Atmospheric Greenhouse Gas inverse modelling
||Atmospheric Greenhouse Gas inverse modelling|
||SNIC Medium Compute|
||Guillaume Monteil <firstname.lastname@example.org>|
||2019-12-01 – 2020-12-01|
The project is a continuation and expansion of the small SNIC 2017/5-70 project. The aim is to support development of the atmospheric inversion group of the department of Physical Geography, at Lund University.
We run a variety of atmospheric inversion systems, with the aim to compute estimate of regional greenhouse gas sources and sinks, using a combination of prior information from process models, such as LPJ-GUESS developed in Lund, and of atmospheric observations of greenhouse gas concentrations, such as observations from the ICOS network. In short, the principle is to use an atmospheric transport model to compute the concentrations corresponding to a “prior” estimate of the fluxes, which are then compared to observed concentrations. The aim of the inversion is then to find the set of fluxes that minimizes the mismatch with observations, while minimizing departures from the prior estimate. That optimal solution is searched for iteratively, which requires many (>100) applications of the transport model.
The usage of atmospheric inversions in the past years has been boosted on a technical side by the development of regional high-density in situ measurement networks, such as ICOS in Europe, and on a political side by the need of assessing the carbon balance of European countries, within the framework of the Paris agreement on climate. Our group is therefore currently expanding, and atmospheric inversions are used or will be used in several granted research projects, for which the current small SNAC project is insufficient.