Seminar: David Ho

The accumulation of anthropogenic GHGs since industrialization is the principal driver of climate change, particularly carbon dioxide (CO2) and methane (CH4). Accurately quantifying their emissions and removals is critical for understanding the carbon cycle and to support mitigation strategies. Traditional bottom-up inventories, based on activity data and emission factors, are often limited by data gaps, uncertainties, and inconsistent reporting standards. Inverse modelling offers a complementary, top-down approach by using atmospheric GHG concentration measurements in combination with Atmospheric Chemistry Transport Models (ACTMs) to infer surface fluxes. This allows for an observation-constrained and more spatially resolved estimate of emissions, especially in regions with limited bottom-up information. In support of the development of an operational Integrated Greenhouse Gas Monitoring System for Germany, or “Integriertes Treibhausgas-Monitoring System für Deutschland (ITMS)” in German, this work focuses on fundamental research in assimilating satellite-based GHG observations to provide independent verification and quantification of GHG emissions. The modular, Ensemble Kalman Filter (EnKF)-based CarbonTracker Data Assimilation Shell (CTDAS) inversion framework is implemented and extended for this purpose.