Yajuan Li

A long-term vertical ozone observational dataset has been provided during 2001–2019 by ozonesonde measurements in Beijing on the North China Plain. Previous studies using this dataset primarily focused on the vertical characteristics of climatological ozone and its variation; however, the driving factors of ozone variation have not been well discussed. In this study, by applying the wavelet analysis method (including continuous wavelet transform and cross wavelet) and sliding correlation coefficients to ~20 years of ozonesonde measurements collected in Beijing, we analyzed the dominant modes of ozone column variability within three height ranges over Beijing (total column ozone: TOT; stratospheric column ozone: SCO; and tropospheric column ozone: TCO). Moreover, we also preliminarily discussed the relationship between these three ozone columns and the El Niño Southern Oscillation (ENSO), Quasi-biennial Oscillation (QBO), and 11-year solar activity cycle. The results revealed that the ozone columns within the three height ranges predominantly adhered to interannual variability patterns, and the short-term variabilities in TOT and SCO may have been related to eruptive volcanic activity. In comparison to the TOT and SCO, the TCO was more susceptible to the forcing influences of high-frequency factors such as pollutant transport. Similar to the results in other mid-latitude regions, strong ENSO and QBO signals were revealed in the interannual ozone column variability over Beijing. The TOT and SCO showed positive anomalous responses to ENSO warm-phase events, and the peak of the ENSO warm phase led the winter peaks of the TOT and SCO by approximately 3–6 months. During the strong cold–warm transition phase in 2009–2012, the TOT and SCO showed a significant positive correlation with the ENSO index. The strong seasonality of the meridional circulation process driven by the QBO led to a significant positive correlation between the QBO index and the TOT and SCO in the interannual cycle, except for two periods of abnormal QBO fluctuations in 2010–2012 and 2015–2017, whereas the TCO showed a time-lagged correlation of approximately 3 months in the annual cycle relative to the QBO due to the influence of the thermodynamic tropopause. In addition, analysis of the F10.7 index and the ozone columns revealed that the ozone columns over Beijing exhibited lagged responses to the peaks of sunspot activity, and there was no obvious correlation between ozone columns and 11-year solar activity cycle. Given the complex driving mechanism of the climatic factors on local ozone variability, the preliminary results obtained in this study still require further validation using longer time series of observational data and the combination of chemical models and more auxiliary data.

Based on the resonance fluorescence scattering mechanism, a narrowband sodium (Na) lidar can measure temperature and wind in the mesosphere and lower thermosphere (MLT) region. By using a narrowband spectral filter, background light noise during the day can be suppressed, allowing for continuous observations. To obtain full-diurnal-cycle temperature and wind measurement results, a complex and precise retrieval process is required, along with necessary corrections to minimize measurement errors. This paper introduces the design of a data acquisition unit for three frequencies in three directions of the Na lidar system in the Chinese Meridian Project (Phase II) and investigates the calibration and retrieval methods for obtaining diurnal temperature and horizontal wind in the MLT region, using a Na Doppler lidar with Faraday anomalous dispersion optical filter (FADOF). Furthermore, these methods are applied to observations conducted by a Na lidar in Beijing, China. The wind and temperature results over full diurnal cycles obtained from the all-solid-state Na Doppler lidar are reported for the first time and compared with temperature measurements from satellite, as well as wind observations from a meteor radar. The comparison demonstrates a reasonable agreement between the results, indicating the rationality of the lidar-retrieved results and the feasibility and effectiveness of the data correction and retrieval method.

