We simulate the impact of historical climate change on summertime surface ozone concentration in the Northern Hemisphere over the past 40 years (1981–2021). Our results show a 1.0 °C (0.25 °C/10 a) rise in temperature, corresponding to an increase of 2.75 ppbv (0.67 ppbv/10 a) in surface ozone concentration. This climate penalty is more severe and regionally widespread than the 0.2–2.0 ppbv ozone increase projected only for India and China by the IPCC AR6 under a scenario of future 1 °C warming.

For the future, building on deep learning's success in different domains, we explore its application to correct surface ozone biases in the state-of-the-art chemistry-climate model. Six statistical models have been developed, and the model Transformer outperforms others due to its advanced architecture. A simple weighted ensemble approach is further proved to enhance performance by 14 % over the best single model Transformer, reducing RMSE to 0.69 ppb. Applied to future scenarios (SSP3-7.0 and SSP3-7.0-lowNTCF), the UKESM1 shows a larger overestimation of ozone changes by up to 25 ppb compared to present-day conditions. Despite biases, UKESM1 captures the non-linear ozone sensitivity to precursors, with temperature-sensitive processes identified as a dominant contributor to biases. We highlight that simulations of future surface ozone are likely to become less accurate under a warmer climate.

臭氧,气候变化