Ocean color remote sensing of phytoplankton physiology

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 University 2School of
Marine Sciences, University of Maine 3Institute
for Computational Earth System Science, UCSB
2
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
3
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
4
Modeling NPP
NPP biomass x physiologic rate
General
NPP Chl x Pbopt
Chl-based
NPP C x m
C-based
Scattering (cp or bbp)
Ratio of Chl to scattering (ChlC)
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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

6
ScatteringChl
From Behrenfeld Boss (2003)
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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)
1. ChlC is consistent with lab data Mean
ChlC0.010, range0.002-0.030 (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)
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ChlC registers physiology
ChlC (mg mg-1)
ChlC (mg mg-1)
Space
Laboratory
Light (moles photons m-2 h-1)
ChlC
ChlC
Growth rate (div. d-1)
Temperature (oC)
after Behrenfeld et al. (2005)
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Model
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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 ChlC to infer physiology (photoacclimation
  nutrient stress)
• Propagate information through water column
• Estimate phytoplankton growth rate (m) and NPP

Carbon-based Production Model (CbPM)
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CbPM details (1)
1. Let surface values of ChlC indicate
level of nutrient-stress -nutrient stress falls
off as e-Dz (Dzdistance from nitracline) 2.
Let cells photoacclimate through the water
column
Chl C
m (divisions d-1)
Ig (Ein m-2 h-1)
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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)
Light limitation
Max. growth rate
Nutrient limitation ( temperature)
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SeaWiFS
FNMOC
WOA01
INPUTS
nLw
Kd(490)
PAR(0)
MLD
NO3
Austin Petzold (1986)
Maritorena et al. (2001)
DNO3 gt 0.5 mM
chl
bbp
Kd(l)
Ed(l)
zno3, Dzno3
Morel (1988)
Photoacclimation
DChlCnut
C
ChlC
PAR(z)
Light limitation
NPP
m
OUTPUTS
if zltMLD, red arrows indicate relationships
exist ONLY when zgtMLD Run with 1 x1 monthly
mean climatologies (1999-2004)
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Results
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Example profiles (1)
Stratified, shallow mixed layer,
oligo- trophic MLD 25m zNO3 110m zeu
105m
Sargasso Sea (35N, 65W, Aug)
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Example profiles (2)
Deep mixed layer, nutrient replete MLD
95m zNO3 0m zeu 40m
North Atlantic (50N, 30W, Apr)
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Example profiles (mean)
Annual mean northern hemisphere
m
NPP
Chl
Depth (m)
mg Chl m-3
d-1
mg C m-3 d-1
- c.f. Morel Berthon (1989)
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Surface patterns
South Pacific (L0) (central gyre)
Equatorial (L3)
Chl (mg Chl m-3) C (mg C m-3) ChlC (mg mg-1)
South Pacific (L2) (non-gyre)
North Atlantic (L3)
Month since 1997
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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 (L0 only)
Annual mean
m (d-1)
m (d-1)
m (d-1)
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NPP patterns
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)
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NPP patterns (2)

• large spatial ( temporal)
• differences in carbon-based
• NPP from chl-based results
• (e.g., gt 50)
• differences due to photo-
• acclimation and nutrient-stress
• related changes in Chl C

mg C m-2 d-1
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Seasonal NPP patterns (N. Atl.)
Western N. Atl
CBPM
VGPM
Eastern N. Atl
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Seasonal NPP patterns
CbPM
VGPM

• seasonal cycles
• dampened in tropics,
• but strengthened and
• delayed in spring
• bloom areas

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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 (qlt-50S) 2 (4) 3 (5)

• Although total NPP doesnt change much (15),
• where and when it occurs does

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Validation
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Surface ChlC at HOT

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

HOT

• Satellite Chl C

1998
1999
2000
2001
2002
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Chl(z) Kd(z) at BATS
Model compared to Bermuda Atlantic Time- series
Study/Bermuda Bio-Optics Project
(BATS/BBOP) HPLC Chl CTD fluorometer
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?NPP at HOT BATS
?NPP (mg C m-2 d-1)
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NPP(z) at HOT
NPP (mg C m-3 d-1)
Serial day since 09/1997
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NPP(z) at HOT
- Uniform mixed layer (step function) v. in situ
incubations - Discrepancies due to satellite
estimates, NOT concept
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Future directions
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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

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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

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Thanks
Princeton Jorge Sarmiento Patrick Shultz Mike
Hiscock UCSB Norm Nelson Stephane
Maritorena Manuela Lorenzi-Kayser
OSU Robert OMalley Julie Arrington Allen
Milligen Giorgio DallOlmo
toby.westberry_at_science.oregonstate.edu
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Extra
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ChlC physiology
3 primary factors Light Temperature
Nutrients
ChlCmax
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
ChlC (mg mg-1)
ChlCmin
Light (moles m-2 h-1)
Laboratory
ChlCmax
Temperature (oC)
ChlCmin
Low Nutrient stress High
Growth rate (div. d-1)
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Depth-resolved CBPM
z0
Uniform
zMLD
Nutrient-limited /or light-limited
photoacclimation
zzNO3
Light-limited photoacclimation
z8
Relative PAR Relative NO3
Iterative such that values at zzi1 depend on
values at zzi
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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)

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The Model (cont)
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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, ChlC - m - NPP
Run with 1 x1 monthly mean climatologies
(1999-2004)
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Example profiles (3)
Deep winter mixing, Very low light, Nutrient
replete MLD gt300m zNO3 0m zeu
Southern Ocean (50S, 130W, Aug)
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Growth rate, m (2)
Annual mean
Annual mean (L0 only)
m (d-1)
m (d-1)
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NPP patterns (Jun-Aug)

• large spatial temporal
• differences in carbon-based
• NPP from Chl-based results
• (e.g., gt 50)
• Chl-based model interprets high
• Chl areas as high NPP
• differences due to photo-
• acclimation and nutrient-stress
• related changes in Chl C

44
NPP patterns (2)

• large spatial temporal
• differences in carbon-based
• NPP from chl-based results
• (e.g., gt 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
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Annual NPP
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
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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_at_science.oregonstate.edu