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Monitoring of Phytoplankton Functional Types in surface waters using ocean color imagery C. Moulin 1, S. Alvain 1,2, Y. Dandonneau 3, L. Bopp 1, H. Loisel.

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Presentation on theme: "Monitoring of Phytoplankton Functional Types in surface waters using ocean color imagery C. Moulin 1, S. Alvain 1,2, Y. Dandonneau 3, L. Bopp 1, H. Loisel."— Presentation transcript:

1 Monitoring of Phytoplankton Functional Types in surface waters using ocean color imagery C. Moulin 1, S. Alvain 1,2, Y. Dandonneau 3, L. Bopp 1, H. Loisel 2 1.LSCE/IPSL, Gif-sur-Yvette, France 2.ELICO, Wimereux, France 3.LOCEAN/IPSL, Paris, France

2 PFT and the Ocean Carbon Cycle PISCES Annual mean Chl Annual mean frequency of diatom blooms Recent global biogeochemical models account for more than one PFT to quantify the marine « biological pump » of CO2 Validation ? SEAWIFS ?

3 Wavelenghts(nm) Normalized water-leaving radiance Chl a (mg.m -3 ) nLw ref (,Chl a ) Chl a, the main ocean color product Our goal is to identify the Phytoplankton Functional Type (PFT) associated with Chl a ?? ???

4 Natural variability of nLw Is it related to PFT (at least partly) ? NOMAD

5 SEAWIFS nLw spectra and PFT We looked for a correlation between anomalies of the SEAWIFS nLw spectrum and the dominant phytoplankton group. Two steps: 1.Develop a normalization technique to remove the 1st order Chl a effect on the nLw spectrum and to evidence a 2nd order spectral variability. 2.Compare nLw* spectra with coincident in situ pigment inventories from the GeP&CO dataset (Dandonneau et al., 2004) to find relationships between nLw* and phytoplankton groups. PHYSAT (Alvain et al., DSRI, 2005) nLw*( ) = nLw( )/nLw ref (, Chl a)

6 The specific normalized water-leaving radiance, nLw* nLw*( ) = nLw( )/nLw ref (,Chla) (nm) 0 0,2 0,4 0,6 0,8 1 1,2 1,4 1,6 1,8 2 2,2 2,4 2,6 2, nLw ref (, Chl a) 555 nm 510 nm 490 nm 443 nm 412 nm

7 nLw* Wavelength Coccoliths Trichodesmium Diatoms Phaeocystis Cyanobacteria Prochlorococcus Haptophytes - The GeP&CO dataset has allowed us to « identify » four groups (Diatoms, Prochlorococcus, Cyanobacteria and Haptophytes). - Three additional groups (Phaeocystis, Coccoliths and Trichodesmium) have still to be validated. Relationships between nLw* and PFT

8 The PHYSAT method nLw obs and Chl-a SeaWiFS nLw* = nLw obs / nLw ref (Chl-a) Identification of the dominant PFT for the pixel Haptophytes-Prochlorococcus-Synechococcus-Diatoms Daily Level-3 GAC data nLw* Wavelength Most frequent dominant PFT for the month January 2001

9 PHYSAT (Monthly Climatology) Haptophytes - Prochlorococcus - SLC - Diatoms - Bloom Cocco. - Phaeocystis January February MarchApril

10 PHYSAT (Monthly Climatology) Haptophytes - Prochlorococcus - SLC - Diatoms - Bloom Cocco. - Phaeocystis May June July August

11 PHYSAT (Monthly Climatology) Haptophytes - Prochlorococcus - SLC - Diatoms - Bloom Cocco. - Phaeocystis SeptemberOctober NovemberDecember

12 Three dominant groups in the Global Ocean Haptophytes Prochlorococcus SLC Diatomées Phaeocystis Relative fraction of total chl-a for each dominant group Prochlorococcus Haptophytes SLC

13 Diatoms ( Relative fraction of total chl-a - Global) Phaeocystis ( Relative fraction of total chl-a - Global) Interannual variability of « blooming » PFTs North Atlantic and Pacific blooms June 2001 Austral Ocean bloom Jan. 2001

14 The 1998 bloom of diatoms in the Equatorial Pacific Equatorial Pacific Area Effect of La Nina ? July 1998 July 1999 Haptophytes Prochlorococcus SLC Diatomées Phaeocystis Relative fraction of total chl-a for each dominant group

15 Conclusions  Major Phytoplankton Functional Types are associated with specific spectral signatures that can be detected from space.  PHYSAT results are globally OK, but further validation is needed (phaeocystis, coccolithophorids, trichodesmiums,…).  PHYSAT allows to monitor the seasonal and inter-annual variability of the distributions of major PFTs.  Diatoms and Phaeocystis are the major blooming PFTs in the Austral Ocean.

16 Define a bio-optical Algorithm for Haptophytes and Diatoms only. In situ Seabam nLw obs and Chl-a PHYSAT data labelized as Haptophytes, SLC, Prochloroc. and Diatoms Perspective (1): Improved bio-optical models Alvain et al., DSRI, 2006

17 Perspectives (2): Model validation PISCES Annual mean Chl Annual mean frequency of diatom blooms ? SEAWIFS

18 Perspectives (3): Intercomparison of PFT’s algorithms PHYSAT is not the only existing method to identify PFTs from space. (but it is the only one that both relies on the analysis of the nLw spectrum and allows a global processing) A recent IOCCG working group is dedicated to the comparison of existing PFT’s algorithms.

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20 THE GEP&CO DATASET 20 pigments were measured daily (5 observations per day) during 12 GeP&CO cruises from France to New Caledonia between November 1999 and July Nov Feb May Aug Oct Feb Apr Jul Oct Jan Apr Jul Gep&Co Shipping track

21 Phaeocystis and Diatoms in the Austral Ocean Climatology of the mixed-layer Depth for January (Boyer Montégut et al., 2004). PHYSAT January 2001 (diatoms, phaeocystis-like) m


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