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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
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)
