1. A global Carbon Cycle Data Assimilation System (CCDAS) and its link to CAMELS Marko Scholze 1, Peter Rayner 2, Wolfgang Knorr 3, Thomas Kaminski 4, Ralf Giering 4 & Heinrich Widmann 3 1 st CarboEurope Integration Workshop, Potsdam, 2004 2 FastOpt 4 3 1

2. QUEST c QUEST is a newly, NERC funded directed programme (5 years). QUEST aims to achieve a better qualitative and quantitative understanding of large-scale processes and interactions in the Earth System, especially the interactions among biological, physical and chemical processes in the atmosphere, ocean and land and their implications for human activities. QUEST mainly focuses on: (1) the contemporary carbon cycle and its interactions with climate and atmospheric chemistry; (2) the natural regulation of atmospheric composition on glacial-interglacial and longer time scales; and (3) the implications of global environmental changes for the sustainable use of resources. QUEST consists of a core team, strategic activities, fellowships, and collaborative grants. QUEST website: http://quest.bris.ac.uk

3. CAMELS c Carbon Assimilation and Modelling of the European Land Surface an EU Framework V Project (Part of the CarboEurope Cluster) CAMELS

4. CAMELS PARTICIPANTS (the “Jockeys”)  Hadley Centre, Met Office, UK – Coordinator: Peter Cox  LSCE, France  MPI-BGC, Germany  UNITUS, Italy  ALTERRA, Netherlands  European Forestry Institute, Finland  CEH, UK  IES/JRC, EC  FastOpt, Germany CAMELS

5. CAMELS AND INVERSE MODELLING CAMELS Goals and General Strategy: Combining Inverse and Forward Model Strategies (material by Peter Cox, Hadley Centre) Carbon Cycle Data Assimilation and Calculation of Uncertainties (CCDAS consortium) CAMELS

6. CAMELS Goals Best estimates and uncertainty bounds for the contemporary and historical land carbon sinks in Europe and elsewhere, isolating the effects of direct land- management. A prototype carbon cycle data assimilation system (CCDAS) exploiting existing data sources (e.g. flux measurements, carbon inventory data, satellite products) and the latest terrestrial ecosystem models (TEMs), in order to produce operational estimates of “Kyoto sinks“.

7. CAMELS CAMELS Motivating Science Questions Where are the current carbon sources and sinks located on the land and how do European sinks compare with other large continental areas? Why do these sources and sinks exist, i.e. what are the relative contributions of CO 2 fertilisation, nitrogen deposition, climate variability, land management and land- use change? How could we make optimal use of existing data sources and the latest models to produce operational estimates of the European land carbon sink?

8. Inverse Modelling Method : Use atmospheric transport model to infer CO 2 sources and sinks most consistent with atmospheric CO 2 measurements. Pros : a) Large-scale; b) Data based (transparency). Cons : a) Uncertain (network too sparse); b) not constrained by ecophysiological understanding; c) net CO 2 flux only (cannot isolate land management). CAMELS

9. Forward Modelling Method : Build “bottom-up” process-based models of land and ocean carbon uptake. Advantages : a) Include physical and ecophysiological constraints; b) Can isolate land-management effects; c) can be used predictively (not just monitoring). Disadvantages : a) Uncertain (gaps in process understanding); b) Do not make optimal use of large-scale observational constraints.

10. CAMELS The Case for Data-Model Fusion Mechanistic Models are needed to separate contributions to the land carbon sink (e.g. as required by KP). Large-scale data constraints (from CO 2 and remote-sensing) are required to provide best estimates and error bars at regional and national scales. Data-Model Fusion = ecophysiological constraints from forward modelling + large-scale CO 2 constraints from inverse modelling

11. CAMELS Flow Diagram CAMELS

12. Combined ‘top-down’/’bottom-up’ Method CCDAS – Carbon Cycle Data Assimilation System CO 2 station concentration Biosphere Model: BETHY Atmospheric Transport Model: TM2 Misfit to observations Model parameterFluxes Misfit 1 Forward Modeling: Parameters –> Misfit Inverse Modeling: Parameter optimization

13. CCDAS set-up 2-stage-assimilation: 1.AVHRR data (Knorr, 2000) 2.Atm. CO 2 data Background fluxes: 1.Fossil emissions (Marland et al., 2001 und Andres et al., 1996) 2.Ocean CO 2 (Takahashi et al., 1999 und Le Quéré et al., 2000) 3.Land-use (Houghton et al., 1990) Transport Model TM2 (Heimann, 1995)

14. BETHY (Biosphere Energy-Transfer-Hydrology Scheme) GPP: C3 photosynthesis – Farquhar et al. (1980) C4 photosynthesis – Collatz et al. (1992) stomata – Knorr (1997) Plant respiration: maintenance resp. = f(N leaf, T) – Farquhar, Ryan (1991) growth resp. ~ NPP – Ryan (1991) Soil respiration: fast/slow pool resp., temperature (Q 10 formulation) and soil moisture dependent Carbon balance: average NPP =  average soil resp. (at each grid point)  <1: source  >1: sink  t=1h  t=1day  lat,  lon = 2 deg

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

16. 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)

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

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

19. Data fit

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

21. Global Growth Rate Calculated as: observed growth rate optimised modeled growth rate Atmospheric CO 2 growth rate

22. Parameters I 3 PFT specific parameters (J max, J max /V max and  ) 18 global parameters 57 parameters in all plus 1 initial value (offset) ParamInitialPredictedPrior unc. (%)Unc. Reduction (%) fautleaf c-cost Q 10 (slow)  (fast) 0.4 1.25 1.5 0.24 1.27 1.35 1.62 2.5 0.5 70 75 39 1 72 78  (TrEv)  (TrDec)  (TmpDec)  (EvCn)  (DecCn)  (C4Gr)  (Crop) 1.0 1.44 0.35 2.48 0.92 0.73 1.56 3.36 25 78 95 62 95 91 90 1

23. Parameters II Relative Error Reduction

24. Carbon Balance latitude N *from Valentini et al. (2000) and others Euroflux (1-26) and other eddy covariance sites* net carbon flux 1980-2000 gC / (m 2 year)

25. Uncertainty in net flux Uncertainty in net carbon flux 1980-200 gC / (m 2 year)

26. Uncertainty in prior net flux Uncertainty in net carbon flux from prior values 1980-2000 gC / (m 2 year)

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

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

29. Interannual Variability I Normalized CO 2 flux and ENSO Lag correlation (low-pass filtered) ENSO and terr. biosph. CO 2 : Correlations seems strong with a maximum at ~4 months lag, for both El Niño and La Niña states.

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

31. 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)

32. Conclusions CCDAS with 58 parameters can fit 20 years of CO 2 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.

33. 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.