Assimilation of Aqua Ocean Chlorophyll Data

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Assimilation of Aqua Ocean Chlorophyll Data
in a Global Three-Dimensional Model Watson
Gregg NASA/Global Modeling and Assimilation Office
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Motivations for Assimilation
1. Data use maximization 2. Parameter
Estimation (model improvement) 3. State and Flux
Estimation 4. Prediction
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NASA Ocean Biogeochemical Model (NOBM)
Winds, ozone, rel. humidity, pressure, precip.
H2O, cloud , LWP, droplet radius, aerosols
Atmospheric Forcing Data
Winds, SST
Dust (Fe)
Sea Ice
Radiative Model
Circulation Model
Heat
Layer Depths
Abundances
Biogeochemical Model
Temp.
Spectral Irradiance
Layer Depths
Current Velocities
Particles
Advection/ Diffusion
Primary Production
Chlorophyll, Nutrients,
Spectral Radiance
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Biogeochemical Model
Phytoplankton
Nutrients
Diatoms
Si
Silica Detritus
Chloro- phytes
NO3
Herbivores
Cyano- bacteria
NH4
Cocco- lithophores
Fe
N/C Detritus
Iron Detritus
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Spectral Absorption
m-1 m2 mg-1
Spectral Scattering
Wavelength (nm)
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North Pacific
North Atlantic
North Central Pacific
North Central Atlantic
North Indian
Equatorial Indian
Equatorial Pacific
Equatorial Atlantic
Chlorophyll (mg m-3)
South Indian
South Pacific
South Atlantic
Antarctic
Day of Year
Statistically positively correlated (P lt 0.05)
all 12 basins
Gregg, W.W., 2002. Tracking the SeaWiFS record
with a coupled physical/biogeochemical/radiative
model of the global oceans. Deep-Sea Research
II 49 81-105. Gregg, W.W., P. Ginoux, P.S.
Schopf, and N.W. Casey, 2003. Phytoplankton and
Iron Validation of a global three-dimensional
ocean biogeochemical model. Deep-Sea Research
II, 50 3143-3169.
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Assimilation of Satellite Ocean Chlorophyll
Conditional Relaxation Analysis Method
M
M,S
Advantages Very strongly weighted toward data,
less susceptible to model errors Fast Disadvan
tages Very susceptible to data errors
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• To keep assimilation model bounded requires
• Smoothing of data (25 monthly mean, 75 daily
  weight)
• 2) Increase model weighting relative to data

0.75
0.25
0.5
0.85
Model Weight (fraction)
0.5
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M
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Motivations for Assimilation
1. Data use maximization 2. Parameter
Estimation (model improvement) 3. State and Flux
Estimation 4. Prediction
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NASA Ocean Biogeochemical EOS Assimilation Model
(OBEAM)
Winds, ozone, rel. humidity, pressure, precip.
H2O, cloud , LWP, droplet radius, aerosols
Atmospheric Forcing Data
Winds, SST
Dust (Fe)
Sea Ice
Radiative Model
Circulation Model
Heat
Layer Depths
Abundances
Biogeochemical Model
Temp.
Spectral Irradiance
Red EOS Data product Green
assimilated variable
Layer Depths
Current Velocities
Particles
Advection/ Diffusion
Primary Production
Chlorophyll, Nutrients, POC?, PIC?
Spectral Radiance
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Feb. 1, 2003
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Motivations for Assimilation
1. Data use maximization 2. Parameter
Estimation (model improvement) 3. State and Flux
Estimation 4. Prediction
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Annual RMS Log Error
v
?
log10Cassim log10Caqua
X 100
RMSmon
n
?
RMSmon
RMSann
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North Pacific
North Atlantic
North Central Pacific
North Central Atlantic
North Indian
Equatorial Indian
Equatorial Pacific
Equatorial Atlantic
Chlorophyll (mg m-3)
South Indian
South Pacific
South Atlantic
Antarctic
Red model monthly mean Diamonds SeaWiFS
monthly mean
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Equatorial Pacific
Diatoms
Chloro
Percent of Total
Cocco
Cyano
1997
2000
1998
1999
2001
2002
2003
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Motivations for Assimilation
1. Data use maximization 2. Parameter
Estimation (model improvement) 3. State and Flux
Estimation 4. Prediction
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Summary and Plans
Initial assimilation results promising Need
further analysis new methodologies Awaiting new
SeaWiFS data Proceed on incorporation of
MODIS/GMAO products
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NASA Ocean Biogeochemical EOS Assimilation Model
(OBEAM)
Winds, ozone, rel. humidity, pressure, precip.
H2O, cloud , LWP, droplet radius, aerosols
Atmospheric Forcing Data
Winds, SST
Dust (Fe)
Sea Ice
Radiative Model
Circulation Model
Heat
Layer Depths
Abundances
Biogeochemical Model
Temp.
Spectral Irradiance
Red EOS Data product Green
assimilated variable
Layer Depths
Current Velocities
Particles
Advection/ Diffusion
Primary Production
Chlorophyll, Nutrients, POC?, PIC?
Spectral Radiance