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

2017 ◽  
Vol 17 (17) ◽  
pp. 10435-10465 ◽  
Author(s):  
Efisio Solazzo ◽  
Christian Hogrefe ◽  
Augustin Colette ◽  
Marta Garcia-Vivanco ◽  
Stefano Galmarini
Keyword(s):  
North America ◽  
Boundary Conditions ◽  
Model Evaluation ◽  
Total Error ◽  
Surface Ozone ◽  
Winter Season ◽  
Evaluation Framework ◽  
Diagnostic Methods ◽  
Base Case ◽  
Air Quality Model

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 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, 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.

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

2017 ◽  
Author(s):  
Efisio Solazzo ◽  
Christian Hogrefe ◽  
Augustin Colette ◽  
Marta Garcia-Vivanco ◽  
Stefano Galmarini
Keyword(s):  
Wind Speed ◽  
North America ◽  
Boundary Conditions ◽  
Time Scale ◽  
Model Evaluation ◽  
Evaluation Framework ◽  
Diagnostic Methods ◽  
Base Case ◽  
Lateral Boundary ◽  
Lateral Boundary Conditions

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).

scholarly journals Unraveling the complex local-scale flows influencing ozone patterns in the southern Great Lakes of North America

2010 ◽  
Vol 10 (8) ◽  
pp. 19763-19810 ◽  
Author(s):  
I. Levy ◽  
P. A. Makar ◽  
D. Sills ◽  
J. Zhang ◽  
K. L. Hayden ◽  
...  
Keyword(s):  
Air Quality ◽  
North America ◽  
Great Lakes ◽  
Lake Erie ◽  
Surface Ozone ◽  
Inversion Layer ◽  
Air Quality Model ◽  
Large Reservoir ◽  
Mixing Ratios ◽  
Ozone Levels

Abstract. This study examines the complexity of various processes influencing summertime ozone levels in the southern Great Lakes region of North America. Results from the Border Air Quality and Meteorology (BAQS-Met) field campaign in the summer of 2007 are examined with respect to land-lake differences and local meteorology using a large array of ground-based measurements, aircraft data and simulation results from a high resolution (2.5 km) regional air-quality model, AURAMS. Analyses of average ozone mixing ratio from the entire BAQS-Met intensive campaign period support previous findings that ozone levels are higher over the southern Great Lakes than over the adjacent land. However, there is great heterogeneity in the spatial distribution of surface ozone over the lakes, particularly over Lake Erie during the day, with higher levels located over the southwestern end of the lake. Results suggest that some of these increased ozone levels are due to local emission sources in large nearby urban centers. The land-lake differences in ozone mixing ratios are most pronounced during the night in a shallow inversion layer of about 200 m above the surface. After sunrise, these differences have a limited effect on the total mass of ozone over the lakes during the day time, though they may cause elevated ozone levels in the lake breeze air. A large reservoir layer of ozone is predicted by the AURAMS model over Lake Erie at night, centered between 600–1000 m above ground and extending into the land over Cleveland. The model also predicts a vertical circulation during the day with an updraft over Detroit-Windsor and downdraft over Lake St. Clair, which transports ozone up to 1500 m above ground and results in high ozone over the lake. Oscillations in ground level ozone mixing ratios were observed on several nights and several ground monitoring sites, with amplitudes of up to 40 ppbv and time periods of 15–40 min.

scholarly journals Solar “brightening” impact on summer surface ozone between 1990 and 2010 in Europe – a model sensitivity study of the influence of the aerosol–radiation interactions

2018 ◽  
Vol 18 (13) ◽  
pp. 9741-9765 ◽  
Author(s):  
Emmanouil Oikonomakis ◽  
Sebnem Aksoyoglu ◽  
Martin Wild ◽  
Giancarlo Ciarelli ◽  
Urs Baltensperger ◽  
...  
Keyword(s):  
Air Quality ◽  
Surface Ozone ◽  
Biogenic Emissions ◽  
Base Case ◽  
Air Quality Model ◽  
Quality Model ◽  
Ozone Concentrations ◽  
Photolysis Rates ◽  
Ozone Trends ◽  
The Impact

