Quantifying the impact of urban greenhouse gas emissions for Munich during the COVID-19 pandemic using WRF V3.9.1.1

<p>During the COVID-19 pandemic lockdowns, human activities are strongly restricted, which results in a reduction in greenhouse gas (GHG) emissions associated with changes in energy consumptions. The Copernicus Atmosphere Monitoring Service (CAMS) reported a 10.3% decrease in CO<sub>2</sub> fossil fuel emissions during the first lockdown (February-July, 2020) of the COVID-19 pandemic throughout Europe. Using our WRF modeling framework built for the Munich area [1,3] and the column measurements from our automated Munich Urban Carbon Column network (MUCCnet, [2]), we aim to quantify the reduction of GHG emissions within Munich during the COVID-19 pandemic.</p><p>Our high-resolution modeling framework can simulate the sources, sinks, and emissions of CO<sub>2</sub> and CH<sub>4 </sub>at a spatial resolution of up to 400m. The initial and boundary conditions for meteorological fields are taken from ERA5 and CAMS data is used for initializing the initial and lateral tracer boundary conditions. Anthropogenic emissions below ~1 km altitude above the ground level are obtained from TNO-GHGco v1.1 at a resolution of 1 km<sup>2</sup>. Various tagged tracers are included to quantify the contribution from different emission categories (such as biogenic emissions from wetlands, emissions from road transport, industry, etc). By refining the vegetation classification using the Dynamic Land Cover map of the Copernicus Global Land Service at 100 m resolution (CGLS-LC100), the urban biogenic signals of CO<sub>2</sub> can be well captured using the diagnostic light-use-efficiency biosphere model VPRM (Vegetation Photosynthesis and Respiration Model), which is driven by MODIS indices. Moreover, we integrate urban canopy information derived from World Urban Database and Access Portal Tools (WUDAPT) classified by local climate zones (LCZs) [4] into our model infrastructure. Incorporating precise urban land use data in WRF helps to capture more urban transport features, improving the model behavior within urban areas.</p><p>We targeted the pandemic period from February to July 2020 and the same period in 2019 to make a comparison. Thanks to our nearly continuous column measurements during the COVID-19 pandemic, we are able to evaluate our simulated GHG concentrations by comparing them to the measurement results. Furthermore, an estimation of GHG emissions reduction in Munich during the targeted period will be obtained by performing a Bayesian inversion approach incorporating the simulated concentration enhancements from tagged tracers in WRF.</p><p>[1] Zhao, X., Chen, J., Marshall, J., Galkowski, M., Gerbig, C., Hachinger, S., Dietrich, F., Lan, L., Knote, C., and van der Gon, H.D., 2020. A semi-operational near-real-time Modelling Infrastructure for assessing GHG emissions in Munich using WRF-GHG. In EGU General Assembly 2020.</p><p>[2] Dietrich, F., Chen, J., Voggenreiter, B., Aigner, P., Nachtigall, N., and Reger, B.: Munich permanent urban greenhouse gas column observing network, Atmos. Meas. Tech. Discuss. https://doi.org/10.5194/amt-2020-300, in review, 2020.</p><p>[3] Zhao, X., Marshall, J., Hachinger, S., Gerbig, C., Frey, M., Hase, F., and Chen, J.: Analysis of total column CO<sub>2</sub> and CH<sub>4</sub> measurements in Berlin with WRF-GHG, Atmos. Chem. Phys., 19, 11279–11302, https://doi.org/10.5194/acp-19-11279-2019, 2019.</p><p>[4] Demuzere, M., Bechtel, B., Middel, A., & Mills, G. (2019). Mapping Europe into local climate zones. PLOS ONE, 14(4), e0214474. https://doi.org/10.1371/journal.pone.0214474.</p>