Aerosol microphysics simulations of the Mt.~Pinatubo eruption with the UM-UKCA composition-climate model

We use a stratosphere–troposphere composition–climate model with interactive sulfur chemistry and aerosol microphysics, to investigate the effect of the 1991 Mount Pinatubo eruption on stratospheric aerosol properties. Satellite measurements indicate that shortly after the eruption, between 14 and 23 Tg of SO2 (7 to 11.5 Tg of sulfur) was present in the tropical stratosphere. Best estimates of the peak global stratospheric aerosol burden are in the range 19 to 26 Tg, or 3.7 to 6.7 Tg of sulfur assuming a composition of between 59 and 77 % H2SO4. In light of this large uncertainty range, we performed two main simulations with 10 and 20 Tg of SO2 injected into the tropical lower stratosphere. Simulated stratospheric aerosol properties through the 1991 to 1995 period are compared against a range of available satellite and in situ measurements. Stratospheric aerosol optical depth (sAOD) and effective radius from both simulations show good qualitative agreement with the observations, with the timing of peak sAOD and decay timescale matching well with the observations in the tropics and mid-latitudes. However, injecting 20 Tg gives a factor of 2 too high stratospheric aerosol mass burden compared to the satellite data, with consequent strong high biases in simulated sAOD and surface area density, with the 10 Tg injection in much better agreement. Our model cannot explain the large fraction of the injected sulfur that the satellite-derived SO2 and aerosol burdens indicate was removed within the first few months after the eruption. We suggest that either there is an additional alternative loss pathway for the SO2 not included in our model (e.g. via accommodation into ash or ice in the volcanic cloud) or that a larger proportion of the injected sulfur was removed via cross-tropopause transport than in our simulations. We also critically evaluate the simulated evolution of the particle size distribution, comparing in detail to balloon-borne optical particle counter (OPC) measurements from Laramie, Wyoming, USA (41° N). Overall, the model captures remarkably well the complex variations in particle concentration profiles across the different OPC size channels. However, for the 19 to 27 km injection height-range used here, both runs have a modest high bias in the lowermost stratosphere for the finest particles (radii less than 250 nm), and the decay timescale is longer in the model for these particles, with a much later return to background conditions. Also, whereas the 10 Tg run compared best to the satellite measurements, a significant low bias is apparent in the coarser size channels in the volcanically perturbed lower stratosphere. Overall, our results suggest that, with appropriate calibration, aerosol microphysics models are capable of capturing the observed variation in particle size distribution in the stratosphere across both volcanically perturbed and quiescent conditions. Furthermore, additional sensitivity simulations suggest that predictions with the models are robust to uncertainties in sub-grid particle formation and nucleation rates in the stratosphere

Using machine-learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets

Monitoring the atmospheric concentrations of greenhouse gases (GHGs) is crucial in order to improve our understanding of their climate impact. However, there are no long-term profile data sets of important GHGs that can be used to gain a better insight into the processes controlling their variations in the atmosphere. Here, we merge chemical transport model (CTM) output and profile measurements from two solar occultation instruments, the HALogen Occultation Experiment (HALOE) and the Atmospheric Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS), to construct long-term (1991–2021), gap-free stratospheric profile data sets (hereafter, TCOM) for two important GHGs. The Extreme Gradient Boosting (XGBoost) regression model is used to estimate the corrections needed to apply to the CTM profiles. For methane (TCOM-CH4), we use both HALOE and ACE satellite profile measurements (1992–2018) to train the XGBoost model while profiles from three later years (2019–2021) are used as an independent evaluation data set. As there are no nitrous oxide (N2O) profile measurements for earlier years, XGBoost-derived correction terms to construct TCOM-N2O profiles are derived using only ACEFTS profiles for the 2004–2018 time period, with profiles from 2019–2021 again being used for the independent evaluation. Overall, both TCOM-CH4 and TCOM-N2O profiles show excellent agreement with the available satellite measurement-based data sets. We find that compared to evaluation profiles, biases in TCOM-CH4 and TCOM-N2O are generally less than 10 % and 50 %, respectively, throughout the stratosphere. Daily zonal mean profile data sets on altitude (15–60 km) and pressure (300–0.1 hPa) levels are publicly available via https://doi.org/10.5281/zenodo.7293740 for TCOM-CH4 (Dhomse, 2022a) and https://doi.org/10.5281/zenodo.7386001 for TCOM-N2O (Dhomse, 2022b)

Quantifying stratospheric ozone trends over 1984–2020: a comparison of ordinary and regularized multivariate regression models

