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

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.

Intercomparison of Magnitudes and Trends in Anthropogenic Surface Emissions From Bottom‐Up Inventories, Top‐Down Estimates, and Emission Scenarios

“…Constraining multiple species in inversion modeling methods can lead to better consistency in simulated atmospheric chemical processes and thus more accurate optimized emissions (e.g., Zheng et al, 2019). Correctly modeling OH fields are also important due to its significant impact on oxidation processes (Müller, 2018;Jiang, 2011;Miyazaki 2020). More generally, the representation of chemical and transport processes in model should be improved.…”

An intercomparison of tropospheric ozone reanalysis products from CAMS, CAMS interim, TCR-1, and TCR-2

“…go.jp/~miyazaki/tcr/ (last access: 1 July 2019; Miyazaki et al, 2015); the TCR-2 reanalysis is available from https://tes.jpl.nasa. gov/chemical-reanalysis/(last access: 1 December 2019; Miyazaki et al, 2019bMiyazaki et al, , 2020b.…”

An updated tropospheric chemistry reanalysis and emission estimates, TCR-2, for 2005–2018

“…The ozone response to precursor's emissions was also found to be strongly sensitive to the chemical mechanisms in the model, which varied by a factor of 2 for end-member models, revealing fundamental differences in the representation of fast chemical and dynamical processes (Miyazaki et al, 2020). The emissions of ozone precursors other than NO x and CO, such as VOCs, have a pronounced influence on the tropospheric chemistry.…”

“…Meanwhile, a strong reanalysis dependence on forecast model performance was found on the near surface concentrations and precursor emissions, associated with insufficient observational constraints (Huijnen et al, 2019;Miyazaki et al, 2020).…”

“…The optimized precursor emission fields can be used to validate bottom-up emission inventories and lightning parameterizations. As changes in tropospheric ozone burden and NO x emissions show a close relation in different future scenarios , evaluations using the estimated emissions and evaluated model response to emissions (Miyazaki et al, 2020) have the potential to evaluate preindustrial, present day, and future model simulations. Short-lived climate pollutants (SLCP) are an increasingly important component of greenhouse gas budgets that limit warming to target temperatures, e.g.,…”