Ocean color remote sensing of phytoplankton physiology primary

Ocean color remote sensing of phytoplankton physiology & primary production Toby K. Westberry 1, Mike J. Behrenfeld 1 Emmanuel Boss 2, David A. Siegel 3 1 Department of Botany, Oregon State University 2 School of Marine Sciences, University of Maine 3 Institute 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 • 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

Modeling NPP General Chl-based C-based NPP ~ [biomass] x physiologic rate NPP ~ [Chl] x Pbopt NPP ~ [C] x m 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)

75 o 90 o 75 o 60 o NP o 30 15 o 0 o 15 o o 30 15 o CP CA SI NA NP NI 45 o NA 0 o 30 o SP 45 o 60 o 75 o 30 o 28 Regional Bins based on seasonal Chl variance SA 45 o 60 o SO 15 o SA SP L 0 L 1 L 2 L 3 L 4 Chlorophyll Variance Level 60 o 45 o SO-all 75 o 90 o ‘cell size domain? ’ bbp (m-1) ‘biomass domain’ excluded C = (bbp – intercept) x scalar = (bbp – 0. 00035) x 13, 000 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)) ‘physiology domain’ Chlorophyll (mg m-3) 2. C ~ 25 -40% of POC (Eppley et al. (1992); Du. Rand et al. (2001); Gundersen et al. (2001), Obuelkheir et al. (2005), Loisel et al. , (2001), Stramski et al. , (1999))

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

Model

Cb. PM 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 (Cb. PM)

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

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

Sea. Wi. FS n. Lw Kd(490) Maritorena et al. (2001) bbp chl C Chl: C NPP PAR(0+) FNMOC WOA 01 MLD NO 3 Austin & Petzold (1986) Kd(l) Morel (1988) Photoacclimation Light limitation INPUTS DNO 3 > 0. 5 m. M Ed(l) PAR(z) m * if z<MLD, * red arrows indicate relationships exist ONLY when z>MLD * Run with 1° x 1° monthly mean climatologies (1999 -2004) zno 3, Dzno 3 DChl: Cnut OUTPUTS

Results

Example profiles (1) Sargasso Sea (35°N, 65°W, Aug) Stratified, shallow mixed layer, oligotrophic MLD =25 m z. NO 3 =110 m zeu =105 m

Example profiles (2) North Atlantic (50°N, 30°W, Apr) Deep mixed layer, nutrient replete MLD =95 m z. NO 3 =0 m zeu =40 m

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

Surface patterns South Pacific (L 0) (central gyre) Equatorial (L 3) Chl (mg Chl m-3) C (mg C m-3) Chl: C (mg mg-1) South Pacific (L 2) (non-gyre) North Atlantic (L 3) 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 (L 0 only) m (d-1)

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

NPP patterns (2) mg C m-2 d-1 • 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

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

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

Annual NPP ∫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%) 2 (4%) 3 (5%) Southern Ocean (q<-50°S) • Although total NPP doesn’t change much (~15%), where and when it occurs does

Validation

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

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

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

NPP (mg C m-3 d-1) NPP(z) at HOT 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 & d. C/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 OSU Robert O’Malley Julie Arrington Allen Milligen Giorgio Dall’Olmo Princeton Jorge Sarmiento Patrick Shultz Mike Hiscock UCSB Norm Nelson Stephane Maritorena Manuela Lorenzi-Kayser toby. westberry@science. oregonstate. edu

Extra

Chl: C (mg mg-1) 3 primary factors Chl: Cmax Chl: Cmin Light Dunaliella tertiolecta 20 o. C Replete nutrients Exponential growth phase Light (moles m-2 h-1) Chl: Cmax Geider (1987) New Phytol. 106: 1 -34 Temperature 16 species = Diatoms = all other species Temperature (o. C) Laws & Bannister (1980) Limnol. Oceanogr. 25: 457 -473 Chl: Cmin Laboratory Chl: C physiology Nutrients Thalassiosira fluviatilis = NO 3 limited cultures = NH 4 limited cultures Low Nutrient stress High Growth rate (div. d-1) = PO 4 limited cultures

Depth-resolved CBPM z=0 Uniform z=MLD Nutrient-limited &/or light-limited + photoacclimation z=z. NO 3 Light-limited + photoacclimation Relative PAR Relative NO 3 z=∞ * Iterative such that values at z=zi+1 depend on values at z=zi *

GSM 01 (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) - Sea. Wi. FS: n. Lw(l), PAR, Kd(490) - GSM 01: Chl a, bbp(443) - FNMOC: MLD - WOA 2001: ZNO 3 OUTPUT ( (z)) - Chl, C, & Chl: C - m - NPP Run with 1° x 1° monthly mean climatologies (1999 -2004)

Example profiles (3) Southern Ocean (50°S, 130°W, Aug) Deep winter mixing, Very low light, Nutrient replete MLD =>300 m z. NO 3 =0 m zeu =

Growth rate, m (2) Annual mean m (d-1) Annual mean (L 0 only) m (d-1)

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

NPP patterns (2) mg C m-2 d-1 • 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 C-based Chl-based • differences due to photo acclimation and nutrient-stress related changes in Chl : C

Annual NPP Annual ∫NPP (Pg C) DMLD CBPM 45 (61) 75 O BY DKd SHOW R SI 8 -10 BA C EAN 26 This model T IST D RE U RIB 52 OW H S O T ON S A E 18 S R O D/ N A N -- DChl O VGPM ? ? N IO 8 ? ? 4 37 29 Models are very sensitive to input sources D∫NPP for change In input

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