Servizio Comunitario di Informazione in materia di Ricerca e Sviluppo - CORDIS


CAMELS Sintesi della relazione

Project ID: EVK2-CT-2002-00151
Finanziato nell'ambito di: FP5-EESD
Paese: Germany

D3.3 Diagnosis of the causes of the European land carbon sink in the context of the Kyoto Protocol

CAMELS uses a novel approach, termed Carbon Cycle Data Assimilation System (CCDAS) that combines both views and adds a few additional elements. An additional innovation is that CAMELS produces consistent uncertainty bounds on carbon fluxes that are essential for policy purposes. It starts from flux measurements at the stand scale, which are used to improve and best parameterise a number of ecosystem models. The exercise also yields uncertainty bounds for ecosystem model parameters, and, by using data from all major biomes, a notion of the representativeness of the models and parameterisations.

The assumption used in CAMELS is that the best way to spatially extrapolate the results from the flux measurements is not through fluxes, but through parameter values that describe the underlying processes. Hence, the parameter values optimised from the site data are used as a priori values in a global carbon cycle data assimilation system (CCDAS). CAMELS has so far produced one prototype CCDAS based on the ecosystem model BETHY: in a first data assimilation step, BETHY takes satellite-observed values of "greenness" to optimise parameters related to water status, phenology, and total plant cover. Next, the adjoint (the first derivative of the code with respect to model parameters) of the physiological and energy balance part of BETHY coupled with the adjoint of the atmospheric transport model TM2 is used to optimise parameter values of BETHY.

This is done by assimilation of atmospheric CO2 concentration measurements. Uncertainties of optimised model parameters can be derived from the Hessian (the second derivative) of the BETHY code with respect to the parameters. By using the Hessian of the BETHY code with respect to the parameters, uncertainties of optimised model parameters can also be derived. These uncertainties, that reflect both the prior information (in a Bayesian context), as well as the information from the large-scale inversion, can finally be translated into uncertainty bounds for CO2 fluxes and any other model diagnostic. Both the adjoint and Hessian codes are generated automatically using the compiler tool TAF, developed by FastOpt. Automatic generation ensures that improvements of BETHY can be used in the assimilation scheme without delay.


Ernst-Detlef SCHULZE, (Professor)
Tel.: +49-364-1643642
Fax: +49-364-1643665