1. Remote Assessment of Phytoplankton Functional Types Using Retrievals of the Particle Size Distribution from Ocean Color Data Tihomir Kostadinov, David Siegel, Stéphane Maritorena ICESS, University of California Santa Barbara NASA Ocean Color Research Team Meeting, New Orleans, LA, May 12, 2010

2. Outline Introduction & Motivation Kostadinov et al. (2009) PSD Algorithm –Algorithm theoretical basis & operation –Uncertainty analysis –Validation Phytoplankton Functional Types Retrieval (Biogeosci. Disc., submitted) –Definition of PFT’s –Validation –Global climatology, seasonal succession

3. Why PFT’s are Important PFT’s are groups of phytoplankton with simil2 耀 biology & biogeochemical roles, e.g.: –physiology –sinking –CO 2 sequestration –DMS production –silicate drawdown Cell SIZE –is a characteristic feature of PFT’s –determines structure and function of pelagic ecosystems Global RS retrieval of the PFT’s is needed Chisholm, 2000

4. PSD’s  PFT Link PSD’s  # & V in any size class Case I assumption – particle load dominated by Chl and covariates Size-defined PFT in terms of % volume = f(PSD parameters): –3 classes – pico, nano, micro –definition does not explicitly take into account taxonomy/biology Existing methods for PFT retrieval are based on HPLC pigments & Chl (e.g. Uitz, Alvain); phytoplankton absorption (e.g. Mouw, Devred)

5. Describing the PSD Power-law Junge-type Size Distribution  = PSD slope D o = 2  m N o = N(D o ), [m -4 ] Example PSD measured by LISST-100X July 21, 2008 Santa Barbara Channel California  : 3.91 N o : 16.7 m -4 log10 of 34 o 12.26’N 119 o 55.69’ W

6. Link to Optics - Mie Scattering Theory Single particle optical properties depend on: –Complex index of refraction m r ( ) = n r – i*n r ’( ) –Size relative to the incident wavelength –Shape & internal composition Mie modeling solves the Maxwell equations for the IOP’s of homogeneous spherical particles Retrievable spectrally Goal of retrieval b bp ( ) efficiency solved by Mie theory

7. PSD Algorithm Scheme Use the LUT’s and b bp (440) &  maps to calculate algorithm base products: PSD slope =  N(2  m) = N o Calculate derived products: Particle # & V in different size classes PFT’s Input Mie model parameters:  = 2.5 to 6 m( ) = n – m’( )i D min ; D max Run Monte Carlo simulation of Mie model with various input combinations & create two mean LUT’s:  = f -1 (  ) log10(b bp (440)/N o ) = g -1 (  ) Operational Satellite Processing Retrieve spectral b bp ( ) and its slope  from R rs ( ) via Loisel et al. (2006) Theoretical LUT Development

8. Global b bp (440) and  Climatology

9. Algorithm LUT’s

10. log10(particles*m -4 ) Mission mean of  (Sept. 1997 – Dec. 2007) Mission mean of    (Sept. 1997 – Dec. 2007) Global  & N o Climatology

11. Endogenous Uncertainties Due to D max and m  (  ) is small compared to its variability  (log10(N o )) higher, due to n

12. PSD Validation w/ Coulter Counter Regional validation uses GAC monthly data instead (N = 363): OK for , great for N o ! In-situ  SeaWiFS  In-situ   SeaWiFS   N =22 Slope = 1.34 R 2 = 0.24 N =22 Slope = 2.05 R 2 = 0.26

13. Partitioning Number Concentration Picoplankton, # m -3 (0.5  m to 2  m) Microplankton, # m -3 (20  m to 50  m) Nanoplankton, # m -3 (2  m to 20  m) Pico’s vary ~100 times Nano’s vary ~ 10,000 times Micro’s vary ~ 10 6 times log10(particles/m 3 )

14. PFT’s Definition by % Volume Partitioning by volume makes more sense –related to biomass, POC, living C Three PFT’s quantitatively defined as % volume concentration contribution = f(  ) : –Picoplankton (0.5 – 2  m equiv. sphere cell diameter) –Nanoplankton (2 – 20  m) –Microplankton (20 – 50  m)

15. PFT’s = f(PSD slope)

16. Partitioning Biovolume – the PFT’s Picoplankton % (0.5  m to 2  m) Microplankton % (20  m to 50  m) Nanoplankton % (2  m to 20  m) Pico’s dominate oligotrophic ocean (>90%) Nano’s in transition regions (~50%) Micro’s only found in upwelling zones & high latitudes (<60%)

17. PFT Validation w/ HPLC Data Uses in-situ HPLC diagnostic pigments (Vidussi et al., 2001) Matched with daily SeaWiFS 9 km data. Satisfactory for pico & micro, poor for nano. N =48 Slope = 1.58 R 2 = 0.34 N =48 Slope = 1.01 R 2 = 0.41 SeaWiFS % pico SeaWiFS % micro In-situ % pico In-situ % micro

18. BATS Seasonal Succession

20. Conclusions First global assessment of PFT’s via the PSD from space Spatial patterns are consistent with current understanding –Oligotrophic oceans have high PSD slopes, low abundances & are dominated by pico’s –Bloom regions have lower PSD slope & are dominated by nano’s & micro’s –Pico’s vary over few orders of magnitude, micro’s – over many. Seasonal succession and relationships to Chl-a are consistent with expectations

21. Acknowledgements David Siegel, Stéphane Maritorena Funding from the NASA Ocean Biology & Biogeochemistry Program Mike Behrenfeld, Hubert Loisel, Emmanuel Boss, Curtis Mobley, Mary Jane Perry, Collin Roesler, Wayne Slade, Giorgio Dall’Olmo, Toby Westberry

22. The End