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

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

64-Regions Inverse Model (using 15 years of interannually varying NCEP/NCAR winds) CS = cs1 + cs2… Inv. Setup Chi2 22 reg 2.15 64 reg 1.11 64+IAV 0.99 Bluemoon:/home/prabir/soft/bayesl3/results_hres1/plots/hr64new.eps Patra et al., Global Biogeochem. Cycles., 2005a,b

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)

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

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

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.

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

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

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

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

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

Simple empirical relations for atmospheric-CO2 growth rate prediction Sources/Increase Rates 1971- 1972 1986-1987 2001-2002 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.

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.

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