Abstract. Accurately representing polar stratospheric clouds (PSCs) in global Chemistry-Climate Models and Earth System Models is important as they play a key role in springtime ozone depletion in polar regions by activating both chlorine and bromine species through heterogeneous reactions and denitrifying the stratosphere. Here, we present and evaluate an updated PSC parameterisation scheme implemented in the UK Earth System Model (UKESM1.1). The scheme includes the kinetic formation of nitric acid trihydrate (NAT) particles, the formation of supercooled ternary solution (STS) droplets assuming thermodynamic equilibrium, and accounts for ice and sulphate aerosols. To evaluate the new scheme, we compare modelled PSC production with satellite observations from the Cloud-Aerosol and Lidar with Orthogonal Polarization (CALIOP) instrument and model concentrations of gas-phase nitric acid and ozone concentrations with observations from the Aura Microwave Limb Sounder (MLS) instrument for the 2008 Antarctic and 2009/2010 Arctic winters. In comparison with the current, simpler scheme, the updated parameterisation increases both the range of PSC types that form and the seasonal variability, resulting in better agreement with CALIOP observations. It also slows the growth of NAT particles and enables nitric acid partitioning between gas, liquid, and solid phases, leading to improved agreement with MLS observations. However, there is comparatively little impact on stratospheric ozone, with the exception near the edge of the polar vortex where the new scheme improves comparisons with MLS observations.

Accurate and efficient modeling of atmospheric composition, including aerosols and trace gases and their interactions with radiation, clouds, and dynamics is essential for improving predictions and understanding of air quality, weather, climate, and related health impacts. The Aerosols and Reactive Trace gases (ART) component extends the ICOsahedral Nonhydrostatic (ICON) modeling framework by enabling online, fully coupled simulations of atmospheric composition processes across scales. ICON-ART includes modules for emissions, transport, gas-phase chemistry, and aerosol microphysics in both the troposphere and stratosphere, allowing for the investigation of feedbacks between atmospheric composition and physical processes from the large-eddy to global scale.This paper presents an updated overview of the ICON-ART framework as implemented in version 2025.10, highlighting recent developments in emission parameterizations, chemical mechanisms, aerosol processes, and coupling to the physical core of ICON via aerosol-radiation and aerosol-cloud interactions. We summarize the structure of the code infrastructure and demonstrate the model's flexibility and scalability across a wide range of applications. ICON-ART provides a unified and modular platform for research and operational use in atmospheric composition, bridging the gap between regional air quality modeling and global Earth system simulations.

Anthropogenic emissions of carbon dioxide (CO2) are the main driver of the change in climate since the industrial revolution. Policy mitigation strategies include reduction of these emissions. The Paris Agreement from 2015 requires the states to report their greenhouse gas emissions on a regular basis. Space-borne remote-sensing measurements of CO2 are considered potentially of great value to monitor CO2 emissions, due to their better coverage in comparison to in-situ instruments. Measuring CO2 from space of an adequate quality is a challenge because of the stringent requirements related to the accuracy and precision of the data products and thus the instruments. In this study, we use the Fast atmospheric traCe gAs retrievaL (FOCAL) algorithm to retrieve maps of the column-averaged dry-air CO2 mole fraction (XCO2) with the goal of quantifying CO2 emissions for specific emission targets using Orbiting Carbon Observatory 3 (OCO-3) snapshot area maps. This data product is planned to be used in the German national Integrated Greenhouse Gas Monitoring System (ITMS), for which an operational data assimilation system for greenhouse gases is being set up for Germany.