Abstract. Surface solar radiation (SSR) observations have indicated an increasing trend in Europe since the mid-1980s, referred to as solar “brightening”. In this study, we used the regional air quality model, CAMx (Comprehensive Air Quality Model with Extensions) to simulate and quantify, with various sensitivity runs (where the year 2010 served as the base case), the effects of increased radiation between 1990 and 2010 on photolysis rates (with the PHOT1, PHOT2 and PHOT3 scenarios, which represented the radiation in 1990) and biogenic volatile organic compound (BVOC) emissions (with the BIO scenario, which represented the biogenic emissions in 1990), and their consequent impacts on summer surface ozone concentrations over Europe between 1990 and 2010. The PHOT1 and PHOT2 scenarios examined the effect of doubling and tripling the anthropogenic PM2.5 concentrations, respectively, while the PHOT3 investigated the impact of an increase in just the sulfate concentrations by a factor of 3.4 (as in 1990), applied only to the calculation of photolysis rates. In the BIO scenario, we reduced the 2010 SSR by 3 % (keeping plant cover and temperature the same), recalculated the biogenic emissions and repeated the base case simulations with the new biogenic emissions. The impact on photolysis rates for all three scenarios was an increase (in 2010 compared to 1990) of 3–6 % which resulted in daytime (10:00–18:00 Local Mean Time – LMT) mean surface ozone differences of 0.2–0.7 ppb (0.5–1.5 %), with the largest hourly difference rising as high as 4–8 ppb (10–16 %). The effect of changes in BVOC emissions on daytime mean surface ozone was much smaller (up to 0.08 ppb, ∼ 0.2 %), as isoprene and terpene (monoterpene and sesquiterpene) emissions increased only by 2.5–3 and 0.7 %, respectively. Overall, the impact of the SSR changes on surface ozone was greater via the effects on photolysis rates compared to the effects on BVOC emissions, and the sensitivity test of their combined impact (the combination of PHOT3 and BIO is denoted as the COMBO scenario) showed nearly additive effects. In addition, all the sensitivity runs were repeated on a second base case with increased NOx emissions to account for any potential underestimation of modeled ozone production; the results did not change significantly in magnitude, but the spatial coverage of the effects was profoundly extended. Finally, the role of the aerosol–radiation interaction (ARI) changes in the European summer surface ozone trends was suggested to be more important when comparing to the order of magnitude of the ozone trends instead of the total ozone concentrations, indicating a potential partial damping of the effects of ozone precursor emissions' reduction.

scholarly journals Error apportionment for atmospheric chemistry-transport models: a new approach to model evaluation

2016 ◽  
Author(s):  
E. Solazzo ◽  
S. Galmarini
Keyword(s):  
Air Quality ◽  
Correlation Coefficient ◽  
Atmospheric Chemistry ◽  
Model Evaluation ◽  
Surface Ozone ◽  
Model Complexity ◽  
Air Quality Model ◽  
Quality Model ◽  
Causes Of Errors ◽  
Bias Variance

Abstract. In this study, methods are proposed to diagnose the causes of errors in air quality (AQ) modelling systems. We investigate the deviation between modelled and observed time series of surface ozone through a revised formulation for breaking down the mean square error (MSE) into bias, variance, and the minimum achievable MSE (mMSE). The bias measures the accuracy and implies the existence of systematic errors and poor representation of data complexity, the variance measures the precision and provides an estimate of the variability of the modelling results in relation to the observed data, and the mMSE reflects unsystematic errors and provides a measure of the associativity between the modelled and the observed fields through the correlation coefficient. Each of the error components is analysed independently and apportioned to resolved process based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) and as a function of model complexity. The apportionment of the error is applied to the AQMEII (Air Quality Model Evaluation International Initiative) group of models, which embrace the majority of regional AQ modelling systems currently used in Europe and North America. The proposed technique has proven to be a compact estimator of the operational metrics commonly used for model evaluation (bias, variance, and correlation coefficient), and has the further benefit of apportioning the error to the originating timescale, thus allowing for a clearer diagnosis of the process that caused the error.

scholarly journals Dynamic Adjustment of Climatological Ozone Boundary Conditions for Air-Quality Forecasts

