Surface ozone interannual variability, trends, and extremes in CCMI models. (15th January 2022)

• Record Type: Journal Article
• Authors: Zhang, Li; Cui, Yu Yan
• Is Part Of: Atmospheric environment. Volume 269(2022)
• Publication Date: 2022-01-15
• Subjects: Ozone -- Interannual variability -- Trend -- Extremes -- Heatwave; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2021.118841
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: Tropospheric ozone exhibits variations at interannual to decadal timescales tied closely to climate variability. The fidelity of current-generation global chemistry-climate models (CCMs) in representing observed variations and extremes of troposphere ozone is an important aspect of model abilities to predict ozone but has not been comprehensively assessed. Here we evaluate multiple state-of-the-art CCMs participating in the Chemistry-Climate Model Initiative phase 1 (CCMI-1) in simulating the interannual variability (IAV), decadal trends, and extremes of surface ozone during 1990–2010 at northern mid-latitudes. We find that current CCMs tend to underestimate observed ozone IAV over the U.S., Europe, and East Asia in both spring and summer and fail to capture the springtime ozone trends in the eastern U.S. (EUS) and Europe. Specified-dynamics simulations constrained by reanalysis data outperform the free-running simulations with CCMs in terms of IAV and decadal trends of surface ozone. Observations show large surface ozone IAV in summer over the EUS, Europe, and East Asia (standard deviation = 3−6 ppbv or relative standard deviation = 8−15%) while all models substantially underestimate observed IAV by a factor of 2–3. Analysis in these populous/polluted regions unveils that the models significantly underestimate ozone extreme anomalies during heatwaves/droughts by 2–9 ppbv (17–55%) over the EUS and by 7–10 ppbv (42–59%) over Europe during the past two decades. These underestimates in summertime extreme anomalies contribute to the model biases in surface ozone IAV. Future model developments are needed to improve the ability of current CCMs to predict ozone extremes during heatwaves and droughts. Highlights: CCMI models underestimate ozone interannual variability at northern mid-latitudes. Specified-dynamics simulations outperform free-running simulations in ozone variation. CCMI models fail to reproduce ozone trends in spring at northern mid-latitudes. CCMI models underestimate ozone extreme anomalies during heatwaves by 15–59%.