Global atmospheric methanol emissions inferred from satellite IASI measurements and aircraft data

We employ an updated retrieval of space-based methanol (CH₃OH) column measurements from the Infrared Atmospheric Sounding Interferometer (IASI) and an emission optimisation framework built on the adjoint of the MAGRITTE chemical transport model to assess terrestrial emissions of methanol to the atmosphere between 2008 and 2019. We first carry out a IASI CH₃OH validation study based on concentration measurements from three airborne campaigns over the U.S. in 2012-2013, using the model and the IASI averaging kernels to compute aircraft-based vertical columns directly comparable to IASI data.IASI is found to underestimate high columns and overestimate low columns in the considered region. A linear regression gives Ω_IASI = 0.46 Ω_airc + 10.6 · 10¹⁵ molec.cm^-2 , with Ω_IASI and Ω_airc the IASI and aircraft-derived vertical columns, respectively. Inverse modelling of terrestrial methanol emissions with the MAGRITTE model based on IASI columns corrected for biases using the above relationship leads to much-improved agreement over most regions against in situ observations from aircraft and surface measurement campaigns as well as column data at eight FTIR stations. The optimized global biogenic methanol emissions (160 Tg yr^-1 ) are 22-60% higher than previous top-down estimates, due to (1) column enhancements caused by the IASI bias-correction over source regions and (2) higher dry deposition velocities in the model over land, compared to previous model studies, based on a parametrisation constrained by field data from 13 campaign studies. The inversion results are less reliable over boreal forests due to shortcomings of both the bias-correction and the dry deposition scheme over these regions. The optimisation suggests large changes in the distribution and seasonality of biogenic emissions, such as enhanced emissions during warm and sunny periods over tropical ecosystems. In these regions, radiation and temperature seem to exert a stronger control on biogenic emissions than is currently accounted for in the MEGAN emission model, possibly due to leaf age effects currently not well accounted for in emission models.