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

Miyazaki, Kazuyuki; Bowman, Kevin W.; Yumimoto, Keiya; Walker, Thomas; Sudo, Kengo

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, inline-formulaNO2, CO, inline-formulaHNO3) 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 %–85 % for ozone and annual mean bias by 39 %–97 % for ozone in the middle troposphere, while simultaneously reducing the tropospheric inline-formulaNO2 column biases by more than 40 % and the negative biases of surface CO in the Northern Hemisphere by 41 %–94 %. For tropospheric mean OH, the multi-model mean meridional hemispheric gradient was reduced from inline-formula1.32±0.03 to inline-formula1.19±0.03, while the multi-model spread was reduced by 24 %–58 % over polluted areas. The uncertainty ranges in the a posteriori emissions due to model errors were quantified in 4 %–31 % for inline-formulaNOx and 13 %–35 % for CO regional emissions. Harnessing assimilation increments in both inline-formulaNOx and ozone, we show that the sensitivity of ozone and inline-formulaNO2 surface concentrations to inline-formulaNOx 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.



Miyazaki, Kazuyuki / Bowman, Kevin W. / Yumimoto, Keiya / et al: Evaluation of a multi-model, multi-constituent assimilation framework for tropospheric chemical reanalysis. 2020. Copernicus Publications.


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