A model based on stacked‐constraints approach for partitioning the light absorption coefficient of seawater into phytoplankton and non‐phytoplankton components

Abstract

[1] Partitioning of the total non-water absorption coefficient of seawater, a nw (l) (i.e., the light absorption coefficient after subtraction of pure water contribution), into phytoplankton, a ph (l), and non-phytoplankton, a dg (l), components is important in the areas of ocean optics, biology, and biogeochemistry. We propose a partitioning model based on stacked-constraints approach, which requires input of a nw (l) at a minimum of six specific light wavelengths. Compared with existing models, our approach requires much less restrictive assumptions about the spectral slope of a dg (l) and the spectral shape of a ph (l). Our model is based on several inequality constraints determined from an extensive, quality-verified set of field data covering oceanic and coastal waters from low to high-latitudes. With these constraints, the model first derives a wide range of speculative solutions for a dg (l) and a ph (l) and then identifies feasible solutions. Final model outputs include the optimal solution and a range of feasible solutions for a dg (l) and a ph (l). The optimal solutions agree well with measurements. For example, the median ratio of the model-derived optimal solutions to measured a dg (l) and a ph (l) at 443 nm is very close to 1, i.e., 1.004 and 0.988, respectively. The median absolute percent difference between the optimal solutions and measured values of a dg (443) and a ph (443) is 6.5% and 12%, respectively. The range of feasible solutions encompasses the measured a dg (l) and a ph (l) with a probability >90% at most wavelengths. Our results support the prospect for the applications of the partitioning model using the input data of a nw (l) collected from various oceanographic and remote-sensing platforms.Citation: Zheng, G., and D. Stramski (2013), A model based on stacked-constraints approach for partitioning the light absorption coefficient of seawater into phytoplankton and non-phytoplankton components,

Evaluation of the Quasi-Analytical Algorithm for estimating the inherent optical properties of seawater from ocean color: Comparison of Arctic and lower-latitude waters

“…In the evaluation analysis, we quantified errors in the QAA-derived a(λ) and b b (λ) and identified and quantified major error sources for the model outputs through sensitivity analysis. We also evaluated the performance of the second part of the QAA that partitions a(λ) into a ph (λ) and a dg (λ) components and compared these results with the performance of a recently developed partitioning model (Zheng & Stramski, 2013). Our findings have important implications for an understanding of the performance of the QAA and potential refinements of this and other inverse models for applications within the world oceans including Arctic waters.…”

“…One restriction for applying the SCM is for waters containing significant amounts of absorbing mineral particles, for which the spectral shape of absorption spectrum shows considerable departures from an exponential function of wavelength (Zheng & Stramski, 2013; see also Babin & Stramski, 2004;Stramski, Babin, & Woźniak, 2007). Such spectral features violate the assumption of the SCM which requires an exponential spectral shape of a dg (λ).…”

“…This analysis enabled the determination of errors in a dg (λ) and a ph (λ) resulting solely from the parameterizations involved in Part II of the QAA. For comparison, we also made a similar analysis by applying the same measurements of a(λ) [more specifically, the non-water absorption coefficient a nw (λ) = a(λ) -a w (λ)] as input to the absorption partitioning model developed by Zheng and Stramski (2013), hereafter referred to as the stacked-constraints model (SCM). Such comparative analysis of the QAA and the SCM was not feasible for the QAA-derived a nw (λ) used as input to the SCM because significant errors associated with the QAA-derived a nw (λ) prevented the SCM from finding solutions for the majority of data considered in this study.…”

Capturing optically important constituents and properties in a marine biogeochemical and ecosystem model

“…11). AMT-15 cell counts were measured by analytical flow cytometry following methods of Heywood et al (2006) and converted to biomass using constant factors (Zubkov et al, 1998) for comparison purposes. The model captures the smallest autotrophs, Prochlorococcus, as having significant abundances through the subtropics and tropic; Synechococcus was more abundant at the northern poleward fringe of the subtropics, and picoeukaryotes were more ubiquitous and more dominant in the subsurface Chl a maximum.…”

“…Additionally, several attempts have been made to construct algorithms to determine the relative contributions from more easily measured quantities, including those from satellites (e.g. Maritorena et al, 2002;Lee et al, 2002Lee et al, , 2007Ciotti and Bricaud, 2006;Werdell et al, 2013;Zheng and Stramski, 2013). Our model provides a unique global three-dimensional perspective.…”