Figure  presents a schematic diagram of the processing chain used to derive the Rrs and the associated coccolithophorid bloom map. The processor initial stages (Sect. 3.1.1–Sect. 3.1.4) are applied to each image in turn. The images are then aggregated into daily composites and monthly climatologies. Each stage of the processor is sequentially discussed below.

In previous ocean-colour-based analyses, coccolithophorid bloom maps are produced as the binary classified output of a supervised multi-spectral algorithm (e.g. ). In this work, the availability of only one visible channel necessitates an alternative method, and the bloom map is instead produced through temporal filtering of the Rrs product, followed by selective masking to subsequently remove false positives.

Temporal filtering of the Rrs product is performed through a comparison of each daily composite to the relevant monthly mean climatological Rrs field (produced by the total aggregator stage). Rrs signals are only classified as blooms where the per-pixel Rrs value is greater than 2 monthly standard deviations above the corresponding monthly mean value. The standard deviation in this case is calculated from the monthly mean products across the entire archive. Pixels that do not match this criterion are assumed to contribute to the background, rather than bloom signal, and are therefore set to zero. The filtered bloom product, written into the daily netCDF4 file as filtered_remote_sensing_product, is then subject to further quality controls in the masking stage, as described below.

High Rrs values, while potentially indicative of coccolithophore blooms, can also occur in regions that are subject to high concentrations of suspended sediment (e.g. estuaries), or where shallow bathymetry and clear water coincide (e.g. shelf regions in oligotrophic areas). To remove these, and other false positives, the final bloom product is derived from the filtered bloom product by subjecting the latter to a number of screening processes, as detailed below.

Firstly, to remove the effects of land contamination, the bloom map is set to zero in all points within three pixels of the land mask. Secondly, following , the bloom map is set to zero in areas between 47∘ N and 47∘ S where the bathymetry is shallower than 100 m. This removes false positives associated with the sea floor, an effect that is most noticeable in the Caribbean and Arafura seas. Thirdly, whilst flagged sea ice has been explicitly removed from the Px5.3 data (see Sect. ), this does not comprehensively remove ice effects. As a result of missed flagging, and of glacial and river run-off, sporadic high Rrs values that are not indicative of blooms still occur at high latitudes. To correct for this, bloom map pixels are set to zero where Rrs≥0.05 (a value far above that which we would expect in water types associated with coccolithophorid blooms). Furthermore, the Rrs product is screened using sea surface temperature (SST) data obtained from contemporaneous ERA-Interim fields, and Rrs is set to zero in pixels where SST <0 ∘C in the Northern Hemisphere, a value at which the coccolithophorid growth rate drops to near zero, even for cold-water strains such as E. huxleyi . Finally, bloom map pixels are set to zero where the total aggregated mean value Rrs is greater than 0.0005, removing the effects of consistent river outflows (e.g. the Amazon and around the Yellow Sea).

The final product suite is annotated with relevant metadata to ensure CF1.8 compliance, completing the processing. The contents of the data file are described fully in the following section.

Figure  shows a comparison between four coccolithophorid blooms detected by ocean colour sensors and the corresponding blooms in the filtered_remote_sensing_reflectance product. In all four cases where bright blooms are detected in the ocean colour sensor observations (Fig. 3a SeaWiFS; Fig. 3c, MERIS; Fig. 3e, g, MODIS) there are spatially corresponding bright patches in the AVHRR imagery. In the MERIS and MODIS cases the AVHRR imagery is from the same day (i.e. on a single overpass basis). In the SeaWiFS case, a 3-day AVHRR composite mean is used, due to differences in cloud cover at the various acquisition times, lowering the intensity of the visible bloom but preserving the spatial coverage of the scene.

There is also evidence from in situ data in the English Channel case (Fig. a and b) that this was indeed a bloom of Emiliania huxleyi from cell counts and in-water radiometry . The northern North Sea feature centred on 56∘ N, 1.5∘ E in Fig. a is possibly the remnants of a bloom which was the subject of an intensive field campaign during June 1999 . Similarly blooms have been documented in the literature in the Barents Sea (Fig. e and f) which are comparable in timing and extent with some limited in situ samples ; report on blooms in the Bering Sea of similar timing and extent to those shown in Fig. g and h.

It is notable that the cloud (and ice) masking algorithms for the ocean colour and AVHRR sensors are in close agreement for the individual scenes shown in Fig. c and d, e and f, and g and h. The discrepancy in the cloud flagging for the SeaWiFS case occurs as a result of the 3-day compositing.