Accurate quantification of long-term trends in stratospheric ozone can be challenging due to their sensitivity to natural variability, the quality of the observational datasets, and non-linear changes in forcing processes as well as the statistical methodologies. Multivariate linear regression (MLR) is the most commonly used tool for ozone trend analysis; however, the complex coupling in many atmospheric processes can make it prone to the issue of over-fitting when using the conventional ordinary-least-squares (OLS) approach. To overcome this issue, here we adopt a regularized (ridge) regression method to estimate ozone trends and quantify the influence of individual processes. We use the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) merged dataset (v2.7) to derive stratospheric ozone profile trends for the period 1984–2020. Besides SWOOSH, we also analyse a machine-learning-based satellite-corrected gap-free global stratospheric ozone profile dataset from a chemical transport model (ML-TOMCAT) and output from a chemical transport model (TOMCAT) simulation forced with European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis. For 1984–1997, we observe smaller negative trends in the SWOOSH stratospheric ozone profile using ridge regression compared to OLS. Except for the tropical lower stratosphere, the largest differences arise in the mid-latitude lowermost stratosphere (>4 % per decade difference at 100 hPa). From 1998 and the onset of ozone recovery in the upper stratosphere, the positive trends estimated using the ridge regression model (∼1 % per decade near 2 hPa) are smaller than those using OLS (∼2 % per decade). In the lower stratosphere, post-1998 negative trends with large uncertainties are observed and ridge-based trend estimates are somewhat smaller and less variable in magnitude compared to the OLS regression. Aside from the tropical lower stratosphere, the largest difference is around 2 % per decade at 100 hPa (with ∼3 % per decade uncertainties for individual trends) in northern mid-latitudes. For both time periods the SWOOSH data produce large negative trends in the tropical lower stratosphere with a correspondingly large difference between the two trend methods. In both cases the ridge method produces a smaller trend. The regression coefficients from both OLS and ridge models, which represent ozone variations associated with natural processes (e.g. the quasi-biennial oscillation, solar variability, El Niño–Southern Oscillation, Arctic Oscillation, Antarctic Oscillation, and Eliassen–Palm flux), highlight the dominance of dynamical processes in controlling lower-stratospheric ozone concentrations. Ridge regression generally yields smaller regression coefficients due to correlated explanatory variables, and care must be exercised when comparing fit coefficients and their statistical significance across different regression methods. Comparing the ML-TOMCAT-based trend estimates with the ERA5-forced model simulation, we find ML-TOMCAT shows significant improvements with much better consistency with the SWOOSH dataset, despite the ML-TOMCAT training period overlapping with SWOOSH only for the Microwave Limb Sounder (MLS) measurement period. The largest inconsistencies with respect to SWOOSH-based trends post-1998 appear in the lower stratosphere where the ERA5-forced model simulation shows positive trends for both the tropics and the mid-latitudes. The large differences between satellite-based data and the ERA5-forced model simulation confirm significant uncertainties in ozone trend estimates, especially in the lower stratosphere, underscoring the need for caution when interpreting results obtained with different regression methods and datasets.

Accurate quantification of long-term trends in stratospheric ozone can be challenging due to their sensitivity to natural variability, the quality of the observational datasets, non-linear changes in forcing processes as well as the statistical methodologies. Multivariate linear regression (MLR) is the most commonly used tool for ozone trend analysis, however, the complex coupling in most atmospheric processes can make it prone to the over-fitting or multi-collinearity-related issues when using the conventional Ordinary Least Squares (OLS) setting. To overcome this issue, we adopt a regularised (Ridge) regression method to estimate ozone trends and quantify the influence of individual processes. Here, we use the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) merged data set (v2.7) to derive stratospheric ozone profile trends for the period 1984–2020. Beside SWOOSH, we also analyse a machine-learning-based satellite-corrected gap-free global stratospheric ozone profile dataset from a chemical transport model (ML-TOMCAT), and output from two chemical transport model (TOMCAT) simulations forced with ECMWF reanalyses ERA-Interim and ERA5. With Ridge regression, the stratospheric ozone profile trends from SWOOSH data show smaller declines during 1984–1997 compared to OLS with the largest differences in the lowermost stratosphere (> 4 % per decade at 100 hPa). Upper stratospheric ozone has increased since 1998 with maximum (~2 % per decade near 2 hPa) in local winter for mid-latitudes. Negative trends with large uncertainties are observed in the lower stratosphere with the most pronounced in the tropics. The largest differences in post-1998 trend estimates between OLS and Ridge regression methods appear in the tropical lower stratosphere (with ~7 % per decade difference at 100 hPa). Ozone variations associated with natural processes such as the quasi-biennial oscillation (QBO), the solar variability, the El Niño–Southern Oscillation (ENSO), the Arctic oscillation (AO) and the Antarctic oscillation (AAO) also indicate that Ridge regression coefficients are somewhat smaller and less variable compared to the OLS-based estimates. Additionally, ML-TOMCAT based trend estimates are consistent with SWOOSH data set. Finally, we argue that the large differences between the satellite-based data and model simulations confirm that there are still large uncertainties in ozone trend estimates especially in the lower stratosphere, and caution is needed when discussing results if explanatory variables used are correlated.

