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The eruption of the Hunga Tonga‐Hunga Ha'apai volcano on 15 January 2022 was one of the most explosive eruptions of the last decades. The amount of water vapor injected into the stratosphere was unprecedented in the observational record, increasing the stratospheric water vapor burden by about 10%. Using model runs from the ATLAS chemistry and transport model and Microwave Limb Sounder (MLS) satellite observations, we show that while 20%–40% more water vapor than usual was entrained into the Antarctic polar vortex in 2023 as it formed, the direct chemical effect of the increased water vapor on Antarctic ozone depletion in June through October was minor (less than 4 DU). This is because low temperatures in the vortex, as occur every year in the Antarctic, limit water vapor to the saturation pressure and thus reset any anomalies through the process of dehydration before they can affect ozone loss.

The stratospheric aerosol layer is important for stratospheric chemistry, climate change and in geo-engineering. Yet the processes governing the transport of sulfur to the stratosphere are poorly quantified. We present model calculations of the chemistry of sulfur dioxide (SO2) and its transport to the stratosphere and perform numerous sensitivity runs to assess the range of uncertainty of these calculations. The transport model is based on backward trajectories from the ATLAS model driven by ECMWF ERA 5 and ERA Interim. A simplified chemical box model constrained by CAMS data is used to calculate the SO2 chemistry. Sensitivity experiments explore the sensitivity to changes in OH, H2O2, DMS, cloud water, cloud pH value and in the driving analysis data. Input parameters were varied and their differences have been explored. The SO2 reaching the stratosphere was quantified and the sources in the troposphere were determined. The model’s results were compared to POSIDON Aircraft measurements.

Abstract In climate modeling, the stratospheric ozone layer is typically only considered in a highly simplified form due to computational constraints. For climate projections, it would be of advantage to include the mutual interactions between stratospheric ozone, temperature, and atmospheric dynamics to accurately represent radiative forcing. The overarching goal of our research is to replace the ozone layer in climate models with a machine-learned neural representation of the stratospheric ozone chemistry that allows for a particularly fast, but accurate and stable simulation. We created a benchmark data set from pairs of input and output variables that we stored from simulations of the ATLAS Chemistry and Transport Model. We analyzed several variants of multilayer perceptrons suitable for physical problems to learn a neural representation of a function that predicts 24-h ozone tendencies based on input variables. We performed a comprehensive hyperparameter optimization of the multilayer perceptron using Bayesian search and Hyperband early stopping. We validated our model by replacing the full chemistry module of ATLAS and comparing computation time, accuracy, and stability. We found that our model had a computation time that was a factor of 700 faster than the full chemistry module. The accuracy of our model compares favorably to the full chemistry module within a 2-year simulation run, also outperforms a previous polynomial approach for fast ozone chemistry, and reproduces seasonality well in both hemispheres. In conclusion, the neural representation of stratospheric ozone chemistry in simulation resulted in an ozone layer that showed a high accuracy, significant speed-up, and stability in a long-term simulation.

The stratospheric aerosol layer is important for stratospheric chemistry, climate change and in geo-engineering. Yet the processes governing the transport of sulfur to the stratosphere are poorly quantified. We present model calculations of the chemistry of sulfur dioxide (SO2) and its transport to the stratosphere and perform numerous sensitivity runs to assess the range of uncertainty of these calculations. The transport model is based on backward trajectories from the ATLAS model driven by ECMWF ERA 5. A simplified chemical box model constrained by CAMS data is used to calculate the SO2 chemistry. Sensitivity experiments explore the sensitivity to changes in OH, H2O2, DMS, cloud water, cloud pH value and in the driving analysis data. Input parameters were varied and their differences have been explored. The effect of El Nino and La Nina on SO2 transport was investigated. The SO2 reaching the stratosphere was quantified and the sources in the troposphere were determined. The model results were compared to POSIDON Aircraft measurements.

