The global distribution of phytoplankton size spectrum and size classes from their light-absorption spectra derived from satellite data

The absorption spectra of phytoplankton in the visible domain hold implicit information on the phytoplankton community structure. Here we use this information to retrieve quantitative information on phytoplankton size structure by developing a novel method to compute the exponent of an assumed power-law for their particle-size spectrum. This quantity, in combination with total chlorophyll-a concentration, can be used to estimate the fractional concentration of chlorophyll in any arbitrarily-defined size class of phytoplankton. We further define and derive expressions for two distinct measures of cell size of mixed. populations, namely, the average spherical diameter of a bio-optically equivalent homogeneous population of cells of equal size, and the average equivalent spherical diameter of a population of cells that follow a power-law particle-size distribution. The method relies on measurements of two quantities of a phytoplankton sample: the concentration of chlorophyll-a, which is an operational index of phytoplankton biomass, and the total absorption coefficient of phytoplankton in the red peak of visible spectrum at 676 nm. A sensitivity analysis confirms that the relative errors in the estimates of the exponent of particle size spectra are reasonably low. The exponents of phytoplankton size spectra, estimated for a large set of in situ data from a variety of oceanic environments (similar to 2400 samples), are within a reasonable range; and the estimated fractions of chlorophyll in pico-, nano- and micro-phytoplankton are generally consistent with those obtained by an independent, indirect method based on diagnostic pigments determined using high-performance liquid chromatography. The estimates of cell size for in situ samples dominated by different phytoplankton types (diatoms, prymnesiophytes, Prochlorococcus, other cyanobacteria and green algae) yield nominal sizes consistent with the taxonomic classification. To estimate the same quantities from satellite-derived ocean-colour data, we combine our method with algorithms for obtaining inherent optical properties from remote sensing. The spatial distribution of the size-spectrum exponent and the chlorophyll fractions of pico-, nano- and micro-phytoplankton estimated from satellite remote sensing are in agreement with the current understanding of the biogeography of phytoplankton functional types in the global oceans. This study contributes to our understanding of the distribution and time evolution of phytoplankton size structure in the global oceans.

Colloidal stability of nanoparticles derived from simulated cloud-processed mineral dusts

Laboratory simulation of cloud processing of three model dust types with distinct Fe-content (Moroccan dust, Libyan dust and Etna ash) and reference goethite and ferrihydrite were conducted in order to gain a better understanding of natural nanomaterial inputs and their environmental fate and bioavailability. The resulting nanoparticles (NPs) were characterised for Fe dissolution kinetics, aggregation/size distribution, micromorphology and colloidal stability of particle suspensions using a multi-method approach. We demonstrated that the: (i) acid-leachable Fe concentration was highest in volcanic ash (1 m Mg(-1) dust) and was followed by Libyan and Moroccan dust with an order of magnitude lower levels; (ii) acid leached Fe concentration in the<20 nm fraction was similar in samples processed in the dark with those under artificial sunlight, but average hydrodynamic diameter of NPs after cloud-processing (pH~6) was larger in the former; iii) NPs formed at pH~6 were smaller and less poly-disperse than those at low pH, whilst unaltered zeta potentials indicated colloidal instability; iv) relative Fe percentage in the finer particles derived from cloud processing does not reflect Fe content of unprocessed dusts (e.g. volcanic ash>Libyan dust). The common occurrence of Fe-rich "natural nanoparticles" in atmospheric dust derived materials may indicate their more ubiquitous presence in the marine environment than previously thought.

Chemical interaction of atmospheric mineral dust-derived nanoparticles with natural seawater — EPS and sunlight-mediated changes

