1. 1 CAMELS Carbon Assimilation and Modelling of the European Land Surface an EU Framework V Project (Part of the CarboEurope Cluster) CAMELS

2. 2 CAMELS PROJECT OVERVIEW CAMELS Goals Background: Kyoto Protocol Background: Inverse Model vs Forward Model Estimates Forward Model Constraints from Atmospheric Variability (“Mickey Mouse Data-Model Fusion” from Cox et al.) Carbon Cycle Data Assimilation (“proper” example from Knorr et al.) Peter Cox, Hadley Centre, Met Office

3. 3 CAMELS 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“.

4. 4 CAMELS Policy Motivation: Kyoto Sinks Article 3.3 : “The net change in greenhouse gas emissions by sources and removals by sinks resulting from direct human-induced land-use change and forestry activities, …… measured as verifiable changes … shall be used to meet the commitments.” Article 3.4 : “……each Party …… shall provide …… data to establish its level of carbon stocks in 1990 and to enable an estimate to be made of its changes in carbon stocks in subsequent years……”

5. 5 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?

6. 6 CAMELS Inverse Modelling Method : Use atmospheric transport model to infer CO 2 sources and sinks most consistent with atmospheric CO 2 measurements. Advantages : a) Large-scale; b) Data based (transparency). Disadvantages : a) Uncertain (network too sparse); b) not constrained by ecophysiological understanding; c) net CO 2 flux only (cannot isolate land management).

7. 7 CAMELS Inverse Model estimates of the carbon sink still have significant uncertainties, and are not strongly constrained by ecophysiological understanding within-model uncertainty between-model uncertainty (Gurney et al., Nature 2002)

8. 8 CAMELS Inverse Modelling - Uncertainties Fan et al. (1998): 1.7 GtC/yr sink in North America. Bousquet et al. (1999): 0.5 +/- 0.6 GtC/yr in North America, 1.3 GtC/yr in Siberia.

9. 9 CAMELS 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. 10 CAMELS Smoothed Mean and Standard Deviation of DGVM Predictions (Cramer et al., 2001) Diagram from Royal Soc. Sinks Report Forward model estimates of the carbon sink still have significant uncertainties, and are not strongly constrained by observations

11. 11 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

12. 12 CAMELS Observed Variability in CO 2 Annual changes in atmospheric CO 2 are dominated by ENSO –after removing anthropogenic rise –rise during El Nino –fall during La Nina –except during major volcanic eruptions  CO 2 - black, Nino3 - red P inatuboEl Chichon

13. 13 CAMELS Soil Respiration Constraint from ENSO Sensitivity (Mickey Mouse Data-Model Fusion) q 10 is the factor by which soil respiration is assumed to increase for each 10 o C warming. Model with q 10 =2 has realistic sensitivity to ENSO. Reconstructions for range of q 10. Infer q 10 =2.1±0.7.

14. 14 CAMELS Influence of Pinatubo Eruption on Atmospheric CO 2 Volcano causes surface cooling model agrees with –obs (red) –“theory” (blue) Cooling causes reduction in CO 2 model agrees with reconstructed volcanic anomaly (blue) phase of ENSO important ?

15. 15 CAMELS Constraint from Sensitivity to Volcanoes Model with q 10 =2 has realistic sensitivity to Pinatubo. Reconstructions for range of q 10. Infer q 10 =1.9±0.4

16. 16 CAMELS Use of Data Constraints in CAMELS Original TEM Optimised TEM for key Sites 20 th Century Simulation of European sink Carbon Cycle Data Assimilation Systems Fluxes of CO 2 and H 2 0, Inventory data Weather data, Land management, N deposition Atmos CO 2, Satellite data LOCAL CONSTRAINTS HISTORICAL CONSTRAINTS SPATIAL CONSTRAINTS

17. 17 CAMELS Flux Measurement in Amazonia

18. 18 CAMELS Interannual Variability in Atmospheric CO 2 Annual CO 2 increase fluctuates by up to 1 ppmv/yr even though emissions increase smoothly IPCC TAR (2001)

19. 19 CAMELS Offline Carbon Cycle Data Assimilation (“proper” example after Wolfgang Knorr et al.) Optimisation Algorithm Sensitivity to TEM parameters, State variables TEM parameters, State variables Surface CO 2 fluxes Offline TEM Atm Transport Model (ATM) Adjoint offline TEM and ATM Simulated fAPAR Satellite fAPAR Simulated CO 2 Concentrations Measured CO 2 Concentrations Climate, soils, Land-use drivers Cost Function

20. 20 CAMELS Slide from Wolfgang Knorr

21. 21 CAMELS Slide from Wolfgang Knorr

22. 22 CAMELS Conclusions CAMELS is an EU FP5 project motivated by the need to develop best estimates plus uncertainty bounds for the European (and global) land carbon sink. CAMELS will make use of local flux measurements, the historical carbon balance, and large-scale constraints from remote-sensing and atmospheric CO 2 measurements. CAMELS ultimate aim is to develop a prototype Carbon Cycle Data Assimilation System.

23. 23 CAMELS CAMELS Workpackages WP1. Data Harmonisation and Consolidation WP2. Model Validation and Uncertainty Analysis WP3. Modelling of the 20 th Century Land Carbon Balance WP4. Development of a System for Carbon Data Assimilation WP5. Dissemination of Information

24. 24 CAMELS CAMELS PARTICIPANTS (the “Jockeys”)  Met Office, UK  LSCE, France  MPI-BGC, Jena  UNITUS, Italy  ALTERRA, Netherlands  European Forestry Institute, Finland  CEH, UK  JRC, EC

25. 25 CAMELS CAMELS Flow Diagram

26. 26 CAMELS Influence of ENSO on CO 2 Variability Hadley Centre Model recreates observed sensitivity to ENSO Ocean and terrestrial fluxes opposite variation with ENSO –consistent with obs land dominates overall response NINO 3 index (K) CO 2 Growth Rate Anomaly (ppmv/yr)

27. 27 CAMELS Forward Modelling - Ocean Uncertainties Ocean Uptake From OCMIP II Models Source: IPCC TAR