The Polar SWIFT model is a fast scheme for calculating the chemistry of stratospheric ozone depletion in the polar vortex in winter. It is intended for use in general circulation models (GCMs) and earth system models (ESMs) to enable the simulation of interactions between the ozone layer and climate when a full stratospheric chemistry scheme is computationally too expensive. In addition to the simulation of chemistry, ozone has to be transported in the GCM. As an alternative to the general schemes for the transport and mixing of tracers in the GCMs, a parameterization of the transport of ozone can be used in order to obtain the total change of ozone as the sum of the change by transport and by chemistry. One of the benefits of this approach is the easy and self-contained coupling to a GCM. Another potential advantage is that a transport parameterization based on reanalysis data and measurements can avoid deficiencies in the representation of transport in the GCMs, such as deficits in the representation of the Brewer–Dobson circulation caused by the gravity wave parameterization. Hence, we present a transport parameterization for the Polar SWIFT model that simulates the change in vortex-averaged ozone by transport in a fast and simple way without the need for a complex transport scheme in the GCM.

The winter 2019/2020 showed the lowest ozone mixing ratios ever observed in the Arctic winter stratosphere. It was the coldest Arctic stratospheric winter on record and was characterized by an unusually strong and long-lasting polar vortex. We study the chemical evolution and ozone depletion in the winter 2019/2020 using the global Chemistry and Transport Model ATLAS. We examine whether the chemical processes in 2019/2020 are more characteristic of typical conditions in Antarctic winters or in average Arctic winters. Model runs for the winter 2019/2020 are compared to simulations of the Arctic winters 2004/2005, 2009/2010, and 2010/2011 and of the Antarctic winters 2006 and 2011, to assess differences in chemical evolution in winters with different meteorological conditions. In some respects, the winter 2019/2020 (and also the winter 2010/2011) was a hybrid between Arctic and Antarctic conditions, for example, with respect to the fraction of chlorine deactivation into HCl versus ClONO2, the amount of denitrification, and the importance of the heterogeneous HOCl + HCl reaction for chlorine activation. The pronounced ozone minimum of less than 0.2 ppm at about 450 K potential temperature that was observed in about 20% of the polar vortex area in 2019/2020 was caused by exceptionally long periods in the history of these air masses with low temperatures in sunlight. Based on a simple extrapolation of observed loss rates, only an additional 21-46 h spent below the upper temperature limit for polar stratospheric cloud formation and in sunlight would have been necessary to reduce ozone to near zero values (0.05 ppm) in these parts of the vortex.

Motivated by previous measurements of very low tropospheric ozone concentrations in the Tropical West Pacific (TWP) and the implied low oxidizing capacity of this key region for transport into the stratosphere in boreal winter (e.g. Rex et al. 2014), we set up an atmospheric research station in Palau (7°N 134°E) as part of the StratoClim campaign. Our analysis of regular balloon-borne tropospheric ozone observations at Palau from 01/2016-12/2019 gives unprecedented insights into transport processes and air mass origin in the TWP. We confirm the year-round dominance of a low ozone background in the mid-troposphere. Layers of enhanced ozone are often anti-correlated with water vapor and occur frequently. Moreover, the occurrence of respective layers shows a strong seasonality. Dry and ozone-rich air masses between 5 and 10 km altitude were observed in 71 % of the profiles from February until April compared to 25 % from August until October. By defining monthly atmospheric background profiles for ozone and relative humidity based on observed statistics, we found that the deviations from this background reveal a bimodal distribution of RH anomalies. A previously proposed universal bimodal structure of free tropospheric ozone in the TWP could not be verified (Pan et al. 2015). Back trajectory calculations (ATLAS) confirm that throughout the year the mid-tropospheric background is controlled by local convective processes and the origin of air masses is thus close to or East of Palau in the Pacific Ocean. Dry and ozone-rich air originates in tropical Asia and reaches Palau in anticyclonic conditions over an area stretching from India to the Philippines. This supports the controversial hypothesis of several studies which attribute ozone enhancement against the ozone-poor background to remote pollution events on the ground such as biomass burning (e.g. Andersen et al. 2016). A potential vorticity analysis revealed no stratospheric influence and we thus propose large-scale de