Abstract. Accurate and efficient modeling of atmospheric composition, including aerosols and trace gases and their interactions with radiation, clouds, and dynamics is essential for improving predictions of air quality, weather, climate, and related health impacts. The ART (Aerosols and Reactive Trace gases) component extends the ICOsahedral Nonhydrostatic (ICON) modeling framework by enabling online, fully coupled simulations of atmospheric composition processes across scales. ART includes modules for emissions, transport, gas-phase chemistry, and aerosol microphysics in both the troposphere and stratosphere, allowing for the investigation of feedbacks between atmospheric composition and physical processes from the large-eddy to global scale. This paper presents an updated overview of the ICON-ART framework as implemented in version 2025.04, highlighting recent developments in emission parameterizations, chemical mechanisms, aerosol processes, and coupling to the physical core of ICON via aerosol–radiation and aerosol–cloud interactions. We summarize the structure of the code infrastructure and demonstrate the model’s flexibility and scalability across a wide range of applications. ICON-ART provides a unified and modular platform for research and operational use in atmospheric composition, bridging the gap between regional air quality modeling and global Earth system simulations.

Atmospheric gravity waves can play a significant role on atmospheric chemistry through temperature fluctuations. A recent modeling study introduced a method to implement subgrid‐scale orographic gravity‐wave‐induced temperature perturbations in the Whole Atmosphere Community Climate Model (WACCM). The model with a wave‐induced temperature parameterization was able to reproduce for example, the influence of mountain wave events on atmospheric chemistry, as highlighted in previous literature. Here we extend the subgrid‐scale wave‐induced temperature parameterization to also include non‐orographic gravity waves arising from frontal activity and convection. We explore the impact of these waves on middle atmosphere chemistry, particularly focusing on reactions that are strongly sensitive to temperature. The non‐orographic gravity waves increase the variability of chemical reaction rates, especially in the lower mesosphere. As an example, we show that this, in turn, leads to increases in the daytime ozone variability. To demonstrate another impact, we briefly investigate the role of non‐orographic gravity waves in cirrus cloud formation in this model. Consistent with findings from the previous study focusing on orographic gravity waves, non‐orographic waves also enhance homogeneous nucleation and increase cirrus clouds. The updated method used enables the global chemistry‐climate model to account for both orographic and non‐orographic gravity‐wave‐induced subgrid‐scale dynamical perturbations in a consistent manner.

Carbon dioxide (CO2) and methane (CH4) are the most important anthropogenic greenhouse gases and the main drivers of climate change. Monitoring their concentrations from space helps detect and quantify anthropogenic emissions, supporting the mitigation efforts urgently needed to meet the primary objective of the Paris Agreement, adopted at the 21st Conference of the Parties to the United Nations Framework Convention on Climate Change (UNFCCC) in 2015, to limit the global average temperature increase to well below 2 °C above pre-industrial levels. In addition, satellite observations can be used to quantify natural sources and sinks, improving our understanding of the carbon cycle. Advancing these goals is one key motivation for the European Copernicus CO2 monitoring mission CO2M. The necessary accuracy and precision requirements for the measured quantities XCO2 and XCH4 (the column-averaged dry-air mole fractions of CO2 and CH4) are demanding. According to the CO2M mission requirements, the spatial and temporal variability of the systematic errors (or spatio-temporal systematic errors) of XCO2 and XCH4 must not exceed 0.5 ppm and 5 ppb, respectively. The stochastic errors due to instrument noise must not exceed 0.7 ppm for XCO2 and 10 ppb for XCH4. Conventional so-called full-physics algorithms for retrieving XCO2 and/or XCH4 from satellite-based measurements of reflected solar radiation are typically computationally intensive and still usually require empirical bias corrections based on supervised machine learning methods. Here we present the retrieval algorithm Neural networks for Remote sensing of Greenhouse gases from CO2M (NRG-CO2M), which derives XCO2 and XCH4 from CO2M radiance measurements with minimal computational effort using artificial neural networks (ANNs). In addition, NRG-CO2M also provides estimates of both the noise-driven uncertainties and the averaging kernels of XCO2 and XCH4 for each sounding. Since CO2M will not be launched until 2026, our study exploits simulated measurements over land surfaces from a comprehensive observing system simulation experiment (OSSE) that includes realistic meteorology, aerosols, surface bidirectional reflectance distribution function (BRDF), solar-induced chlorophyll fluorescence (SIF), and CO2 and CH4 concentrations. We created a novel hybrid learning approach that combines advantages of simulation-based and measurement-based training data to ensure coverage of a wide range of XCO2 and XCH4 values, making the training data representative of future concentrations as well. The algorithm's postprocessing is designed to achieve a high data yield of about 80 % of all cloud-free soundings. The spatio-temporal systematic errors of XCO2 and XCH4 are 0.44 ppm and 2.45 ppb, respectively. The average single sounding precision is 0.41 ppm for XCO2 and 2.74 ppb for XCH4. Therefore, the presented retrieval method has the potential to meet the demanding CO2M mission requirements for XCO2 and XCH4. While the presented results are a solid proof of concept, the actual achievable quality can only be determined once NRG-CO2M is trained on real data, where it is confronted, e.g., with unknown instrument effects and systematic errors in the training truth.

