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CAMELS

CAMELS. C arbon A ssimilation and M odelling of the E uropean L and S urface an EU Framework V Project (Part of the CarboEurope Cluster). CAMELS PROJECT OVERVIEW. Peter Cox, Hadley Centre, Met Office. CAMELS Goals Background: Kyoto Protocol

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CAMELS

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

  2. CAMELS PROJECT OVERVIEW Peter Cox, Hadley Centre, Met Office • 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 etal.)

  3. 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. 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 … shallbe 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. 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 CO2 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. Inverse Modelling Method : Use atmospheric transport model to infer CO2 sources and sinks most consistent with atmospheric CO2 measurements. Advantages : a)Large-scale; b) Data based (transparency). Disadvantages : a)Uncertain (network too sparse); b) not constrained by ecophysiological understanding; c) net CO2 flux only (cannot isolate land management).

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

  11. 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 CO2 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 CO2 constraints from inverse modelling

  12. Observed Variability in CO2 • Annual changes in atmospheric CO2 are dominated by ENSO • after removing anthropogenic rise • rise during El Nino • fall during La Nina • except during major volcanic eruptions El Chichon Pinatubo CO2 - black, Nino3 - red

  13. Soil Respiration Constraint from ENSO Sensitivity(Mickey Mouse Data-Model Fusion) • q10 is the factor by which soil respiration is assumed to increase for each 10oC warming. • Model with q10=2 has realistic sensitivity to ENSO. • Reconstructions for range of q10. • Infer q10=2.1±0.7.

  14. Influence of Pinatubo Eruption on Atmospheric CO2 • Volcano causes surface cooling • model agrees with • obs (red) • “theory” (blue) • Cooling causes reduction in CO2 • model agrees with reconstructed volcanic anomaly (blue) • phase of ENSO important ?

  15. Constraint from Sensitivity to Volcanoes • Model with q10=2 has realistic sensitivity to Pinatubo. • Reconstructions for range of q10. • Infer q10=1.9±0.4

  16. Use of Data Constraints in CAMELS LOCAL CONSTRAINTS HISTORICAL CONSTRAINTS SPATIAL CONSTRAINTS Weather data, Land management, N deposition Fluxes of CO2 and H20, Inventory data Atmos CO2, Satellite data Carbon Cycle Data Assimilation Systems 20th Century Simulation of European sink Optimised TEM for key Sites Original TEM

  17. Flux Measurement in Amazonia

  18. Interannual Variability in Atmospheric CO2 IPCC TAR (2001) Annual CO2 increase fluctuates by up to 1 ppmv/yr even though emissions increase smoothly

  19. Offline Carbon Cycle Data Assimilation(“proper” example after Wolfgang Knorr et al.) Surface CO2 fluxes TEM parameters, State variables Offline TEM Atm Transport Model (ATM) Simulated fAPAR Simulated CO2 Concentrations Climate, soils, Land-use drivers Cost Function Optimisation Algorithm Satellite fAPAR Measured CO2 Concentrations Sensitivity to TEM parameters, State variables Adjoint offline TEM and ATM

  20. Slide from Wolfgang Knorr

  21. Slide from Wolfgang Knorr Slide from Wolfgang Knorr

  22. 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 CO2 measurements. • CAMELS ultimate aim is to develop a prototype Carbon Cycle Data Assimilation System.

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

  24. 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. CAMELS Flow Diagram

  26. Influence of ENSO on CO2 Variability • Hadley Centre Model recreates observed sensitivity to ENSO • Ocean and terrestrial fluxes opposite variation with ENSO • consistent with obs • land dominates overall response CO2 Growth Rate Anomaly (ppmv/yr) NINO 3 index (K)

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

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