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O. Kopelevich. Shirshov Institute of Oceanology RAS, Moscow

The problem of reliable detection of coccolitophore blooms in the Black Sea from satellite ocean color data. O. Kopelevich. Shirshov Institute of Oceanology RAS, Moscow. What is the coccolithophore bloom?.

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O. Kopelevich. Shirshov Institute of Oceanology RAS, Moscow

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  1. The problem of reliable detection ofcoccolitophore blooms in the Black Seafrom satellite ocean colordata O. Kopelevich. Shirshov Institute of Oceanology RAS, Moscow

  2. What is the coccolithophore bloom? Coccolithophorideis single-celled algae with spherical cells surrounded by disk-shaped plates (coccoliths) consisting of calcium carbonate, CaCO3. From the optical point of view, coccolithophores stand out by their optical characteristics, having a strong light scattering, weakly selective. This makes it possible to detect the CB from satellite observation in the visible spectrum. Coccolithophoride is the most powerful producer of CaCO3 and coccolithophore blooms (CB) may have a significant impact on the exchange of CO2 between the ocean and atmosphere, the greenhouse effect and thus to global climate change. Goal of the studies: To assess a possibility of reliable identification of the coccolithophore blooms (CB) in the Black Sea from data of satellite ocean color sensors.

  3. Changeability of the monthly means of the particle backscattering coefficients bbpin different sub-regions of the Black Sea 2 – Danube area; 6 – western deep part 7 – eastern deep part; 8 – eastern shelf part.

  4. True color images with turbid inhomogeneities in the Black and the Barens seas 4 1 2 3 • Danube river run-off. MODIS –Aqua. 20/05/ 2009. • The eastern psrt, SeaWiFS. 25/06/1998. • SeaWiFS. 11/06/2000. • The Barents Sea. MODIS-Aqua. 31/08/2010

  5. Existing algorithms used for the Black Sea SeaDAS algorithm(the merged 2- and 3-bands algorithms developed by Gordon, Balch 1999 and Gordon et al. 2001) can derive calcite (CaCO3) concentration. The algorithm is divided into two stages: first the backscattering coefficient bb is calculated, and then calcite concentration via empirical relationship. Calcite concentration was chosen as the parameter to be calculated because the backscattering, normalized to calcite concentration (bb/Ca), showed significantly less variation than the backscattering, normalized to coccolith concentration, and the calcite-specific backscatter of the natural bloom fell between the bb/Ca for detached coccoliths and the bb/Ca for a mixture of intact plated cells and detached coccoliths (Gordon, Balch 1999). IORAS algorithm for retrieval of coccolithophoride concentration Ncoc from satellite data on the particle backscattering bbp was created on basis of the data set on the sub-surface radiance reflectance and coccolithophoride concentration concurrently measured in the eastern part of the Black Sea in June 2004-2006 (Burenkov et al. 2001, 2007) Ncoc = 768 bbp1.55, n=48, r2=0.54,   where n is number of pairs, r2 is coefficient of determination, Ncoc is coccolithophoride concentration in 106 cells/l, bbp(550) in m-1. Ncoc was chosen as the calculated parameter because it was available from measurements data, whereas calcite concentration was not measured.

  6. The obvious drawback of the SeaDAS algorithm, mentioned by the authors themselves, is that all particulate backscatter is assumed to be calcite-related, and non-calcite related backscattering is not accounted for. This shortcoming is particularly significant for the Black Sea which is strongly influenced by river run-off, and concentration of the particulate matter brought by rivers is increased just in June after the maximum of river discharge in the second half of May. The current SIO RAS algorithm also cannot distinguished between cases of contributions arising from coccolithophore –related and non-coccolithophore backscattering.

  7. The bbpdistributions and spectral radiance reflectance in selected points

  8. Spatial distributions of the particle backscattering coefficient (top) and the sub-surface spectral radiance reflectance () (bottom) In the Black and the Barents seas Black Sea_12/06/2004 Black Sea_13.06.2006 Barents Sea 19.08.2009

  9. Values of bbp, m-1, ratio of (488)/(443),the coccolithophoride cell concentration Ncoc,106cell/l derived from MODIS data (in the Black Sea) and in situ measured (L.A. Pautova, V.A. Silkin)

  10. . Some results of field studies in coastal zone of the eastern part of the Black Sea in June 2010 Optical studies included measurements of spectral radiance reflectance just beneath the sea surface by a floating spectroradiometer. Water samples were taken for laboratory determination of biological and biogeochemical characteristics (phytoplankton - diatoms, peridinium,coccolithophorides and coccolithes, small flagellates, picoplankton, biomass, concentrations of suspended matter, chlorophyll and pheophytin, organic and carbonate carbon, Al, Si, Ph. Stations in June 2010, its depth and measured parameters (Sechhi depth, m, chlorophyll concentration (Chl), mg m-3, total suspended matter concentration (TSM), mg/l

  11. Conclusion • In the Black Sea the contributions to bbp arising from “not coccolithophore” particles should be considered. In June, when CВ are observed, an origin of “not coccolithophore” particles is mainly river run-off. An indicator of the river run-off effect may be the slope of the (spectrum at short wavelength region, depending on the yellow substance absorption. Additional information about the sources of terrigenous runoff, transport and transformatioт of suspended and color dissolved organic matter may be also helpful. • Using data on () at the short wavelength region will require a greater accuracy of atmospheric correction. Apparently, there is also a need to develop regional algorithms, as it was done for the Barents Sea and for the Caspian Sea (Kopelevich et al. 2003, 2009). • Calcite concentration should be used as a parameter to be determined, as it is done in the SeaDAS algorithm. • The river run-off in the Black Sea plays an important role in the observed phenomenon,as a direct (growing turbid due to terrigenous particles brought • by rivers) and indirect (supplying nutrients stimulating coccolithophore blooms).

  12. Acknowledgement The presented study have been performed by specialists from the Ocean Optics Laboratory SIO RAS V.A. Artemiev, V.I. Burenkov, A.V. Grigoriev, S.V. Sheberstov, S.V. Vazyulya. Our sincere gratitude to L.A. Pautova and V.A. Silkin for the data on phytoplankton, including coccolithophoride and coccolite concentration.

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