Modeling and Data Assimilation in Support of ACE Watson Gregg

1. Modeling and Data Assimilation in Support of ACE Watson Gregg NASA/GSFC/Global Modeling and Assimilation Office Supporting data and publications: Google gmao, click Research, then Ocean Biology Modeling (http://gmao.gsfc.nasa.gov/research/oceanbiology)

2. NASA Ocean Biogeochemical Model (NOBM) Chlorophyll,Phytoplankton Groups Primary Production Nutrients DOC, DIC, pCO2, FCO2 Spectral Irradiance/Radiance Outputs: Winds, ozone, relative humidity,pressure, precip. water, clouds (cover, τc), aerosols (τa, ωa, asym) Dust (Fe) Sea Ice Winds SST atm pCO2 Radiative Model (OASIM) Ed(λ) Es(λ) Ed(λ) Es(λ) IOP Layer Depths Biogeochemical Processes Model Circulation Model (Poseidon) Temperature, Layer Depths Advection-diffusion Global model grid: domain: 84S to 72N1.25 lon., 2/3 lat.14 layers

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

4. Biogeochemical Processes Model Carbon Component pCO2 (air) Winds, Surface pressure pCO2 (water) Phyto- plankton Herbivores Dissolved Inorganic Carbon Dissolved Organic Carbon N/C Detritus

5. Validation Variable Global Difference % Correlation over Basins Nitrate 18.9% 0.905 P<0.05 Ammonia Not tested Not tested Silica 5.4% 0.952 P<0.05 Dissolved Iron 45% 0.933 P<0.05 Diatoms 15.5% 0.850 P<0.05 Chlorophytes -16.2% 0.020 NS Cyanobacteria 7.9% 0.970 P<0.05 Coccolithophores -2.6% 0.700 P<0.05 Total Chlorophyll vs In situ -17.1% 0.787 P<0.05 vs SeaWiFS -8.0% 0.618 P<0.05 vs Aqua 1.1% 0.469 NS Herbivores Not tested Not tested Detritus Not tested Not tested Diss. Inorganic Carbon 0.1% 0.972 P<0.05 pCO2 0.0% 0.765 P<0.05 Air-sea carbon flux 3.1% 0.741 P<0.05

6. OASIM CO2 Water vapor Ozone Oxygen Molecules, aerosols LwN Ed  Ed, Es Es Ed Es  Ed, Es air sea Surface Clear Sky Spectral Irradiance (PAR wavelengths): RMS=6.6% Integrated PAR: RMS=5.1% Total Surface Irradiance (direct+diffuse; spectrally integrated; clear/cloudy): bias=1.6 W m-2 (0.8%) RMS=20.1 W m-2 (11%) r=0.89 (P<0.05) Eu (1 - ) Es (1 - ) Ed

7. CDOM detritus OASIM In-water Radiative Model OASIM Upwelling Irradiance (Forward Model) 450 350 375 400 425 475 500 525 550 575 600 625 650 675 700 a(λ), bb(λ) ap(λ), bbp(λ) aw(λ), bbw(λ) Chlorophyll components: diatoms chlorophytes cyanobacteria coccolithophores water ad(λ), bbd(λ) aCDOM(λ)

8. Modeling Water-Leaving Radiances (with assimilated chlorophyll) Tropical Rivers (CDOM) mW cm-2 um-1 sr-1 Cocco- lithophores

9. Data Assimilation In ocean biology, Two Classes: Variational (e.g., adjoint, 4DVar) Sequential (e.g., Kalman Filter) We use Sequential Methodologies, Conditional Relaxation Analysis Method Ensemble Kalman Filter Routinely assimilating SeaWiFS and Aqua Chlorophyll Data

10. mg m-3 Bias Uncertainty N SeaWiFS -1.3% 32.7% 2086 Free-run Model -1.4% 61.8% 4465 Assimilation Model 0.1% 33.4% 4465 vs. In Situ Data

11. Potential Support for ACE Pre-Launch Observing Simulation System Experiments (OSSE’s) GMAO signature Previously done for SeaWiFS e.g., orbit selection, sampling strategy (targeted sampling), band selection, potential algorithm effectiveness, various aspects of instrument design Development of a globally representative, dynamic simulated data set Post-Launch Data Assimilation: Reasonableness of derived products in the context of an interdependent set of variables Removal of Sampling Biases caused by clouds, aerosols, low light, others