1. Carbon-based primary production & phytoplankton physiology from ocean color data
Toby Westberry1, Mike Behrenfeld1, Emmanuel Boss2, Dave Siegel3, Allen Milligan1
1Department of Botany & Plant Pathology, Oregon State University, 2School of Marine Sciences, University of Maine, 3Institute for Computational Earth System Science, University of California Santa Barbara
1. Introduction
2. Model
Historically, net primary production (NPP) has been modeled as a function of chlorophyll concentration, allowing for a natural application to ocean color satellite data. However, cellular chlorophyll content is highly variable and is affected by photoacclimation and nutrient stress which act to confound global NPP model results. An approach to alleviate these limitations was recently introduced providing satellite NPP estimates based on conversion of backscattering to phytoplankton carbon and mixed layer phytoplankton growth rates (m, day-1) derived from chlorophyll:carbon ratios (Carbon Based Productivity Model (CBPM), see Behrenfeld et al., 2005).  Here, the CBPM is extended to provide full vertical profiles of m and NPP given knowledge of the mixed layer depth and the nitracline depth, which allow appropriate parameterization of photoacclimation and nutrient stress through the water column. This depth resolved approach accurately reconstructs the underwater light field, and produces biological profile data (C, Chl, NPP) which are broadly consistent with laboratory- and field-based measurements. Direct validation using regional in situ datasets of phytoplankton chlorophyll:carbon, cellular growth rates, and measured NPP rates support the findings presented here.
Conceptual model (ex., strongly stratified case)
Some details
- within ML, phytoplankton are acclimated to
median PAR in ML. Below this, they are
acclimated to ambient PAR
- Kd(490) is expanded to give Kd(l) using Austin &
Petzold (1986)
- Kd(l,z) below ML is estimated from [Chl]
(Morel, 1988) + difference between this
method & Austin & Petzold (1986) at surface
- each property (Kd,PAR,Chl,C,m,NPP) is dependent
on values above it (=iterative!)
z=0
Uniform
z=MLD
Propagation (and relaxation)
of surface nutrient stress
Photoacclimated Chl:C
Nutrient-limited &/or light-limited
+ photoacclimation y0
z=zNO3
Light-limited + photoacclimation
- Concept of a non-zero minimum
Chl:C (y0) when m=0 is important!
- can be seen in in situ data
(e.g., Laws & Bannister (1980))
- required to make model
formulations self-consistent
Light limitation
Nutrient stress
z=∞
Relative PAR
Relative NO3
2 main ingredients for modeling NPP : 1) biomass & 2) physiology
NPP ~ [biomass] x physiologic rate
Can have any combination of above processes depending on PAR(z) and depths of mixed layer and nitracline
General
Most often the case:
Key Points
Invert ocean color data to estimate [Chl] & bbp(443)
(Garver & Siegel, 1997; Maritorena et al., 2001)
Relate bbp(443) to carbon biomass (mg C m-2)
Use satellite Chl:C to infer phytoplankton physiology in mixed layer
(photoacclimation & nutrient stress)
Iteratively propagate properties below mixed layer as (PAR,zMLD, zNO3)
keeping track of photoacclimation to variable nutrient and light stress
Data Sources & implementation
NPP ~ [Chl] x Pbopt x …
(e.g., Antoine & Morel, 1996; Behrenfeld & Falkowski,
1997; others)
Intercept represents stable component of bbp due to background particles (i.e., bacteria, colloids, detritus, etc.) and scalar was chosen such that phytoplankton C ~30% of scattering-based POC estimates
Chl-based
bbp (m-1)
INPUTS
Problem: [Chl] does not adequately characterize “biomass” for
modeling NPP due to photoacclimation and nutrient-
stress related changes in cellular chlorophyll
- SeaWiFS: nLw(l) [Chl], bbp(443)
PAR
Kd(490) Kd(l)
- FNMOC: MLD
- WOA 2001: [NO3] ZNO3
OUTPUTS
Maritorena et al. (2001)
- Chl(z), C(z), & Chl:C(z)
- m(z)
- NPP(z)
- Kd(l,z), PAR(z), Zeu?
Austin & Petzold (1986)
Chl (mg m-3)
Solution: estimate biomass independently of physiology
NPP ~ [C] x m
Can estimate these from
ocean color measurements
C-based
NO3>0.5mM
Chl : C
m (divisions d-1)
**1° x1° monthly mean climatologies ( )**
Scattering
(cp or bbp)
Ratio of Chl to
scattering (Chl:C)
Carbon-Based Production Model (CBPM)
Ig (Ein m-2 h-1)
3. Data & Results
Phytoplankton growth rates, mz=0
Depth-integrated NPP temporal patterns
Example, single station (pixel:
Summer (Jun-Aug)
Ex. 1. North Atlantic
Ex 2. Tropical Atlantic
Western N. Atl
Eastern Pacific (20°N, -110°E, Aug)
All data
Oligotrophic gyres (L0)
CBPM
In N. Atlantic, both the
onset and peak of the
spring bloom occur 1-2
months later than Chl-
based model indicates
Seasonality is amplified at
higher latitudes & dampened
in tropics (difference btwn
summer & winter)
Subsurface profile features:
variable Kd(l,z)
subsurface Chl a maximum
realistic 1% light levels
variable C biomass
realistic NPP profiles
VGPM
m represents a net community
growth rate and reflects growth,
respiration, and other losses
(grazing, mixing, etc.) of a mixed
population
Surface phytoplankton growth
rates are broadly consistent with
expected values
Median values of m are ~0.4
Open ocean growth rates range
from ~ divisions d-1
Depth (m)
Eastern N. Atl
Winter (Dec-Feb)
# occur.
m (d-1)
m (d-1)
CBPM
VGPM
Summary/Conclusions
Mean NPP profiles
We have developed a spectral- & depth-resolved NPP model based on independent C & Chl estimates from ocean color data
Ability to distinguish DChl due to photoacclimation v. growth
Estimates of the phytoplankton growth rate, m, are also derived as part of this approach and provide valuable insight into phytoplankton physiology
Spatial & temporal patterns in NPP are markedly different than if using a Chl-based model (e.g., VGPM of Behrenfeld & Falkowski, 1997)
Ongoing validation with various diagnostics (PAR, Chl, m, NPP) suggest the model is performing well
m (d-1)
Validation example: NPP at Hawaii Ocean Time-series
Depth-integrated NPP spatial patterns
Summer (Jun-Aug)
Summer (Jun-Aug)
Large spatial differences between C-based
and Chl-based NPP estimates (e.g., N.
Atlantic, Eq. upwelling) lead to redistribution
of NPP in time and space
If classify pixels according to
average seasonal Chl variance
(see above image), such that
L0 L4
~Oligotrophic ~High Lat.
we can look at mean m & NPP
profiles within those regions
Depth (m)
∫NPP
VGPM
This model
(Pg C yr-1)
Annual 45 49
Gyres
5 (11%)
10 (20%)
High latitudes
19 (42%)
13 (27%)
Subtropics
18 (40%)
23 (47%)
Southern Ocean
(q<-50°S)
2 (4%)
3 (6%)
Depth (m)
Winter (Dec-Feb)
Winter (Dec-Feb)
NPP (mg C m-3 d-1)
**Annual NPP (and % of total) in each region**
C-based NPP (mg C m-2 d-1)
Chl-based NPP (mg C m-2 d-1)