Ocean color remote sensing of phytoplankton physiology & primary production Toby K. Westberry 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 University2School of Marine Sciences, University of Maine

3Institute for Computational Earth System Science, UCSB

Outline1. 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

NPP ~ [biomass] x physiologic rate

NPP ~ [Chl] x Pbopt

NPP ~ [C] x

Scattering (cp or bbp)

Ratio of Chl to scattering (Chl:C)

General

Chl-based

C-based

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)

0.0 0.2 0.4 0.6 0.8

0.000

0.001

0.002

0.003

0.004

0.005

bb

p (

m-1)

Chlorophyll (mg m-3)

‘physiology domain’

‘biomass domain’

C = (bbp – intercept) x scalar

75o

0o

15o

30o

90o

60o

45o

60o

75o90o

15o

30o

45o

75o

0o

15o

30o

90o

60o

45o

60o

75o90o

15o

30o

45o

NP

SP SA

NANP

SP

CP

SA

NA

SI

NICA

SO

L0

L1

L2

L3

L4

SO-all

Ch

loro

ph

yll

Varia

nce L

evel

excluded

‘cell size domain?’

= (bbp – 0.00035) x 13,000

28 Regional Bins based on seasonal Chl

variance

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))

Chl:C

(m

g m

g-1)

Light (moles photons m-2 h-1)

Growth rate (div. d-1)

Chl:C

Low Nutrient stress High

Labora

tory

Temperature (oC)

Low Nutrient stress High

Chl:C

(mg m

g-1)

Chl:C

Space

after Behrenfeld et al. (2005)

Chl:C registers physiology

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 () and NPP

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-z (z=distance from nitracline)

2. Let cells photoacclimate through the water column

Ig (Ein m-2 h-1)

Chl :

C

(div

ision

s d-1)

CbPM details (2)

3. Spectral accounting for underwater light field

-both irradiance & attenuation

4. Phytoplankton growth rate,

5. Net primary production, NPP(z) = (z) x C(z)

))(3(

0max

0max 1 zPAR

TNCchl

Cchl

exy

yx

Light limitationNutrient limitation(& temperature)

Max. growth rate

Ig (Ein m-2 h-1)

Ch

l :

C

(div

ision

s d-1)

nLw

C

chlbbp

Kd(490) PAR(0+) MLD NO3

Kd() Ed()

Chl:C

zno3, zno3

PAR(z)

SeaWiFS FNMOC WOA01

Austin & Petzold (1986)Maritorena et al. (2001) NO3 > 0.5 M

Morel (1988)

Chl:Cnut

Photoacclimation

NPP

Light limitation

INPUTS

OUTPUTS

* if z<MLD, * red arrows indicate relationships exist ONLY when z>MLD* Run with 1° x1° monthly mean climatologies (1999-2004)

0dzXd

Results

Example profiles (1)

Stratified, shallow mixed layer, oligo-trophic

MLD =25mzNO3 =110mzeu =105m

Sargasso Sea (35°N, 65°W, Aug)

Example profiles (2)

Deep mixed layer, nutrient replete

MLD =95mzNO3 =0mzeu =40m

North Atlantic (50°N, 30°W, Apr)

Chl NPPD

epth

(m

)

mg Chl m-3 d-1 mg C m-3 d-1

Example profiles (mean)

- c.f. Morel & Berthon (1989)

Annual mean northern hemisphere

South Pacific (L0)(central gyre)

Equatorial (L3)

South Pacific (L2)(non-gyre)

North Atlantic (L3)

Surface patterns

Month # since 1997

Chl (mg Chl m-3)

C (mg C m-3)

Chl:C (mg mg-1)

Summer (Jun-Aug)

Winter (Dec-Feb)

(d-1)

Growth rate, • Persistently elevated in upwelling regions

• Chronically depressed in open ocean

• Can see effects of mixing depth & micro-nutrient limitation

(d-1) (d-1)

Annual mean Annual mean (L0 only)

NPP patterns

∫NPP (mg C m-2 d-1)

Summer (Jun-Aug)

Winter (Dec-Feb)

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

• Large seasonal cycle at high latitudes (ex., N. Atl.)