The direct and indirect release of carbon dioxide (CO2) by human activities into the atmosphere has been the main driver of anthropogenic climate change since the industrial revolution. The Paris Agreement from 2015 requires regular country-based reports of greenhouse gas emissions. Inverse modeling of observed concentrations of greenhouse gases is one important approach to verify reported emissions. The future constellation of Copernicus Anthropogenic CO2 Monitoring (CO2M) satellites is dedicated to greenhouse gas measurements with high spectral and spatial resolution and wide coverage. The requirements for the performance of the instruments and retrieval algorithms for the column-averaged dry-air mole fraction (XCO2) are stringent in order to identify, assess and monitor CO2 emissions from space. In this study, we analyze the impact of avoiding detector saturation on the precision and spatial coverage of XCO2. We use the Fast atmOspheric traCe gAs retrievaL (FOCAL) algorithm, which has been selected to be one of the operational greenhouse gas retrieval algorithms to be implemented within the CO2M ground segment. In order to avoid saturation, the number of read-outs per sampling time can be increased and the signals can be co-added on board, which we refer to as “temporal oversampling” in this study. We use a subsampled 1-year dataset of simulated radiances to define the temporal oversampling factors (OSFs) that are sufficient to avoid detector saturation and then apply the defined OSF combinations globally. We find that OSFs larger than 1 will lead to a significant decrease in the number of saturated observations, with some impact on the median XCO2 precision, concluding that OSFs larger than 1 should be considered for the satellite mission. These results are based on simulated radiances. Consequently, the real impact on precision should be analyzed in more detail during the commissioning phase of the satellite.

Carbon dioxide (CO2) and methane (CH4) are the most important anthropogenic greenhouse gases because they are the main drivers of climate change. Monitoring their concentrations from space can help to detect and quantify anthropogenic emissions, supporting the mitigation efforts urgently needed to fulfill the Paris Agreement. Additionally, it can help to better understand the processes of the carbon cycle and thus allow better climate projections. These are key objectives of the European Copernicus CO2 Monitoring Mission CO2M, scheduled for launch in 2026, for which three retrieval algorithms are currently being developed and implemented in the EUMETSAT ground segment. These are so called conventional retrieval techniques that base on radiative transfer calculations. Despite shortcuts and approximations, the vast amount of satellite data makes them computationally expensive, requiring thousands of CPU cores. Although conventional retrieval methods base on physical principles, they typically require empirical data-driven methods to correct for biases in order to meet the demanding accuracy and precision requirements. The biases arise, e.g., from inaccuracies of the radiative transfer computations or unknown instrumental issues. Machine learning methods have the potential to combine both steps into a single data-driven retrieval algorithm, reducing the computational cost by several orders of magnitude. We used the radiative transfer model SCIATRAN to simulate two years (2015 and 2020) of sub-sampled realistic radiances of three instruments on board CO2M: the main instrument CO2I (CO2 imager), MAP (multi angle polarimeter), and CLIM (cloud imager). We use data from the first year of this data set to train artificial neural networks (ANNs) to retrieve XCO2 and XCH4 (the column-average dry-air mole fraction of atmospheric CO2 and CH4, respectively) plus related uncertainties and column averaging kernels. We will introduce a method which allows us to modify the training data making it representative for a wider range of atmospheric states. This ensures that the ANNs learn from the spectral signatures of CO2 and CH4 and that learning from spurious correlations is minimized. Despite the annual growth of CO2 and CH4, we will show that the ANNs trained with data from 2015 have almost the same quality when applied to data from 2020. We will analyze and compare the performance of different input vector settings, e.g., with and without MAP data and will discuss potential advantages or disadvantages of our ANN approach.