Figure  shows the global spatial coverage of the data set, presented as decadal means of the filtered Rrs bloom product for four decadal periods. Comparing Fig. a with the CZCS era (1978–1986) coccolithophorid bloom map produced by suggests that the bloom signals in the North Atlantic, North Sea and Norwegian Sea and on the Argentinian coast are well captured. The presence of blooms in the Black Sea and sporadically throughout the Mediterranean is also consistent between the two. However, due to the coastal masking, the high signals around Newfoundland are not captured here. In addition, no signals are detected in the Arafura Sea or within the Indonesian archipelago, as these areas have been specifically masked due to shallow bathymetry. The signals along the coast of Greenland and in the Southern Ocean are stronger than anticipated, likely due to some remaining influence of ice and glacial river outflow.

Mean values for the 1990–1999 period suggest the presence of coccolithophorid blooms in the North Atlantic, Norwegian Sea, Baltic Sea, Bering Sea and Southern Ocean, which is consistent with previous findings of . Similarly, the Rrs-based bloom product correctly attributes signals to the Benguela upwelling and in the western North Pacific Ocean. While they do not cover identical periods to the record shown here, increased Rrs in the Black, Norwegian and Baltic seas between the and studies is well replicated. A reduction of Rrs along the coast of Argentina also appears to be appropriately captured .

The change in bloom patterns between the CZCS era (Fig. a) and the SeaWiFS (Fig. b and c) mirrors the findings of , with an intensification of bloom expression and increase in coverage in the North Atlantic. Following this, despite a substantial decrease in mid-latitude bloom expression in Fig. d, there is a marked increase in the intensity of blooms in the Barents Sea, consistent with .

The differences between the bloom extent and intensity observed by the ocean colour and AVHRR sensors in Fig.  can, in part, be attributed to a combination of lower spatial resolution and radiometric sensitivity of the AVHRR sensor. The typical spatial resolution of ocean colour sensors is between 300 m and 1.1 km, whereas the Px5.3 product is 0.1∘. This equates to 2 orders of magnitude of coarsening and will have a particularly pronounced effect where strong spatial radiometric heterogeneities exist within blooms (see e.g. ), resulting in lower overall bloom reflectances. This coupled with the lower radiometric sensitivity of AVHRR (3 %) will exacerbate the overall “dimming” of the bloom. It is unlikely that blooms of non-calcifying phytoplankton with low backscattering will be detectable using this approach.

Given the expected “bloom dimming” (as discussed in Sect. ), it is necessary to establish the concentration at which coccolithophorid blooms can be detected using this approach. Comparing Fig. a, c, e and g, we can see that only the densest parts of the bloom are detected by AVHRR. This will be in the parts of the bloom undergoing the closing or senescent stages where the reflectance signal will be dominated by coccoliths rather than live coccolithophore cells.

in their detailed study of the biogeochemistry of coccolithophore blooms in the North Atlantic calculated that the AVHRR threshold of detectability for coccoliths is around 2×104 liths mL-1. A similar type of calculation using the equations found in and accounting for shifts in wavelength (0.546 to 0.630 µm) yields a threshold of approximately 105 liths mL-1. The bloom shown in Fig. a is well described by . When comparing the in situ data from Figs. 1 and 3a with the bloom pattern here, it is apparent that only the near-shore transect point is bright enough to be detected by AVHRR. However, for the same bloom showed that the number of free liths enumerated via microscopy using manual counting of samples fixed in buffered formalin, as used in , yielded a factor of 2 greater than that using flow cytometry. This they attributed to the fixation process causing the coccolithophores to shed even more of their liths and was proved by comparing fresh and preserved samples of cultured E. huxleyi using flow cytometry. Additionally achieved closer modelled optical closure when using the flow cytometric rather than the manual counts. This would therefore reduce the threshold limit described above to around 5×104 liths mL-1. As the Px5.3 data are cross-calibrated across all sensors, we do not expect this sensitivity threshold to be platform-specific.

The availability of only a single visible channel greatly reduces the ability of AVHRR to spectrally determine in-water constituents. Consequently, coccolithophorid blooms are predominantly identified through the removal of likely false positives (e.g. river plumes, coastal influences, bathymetry and sea ice). Whilst coccolithophorid blooms are known to occur across the eastern , north-western and western Mediterranean , as shown in the bloom map of , they do not do so to the extent that they are expressed in the Rrs product derived here (Fig. b). We partially attribute the high Rrs values to the sporadic presence of Saharan dust. This requires specific atmospheric correction methods that are not implemented here and would require ancillary dust information that is of limited availability on the timescale covered by this data set.