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

Seasonal NPP patterns (N. Atl.)

Western N. Atl

Eastern N. Atl

CBPM

VGPM

Seasonal NPP patterns

CbPM

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%)

Southern Ocean(<-50°S)

2 (4%) 3 (5%)

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

Validation

Surface Chl:C at HOTC

hlor

ophy

llb bp

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

Chl:C

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

1998 1999 2000 2001 2002

• Prochlorococcus cellular fluorescence at HOT ~(in situ Chl : C) (Winn et al., 1995)

• Satellite Chl :C

HOT

Chl(z) & Kd(z) at BATS

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

∫NPP at HOT & BATS∫N

PP (

mg C

m-2 d

-1)

NPP(z) at HOTN

PP (

mg C

m-3 d

-1)

Serial day since 09/1997

- Uniform mixed layer (step function) v. in situ incubations

- Discrepancies due to satellite estimates, NOT concept

NPP(z) at HOT

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 & 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

PrincetonJorge SarmientoPatrick ShultzMike Hiscock

UCSBNorm NelsonStephane MaritorenaManuela Lorenzi-Kayser

toby.westberry@science.oregonstate.edu

OSURobert O’MalleyJulie Arrington Allen MilligenGiorgio Dall’Olmo

Extra

0 5 10 15 20 25 300.01

0.04

0.07

0.10

0.13

0.16

0.19

0 1 2 3

0.005

0.020

0.035

0.050

0.065

0.080

0.20.40.60.81.00.001

0.006

0.011

0.016

Ch

l:C

(m

g m

g-1)

Light (moles m-2 h-1)

Temperature (oC)

Ch

l:C

max

Growth rate (div. d-1)

Ch

l:C

min

Low Nutrient stress High

3 primary factors

Light

Temperature

Nutrients

Chl:C physiologyLa

bora

tory

Chl:Cmax

Chl:Cmin

Dunaliella tertiolecta20 oCReplete nutrientsExponential 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

Nutrient-limited &/or light-limited + photoacclimation

Uniform

Light-limited + photoacclimation

z=zNO3

z=MLD

z=0

z=∞Relative PAR Relative NO3

Depth-resolved CBPM

* 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)

0

0

2

*1 0 0

0

0

( )

(

( ) ( / )( )

( ) ( /) () exp[ ( )])( )

i

bwi

i

bp

bw bp cph dm

bbRrs g

b b ahl a SC

The Model (con’t)

700

400

))1,((),()( dezEdzPAR zzKd

satC

chl

C

chl

)3(

max

max 1 mldPAR

TNCchl

Cchl

exx

zC

ChlzPAR exezC

Chl 075.0)(3 1)022.0045.0(022.0)(

)(3)(3 1)022.0045.0(022.0/2)( zPARzPARsatC

Chl exexz

CBPM data sources

- SeaWiFS: nLw(), PAR, Kd(490)- GSM01: Chl a, bbp(443)- FNMOC: MLD- WOA 2001: ZNO3

- Chl, C, & Chl:C- - NPP

INPUT (surface) OUTPUT ((z))

Run with 1° x1° monthly mean climatologies (1999-2004)

Example profiles (3)

Deep winter mixing,Very low light, Nutrient replete

MLD =>300mzNO3 =0mzeu =

Southern Ocean (50°S, 130°W, Aug)

(d-1) (d-1)

Annual mean Annual mean (L0 only)

Growth rate, (2)

NPP patterns (Jun-Aug)

This work

∫NPP (mg C m-2 d-1)VGPM (Chl-based model)

∫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

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-basedChl-based

Annual NPP

Models are very sensitive to input sources

VGPM CBPM This model

Annual ∫NPP (Pg C)

45 (61) 75 52

MLD -- 18 8

Chl 8-10 ?? 4

Kd 26 37 29

∫NPP for changeIn input

OR SHOW BY OCEAN BASIN AND/OR SEASON TO SHOW REDISTRIBUTION??

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