第八届青年地学论坛

We use the TOMCAT 3-D off-line chemical transport model (CTM) to investigate the seasonal ozone trends and solar response in stratospheric ozone. The model simulations forced with European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses (ERA-Interim and ERA5) data, A_ERAI and B_ERA5, are compared to observation-based data sets, the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH, Davis et al., 2016) database (1984-2020) and machine learning based satellite-corrected data (ML-TOMCAT, Dhomse et al., 2021) for the 1979-2020 time period. We find large differences between the modeled and observed ozone profiles and simulation B_ERA5 does not perform better in simulating the observed stratospheric ozone when compared to A_ERAI (Li et al., 2022).
We employ a multi-variate regression model (MLR) to estimate the trends and solar cycle signals (SCS) in both the modelled and observed ozone profiles. Both the ordinary least squares (OLS) and regularised regression methods (Ridge/Lasso) are used for comparison. The MLR includes independent linear trends before and after peak stratospheric halogen loading in 1997, Quasi-Biennial Oscillation (QBO) terms at 30 hPa and 10 hPa, El-Nino Southern Oscillation (ENSO), Arctic Oscillation (AO), Antarctic Oscillation (AAO) index as well as the vertical component of the E-P flux or the age-of-air (AoA) tracer to account for the effects of dynamical variability. We find that both ozone trends and SCS vary with the fit methods, datasets and analysis periods. ML-TOMCAT data agree better with SWOOSH data in ozone trends and SCS than the two model simulations do. AoA associated transport changes the lower stratospheric ozone trends as well as the solar cycle estimates, which suggests the stratospheric dynamics might have influenced the stratospheric ozone, particularly in the lower stratosphere, resulting in the significantly different SCS.
References:
Davis, S.M., Rosenlof, K.H., Hassler, B., Hurst, D.F., Read, W.G., Vömel, H., Selkirk, H., Fujiwara, M. and Damadeo, R., 2016. The Stratospheric Water and Ozone Satellite Homogenized (SWOOSH) database: a long-term database for climate studies. Earth system science data, 8(2), pp.461-490.
Dhomse, S.S., Arosio, C., Feng, W., Rozanov, A., Weber, M. and Chipperfield, M.P., 2021. ML-TOMCAT: machine-learning-based satellite-corrected global stratospheric ozone profile data set from a chemical transport model. Earth System Science Data, 13(12), pp.5711-5729.
Li, Y., Dhomse, S. S., Chipperfield, M. P., Feng, W., Chrysanthou, A., Xia, Y., and Guo, D., 2022. Effects of reanalysis forcing fields on ozone trends and age of air from a chemical transport model, Atmos. Chem. Phys., 22, 10635–10656.
 

ozone,trends,stratosphere,solar response