Development of a regional CO2 fluxes data assimilation system

The amplitude of global warming in the next century depends for a large part on the behavior of terrestrial ecosystems. They currently absorb a significant fraction (~25%) of our CO2 emissions, thus limiting the amplitude of global warming. They are also a major source of uncertainties in future climate predictions, as it is unclear how their capacity to absorb carbon will evolve in a changing climate.


Future climate predictions rely on vegetation models to compute the sensitivity of terrestrial ecosystems to a large number of climatic parameters (solar intensity, temperature, precipitations, frequency of “extreme” climatic events, etc.). To evaluate these models, it is common to test their ability to simulate known past events. However, testing specifically their capacity to simulate surface-atmosphere CO2 exchanges is difficult, since no large-scale direct observation of this flux is available.
In this project, we aim at producing a robust, high-resolution, estimate of European surface-atmosphere CO2 exchanges, using a so-called inverse approach. In an inverse approach, CO2 fluxes are estimated using observations of the atmospheric CO2 concentrations, without having to make assumptions on the physical drivers of these fluxes.

The link between observed concentration and surface fluxes is established using an atmospheric transport model, which simulates the dispersion of CO2 by atmospheric mixing (winds, convection, diffusion, etc.). From a first guess of the CO2 surface exchanges, we calculate the corresponding concentrations. From the difference with the observed concentrations, we derive a correction to the flux estimate.
This approach relies on both accurate numerical models to compute the atmospheric transport, and on robust statistical methods to derive the best possible CO2 flux correction.

Research group

PI:Marko Scholze
Department of Physical Geography and Ecosystem Science, Lund University
Guillaume Monteil
Department of Physical Geography and Ecosystem Science, Lund University