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Nitrogen dioxide (NO2) air pollution provides valuable information for quantifying NOx (NOx = NO + NO2) emissions and exposures. This study presents a comprehensive method to estimate average tropospheric NO2 emission strengths derived from 4-year (May 2018–June 2022) TROPOspheric Monitoring Instrument (TROPOMI) observations by combining a wind-assigned anomaly approach and a machine learning (ML) method, the so-called gradient descent algorithm. This combined approach is firstly applied to the Saudi Arabian capital city of Riyadh, as a test site, and yields a total emission rate of 1.09×1026 molec. s−1. The ML-trained anomalies fit very well with the wind-assigned anomalies, with an R2 value of 1.0 and a slope of 0.99. Hotspots of NO2 emissions are apparent at several sites: over a cement plant and power plants as well as over areas along highways. Using the same approach, an emission rate of 1.99×1025 molec. s−1 is estimated in the Madrid metropolitan area, Spain. Both the estimate and spatial pattern are comparable with the Copernicus Atmosphere Monitoring Service (CAMS) inventory. Weekly variations in NO2 emission are highly related to anthropogenic activities, such as the transport sector. The NO2 emissions were reduced by 16 % at weekends in Riyadh, and high reductions were found near the city center and in areas along the highway. An average weekend reduction estimate of 28 % was found in Madrid. The regions with dominant sources are located in the east of Madrid, where residential areas and the Madrid-Barajas airport are located. Additionally, due to the COVID-19 lockdowns, the NO2 emissions decreased by 21 % in March–June 2020 in Riyadh compared with the same period in 2019. A much higher reduction (62 %) is estimated for Madrid, where a very strict lockdown policy was implemented. The high emission strengths during lockdown only persist in the residential areas, and they cover smaller areas on weekdays compared with weekends. The spatial patterns of NO2 emission strengths during lockdown are similar to those observed at weekends in both cities. Although our analysis is limited to two cities as test examples, the method has proven to provide reliable and consistent results. It is expected to be suitable for other trace gases and other target regions. However, it might become challenging in some areas with complicated emission sources and topography, and specific NO2 decay times in different regions and seasons should be taken into account. These impacting factors should be considered in the future model to further reduce the uncertainty budget.

Satellite data records of stratospheric water vapour have been compared to balloon-borne frost point hygrometer (FP) profiles that are coincident in space and time. The satellite data records of 15 different instruments cover water vapour data available from January 2000 through December 2016. The hygrometer data are from 27 stations all over the world in the same period. For the comparison, real or constructed averaging kernels have been applied to the hygrometer profiles to adjust them to the measurement characteristics of the satellite instruments. For bias evaluation, we have compared satellite profiles averaged over the available temporal coverage to the means of coincident FP profiles for individual stations. For drift determinations, we analyzed timeseries of relative differences between spatiotemporally coincident satellite and hygrometer profiles at individual stations. In a synopsis we have also calculated the mean biases and drifts (and their respective uncertainties) for each satellite record over all applicable hygrometer stations in three altitude ranges (10–30 hPa, 30–100 hPa, and 100 hPa to tropopause). Most of the satellite data have biases < 10 % and average drifts < 1 % yr−1 in at least one of the respective altitude ranges. Virtually all biases are significant in the sense that their uncertainty range in terms of twice the standard error of the mean does not include zero. Statistically significant drifts (95 % confidence) are detected for 35 % of the ~1200 timeseries of relative differences between satellites and hygrometers.

