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Volume 17, issue 17 | Copyright

Special issue: Global and regional assessment of intercontinental transport...

Atmos. Chem. Phys., 17, 10435-10465, 2017
https://doi.org/10.5194/acp-17-10435-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 07 Sep 2017

Research article | 07 Sep 2017

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 Efisio Solazzo et al.
  • 1European Commission, Joint Research Centre (JRC), Directorate for Energy, Transport and Climate, Air and Climate Unit, Ispra (VA), Italy
  • 2Environmental Protection Agency, Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, Research Triangle Park, NC 27711, USA
  • 3INERIS, Institut National de l'Environnement Industriel et des Risques, Parc Alata, 60550 Verneuil-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 timescale 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 timescale and the fields that the two models are most sensitive to. The representation of planetary boundary layer (PBL) dynamics is pivotal to both models. In particular, (i) the fluctuations slower than ∼1.5 days account for 70–85% of the mean square error of the full (undecomposed) ozone time series; (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 9ppb (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, while the error in NO2 significantly impacts the ozone error produced by Chimere; (v) the response of the models to variations of anthropogenic emissions and boundary conditions show a pronounced spatial heterogeneity, while the seasonal variability of the response is found to be less marked. Only during the winter season does the zeroing of boundary values for North America produce a spatially uniform deterioration of the model accuracy across the majority of the continent.

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The work here is conducted within the frame of AQMEII3 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 aids the understanding of the causes of model error, providing more useful information to model developers and users than can be gained from common evaluations.
The work here is conducted within the frame of AQMEII3 and promotes the use of diagnostic...
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