Simulating global fire regimes & biomass burning with vegetation-fire models Kirsten Thonicke 1, Allan Spessa 2 & I. Colin Prentice 1 1 2

Challenges to estimate global fire emissions: Wildfire emission models E x = Area burnt*Fuel load*Combustion Efficiency*EF x to simulate vegetation - fire interactions: Mechanistic fire models in DGVMs –Vegetation dynamics & composition on fuel characteristics –Burning conditions (fire behaviour & intensity) determine biomass burnt, thus trace gas emissions –Actual vs. potential vegetation (Human impact) Reduce uncertainties Inventory & satellite data  Inter- annual variability  Different climate conditions Burning conditions Affected vegetation

Vegetation-fire model: Our approach

SPread and IntensiTy of FIRE (SPITFIRE) Embedded in Lund-Potsdam-Jena DGVM –litter carbon pool (leaves, sapwood, heartwood) reclassified into dead fuel classes (1, 10, 100, 1000-hr) –live grass (higher moisture content than dry fuel)  fire spread –Tree architecture  fire behaviour & post-fire mortality –Post-fire mortality  Vegetation composition & fuel availability –More fire processes = more PFT parameters  fuel characteristics & fire traits Resolution: –0.5° x 0.5° grid cell –Daily: fire processes –Monthly: calculating trace gas emissions –Annual: update of vegetation dynamics

Distribution of precipitation according to no. wet days (Gerten et al. J.Hydr. 2004)  daily estimation of fire danger Fire danger index FDI = Probability that an ignition leads to a spreading fire Litter moisture per fuel class = f(NI) Fire Danger Index No. ignitions Spread Effects Emissions (Nesterov 1949)

“Frame” for potential fires  Fuel availability (as simulated by LPJ)  Climate Fire Danger Index No. ignitions Spread Effects Emissions

Expected number of fires E[n f ]=E[N ig ]*FDI with E[n ig ]=E[n l,ig ]+E[n h,ig ] –Lightning –Human-caused ignitions (after Venevsky et al. 2002) Depending on human population density Population growth : RIVM Database (NL) Spatial: rural vs. urban lifestyle Temporal: average no. ignitions per grid cell or region (intentional & negligence) Minimum intensity to sustain a fire Fire Danger Index No. ignitions Spread Effects Emissions

a)Human-caused ignitions per region: - Intentional > negligence Fire Danger Index No. ignitions Spread Effects Emissions

b) Estimated for case study regions (grid cell) Fire Danger Index No. ignitions Spread Effects Emissions Canada: LFDB + small fires + grassland fires Siberia Northern Australia

Conditions of an average fire Fire spread after Rothermel –Potential fuel load –Fuel characteristics Litter moisture Surface-area-to-volume ratio Fuel bulk density –Wind speed (NCEP re-analysis data) Fuel consumption after rate of spread –Litter moisture Assume elliptical fire shape Fire Danger Index No. ignitions Spread Effects Emissions Per PFT Fuel class

Human-dominated fire regimes (regional estimate) & constant wind speed Fire Danger Index No. ignitions Spread Effects Emissions

Surface fire intensity I surface =H*ROS*  (fuel consumed) Scorch height per PFT Crown scorch (CK) per PFT SH of fire vs. tree height & crown length Fire Danger Index No. ignitions Spread Effects Emissions

Low intensities in savannahs High intensities in forest ecosystems Fire Danger Index No. ignitions Spread Effects Emissions

Post-fire mortality P m = P m (CK) & P m (cambial damage) –Mortality from crown scorch = r(CK)*CK 3 –Cambial damage = residence time of fire  l / critical time for cambial damage  c  c = 2.9 * BT 2 with BT- Bark thickness –Biomass of killed trees to litter pool  available for burning in the following year Fire Danger Index No. ignitions Spread Effects Emissions

Carbon release to atmosphere –Surface fire –Crown scorch Plant material from killed plants to respective dead fuel classes Emission factor (Andreae & Merlet 2001, Andreae pers. comm. 2003) –CO 2, CO, CH 4, VOC, NO x, Total Particulate Matter Fire Danger Index No. ignitions Spread Effects Emissions

Carbon release to atmosphere –Surface fire –Crown scorch Fire Danger Index No. ignitions Spread Effects Emissions

Emission factor (Andreae & Merlet 2001, Andreae pers. Comm. 2003) –CO2, CO, CH4, VOC, NOx, Total Particulate Matter Fire Danger Index No. ignitions Spread Effects Emissions

Next steps Evaluation of interannual variability & seasonality Variability in area burnt, fire intensity in relation to biomass burning Comparison of biomass burning estimates –Methods –Uncertainties