<p>We introduce a Multi-mOdel Multi-cOnstituent Chemical data assimilation (MOMO-Chem) framework that directly accounts for model error in transport and chemistry, and we integrate a portfolio of data assimilation analyses obtained using multiple forward chemical transport models in a state-of-the-art ensemble Kalman filter data assimilation system. The data assimilation simultaneously optimizes both concentrations and emissions of multiple species through ingestion of a suite of measurements (ozone, <span classCombining double low line"inline-formula">NO2</span>, CO, <span classCombining double low line"inline-formula">HNO3</span>) from multiple satellite sensors. In spite of substantial model differences, the observational density and accuracy was sufficient for the assimilation to reduce the multi-model spread by 20&thinsp;%-85&thinsp;% for ozone and annual mean bias by 39&thinsp;%-97&thinsp;% for ozone in the middle troposphere, while simultaneously reducing the tropospheric <span classCombining double low line"inline-formula">NO2</span> column biases by more than 40&thinsp;% and the negative biases of surface CO in the Northern Hemisphere by 41&thinsp;%-94&thinsp;%. For tropospheric mean OH, the multi-model mean meridional hemispheric gradient was reduced from <span classCombining double low line"inline-formula">1.32±0.03</span> to <span classCombining double low line"inline-formula">1.19±0.03</span>, while the multi-model spread was reduced by 24&thinsp;%-58&thinsp;% over polluted areas. The uncertainty ranges in the a posteriori emissions due to model errors were quantified in 4&thinsp;%-31&thinsp;% for <span classCombining double low line"inline-formula">NO<i>x</i></span> and 13&thinsp;%-35&thinsp;% for CO regional emissions. Harnessing assimilation increments in both <span classCombining double low line"inline-formula">NO<i>x</i></span> and ozone, we show that the sensitivity of ozone and <span classCombining double low line"inline-formula">NO2</span> surface concentrations to <span classCombining double low line"inline-formula">NO<i>x</i></span> emissions varied by a factor of 2 for end-member models, revealing fundamental differences in the representation of fast chemical and dynamical processes. A systematic investigation of model ozone response and analysis increment in MOMO-Chem could benefit evaluation of future prediction of the chemistry-climate system as a hierarchical emergent constraint.</p>.

Atmospheric Chemistry and Physics
Department of Civil and Environmental Engineering

Miyazaki, K. (Kazuyuki), Bowman, W.K. (W. Kevin), Yumimoto, K. (Keiya), Walker, T.W, & Sudo, K. (Kengo). (2020). Evaluation of a multi-model, multi-constituent assimilation framework for tropospheric chemical reanalysis. Atmospheric Chemistry and Physics, 20(2), 931–967. doi:10.5194/acp-20-931-2020