Accurate quantification of long-term trends in stratospheric ozone can be challenging due to their sensitivity to natural variability, the quality of the observational datasets, non-linear changes in forcing processes as well as the statistical methodologies. Multivariate linear regression (MLR) is the most commonly used tool for ozone trend analysis, however, the complex coupling in most atmospheric processes can make it prone to the over-fitting or multi-collinearity-related issues when using the conventional Ordinary Least Squares (OLS) setting. To overcome this issue, we adopt a regularised (Ridge) regression method to estimate ozone trends and quantify the influence of individual processes. Here, we use the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) merged data set (v2.7) to derive stratospheric ozone profile trends for the period 1984–2020. Beside SWOOSH, we also analyse a machine-learning-based satellite-corrected gap-free global stratospheric ozone profile dataset from a chemical transport model (ML-TOMCAT), and output from two chemical transport model (TOMCAT) simulations forced with ECMWF reanalyses ERA-Interim and ERA5. With Ridge regression, the stratospheric ozone profile trends from SWOOSH data show smaller declines during 1984–1997 compared to OLS with the largest differences in the lowermost stratosphere (> 4 % per decade at 100 hPa). Upper stratospheric ozone has increased since 1998 with maximum (~2 % per decade near 2 hPa) in local winter for mid-latitudes. Negative trends with large uncertainties are observed in the lower stratosphere with the most pronounced in the tropics. The largest differences in post-1998 trend estimates between OLS and Ridge regression methods appear in the tropical lower stratosphere (with ~7 % per decade difference at 100 hPa). Ozone variations associated with natural processes such as the quasi-biennial oscillation (QBO), the solar variability, the El Niño–Southern Oscillation (ENSO), the Arctic oscillation (AO) and the Antarctic oscillation (AAO) also indicate that Ridge regression coefficients are somewhat smaller and less variable compared to the OLS-based estimates. Additionally, ML-TOMCAT based trend estimates are consistent with SWOOSH data set. Finally, we argue that the large differences between the satellite-based data and model simulations confirm that there are still large uncertainties in ozone trend estimates especially in the lower stratosphere, and caution is needed when discussing results if explanatory variables used are correlated.

