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

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

Carbon v. Chlophyll Chl is NOT biomass 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 Chlorophyll a Pros: uniquely algal trait; easily measured; detectable from space; large historical database; NPP ~ 0(chl) Cons: chl synthesis depends on physiological state! Carbon Pros: -represents actual biomass; can be directly related to export flux Cons: -difficult to measure Chl is NOT biomass

Modeling NPP NPP ~ [biomass] x physiologic rate NPP ~ [Chl] x Pbopt General NPP ~ [Chl] x Pbopt Chl-based NPP ~ [C] x m C-based However, chl contains a mixture of biomass AND physiology, so that getting Pbopt is complex Chl does NOT represent biomass, C does ! Scattering (cp or bbp) Ratio of Chl to scattering (Chl:C)

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

Scattering:Chl From Behrenfeld & Boss (2003)

1. Chl:C is consistent with lab data NP SP SA NA CP SI NI CA SO L0 L1 L2 L3 L4 SO-all Variance Level Chlorophyll excluded 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) Intercept = 0.00017 m-1 based on Stramski & Kiefer (1991) & Cho & Azam (1990) Phytoplankton Carbon = 25 – 35% POC Eppley et al. (1992), DuRand et al. (2001), Gundersen et al. (2001), Obuelkheir et al. (2005) 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)

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

Model

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 Results 1. large spatial & temporal differences in carbon-based NPP from chl-based results (e.g., > ±50%) 2. seasonal cycles dampened in tropics, but strengthened and delayed in “spring bloom” areas 3. differences due to photoacclimation and nutrient-stress related changes in Chl : C Carbon-based Production Model (CbPM)

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)

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 Nutrient limitation (& temperature) Light limitation

* red arrows indicate relationships exist ONLY when z>MLD 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) C Chl:C Photoacclimation PAR(z) DChl:Cnut 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)

Results

Example profiles (1) Stratified, shallow mixed layer, oligo- trophic Sargasso Sea (35°N, 65°W, Aug) Stratified, shallow mixed layer, oligo- trophic MLD =25m zNO3 =110m zeu =105m Uniform mixed layer Subsurface Chl a max. Realistic K(l), PAR, and euphotic depth

Example profiles (2) Deep mixed layer, nutrient replete MLD =95m North Atlantic (50°N, 30°W, Apr) Deep mixed layer, nutrient replete MLD =95m zNO3 =0m zeu =40m Uniform mixed layer Subsurface Chl a max. Realistic K(l), PAR, and euphotic depth

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

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

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 Annual mean (L0 only) m (d-1) m (d-1) m (d-1)

NPP patterns O(1) looks like Chl - gyres, upwelling, seasonal blooms 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)

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 differences due to photoacclimation and nutrient-stress manifest in the Chl : C

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

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

Annual NPP Although total NPP doesn’t change much (~15%), ∫NPP (Pg C) VGPM This model Annual 45 52 Gyres 5 (11%) 13 (26%) High latitudes 19 (42%) 12 (23%) Subtropics? 18 (39%) 25 (48%) Southern Ocean (q<-50°S) 2 (4%) 3 (5%) Add in comments about growth rate and ability to calc standing stocks, Uncertainties, etc. Although total NPP doesn’t change much (~15%), where and when it occurs does

Validation

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

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

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

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

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

Future directions

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

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

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

Extra

Laboratory 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)

Depth-resolved CBPM Uniform Nutrient-limited &/or light-limited 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 *

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)

The Model (con’t)

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)

Example profiles (3) Deep winter mixing, Very low light, Southern Ocean (50°S, 130°W, Aug) Deep winter mixing, Very low light, Nutrient replete MLD =>300m zNO3 =0m zeu = Uniform mixed layer Subsurface Chl a max. Realistic K(l), PAR, and euphotic depth

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

NPP patterns (Jun-Aug) This work ∫NPP (mg C m-2 d-1) 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 VGPM (Chl-based model) ∫NPP (mg C m-2 d-1)

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 C-based Chl-based differences due to photoacclimation and nutrient-stress manifest in the Chl : C

Annual NPP Models are very sensitive to input sources VGPM CBPM This model Annual ∫NPP (Pg C) 45 (61) 75 52 DMLD -- 18 8 DChl 8-10 ?? 4 DKd 26 37 29 OR SHOW BY OCEAN BASIN AND/OR SEASON TO SHOW REDISTRIBUTION?? D∫NPP for change In input Models are very sensitive to input sources

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