Ocean color remote sensing of phytoplankton physiology & primary production

1. Ocean color remote sensing of phytoplankton physiology & primary production Toby K. Westberry1, Mike J. Behrenfeld1 Emmanuel Boss2, David A. Siegel3 1Department of Botany, Oregon State University 2School of Marine Sciences, University of Maine 3Institute for Computational Earth System Science, UCSB

2. Outline 1. Introduction to problem - Phytoplankton Chl v. Carbon - NPP modeling 2. Model - bio-optics - physiology - photoacc./light limitation/nutrient stress 3. Results - surface & depth patterns - global patterns 4. Validation 5. Future directions

3. Carbon v. Chlophyll • How to quantify phytoplankton • Historically, net primary production (NPP) has been modeled as a function of chlorophyll concentration • BUT, cellular chlorophyll content is highly variable and is affected by acclimation to light & nutrient stress and species composition Chl is NOT biomass

4. Modeling NPP NPP ~ [biomass] x physiologic rate General NPP ~ [Chl] x Pbopt Chl-based NPP ~ [C] x m C-based Scattering (cp or bbp) Ratio of Chl to scattering (Chl:C)

5. Phytoplankton C • Scattering covaries with particle abundance • (Stramski & Kiefer, 1991; Bishop, 1999; Babin et al., 2003) • Scattering also covaries with phytoplankton carbon • (Behrenfeld & Boss, 2003; Behrenfeld et al., 2005) • Chlorophyll variations independent of carbon (C) are an • index of changing cellular pigmentation

6. Scattering:Chl From Behrenfeld & Boss (2003)

7. 90o 90o 75o 75o 60o 60o L0 45o 45o NP NA 30o 30o NA L1 NP 15o 15o Chlorophyll Variance Level L2 NI CP CA 0o 0o L3 SI 15o 15o SA L4 SP 30o 30o SP SA SO-all 45o 45o 60o 60o SO 75o 75o excluded 90o 90o 28 Regional Bins based on seasonal Chl variance ‘cell size domain?’ C = (bbp – intercept) x scalar = (bbp – 0.00035) x 13,000 ‘biomass domain’ bbp (m-1) 1. Chl:C is consistent with lab data Mean Chl:C=0.010, range=0.002-0.030 (see synthesis in Behrenfeld et al. (2002)) 2. C ~ 25-40% of POC (Eppley et al. (1992); DuRand et al. (2001); Gundersen et al. (2001), Obuelkheir et al. (2005), Loisel et al., (2001), Stramski et al., (1999)) ‘physiology domain’ Chlorophyll (mg m-3)

8. Low Nutrient stress High Low Nutrient stress High Chl:C registers physiology Chl:C (mg mg-1) Chl:C (mg mg-1) Space Laboratory Light (moles photons m-2 h-1) Chl:C Chl:C Growth rate (div. d-1) Temperature (oC) after Behrenfeld et al. (2005)

9. Model

10. CbPM overview • Invert ocean color data to estimate [Chl a] & bbp(443) • (Garver & Siegel, 1997; Maritorena et al., 2001) • Relate bbp(443) to carbon biomass (mg C m-3) • (Behrenfeld et al., 2005) • Use Chl:C to infer physiology (photoacclimation & nutrient stress) • Propagate information through water column • Estimate phytoplankton growth rate (m) and NPP Carbon-based Production Model (CbPM)

11. CbPM details (1) 1. Let surface values of Chl:C indicate level of nutrient-stress -nutrient stress falls off as e-Dz(Dz=distance from nitracline) 2. Let cells photoacclimate through the water column Chl : C m (divisions d-1) Ig (Ein m-2 h-1)

12. CbPM details (2) 3. Spectral accounting for underwater light field -both irradiance & attenuation 4. Phytoplankton growth rate, m 5. Net primary production, NPP(z) = m(z) x C(z) Chl : C m (divisions d-1) Ig (Ein m-2 h-1) Max. growth rate Light limitation Nutrient limitation (& temperature)

13. SeaWiFS FNMOC WOA01 INPUTS nLw Kd(490) PAR(0+) MLD NO3 Maritorena et al. (2001) Austin & Petzold (1986) DNO3 > 0.5 mM bbp chl Kd(l) Ed(l) zno3, Dzno3 Morel (1988) Photoacclimation DChl:Cnut C Chl:C PAR(z) Light limitation NPP m OUTPUTS * if z<MLD, * red arrows indicate relationships exist ONLY when z>MLD * Run with 1° x1° monthly mean climatologies (1999-2004)

14. Results

15. Example profiles (1) Sargasso Sea (35°N, 65°W, Aug) Stratified, shallow mixed layer, oligo- trophic MLD =25m zNO3 =110m zeu =105m

16. Example profiles (2) North Atlantic (50°N, 30°W, Apr) Deep mixed layer, nutrient replete MLD =95m zNO3 =0m zeu =40m

17. Example profiles (mean) Annual mean northern hemisphere m NPP Chl Depth (m) mg Chl m-3 d-1 mg C m-3 d-1 - c.f. Morel & Berthon (1989)

18. Surface patterns South Pacific (L0) (central gyre) Equatorial (L3) Chl (mg Chl m-3) C (mg C m-3) Chl:C (mg mg-1) South Pacific (L2) (non-gyre) North Atlantic (L3) Month # since 1997

19. Growth rate, m Summer (Jun-Aug) • Persistently elevated in upwelling • regions • Chronically depressed in open ocean • Can see effects of mixing depth & • micro-nutrient limitation Winter (Dec-Feb) Annual mean (L0 only) Annual mean m(d-1) m (d-1) m (d-1)

