Transcript Carbon-Based Net Primary Production and Phytoplankton

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
• 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)
75o
90o
90o
75o
60o
NP
30o
15o
0o
15o
CP
CA
SI
15o
SP
45o
60o
45o
60o
75o
90o
0.005
15o
30o
SA
SO
75o
0o
SA
SP
30o
30o
NA
NP
NI
45o
NA
28 Regional
Bins based on
seasonal Chl
variance
SO-all
excluded
90o
‘cell size domain?’
0.004
C = (bbp – intercept) x scalar
= (bbp – 0.00035) x 13,000
‘biomass domain’
bbp (m-1)
L0
L1
L2
L3
L4
Chlorophyll
Variance Level
60o
45o
0.003
0.002
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))
0.001
‘physiology domain’
0.000
0.0
0.2
0.4
Chlorophyll (mg
2. C ~ 25-40% of POC
0.6
m-3)
0.8
(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 (mg mg-1)
Light (moles photons m-2 h-1)
Space
Chl:C (mg mg-1)
Chl:C
Chl:C
Laboratory
Chl:C registers physiology
Low
Nutrient stress
High
Growth rate (div. d-1)
Low
Nutrient stress
High
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
Carbon-based Production Model (CbPM)
CbPM 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)
CbPM details (2)
3. Spectral accounting for underwater light field
-both irradiance & attenuation
m  mmax
x
Max. growth rate
 y0
chlC N T max  y0
chl
C
Chl : C
m (divisions d-1)
4. Phytoplankton growth rate, m
x
Nutrient limitation
(& temperature)
1  e( 3PAR( z ))
Ig (Ein m-2 h-1)
Light limitation
5. Net primary production, NPP(z) = m(z) x C(z)
SeaWiFS
nLw
Maritorena et al. (2001)
bbp
PAR(0+)
Kd(490)
chl
FNMOC
WOA01
MLD
NO3
DNO3 > 0.5 mM
Austin & Petzold (1986)
Kd(l)
Morel (1988)
Ed(l)
Photoacclimation
C
Chl:C
NPP
d X 0
* if z<MLD, dz
Light limitation
INPUTS
PAR(z)
m
* red arrows indicate relationships exist ONLY when z>MLD
* Run with 1° x1° monthly mean climatologies (1999-2004)
zno3, Dzno3
DChl:Cnut
OUTPUTS
Results
Example profiles (1)
Sargasso Sea (35°N, 65°W, Aug)
Stratified, shallow
mixed layer, oligotrophic
MLD =25m
zNO3 =110m
zeu =105m
Example profiles (2)
North Atlantic (50°N, 30°W, Apr)
Deep mixed layer,
nutrient replete
MLD =95m
zNO3 =0m
zeu =40m
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 (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
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 photoacclimation and nutrient-stress
related changes in Chl : C
Seasonal NPP patterns (N. Atl.)
Western N. Atl
CBPM
VGPM
Eastern N. Atl
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%)
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
0.16
0.020
0.14
0.018
0.12
0.016
0.10
0.014
0.08
0.012
0.06
0.010
0.04
0.008
0.02
0.006
0.00
0.004
1998
1999
2000
2001
2002
Chl:C
Chlorophyll
bbp
• Satellite Chl :C
Chl(z) & Kd(z) at BATS
Model compared to
Bermuda Atlantic Timeseries Study/Bermuda BioOptics 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 & 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
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
[email protected]
Extra
3 primary factors
0.080
Chl:Cmax
0.065
0.050
Light
Chl:Cmin
0.035
Dunaliella tertiolecta
20 oC
Replete nutrients
Exponential growth phase
0.020
0.005
0
1
2
3
Light (moles m-2 h-1)
0.19
Chl:Cmax
0.16
Geider (1987) New Phytol. 106: 1-34
0.13
Temperature
0.10
16 species
= Diatoms
0.07
= all other species
0.04
0.01
0
5
10
15
20
25
30
Temperature (oC)
Laws & Bannister (1980)
Limnol. Oceanogr. 25: 457-473
0.016
Chl:Cmin
Laboratory
Chl:C (mg mg-1)
Chl:C physiology
0.011
Nutrients
Thalassiosira fluviatilis
= NO3 limited cultures
0.006
= NH4 limited cultures
0.001
Low
1.0
Nutrient stress
0.8
0.6
0.4
High
0.2
Growth rate (div. d-1)
= PO4 limited cultures
Depth-resolved CBPM
z=0
Uniform
z=MLD
Nutrient-limited &/or light-limited
+ photoacclimation
z=zNO3
Light-limited + photoacclimation
Relative PAR
Relative NO3
z=∞
* Iterative such that values at z=zi+1 depend on values at z=zi *
GSM01
(Maritorena et al., 2002)
i


bbw (l )  bbp (l0 )(l / l0 )


Rrs(l )   gi 


*
b
(
l
)

b
(
l
)
(
l
/
l
)

C
hl

a
(
l
)

a
(
l
)
exp[

S
(
l

l
)]
i 1

bp
0
0
ph
cdm
0
0 
 bw

2

• 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)
700
PAR( z ) 
 Ed( z, l )  e
(  K d ( l , z 1) z )
chl  chl 
 
C  C  sat
dl
400



Chl
 0.075 Dz
( z )  0.022  (0.045  0.022 )e 3 PAR( z ) x D Chl
C 1 e
C
m  mmax
m ( z)  2 x
chl
C
chl
C N T max
x
Chl
C sat
 

x

1  e( 3PARmld )

/ 0.022 (0.045 0.022)e3PAR( z ) x 1 e3PAR( z )

CBPM data sources
-
INPUT (surface)
SeaWiFS: nLw(l), PAR, Kd(490)
GSM01: Chl a, bbp(443)
FNMOC: MLD
WOA 2001: ZNO3
OUTPUT ((z))
- Chl, C, &
Chl:C
-m
- NPP
Run with 1° x1° monthly mean climatologies (1999-2004)
Example profiles (3)
Southern Ocean (50°S, 130°W, Aug)
Deep winter mixing,
Very low light,
Nutrient replete
MLD =>300m
zNO3 =0m
zeu =
Growth rate, m (2)
Annual mean
m (d-1)
Annual mean (L0 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 photoacclimation 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 photoacclimation and nutrient-stress
related changes in Chl : C
Annual NPP
VGPM
CBPM
This model
45 (61)
75
52
DMLD
--
18
8
DChl
8-10
??
4
DKd
26
37
29
Annual ∫NPP
(Pg C)
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)
[email protected]