Abstract
Abstract. 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, NO2, CO, HNO3) 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 NO2 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 1.32±0.03 to 1.19±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 NOx and 13 %–35 % for CO regional emissions. Harnessing assimilation increments in both NOx and ozone, we show that the sensitivity of ozone and NO2 surface concentrations to NOx 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.
Highlights
Data assimilation is a technique for combining different observational data sets with a model, taking into consideration of the characteristics of individual measurements and model dynamics (e.g., Kalnay, 2003; Lahoz and Schneider, 2014)
The state vectors for the MIROC-Chem and MIROC-Chem-H systems include a correction factor for emission diurnal variability to improve the representation of diurnal emission variability using the Ozone Monitoring Instrument (OMI) and SCIAMACHY retrievals obtained at different overpass times, based on the scheme developed by Miyazaki et al (2017)
We developed the MOMO-Chem framework to integrate a portfolio of data assimilation analyses obtained using forward chemical transport models (CTMs) (GEOS-Chem, atmospheric general circulation model (AGCM)-CHASER, MIROCChem, MIROC-Chem-H) in a state-of-the-art ensemble Kalman filter data assimilation system
Summary
Data assimilation is a technique for combining different observational data sets with a model, taking into consideration of the characteristics of individual measurements and model dynamics (e.g., Kalnay, 2003; Lahoz and Schneider, 2014). Xue and Zhang (2014) extended data assimilation to the multi-model Bayesian model averaging analysis framework, in which the posterior model weight for each model is determined through Bayes’ theorem reflecting the prior probability of each model and the analysis consistency with the observations This approach requires a framework to execute and update multiplemodel states continuously, which is difficult with multiple state-of-the-art CTMs that have been optimized using different platforms. Uncertainty-weighed multi-model integrated analysis fields would provide unique information that is less dependent on individual model performance and is fundamentally different from averages of individual data assimilation analyses. This study demonstrates, for the first time, the importance of forecast model performance on data assimilation analysis of tropospheric composition and emissions, by utilizing four different CTM frameworks and applying a common EnKF approach. Using the same data assimilation settings and assimilating almost the same multi-constituent observations from multiple satellite sensors, we examine how model bias affects tropospheric chemistry data assimilation performance, including emission estimation, and provide integrated data assimilation analysis fields from an ensemble of analyses that ingested multiple models and multi-constituent measurements
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