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The light absorption coefficients of particulate and dissolved materials are the main factors determining the light propagation of the visible part of the spectrum and are, thus, important for developing ocean color algorithms. While these absorption properties have recently been documented by a few studies for the Arctic Ocean (e.g., Matsuoka et al., 2007, 2011; Ben Mustapha et al., 2012), the datasets used in the literature were sparse and individually insufficient to draw a general view of the basin-wide spatial and temporal variations in absorption. To achieve such a task, we built a large absorption database at the pan-Arctic scale by pooling the majority of published datasets and merging new datasets. Our results showed that the total non-water absorption coefficients measured in the Eastern Arctic Ocean (EAO; Siberian side) are significantly higher than in the Western Arctic Ocean (WAO; North American side). This higher absorption is explained by higher concentration of colored dissolved organic matter (CDOM) in watersheds on the Siberian side, which contains a large amount of dissolved organic carbon (DOC) compared to waters off North America. In contrast, the relationship between the phytoplankton absorption (aφ(λ)) and chlorophyll a (chl a) concentration in the EAO was not significantly different from that in the WAO. Because our semi-analytical CDOM absorption algorithm is based on chl a-specific aφ(λ) values (Matsuoka et al., 2013), this result indirectly suggests that CDOM absorption can be appropriately derived not only for the WAO but also for the EAO using ocean color data. Derived CDOM absorption values were reasonable compared to in situ measurements. By combining this algorithm with empirical DOC vs. CDOM relationships, a semi-analytical algorithm for estimating DOC concentrations for coastal waters at the Pan-Arctic scale is presented and applied to satellite ocean color data.

A next-generation in-water profiler designed to measure the apparent optical properties (AOPs) of seawater was developed and validated across a wide dynamic range of in-water properties. The new free-falling instrument, the Compact-Optical Profiling System (C-OPS), was based on a cluster of 19 state-of-the-art microradiometers spanning 320–780 nm and a new kite-shaped backplane design. The kite-shaped backplane includes tunable ballast, a hydrobaric buoyancy chamber, plus pitch and roll adjustments, to provide unprecedented stability and vertical resolution in near-surface waters. A unique data set was collected as part of the development activity and the first major field campaign that used the new instrument, the Malina expedition to the Beaufort Sea in the vicinity of the Mackenzie River outflow. The data were of sufficient resolution and quality to show that errors – more correctly, uncertainties – in the execution of data sampling protocols were measurable at the 1% and 1 cm level with C-OPS. A sensitivity analysis as a function of three water types established by the peak in the remote sensing reflectance spectrum, Rrs(λ), revealed which water types and which parts of the spectrum were the most sensitive to data acquisition uncertainties. Shallow riverine waters were the most sensitive water type, and the ultraviolet and near-infrared were the most sensitive parts of the spectrum. The sensitivity analysis also showed how the use of data products based on band ratios significantly mitigated the influence of data acquisition uncertainties. The unprecedented vertical resolution provided high quality data products at the spectral end members, which subsequently supported an alternative classification capability based on the spectral diffuse attenuation coefficient, Kd(λ). The Kd(320) and Kd(780) data showed how complex coastal systems can be distinguished two-dimensionally and how near-ice water masses are different from the open ocean. Finally, an algorithm for predicting the spectral absorption due to colored dissolved organic matter (CDOM), denoted aCDOM(λ), was developed using the Kd(320)/Kd(780) ratio, which was based on a linear relationship with respect to aCDOM(440), with over 99% of the variance explained. The robustness of the approach was established by expanding the use of the algorithm to include a geographically different coastal environment, the Southern Mid-Atlantic Bight, with no significant change in accuracy (approximately 98% of the variance explained). Alternative spectral end members reminiscent of next-generation (340 and 710 nm) as well as legacy satellite missions (412 and 670 nm) were also used to accurately derive aCDOM(440) from Kd(λ) ratios (94% or more of the variance explained).

A statistical examination of the available data on the ratio of antibody to antigen in specific precipitates made at or near the optimum shows a definite correlation between the ratio and the molecular weight of antigen (regression coefficient = -0.529 (+/-0.014)). The authors' assumption that at this point the antigen molecules are just about covered by a layer of antibody behaving as contiguous spherical ("Svedberg") units of weight 35,200 leads to predicted ratios that in general agree well with those found, though individual experimental determinations may deviate considerably.