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MODELING PHYTOPLANKTON COMMUNITY STRUCTURE: PIGMENTS AND SCATTERING PROPERTIES

MODELING PHYTOPLANKTON COMMUNITY STRUCTURE: PIGMENTS AND SCATTERING PROPERTIES Stephanie Dutkiewicz 1 Anna Hickman 2 , Oliver Jahn 1 , Watson Gregg 3 , Mick Follows 1 1. Massachusetts Institute of Technology 2. University of Essex 3. NASA Goddard Space Flight Center. stephanie dutkiewicz

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MODELING PHYTOPLANKTON COMMUNITY STRUCTURE: PIGMENTS AND SCATTERING PROPERTIES

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  1. MODELING PHYTOPLANKTON COMMUNITY STRUCTURE: PIGMENTS AND SCATTERING PROPERTIES Stephanie Dutkiewicz1 Anna Hickman2, Oliver Jahn1, Watson Gregg3, Mick Follows1 1. Massachusetts Institute of Technology 2. University of Essex 3. NASA Goddard Space Flight Center stephanie dutkiewicz http://ocean.mit.edu/~stephd

  2. modeling the marine ecosystem light many (100+) phytoplankton zooplankton nutrients PO4 NO3 Fe Si grazing rates randomly assigned growth rates detritus some sinks out to depths Darwin Project Model (Follows et al., Science 2007) http://darwinproject.mit.edu

  3. modeling the marine ecosystem light environment 1 phytoplankton nutrients zooplankton PO4 NO3 Fe Si grazing rates randomly assigned growth rates detritus some sinks out to depths Darwin Project Model (Follows et al., Science 2007) http://darwinproject.mit.edu

  4. modeling the marine ecosystem light environment 2 phytoplankton zooplankton nutrients PO4 NO3 Fe Si grazing rates randomly assigned growth rates detritus some sinks out to depths Darwin Project Model (Follows et al., Science 2007) http://darwinproject.mit.edu

  5. Initial Biomass of 100 phytoplankton types log10 (biomass) http://darwinproject.mit.edu

  6. Annual Biomass after 10 years simulation log10 (biomass) http://darwinproject.mit.edu

  7. EMERGENT COMMUNITY • By putting in appropriate trait trade-offs, • environment selects the appropriate • community structure: • K versus r strategies • (Dutkiewicz et al, GBC, 2009) • -nitrogen fixing • (Monteiro et al, GBC, 2010,2011) • -nitrate assimilation ability • (Bragg et al, PlosOne 2010) • -size/grazing pressure • (Ward et al. in prep) • -pigments/absorption • (Hickman et al, MEPS, 2010) Phytoplankton Functional Types stephanie dutkiewicz http://ocean.mit.edu/~stephd

  8. EMERGENT COMMUNITY • By putting in appropriate trait trade-offs, • environment selects the appropriate • community structure: • K versus r strategies • (Dutkiewicz et al, GBC, 2009) • -nitrogen fixing • (Monteiro et al, GBC, 2010,2011) • -nitrate assimilation ability • (Bragg et al, PlosOne 2010) • -size/grazing pressure • (Ward et al. in prep) • -pigments/absorption • (Hickman et al, MEPS, 2010) Phytoplankton Functional Types stephanie dutkiewicz http://ocean.mit.edu/~stephd

  9. ONE DIMENSIONAL MODEL Vertical distribution of phytoplankton types OBSERVATIONS AMT15 (Data courtesy: M. Zubkov, J. Heywood) (Hickman et al, MEPS, 2010) stephanie dutkiewicz http://ocean.mit.edu/~stephd

  10. ONE DIMENSIONAL MODEL • Pigments as trait • Different pigment allow • absorption of light • at different wavebands Absorption Spectra: Solid (PS specific); dashed (all pigments) wavelength (nm) Culture date from L. Moore, D. Suggett (Hickman et al, MEPS, 2010) stephanie dutkiewicz http://ocean.mit.edu/~stephd

  11. ONE DIMENSIONAL MODEL Vertical distribution of phytoplankton types OBSERVATIONS MODEL AMT15 (Data courtesy: M. Zubkov, J. Heywood) (Hickman et al, MEPS, 2010) stephanie dutkiewicz http://ocean.mit.edu/~stephd