We present the first high resolution measurements of pollutant trace gases in the Asian Summer Monsoon Upper Troposphere and Lowermost Stratosphere (UTLS) from the Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA) during the StratoClim (Stratospheric and upper tropospheric processes for better climate predictions) campaign with base in Kathamandu, Nepal, 2017. Measurements of peroxyacetyl nitrate (PAN), acetylene (C2H2), and formic acid (HCOOH) show strong local enhancements up to altitudes of 16 km. More than 500 pptv of PAN, more than 200 pptv of C2H2, and more than 200 pptv of HCOOH are observed. An observed local maximum of PAN and C2H2 at altitudes up to 18 km, reaching to the lowermost stratosphere, instead has been transported for a longer time. A local minimum of HCOOH is correlated with a maximum of ammonia (NH3), which suggests different wash out efficiencies of these species in the same air masses. To study the influence of convective transport to the measured pollution trace gas occurrences in detail, a trajectory analysis of the models ATLAS and TRACZILLA examined backward trajectories, starting at geolocations of GLORIA measurements with enhanced pollution trace gases. Both trajectory schemes implemented advanced techniques for detection of convective events. These convective events along trajectories leading to GLORIA measurements with enhanced pollutants are located close to regions, where satellite measurements by OMI show enhanced tropospheric columns of nitrogen dioxide (NO2) in the days prior to the observation. As an application of these highly resolved measurements, a comparison to the atmospheric models CAMS and EMAC is performed. It is demonstrated that these simulation results are able to reproduce large scale structures of the pollution trace gas distributions if the convective influence on the measured air masses is captured by the meteorological fields used by these simulations. Both models do not have sufficient hor

In the Antarctic ozone hole, ozone mixing ratios have been decreasing to extremely low values of 0.01–0.1 ppm in nearly all spring seasons since the late 1980s, corresponding to 95–99% local chemical loss. In contrast, Arctic ozone loss has been much more limited and mixing ratios have never before fallen below 0.5 ppm. In Arctic spring 2020, however, ozonesonde measurements in the most depleted parts of the polar vortex show a highly depleted layer, with ozone loss averaged over sondes peaking at 93% at 18 km. Typical minimum mixing ratios of 0.2 ppm were observed, with individual profiles showing values as low as 0.13 ppm (96% loss). The reason for the unprecedented chemical loss was an unusually strong, long-lasting, and cold polar vortex, showing that for individual winters the effect of the slow decline of ozone-depleting substances on ozone depletion may be counteracted by low temperatures.

Motivated by previous measurements of very low tropospheric ozone concentrations in the Tropical West Pacific (TWP) and the implied low oxidizing capacity of this key region for transport into the stratosphere (e.g. Rex et al. 2014), we set up an atmospheric research station in Palau (7° N 134° E). Our analysis of regular balloon-borne tropospheric ozone observations at Palau from 01/2016-10/2019 confirms the year-round dominance of a low ozone background in the mid-troposphere. Layers of enhanced ozone are often anti-correlated with water vapor and occur frequently. Moreover, the occurrence of respective layers shows a strong seasonality. Dry and ozone-rich air masses between 5 and 10 km altitude were observed in 71 % of the profiles from February until April compared to 25 % from August until October. By defining monthly atmospheric background profiles for ozone and relative humidity based on observed statistics, we found that the deviations from this background reveal a bimodal distribution of RH anomalies. A previously proposed universal bimodal structure of free tropospheric ozone in the TWP could not be verified (Pan et al. 2015). Back trajectory calculations confirm that throughout the year the mid-tropospheric background is controlled by local convective processes and the origin of air masses is thus close to or East of Palau in the Pacific Ocean. Dry and ozone-rich air originates in tropical Asia and reaches Palau in anticyclonic conditions over an area stretching from India to the Philippines. This supports the hypothesis of several studies which attribute ozone enhancement against the low ozone background to remote pollution events on the ground such as biomass burning (e.g. Andersen et al. 2016). A potential vorticity analysis revealed no stratospheric influence and we thus propose large-scale descent within the tropical troposphere as responsible for dehydration of air masses on their way to Palau.

