TransCom 3 Level 2 Base CaseInter-annual CO2 Flux Inversion

ResultsCurrent Status

David Baker, Rachel Law, Kevin Gurney, Peter Rayner, TransCom3 L2 modelers*, and the producers of the

GLOBALVIEW-CO2 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 InversionsInversion Type Fluxes Data References

Long-term mean R S1

Gurney, et al (2002), Nature, 415

Gurney, et al (2003), Tellus, 55B

Seasonal R*12 S1*12 Gurney, et al (2004), GBC, 18

Inter-annual R*12*Y S2*12*Y Baker, et al (2005), GBC, in review

R = 22 regions

S1 = 78 stations (GLOBALVIEW-CO2, 2003)

S2 = 76 stations (GLOBALVIEW-CO2, 2004)

Y = 16 years (1988-2003)

• IAV paper submitted Dec 2004• Reviews back early Feb 2005• Revision resubmitted April 2005

Base Case Assumptions

Nov 2002 [for T3 L3]• 1988-2001 (14

years)• GLOBALVIEW- CO2

(2002), 76 sites [chosen to have >68% data coverage ]; interpolated data used to fill all gaps.

• Data uncertainties calculated from GV 1979-2002 rsd (GV) as: 2 = (0.3 ppmv) 2 + GV

2 [non-seasonal]

June 2004• 1988-2002 (15

years)• GLOBALVIEW- CO2

(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 2004A 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-CO2 (2004)

Method• Find optimal fluxes x to minimize

where:x are the CO2 fluxes to be solved for,H is the transport matrix, relating fluxes to concentrationsz are the observed concentrations, minus the effect of pre-subtracted tracers (fossil fuel, and seasonal CASA & Takahashi)R is the covariance matrix for z,xo is an a priori estimate of the fluxes,Pxo is the covariance matrix for xo

Solution:

)()()()( 11 oToToJ xxPxxzHxRzHx

x

)(

)()(ˆ

111ˆ

11111

o

oo

T

oTT

xx

xx

PHRHP

xPzRHPHRHx

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

12 + 1 = 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 seasonalityfrom the monthly estimate, xmon

• xmon = xdeseas + xseas = xmean + xIAV + xseas

• xdeseas computed by passing a 13-point running mean over xmon

• xseas = xmon - xdeseas (zero annual mean seasonal cycle)

• xmean = the 1988-2003 mean of xdeseas

• xIAV = xdeseas - xmean (zero mean, 1988-2002)

• 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 xIAV 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 <0.00001

<0.00001

<0.00001

<0.00001

Total Flux (Land+Ocean)

<0.00001

<0.00001

<0.00001

~0.01

June 2005 results

Land & Ocean Fluxes

<0.00001

<0.00001

<0.00001

<0.00001

0.000036

0.00013

0.0057(0.37)

June 2004 results

June 2005 results

Land & Ocean Fluxes

<0.00001

<0.00001<0.001 <0.00001

<0.00001

(~0.03)

(~0.05)(~0.03)

(~0.02)

<0.001 (~0.05)

<0.001

(~0.02)

June 2004 results

0.00003

0.052

0.006

(0.116)

<0.001

<0.01

(~0.02)

(~0.11)

June 2005 results

June 2004 results<0.00001

<0.00001

<0.00001

0.00014

<0.00001

0.0022

<0.00001

~0.01

(~0.02)

<0.01

<0.01

<0.00001

(~0.11)

June 2005 results

Comparison of the 1992-96 Mean Fluxes

“IAV” = current IAV inversion

Mean Seasonal Cycle1991-2000

Prior Prior, no def. G04 1992-96

Mean Seasonal Cycle1991-2000

Prior G04 1992-96

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