TransCom 3 Level 2 Base Case Inter-annual CO 2 Flux Inversion Results Current Status David Baker, Rachel Law, Kevin Gurney, Peter Rayner, TransCom3 L2 modelers*, and the producers of the GLOBALVIEW-CO 2 data product * (P. Bousquet, L. Bruhwiler, Y-H Chen, P. Ciais, I. Fung, K. Gurney, M. Heimann, J. John, T. Maki, S. Maksyutov, P. Peylin, M. Prather, B. Pak, S. Taguchi, Z. Zhu) 15 June 2005

TransCom 3 Base-case Inversions Inversion TypeFluxesDataReferences Long-term meanRS1S1 Gurney, et al (2002), Nature, 415 Gurney, et al (2003), Tellus, 55B SeasonalR*12S 1 *12Gurney, et al (2004), GBC, 18 Inter-annualR*12*YS 2 *12*YBaker, et al (2005), GBC, in review R = 22 regions S 1 = 78 stations (GLOBALVIEW-CO 2, 2003) S 2 = 76 stations (GLOBALVIEW-CO 2, 2004) Y = 16 years ( ) IAV paper submitted Dec 2004 Reviews back early Feb 2005 Revision resubmitted April 2005

Base Case Assumptions Nov 2002 [for T3 L3] (14 years) GLOBALVIEW- CO 2 (2002), 76 sites [chosen to have >68% data coverage ]; interpolated data used to fill all gaps. Data uncertainties calculated from GV rsd (  GV ) as:  2 = (0.3 ppmv ) 2 +  GV 2 [non-seasonal] June (15 years) GLOBALVIEW- CO 2 (2003), 78 sites; the previous 76 + CPT_36C0 + HAT_20C0; also SYO_00D0 changed to SYO_09C0 New seasonally- and interannual-varying data uncertainties

Base Case Assumptions Nov 2002 [for T3 L3] A priori fluxes – same as in Level 1, constant across year A priori flux errors – twice Level 1 June 2004 A priori fluxes – Kevin’s seasonally-varying ones from the seasonal inversion A priori flux errors a) Kevin’s seasonally- varying ones b) ditto for land regions,  2 =  2 L1 + (0.5 PgC/yr ) 2 for ocean regions

Base Case Assumptions Changes since June 2004 Added the CSU model results back in (13 models total) Included 2003 data from GLOBALVIEW-CO 2 (2004)

Method Find optimal fluxes x to minimize where: x are the CO 2 fluxes to be solved for, H is the transport matrix, relating fluxes to concentrations z are the observed concentrations, minus the effect of pre-subtracted tracers (fossil fuel, and seasonal CASA & Takahashi) R is the covariance matrix for z, x o is an a priori estimate of the fluxes, P xo is the covariance matrix for x o Solution:

Time-dependent basis functions for 13 transport models were submitted in Level 2: CSU (Gurney) † GCTM (Baker) GISS-UCB (Fung) GISS-UCI (Prather) JMA-CDT (Maki) MATCH (Chen) † not used before, but now added back in = 13 models used MATCH (Law) MATCH (Bruhwiler) NIES (Maksyutov) NIRE (Taguchi) TM2 (LSCE) TM3 (Heimann) PCTM (Zhu)

EUROPE: Monthly Flux Deseasonalized Flux “IAV” = Deseasonalized Flux, Mean Subtracted Off IAV with error ranges 13-model mean 1  internal error1  model spread

Computation of the inter-annual variability (IAV), long-term mean, and seasonality from the monthly estimate, x mon x mon = x deseas + x seas = x mean + x IAV + x seas x deseas computed by passing a 13-point running mean over x mon x seas = x mon - x deseas (zero annual mean seasonal cycle) x mean = the mean of x deseas x IAV = x deseas - x mean (zero mean, ) Corresponding errors also computed

1  Estimation Uncertainties and Transport Errors

Chi-square Significance Test We try to reject the null hypothesis that the estimated IAV is due solely to the combined effect of both transport error and random estimation error, superimposed on zero IAV Compare the variance of x IAV with the combined variance the transport and random errors: use  2 test ( =14; 15 independent years – 1 for mean)

Probability from  2 test that null hypothesis is correct

Total Flux (Land+Ocean) June 2004 results <

Total Flux (Land+Ocean) < ~0.01 June 2005 results

Land & Ocean Fluxes < (0.37) June 2004 results

June 2005 results Land & Ocean Fluxes < <0.001< (~0.03) (~0.05)(~0.03)

(~0.02) <0.001(~0.05) <0.001 (~0.02)

June 2004 results (0.116)

<0.001 <0.01 (~0.02) (~0.11) June 2005 results

June 2004 results < <

< ~0.01 (~0.02) <0.01 < (~0.11) June 2005 results

Comparison of the Mean Fluxes “IAV” = current IAV inversion

Mean Seasonal Cycle Prior Prior, no def. G

Mean Seasonal Cycle Prior G

Seasonal Cycle Amplitude [PgC/yr]

Conclusions Inter-model differences in long-term mean fluxes are larger than in the flux inter-annual variability IAV for latitudinal land & ocean partition is robust (except for Southern S. America); continent/basin partition of IAV in north is of marginal significance; in tropics, IAV is significant for the Tropical Pacific and Australasia The IAV for the 22 regions is significant for only a few land regions and about half the ocean regions. Probable physical drivers for Tropical Asia (fires) & East Pacific (El Niño); other regions less clear… Good agreement between the three types of inversions (annual-mean, seasonal, inter-annual) in mean & seasonality