1. Interannual variability in CO2 fluxes derived from 64-region inversion of atmospheric CO2 data Prabir K. Patra*, Shamil Maksyutov*, Misa Ishizawa*, Takakiyo Nakazawa #, Taro Takahashi $, and Gen Inoue & *Frontier Research System for Global Change, Yokohama # Graduate School of Science, Tohoku University, Sendai $ Lamont-Doherty Earth Observatory, Columbia University, New York & National Institute for Environmental Studies, TSukuba Acknowledgment: TranCom-3 Developers for the TDI CODE TransCom-3 Meeting, Tsukuba, June 2004

2. Plan of the Talk Basic tools –Transport model (simulation of fluxes) –Inverse model (least-squares fitting to data) Results and Discussion –Testing of the results (networks, resolutions) –Comparisons with previous results –Climate controls on flux anomaly Conclusions

3. The transport equations is: where, q k is the tracer concentration with index k, S is the source function, V (  ) denote the horizontal (vertical) components of winds, F k represents the PBL flux or convective transport. We have used: the NCEP/NCAR reanalysis data for pressure level fields monthly PBL heights are cyclostationary (from NASA - DAO) global distribution of yearly or monthly sources (cyclostat.) NIES/FRSGC Tracer Transport Model: Basic Principles

4. Background CO2 fluxes: Three Types The fossil fuel emission do not have seasonality. Oceanic sources and sinks are weaker compared to the land and less heterogeneous.

5. Transport Model Simulations: Combined (FOS, NEP, OCN) signals of CO2 at various layers of the atmosphere (left panels) and the estimated RSDs (right panels). Patra et al., J. Geophys. Res., 2003

6. The problem of surface source (S) inversion is mathematically the inversion of the forward problem:, where the G a linear operator representing atmospheric transport (no chemistry). The results are CO2 fluxes with uncertainty: Inverse Model: Basic Equations Estimated Flux Estimated Flux Cov. A Priori Flux A Priori Flux Cov. Atmospheric CO2 Data

7. Development of 64-Regions Inverse Model Patra et al., Global Biogeochem. Cycles, submitted

8. Testing Inversion results: fitting to the data

9. Testing Inversion results: χ 2 tests

10. Averages of CO2 Fluxes for 1990s Estimates This Work IPCC 2001 Gurney et al., 2004 Bousquet et al, 2000 Rodenbeck et al., 2003 Land- 1.15 {0.14*} -1.40 (0.70) -1.54 (0.73) -1.40 (0.80) -1.20 (0.4) Ocean-1.88 {0.18*} -1.70 (0.50) -1.35 (0.76) -1.80 (0.60) -1.70 (0.40) Global-3.03-3.10-2.89-3.20-2.90 Patra et al., 2004a, Global Biogeochem. Cycles, submitted * Spread based on sensitivity tests

11. Land and Ocean Flux - sensitivity Patra et al., 2004a, Global Biogeochem. Cycles, submitted

12. Comparison of Ocean Flux Anomalies …

13. Anomaly in Land and Ocean CO 2 Fluxes – ENSO effect

14. Region-aggregated Fluxes

15. Comparison of oceanic flux anomaly: observation and model Patra et al., 2004a, Global Biogeochem. Cycles, Submitted Flux anom. (Pg-C per Year) Equatorial Pacific North Pacific

16. Regional Land Fluxes Patra et al., 2004b, Global Biogeochem. Cycles, Submitted

17. Various types of fires Indonesia ablaze, 1998. These widespread fires released massive amounts of carbon into the atmosphere

18. Comparison of land flux anomalies: Observations /estimations, and Biome-BGC ecosystem model fluxes Patra et al., 2004b

19. Capturing the time evolution of fires

20. Correlation Analysis CO2 Flux Anomaly With MEI ENSO Index CO2 Flux Anomaly With IOD Index

21. EOF Distribution of Flux Anomaly

22. Flux Anom./PC vs. Met./Clim. Index Region/PC ENSOIODRain* Temp. Temp. N. A. -0.41-0.34 -0.39 0.30 Trop. S. A. 0.49 0.51 -0.51 0.66 Temp. Asia -0.190.17 0.11-0.15 Trop. Africa 0.73 0.44 0.37 0.47 South Africa 0.66 0.54 0.00 0.07 Trop. Asia 0.53 0.46 -0.68 0.55 Australia 0.36 0.35 -0.19 0.18 PC - 1 0.91 0.76 PC - 2 -0.46 -0.22 PC - 3 0.25 -0.13 * CO2 flux anomaly lags 3-month the rainfall anomaly. In Tropics: CO2 flux & Temp: +ve CO2 flux & Rain : -ve

23. Studying CO2 Growth Rate at MLO (! A Classic Problem !)

24. In search of simple empirical relations Increas e Rates 197219872003 El Nino4.72.30.8 Boreal Fire 0.00.51.1- 3.3 CO2 Gr. Rate 1.81.41.8 Green diamond: van der Werf et al. Vertical bar: Kasischke and Bruhwiler

25. Conclusions 1.We have derived CO 2 fluxes from 42 land and 22 ocean regions. 2.The inverse method fairly successfully captures the flux variability due to climate variation. 3.The highest influence of weather/climate is observed over the tropical lands. 4.Major modes of CO 2 flux variability are connected to ENSO/IOD, Biomass burning (indirect climate forcing?). 5.Interannual variability in MLO CO2 growth rates are mostly of natural origin (as in Keeling et al., 1995)