Polar stratospheric clouds (PSCs) play a fundamental role in depleting stratospheric ozone. Heterogeneous reactions on their surfaces increase the concentration of active chlorine, which can catalytically destroy ozone and prolong ozone depletion by denitrifying and dehydrating the stratosphere. However, parametrisations of PSC formation is poorly included in global chemistry-climate models due to the complexity of the microphysical processes involved in PSC particle formation. This limits our ability to project the future recovery of the stratospheric ozone and the resulting climate impacts.In this work, the representation of PSCs in the UK Earth System Model (UKESM) has been improved by refining the particle formation schemes to 1) kinetically determine the growth of nitric acid trihydrate particles rather than using a thermodynamic assumption, and 2) include the growth of supercooled ternary solution particles through the uptake of nitric acid rather than using a sulphate aerosol climatology. To validate these changes, the simulated PSCs are converted into optical properties and evaluated against satellite data. Here, a comparison of the results from the new PSC scheme in the UKESM with the observations from the satellite-borne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) is presented. Whether the changes to the formation parameterisation improve the model’s ability to accurately simulate PSCs both temporally and spatially in the Northern and Southern hemisphere is assessed and the effect on stratospheric denitrification in the model is examined.

The Anthropogenic Carbon Dioxide Monitoring (CO2M) mission is a constellation of satellites currently planned to be launched in 2026. CO2M is planned to be a core component of a Monitoring and Verification Support (MVS) service capacity under development as part of the Copernicus Atmosphere Monitoring Service (CAMS). The CO2M radiance measurements will be used to retrieve column-averaged dry-air mole fractions of atmospheric carbon dioxide (XCO2), methane (XCH4) and total columns of nitrogen dioxide (NO2). Using appropriate inverse modelling, the atmospheric greenhouse gas (GHG) observations will be used to derive United Nations Framework Convention on Climate Change (UNFCCC) COP 21 Paris Agreement relevant information on GHG sources and sinks. This challenging application requires highly accurate XCO2 and XCH4 retrievals. Three different retrieval algorithms to derive XCO2 and XCH4 are currently under development for the operational processing system at EUMETSAT. One of these algorithms uses the heritage of the FOCAL (Fast atmOspheric traCe gAs retrievaL) method, which has already successfully been applied to measurements from other satellites. Here, we show recent results generated using the CO2M version of FOCAL, called FOCAL-CO2M. To assess the quality of the FOCAL-CO2M retrievals, a large set of representative simulated radiance spectra has been generated using the radiative transfer model SCIATRAN. These simulations consider the planned viewing geometry of the CO2 instrument and corresponding geophysical scene data (including different types of aerosols and varying surface properties), which were taken from model data for the year 2015. We consider instrument noise and systematic errors caused by the retrieval method but have not considered additional error sources due to, for example, instrumental issues, spectroscopy or meteorology. On the other hand, we have also not taken advantage in this study of CO2M's MAP (multi-angle polarimeter) instrument, which will provide additional information on aerosols and cirrus clouds. By application of the FOCAL retrieval to these simulated data, confidence is gained that the FOCAL method is able to fulfil the challenging requirements for systematic errors for the CO2M mission (spatio-temporal bias ≤ 0.5 ppm for XCO2 and ≤ 5 ppb for XCH4).