20. NPP patterns Summer (Jun-Aug) • O(1) looks like Chl • - gyres, upwelling, • seasonal blooms • Large seasonal cycle at • high latitudes (ex., N. Atl.) Winter (Dec-Feb) ∫NPP (mg C m-2 d-1)

21. NPP patterns (2) • large spatial (& temporal) • differences in carbon-based • NPP from chl-based results • (e.g., > ±50%) • differences due to photo- • acclimation and nutrient-stress • related changes in Chl : C mg C m-2 d-1

22. Seasonal NPP patterns (N. Atl.) Western N. Atl CBPM VGPM Eastern N. Atl

23. Seasonal NPP patterns CbPM VGPM • seasonal cycles • dampened in tropics, • but strengthened and • delayed in “spring • bloom” areas

24. Annual NPP • Although total NPP doesn’t change much (~15%), • where and when it occurs does

25. Validation

26. Surface Chl:C at HOT • Prochlorococcus cellular • fluorescence at HOT • ~(in situ Chl : C) • (Winn et al., 1995) HOT • Satellite Chl :C 1998 1999 2000 2001 2002

27. Chl(z) & Kd(z) at BATS Model compared to Bermuda Atlantic Time- series Study/Bermuda Bio-Optics Project (BATS/BBOP) HPLC Chl & CTD fluorometer

28. ∫NPP at HOT & BATS ∫NPP (mg C m-2 d-1)

29. NPP(z) at HOT NPP (mg C m-3 d-1) Serial day since 09/1997

30. NPP(z) at HOT - Uniform mixed layer (step function) v. in situ incubations - Discrepancies due to satellite estimates, NOT concept

31. Future directions

32. Next steps (model) • Sensitivity to inputs (e.g., MLD, MODIS) • Error budget • Inclusion of CDOM(z) • Change photoacclimation with depth • change bbp to C relationship • -diatoms, coccolithophorids, coastal • Further validation

33. Next steps (applications) • Look at finer spatial/temporal scales • Knowledge of m & dC/dt allow statements about loss • processes • Recycling efficiency (wrt nutrients) • Characterization of ocean in terms of nutrient and light • limitation patterns • Inclusion of concepts/data into coupled models

34. Thanks Princeton Jorge Sarmiento Patrick Shultz Mike Hiscock UCSB Norm Nelson Stephane Maritorena Manuela Lorenzi-Kayser OSU Robert O’Malley Julie Arrington Allen Milligen Giorgio Dall’Olmo toby.westberry@science.oregonstate.edu

35. Extra

36. Chl:C physiology 3 primary factors Light Temperature Nutrients Chl:Cmax Dunaliella tertiolecta 20 oC Replete nutrients Exponential growth phase Geider (1987) New Phytol. 106: 1-34 16 species = Diatoms = all other species Laws & Bannister (1980) Limnol. Oceanogr. 25: 457-473 Thalassiosira fluviatilis = NO3 limited cultures = NH4 limited cultures = PO4 limited cultures Chl:C (mg mg-1) Chl:Cmin Light (moles m-2 h-1) Laboratory Chl:Cmax Temperature (oC) Chl:Cmin Low Nutrient stress High Growth rate (div. d-1)

37. Depth-resolved CBPM z=0 Uniform z=MLD Nutrient-limited &/or light-limited + photoacclimation z=zNO3 Light-limited + photoacclimation z=∞ Relative PAR Relative NO3 * Iterative such that values at z=zi+1 depend on values at z=zi *

38. GSM01 (Maritorena et al., 2002) • Non-linear least squares problem with 3 unknowns and 5 equations • Solved by minimization of of squared sum of residuals(between obs & estimate) • Result is Chl, acdm(443), bbp(443)

39. The Model (con’t)

40. CBPM data sources INPUT (surface) OUTPUT ((z)) - SeaWiFS: nLw(l), PAR, Kd(490) - GSM01: Chl a, bbp(443) - FNMOC: MLD - WOA 2001: ZNO3 - Chl, C, & Chl:C - m - NPP Run with 1° x1° monthly mean climatologies (1999-2004)

41. Example profiles (3) Southern Ocean (50°S, 130°W, Aug) Deep winter mixing, Very low light, Nutrient replete MLD =>300m zNO3 =0m zeu =

42. Growth rate, m (2) Annual mean Annual mean (L0 only) m (d-1) m (d-1)

43. This work VGPM (Chl-based model) ∫NPP (mg C m-2 d-1) ∫NPP (mg C m-2 d-1) NPP patterns(Jun-Aug) • large spatial & temporal • differences in carbon-based • NPP from Chl-based results • (e.g., > ±50%) • Chl-based model interprets high • Chl areas as high NPP • differences due to photo- • acclimation and nutrient-stress • related changes in Chl : C

44. C-based Chl-based NPP patterns (2) • large spatial & temporal • differences in carbon-based • NPP from chl-based results • (e.g., > ±50%) • seasonal cycles dampened in • tropics, but strengthened and • delayed in “spring bloom” • areas • differences due to photo- • acclimation and nutrient-stress • related changes in Chl : C mg C m-2 d-1

45. Annual NPP OR SHOW BY OCEAN BASIN AND/OR SEASON TO SHOW REDISTRIBUTION?? D∫NPP for change In input Models are very sensitive to input sources

46. Conclusions • Spectral, depth-resolved NPP model that includes • photoacclimation, light & nutrient limitation • - based on phytoplankton scattering-carbon relationship • Consistencies with field data  ongoing validation • Spatial patterns in ∫PP markedly different than Chl-based models • - also different seasonal cycles (timing/magnitude) toby.westberry@science.oregonstate.edu