Assessment of satellite observation-based wildfire emissions inventories using TROPOMI data and IFS-COMPO model simulations

Abstract. Fires are a key component of the global carbon cycle and humans are changing their characteristics. Fire emission monitoring is important to keep track of those changes and TROPOMI satellite observations of tropospheric nitrogen dioxide, carbon monoxide and the absorbing aerosol index can be used to quantify and verify the accuracy and precision of global wildfire emission estimates on a daily basis. Here we use TROPOMI observations to evaluate a new fire emission database based on Global Fire Atlas input for the Sense4Fire project (GFA-S4F) and from the Copernicus Atmosphere Monitoring (CAMS) Global Fire Assimilation System (GFAS) for a number of test regions worldwide representative of the most important wildfire type environments. The main focus is on Amazon and Cerrado biomes (tropical rain forests and deforestation) during August–September 2020, but analyses are also made for a region in sub-Saharan Africa (savannah) as well as two regions in Siberia (steppe and boreal forests/tundra). GFA-S4F and GFAS fire emissions are used as input for global atmospheric composition model simulations based on IFS-COMPO, i.e. an extension of ECMWF’s Integrated Forecasting System (IFS) for simulating atmospheric composition. Comparing the model output with the TROPOMI observations then provides an indirect check on the realism of these emission estimates. Furthermore, for tropospheric nitrogen dioxide the IFS-COMPO model simulations are also used to estimate the model sensitivity of tropospheric nitrogen dioxide columns with respect to fire emission changes. This local relationship is used to optimize the fire NO_x emissions directly using the Sentinel-5p nitrogen dioxide observations.

The results reveal that for small fires emission nitrogen dioxide estimates are realistic on average albeit with a large spread, i.e. for individual fires emissions can be significantly under or overestimated regardless of emission database. However, for large fires nitrogen dioxide emissions are systematically and largely overestimated in all four regions. The overestimation can be an order of magnitude or even more. For area total nitrogen dioxide emissions this “large fire bias” is of minor importance, i.e. total nitrogen dioxide emissions are dominated by small fires. The GFA-S4F emission estimates were characterized by a larger positive bias for large fire NO₂ emission cases compared to GFAS. The source of this bias is not well understood. With optimized NO₂ emissions by direct adjustment of emission using TROPOMI nitrogen dioxide observations the large positive bias can efficiently be resolved. Combined with an update of soil NO_x emissions – causing too low background NO_x levels – a fairly good agreement between IFS-COMPO and TROPOMI was reached.

Carbon monoxide was generally underestimated using GFAS emission (~50 % on average for the selected regions). Updating carbon monoxide emissions over the Amazon region by incorporating more Sentinel satellite data (GFA-S4F) did reduce this fire CO bias significantly (to ~25 % on average).

Overall, the results show that TROPOMI data allows for systematically identifying uncertainties and errors in satellite-data based fire emissions. The results also suggest that the use of dynamic emission factors may further improve satellite based global emissions inventories. In addition, the results also highlight that the use of TROPOMI data could be much more detailed and refined towards assessing individual fires on a daily basis for better understanding fire dynamics and to improve and diversify fire emission factors.