Jochen Theloke

Can we evaluate a fine-grained emission model using high-resolution atmospheric transport modelling and regional fossil fuel CO2 observations?

Quantifying carbon dioxide emissions from fossil fuel burning (FFCO2) is a crucial task to assess continental carbon fluxes and to track anthropogenic emissions changes in the future. In the present study, we investigate potentials and challenges when combining observational data with simulations using high-resolution atmospheric transport and emission modelling. These challenges concern, for example, erroneous vertical mixing or uncertainties in the disaggregation of national total emissions to higher spatial and temporal resolution. In our study, the hourly regional fossil fuel CO2 offset (ΔFFCO2) is simulated by transporting emissions from a 5 min×5 min emission model (IER2005) that provides FFCO2 emissions from different emission categories. Our Lagrangian particle dispersion model (STILT) is driven by 25 km×25 km meteorological data from the European Center for Medium-Range Weather Forecast (ECMWF). We evaluate this modelling framework (STILT/ECMWF+IER2005) for the year 2005 using hourly ΔFFCO2 estimates derived from 14C, CO and 222Radon (222Rn) observations at an urban site in south-western Germany (Heidelberg). Analysing the mean diurnal cycles of ΔFFCO2 for different seasons, we find that the large seasonal and diurnal variation of emission factors used in the bottom-up emission model (spanning one order of magnitude) are adequate. Furthermore, we show that the use of 222Rn as an independent tracer helps to overcome problems in timing as well as strength of the vertical mixing in the transport model. By applying this variability correction, the model-observation agreement is significantly improved for simulated ΔFFCO2. We found a significant overestimation of ΔFFCO2 concentrations during situations where the air masses predominantly originate from densely populated areas. This is most likely caused by the spatial disaggregation methodology for the residential emissions, which to some extent relies on a constant per capita-based distribution. In the case of domestic heating emissions, this does not appear to be sufficient.

Discrepancies Between Top-Down and Bottom-Up Emission Inventories of Megacities: The Causes and Relevance for Modeling Concentrations and Exposure

A state-of-the-art regional European emission data base is combined and cross-checked with bottom-up emission inventories of Paris, London, Rhine-Ruhr area (Germany) and the Po-valley (Italy). It is shown that the allocation of the emission in the regional top-down inventory can deviate substantially from the megacity bottom-up inventories. For example, the PM10 and NOx in local inventories are respectively 26% and 62% (London), 33% and 95% (Paris), 55% and 108% (Rhine-Ruhr) and 110% and 107% (Po valley) of the emission allocated to the same area in the regional inventory. The match for the Po Valley is reasonable but if we zoom in on a city level (Milan) similar problems as seen in Paris and London surface. We conclude that the European scale inventory is consistent with official reported national emissions but local-national-regional scale inventories are not consistent. Since the discrepancies are large, predicted concentrations and population exposure estimates may be significantly different. Our work shows the importance of regionalization of emissions for model input and argues that consistency between emission inventories at different scales deserves more attention.

Temporal and Spatial Distribution of Carbon Emissions

In the early stages of research in climate change, the focus was on the quantification of global and regional carbon cycles. At this stage, the accurate determination of location and time of emissions played a less prominent role. But with the growing need for verification experiments and prior information for atmospheric transport models (Chaps. 3, 12) to underpin and support policy development, and thus the application of climate and atmospheric dispersion models, it became evident that both anthropogenic and biogenic carbon emissions had to be provided in a spatial and temporal resolution matching the requirements of said models. The methodology for the temporal and spatial resolution of anthropogenic emissions, in general, has been around for some time. Some of the techniques described in the following sections have, for instance, been developed for the application of atmospheric dispersion models to quantify acid deposition and ambient air concentrations of tropospheric ozone. One major activity during the 1990s in this field has been the work within the EUROTRAC subproject Generation and Evaluation of Emission Data (GENEMIS), which has been documented in Friedrich and Reis (2004).