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Results from the Carbon Cycle Data Assimilation System (CCDAS)

4. Fast Opt. Results from the Carbon Cycle Data Assimilation System (CCDAS). Marko Scholze 1 , Peter Rayner 2 , Wolfgang Knorr 1 Heinrich Widmann 3, Thomas Kaminski 4 & Ralf Giering 4. 3. 1. 2. Misfit 1. CO 2 station concentration. Inverse Modeling: Parameter optimization.

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Results from the Carbon Cycle Data Assimilation System (CCDAS)

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  1. 4 FastOpt Results from the Carbon Cycle Data Assimilation System (CCDAS) Marko Scholze1, Peter Rayner2, Wolfgang Knorr1 Heinrich Widmann3, Thomas Kaminski4 & Ralf Giering4 3 1 2

  2. Misfit 1 CO2 station concentration InverseModeling: Parameter optimization Fluxes Model parameter Methodology sketchCCDAS – Carbon Cycle Data Assimilation System ForwardModeling: Parameters –> Misfit Misfit to observations Atmospheric Transport Model: TM2 Biosphere Model: BETHY

  3. CCDAS set-up • Background fluxes: • Fossil emissions (Marland et al., 2001 und Andres et al., 1996) • Ocean CO2(Takahashi et al., 1999 und Le Quéré et al., 2000) • Land-use (Houghton et al., 1990) Transport Model TM2(Heimann, 1995)

  4. BETHY(Biosphere Energy-Transfer-Hydrology Scheme) lat, lon = 2 deg • GPP: C3 photosynthesis – Farquhar et al. (1980) C4 photosynthesis – Collatz et al. (1992) stomata – Knorr (1997) • Plant respiration: maintenance resp. = f(Nleaf,T) – Farquhar, Ryan (1991) growth resp. ~ NPP – Ryan (1991) • Soil respiration: fast/slow pool resp., temperature (Q10 formulation) and soil moisture dependant • Carbon balance: average NPP =  average soil resp. (at each grid point) t=1h t=1h t=1day b<1: source b>1: sink

  5. where • is a model mapping parameters to observable quantities • is a set of observations • error covariance matrix  need of (adjoint of the model) Methodology Minimize cost function such as (Bayesian form):

  6. = inverse Hessian • Covariance (uncertainties) of prognostic quantities Calculation of uncertainties • Error covariance of parameters

  7. Improvements and further applications since Rayner et al. 2005 • Fate of terrestrial C under climate change • Including biomass burning • Uncertainties of prognostic (2000-2004) net fluxes (still calculating) • Improved carbon balance • Improved spin-up of fast soil pool • Weaker prior constraint on parameters

  8. Seasonal cycle of CO2 at Barrow, Alaska The red line is the simulation of R05 while the green line Is the improved simulation. Observations are shown by diamonds.

  9. Global atmospheric growth rate Weighted sum of Mauna Loa (0.75) and South Pole (0.25) concentrations

  10. Parameters I • 3 PFT specific parameters (Jmax, Jmax/Vmax and ) • 18 global parameters • 56 parameters in all plus 1 initial value (offset)

  11. Parameters II Relative Error Reduction

  12. Some values of global fluxes Value Gt C/yr

  13. Uncertainty in net carbon flux 1980-2000 gC / (m2 year) Carbon Balance net carbon flux 1980-2000 gC / (m2 year)

  14. Terrestrial C cycling under climate change

  15. Finding : • Assume P(t = 1979) • Adjust  to yield NEP(t = 1979-200)  iterative process Off-line model for prognostic slow pool Some equations: P: slow pool, rF: fast resp., fS: allocation fast to slow pool : soil moisture Ta: air temperature

  16. Initial slow pool size

  17. Decadal mean global NEP 1980-2090 Red lines indicate simulations with climate change and black lines with no climate change. Solid lines indicate simulations with optimized parameters and broken lines with a priori parameters.

  18. A biomass burning climatology (monthly resolved) based on the v. d. Werf data is used as a yearly basis function for the optimisation • Land is divided into the 11 TransCom-3 regions • That means: 11 regions * 21 yr = 231 additional parameters van der Werf et al., 2004, Continental-Scale Partitioning of fire emissions during the 1997 to 2001 El Niño/La Niña Period. Science, 303, 73-76. Including biomass burning

  19. Parameters revisited

  20. Global fluxes revisited Mean value 1980-1999 Gt C/yr

  21. Global growth rate revisited Atmospheric CO2 growth rate observed no fire with fire Calculated as:

  22. Interannual variability in biomass burning estimate Gt C/yr year blue bars CCDAS red bars v. d. Werf et al.

  23. Conclusions & Outlook • Prognostic future net carbon flux under climate change: more productive & more sensitive • More processes: fire (‘weak constraint’ as a first step) • More components: ocean (not-shown, but “free” optimization indicates no big changes, ideally also process-based) • Prognostic uncertainties on net carbon flux for 2000-2004: calculations finished by now.. • More data: inventories, regional inversions and budgets, satellite CO2 columns, isotopes, O2/N2

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