Quantifying the constraint of biospheric process parameters by CO2 concentration and flux measurement networks through a carbon cycle data assimilation system
- 1Laboratoire des Sciences du Climat et de l'Environnement (LSCE), UMR8212, Ormes des merisiers, 91191 Gif-sur-Yvette, France
- 2School of Earth Sciences, University of Melbourne, Melbourne, Australia
- 3School of Earth Sciences, University of Bristol, Queen's Road, Bristol BS8 1RJ, UK
- 4FastOpt, Lerchenstraße 28a, 22767 Hamburg, Germany
- *now at: the European Commission Joint Research Centre, Institute for Environment and Sustainability, 21027 Ispra (Va), Italy
Abstract. The sensitivity of the process parameters of the Biosphere Energy Transfer HYdrology (BETHY) model to choices of atmospheric concentration network, high frequency terrestrial fluxes, and the choice of flux measurement network is investigated by using a carbon cycle data assimilation system. We use BETHY-generated fluxes as a proxy of flux measurements. Results show that monthly mean or low-frequency observations of CO2 concentration provide strong constraints on parameters relevant for net flux (NEP) but only weak constraints for parameters controlling gross fluxes. The use of high-frequency CO2 concentration observations, which has led to great refinement of spatial scales in inversions of net flux, adds little to the observing system in the Carbon Cycle Data Assimilation System (CCDAS) case. This unexpected result is explained by the fact that the stations of the CO2 concentration network we use are not well placed to measure such high frequency signals. Indeed, CO2 concentration sensitivities relevant for such high frequency fluxes are found to be largely confined in the vicinity of the corresponding fluxes, and are therefore not well observed by background monitoring stations. In contrast, our results clearly show the potential of flux measurements to better constrain the model parameters relevant for gross primary productivity (GPP) and net primary productivity (NPP). Given uncertainties in the spatial description of ecosystem functions, we recommend a combined observing strategy.