Accurate quantification of long-term trends in stratospheric ozone can be challenging due to their sensitivity to natural variability, the quality of the observational datasets, non-linear changes in forcing processes as well as the statistical methodologies. Multivariate linear regression (MLR) is the most commonly used tool for ozone trend analysis, however, the complex coupling in most atmospheric processes can make it prone to the over-fitting or multi-collinearity-related issues when using the conventional Ordinary Least Squares (OLS) setting. To overcome this issue, we adopt a regularised (Ridge) regression method to estimate ozone trends and quantify the influence of individual processes. Here, we use the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) merged data set (v2.7) to derive stratospheric ozone profile trends for the period 1984–2020. Beside SWOOSH, we also analyse a machine-learning-based satellite-corrected gap-free global stratospheric ozone profile dataset from a chemical transport model (ML-TOMCAT), and output from two chemical transport model (TOMCAT) simulations forced with ECMWF reanalyses ERA-Interim and ERA5. With Ridge regression, the stratospheric ozone profile trends from SWOOSH data show smaller declines during 1984–1997 compared to OLS with the largest differences in the lowermost stratosphere (> 4 % per decade at 100 hPa). Upper stratospheric ozone has increased since 1998 with maximum (~2 % per decade near 2 hPa) in local winter for mid-latitudes. Negative trends with large uncertainties are observed in the lower stratosphere with the most pronounced in the tropics. The largest differences in post-1998 trend estimates between OLS and Ridge regression methods appear in the tropical lower stratosphere (with ~7 % per decade difference at 100 hPa). Ozone variations associated with natural processes such as the quasi-biennial oscillation (QBO), the solar variability, the El Niño–Southern Oscillation (ENSO), the Arctic oscillation (AO) and the Antarctic oscillation (AAO) also indicate that Ridge regression coefficients are somewhat smaller and less variable compared to the OLS-based estimates. Additionally, ML-TOMCAT based trend estimates are consistent with SWOOSH data set. Finally, we argue that the large differences between the satellite-based data and model simulations confirm that there are still large uncertainties in ozone trend estimates especially in the lower stratosphere, and caution is needed when discussing results if explanatory variables used are correlated.

Accurate quantification of long-term trends in stratospheric ozone can be challenging due to their sensitivity to natural variability, the quality of the observational datasets, non-linear changes in forcing processes as well as the statistical methodologies. Multivariate linear regression (MLR) is the most commonly used tool for ozone trend analysis, however, the complex coupling in most atmospheric processes can make it prone to the over-fitting or multi-collinearity-related issues when using the conventional Ordinary Least Squares (OLS) setting. To overcome this issue, we adopt a regularised (Ridge) regression method to estimate ozone trends and quantify the influence of individual processes. Here, we use the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) merged data set (v2.7) to derive stratospheric ozone profile trends for the period 1984–2020. Beside SWOOSH, we also analyse a machine-learning-based satellite-corrected gap-free global stratospheric ozone profile dataset from a chemical transport model (ML-TOMCAT), and output from two chemical transport model (TOMCAT) simulations forced with ECMWF reanalyses ERA-Interim and ERA5. With Ridge regression, the stratospheric ozone profile trends from SWOOSH data show smaller declines during 1984–1997 compared to OLS with the largest differences in the lowermost stratosphere (> 4 % per decade at 100 hPa). Upper stratospheric ozone has increased since 1998 with maximum (~2 % per decade near 2 hPa) in local winter for mid-latitudes. Negative trends with large uncertainties are observed in the lower stratosphere with the most pronounced in the tropics. The largest differences in post-1998 trend estimates between OLS and Ridge regression methods appear in the tropical lower stratosphere (with ~7 % per decade difference at 100 hPa). Ozone variations associated with natural processes such as the quasi-biennial oscillation (QBO), the solar variability, the El Niño–Southern Oscillation (ENSO), the Arctic oscillation (AO) and the Antarctic oscillation (AAO) also indicate that Ridge regression coefficients are somewhat smaller and less variable compared to the OLS-based estimates. Additionally, ML-TOMCAT based trend estimates are consistent with SWOOSH data set. Finally, we argue that the large differences between the satellite-based data and model simulations confirm that there are still large uncertainties in ozone trend estimates especially in the lower stratosphere, and caution is needed when discussing results if explanatory variables used are correlated.

