Advanced error diagnostics of the CMAQ and Chimere modelling systems within the AQMEII3 model evaluation framework

Abstract

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 NO x 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 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 NO 2 , while the error in NO 2 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.

Sensitivity of stomatal conductance to soil moisture: implications for tropospheric ozone

“…Furthermore, Mészáros et al (2009) pointed out that variation in surface resistance can involve differences in variability in total deposition velocity of up to 2 or 3 times, also indicating soil moisture as a key variable controlling the O 3 dry deposition. Moreover, our results are in agreement with Solazzo et al (2017), which created a diagnostic methodology for model evaluation; using CHIMERE, they showed that setting the ozone dry deposition velocity to zero causes a profound change of the error structure of O 3 concentration with significant impacts on not only the bias but also the variance and covariance terms (Solazzo et al, 2017). All these studies highlight that more sophisticated parameterizations of stomatal conductance are required in deposition models to reduce their uncertainty.…”

“…This result is in agreement with a previous study which showed how, within CHIMERE, the deposition not only acts as a shifting term on the modelled concentration but also influences the variability and timing of ozone (Solazzo et al, 2017).…”

Model Performance Differences in Sulfate Aerosol in Winter over Japan Based on Regional Chemical Transport Models of CMAQ and CAMx

“…As we discussed in relation to the common model underestimation at Nagoya (Figure 4), further studies of the effects of meteorological parameters are needed, and this direction was also suggested by another model intercomparison study in Europe [31]. Another important factor is the vertical distribution of air pollutants simulated by the models.…”

Response to Reviewer 2

“…In AQMEII, all participating groups were required to upload modeled hourly surface concentrations to the ENSEMBLE system at EC-JRC, at specified monitoring stations in EU and NA, as well as surface gridded data Im et al, 2015a, b;Solazzo et al, 2017b). This study investigates the impacts of emission perturbations and boundary conditions on O 3 , NO 2 , CO, SO 2 , PM 10 and PM 2.5 levels over Europe and North America.…”

“…The model biases can be attributed to meteorology, in particular wind speed and planetary boundary layer (PBL) height, as well as the aerosol mechanisms used in different models that can underestimate either the inorganic aerosols (e.g., IT2) or the secondary organic aerosols (e.g., DK1), leading to underestimations in simulated PM mass. As discussed in Solazzo et al (2017b), the EU3 region that covers the central Europe including the Alps has the largest errors in terms of wind speed, mainly attributed to the diurnal component of the error, with some models having also large errors in the synoptic component. This region also represents the lowest correlation coefficients for all models.…”