1. Sensitivity of observational dataset to CO 2 flux inversion Takashi Maki, Kazumi Kamide Atmospheric Environment Division Japan Meteorological Agency

2. Backgrounds Current carbon cycle analysis, GLOBALVIEW (NOAA/CMDL) is the standard dataset. In GLOBALVIEW, CO2 data are smoothed, interpolated and extrapolated. In higher resolution analysis in time and space, we tend to need well selected raw data. We tried to compare estimated flux by smoothed data and raw data.

3. Methods Using smoothed dataset (from WDCGG analysis or GLOBALVIEW) and raw data (from WDCGG) with the same scenarios. The scenarios consist of annual mean inversion and time dependent inversion. We did not analyze absolute value of estimate flux and flux uncertainties, but analyze sensitivity of observational dataset. WDCGG: World Data Centre for Greenhouse Gases (JMA) http://gaw.kishou.go.jp

4. Our Model (JMA CDTM) Off-line transport model based upon JMA operational Global spectral model (GSM9603). Resolution2.5 x 2.5 deg (Horizontal) 32 layers (Surface to 10hPa) AdvectionSemi-Lagrangian (Horizontal) Box-scheme (Vertical) DiffusionCumulus convection (Kuo) Turbulent diffusion ( Mellor-Yamada ) Shallow convection (Tiedtke)

5. Annual mean inversion case Periods1997 – 2001(1996 is spin-up) WindsJMA operational analysis(1997-2001) DataFrom WDCGG (selected) monthly data Smoothed or raw data Uncertainty Residual from smoothed data SiteSelected by inversion (see later page) Prior fluxAs in TransCom 3 Level 1(Background) Previous year (each region) InversionAs in TransCom 3 Level 1

6. Terrestrial and Oceanic regions From TransCom 3 HP(http://transcom.colostate.edu/TransCom_3/transcom_3.html)

7. Site Selection We adopt sites where the misfit of inversed data and observational data is smaller than 2ppm in every year. This is the first selection. Also we select sites where average misfit is smaller than 0.75ppm from 1997 to 2001. We continue these selections until we reject no site. From these selections, we can select sites where there are small effect of anthropogenic and local sources. Finally, we choose the same 71 sites (from 91 sites) for annual mean scenarios.

8. Selected sites (Organization) OrganizationssitesOrganizationssites NOAA/CMDL41CAMM1 MRI13INM1 CSIRO6ISAC1 JMA3NIPR1 NIES2SAWS1 UBA1 Thank you very much for submitting data to WDCGG!

9. Selected sites We can make use of higher altitude (Tokyo – Sydney) data! Our model can represent vertical profile of CO 2 concentrations.

10. Selected sites Region NConstrain Region NConstrain Bor. N. America 11.25 N. Pacific 15*28.14 Temp. N. America 33.60 W. Pacific 11*26.20 Trop. America 00.00 E. Pacific 35.75 South America 12.75 S. Pacific 49.34 Tropical Africa 13.82 Northern Ocean 47.06 S. Africa 00.00 N. Atlantic 46.52 Boreal Eurasia 00.00 Tropical Atlantic 25.76 Temp. Eurasia 32.75 S. Atlantic 14.31 S.E. Asia 1*3.82 Southern Ocean 8**45.67 Australasia 4*2.75 Trop. Indian Ocean 12.13 Europe 33.82 S. Indian Ocean 16.85 Constrain is defined as sum of (1/uncertainty) in the region. * : contain aircraft data, **:contain Antarctic sites

11. Estimated flux from last year (Raw) In 1998, Tropical land regions are remarkable source of CO2. We analyze tropical land flux in our presentation.

12. Difference between smoothed and raw data (annual mean) Largest standard deviation from 1997 – 2001. StationRegionUnit : ppm 1Tae-ahn PeninsulaTemp. Eurasia0.272 2Cape FergusonAustralasia0.228 3Pacific Ocean(5N)W. Pacific0.213 4Shemya IslandN. Pacific0.190 5Pacific Ocean(20N)N. Pacific0.180 6Estevan PointTemp. N. America0.173 7SchauinslandEurope0.171 8Pacific Ocean(15N)N. Pacific0.171 The difference appears relatively constrained regions!