Laboratory studies were conducted to investigate the interactions of nanoparticles (NPs) formed via simulated cloud processing of mineral dust with seawater under environmentally relevant conditions. The effect of sunlight and the presence of exopolymeric substances (EPS) were assessed on the: (1) colloidal stability of the nanoparticle aggregates (i.e. size distribution, zeta potential, polydispersity); (2) micromorphology and (3) Fe dissolution from particles. We have demonstrated that: (i) synthetic nano-ferrihydrite has distinct aggregation behaviour from NPs formed from mineral dusts in that the average hydrodynamic diameter remained unaltered upon dispersion in seawater (~1500 nm), whilst all dust derived NPs increased about three fold in aggregate size; (ii) relatively stable and monodisperse aggregates of NPs formed during simulated cloud processing of mineral dust become more polydisperse and unstable in contact with seawater; (iii) EPS forms stable aggregates with both the ferrihydrite and the dust derived NPs whose hydrodynamic diameter remains unchanged in seawater over 24h; (iv) dissolved Fe concentration from NPs, measured here as <3 kDa filter-fraction, is consistently >30% higher in seawater in the presence of EPS and the effect is even more pronounced in the absence of light; (v) micromorphology of nanoparticles from mineral dusts closely resemble that of synthetic ferrihydrite in MQ water, but in seawater with EPS they form less compact aggregates, highly variable in size, possibly due to EPS-mediated steric and electrostatic interactions. The larger scale implications on real systems of the EPS solubilising effect on Fe and other metals with the additional enhancement of colloidal stability of the resulting aggregates are discussed.

On the temporal consistency of chlorophyll products derived from three ocean-colour sensors

Satellite ocean-colour sensors have life spans lasting typically five-to-ten years. Detection of long-term trends in chlorophyll-a concentration (Chl-a) using satellite ocean colour thus requires the combination of different ocean-colour missions with sufficient overlap to allow for cross-calibration. A further requirement is that the different sensors perform at a sufficient standard to capture seasonal and inter-annual fluctuations in ocean colour. For over eight years, the SeaWiFS, MODIS-Aqua and MERIS ocean-colour sensors operated in parallel. In this paper, we evaluate the temporal consistency in the monthly Chl-a time-series and in monthly inter-annual variations in Chl-a among these three sensors over the 2002–2010 time period. By subsampling the monthly Chl-a data from the three sensors consistently, we found that the Chl-a time-series and Chl-a anomalies among sensors were significantly correlated for >90% of the global ocean. These correlations were also relatively insensitive to the choice of three Chl-a algorithms and two atmospheric-correction algorithms. Furthermore, on the subsampled time-series, correlations between Chl-a and time, and correlations between Chl-a and physical variables (sea-surface temperature and sea-surface height) were not significantly different for >92% of the global ocean. The correlations in Chl-a and physical variables observed for all three sensors also reflect previous theories on coupling between physical processes and phytoplankton biomass. The results support the combining of Chl-a data from SeaWiFS, MODIS-Aqua and MERIS sensors, for use in long-term Chl-a trend analysis, and highlight the importance of accounting for differences in spatial sampling among sensors when combining ocean-colour observations.

Uncertainty estimates of remote sensing reflectance derived from comparison of ocean color satellite data sets

Assigning uncertainty to ocean-color satellite products is a requirement to allow informed use of these data. Here, uncertainty estimates are derived using the comparison on a 12th-degree grid of coincident daily records of the remote-sensing reflectance RRS obtained with the same processing chain from three satellite missions, MERIS, MODIS and SeaWiFS. The approach is spatially resolved and produces σ, the part of the RRS uncertainty budget associated with random effects. The global average of σ decreases with wavelength from approximately 0.7– 0.9 10−3 sr−1 at 412 nm to 0.05–0.1 10−3 sr−1 at the red band, with uncertainties on σ evaluated as 20–30% between 412 and 555 nm, and 30–40% at 670 nm. The distribution of σ shows a restricted spatial variability and small variations with season, which makes the multi-annual global distribution of σ an estimate applicable to all retrievals of the considered missions. The comparison of σ with other uncertainty estimates derived from field data or with the support of algorithms provides a consistent picture. When translated in relative terms, and assuming a relatively low bias, the distribution of σ suggests that the objective of a 5% uncertainty is fulfilled between 412 and 490 nm for oligotrophic waters (chlorophyll-a concentration below 0.1 mg m−3). This study also provides comparison statistics. Spectrally, the mean absolute relative difference between RRS from different missions shows a characteristic U-shape with both ends at blue and red wavelengths inversely related to the amplitude of RRS. On average and for the considered data sets, SeaWiFS RRS tend to be slightly higher than MODIS RRS, which in turn appear higher than MERIS RRS. Biases between mission-specific RRS may exhibit a seasonal dependence, particularly in the subtropical belt.