Reynolds, Rick: Scripps Institution of Oceanography, UCSD (Project Lead)
Stramski, Dariusz: UCSD (Co-Investigator)
Project Funding:
2010 - 2014
NRA: 2009 NASA: Ocean Biology and Biogeochemistry
Funded by NASA
Abstract:
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.
Publications:
Neukermans, G., Reynolds, R. A., Stramski, D. 2016. Optical classification and characterization of marine particle assemblages within the western Arctic Ocean. Limnology and Oceanography. 61(4), 1472-1494. DOI: 10.1002/lno.10316
Neukermans, G., Reynolds, R. A., Stramski, D. 2016. Optical classification and characterization of marine particle assemblages within the western Arctic Ocean. Limnology and Oceanography. 61(4), 1472-1494. DOI: 10.1002/lno.10316
Reynolds, R. A., Stramski, D. 2019. Optical characterization of marine phytoplankton assemblages within surface waters of the western Arctic Ocean. Limnology and Oceanography. 64(6), 2478-2496. DOI: 10.1002/lno.11199
Reynolds, R. A., Stramski, D., Neukermans, G. 2016. Optical backscattering by particles in Arctic seawater and relationships to particle mass concentration, size distribution, and bulk composition. Limnology and Oceanography. 61(5), 1869-1890. DOI: 10.1002/lno.10341
Runyan, H., Reynolds, R. A., Stramski, D. 2020. Evaluation of Particle Size Distribution Metrics to Estimate the Relative Contributions of Different Size Fractions Based on Measurements in Arctic Waters. Journal of Geophysical Research: Oceans. 125(6). DOI: 10.1029/2020JC016218
Zheng, G., Stramski, D., Reynolds, R. A. 2014. Evaluation of the Quasi-Analytical Algorithm for estimating the inherent optical properties of seawater from ocean color: Comparison of Arctic and lower-latitude waters. Remote Sensing of Environment. 155, 194-209. DOI: 10.1016/j.rse.2014.08.020
Arrigo, K. R., Perovich, D. K., Pickart, R. S., Brown, Z. W., van Dijken, G. L., Lowry, K. E., Mills, M. M., Palmer, M. A., Balch, W. M., Bahr, F., Bates, N. R., Benitez-Nelson, C., Bowler, B., Brownlee, E., Ehn, J. K., Frey, K. E., Garley, R., Laney, S. R., Lubelczyk, L., Mathis, J., Matsuoka, A., Mitchell, B. G., Moore, G. W. K., Ortega-Retuerta, E., Pal, S., Polashenski, C. M., Reynolds, R. A., Schieber, B., Sosik, H. M., Stephens, M., Swift, J. H. 2012. Massive Phytoplankton Blooms Under Arctic Sea Ice. Science. 336(6087), 1408-1408. DOI: 10.1126/science.1215065
Yang, Q., Stramski, D., He, M. 2013. Modeling the effects of near-surface plumes of suspended particulate matter on remote-sensing reflectance of coastal waters. Applied Optics. 52(3), 359. DOI: 10.1364/AO.52.000359
Zheng, G., Stramski, D. 2013. A model based on stacked-constraints approach for partitioning the light absorption coefficient of seawater into phytoplankton and non-phytoplankton components. Journal of Geophysical Research: Oceans. 118(4), 2155-2174. DOI: 10.1002/jgrc.20115
Zheng, G., Stramski, D. 2013. A model for partitioning the light absorption coefficient of suspended marine particles into phytoplankton and nonalgal components. Journal of Geophysical Research: Oceans. 118(6), 2977-2991. DOI: 10.1002/jgrc.20206
Neukermans, G., Reynolds, R. A., Stramski, D. 2014. Contrasting inherent optical properties and particle characteristics between an under-ice phytoplankton bloom and open water in the Chukchi Sea. Deep Sea Research Part II: Topical Studies in Oceanography. 105, 59-73. DOI: 10.1016/j.dsr2.2014.03.014
Arrigo, K. R., Perovich, D. K., Pickart, R. S., Brown, Z. W., van Dijken, G. L., Lowry, K. E., Mills, M. M., Palmer, M. A., Balch, W. M., Bates, N. R., Benitez-Nelson, C. R., Brownlee, E., Frey, K. E., Laney, S. R., Mathis, J., Matsuoka, A., Greg Mitchell, B., Moore, G. W. K., Reynolds, R. A., Sosik, H. M., Swift, J. H. 2014. Phytoplankton blooms beneath the sea ice in the Chukchi sea. Deep Sea Research Part II: Topical Studies in Oceanography. 105, 1-16. DOI: 10.1016/j.dsr2.2014.03.018
More details may be found in the following project profile(s):