The strong coupling between atmospheric circulation, moisture pathways and atmospheric diabatic heating is responsible for most climate feedback mechanisms and controls the evolution of severe weather events. However, diabatic heating rates obtained from current meteorological reanalysis show significant inconsistencies. Here, we theoretically assess with an Observation System Simulation Experiment (OSSE) the potential of the MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water (MUSICA) Infrared Atmospheric Sounding interferometer (IASI) mid-tropospheric water isotopologue data for constraining uncertainties in meteorological analysis fields. For this purpose, we use the Isotope-incorporated General Spectral Model (IsoGSM) together with a Local Ensemble Transform Kalman Filter (LETKF) and assimilate synthetic MUSICA IASI water vapour isotopologue observations. We perform four ensemble simulations, three where synthetic IASI isotopologue and humidity data are assimilated and another one where no observational data at all are assimilated. By comparing the ensemble simulations with data assimilation with water isotopologues to the one without any data assimilation we can, in contrast to the former study by Toride et al. (2021) where additionally conventional observations where considered, assess the direct impact of the IASI δD and water vapour data on the meteorological variables, especially on the heating rates and vertical velocity. The assessment is performed for the tropics in the latitude range from 10° S to 10° N. When the synthetic isotopologue data are assimilated, we derive reduced Root-Mean-Square Deviations (RMSDs) and improved skills with respect to meteorological variables (improvement by about 11–17%). When only IASI δD is assimilated the improvement in vertical velocity and heating rate is minor (up to a few percent) and restricted to the mid-troposphere. However, when water vapour alone or δD is assimilated additionally to water vapour, heating rates and vertical motion can be improved throughout the troposphere. The highest improvements, however, are derived when δD is assimilated additionally to water vapour confirming that water isotopologues hold different aspects of information than water vapour. Thus, in consequence δD offers, especially when assimilated additionally to water vapour, potential for improving diabatic heating and precipitation and thus meteorological analysis, weather forecasts and climate predictions in the tropical regions.

We present a global and multi-annual space-borne dataset of tropospheric { H2O , δ D } pairs that is based on radiance measurements from the nadir thermal infrared sensor IASI (Infrared Atmospheric Sounding Interferometer) on board the Metop satellites of EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites). This dataset is an a posteriori processed extension of the MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) IASI full product dataset as presented in Schneider et al. ( 2021 b ) . From the independently retrieved H2O and δ D proxy states, their a priori settings and constraints, and their error covariances provided by the IASI full product dataset, we generate an optimal estimation product for pairs of H2O and δ D . Here, this standard MUSICA method for deriving { H2O , δ D } pairs is extended using an a posteriori reduction of the constraints for improving the retrieval sensitivity at dry conditions. By applying this improved water isotopologue post-processing for all cloud-free MUSICA IASI retrievals, this yields a { H2O , δ D } pair dataset for the whole period from October 2014 to December 2020 with global coverage twice per day (local morning and evening overpass times). In total, the dataset covers more than 1500 million individually processed observations. The retrievals are most sensitive to variations in { H2O , δ D } pairs within the free troposphere, with up to 30 % of all retrievals containing vertical profile information in the { H2O , δ D } pair product. After applying appropriate quality filters, the largest number of reliable pair data arises for tropical and subtropical summer regions, but higher latitudes also show a considerable amount of reliable data. Exemplary time series over the tropical Atlantic and West Africa are chosen to illustrate the potential of the MUSICA IASI { H2O , δ D } pair data for atmospheric moisture pathway studies. Furthermore, in order to facilitate the application of this rather comprehensive MUSICA IASI { H2O , δ D } pair dataset (referred to as Level-2), we additionally provide the data in a re-gridded and simplified format (Level-3) with focus on the quality-filtered { H2O , δ D } pairs in the free troposphere. A technical documentation for guiding the use of both datasets is attached as the Supplement. Finally, the Level-2 dataset is referenced with the DOI https://doi.org/10.35097/415 ( Diekmann et al. , 2021 a ) and the Level-3 dataset with DOI https://doi.org/10.35097/495 ( Diekmann et al. , 2021 b ) .

Nineteen limb-viewing data sets (occultation, passive thermal, and UV scattering) and two nadir upper tropospheric humidity (UTH) data sets are intercompared and also compared to frost-point hygrometer balloon sondes. The upper troposphere considered here covers the pressure range from 300–100 hPa. UTH is a challenging measurement, because concentrations vary between 2–1000 ppmv (parts per million by volume), with sharp changes in vertical gradients near the tropopause. Cloudiness in this region also makes the measurement challenging. The atmospheric temperature is also highly variable ranging from 180–250 K. The assessment of satellite-measured UTH is based on coincident comparisons with balloon frost-point hygrometer sondes, multi-month mapped comparisons, zonal mean time series comparisons, and coincident satellite-to-satellite comparisons. While the satellite fields show similar features in maps and time series, quantitatively they can differ by a factor of 2 in concentration, with strong dependencies on the amount of UTH. Additionally, time-lag response-corrected Vaisala RS92 radiosondes are compared to satellites and the frost-point hygrometer measurements. In summary, most satellite data sets reviewed here show on average ∼30 % agreement amongst themselves and frost-point data but with an additional ∼30 % variability about the mean bias. The Vaisala RS92 sonde, even with a time-lag correction, shows poor behavior for pressures less than 200 hPa.

