A tropospheric chemistry reanalysis based multi-constituent satellite data assimilation
I will present the results from a ten-year tropospheric chemistry reanalysis for the period 2005–2014 obtained by assimilating multiple data sets from the OMI, MLS, TES, and MOPITT satellite instruments. The reanalysis calculation was conducted using a global chemical transport model and an ensemble Kalman filter data assimilation approach that simultaneously optimizes the chemical concentrations of various species and emissions of several precursors. The optimization of both the multiple species concentration and the emission fields is an efficient method to correct the entire tropospheric profile and its year-to-year variations, and to adjust various tracers chemically linked to the species assimilated, while taking their feedbacks into account. This is also expected to improve the emission inversion because the emission estimates are influenced by biases in the modelled tropospheric chemistry, which can be partly corrected by also optimizing the concentrations. We analyzed detailed distributions of the estimated emission distributions for all major regions, the diurnal and seasonal variability, and the development of these emissions over the past ten-year period. The assimilation of multiple chemical data sets with different vertical sensitivity profiles also provides comprehensive constraints on the global lightning NOx source and its year-to-year variations. In summary, the consistent concentration and emission products provide comprehensive information on atmospheric composition variability in order to improve the understanding of the processes controlling the atmospheric environment, including OH, and their roles in changing climate.