Hindcasting and forecasting of regional methane from coal mine emissions in the Upper Silesian Coal Basin using the online nested global regional chemistry--climate model MECO(n) (MESSy v2.53)

Kurzfassung

Methane is the second most important greenhouse gas in terms of anthropogenic radiative forcing. Since pre-industrial times, the globally averaged dry mole fraction of methane in the atmosphere has increased considerably. Emissions from coal mining are one of the primary anthropogenic methane sources. However, our knowledge about different sources and sinks of methane is still subject to great uncertainties. Comprehensive measurement campaigns and reliable chemistry–climate models, are required to fully understand the global methane budget and to further develop future climate mitigation strategies. The CoMet 1.0 campaign (May to June 2018) combined airborne in situ, as well as passive and active remote sensing measurements to quantify the emissions from coal mining in the Upper Silesian Coal Basin (USCB, Poland). Roughly 502 kt of methane is emitted from the ventilation shafts per year. In order to help with the flight planning during the campaigns, we performed 6 d forecasts using the online coupled, three-time nested global and regional chemistry–climate model MECO(n). We applied three-nested COSMO/MESSy instances going down to a spatial resolution of 2.8 km over the USCB. The nested global–regional model system allows for the separation of local emission contributions from fluctuations in the background methane. Here, we introduce the forecast set-up and assess the impact of the model's spatial resolution on the simulation of methane plumes from the ventilation shafts. Uncertainties in simulated methane mixing ratios are estimated by comparing different airborne measurements to the simulations. Results show that MECO(3) is able to simulate the observed methane plumes and the large-scale patterns (including vertically integrated values) reasonably well. Furthermore, we obtain reasonable forecast results up to forecast day four.