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.

Flexible C : N ratio enhances metabolism of large phytoplankton when resource supply is intermittent

Phytoplankton cell size influences particle sinking rate, food web interactions and biogeographical distributions. We present a model in which the uptake, storage and assimilation of nitrogen and carbon are explicitly resolved in different-sized phytoplankton cells. In the model, metabolism and cellular C :N ratio are influenced by the accumulation of carbon polymers such as carbohydrate and lipid, which is greatest when cells are nutrient starved, or exposed to high light. Allometric relations and empirical data sets are used to constrain the range of possible C : N, and indicate that larger cells can accumulate significantly more carbon storage compounds than smaller cells. When forced with extended periods of darkness combined with brief exposure to saturating irradiance, the model predicts organisms large enough to accumulate significant carbon reserves may on average synthesize protein and other functional apparatus up to five times faster than smaller organisms. The advantage of storage in terms of average daily protein synthesis rate is greatest when modeled organisms were previously nutrient starved, and carbon storage reservoirs saturated. Small organisms may therefore be at a disadvantage in terms of average daily growth rate in environments that involve prolonged periods of darkness and intermittent nutrient limitation. We suggest this mechanism is a significant constraint on phytoplankton C :N variability and cell size distribution in different oceanic regimes.

Coccolithophore bloom size variation in response to the regional environment of the subarctic North Atlantic

Several environmental/physical variables derived from satellite and in situ data sets were used to understand the variability of coccolithophore abundance in the subarctic North Atlantic. The 7-yr (1997–2004) time-series analysis showed that the combined effects of high solar radiation, shallow mixed layer depth (<20 m), and increased temperatures explained >89% of the coccolithophore variation. The June 1998 bloom, which was associated with high light intensity, unusually high sea-surface temperature, and a very shallow mixed layer, was found to be one of the most extensive (>995,000 km2) blooms ever recorded. There was a pronounced sea-surface temperature shift in the mid-1990s with a peak in 1998, suggesting that exceptionally large blooms are caused by pronounced environmental conditions and the variability of the physical environment strongly affects the spatial extent of these blooms. Consequently, if the physical environment varies, the effects of these blooms on the atmospheric and oceanic environment will vary as well.

A multicomponent model of phytoplankton size structure

Size-fractionated filtration (SFF) is a direct method for estimating pigment concentration in various size classes. It is also common practice to infer the size structure of phytoplankton communities from diagnostic pigments estimated by high-performance liquid chromatography (HPLC). In this paper, the three-component model of Brewin et al. (2010) was fitted to coincident data from HPLC and from SFF collected along Atlantic Meridional Transect cruises. The model accounted for the variability in each data set, but the fitted model parameters differed for the two data sets. Both HPLC and SFF data supported the conceptual framework of the three-component model, which assumes that the chlorophyll concentration in small cells increases to an asymptotic maximum, beyond which further increase in chlorophyll is achieved by the addition of larger celled phytoplankton. The three-component model was extended to a multicomponent model of size structure using observed relationships between model parameters and assuming that the asymptotic concentration that can be reached by cells increased linearly with increase in the upper bound on the cell size. The multicomponent model was verified using independent SFF data for a variety of size fractions and found to perform well (0.628 ≤ r ≤ 0.989) lending support for the underlying assumptions. An advantage of the multicomponent model over the three-component model is that, for the same number of parameters, it can be applied to any size range in a continuous fashion. The multicomponent model provides a useful tool for studying the distribution of phytoplankton size structure at large scales.