Fires and the Contemporary Global Carbon Cycle Guido van der Werf (Free University, Amsterdam, Netherlands) In collaboration with: Jim Randerson (UCI, CA, USA) Louis Giglio (SSAI, MD, USA) Prasad Kasibhatla (Duke University, NC, USA) Jim Collatz (NASA GSFC, MD, USA)

Main Objectives 1.Quantify the contemporary amount of biomass burned on a global scale 2.Assess the role of biomass burning in the global CO 2 and CH 4 cycle 3.Determine the climate sensitivity of biomass burning

‘Classical’ approach: Emissions = Σ x,t A × B × CC × EF Giglio et al, submitted to ACPD

Net Primary Production Allocation =f(treecover) Aboveground Biomass C Belowground Biomass C Combustion Belowground Litter C Aboveground Litter C f(A,CC,M)f(A,CC) Respiration Fuelwood collection Herbivore consumption A = area burnt CC = combustion completeness M = fire induced mortality Fires embedded in the CASA satellite-driven biogeochemical model

Annual fire emissions, averaged over the 1997 – 2004 period

Annual fire emissions as percentage of NPP, averaged over the 1997 – 2004 period

Fuel consumption as calculated by CASA Emission (g) per m 2 burned

On a global scale, IAV in burned area and emissions are decoupled BA driven by savannas Emissions driven by forest fires (including deforestation) 1997 peat burning in Asia

Observed CO anomalies Boreal region CO (forward modeling) Atmospheric CO as a constrain on fire emissions

CO: forward modeling optimized in inversion to fit observations CO 2 : fires explain ~2/3 of the growth rate anomaly (60% SE-Asia, 30% C+S America, 10% Boreal) CH 4 : fires explain most of the growth rate anomaly. Contribution of IAV in fire activity to CO 2 and CH 4 growth rates

Mismatch in seasonality between top-down and bottom-up observations (southern Africa) Atmosphere: peak in September - October Satellite surface observations: peak in June – July - August

Effect of IAV in precipitation on fire activity

Concluding remarks 1.Global (vegetation) fire emissions as calculated by our modeling framework is ~2.5 Pg C / year, largest uncertainties in deforestation regions 2.IAV in fire emissions contributed significantly to variability in the growth rate of CO 2 and CH 4. 3.IAV in fire emissions is dominated by IAV in forest fires. Implications: IAV in burned area and emissions are decoupled IAV in CO and CH 4 is larger than IAV in C or CO 2 4.(Multi-specie) inversions and high resolution (deforestation) fire modelling ideally employed to further improve estimates