Global crude steel production is expected to continue to increase in the coming decades to meet the demands of the growing world population. Currently, the dominant steelmaking technology worldwide is the conventional highly CO2-intensive blast furnace–basic oxygen furnace production route (also known as the Linz–Donawitz process), which uses iron ore as raw material and coke as a reducing agent. As a result, large quantities of special gases that are rich in carbon monoxide (CO) are by-products of the various stages of the steelmaking process. Given the challenges associated with satellite-based estimates of carbon dioxide (CO2) emissions at the scale of emitting installations due to significant background levels, co-emitted CO may serve as a valuable indicator of the carbon footprint of steel plants. We show that regional CO release from steel production sites can be monitored from space using 5 years of measurements (2018–2022) from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite, benefiting from its relatively high spatial resolution and daily global coverage. We analyse all German steel plants with blast furnaces and basic oxygen furnaces and obtain associated CO emissions in the range of 50–400 kt yr−1 per site. A comparison with the respective CO2 emissions on the level of emitting installations available from emissions trading data of the European Union Emissions Trading System yields a linear relationship with a sector-specific CO/CO2 emission ratio for the analysed steelworks of 3.24 % [2.73–3.89; 1σ], suggesting the feasibility of using CO as a proxy for CO2 emissions from comparable steel production sites. An evaluation at other steel production sites indicates that the derived CO/CO2 emission ratio is also representative of other highly optimised state-of-the-art Linz–Donawitz steelworks outside Germany and that the emission ratio is potentially valuable for estimating sector-specific CO2 emissions from remotely sensed CO emissions, provided that the underlying CO emission estimate is not affected by other sources.

The ICOsahedral Non-hydrostatic (ICON) modelling system was originally developed by DWD and MPI-M for a range of weather (forecast) and climate applications. An Aerosols and Reactive Tracers (ART) module was added by KIT to enable a comprehensive assessment of composition interactions within the atmospheric domain. Recognising that atmospheric processes happen on a multitude of temporal and spatial scales, flexible horizontal and vertical grid options are a key element of versatile model configurations in use. Here, we present a selection of results from different ICON-ART configurations that explore (stratospheric) ozone-climate interactions and stratosphere-troposphere coupling – e.g. regional climatic impacts of the ozone hole (and ozone losses in other regions) and global warming induced changes in jet-streams – in different types of integrations. In addition, we explore the potential to forecast “chemical weather” with ICON-ART, including environmental (UV) indices.Starting with time-slice experiments, we provide a range of examples using the ICON-ART modelling system to investigate (idealised) climate change scenarios with respect to different threshold temperatures (reached under global warming) and the climatic impact of the ozone hole (and ozone losses in other regions). For the latter, halogen induced depletion of (stratospheric) ozone can be switched on and off in our modelling world. We illustrate how such integrations allow the unambiguous attribution of certain climate change effects, e.g. the contribution of the ozone hole (and other regional ozone losses) to regional surface warming in Antarctica and changes to regional and global “effective radiative forcing”, and the change of jet stream variability under global warming. Moving on, we explore the capability of ICON-ART to work with regionally nested grids to capture accurately smaller spatial scales and to provide “meaningful” forecasts of environmental (UV) indices, thus, demonstrating comprehensively the seamless philosophy regarding processes, scales and applications with the flexible ICON-ART modelling system.

