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Vulnerability of Carbon Cycle to Drought and Fire: Effects on Interannual CO2 Variability

Explore how drought and fire impact global CO2 flux variability through atmospheric-CO2 inversion modeling using the TDI framework. Assess the significance of drought and fire on terrestrial carbon cycles and understand CO2 growth rates. Discuss implications for CO2 flux determination, observational networks, and climate influences on flux variability.

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Vulnerability of Carbon Cycle to Drought and Fire: Effects on Interannual CO2 Variability

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  1. 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 Effects of drought and fire on interannual variability in CO2 as derived using atmospheric-CO2 inversion

  2. 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

  3. 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 Patra et al., Global Biogeochem. Cycles., 2005a,b

  4. 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)

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

  6. Comparison with other estimates and the main controlling factor for CO2 flux interannual variability Patra et al., Global Biogeochem. Cycles., 2005a,b

  7. Comparison of average ocean fluxes – ocean inv. (Fletcher), atmos. inv. (Patra, Roedenbeck, TransCom) Patra et al., Atmos. Chem. Phys., submitted, 2006.

  8. Effect of Draught on Regional Land Fluxes Patra et al., Global Biogeochem. Cycles., 2005b

  9. CO2 regional flux anomalies:TDI, Biome-BGC/draught, bottom-up estimates Patra et al., Global Biogeochem. Cycles., 2005b

  10. Fire 100%; BGC 25% Fire 62%; BGC 9% Fire 10%; BGC 86% Fire 10%; BGC 78% Fire 30%; BGC -3% Fire 70%; BGC 20% CO2 regional flux anomalies:TDI, Biome-BGC/draught, bottom-up estimatesand fire emissions

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

  12. Studying CO2 Growth Rate at Mauna Loa, Hawaii using TDI model fluxes Patra et al., Tellus, 2005c.

  13. Simple empirical relations for atmospheric-CO2 growth rate prediction * this flux is confined to NH only Green diamond: van der Werf et al. Vertical bar: Kasischke and Bruhwiler Patra et al., Tellus, 2005c.

  14. Simulation of CO2 Growth Rates and seasonal cycles using TDI fluxes Patra et al., ACP, 2006.

  15. 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

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