Developing effective global strategies for climate mitigation requires an independent assessment of the greenhouse gas emission inventory at the urban scale. In the framework of the Dutch Ruisdael Observatory infrastructure project, we have enhanced the Dutch Atmospheric Large-Eddy-Simulation (DALES) model to simulate carbon dioxide (CO2) plume emission and three-dimensional dispersion within the turbulent boundary layer. The unique ability to explicitly resolve turbulent structures a the hectometer resolution (100 m) makes DALES particularly suitable for detailed realistic simulations of both singular high-emitting point sources and urban emissions, aligning with the goals of Ruisdael Observatory. The model setup involves a high-resolution simulation (100 m × 100 m) covering the main urban area of the Netherlands (51.5–52.5° N, 3.75–6.45° E). The model integrates meteorological forcing from the HARMONIE-AROME weather forecasting model, background CO2 levels from the CAMS reanalysis, and point source emissions and downscaled area emissions derived from the 1 km × 1 km emission inventory from the national registry. The latter are prepared using a sector-specific downscaling workflow, covering major emission categories. Biogenic CO2 exchanges from grasslands and forests are interactively included in the hectometer calculations within the heterogeneous land–surface model of DALES. Our evaluation strategy is twofold, comparing DALES simulations with (i) the state-of-the art LOTOS-EUROS model simulations and (ii) Ruisdael surface observations of the urban background in the Rotterdam area at Westmaas and Slufter and in situ rural Cabauw tower measurements. Our comprehensive statistical analysis confirmed the effectiveness of DALES at modeling the urban-scale CO2 emission distribution and plume dispersion under turbulent conditions but also revealed potential limitations and areas for further improvement. Thus, our new model framework provides valuable insights into the role of anthropogenic and biogenic contributions to local CO2 levels, as well as the transport and dispersion of CO2 emissions. This supports emission uncertainty reduction using atmospheric measurements and contributes to the development of effective regional climate mitigation strategies.

Southeast Australia experienced intensive and geographically extensive wildfires during the 2019–2020 summer season^1,2. The fires released substantial amounts of carbon dioxide into the atmosphere³. However, existing emission estimates based on fire inventories are uncertain⁴, and vary by up to a factor of four for this event. Here we constrain emission estimates with the help of satellite observations of carbon monoxide⁵, an analytical Bayesian inversion⁶ and observed ratios between emitted carbon dioxide and carbon monoxide⁷. We estimate emissions of carbon dioxide to be 715 teragrams (range 517–867) from November 2019 to January 2020. This is more than twice the estimate derived by five different fire inventories^8–12, and broadly consistent with estimates based on a bottom-up bootstrap analysis of this fire episode¹³. Although fires occur regularly in the savannas in northern Australia, the recent episodes were extremely large in scale and intensity, burning unusually large areas of eucalyptus forest in the southeast¹³. The fires were driven partly by climate change^14,15, making better-constrained emission estimates particularly important. This is because the build-up of atmospheric carbon dioxide may become increasingly dependent on fire-driven climate–carbon feedbacks, as highlighted by this event¹⁶.

The global fire emission inventories depend on ground and airborne measurements of species-specific emission factors (EFs), which translate dry matter losses due to fires to actual trace gas and aerosol emissions. The EFs of nitrogen oxides (NOx) and carbon monoxide (CO) can function as a proxy for combustion efficiency to distinguish flaming from smoldering combustion. The uncertainties in these EFs remain large as they are limited by the spatial and temporal representativeness of the measurements. The global coverage of satellite observations has the advantage of filling this gap, making these measurements highly complementary to ground-based or airborne data. We present a new analysis of biomass burning pollutants using space-borne data to investigate the spatiotemporal efficiency of fire combustion. Column measurements of nitrogen dioxide and carbon monoxide from the TROPOspheric Monitoring Instrument (TROPOMI) are used to quantify the relative atmospheric enhancements of these species over different fire-prone regions around the world. We find spatial and temporal patterns in the classCombining double low linespan classCombining double low lineratio that point to distinct differences in biomass burning behavior. Such differences are induced by the burning phase of the fire (e.g., high-temperature flaming vs. low-temperature smoldering combustion) and burning practice (e.g., the combustion of logs, coarse woody debris and soil organic matter vs. the combustion of fine fuels such as savanna grasses). The sampling techniques and the signal-to-noise ratio of the retrieved <span classCombining double low signals were quantified with WRF-Chem experiments and showed similar distinct differences in combustion types. The TROPOMI measurements show that the fraction of surface smoldering combustion is much larger for the boreal forest fires in the upper Northern Hemisphere and peatland fires in Indonesia. These types of fires cause a much larger increase (3 to 6 times) in <span classCombining double low lineinline-formula relative to span classCombining double low lineinline-formula than elsewhere in the world. The high spatial and temporal resolution of TROPOMI also enables the detection of spatial gradients in combustion efficiency at smaller regional scales. For instance, in the Amazon, we found higher combustion efficiency (up to 3-fold) for savanna fires than for the nearby tropical deforestation fires. Out of two investigated fire emission products, the TROPOMI measurements support the broad spatial pattern of combustion efficiency rooted in GFED4s. Meanwhile, TROPOMI data also add new insights into regional variability in combustion characteristics that are not well represented in the different emission inventories, which can help the fire modeling community to improve their representation of the spatiotemporal variability in EFs.