378. Published 2020 Dec 15. doi:10.3390/ijerph17249378
Dutkiewicz, S., Hickman, A.E., Jahn, O., Henson, S., Beaulieu, C. and Monier, E., 2019. Ocean colour signature of climate change. Nature communications, 10(1), p.578.
Tang, W., Llort, J., Weis, J. et al. Widespread phytoplankton blooms triggered by 2019–2020 Australian wildfires. Nature 597, 370–375 (2021). https://doi.org/10.1038/s41586-
Kulk, Gemma & Platt, Trevor & Dingle, James & Jackson, Thomas & Jönsson, Bror & Bouman, Heather & Babin, Marcel & Brewin, Bob & Doblin, Martina & Estrada, Marta & Figueiras, F.G. & Furuya, Ken & González-Benítez, Natalia & Gudfinnsson, Hafsteinn & Gudmundsson, Kristinn & Huang, Bangqin & Isada, Tomonori & Kovač, Žarko & Lutz, Vivian & Sathyendranath, Shubha. (2020). Primary Production, an Index of Climate Change in the Ocean: Satellite-Based Estimates over Two Decades. Remote Sensing. 12. 826. https://doi.org/10.3390/rs12050826
Daniel K. Okamoto, Stephen Schroeter, Daniel C. Reed (2019) Effects of Ocean Climate on Spatiotemporal Variation in Sea Urchin Settlement and Recruitment, bioRxiv 387282; doi: https://doi.org/10.1101/387282