2010 ◽  
Vol 10 (6) ◽  
pp. 13643-13688 ◽  
Author(s):  
P. A. Makar ◽  
W. Gong ◽  
C. Mooney ◽  
J. Zhang ◽  
D. Davignon ◽  
...  
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Boundary Conditions ◽  
Surface Ozone ◽  
Daily Maximum ◽  
Tropopause Height ◽  
Dynamic Adjustment ◽  
Air Quality Model ◽  
Maximum Surface ◽  
Ozone Profile

Abstract. Ten different approaches for applying lateral and top climatological boundary conditions for ozone have been evaluated using the off-line regional air-quality model AURAMS. All ten approaches employ the same climatological ozone profiles, but differ in the manner in which they are applied, via the inclusion or exclusion of (i) a dynamic adjustment of the climatological ozone profile in response to the model-predicted tropopause height, (ii) a sponge zone for ozone on the model top, (iii) upward extrapolation of the climatological ozone profile, and (iv) different mass consistency corrections. The model performance for each approach was evaluated against North American surface ozone and ozonesonde observations from the BAQS-Met field study period in the summer of 2007. The original daily one-hour maximum surface ozone biases of about +15 ppbv were greatly reduced (halved) in some simulations using alternative methodologies. However, comparisons to ozonesonde observations showed that the reduction in surface ozone bias sometimes came at the cost of significant positive biases in ozone concentrations in the free troposphere and upper troposphere. The best overall performance throughout the troposphere was achieved using a methodology that included dynamic tropopause height adjustment, no sponge zone at the model top, extrapolation of ozone when required above the limit of the climatology, and no mass consistency corrections (global mass conservation was still enforced). The simulation using this model version had a one-hour daily maximum surface ozone bias of +8.6 ppbv, with small reductions in model correlation, and the best comparison to ozonesonde profiles. This recommended and original methodologies were compared for two further case studies: a high-resolution simulation of the BAQS-Met measurement intensive, and a study of the downwind region of the Canadian Rockies. Significant improvements were noted for the high resolution simulations during the BAQS-Met measurement intensive period, both in formal statistical comparisons and time series comparisons of events at surface stations. The tests for the downwind-Rockies region showed that the coupling between vertical transport associated with troposphere/stratosphere exchange, and that associated with boundary layer turbulent mixing, may contribute to ozone positive biases.

scholarly journals Unraveling the complex local-scale flows influencing ozone patterns in the southern Great Lakes of North America

2010 ◽  
Vol 10 (22) ◽  
pp. 10895-10915 ◽  
Author(s):  
I. Levy ◽  
P. A. Makar ◽  
D. Sills ◽  
J. Zhang ◽  
K. L. Hayden ◽  
...  
Keyword(s):  
Air Quality ◽  
North America ◽  
Great Lakes ◽  
Lake Erie ◽  
Surface Ozone ◽  
Inversion Layer ◽  
Air Quality Model ◽  
Quality Model ◽  
Mixing Ratios ◽  
Ozone Levels

Abstract. This study examines the complexity of various processes influencing summertime ozone levels in the southern Great Lakes region of North America. Results from the Border Air Quality and Meteorology (BAQS-Met) field campaign in the summer of 2007 are examined with respect to land-lake differences and local meteorology using a large array of ground-based measurements, aircraft data, and simulation results from a high resolution (2.5 km) regional air-quality model, AURAMS. Analyses of average ozone mixing ratio from the entire BAQS-Met intensive campaign period support previous findings that ozone levels are higher over the southern Great Lakes than over the adjacent land. However, there is great heterogeneity in the spatial distribution of surface ozone over the lakes, particularly over Lake Erie during the day, with higher levels located over the southwestern end of the lake. Model results suggest that some of these increased ozone levels are due to local emission sources in large nearby urban centers. While an ozone reservoir layer is predicted by the AURAMS model over Lake Erie at night, the land-lake differences in ozone mixing ratios are most pronounced during the night in a shallow inversion layer of about 200 m above the surface. After sunrise, these differences have a limited effect on the total mass of ozone over the lakes and land during the day, though they do cause elevated ozone levels in the lake-breeze air in some locations. The model also predicts a mean vertical circulation during the day with an updraft over Detroit-Windsor and downdraft over Lake St. Clair, which transports ozone up to 1500 m above ground and results in high ozone over the lake. Oscillations in ground-level ozone mixing ratios were observed on several nights and at several ground monitoring sites, with amplitudes of up to 40 ppbv and time periods of 15–40 min. Several possible mechanisms for these oscillations are discussed, but a complete understanding of their causes is not possible given current data and knowledge.

