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Abstract

Though they cover less than 3 % of the global land area, urban areas are responsible for over 70 % of the global
greenhouse gas (GHG) emissions and contain 55 % of the global population. A quantitative tracking of GHG emissions in
urban areas is therefore of great importance, with the aim of accurately assessing the amount of emissions and identifying the
emission sources. The Weather Research and Forecasting model (WRF) coupled with GHG modules (WRF-GHG) developed
for mesoscale atmospheric GHG 5 transport, can predict column-averaged abundances of CO2 and CH4 (XCO2 and XCH4).
In this study, we use WRF-GHG to model the Berlin area at a high spatial resolution of 1 km. The simulated wind and
concentration fields were compared with the measurements from a campaign performed around Berlin in 2014 (Hase et al.,
2015). The measured and simulated wind fields mostly demonstrate good agreement and the simulated XCO2 agrees well with
the measurement. In contrast, a bias in the simulated XCH4 of around 2.7 % is found, caused by relatively high initialization
10 values for the background concentration field. We find that an analysis using differential column methodology (DCM) works
well for the XCH4 comparison, as corresponding background biases then cancel out. From the tracer analysis, we find that the
enhancement of XCH4 is highly dependent on human activities. The XCO2 signal in the vicinity of Berlin is dominated by
anthropogenic behavior rather than biogenic activities. We conclude that DCM is an effective method for comparing models
to observations independently of biases caused, e.g., by initial conditions. It allows us to use our high resolution WRF-GHG
15 model to detect and understand sources of GHG emissions quantitatively in urban areas.