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

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.

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)

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)

Annual mean inversion case Periods1997 – 2001(1996 is spin-up) WindsJMA operational analysis( ) 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

Terrestrial and Oceanic regions From TransCom 3 HP(

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 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.

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

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

Selected sites Region NConstrain Region NConstrain Bor. N. America N. Pacific 15*28.14 Temp. N. America W. Pacific 11*26.20 Trop. America E. Pacific South America S. Pacific Tropical Africa Northern Ocean S. Africa N. Atlantic Boreal Eurasia Tropical Atlantic Temp. Eurasia S. Atlantic S.E. Asia 1*3.82 Southern Ocean 8**45.67 Australasia 4*2.75 Trop. Indian Ocean Europe S. Indian Ocean Constrain is defined as sum of (1/uncertainty) in the region. * : contain aircraft data, **:contain Antarctic sites

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

Difference between smoothed and raw data (annual mean) Largest standard deviation from 1997 – StationRegionUnit : ppm 1Tae-ahn PeninsulaTemp. Eurasia Cape FergusonAustralasia Pacific Ocean(5N)W. Pacific Shemya IslandN. Pacific Pacific Ocean(20N)N. Pacific Estevan PointTemp. N. America SchauinslandEurope Pacific Ocean(15N)N. Pacific0.171 The difference appears relatively constrained regions!

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

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

Standard deviation of estimated flux from 1997 to Smoothed analysis tend to show larger inter-annual variability! , Unit: GtC/ySmoothedRaw L03: Tropical America L05: Tropical Africa L06: South Africa L08: Temperate Eurasia L09: Southeast Asia Average of all regions

Estimated flux difference in each region Averaging from 1997 – Unit is GtC/y. Red regions are larger than (average + 1 sigma) of all regions. Smoothed-RawSt. Dev.Smoothed-RawSt. Dev. L O L O L O L O L O L O L O L O L O L O L O Average Correlation coefficient0.978

Summary of annual mean case We can select site using inverse method. Sensitivity of dataset is not so large. Average correlation coefficients is about 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.

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

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

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

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

Standard deviation of estimated flux from each climate ( ) Raw data analysis tend to show higher seasonal variability! , Unit: GtCSmoothedRaw L03: Tropical America L05: Tropical Africa L06: South Africa L08: Temperate Asia L09: Southeast Asia Total flux

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. L O L O L O L O L O L O L O L O L O L O L O Average Correlation coefficient0.950

Summary of time dependent case Sensitivity of dataset appeared. Average correlation coefficients is about 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.

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.

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.

References (Data) GLOBALVIEW-CO2, Cooperative Atmospheric Data Integration Project - Carbon Dioxide, CD-ROM, NOAA CMDL, Boulder, Colorado, 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 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, , 1994.

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, , 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, , 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 J. Meteor. Soc. Japan, 78,

References (Inversion) Enting, I 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, 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!!

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