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...

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Chlorophyll-a (Chl a) is a key parameter for the assessment of water quality in coastal and shelf environments. The availability of satellite ocean colour offers the potential of monitoring these regions at unprecedented spatial and temporal scales, as long as a high level of accuracy can be achieved. To use satellite derived Chl a to monitor these environments, it is imperative that rigorous accuracy assessments are undertaken to select the most accurate ocean colour algorithm(s). To this end, the accuracy of a range of ocean colour Chl a algorithms for use with Medium Imaging Resolution Spectrometer (MERIS) Level 2 (L2) Remote Sensing Reflectance (Rrs), using two different atmospheric correction (AC) processors (COASTCOLOUR and MERIS Ground Segment processor version 8.0-MEGS8.0), were assessed in North West European waters. A total of 594 measurements of Rrs(λ) and/or Chl a were made in the North Sea, Mediterranean Sea, along the Portuguese Coast, English Channel and Celtic Sea between June 2001 and March 2012, where Chl a varied from 0.2 to 35 mg m− 3. The following algorithms were compared: MERIS Case 1 water Chl a algorithm OC4Me, the MERIS Case 2 algorithm Algal Pigment 2 (AP2), the MODIS-Aqua Case 1 Chl a algorithm OC3 adapted for MERIS (OC3Me), the MODIS-Aqua Garver-Siegel-Maritorena algorithm (GSM) adapted for MERIS and the Gohin et al. (2002) algorithm for MERIS (OC5Me). For both COASTCOLOUR and MEGS8.0 processors, OC5Me was the most accurate Chl a algorithm, which was within ~ 25% of in situ values in these coastal and shelf waters. The uncertainty in MEGS8.0 Rrs(442) (~ 17%) was slightly higher compared to COASTCOLOUR (~ 12%) from 0.3 to 7 mg m− 3 Chl a, but for Rrs(560) the uncertainty was lower for MEGS8.0 (~ 10%) compared to COASTCOLOUR (~ 13%), which meant that MEGS8.0 Chl a was more accurate than COASTCOLOUR for all of the Chl a algorithms tested. Compared to OC5Me, OC4Me tended to overestimate Chl a, which was caused by non-algal SPM especially at values > 14 g m− 3. GSM also overestimated Chl a, which was caused by variations in absorption Please note that this is an author-produced PDF of an article accepted for publication following peer review. The definitive publisher-authenticated version is available on the publisher Web site. coefficient of coloured dissolved organic matter at 442 nm (aCDOM(442)). AP2 consistently underestimated Chl a, especially when non-algal SPM was > 4 g m− 3. Highlights ► Accuracy of MERIS ocean colour algorithms was assessed in NW European waters. ► Both COASTCOLOUR and MEGS8.0 processors were compared for computation of Chl a. ► MEGS8.0 OC5Me was the most accurate algorithm; OC4 & AP2 overestimated Chl a. ► Computation of uncertainties in MERIS R rs over the range in Chl a was conducted.

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El Niño-Southern Oscillation (ENSO) is regarded as the main driver of phytoplankton inter-annual variability. Remotely sensed surface chlorophyll-a (Chl-a), has made it possible to examine phytoplankton variability at a resolution and scale that allows for the investigation of climate signals such as ENSO. We provide empirical evidence of an immediate and lagged influence of ENSO on SeaWiFS and MODIS-Aqua derived global Chl-a concentrations. We use 13 years of Chl-a remotely sensed observations along with sea surface temperature (SST) observations across the Tropical and South Pacific to isolate and examine the spatial development of Chl-a anomalies during ENSO: its canonical or eastern Pacific (EP) mode, and El Niño Modoki or central Pacific (CP) mode, using the extended empirical orthogonal function (EEOF) technique. We describe how an EP ENSO phase transition affects Chl-a, and identify an interannual CP mode of variability induced spatial pattern. We argue that when ENSO is analysed as a propagating signal by the EEOF, CP ENSO is found to be more influential on Chl-a interannual to decadal variability than the canonical EP ENSO. Our results cannot confirm the independence of the two ENSO modes but clearly demonstrate that both ENSO flavors manifest a distinct