Atmospheric Greenhouse Gas inverse modelling
Title: Atmospheric Greenhouse Gas inverse modelling
SNIC Project: SNIC 2021/5-549
Project Type: SNIC Medium Compute
Principal Investigator: Guillaume Monteil <guillaume.monteil@nateko.lu.se>
Affiliation: Lunds universitet
Duration: 2021-12-01 – 2022-12-01
Classification: 10501
Homepage: https://lumia.nateko.lu.se
Keywords:

Abstract

The project is a continuation and expansion of the small SNIC 2020/5-631 project. The aim is to support development of the atmospheric inversion group of the department of Physical Geography, at Lund University. We develop the LUMIA regional atmospheric inversion system, which aims to compute estimates 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. LUMIA inversions are used operationally in several large international projects (e.g., VERIFY, CoCO2), and as a research tool for several research projects at LU (use of 14CO2 observations to distinguish natural and anthropogenic CO2 (14C-FFDAS); development of new inversion techniques; atmospheric inversions using black carbon measurements). The aim of this project is to support these research efforts.