1. Effects of drought and fire on interannual variability in CO2 as derived using atmospheric-CO2 inversion
Prabir K. Patra
Acknowledgements to:
M. Ishizawa, S. Maksyutov, S. Venevsky, G. Inoue, T. Nakazawa
GCP/ESF: Vulnerability of the Carbon Cycle to Drought and Fire
5-8 June 2006

2. Plan of the talk
Introduction to 64-region TDI framework (based on CSIRO model; Rayner et al.)
Interannual variability and magnitudes of global and regional fluxes
Effect of draught and fires on terrestrial carbon cycle
Utility of TDI derived fluxes to understand the atmospheric-CO2 growth rates

3. 64-Regions Inverse Model (using 15 years of interannually varying NCEP/NCAR winds)
CS = cs1 + cs2…
Inv. Setup Chi2
22 reg
64 reg
64+IAV
Bluemoon:/home/prabir/soft/bayesl3/results_hres1/plots/hr64new.eps
Patra et al., Global Biogeochem. Cycles., 2005a,b

4. Basic Equations in the Inverse Model:
Forward model simulation of an atmospheric tracer (e.g. CO2) mathematically is:
, where G is a linear operator representing atmospheric transport (no chemistry).
Inverse model equations for CO2 fluxes and uncertainties:
r: inverse model region, s: observation station, t: time -1
Estimated
Flux (r,t)
Atmospheric
CO2 Data (s,t)
A Priori
Flux (r,t)
Estimated
Flux Cov. (r,t)
A Priori Flux
Cov. (r,t)
Transport Model
Simulation (s,t)

5. Sensitivity of CO2 fluxes to initial conditions
12-month running averages are shown
Patra et al., Global Biogeochem. Cycles., 2005a

6. Comparison with other estimates and the main controlling factor for CO2 flux interannual variability
bluemoon:/home/prabir/soft/bayesl3/results_hres2/comp_frsar.eps
Patra et al., Global Biogeochem. Cycles., 2005a,b

7. Comparison of average ocean fluxes – ocean inv. (Fletcher), atmos. inv
Comparison of average ocean fluxes – ocean inv. (Fletcher), atmos. inv. (Patra, Roedenbeck, TransCom)
Patra et al., Atmos. Chem. Phys., submitted, 2006.

8. Effect of Draught on Regional Land Fluxes
cumulus:/temp03/prabir/data/plotsco2/flux_tsn.eps
Patra et al., Global Biogeochem. Cycles., 2005b

9. CO2 regional flux anomalies: TDI, Biome-BGC /draught, bottom-up estimates
bluemoon:/home/prabir/soft/bayesl3/results_hres2/boreal_new.eps
Patra et al., Global Biogeochem. Cycles., 2005b

10. CO2 regional flux anomalies: TDI, Biome-BGC /draught, bottom-up estimates and fire emissions
Fire 62%; BGC 9%
Fire 10%; BGC 78%
Fire 70%; BGC 20%
Fire 100%; BGC 25%
Fire 10%; BGC 86%
Fire 30%; BGC -3%
bluemoon:/home/prabir/soft/bayesl3/results_hres2/boreal_new.eps

11. Regional Flux Anomaly (1994-2004) : Europe Ciais et al. , 2005 : 0
Regional Flux Anomaly ( ) : Europe Ciais et al., 2005 : 0.5 Pg-C for 2003

12. Studying CO2 Growth Rate at Mauna Loa, Hawaii using TDI model fluxes
Patra et al., Tellus, 2005c.
bluemoon:/home/prabir/Pubctn/2004/CO2anom/trends/mlo_sion.eps

13. Simple empirical relations for atmospheric-CO2 growth rate prediction
Sources/Increase Rates
1971-
1972
El Nino (Gt-C)
4.0
2.3
2.1
Boreal Fire (Gt-C)
0.0
0.5*
0.28*
CO2 Gr. Rate
(estimated) (ppm/yr)
1.9
1.6
1.3
(observed) (ppm/yr)
1.8
1.5
bluemoon:/home/prabir/Pubctn/2004/CO2anom/trends/mlo_sion.eps
* this flux is confined to NH only
Green diamond: van der Werf et al.
Vertical bar: Kasischke and Bruhwiler
Patra et al., Tellus, 2005c.

14. Simulation of CO2 Growth Rates and seasonal cycles using TDI fluxes
bluemoon:/home/prabir/Pubctn/2004/CO2anom/trends/mlo_sion.eps
Patra et al.,
ACP, 2006.

15. Conclusions CO2 flux determination primarily depend on
Selection of observational networks
Forward transport modelling (less on techniques)
The flux variability over land and ocean are linked fundamentally to the climate, e.g., ENSO, NAO, PDO…
This enables us to establish a CO2 growth rate prediction model based on empirical relations.
Interannual variability in terrestrial ecosystem fluxes, and thus atmospheric CO2 are primarily controlled by draught and fire