We use TOMCAT, a 3-dimensional (3D) offline chemical transport model (CTM) forced by two different meteorological reanalysis data sets (ERA-Interim and ERA5) from the European Centre for Medium-Range weather Forecasts (ECMWF) to analyse seasonal behaviour and long-term trends in stratospheric ozone and mean age of air. The model-simulated ozone variations are evaluated against two observation-based data sets. For total column ozone (TCO) comparisons, we use the Copernicus Climate Change Service (C3S) data (1979–2019), while for ozone profiles we use the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) data set (1984–2019). We find that the CTM simulations forced by ERA-Interim (A_ERAI) and ERA5 (B_ERA5) can both successfully reproduce the spatial and temporal variations in stratospheric ozone. Also, modelled TCO anomalies from B_ERA5 show better agreement with C3S than A_ERAI, especially in Northern Hemisphere (NH) mid latitudes, except that it gives somewhat larger positive biases (> 15 DU, Dobson units) during winter–spring seasons. Ozone profile comparisons against SWOOSH data show larger differences between the two simulations. In the lower stratosphere, ozone differences can be directly attributed to the representation of dynamical processes, whereas in the upper stratosphere they can be directly linked to the differences in temperatures between ERAI and ERA5 data sets. Although TCO anomalies from B_ERA5 show relatively better agreement with C3S compared to A_ERAI, a comparison with SWOOSH data does not confirm that B_ERA5 performs better at simulating the variations in the stratospheric ozone profiles. We employ a multivariate regression model to quantify the TCO and ozone profile trends before and after peak stratospheric halogen loading in 1997. Our results show that, compared to C3S, TCO recovery trends (since 1998) in simulation B_ERA5 are significantly overestimated in the Southern Hemisphere (SH) mid latitudes, while for A_ERAI in the NH mid latitudes, simulated ozone trends remain negative. Similarly, in the lower stratosphere, B_ERA5 shows positive ozone recovery trends for both NH and SH mid latitudes. In contrast, both SWOOSH and A_ERAI show opposite (negative) trends in the NH mid latitudes. Furthermore, we analyse age of air (AoA) trends to diagnose transport differences between the two reanalysis data sets. Simulation B_ERA5 shows a positive AoA trend after 1998 and somewhat older age in the NH lower stratosphere compared to A_ERAI, indicating that a slower Brewer–Dobson circulation does not translate into reduced wintertime ozone buildup in the NH extratropical lower stratosphere. Overall, our results show that models forced by the most recent ERA5 reanalyses may not yet be capable of reproducing observed changes in stratospheric ozone, particularly in the lower stratosphere.

Various observation-based datasets have confirmed positive zonal mean column ozone trends at midlatitudes as a result of the successful implementation of the Montreal Protocol. However, there is still uncertainty about the longitudinal variation of these trends and the direction and magnitude of ozone changes at low latitudes. Here, we use the extended Copernicus Climate Change Service (C3S) dataset (1979–2017) to investigate the long-term variations in total column ozone (TCO) over the Tibetan Plateau (TP) for different seasons. We use piecewise linear trend (PWLT) and equivalent effective stratospheric chlorine loading (EESC)-based multivariate regression models with various proxies to attribute the influence of dynamical and chemical processes on the TCO variability. We also compare the seasonal behaviour of the relative total ozone low (TOL) over the TP with the zonal mean at the same latitude. Both regression models show that the TP column ozone trends change from negative trends from 1979 to 1996 to small positive trends from 1997 to 2017, although the later positive trend based on PWLT is not statistically significant. The wintertime positive trend starting from 1997 is larger than that in summer, but both seasonal TP recovery rates are smaller than the zonal means over the same latitude band. For TP column ozone, both regression models suggest that the geopotential height at 150 hPa (GH150) is a more suitable and realistic dynamical proxy compared to a surface temperature proxy used in some previous studies. Our analysis also shows that the wintertime GH150 plays an important role in determining summertime TCO over the TP through persistence of the ozone signal. For the zonal mean column ozone at this latitude, the quasi-biennial oscillation (QBO) is nonetheless the dominant dynamical proxy. We also use a 3-D chemical transport model to diagnose the contributions of different proxies for the TP region. The role of GH150 variability is illustrated by using two sensitivity experiments with repeating dynamics of 2004 and 2008. The simulated ozone profiles clearly show that wintertime TP ozone concentrations are largely controlled by tropics to midlatitude pathways, whereas in summer variations associated with tropical processes play an important role. These model results confirm that the long-term trends of TCO over the TP are dominated by different processes in winter and summer. The different TP recovery rates relative to the zonal means at the same latitude band are largely determined by wintertime dynamical processes.