Sack Lunch Seminar (SLS)

A Global Carbon Cycle Data Assimilation System (CCDAS) to Infer Terrestrial CO2 Fluxes and Their Uncertainties





Atmospheric inversion studies have become an important tool for identifying terrestrial sources and sinks of CO2 at the interannual time scale. Such traditional top-down studies have so far delivered important insights into the atmosphere-biosphere and atmosphere-ocean CO2 exchanges fluxes. However, they suffer from the inverse problem being seriously under determined and they do not have any prognostic power, i.e. they cannot predict the evolution of future CO2 fluxes. These inverse methods are usually contrasted with bottom-up approaches using process-based terrestrial or oceanic models capable of predicting CO2 fluxes. These models, however, cannot take into account the information contained in CO2 measurements from the extensive flask sampling network. I will present results from a carbon cycle data assimilation system (CCDAS) in which atmospheric CO2 concentration data are assimilated into a terrestrial biosphere model. CCDAS calibrates values of the parameters controlling the function of the processes within the terrestrial biosphere model against these observations. The model is run forward using these optimized parameters and calculates quantities of interest such as the net carbon flux. This can be done in a diagnostic mode, calculating fluxes for the same period as the assimilation consistent both with observations and model dynamics. If, however, the model is prognostic one can run it for other periods, either the future or the past. The assimilation procedure also allows to calculate uncertainties on the parameters, which can then be propagated onto diagnostic/prognostic quantities. I will report on results from a hindcasting experiment for prognostically calculated net CO2 fluxes plus uncertainties for the years 2000 to 2003 as well as on the most recent developments to include more observational constraints such as remotely sensed vegetation activity.