scholarly journals Evaluation of a regional air quality model using satellite column NO<sub>2</sub>: treatment of observation errors and model boundary conditions and emissions

2014 ◽  
Vol 14 (15) ◽  
pp. 21749-21784
Author(s):  
R. J. Pope ◽  
M. P. Chipperfield ◽  
N. H. Savage ◽  
C. Ordóñez ◽  
L. S. Neal ◽  
...  
Keyword(s):  
Air Quality ◽  
Boundary Conditions ◽  
Total Error ◽  
Critical Evaluation ◽  
Atmospheric Composition ◽  
Air Quality Model ◽  
Ozone Monitoring Instrument ◽  
Quality Model ◽  
Source Power ◽  
In Situ Data

Abstract. We compare tropospheric column NO2 between the UK Met Office operational Air Quality in the Unified Model (AQUM) and satellite observations from the Ozone Monitoring Instrument (OMI) for 2006. Column NO2 retrievals from satellite instruments are prone to large uncertainty from random, systematic and smoothing errors. We present an algorithm to reduce the random error of time-averaged observations, once smoothing errors have been removed with application of satellite averaging kernels to the model data. This reduces the total error in seasonal mean columns by 30–90%, which allows critical evaluation of the model. The standard AQUM configuration evaluated here uses chemical lateral boundary conditions (LBCs) from the GEMS (Global and regional Earth-system Monitoring using Satellite and in-situ data) reanalysis. In summer the standard AQUM overestimates column NO2 in northern England and Scotland, but underestimates it over continental Europe. In winter, the model overestimates column NO2 across the domain. We show that missing heterogeneous hydrolysis of N2O5 in AQUM is a significant sink of column NO2 and that the introduction of this process corrects some of the winter biases. The sensitivity of AQUM summer column NO2 to different chemical LBCs and NOx emissions datasets are investigated. Using Monitoring Atmospheric Composition and Climate (MACC) LBCs increases AQUM O3 concentrations compared with the default GEMS LBCs. This enhances the NOx-O3 coupling leading to increased AQUM column NO2 in both summer and winter degrading the comparisons with OMI. Sensitivity experiments suggest that the cause of the remaining northern England and Scotland summer column NO2 overestimation is the representation of point source (power station) emissions in the model.

scholarly journals Error apportionment for atmospheric chemistry-transport models – a new approach to model evaluation

2016 ◽  
Vol 16 (10) ◽  
pp. 6263-6283 ◽  
Author(s):  
Efisio Solazzo ◽  
Stefano Galmarini
Keyword(s):  
Air Quality ◽  
Correlation Coefficient ◽  
Atmospheric Chemistry ◽  
Model Evaluation ◽  
Surface Ozone ◽  
Air Quality Model ◽  
Quality Model ◽  
Evaluation Bias ◽  
Causes Of Errors ◽  
Bias Variance

Abstract. In this study, methods are proposed to diagnose the causes of errors in air quality (AQ) modelling systems. We investigate the deviation between modelled and observed time series of surface ozone through a revised formulation for breaking down the mean square error (MSE) into bias, variance and the minimum achievable MSE (mMSE). The bias measures the accuracy and implies the existence of systematic errors and poor representation of data complexity, the variance measures the precision and provides an estimate of the variability of the modelling results in relation to the observed data, and the mMSE reflects unsystematic errors and provides a measure of the associativity between the modelled and the observed fields through the correlation coefficient. Each of the error components is analysed independently and apportioned to resolved processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) and as a function of model complexity.The apportionment of the error is applied to the AQMEII (Air Quality Model Evaluation International Initiative) group of models, which embrace the majority of regional AQ modelling systems currently used in Europe and North America.The proposed technique has proven to be a compact estimator of the operational metrics commonly used for model evaluation (bias, variance, and correlation coefficient), and has the further benefit of apportioning the error to the originating timescale, thus allowing for a clearer diagnosis of the processes that caused the error.

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