13. Estimated flux shows some difference between smoothed and raw data (see later page). Estimated flux uncertainties are completely same in all regions (Data uncertainties are same)! Difference of estimated flux between smoothed and raw data Flux uncertainty Model Transport Prior flux uncertainty Data uncertainty

14. Estimated flux variability Smoothed analysis tend to show larger inter-annual variability!

15. Standard deviation of estimated flux from 1997 to 2001. Smoothed analysis tend to show larger inter-annual variability! 1997-2001, Unit: GtC/ySmoothedRaw L03: Tropical America0.3550.301 L05: Tropical Africa0.2470.181 L06: South Africa0.2400.173 L08: Temperate Eurasia0.0790.064 L09: Southeast Asia0.3200.312 Average of all regions0.1330.122

16. Estimated flux difference in each region Averaging from 1997 – 2001. Unit is GtC/y. Red regions are larger than (average + 1 sigma) of all regions. Smoothed-RawSt. Dev.Smoothed-RawSt. Dev. L010.0050.010O010.0040.019 L02-0.0550.018O020.0010.006 L030.0270.067O03-0.0030.015 L040.1050.027O04-0.0530.021 L05-0.0490.074O05-0.0080.003 L06-0.0860.077O06-0.0060.002 L07-0.0810.028O070.0110.008 L080.1040.029O080.0180.010 L09-0.0340.009O090.0020.012 L10-0.0030.014O10-0.0130.015 L110.0910.031O11-0.0150.008 Average Correlation coefficient0.978

17. Summary of annual mean case We can select site using inverse method. Sensitivity of dataset is not so large. Average correlation coefficients is about 0.98. Smoothed data analysis shows larger inter- annual variability than raw data analysis. Difference between estimated flux by smoothed and raw data appears in less constrained regions and local source dominant regions. Difference of each site does not always affect the estimated flux in the region.

18. Time dependent case Periods1990 – 2000 (1988, 1989 is spin-up) WindsJMA operational analysis (1997) DataFrom GLOBALVIEW 2002 (Smoothed) → As in TransCom 3 Level 3 From WDCGG monthly data (Raw) Uncertainty As in TransCom 3 Level 3 SiteAs in TransCom 3 Level 3 (76 Sites) FluxAs in TransCom 3 Level 3 InversionAs in TransCom 3 Level 3

19. Used sites (as in T3 L3) Region NConstrain Region NConstrain Bor. N. America 21.87 N. Pacific 14*20.41 Temp. N. America 75.75 W. Pacific 9*16.93 Trop. America 00.00 E. Pacific 23.46 South America 00.00 S. Pacific 11.89 Tropical Africa 00.00 Northern Ocean 76.33 S. Africa 00.00 N. Atlantic 43.99 Boreal Eurasia 00.00 Tropical Atlantic 23.27 Temp. Eurasia 52.97 S. Atlantic 00.00 S.E. Asia 1*2.04 Southern Ocean 8**22.86 Australasia 23.74 Trop. Indian Ocean 11.27 Europe 64.53 S. Indian Ocean 513.28 Constrain is defined as sum of (1/uncertainty) in the region. * : contain aircraft data, **: contain Antarctic sites

20. Difference between smoothed and raw data (monthly mean) Largest standard deviation sites from 1990 – 2001. StationRegionUnit: ppm 1GosanTemp. Eurasia2.68 2SchauinslandEurope2.60 3GriftonTemp. N. America2.24 4Estevan PointTemp. N. America1.06 5 Plateau Rosa Europe0.90 6Baltic SeaEurope0.89 7Ulaan UulTemp. Eurasia0.80 8Cold BayBor. N. America0.79 Large difference appears almost in land regions.

21. Estimated flux variability from climate Climate : Average from 1990 to 2001 monthly flux. Raw analysis data tend to show larger seasonal variability. GtC/Month

22. Standard deviation of estimated flux from each climate (1990-2000) Raw data analysis tend to show higher seasonal variability! 1990-2000, Unit: GtCSmoothedRaw L03: Tropical America0.0560.058 L05: Tropical Africa0.0850.097 L06: South Africa0.0580.061 L08: Temperate Asia0.1030.122 L09: Southeast Asia0.0780.094 Total flux0.2040.250

23. Standard deviation between fluxes Less constrained or local source dominant regions tend to differ! Red regions are larger than (average + 1 sigma) of all regions. Smoothed-RawFlux unc.Smoothed-RawFlux unc. L010.0220.333O010.0240.309 L020.0450.467O020.0170.204 L030.0250.711O030.0110.206 L040.0130.627O040.0290.401 L050.0480.675O050.0070.126 L060.0240.724O060.0060.202 L070.0540.485O070.0050.202 L080.0770.516O080.0050.256 L090.0510.384O090.0120.225 L100.0120.172O100.0230.309 L110.0760.397O110.0120.214 Average Correlation coefficient0.950

24. Summary of time dependent case Sensitivity of dataset appeared. Average correlation coefficients is about 0.95. Smoothed data analysis shows smaller seasonal variability than raw data analysis in time dependent inversion. Difference between estimated flux by smoothed and raw data appears in less constrained or local source dominant regions.

