Assimilation of Aqua Ocean Chlorophyll Data in a Global Three-Dimensional Model Watson Gregg NASA/Global Modeling and Assimilation Office.

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Assimilation of Aqua Ocean Chlorophyll Data in a Global Three-Dimensional Model Watson Gregg NASA/Global Modeling and Assimilation Office

Motivations for Assimilation 1. Data use maximization 2. Parameter Estimation (model improvement) 3. State and Flux Estimation 4. Prediction

Atmospheric Forcing Data Radiative Model Layer Depths Circulation Model Biogeochemical Model Advection/ Diffusion Winds, SST Winds, ozone, rel. humidity, pressure, precip. H 2 O, cloud %, LWP, droplet radius, aerosols Layer Depths Current Velocities Particles Abundances Spectral Irradiance Temp. Spectral Radiance Primary Production Chlorophyll, Nutrients, Dust (Fe) Sea Ice NASA Ocean Biogeochemical Model (NOBM) Heat

Biogeochemical Model Diatoms Chloro- phytes Cyano- bacteria Cocco- lithophores Si NO 3 NH 4 Herbivores N/C Detritus Fe Silica Detritus Phytoplankton Nutrients Iron Detritus

Spectral Absorption Spectral Scattering m -1 ; m 2 mg -1 Wavelength (nm)

North Pacific North IndianEquatorial Indian North AtlanticNorth Central PacificNorth Central Atlantic Equatorial PacificEquatorial Atlantic South IndianSouth PacificSouth AtlanticAntarctic Day of Year Chlorophyll (mg m -3 ) Statistically positively correlated (P < 0.05) all 12 basins Gregg, W.W., Tracking the SeaWiFS record with a coupled physical/biogeochemical/radiative model of the global oceans. Deep-Sea Research II 49: Gregg, W.W., P. Ginoux, P.S. Schopf, and N.W. Casey, Phytoplankton and Iron: Validation of a global three-dimensional ocean biogeochemical model. Deep-Sea Research II, 50:

Assimilation of Satellite Ocean Chlorophyll Conditional Relaxation Analysis Method Advantages: Very strongly weighted toward data, less susceptible to model errors Fast Disadvantages Very susceptible to data errors 2 M =M,S 2

To keep assimilation model bounded requires: 1)Smoothing of data (25% monthly mean, 75% daily weight) 2) Increase model weighting relative to data Model Weight (fraction)

M

Motivations for Assimilation 1. Data use maximization 2. Parameter Estimation (model improvement) 3. State and Flux Estimation 4. Prediction

Atmospheric Forcing Data Radiative Model Layer Depths Circulation Model Biogeochemical Model Advection/ Diffusion Winds, SST Winds, ozone, rel. humidity, pressure, precip. H 2 O, cloud %, LWP, droplet radius, aerosols Layer Depths Current Velocities Particles Abundances Spectral Irradiance Temp. Spectral Radiance Primary Production Chlorophyll, Nutrients, POC?, PIC? Dust (Fe) Sea Ice NASA Ocean Biogeochemical EOS Assimilation Model (OBEAM) Heat Red = EOS Data product Green = assimilated variable

Feb. 1, 2003

Motivations for Assimilation 1. Data use maximization 2. Parameter Estimation (model improvement) 3. State and Flux Estimation 4. Prediction

Annual RMS Log Error RMS mon = log 10 C assim – log 10 C aqua n √ RMS ann = ∑ ∑ RMS mon 12 X 100

North Pacific North IndianEquatorial Indian North AtlanticNorth Central PacificNorth Central Atlantic Equatorial PacificEquatorial Atlantic South IndianSouth PacificSouth AtlanticAntarctic Chlorophyll (mg m -3 ) Red = model monthly mean Diamonds = SeaWiFS monthly mean

Percent of Total Equatorial Pacific Diatoms Cocco Cyano Chloro

Motivations for Assimilation 1. Data use maximization 2. Parameter Estimation (model improvement) 3. State and Flux Estimation 4. Prediction

Summary and Plans Initial assimilation results promising Need further analysis new methodologies Awaiting new SeaWiFS data Proceed on incorporation of MODIS/GMAO products

Atmospheric Forcing Data Radiative Model Layer Depths Circulation Model Biogeochemical Model Advection/ Diffusion Winds, SST Winds, ozone, rel. humidity, pressure, precip. H 2 O, cloud %, LWP, droplet radius, aerosols Layer Depths Current Velocities Particles Abundances Spectral Irradiance Temp. Spectral Radiance Primary Production Chlorophyll, Nutrients, POC?, PIC? Dust (Fe) Sea Ice NASA Ocean Biogeochemical EOS Assimilation Model (OBEAM) Heat Red = EOS Data product Green = assimilated variable