In support of the first Tropospheric Ozone Assessment Report (TOAR) a relational database of global surface ozone observations has been developed and populated with hourly measurement data and enhanced metadata. A comprehensive suite of ozone data products including standard statistics, health and vegetation impact metrics, and trend information, are made available through a common data portal and a web interface. These data form the basis of the TOAR analyses focusing on human health, vegetation, and climate relevant ozone issues, which are part of this special feature.Cooperation among many data centers and individual researchers worldwide made it possible to build the world's largest collection of in-situ hourly surface ozone data covering the period from 1970 to 2015. By combining the data from almost 10,000 measurement sites around the world with global metadata information, new analyses of surface ozone have become possible, such as the first globally consistent characterisations of measurement sites as either urban or rural/remote. Exploitation of these global metadata allows for new insights into the global distribution, and seasonal and long-term changes of tropospheric ozone and they enable TOAR to perform the first, globally consistent analysis of present-day ozone concentrations and recent ozone changes with relevance to health, agriculture, and climate.Considerable effort was made to harmonize and synthesize data formats and metadata information from various networks and individual data submissions. Extensive quality control was applied to identify questionable and erroneous data, including changes in apparent instrument offsets or calibrations. Such data were excluded from TOAR data products. Limitations of a posteriori data quality assurance are discussed. As a result of the work presented here, global coverage of surface ozone data for scientific analysis has been significantly extended. Yet, large gaps remain in the surface observation network both in Schultz et al: Tropospheric Ozone Assessment Report Art. 58, page 2 of 26 terms of regions without monitoring, and in terms of regions that have monitoring programs but no public access to the data archive. Therefore future improvements to the database will require not only improved data harmonization, but also expanded data sharing and increased monitoring in data-sparse regions.

Abstract. The high density of European surface ozone monitoring sites provides unique opportunities for the investigation of regional ozone representativeness and for the evaluation of chemistry climate models. The regional representativeness of European ozone measurements is examined through a cluster analysis (CA) of 4 years of 3-hourly ozone data from 1492 European surface monitoring stations in the Airbase database; the time resolution corresponds to the output frequency of the model that is compared to the data in this study. K-means clustering is implemented for seasonal–diurnal variations (i) in absolute mixing ratio units and (ii) normalized by the overall mean ozone mixing ratio at each site. Statistical tests suggest that each CA can distinguish between four and five different ozone pollution regimes. The individual clusters reveal differences in seasonal–diurnal cycles, showing typical patterns of the ozone behavior for more polluted stations or more rural background. The robustness of the clustering was tested with a series of k-means runs decreasing randomly the size of the initial data set or lengths of the time series. Except for the Po Valley, the clustering does not provide a regional differentiation, as the member stations within each cluster are generally distributed all over Europe. The typical seasonal, diurnal, and weekly cycles of each cluster are compared to the output of the multi-year global reanalysis produced within the Monitoring of Atmospheric Composition and Climate (MACC) project. While the MACC reanalysis generally captures the shape of the diurnal cycles and the diurnal amplitudes, it is not able to reproduce the seasonal cycles very well and it exhibits a high bias up to 12 nmol mol−1. The bias decreases from more polluted clusters to cleaner ones. Also, the seasonal and weekly cycles and frequency distributions of ozone mixing ratios are better described for clusters with relatively clean signatures. Due to relative sparsity of CO and NOx measurements these were not included in the CA. However, simulated CO and NOx mixing ratios are consistent with the general classification into more polluted and more background sites. Mean CO mixing ratios are within 140–145 nmol mol−1 (CL1–CL3) and 130–135 nmol mol−1 (CL4 and CL5), and NOx mixing ratios are within 4–6 nmol mol−1 and 2–3 nmol mol−1, respectively. These results confirm that relatively coarse-scale global models are more suitable for simulation of regional background concentrations, which are less variable in space and time. We conclude that CA of surface ozone observations provides a powerful and robust way to stratify sets of stations, being thus more suitable for model evaluation.

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Table S1. The final list of 1492 stations used in the CA. Indicated number of data points, removed with the automatic data quality filter: "N mis."number of missing data points; "N flag."number of all data points below zero, above threshold, near missing value, or outliers; "N valid"total number of valid data points. Last two columnsnumber of cluster each station belongs to. station ID station name lon. lat. alt., m N mis. N flag. N valid CL,

<p><strong>Abstract.</strong> The high density of European surface ozone monitoring sites provides unique opportunities for the investigation of regional ozone representativeness and for the evaluation of chemistry climate models. The regional representativeness of European ozone measurements is investigated through a cluster analysis (CA) of 4 years of three-hourly ozone data from 1492 European surface monitoring stations in the Airbase database; the time resolution corresponds to the output frequency of the model that is compared to the data in this study. K-means clustering is implemented for seasonal-diurnal variations (i) in absolute mixing ratio units, and (ii) normalized by the overall mean ozone mixing ratio at each site. Statistical tests suggest that each CA can distinguish between 4 and 5 different ozone pollution regimes. The individual clusters reveal differences in seasonal-diurnal cycles, showing typical patterns of the ozone behavior for more polluted stations or more rural background. The robustness of the clustering was tested with a series of k-means runs decreasing randomly the size of the initial data set or lengths of the timeseries. Except for the Po Valley, the clustering does not provide a regional differentiation, as the member stations within each cluster are generally distributed all over Europe. The typical seasonal, diurnal, and weekly cycles of each cluster are compared to the output of the multi-year global reanalysis produced within the Monitoring of Atmospheric Composition and Climate (MACC) project. While the MACC reanalysis generally captures the shape of the diurnal cycles and the diurnal amplitudes it is not able to reproduce the seasonal cycles very well and it exhibits a high bias up to 12 nmol/mol. The bias decreases from more polluted clusters to cleaner ones. Also, the seasonal and weekly cycles and frequency distributions of ozone mixing ratios are better described for clusters with relatively clean signatures. Due to relative sparsity of CO and NOx measurements these were not included in the cluster analysis. However, simulated CO and NOx mixing ratios are consistent with the general classification into more polluted and more background sites. Mean CO mixing ratios are ≈ 140–145 nmol/mol (CL1 – CL3) and ≈ 130–135 nmol/mol (CL4 and CL5), and NOx mixing ratios are ≈ 4–6 nmol/mol and ≈ 2–3 nmol/mol, respectively. These results confirm that relatively coarse scale global models are more suitable for simulation of regional background concentrations, which are less variable in space and time. We conclude that cluster analysis of surface ozone observations provides a powerful and robust way to stratify sets of stations being thus more suitable for model evaluation.</p>