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Global process-oriented fire model SEVER-FIRE: structure, results, perspectives

Global process-oriented fire model SEVER-FIRE: structure, results, perspectives. Sergey Venevsky. Hadley Centre, MetOffice. SEVER dynamic global vegetation model (Venevsky, Maksyutov, 2005). Plant functional types distribution. Precip. Temp, SWR. Precip convect. CO 2. H 2 O. NPP.

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Global process-oriented fire model SEVER-FIRE: structure, results, perspectives

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  1. Global process-oriented fire model SEVER-FIRE:structure, results, perspectives Sergey Venevsky Hadley Centre, MetOffice

  2. SEVER dynamic global vegetation model (Venevsky, Maksyutov, 2005) Plant functional types distribution Precip Temp, SWR Precip convect CO2 H2O NPP Vegetation composition FPC C fire Rh NEE=Rh+C fire-NPP 0.5°x0.5° grid cell Daily time step Annual time step

  3. Input data for SEVER Global SOIL classification data (9 classes) at 0.5°x0.5°– as in Sitch, et.al.2003 CLIMATE data: 6 –hour NCEP reanalysis climate data for 1956-2002 at coarse resolution T64 (approx 2°x2°) was interpolated linearly with correction to elevation to 0.5°x0.5° spatial resolution and a daily time step INPUT DAILY CLIMATE DATA: a) No fire emissions considered: b) complete set

  4. SEVER DGVM (examples of calculations) NCEP climatology interpolated to 0.5x0.5 dergees Comparison of SEVER DGVM with LPJ DGVM (new version) Correlation observed against calculated NEE monthly averaged for all sites SEVER: R2=0.75 LPJ (new version) : R2=0.51

  5. Fires and fire emissions: few facts • Natural and anthropogenic fires affects atmospheric chemistry and climate by injection of large amounts of aerosols and greenhouse gases (Andreae et.al., 1988) • Totally fires consume 5% of terrestrial net primary production, resulting in emiting of 2-3 PgC/year (Randerson, et.al., 2002). This constitutes a half to 2/3 of current fossil fuel emissions • Majority of fires are human induced – 97% in Spain (Vasques, et.al, 2002), 60-80% in Russia (Shvidenko and Nilsson, 2002) • Lightning fires account for disproportionally area burnt in boreal zone – 38% of 10000 fires that occur each year in Canada result in 82% of area burned nationwide

  6. SEVER-FIRE global mechanistic fire model Carbon fire emission 365 lon lat Day Fire moisture extinction level Flammability threshold Lightning and human ignition 1 Spread and termination Fire weather danger

  7. Fire danger weather index (FDWI) 100 4000 Nesterov Index (T, Tdew) * Moisture defined probability of fire Temperature defined probability of fire

  8. Human induced ignitions: conceptual scheme & * Population density * Urban/ rural * * Wealth status Timing accessibility

  9. Lightning ignitions: conceptual scheme LPC/LCC flashes Cumulonimbus + + + Moisture + Duff Elevation - - - Moisture Duff Smoldering probability

  10. Fire spread, termination and emissions FDWI=0 Front rate of spread and area burnt Wind Emission & mortality factors Fuel bulk density C fire 0.5° x 0.5° Moisture Distance from megapolis

  11. Input Output SEVER-FIRE

  12. Global data sets used for SEVER- FIRE 1.Elevation data (1 min) of US GS2. Population density (Popden 5 min) from the University of Michigan3. Population growth dynamics for 1950-2050 and urban/rural ratio by five world economic regions from the UN Population Program4. Wealth Index was recalculated from the data of the UN Human Settlements Program for 250 world cities (Svirejeva-Hopkins, et.al., 2000)

  13. Step validation of SEVER- FIRE • Number of cloud-to-ground (CG) flashes is validated using data of the Optical Transient Detector for the continents (Christiensen et.al, 2002) • Number of lightning/human fires is validated using data for Canada (Stocks, et.al., 2002) and Spain (Vasques, et.al.,2002) • Areas burnt for Canada (Stocks, et.al., 2002), Spain (Vasques, et.al.,2002), Africa (Barbosa, et.al., 1998). Example of complete step valdation for North-West Alberta, Canada (Wielgolaski, et.al., 2002), for lightning fires:

  14. Example of validation of SEVER- FIREagainst satelitte derived areas burnt African countries (Barbosa, et.al. 1999)

  15. Areas burnt during 1997-2001 simulated by SEVER-FIRE

  16. Number of fires during 1997-2001 simulated by SEVER-FIRE

  17. Anomalies for number of lightning/human fires during 1997-1998 El Ninio event Number of lightning fires In 1997-1998 minus averaged for 1997-2001 Number of human fires In 1997-1998 minus averaged for 1997-2001

  18. Pattern of annual CO2 emissionfrom lightning fires during 1997-2001 simulated by SEVER-FIRE

  19. Pattern of annual CO2 emissionfrom human-induced fires during 1997-2001 simulated by SEVER-FIRE

  20. 1997-1998 emissions anomaly(human-induced fires) simulated by SEVER-FIRE

  21. 1997-1998 emissions anomaly(lightning fires) simulated by SEVER-FIRE

  22. Comparison SEVER-FIRE vs CASA estimates, based on satelitte derived areas burnt SEVER CASA (van der Werf et.al., 2004)

  23. Simulated global CO2 fire emissionduring 1971-2002 (human and lightning cases) El Niño Total averaged for 1971-2002 annual fire emissions 3581 TgC (3530 TgC for 1997-2001,van der Werf et.al, 2004)

  24. Conclusions: • Human fires emissions exceed lightning ones with ratio 2 /1, Lightning fires emissions, however, play significant role despite of their realtively small number (5% from total global number of fires). • Human fires emissions have increasing trend during last twenty years. Lightning fire emissions do not show such a trend • During El Niño event 1997-1998 human induced fire emissions significantly increased, while lightning induced fire emissions varied as usually • The emission anomaly during El Niño event for human fires are highest in tropics: North Eastern Brazil, Southern Africa, South East Asia, but also is high in North Eastern Siberia and Central Canada • The anomaly for lightning fires is dispersed equally, with exception of peaks in Jakutia (North Eastern Siberia), Candian Rockies and Papua New Guinea

  25. Comparison of SEVER-FIRE with Globe-FIRE (Thonicke, et.al.) Fire return interval 1901-1998 SEVER-FIRE Fire return interval 1990-2000 SEVER-FIRE

  26. Glob-FIRE: Fraction of area burnt in relation to length of fire season – Some problems 1.0 • scale dependent by design, fixed to spatial resolution 0.5x0.5 grad • too small number of points for a regression • set of observed points consists from two independent clusters (Australia, Portugal) • Glob-FIRE has rather poor • performance in key regions • Boreal • Tropical forest

  27. Comparison of SEVER-FIRE with Reg-FIRE (Venevsky, et.al.) New global functions Mistake in the scheme Flexible fire duration Globalisation of Reg-FIRE spatially and temporaly

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