- Related Research Areas
- Carbon Cycle & Ecosystems
We propose to develop a scientific framework and operational algorithms that will permit utilization of optical measurements from in situ and remote sensors to detect biogeochemically important features of seawater particle assemblages in the Arctic. The data products will include different measures of bulk particle concentration (total mass, SPM; organic carbon, POC; chlorophyll a, Chl), composition of the assemblage (ratios of inorganic to organic matter, carbon to chlorophyll), and parameters related to the particle size distribution (median and other percentile diameters). Our approach utilizes a combination of field measurements, optical modeling, and satellite observations. During the two field campaigns in the Chukchi and Beaufort Seas, we will collect a unique dataset consisting of a detailed characterization of the seawater particle assemblage in conjunction with optical measurements of absorption and scattering by particles. A novel and important feature of this field work is that a combination of traditional and new instrumentation will be used to measure the particle size distribution over a broad size range (~0.05 to 200 micrometers), including the submicron range which has been rarely measured. The field data will be utilized to explore linkages between inherent optical properties (IOPs) and characteristics of the particle assemblage, and to develop and parameterize inversion algorithms for retrieving this information from measurements of IOPs and ocean color. We propose a new multi-step empirical approach, which is fundamentally different from common empirical algorithms developed over the past several decades. Instead of using a single empirical relationship for correlating the optical measurement with the concentration of interest (Chl, POC, or SPM), our algorithm will consist of several relationships linking the IOPs with various particulate characteristics. In particular, we envision that the algorithm will begin with the estimation of particle composition and size parameters, and subsequent classification of data into different composition or composition/size categories, such as organic-dominated, mineral-dominated, and mixed types of particulate assemblages. In the final steps of the algorithm, the particle concentration (SPM, POC, Chl) will be estimated from relationships established separately for these different particulate categories. We anticipate that this concept and algorithm design will provide not only new enhanced observational capabilities from optical measurements, but will also lead to improved performance of traditional ocean color observations, especially in optically complex waters such as Arctic regions influenced by large terrigenous inputs of particulate and dissolved materials. Models deriving IOPs from remote-sensing reflectance will be explored to permit use of our IOP-based algorithm for estimating particulate characteristics from ocean color. We will apply these algorithms to satellite imagery in order to derive regional scale maps of near surface particle properties within the study region, and examine spatial and temporal variability in these characteristics within the context of environmental and biological forcing. The proposed research will have a broad impact by enabling investigations in to how characteristics of suspended particles and associated biogeochemical processes within the Arctic Ocean change in response to climate variability.
Project PI: Rick Reynolds/Scripps Institution of Oceanography, UCSD
Scripps Institution of Oceanography, UCSD 9500 Gilman Drive La Jolla CA, 92093 Mail Code: 0238
Phone: (858) 822-4407
Fax: (858) 534-7641
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