Polar stratospheric clouds (PSCs) play a key role in the polar chemistry of the stratosphere. Nitric acid trihydrate (NAT) particles have been shown to lead to denitrification of the lower stratosphere. While the existence of large NAT particles (NAT "rocks") has been verified by many measurements, especially in the Northern Hemisphere (NH), most current chemistry-climate models use simplified parametrizations, often based on evaluations in the Southern Hemisphere where the polar vortex is stable enough that accounting for NAT rocks is not as important as in the NH. Here, we evaluate the probability density functions of various gaseous species in the polar vortex using one such model, the Whole Atmosphere Community Climate Model (WACCM), and compare these with measurements by the Michelson Interferometer for Passive Atmospheric Sounding onboard the Environmental Satellite (MIPAS/Envisat) and two ozonesonde stations for a range of years and in both hemispheres. Using the maximum difference between the distributions of MIPAS and WACCM as a measure of coherence, we find better agreement for HNO3 when reducing the NAT number density from the standard value of 1 x 10−2 cm-3 used in this model to 5 × 10−4 cm−3 for almost all spring seasons during the MIPAS period in both hemispheres. The distributions of ClONO2 and O3 are not greatly affected by the choice of NAT density. The average difference of WACCM to ozonesondes supports the need to reduce the NAT number density in the model. Therefore, this study suggests using a NAT number density of 5 × 10−4 cm−3 for future simulations with WACCM.

Solving a system of hundreds of chemical differential equations in environmental simulations has a major computational complexity, and thereby requires high performance computing resources, which is a challenge as the spatio-temporal resolution increases. Machine learning methods and specially deep learning can offer an approximation of simulations with some factor of speed-up while using less compute resources. In this work, we introduce a neural network based approach (ICONET) to forecast trace gas concentrations without executing the traditional compute-intensive atmospheric simulations. ICONET is equipped with a multifeature Long Short Term Memory (LSTM) model to forecast atmospheric chemicals iteratively in time. We generated the training and test dataset, our target dataset for ICONET, by execution of an atmospheric chemistry simulation in ICON-ART. Applying the ICONET trained model to forecast a test dataset results in a good fit of the forecast values to our target dataset. We discussed appropriate metrics to evaluate the quality of models and presented the quality of the ICONET forecasts with RMSE and KGE metrics. The variety in the nature of trace gases limits the model's learning and forecast skills according to the respective trace gas. In addition to the quality of the ICONET forecasts, we described the computational efficiency of ICONET as its run time speed-up in comparison to the run time of the ICON-ART simulation. The ICONET forecast showed a speed-up factor of 3.1 over the run time of the atmospheric chemistry simulation of ICON-ART, which is a significant achievement, especially when considering the importance of ensemble simulation.

Polar stratospheric clouds (PSCs) play a key role in the polar chemistry of the stratosphere. Nitric acid trihydrate (NAT) particles have been shown to lead to denitrification of the lower stratosphere. While the existence of large NAT particles (NAT "rocks") has been verified by many measurements, especially in the Northern Hemisphere (NH), most current chemistry-climate models use simplified parameterizations, often based on evaluations in the Southern Hemisphere where the polar vortex is stable enough that accounting for NAT rocks is not as important as in the NH. Here, we evaluate the probability density functions of various gaseous species in the polar vortex using one such model, the Whole Atmosphere Community Climate Model (WACCM), and compare these with measurements by the Michelson Interferometer for Passive Atmospheric Sounding onboard the Environmental Satellite (MIPAS/Envisat) and two ozonesonde stations for a range of years and in both hemispheres. Using the maximum difference between the distributions of MIPAS and WACCM as a measure of coherence, we find better agreement for HNO3 when reducing the NAT number density from the standard value of 10(-2) used in this model to 5 x 10(-4) cm(-3) for almost all spring seasons during the MIPAS period in both hemispheres. The distributions of ClONO2 and O-3 are not greatly affected by the NAT density. The average difference between WACCM and ozonesondes supports the need to reduce the NAT number density in the model. Therefore, this study suggests using a NAT number density of 5 x 10(-4) cm(-3) for future simulations with WACCM.

