Fast Opt

1. Inferring terrestrial CO2 fluxesfrom a global-scale Carbon Cycle Data Assimilation System (CCDAS) Marko Scholze1, Peter Rayner2, Wolfgang Knorr1, Thomas Kaminski3, Ralf Giering3 & Heinrich Widmann1 Atmospheric Science Seminars, Harvard University, 16th January 2004 1 2 3 FastOpt

2. Overview • Motivation • Top-down vs. bottom-up approach • CCDAS set-up • Calculation and propagation of uncertainties • Data fit • Global results • Conclusions and outlook

3. Motivation after Joos, 1996

4. Motivation Sketch of the global carbon cycle • Where are the sources/sinks? • Which are the important processes? • How do they evolve? Fluxes in Gt C yr-1, pools in Gt C, after Prentice et al., 2001.

5. „top-down“ vs. „bottom-up“ atm. CO2 data atmospheric inversion (Transport Model) Process Model climateand other driving data • Advantages: • Fluxes consistent with • atm. data • Estimation of uncertainties • Disadvantages: • No process information • Coarse resolution net CO2 flux at the surface • Advantages: • Process understanding • -> prognostic modeling • High resolution • Disadvantages: • Global validation difficult • Parameter validity

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

7. CCDAS set-up • 2-stage-assimilation: • AVHRR data • (Knorr, 2000) • Atm. CO2 data • 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)

8. Station network 41 stations from Globalview (2001), no gap-filling, monthly values 1979-1999. Annual uncertainty values from Globalview (2001).

9. Terminology GPP Gross primary productivity (photosynthesis) NPP Net primary productivity (plant growth) NEP Net ecosystem productivity (undisturbed C storage) NBP Net biome productivity (C storage)

10. 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 = b average soil resp. (at each grid point) t=1h t=1h t=1day b<1: source b>1: sink

11. Calibration Step Flow of information in CCDAS. Oval boxes represent the various quantities. Rectangular boxes denote mappings between these fields.

12. Prognostic Step Oval boxes represent the various quantities. Rectangular boxes denote mappings between these fields.

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

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

15. Gradient Method cost function J (p) 1st derivative (gradient) of J (p) to model parameters p: yields direction of steepest descent. 2nd derivative (Hessian) of J (p): yields curvature of J. Approximates covariance of parameters. Model parameter space (p) Figure from Tarantola, 1987

16. Data fit

17. Seasonal cycle Barrow Niwot Ridge observed seasonal cycle optimised modeled seasonal cycle

18. Global Growth Rate observed growth rate optimised modeled growth rate Atmospheric CO2 growth rate Calculated as:

19. Parameters I • 3 PFT specific parameters (Jmax, Jmax/Vmax and b) • 18 global parameters • 57 parameters in all plus 1 initial value (offset)

20. Parameters II Relative Error Reduction

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

22. Carbon Balance Euroflux (1-26) and other eddy covariance sites* latitude N *from Valentini et al. (2000) and others net carbon flux 1980-2000 gC / (m2 year)

23. Uncertainty in net flux Uncertainty in net carbon flux 1980-200 gC / (m2 year)

24. Uncertainty in prior net flux Uncertainty in net carbon flux from prior values 1980-2000 gC / (m2 year)

25. NEP anomalies: global and tropical global flux anomalies tropical (20S to 20N) flux anomalies

26. IAV and processes Major El Niño events Major La Niña event Post Pinatubo period

27. Interannual Variability I Normalized CO2 flux and ENSO ENSO and terr. biosph. CO2: Correlations seems strong with a maximum at ~4 months lag, for both El Niño and La Niña states. Lag correlation (low-pass filtered)

28. Interannual Variabiliy II Lagged correlation on grid-cell basis at 99% significance correlation coefficient

29. Regional Net Carbon Balance and Uncertainties

30. Conclusions • CCDAS with 58 parameters can fit 20 years of CO2 concentration data. • Significant reduction of uncertainty for ~15 parameters. • Terr. biosphere response to climate fluctuations dominated by El Nino. • A tool to test model with uncertain parameters and to deliver a posterior uncertainties on parameters and prognostics.

31. Future • Explore more parameter configurations. • Include missing processes (e.g. fire). • Upgrade transport model and extend data. • Include more data constraints (eddy fluxes, isotopes, high frequency data, satellites) -> scaling issue. • Projections of prognostics and uncertainties into future. • Extend approach to ocean carbon cycle.