1. Near-Real Time Analysis of the Modern Carbon Cycle by Model-Data Fusion Scott Denning, David Baker, Chris O’Dell, Denis O’Brien, Tomohiro Oda, Nick Parazoo, Ian Baker, Becky McKeown (CSU Atmospheric Science & CIRA) Andy Jacobson, Pieter Tans (NOAA ESRL), Scott Doney, Ivan Lima (WHOI), Jim Collatz, Randy Kawa (NASA GSFC) Acknowledgements: Support by NOAA, NASA, DoE

2. Component Models Fossil Fuel Emissions (EDGAR plus DMSP, monthly) Ocean Biogeochemistry, Ecosystems, & Air-Sea Gas Exchange (WHOI, daily) Terrestrial photosynthesis & respiration (SiB4, hourly) Biomass burning (GFED, 8-day) Atmospheric Transport (GEOS5-PCTM, 3-hourly) CarbonTracker: CASA+GFED+Ocean+TM5+in-situ

3. Global Fossil Fuel Emissions (2009) Supply-Based Inventory Methods (“follow the fuel”)

4. Regional Emissions Estimates Oda et al (2010) emissions scaled to DMSP lights

5. Ocean Carbon Model Physical ocean model assimilates sea-surface topography and SST (based on SODA, Carton et al) Physics assimilation provides w and delivers nutrients to surface Ocean ecosystem model and biogeochemistry assimilates SeaWiFS and MODIS to predict sea-surface pCO 2 Surface winds from GEOS to compute gas exchange and CO 2 flux

7. Terrestrial Ecosystems Extension of SiB w/Prognostic phenology MODIS data assimilation for fPAR/LAI Crop physiology, phenology, and mapping Time-scale separation of GPP & Resp Development and testing in field experiments with mesoscale model

9. Atmospheric Transport Models Parameterized Chemical Transport Model (PCTM) developed at NASA GSFC –Identical algorithms & code from GEOS-5 –Driven by stored winds, turbulence, cloud mass fluxes from model reanalysis –0.5° x 0.67° lat x lon, 55 levels TM5 –3° x 2° lat x lon, 40 levels –Driven by ECMWF winds and clouds

10. CarbonTracker : Optimization of in-situ CO 2 data

11. SiB+WHOI+FF+GFED+T3sinks->PCTM X CO2 Sampled Daily on GOSAT Orbit

12. ALL FILTERED 12 N = 865806 GOSAT Column CO2 from Space ACOS B2.9 Coverage N ~ 250000

13. GOSAT−PCTM GOSAT−CT CT−PCTM Dark Blue: Oceans (glint, high gain) Cyan: land, medium gain (desert areas only, bright) Green: land, high gain (all other land areas, dark)

14. Bias [ppm]: Dark land -1.03 -0.80 -0.23 Bright land 0.31 0.20 0.11 Ocean 0.07 0.31 -0.24 Standard deviation [ppm]: Dark land 1.89 1.86 0.86 Bright land 1.70 1.61 0.49 Ocean 1.36 1.34 0.35 Comparison of GOSAT to Models ACOS - PCTM ACOS - CT PCTM - CT

15. Conclusions Two different comprehensive analysis systems agree with > 250k GOSAT X CO2 retrievals to better than 2 ppm (bias < 1 ppm; s < 2 ppm) Ocean(!) and medium-gain land retrievals seem to agree better with models than high-gain land Scope for “removing retrieval biases” using fits to albedo, airmass, etc may reduce single-shot random errors to better than 1 ppm System can be used to estimate source/sink in near real time (a few weeks after the fact)