  12. NEW DEVELOPMENTS more sophisticated treatment of light stream: - spectral surface input (OASIM – Watson Gregg) - radiative transfer code: 3 light streams (Iterative solver Oliver Jahn: following Aas, 1987; Ackelson et al 1994, Gregg and Casey, 2009) - resolve absorption, scattering and backscattering stephanie dutkiewicz http://ocean.mit.edu/~stephd

  13. detritus CDOM DEVELOPMENTS: RADIATIVE TRANSFER In collaboration with Anna Hickman, Oliver Jahn, Watson Gregg 400 425 450 475 500 525 550 575 600 625 650 675 700 a(λ), b(λ), bb(λ) ap(λ), bp(λ), bbp(λ) aw(λ), bw(λ), bbw(λ) ad(λ), bd(λ), bbd(λ) Phytoplankton: diatoms coccolithophores large Eukaryotes pico-eukaryotes Synechococcus Prochloroccus Trichodesmium water aCDOM(λ) function of Chl explicit, under development Slide modified from Watson Gregg explicit

  14. NEW DEVELOPMENTS ADDITIONAL FUNCTONAL TYPES Scattering data from Gregg+Casey, 2009; Morel et al 1993 Absorption data from L. Moore, D. Suggett In collaboration with Anna Hickman, Oliver Jahn, Watson Gregg stephanie dutkiewicz http://ocean.mit.edu/~stephd

  15. NEW DEVELOPMENTS UPWELLING RADIANCE: July • Model Output: • upwelling radiance • water leaving radiance • backscattering • (total, detrital, phytoplankton) • absorption • (total, CDOM, phytoplankton) • forward scattering • pigments 450nm 500nm 550nm stephanie dutkiewicz http://ocean.mit.edu/~stephd

  16. NEW DEVELOPMENTS: PRELIMINARY RESULTS JULY log10 backscatter by phytoplankon sum bphym(1/m) 450nm log10 phytoplankon biomass (uMP) Coccolithophore fraction biomass stephanie dutkiewicz http://ocean.mit.edu/~stephd

  17. Remote sensing beginning to resolve aspects of phytoplankton community and functionality: e.g. PHYSAT (Alvain et al), PHYTODAS (Bracher et al), Aiken et al, Sathyendranath et al, Balch et al, Hirata et al, Uitz et al, Giotti+Bricaud, Mouw+Yoder, Kostadinov et al, etc Models also resolving community structure: By resolving optical properties of model ocean can we relate more to the remotely sensed products? stephanie dutkiewicz http://ocean.mit.edu/~stephd

  18. SUMMARY • We are currently working to include radiative transfer code (spectral) and explicit absorption and backscattering. • will provide a closer link with satellite (and other • optical) studies • additional remote sensed products could be used to • validate model • - potential for data assimilation • model may then help untangle the mechanisms leading • to variability and trend observed in satellite products stephanie dutkiewicz http://ocean.mit.edu/~stephd

  19. MODELS HELP WITH OBS DESIGN Correlation between model variables • pCO2 well correlated with bloom • but year integrated CO2 Flux is not well correlated • with biological variability in subpolar Bennington, McKinley, Dutkiewicz, Ullman; GBC, 2009 stephanie dutkiewicz http://ocean.mit.edu/~stephd

  20. MODELS HELP WITH OBS DESIGN Number of years for trend to be visible from natural variability • 3 models 2000-2100 A2 scenario • average of about 40 years of continuous • and consistent measurements needed Henson et al, BG, 2010 stephanie dutkiewicz http://ocean.mit.edu/~stephd

  21. stephanie dutkiewicz http://ocean.mit.edu/~stephd

  22. DEVELOPMENTS: RADIATIVE TRANSFER MODEL OASIM: Ocean-Atmosphere Spectral Irradiance Model CO2 O2 W V O3 aerosols Gregg and Casey, 2009 Ed Es Lw  Ed, Es air sea Eu (1 - ) Es (1 - ) Ed Ed = direct irradiance Es = diffuse downwelling Eu = upwelling radiance ρ = surface reflectance Lw = water leaving radiance

  23. Three-Stream Ocean Irradiance Module following Aas(1987), Ackleson et al (1994), Gregg and Casey (2009) Iterative solver (repeated down/up integration) Oliver Jahn

  24. RADTRANS: approximations • Gregg's truncation (downward integration only) • Downward decaying modes only (à la Aas) • Iterative solver (repeated down/up integration) Lw Ed Es Eu I Eu Es Ed I Es Eu Ed Oliver Jahn

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