Intercomparison of global ground-level ozone datasets for health-relevant metrics

Abstract. Ground-level ozone is a significant air pollutant that detrimentally affects human health and agriculture. Global ground-level ozone concentrations have been estimated using chemical reanalyses, geostatistical methods, and machine learning, but these datasets have not been compared systematically. We compare six global ground-level ozone datasets (three chemical reanalyses, two machine learning, one geostatistics) against one another and relative to observations, for the ozone season daily maximum 8-hour average mixing ratio, for 2006 to 2016. Results show significant differences among datasets in global average ozone, as large as 5–10 ppb, multi-year trends, and regional distributions. For example, in Europe, the three chemical reanalyses show an increasing trend while the other datasets show no increase. Among the six datasets, the population exposed to over 50 ppb varies from 60.8 % to 99 % in East Asia, 17 % to 88 % in North America, and 9 % to 77 % in Europe (2006–2016 average). These differences are large enough to impact health and other applications. Comparing with Tropospheric Ozone Assessment Report (TOAR) II ground-level observations, most datasets overestimate ozone, particularly at lower observed concentrations. In 2016, across all stations, R² ranges among the six datasets from 0.35 to 0.63, and RMSE from 5.28 to 13.49 ppb. Performance further declines when considering only stations with observations above 50 ppb. Although some datasets share some of the same input data, we found important differences among these datasets, likely from variations in approaches, resolution, and other input data, highlighting the importance of continued research on global ozone distributions.