Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
doi:10.5194/acp-2017-257
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
24 Mar 2017
Review status
This discussion paper is under review for the journal Atmospheric Chemistry and Physics (ACP).
Advanced error diagnostics of the CMAQ and Chimere modelling systems within the AQMEII3 model evaluation framework
Efisio Solazzo1, Christian Hogrefe2, Augustin Colette3, Marta Garcia-Vivanco3,4, and Stefano Galmarini5 1European Commission, Joint Research Centre (JRC), Directorate for Energy, Transport and Climate, Air and Climate Unit, Ispra (VA), Italy
2Atmospheric Model Application and Analysis Branch – Computational Exposure Division – NERL, ORD, U.S. EPA
3INERIS, Institut National de l'Environnement Industriel et des Risques, Parc Alata, 60550 Vern euil-en-Halatte, France
4CIEMAT, Avda Complutense 40, Madrid, Spain
5European Commission, Joint Research Centre (JRC), Directorate for Sustainable Resources, Food and Security Unit, Ispra (VA), Italy
Abstract. The work here complements the overview analysis of the modelling systems participating in the third phase of the Air Quality Model Evaluation International Initiative (AQMEII3) by focusing on the performance for hourly surface ozone by two modelling systems, Chimere for Europe and CMAQ for North America.

The evaluation strategy outlined in the course of the three phases of the AQMEII activity, aimed to build up a diagnostic methodology for model evaluation, is pursued here and novel diagnostic methods are proposed. In addition to evaluating the base case simulation in which all model components are configured in their standard mode, the analysis also makes use of sensitivity simulations in which the models have been applied by altering and/or zeroing lateral boundary conditions, emissions of anthropogenic precursors, and ozone dry deposition.

To help understand of the causes of model deficiencies, the error components (bias, variance, and covariance) of the base case and of the sensitivity runs are analysed in conjunction with time-scale considerations and error modelling using the available error fields of temperature, wind speed, and NOx concentration.

The results reveal the effectiveness and diagnostic power of the methods devised (which remains the main scope of this study), allowing the detection of the time scale and the fields that the two models are most sensitive to. The representation of planetary boundary layers (PBL) dynamics is pivotal to both models. In particular: i) The fluctuations slower than −1.5 days account for 70–85 % of the total ozone quadratic error; ii) A recursive, systematic error with daily periodicity is detected, responsible for 10–20 % of the quadratic total error; iii) Errors in representing the timing of the daily transition between stability regimes in the PBL are responsible for a covariance error as large as 9 ppb (as much as the standard deviation of the network-average ozone observations in summer in both Europe and North America); iv) The CMAQ ozone error has a weak/negligible dependence on the errors in NO2 and wind speed, while the error in NO2 significantly impacts the ozone error produced by Chimere; v) On a continent wide monitoring network-average, a zeroing out of anthropogenic emissions produces an error increase of 45 % (25 %) during summer and of 56 % (null) during winter for Chimere (CMAQ), while a zeroing out of lateral boundary conditions results in an ozone error increase of 30 % during summer and of 180 % during winter (CMAQ).


Citation: Solazzo, E., Hogrefe, C., Colette, A., Garcia-Vivanco, M., and Galmarini, S.: Advanced error diagnostics of the CMAQ and Chimere modelling systems within the AQMEII3 model evaluation framework, Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2017-257, in review, 2017.
Efisio Solazzo et al.
Efisio Solazzo et al.
Efisio Solazzo et al.

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Short summary
The work here is conducted within the frame of the AQMEII3 activity, and promotes the use of diagnostic methods for the evaluation of air quality models. We highlight the need to move away from aggregated error metrics and to focus on the quality of the information that can be extracted from the model and the observation. This will aid in the understanding of the causes of model error, thus providing more useful information to model developers and users than can be gained from aggregate metrics.
The work here is conducted within the frame of the AQMEII3 activity, and promotes the use of...
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