Nitrogen dioxide (NO2) air pollution provides valuable information for quantifying NOx emissions and exposures. This study presents a comprehensive method to estimate average tropospheric NO2 emission strengths derived from three-year (April 2018 – March 2021) TROPOMI observations by combining a wind-assigned anomaly approach and a Machine Learning (ML) method, the so-called Gradient Descent. This combined approach is firstly applied to the Saudi Arabian capital city Riyadh, as a test site, and yields a total emission rate of 1.04×1026 molec./s. The ML-trained anomalies fit very well with the wind-assigned anomalies with an R2 value of 1.0 and a slope of 0.99. Hotspots of NO2 emissions are apparent at several sites where the cement plant and power plants are located and over areas along the highways. Using the same approach, an emission rate of 1.80×1025 molec./s is estimated in the Madrid metropolitan area, Spain. Both the estimate and spatial pattern are comparable to the CAMS inventory. Weekly variations of NO2 emission are highly related to anthropogenic activities, such as the transport sector. The NO2 emissions were reduced by 24 % at weekends in Riyadh, and high reductions are found near the city center and the areas along the highway. An average weekend reduction estimate of 30 % in Madrid is found. The regions with dominant sources are located in the east of Madrid, where the residential areas and the Madrid-Barajas airport are located. Additionally, the NO2 emissions decreased by 21 % in March–June 2020 compared to the same period in 2019 induced by the COVID-19 lockdowns in Riyadh. A much higher reduction (60 %) is estimated for Madrid where a very strict lockdown policy was implemented. The high emission strengths during lockdown only persist in the residential areas and cover smaller areas during weekdays than at weekends. The spatial patterns of NO2 emission strengths during lockdown are similar to those observed at weekends in both cities. Though our analysis is limited to two cities as testing examples, the method has proved to provide reliable and consistent results. Therefore, it is expected to be suitable for other trace gases and other target regions.

[EN] Future of the Earth-atmosphere system will depend, to a large extent, on our capability of understanding all the processes driving climate change and, in this context, of outstanding importance are the monitoring and the investigation of greenhouse gases (GHGs), as main drivers of the Earth’s climate change. With this idea the project INMENSE (IASI for Surveying Methane and Nitrous Oxide in the Troposphere) was born, which aims to improve our current understanding of the atmospheric budgets of two of the most important well-mixed greenhouse gases, methane (CH4) and nitrous oxide (N2O). To this end, INMENSE has generated a new global observational data set of middle/upper tropospheric concentrations of CH4 and N2O from the space-based remote sensor IASI (Infrared Atmospheric Sounding Interferometer), on board the meteorological satellites EUMETSAT/Metop. In this work the INMENSE IASI CH4 and N2O products are presented, characterized and comprehensively validated by using a multiplatform reference database (aircraft vertical profiles, ground-based in-situ and remote-sensing observations). This extensive validation exercise suggests that the IASI CH4 and N2O products shows a precision between 1-3% and a bias of 2% as well as they are consistent temporally and spatially. Finally, the CH4 and N2O IASI observations over the Iberian Peninsula have been compared to MOCAGE chemical transport simulations, assessing the degree of agreement between both datasets., [ES] El futuro del sistema Tierra-atmósfera dependerá, en gran medida, de nuestra capacidad para entender todos los procesos que están conduciendo al calentamiento global, y sus consecuencias climáticas, y, en este contexto, los gases de efecto invernadero (GEIs) juegan un papel clave como uno de los principales motores del cambio climático. Con esta idea nace el proyecto de investigación INMENSE (IASI for Surveying Methane and Nitrous Oxide in the Troposphere), el cual tiene como objetivo mejorar nuestra compresión de los balances atmosféricos de dos de los más importantes GEIs, el metano (CH4) y el óxido nitroso (N2O). Para ello, el proyecto INMENSE ha generado nuevas observaciones de concentraciones de CH4 y N2O en la troposfera media/alta a escala global a partir de las medidas satelitales del sensor IASI (Infrared Atmospheric Sounding Interferometer), que vuela a bordo de los satélites meteorológicos EUMETSAT/Metop. En este trabajo los productos INMENSE IASI de CH4 y N2O son presentados, caracterizados y extensamente validados usando una base de datos de referencia multiplataforma (perfiles verticales de avión, medidas in-situ y remotas desde superficie). Esta completa validación sugiere que los productos IASI de CH4 y N2O tienen una precisión entre el 1-3% y un error sistemático del 2%, así como que son consistentes temporal y espacialmente. Por último, las observaciones IASI de CH4 y N2O sobre la Península Ibérica han sido comparadas a las simulaciones del modelo de transporte químico MOCAGE, analizando el grado de acuerdo entre ambas bases de datos., Este trabajo se ha beneficiado de los fondos recibidos del ERC (European Research Council) para el proyecto MUSICA (FP7/(2007-2013)/ERC n◦256961), del Deutsche Forschungsgemeinschaft para el proyecto MOTIV (Geschäftszeichen SCHN1126/21), del Ministerio de Economía y Competitividad de España a través de los proyectos NOVIA (CGL2012-37505) e INMENSE (CGL2016-80688P), del Ministerio de Educación, Cultura y Deporte a través del programa “José Castillejo” (CAS14/00282), y de EUMETSAT para el proyecto VALIASI. Además, nos gustaría agradecer a la red NDACC, al programa de GAW/WMO, así como a la NSF (National Science Foundation) y a la NOAA (National Oceanic and Atmospheric Administration) por su soporte en la adquisición de las medidas FTIR, in-situ y campañas HIPPO, respectivamente.

