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Assimilation of Aqua Ocean Chlorophyll Data in a Global Three-Dimensional Model

This study focuses on the assimilation of Aqua ocean chlorophyll data into a global three-dimensional model to improve parameter estimation, state estimation, and prediction of biogeochemical processes in the ocean. The NASA Ocean Biogeochemical EOS Assimilation Model (OBEAM) is used to assimilate various atmospheric and oceanic data to enhance the accuracy of the model predictions. The results show promising improvements, and further analysis and incorporation of additional satellite data are planned.

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Assimilation of Aqua Ocean Chlorophyll Data in a Global Three-Dimensional Model

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

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

  3. 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

  4. Biogeochemical Model Nutrients Phytoplankton Si Diatoms Silica Detritus NO3 Chloro- phytes Herbivores NH4 Cyano- bacteria Fe Cocco- lithophores N/C Detritus Iron Detritus

  5. Spectral Absorption m-1; m2 mg-1 Spectral Scattering Wavelength (nm)

  6. 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 < 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.

  7. 2 2 Assimilation of Satellite Ocean Chlorophyll Conditional Relaxation Analysis Method M = M,S Advantages: Very strongly weighted toward data, less susceptible to model errors Fast Disadvantages Very susceptible to data errors

  8. To keep assimilation model bounded requires: • Smoothing of data (25% monthly mean, 75% daily weight) • 2) Increase model weighting relative to data 0.75 0.85 0.5 0.25 Model Weight (fraction) 0.5

  9. M

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

  11. 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

  12. Feb. 1, 2003

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

  14. Annual RMS Log Error √ ∑ log10Cassim – log10Caqua X 100 RMSmon = n ∑ RMSmon RMSann = 12

  15. 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

  16. Equatorial Pacific Diatoms Chloro Percent of Total Cocco Cyano 1997 2001 1998 1999 2000 2002 2003

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

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

  19. 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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