Interannual Variability in Terrestrial Carbon Exchange

1. Interannual variability in terrestrial carbon exchange using an ecosystem-fire model and inverse model results Sergey Venevsky(1), Prabir K. Patra(2), Shamil Maksyutov(3), and Gen Inoue(3) (1) Obukhov Institute for Atmospheric Physics, Moscow 109017, Russia (2) Frontier Research Center for Global Change/JAMSTEC, Yokohama 2360001, Japan (3) National Institute for Environmental Studies, Tsukuba 305-8506, Japan

2. Outline of the presentation • Modelling of land-atmosphere carbon fluxes at global and regional scale – dynamic global vegetation model SEVER. • Description of human and lightning induced fires at global scale –SEVER-FIRE • Monthly-mean CO2 fluxes from 11land regions using a time-dependent inverse (TDI) model. • Comparison of the regional net carbon exchange fluxes simulated by TDI and SEVER.

3. SEVER dynamic global vegetation model (Venevsky and 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

4. Technical realisation of SEVER – modification of the LPJ DGVM to a daily time step • . State-of-the-art dynamic global vegetation model LPJ DGVM (Sitch, et.al., 2003, Thonicke et.al., 2001, Venevsky et.al., 2002) was taken as a basis for technical realisation of SEVER. • Advantages of the LPJ DGVM: • modular stucture with identified processes of vegetation dynamics and soil/biosphere biogeochemistry • successfuly reproduces vegetation composition and vegetation/soil carbon pools and fluxes at global scale • Code is avaiable on the Internet • Disadvantage: • Pseudodaily approach Temp GPP Tmonth gppmidmonth day day 1 1 1 31 1 16 31 1 DESIGN OF SEVER (Venevsky, Maksyutov, 2005): New radiation routine Modification of LPJ modules from month/mid-month to a daily step New fire model New soil temperature routine

5. SEVER/DGVM – Technical details SOIL classification data: 9 classes at 0.5°x0.5° (Sitch et.al., 2003) CLIMATE data: 6-hr NCEP/NCAR reanalysis data for 1956-2002 at T62 resolution were interpolated to 0.5°x0.5° with correction to elevation. a) No fire emissions considered: b) complete set

6. Comparison of SEVER DGVM with LPJ DGVM (new version) Correlations (r)between the observed and calculated monthly-mean NEE for all sites SEVER: r2=0.75 LPJ (new version) : r2=0.51

7. 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

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. Stepwise validation of SEVER- FIRE model (1) • 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)

11. Step validation of SEVER- FIRE model (2) • Areas burnt for Canada (Stocks et.al., 2002), Spain (Vasques et.al.,2002), Africa (Barbosa et.al., 1998). Examples of complete step validation for North-West Alberta, Canada (Wielgolaski et.al., 2002), for lightning fires:

12. Annual Flux (1997-2001) Flux Anomaly (1997-1998) Lightning fires Human induced fires

13. Comparison SEVER-FIRE vs CASA estimates, based on satelitte derived area burnt and CO SEVER CASA (van der Werf et.al., 2004)

14. Comparison of SEVER/DGVM fire flux with MODIS fire counts (seasonal cycle and spatial pattern) gC/m2/mon

15. 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,Randerson, 2005)

16. Time-dependent Inverse Model (64 regions): Monthly fluxes (S) and associated covariance (CS) are calculated as: G = Transport model operator, D = atmospheric CO2 data, CD = Data Covariance (1) (2) Rayner et al., 1999 Gurney et al., 2004 Patra et al., GBC, October 2005

17. Overview of TDI Results– Global and hemisphericscale CO2 flux anomaly Anomaly = monthly fluxes – mean seasonal cycle Patra et al., GBC, October 2005 Patra et al., GBC, July2005

18. Climate control on regional CO2 flux anomaly(Patra et al., GBC, 16 July 2005)

19. Comparison of CO2 flux anomalies – TDI vs SEVER/DGVM A B

20. Regional Flux Anomaly (1994-2004) : EuropeCiais et al., 2005 : 0.5 Pg-C for 2003

21. Processes associated with interannual CO2 flux variability– TDI vs SEVER/DGVM A B

22. Seasonal Cycle and long-term means of CO2 fluxes – TDI vs SEVER/DGVM

23. Conclusions • Human induced fire, increased in last 20 yrs, carbon emission exceedsthat from lightning, with a ratio 68/32, despite of small number of lightning fires (1/20 of human fire). • We simulated greater human induced fires during the 1997-98 El Niño event, and the emissions are highest in the tropics. • The ecosystem model simulated flux anomalies are fairly in phase and amplitude with those estimated using inverse modelling atmospheric CO2. • However, there still exists significant disagreements between the inversion and ecosystem flux amplitudes at the regional scale.