Fast Opt

1. A global Carbon Cycle Data Assimilation System (CCDAS) to infer atmosphere-biosphere CO2 exchanges and their uncertainties Marko Scholze1, Peter Rayner2, Wolfgang Knorr1, Thomas Kaminski3, Ralf Giering3 & Heinrich Widmann1 TransCom Tsukuba, 2004 1 2 3 FastOpt

2. Overview • CCDAS set-up • Calculation and propagation of uncertainties • Data fit • Global results • New developments • Conclusions and outlook

3. Combined ‘top-down’/’bottom-up’ 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

4. 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)

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

6. 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)

7. 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 dependent • Carbon balance: average NPP = b average soil resp. (at each grid point) t=1h t=1h t=1day b<1: source b>1: sink

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

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

10. 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)

11. Calculation of uncertainties = inverse Hessian • Covariance (uncertainties) of prognostic quantities • Error covariance of parameters • Adjoint, Hessian, and Jacobian code generated automatically from model code by TAF

12. 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

13. Data fit

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

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

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

17. Parameters II Relative Error Reduction

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

19. 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)

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

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

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

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

24. 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)

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

26. Low-resolution CCDAS • A fully functional low resolution version of CCDAS, BETHY runs on the TM2 grid (appr. 10° x 7.8°) • 506 vegetation points compared to 8776 (high-res.) • About a factor of 20 faster than high-res. Version -> ideal for developing, testing and debugging • On a global scale results are comparable (can be used for pre-optimising)

27. Including the ocean • A 1 GtC/month pulse lasting for three months is used as a basis function for the optimisation • Oceans are divided into the 11 TransCom-3 regions • That means: 11 regions * 12 months * 21 yr / 3 months = 924 additional parameters • Test case: • all 924 parameters have a prior of 0. (assuming that our background ocean flux is correct) • each pulse has an uncertainty of 0.1 GtC/month giving an annual uncertainty of ~2 GtC for the total ocean flux

28. Including the ocean Global land flux Seasonality at MLO Observations High resolution standard model Low resolution model Low-res incl. ocean basis functions

29. Conclusions • CCDAS with 58 parameters can fit 20 years of CO2 concentration data; ~15 directions can be resolved • 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. • With the ability of including ocean basis functions in the optimisation procedure CCDAS comprises a ‘normal’ atmospheric inversion.

30. 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 a prognostic ocean carbon cycle model.

31. Visit: http://www.ccdas.org