Remote sensing of assimilation number for marine phytoplankton
Stéphane Saux Picart, Shubha Sathyendranath, Mark Dowell, Trevor Platt
44th International Liege Colloquium on Ocean Dynamics - May 2012
Aim of the study
Challenge: Estimate phytoplankton primary production from space
Aim of the study
Challenge: Estimate phytoplankton primary production from space
Primary production can be computed
using a photosynthesis-light model:
B)
P B (z) = P B (I (z); αB , Pm
Superscript B indicates
normalisation to chlorophyll
biomass B.
P B Normalised production;
z: depth; I : irradiance;
αB : initial slope;
B : assimilation number
Pm
Aim of the study
Challenge: Estimate phytoplankton primary production from space
Primary production can be computed
using a photosynthesis-light model:
B)
P B (z) = P B (I (z); αB , Pm
Superscript B indicates
normalisation to chlorophyll
biomass B.
P B Normalised production;
z: depth; I : irradiance;
αB : initial slope;
B : assimilation number
Pm
Specific objective of the study:
Estimate the assimilation number globally from remote sensing data
General approach
I
B
If no photo-inhibition, maximum primary production Pmax = BPm
I
Maximum primary production is also given by Pmax =
where Cp is the phytoplankton carbon concentration
I
Maximum growth rate: µmax =
dCp
|
dt max
1 dCp
|
Cp dt max
Cp
B =µ
Assimilation number: Pm
max B = χµmax
where χ is the carbon-to-chlorophyll ratio
General approach
I
B
If no photo-inhibition, maximum primary production Pmax = BPm
I
Maximum primary production is also given by Pmax =
where Cp is the phytoplankton carbon concentration
I
Maximum growth rate: µmax =
dCp
|
dt max
1 dCp
|
Cp dt max
Cp
B =µ
Assimilation number: Pm
max B = χµmax
where χ is the carbon-to-chlorophyll ratio
Eppley (1972) defines the maximum
growth rate as a function of
temperature:
µmax = 0.851(1.066T )
ln 2
24
Two models
Two ways of estimating carbon-to-chlorophyll ratio:
(1.81+0.63∗log10 (Chl))
1. Sathyendranath et al. 2004: χs = 10
Chl
(gives an upper limit of χ)
i−1
h
[N]
2. Cloern et al. 1995: χc = 0.003 + 0.0154 exp(0.05T ) exp(−0.059I ) K +[N]
N
where N is the nutrient concentration
Two models
Two ways of estimating carbon-to-chlorophyll ratio:
(1.81+0.63∗log10 (Chl))
1. Sathyendranath et al. 2004: χs = 10
Chl
(gives an upper limit of χ)
i−1
h
[N]
2. Cloern et al. 1995: χc = 0.003 + 0.0154 exp(0.05T ) exp(−0.059I ) K +[N]
N
where N is the nutrient concentration
Combining these two models and the maximum growth rate formulation from Eppley (1979), we
can approach computation of assimilation number as follows:
I
B = f (I , T , Chl) = χs (n T + exp(n T ))(n I + exp(n I ))µ
Model LTB: Pm
max
1
2
3
4
I
B = f (I , T , N) = χc µ
Model LTN: Pm
max
n1 to n4 are parameters estimated by least squares optimisation between measured and modeled
assimilation number
Sensitivity of the models
I
Monotonically decreasing when Chl and N are increasing.
I
LTN monotonically and almost linearly increases as T increases. LTB reaches a maximum at
26 ◦ C and then deacreases.
I
LTB increase linearly with I , LTN has a stronger dependency on light.
Results: in-situ
B , Chl, T , mixed layer depth,
Model applied to a large database (> 700 measurements): Pm
surface PAR, nitrates
=⇒ Gaps filled with climatological data (for surface PAR and nitrates)
=⇒ North West Atlantic, Gulf of Mexico and Arabian Sea, Middle of Atlantic
Results: in-situ
B , Chl, T , mixed layer depth,
Model applied to a large database (> 700 measurements): Pm
surface PAR, nitrates
=⇒ Gaps filled with climatological data (for surface PAR and nitrates)
=⇒ North West Atlantic, Gulf of Mexico and Arabian Sea, Middle of Atlantic
LTB
20
North West Atlantic
Tropics
Off European shelf
PmB estimated [mgC (mgChl)−1 h−1 ]
PmB estimated [mgC (mgChl)−1 h−1 ]
20
LTN
15
10
15
10
5
00
North West Atlantic
Tropics
Off european shelf
5
10
15
PmB measured [mgC (mgChl)−1 h−1 ]
20
5
00
5
10
15
PmB measured [mgC (mgChl)−1 h−1 ]
20
Temperature dependency
PmB [mgC (mgChl)−1 h−1 ]
20
Model LTN: PmB = f(I,T,N)
Model LTB: PmB = f(I,T,Chl)
in-situ data
15
10
5
0
0
5
10
15
20
Temperature [ ◦ C]
25
30
Application to remote sensing data
Data used (2004):
I
SeaWiFS monthly climatology of photosynthetically active radiation.
I
SeaWiFS monthly climatology of surface chlorophyll-a concentration.
I
MODIS monthly climatology of sea surface temperature.
I
Nitrate World Ocean Atlas (Garcia et al., 2006).
I
Mixed layer depth (de Boyer Montégut et al., 2004).
Data down-scaled to 90 km in order to save computational time.
Two areas are extracted: North West Atlantic and Arabian Sea.
Application to remote sensing data
Application to remote sensing data
LTB
LTN
Conclusion
I
B.
Two different empirical methods to estimate Pm
I
On average and glogally LTB model gives higher estimated of assimilation number in
oligotrophic waters.
I
LTN model gives higher assimilation number in nutrient-rich coastal areas.
Future work:
I
Collect more data to validate the models (in other regions).
I
Develop a methodology to estimate the initial slope of the P-I curve.
I
Apply to estimate primary production from remote sensing data solely.
Aknowledgments
CoastColour - ESA project
UK NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS)
Thank you
Contact
Stéphane SAUX PICART: [email protected]