25. Summary of our presentation Sensitivity of dataset (smoothed or raw) is not so large in annual mean inversion. The sensitivity become larger in time-dependent inversion. → If we tried to estimate higher resolution in time and space, the importance of data quality become clear. Difference between estimated flux by smoothed and raw data appears not only local source dominant regions but also less constrained regions. → We have to put new observational sites in less constrained region and use good model in order to avoid making less constrained region as source dump. We have an option to select site using inversion.

26. Our future plans Using inter-annual analyzed wind in the time dependent inversion. Using higher resolution version (If possible, On- line version) of CDTM. Using more regions than now. Joint experiment with FRSGC using Earth Simulator (From this year)! We hope this experiment could contribute TransCom 3 (4?)! We need more computational resources.

27. References (Data) GLOBALVIEW-CO2, Cooperative Atmospheric Data Integration Project - Carbon Dioxide, CD-ROM, NOAA CMDL, Boulder, Colorado, 2002. WMO WDCGG Data Summary. GAW Data Vol. IV - Greenhouse Gases and Other Atmospheric Gases. WDCGG No. 27. World Meteorological Organization, Global Atmosphere Watch, World Centre for Greenhouse Gases (WDCGG), Japan Meteorological Agency, Tokyo 2003. Conway, T. J., P. P. Tans, L. S. Waterman, K. W. Thoning, D. R. Kitzis, K. A. Masarie, and N. Zhang, Evidence for interannual variability of the carbon cycle from the National Oceanic and Atmospheric Administration/Climate Monitoring and Diagnostics Laboratory global air sampling network, J. Geophys. Res., 99, 22831-22855, 1994.

28. References (Data) Matsueda, H., H. Inoue, Y. Sawa, Y. Tsutsumi, and M. Ishii, Carbon monoxide in the upper troposphere over the western Pacific between 1993 and 1996, J. Geophys. Res., 103, 19093-19110, 1998. Rayner, P. J., I. G. Enting, R. J. Francey and R. Langenfelds, Reconstructing the recent carbon cycle from atmospheric CO2,d13C and O2/N2 observations, Tellus, 51B, 213-232, 1999. Watanabe, F., O. Uchino, Y. Joo, M. Aono, K. Higashijima, Y. Hirano, K. Tsuboi, and K. Suda, 2000: Interannual variation of growth rate of atmospheric carbon dioxide concentration observed at the JMA's three monitoring stations: Large increase in concentration of atmospheric carbon dioxide in 1998. J. Meteor. Soc. Japan, 78, 673-682.

29. References (Inversion) Enting, I. 2002. Inverse Problems in Atmospheric Constituent Transport. Cambridge University Press, Cambridge, U. K. Gurney, K., Law, R., Rayner, P., and A.S. Denning, "TransCom 3 Experimental Protocol," Department of Atmospheric Science, Colorado State University, USA, Paper No. 707, 2000. Tarantola, A. (1987), The least-squares (12-norm) criterion, in Inverse Problem Theory: Methods for Data Fitting and Parameter Estimation, chap. 4, pp. 187– 287, Elsevier Sci., New York. Thank you very much for providing level 1 – 3 inversion codes!!

30. Appendix: WDCGG Data policy WMO WDCGG Data The WDCGG acknowledges the support of the organizations and individual researchers that provide their measurement data for greenhouse and other related gases. Such data contributors should receive fair credit for their work. When you use and publish data and information in the WDCGG's publications or on this web site, please make reference properly to the contributors and data source and inform both WDCGG and the data contributors. If your work substantially depends on the data, it is recommended to contact the data contributors at an early stage to discuss co-authorship and other necessary arrangements. The following is an example of the citation when you need to cite data from the WDCGG website as a reference: Tsutsumi, Y, M. Yoshida, S. Iwano, O. Yamamoto, M. Kamada, H. Morishita, Atmospheric CO2 monthly mean oncentration, Ryori, WMO WDCGG, JMA, Tokyo, 21 May 2003.