Accurate forecasts of the atmosphere demand large-scale simulations with high spatio-temporal resolution. Atmospheric chemistry modeling, for example, usually requires solving a system of hundreds of coupled ordinary partial differential equations. Due to the computational complexity, large high performance computing resources are required, which is a challenge as the spatio-temporal resolution increases. Machine learning methods and specially deep learning can offer an approximation of the simulations with some factor of speed-up while using less compute resources. The goal of this study is to investigate the feasibility, opportunities but also challenges and pitfalls of replacing the compute-intensive chemistry of a state-of-the-art atmospheric chemistry model with a trained neural network model to forecast the concentration of trace gases at each grid cell and to reduce the computational complexity of the simulation. In this work, we introduce a neural network model (ICONET) to forecast trace gas concentrations without executing the traditional compute-intensive atmospheric simulations. ICONET is equipped with a multifeature Long Short Term Memory (LSTM) model to forecast atmospheric chemicals iteratively in time. We generated the training and test dataset, our ground truth for ICONET, by execution of an atmospheric chemistry simulation in ICON-ART. Applying the ICONET trained model to forecast a test dataset results in a good fit of the forecast values compared to our ground truth dataset. We discuss appropriate metrics to evaluate the quality of models and present the quality of the ICONET forecasts with RMSE and KGE metrics. The variety in the nature of trace gases limits the model's learning and forecast skills according to the variable. In addition to the quality of the ICONET forecasts, we described the computational efficiency of ICONET as its run time speed-up in comparison to the run time of the ICON-ART simulation. The ICONET forecast showed a speed-up factor of 3.1 over the run time of the atmospheric chemistry simulation of ICON-ART, which is a significant achievement, especially when considering the importance of ensemble simulations.

Many chemical processes depend non-linearly on temperature. Gravity-wave-induced temperature perturbations have been shown to affect atmospheric chemistry, but accounting for this process in chemistry-climate models has been a challenge because many gravity waves have scales smaller than the typical model resolution. Here, we present a method to account for subgrid-scale orographic gravity-wave-induced temperature perturbations on the global scale for the Whole Atmosphere Community Climate Model. Temperature perturbation amplitudes (T)over cap> consistent with the model's subgrid-scale gravity wave parameterization are derived and then used as a sinusoidal temperature perturbation in the model's chemistry solver. Because of limitations in the parameterization, we explore scaling of (T)over cap> between 0.6 and 1 based on comparisons to altitude-dependent (T) over cap distributions of satellite and reanalysis data, where we discuss uncertainties. We probe the impact on the chemistry from the grid-point to global scales, and show that the parameterization is able to represent mountain wave events as reported by previous literature. The gravity waves for example, lead to increased surface area densities of stratospheric aerosols. This increases chlorine activation, with impacts on the associated chemical composition. We obtain large local changes in some chemical species (e.g., active chlorine, NOx, N2O5) which are likely to be important for comparisons to airborne or satellite observations, but the changes to ozone loss are more modest. This approach enables the chemistry-climate modeling community to account for subgrid-scale gravity wave temperature perturbations interactively, consistent with the internal parameterizations and are expected to yield more realistic interactions and better representation of the chemistry. Plain Language Summary Sub-grid scale gravity waves have long been incorporated in the momentum budgets of global chemistry-climate models using parameterizations, but their associated impacts on temperature dependent chemistry within the models have not been included in a self-consistent way. Here we present an approach to modeling these chemical impacts in the Whole Atmosphere Community Climate Model. We obtain large local changes in some chemical species (e.g., active chlorine, NOx, N2O5) but smaller impacts on ozone. The approach can be expected to advance the ability of the chemistry-climate modeling community to examine gravity wave effects on a wide range of chemical problems.

