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. We introduce a Multi-mOdel Multi-cOnstituent Chemicaldata assimilation (MOMO-Chem) framework that directly accounts for modelerror in transport and chemistry, and we integrate a portfolio of dataassimilation analyses obtained using multiple forward chemical transportmodels in a state-of-the-art ensemble Kalman filter data assimilationsystem. The data assimilation simultaneously optimizes both concentrationsand emissions of multiple species through ingestion of a suite ofmeasurements (ozone, NO2 , CO, HNO3 ) from multiple satellitesensors. In spite of substantial model differences, the observationaldensity and accuracy was sufficient for the assimilation to reduce themulti-model spread by 20 %–85 % for ozone and annual mean bias by39 %–97 % for ozone in the middle troposphere, while simultaneouslyreducing the tropospheric NO2 column biases by more than 40 % andthe negative biases of surface CO in the Northern Hemisphere by 41 %–94 %.For tropospheric mean OH, the multi-model mean meridional hemisphericgradient was reduced from 1.32±0.03 to 1.19±0.03 , while themulti-model spread was reduced by 24 %–58 % over polluted areas. Theuncertainty ranges in the a posteriori emissions due to model errors werequantified in 4 %–31 % for NOx and 13 %–35 % for CO regional emissions.Harnessing assimilation increments in both NOx and ozone, we show that thesensitivity of ozone and NO2 surface concentrations to NOx emissionsvaried by a factor of 2 for end-member models, revealing fundamentaldifferences in the representation of fast chemical and dynamical processes.A systematic investigation of model ozone response and analysis increment inMOMO-Chem could benefit evaluation of future prediction of the chemistry–climatesystem as a hierarchical emergent constraint.

Evaluation of a multi-model, multi-constituent assimilation framework for tropospheric chemical reanalysis