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

Arctic Ozone depletion in 2015/16 and comparison with previous winters

International audienceThree-dimensional chemical transport models (CTMs) and coupled chemistry-climate models (CCMs) have been widely used to study the dynamical and chemical processes which control polar ozone depletion. Despite generally good agreement between modelled and observed loss, there are still some uncertainties in our understanding and limitations of the models. For example, the accuracy of modelled springtime ozone loss depends critically on the transport, chemistry and treatment of polar stratospheric clouds (PSCs). There is also increasing evidence that descent of NOx from the mesosphere can impact ozone at high latitudes, even in the absence of halogen-catalysed loss. CCMs used for future predictions need to accurately include these processes.At the time of writing (late February 2016) the current Arctic winter has been cold with extensive PSC ac- tivity. The local maximum modelled O3 loss is currently ∼50% (www.see.leeds.ac.uk/tomcat), which is similar to that from previous cold years with record ozone depletion (e.g. 2010/11). This is interesting in its own right, but will also provide new meteorological conditions under which to test modelled loss against observations.We will present results from the TOMCAT/SLIMCAT off-line 3-D CTM and the NCAR Whole Atmo- sphere Community Climate Model (WACCM), which we have recently extended by including mesospheric ion chemistry. Both models are forced or nudged by the ECMWF ERA-Interim 4D-var reanalyses. First we will focus on the cold Arctic winter/spring 2015/16 and quantify the amount of chemical ozone loss using both models and satellite observations. Then we will investigate the chorine activation and denitrification as well as mesospheric NOx/HOx impact on stratospheric ozone. Other sensitivities experiments will be also discussed

Investigation of spatial and temporal variability in lower tropospheric ozone from RAL Space UV–Vis satellite products

Ozone is a potent air pollutant in the lower troposphere and an important short-lived climate forcer (SLCF) in the upper troposphere. Studies using satellite data to investigate spatiotemporal variability of troposphere ozone (TO3) have predominantly focussed on the tropospheric column metric. This is the first study to investigate long-term spatiotemporal variability in lower tropospheric column ozone (LTCO3, surface–450 hPa sub-column) by merging multiple European Space Agency–Climate Change Initiative (ESA-CCI) products produced by the Rutherford Appleton Laboratory (RAL) Space. We find that in the LTCO3, the degree of freedom of signal (DOFS) from these products varies with latitude range and season and is up to 0.8, indicating that the retrievals contain useful information on lower TO3. The spatial and seasonal variation of the RAL Space products are in good agreement with each other, but there are systematic offsets of up to 3.0–5.0 DU between them. Comparison with ozonesondes shows that the Global Ozone Monitoring Experiment (GOME-1, 1996–2003), the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY, 2003–2010) and the Ozone Monitoring Instrument (OMI, 2005–2017) have stable LTCO3 records over their respective periods, which can be merged together. However, GOME-2 (2008–2018) shows substantial drift in its bias with respect to ozonesondes. We have therefore constructed a robust merged data set of LTCO3 from GOME-1, SCIAMACHY and OMI between 1996 and 2017. Comparing the LTCO3 differences between the 1996–2000 and 2013–2017 5-year averages, we find sizeable positive increases (3.0–5.0 DU) in the tropics/sub-tropics, while in the northern mid-latitudes, we find small-scale differences in LTCO3. Therefore, we conclude that there has been a substantial increase in tropical/sub-tropical LTCO3 during the satellite era, which is consistent with tropospheric column ozone (TCO3) records from overlapping time periods (e.g. 2005–2016)

Antarctic Vortex Dehydration in 2023 as a Substantial Removal Pathway for Hunga Tonga‐Hunga Ha'apai Water Vapor

The January 2022 eruption of Hunga Tonga-Hunga Ha'apai (HTHH) injected a huge amount (∼150 Tg) of water vapor (H₂O) into the stratosphere, along with small amount of SO₂. An off-line 3-D chemical transport model (CTM) successfully reproduces the spread of the injected H₂O through October 2023 as observed by the Microwave Limb Sounder. Dehydration in the 2023 Antarctic polar vortex caused the first substantial (∼20 Tg) removal of HTHH H₂O from the stratosphere. The CTM indicates that this process will dominate removal of HTHH H₂O for the coming years, giving an overall e-folding timescale of 4 years; around 25 Tg of the injected H₂O is predicted to still remain in the stratosphere by 2030. Following relatively low Antarctic column ozone in midwinter 2023 due to transport effects, additional springtime depletion due to H₂O-related chemistry was small and maximized at the vortex edge (10 DU in column)

Quantifying the tropospheric ozone radiative effect and its temporal evolution in the satellite era

Using state-of-the-art satellite ozone profile products, and a chemical transport model, we provide an updated estimate of the tropospheric ozone radiative effect (TO3RE) and observational constraint on its variability over the decade 2008–2017. Previous studies have shown the short-term (i.e. a few years) globally weighted average TO3RE to be 1.17 ± 0.03 W m−2. However, from our analysis, using decadal (2008–2017) ozone profile datasets from the Infrared Atmospheric Sounding Interferometer, average TO3RE ranges between 1.21 and 1.26 W m−2. Over this decade, the modelled and observational TO3RE linear trends show a negligible change (e.g.  ± 0.1 % yr−1). Two model sensitivity experiments fixing emissions and meteorology to 1 year (i.e. start year – 2008) show that temporal changes in ozone precursor emissions (increasing contribution) and meteorological factors (decreasing contribution) have counteracting tendencies, leading to a negligible globally weighted average TO3RE trend