. Polar stratospheric clouds (PSCs) play a key role in the polar chemistry of the stratosphere. Nitric acid trihydrate (NAT) particles have been shown to lead to denitrification of the lower stratosphere. While the existence of large NAT particles (NAT "rocks") has been verified by many measurements especially in the Northern Hemisphere (NH), most current chemistry-climate models use simplified parametrizations (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) parameterizations, often based on evaluations in the Southern Hemisphere where the polar vortex is stable enough that accounting for NAT rocks is not as important as in the NH. Here, we 5 evaluate the probability density functions of various gaseous species in the polar vortex using one such model, the Whole Atmosphere Community Climate Model (WACCM), and compare these with measurements by the Michelson Interferometer for Passive Atmospheric Sounding onboard the Environmental Satellite (MIPAS/Envisat) and two ozonesonde stations for a range of years and in both hemispheres. Using the maximum difference between the distributions of MIPAS and WACCM as a measure of coherence, we find better agreement for HNO 3 when reducing the NAT number density from the standard value 10 of 10 − 2 used in this model to 5 × 10 − 4 cm − 3 for almost all spring seasons during the MIPAS period in both hemispheres. The distributions of ClONO 2 and O 3 are not greatly affected by the NAT density. The average difference of WACCM to ozonesondes supports the need to reduce the NAT number density in the model. Therefore, this study suggests to use a NAT number density of 5 × 10 − 4 cm − 3 for future simulations with WACCM.

Abstract. Water vapour and ozone are important for the thermal and radiative balance of the upper troposphere (UT) and lowermost stratosphere (LMS). Both species are modulated by transport processes. Chemical and microphysical processes affect them differently. Thus, representing the different processes and their interactions is a challenging task for dynamical cores, chemical modules and microphysical parameterisations of state-of-the-art atmospheric model components. To test and improve the models, high resolution measurements of the UT/LMS are required. Here, we use measurements taken in a challenging case study by the GLORIA (Gimballed Limb Observer for Radiance Imaging of the Atmosphere) instrument on HALO. The German research aircraft HALO (High Altitude and LOng range research aircraft) performed a research flight on 26 February 2016, which covered deeply subsided air masses of the aged 2015/16 Arctic vortex, high-latitude LMS air masses, a highly textured troposphere-to-stratosphere exchange mixing region, and high-altitude cirrus clouds. Therefore, it provides a multifaceted case study for comparing GLORIA observations with state-of-the-art atmospheric model simulations in a complex UT/LMS region at a late stage of the Arctic winter 2015/16. Using GLORIA observations in this manifold scenario, we test the ability of the numerical weather prediction (NWP)-model ICON (ICOsahedral Nonhydrostatic) with the extension ART (Aerosols and Reactive Trace gases) and the chemistry-climate model (CCM) EMAC (ECHAM5/MESSy Atmospheric Chemistry) to model the UT/LMS composition of water vapour (H2O), ozone (O3), nitric acid (HNO3) and clouds. Within the scales resolved by the respective model, we find good overall agreement of both models with GLORIA. The applied high-resolution ICON-ART setup involving a R2B7 nest (local grid refinement with a horizontal resolution of about 20 km), covering the HALO flight region, reproduces mesoscale dynamical structures well. An observed troposphere-to-stratosphere exchange connected to an occluded Icelandic low is clearly reproduced by the model. Given the lower resolution (T106) of the nudged simulation of the EMAC model, we find that this model also reproduces these features well. Overall, trace gas mixing ratios simulated by both models are in a realistic range, and major cloud systems observed by GLORIA are mostly reproduced. However, we find both models to be affected by a well-known systematic moist-bias in the LMS. Further biases are diagnosed in the ICON-ART O3, EMAC H2O and EMAC HNO3 distributions. Finally, we use sensitivity simulations to investigate (i) short-term cirrus cloud impacts on the H2O distribution (ICON-ART), (ii) the overall impact of polar winter chemistry and microphysical processing on O3 and HNO3 (ICON-ART/EMAC), (iii) the impact of the model resolution on simulated parameters (EMAC), and (iv) consequences of scavenging processes by cloud particles (EMAC). We find that changing of the horizontal model resolution results in notable systematic changes for all species in the LMS, while scavenging processes play only a role in case of HNO3. We need to understand the representativeness of our results. However, this is a unique opportunity to characterise model biases that potentially affect forecasts and projection (adversely), and to discover deficits and define paths for further model improvements.