Due to the intensive ozone research in recent decades, the processes that influence stratospheric ozone are well understood. The chemistry and transport model ATLAS was developed to simulate the chemistry and transport of stratospheric ozone globally. The chemical rate of change of ozone is calculated at each model point and time step of the model by solving a system of differential equations that requires 55 input parameters (chemical species, temperatures, ...). But the computational e!ort to solve this complex system of differential equations is very high, and with respect to the overall limited computation time, this prevents the inclusion of ozone chemistry into ESMs. This project proposes a data-driven machine learning approach to predict the rate of change of stratospheric ozone. To derive a data set from modelled data, ATLAS was run for several short model runs. The rate of change of ozone and 55 parameters were stored at each model point and time step. By observing the co-variances of the high-dimensional feature-space, a large data set with reduced dimensionality has been created. A supervised learning algorithm used this data set of input and output pairs to train a deep feed- forward neural network (NN). This involved the identification and optimisation of several hyperparameters and to find a well- functioning combination of depth (number of layers) and width (number of neurons per layer). In this way, the NN model capacity is optimised with respect to the data itself. To evaluate this approach, the results were compared with another data-driven approach called SWIFT. The SWIFT model employs a repro-modelling approach that uses polynomials to approximate the rate of change of ozone. The resulting NN model is not only capable of learning the context of an eleven-dimensional hyperplane, but also improves the RMSE by about one order of magnitude compared to SWIFT’s previous polynomial approach. In addition, the deviations of the predictions at the boundari

We present a Lagrangian convective transport scheme developed for global chemistry and transport models, which considers the variable residence time that an air parcel spends in convection. This is particularly important for accurately simulating the tropospheric chemistry of short-lived species, e.g., for determining the time available for heterogeneous chemical processes on the surface of cloud droplets. In current Lagrangian convective transport schemes air parcels are stochastically redistributed within a fixed time step according to estimated probabilities for convective entrainment as well as the altitude of detrainment. We introduce a new scheme that extends this approach by modeling the variable time that an air parcel spends in convection by estimating vertical updraft velocities. Vertical updraft velocities are obtained by combining convective mass fluxes from meteorological analysis data with a parameterization of convective area fraction profiles. We implement two different parameterizations: a parameterization using an observed constant convective area fraction profile and a parameterization that uses randomly drawn profiles to allow for variability. Our scheme is driven by convective mass fluxes and detrainment rates that originate from an external convective parameterization, which can be obtained from meteorological analysis data or from general circulation models. We study the effect of allowing for a variable time that an air parcel spends in convection by performing simulations in which our scheme is implemented into the trajectory module of the ATLAS chemistry and transport model and is driven by the ECMWF ERA-Interim reanalysis data. In particular, we show that the redistribution of air parcels in our scheme conserves the vertical mass distribution and that the scheme is able to reproduce the convective mass fluxes and detrainment rates of ERA-Interim. We further show that the estimated vertical updraft velocities of our scheme are able to repro

Stratospheric ozone loss inside the Arctic polar vortex for the winters between 2004–2005 and 2012–2013 has been quantified using measurements from the space-borne Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS). For the first time, an evaluation has been performed of six different ozone loss estimation methods based on the same single observational dataset to determine the Arctic ozone loss (mixing ratio loss profiles and the partial-column ozone losses between 380 and 550 K). The methods used are the tracer-tracer correlation, the artificial tracer correlation, the average vortex profile descent, and the passive subtraction with model output from both Lagrangian and Eulerian chemical transport models (CTMs). For the tracer-tracer, the artificial tracer, and the average vortex profile descent approaches, various tracers have been used that are also measured by ACE-FTS. From these seven tracers investigated (CH4, N2O, HF, OCS, CFC-11, CFC-12, and CFC-113), we found that CH4, N2O, HF, and CFC-12 are the most suitable tracers for investigating polar stratospheric ozone depletion with ACE-FTS v3.5. The ozone loss estimates (in terms of the mixing ratio as well as total column ozone) are generally in good agreement between the different methods and among the different tracers. However, using the average vortex profile descent technique typically leads to smaller maximum losses (by approximately 15–30 DU) compared to all other methods. The passive subtraction method using output from CTMs generally results in slightly larger losses compared to the techniques that use ACE-FTS measurements only. The ozone loss computed, using both measurements and models, shows the greatest loss during the 2010–2011 Arctic winter. For that year, our results show that maximum ozone loss (2.1–2.7 ppmv) occurred at 460 K. The estimated partial-column ozone loss inside the polar vortex (between 380 and 550 K) using the different methods is 66–103, 61–95, 59–96, 41–89