An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)

Abstract

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were u...

Primary Production, an Index of Climate Change in the Ocean: Satellite-Based Estimates over Two Decades

“…The model has consistently performed well when compared with other models [5,35,53] and has been implemented on a global scale [2]. In the present study, considerable improvements have been made to the global coverage of the parameter database, while data provided by the Ocean Colour Climate Change Initiative (OC-CCI) project [10,54] allowed for the use of over 20 years of remote-sensing observations. The OC-CCI products [10] are multisensor products (reducing missing data), in which biases between sensors have been corrected (avoiding artificial trends in data arising from systematic differences between biases) and have been processed with a common protocol for calculation of chlorophyll-a concentration (minimising any systematic differences arising from differences between algorithms).…”

“…Yet, trends in biological fields estimated from remote-sensing observations have not been taken into account in recent studies on global carbon budgets and pools and fluxes of carbon in the ocean [8,9]. In recent years, considerable efforts have been made to correct inter-sensor biases and merge data from multiple ocean-colour satellite sensors to provide a long (over two decades) record of phytoplankton biomass in the global oceans through the Ocean Colour Climate Change Initiative of the European Space Agency [10]. This time series now offers the opportunity to undertake a systematic study of changes in phytoplankton primary production over the last 20 years.…”

A New Definition of the South‐East Madagascar Bloom and Analysis of Its Variability

“…The version 3 chlorophyll‐a concentration product, provided by the ESA OC‐CCI (https://www.esa-oceancolour-cci.org; Lavender et al, ; Satyendranath et al, ) was used in this study. It combines data from Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS), MEdium Resolution Imaging Spectrometer, and MOderate Resolution Imaging Spectroradiometer‐Aqua (MODIS) to provide a continuous time series of surface chlorophyll‐a concentration from September 1997 to December 2016.…”

Retrieval of Particulate Backscattering Using Field and Satellite Radiometry: Assessment of the QAA Algorithm

“…Currently, in the European Space Agency (ESA) Ocean Colour (OC) Climate Change Initiative (CCI), QAA is the selected algorithm to retrieve b bp . Specifically, the ESA OC-CCI project aims at creating a long-term, consistent, uncertainty-characterized time series of ocean color products, for use in climate-change studies [5,31]. In such a context, while in the case of Chl the uncertainties are fully provided, the b bp satellite products lack such information that is also crucial for POC and C phyto estimations [1,32].…”