Abstract. Owing to their important roles in biogeochemical cycles, phytoplankton functional types (PFTs) have been the aim of an increasing number of ocean color algorithms. Yet, none of the existing methods are based on phytoplankton carbon (C) biomass, which is a fundamental biogeochemical and ecological variable and the “unit of accounting” in Earth system models. We present a novel bio-optical algorithm to retrieve size-partitioned phytoplankton carbon from ocean color satellite data. The algorithm is based on existing methods to estimate particle volume from a power-law particle size distribution (PSD). Volume is converted to carbon concentrations using a compilation of allometric relationships. We quantify absolute and fractional biomass in three PFTs based on size – picophytoplankton (0.5–2 µm in diameter), nanophytoplankton (2–20 µm) and microphytoplankton (20–50 µm). The mean spatial distributions of total phytoplankton C biomass and individual PFTs, derived from global SeaWiFS monthly ocean color data, are consistent with current understanding of oceanic ecosystems, i.e., oligotrophic regions are characterized by low biomass and dominance of picoplankton, whereas eutrophic regions have high biomass to which nanoplankton and microplankton contribute relatively larger fractions. Global climatological, spatially integrated phytoplankton carbon biomass standing stock estimates using our PSD-based approach yield  ∼  0.25 Gt of C, consistent with analogous estimates from two other ocean color algorithms and several state-of-the-art Earth system models. Satisfactory in situ closure observed between PSD and POC measurements lends support to the theoretical basis of the PSD-based algorithm. Uncertainty budget analyses indicate that absolute carbon concentration uncertainties are driven by the PSD parameter No which determines particle number concentration to first order, while uncertainties in PFTs' fractional contributions to total C biomass are mostly due to the allometric coefficients. The C algorithm presented here, which is not empirically constrained a priori, partitions biomass in size classes and introduces improvement over the assumptions of the other approaches. However, the range of phytoplankton C biomass spatial variability globally is larger than estimated by any other models considered here, which suggests an empirical correction to the No parameter is needed, based on PSD validation statistics. These corrected absolute carbon biomass concentrations validate well against in situ POC observations.

Abstract. Owing to their important roles in biogeochemical cycles, phytoplankton functional types (PFTs) have been the aim of an increasing number of ocean color algorithms. Yet, none of the existing methods are based on phytoplankton carbon (C) biomass, which is a fundamental biogeochemical and ecological variable and the "unit of accounting" in Earth System models. We present a novel bio-optical algorithm to retrieve size-partitioned phytoplankton carbon from ocean color satellite data. The algorithm is based on existing algorithms to estimate particle volume from a power-law particle size distribution (PSD). Volume is converted to carbon concentrations using a compilation of allometric relationships. We quantify absolute and fractional biomass in three PFTs based on size – picophytoplankton (0.5–2 μm in diameter), nanophytoplankton (2–20 μm) and microphytoplankton (20–50 μm). The mean spatial distributions of total phytoplankton C biomass and individual PFTs, derived from global SeaWiFS monthly ocean color data, are consistent with current understanding of oceanic ecosystems, i.e. oligotrophic regions are characterized by low biomass and dominance of picoplankton, whereas eutrophic regions have large biomass to which nanoplankton and microplankton contribute relatively larger fractions. Global spatially integrated phytoplankton carbon biomass standing stock estimates using our PSD-based approach yield on average ~0.2–0.3 Gt of C, consistent with analogous estimates from two other ocean color algorithms, and several state-of-the-art Earth System models. However, the range of phytoplankton C biomass spatial variability globally is larger than estimated by any other models considered here, because the PSD-based algorithm is not a priori empirically constrained and introduces improvement over the assumptions of the other approaches. Satisfactory in situ closure observed between PSD and POC measurements lends support to the theoretical basis of the PSD-based algorithm. Uncertainty budget analyses indicate that absolute carbon concentration uncertainties are driven by the PSD parameter No which determines particle number concentration to first order, while uncertainties in PFTs' fractional contributions to total C biomass are mostly due to the allometric coefficients.

Diarrhea in MC mostly belongs to the secretory type. The major pathophysiological mechanism in LC could be explained by a decrease of active sodium absorption. In CC, decreased Cl/HCO3 exchange rate and increased chloride secretion are coexistent pathways.

Temporally and spatially dense estimates of oceanic phytoplankton net primary productivity (NPP), which are commonly derived by mathematical models from satellite observations of ocean colour, are a cornerstone of current research efforts focused on the state and variability of ecosystems, biogeochemical cycles and climate. Using two exemplary NPP models, it was examined how uncertainties in model input terms might affect the accuracy of the output. In the first part of the dissertation, the response of NPP estimates to perturbing input values of mixed layer depth (MLD) was analyzed. Four series of NPP fields, two global and two covering the North Atlantic, were computed in monthly intervals during a period of several years. Each of the series resulted from identical remote sensing data but different MLD input. Due to the influence of MLD on the availability of light for photosynthesis, the NPP estimates were overall inversely related to MLD. However, the degree of this relationship varied considerably in space and time over most of the world ocean. During summer, NPP at middle and high latitudes was appreciably sensitive even to small MLD fluctuations, but had little or no response to large MLD perturbations in winter. On the other hand, subtropical regions were characterized by a largely opposite seasonal pattern. Tropical areas showed no seasonality and, apart from the equatorial Pacific, exhibited little sensitivity of NPP to MLD uncertainties. The observed variability in the NPP response was attributed not only to the model's nonlinearity, but also to the presence of the photosynthetic saturation/limitation thresholds, as well as to the coincident sea surface irradiance and, in particular, the diffuse attenuation coefficient for downward irradiance (K d). It was shown that K d could be used as an indicator of the NPP sensitivity to uncertainties in MLD, the greatest sensitivity being associated with very large K d values. Maximum differences between areally integrated annual NPP estimates, based on different MLD input, were about 20-30% in the North Atlantic subpolar gyre, about 15-20% in the eastern part of the North Atlantic subtropical gyre, and less than 10% over the global ocean. In the second part of the thesis, uncertainties in input terms were propagated through one of the most widely used NPP models via a Monte Carlo method, which enabled distinguishing between random and systematic uncertainty components. The study was based on monthly averaged global remote sensing observations from 2005. Although, due to computational requirements, the analysis was restricted to one year only, the results were remarkably stable in time and space, suggesting that they might also be valid for other years covered by the satellite observations. The typical distribution of uncertainty around the model output was lognormal-like. The average random uncertainty in NPP, expressed as the coefficient of variation, was 108%. The nominal NPP values in individual grid cells were typically overestimated by 6%, relative to the m...