Communication à un colloque
Résumé : Nitrous acid (HONO) plays a key role in atmospheric chemistry as a major source – through rapid photolysis – of the hydroxyl radical (OH), the primary oxidant in the Earth's atmosphere. However, significant uncertainties remain on the spatial and temporal variability of HONO, on its formation pathways in the atmosphere, and on the contribution of its primary emissions over its secondary formation. Recently, spaceborne measurements in the UV-Vis spectral domain, taken in the early afternoon with the S5P/TROPOMI instrument, have provided a first global picture of HONO in fresh biomass burning plumes, demonstrating the importance of satellite data for improving our representation of atmospheric HONO. With daily overpass times in the early morning and early evening, the polar-orbiting IASI/Metop instruments and their global measurements of the Earth's radiance in the thermal infrared, have the potential to contribute to tackling remaining uncertainties on HONO and to complement the TROPOMI measurements.So far detected by infrared satellite sounders in the exceptional 2009 and 2019/2020 Australian bushfires only, we use a sensitive detection method to demonstrate that unambiguous HONO enhancements can also be identified in IASI spectra recorded in concentrated fire plumesworldwide. With this method, we analyse the long, unique observational timeseries (2007-2022) of IASI and we report a 15-year record of fire events in which HONO has been detected. This dataset reveals first that HONO is primarily captured by IASI at the Northern Hemisphere mid and high latitudes, and secondly that the IASI evening measurements allow a significantly higher number of HONO detections than during daytime despite the overall weaker thermal contrast and lower measurement sensitivity affecting such night-time observations. We discuss different factors that can explain these features, such as the sharp intra-day variability of HONO, the links with the diurnal variations and intensity of fires, and the vertical sensitivity of the IASI measurements. We apply a retrieval approach based on an artificial neural network to quantify the vertical abundance of HONO in IASI measurements. For selected fires, we analyse the temporal evolution of the HONO total columns along with TROPOMI data.