Intensive coal mining activities are in the Upper Silesian Coal Basin (USCB) in southern Poland, resulting in large amounts of methane (CH4) emissions. Annual CH4 emission reached to 448 kt according to the European Pollutant Release and Transfer Register (E-PRTR, 2017). As a CH4 emission hot spot in Europe, it is of importance to investigate its emission sources and accurate emission estimates. In this study, we use satellite-based column-averaged dry-air molar fraction observations of CH4 (XCH4) from the TROPOspheric Monitoring Instrument (TROPOMI) and tropospheric XCH4 (TXCH4) from the Infrared Atmospheric Sounding Interferometer (IASI), together with the high-resolution model forecast XCH4 and TXCH4 from the Copernicus Atmosphere Monitoring Service (CAMS) to estimate the CH4 emission rate averaged over three years in the USCB region (49.3°–50.8° N and 18°–20° E). Using the CAMS inventory as the a priori knowledge of the sources, together with ERA5 wind at 330 m, the wind-assigned XCH4 anomalies for two opposite wind directions are calculated, which yields an estimated CH4 emission of 9.6E26 ± 1.4E25 molec./s for CAMS XCH4 and 9.1E26 ± 1.2E25 molec./s for CAMS TXCH4. These values are very close to the total emission of the CAMS inventory (9.7E26 molec./s). Very good agreements between CAMS and the wind-assigned model results (R2 = 0.89 for XCH4 and TXCH4) indicate that our wind-assigned method is quite robust. The similar estimates of XCH4 and TXCH4 also imply that for a strong source, the dynamically induced variations of the CH4 mixing ratio in the upper troposphere and lower stratosphere region is of secondary importance. This wind-assigned method is further applied to the TROPOMI XCH4 and TROPOMI+IASI TXCH4 with using the Carbon dioxide and Methane (CoMet) inventory performed in 2018. The calculated averaged total CH4 emission over the USCB region is about 5.7E26 ± 4.9E24 molec./s for TROPOMI XCH4 and 5.2E26 ± 2.2E25 molec./s for TROPOMI+IASI TXCH4. These results are very close and thus in agreement to the emissions given in the E-PRTR inventory (5.33E26 molec./s) and the CoMet inventory (6.6E26 molec./s). Since the wind speed is increasing with altitude, sensitivity tests show that higher CH4 emission strengths are yielded with increasing altitude and vice versa. About 23 % lower and 13 % higher estimates are obtained when using lower wind information at 10 m and higher wind information at 500 m instead of 330 m, respectively. When using different wind coverage and different wind segmentation, an uncertainty of 4.2 % and −2.1% is obtained, respectively. These results suggest that our wind-assigned method is quite robust and might also serve as a simple method to estimate CH4 or CO2 emissions for other regions.

Nineteen limb viewing (occultation and passive thermal) and two nadir humidity data sets are intercompared and also compared to frostpoint hygrometer balloon sondes. The upper troposphere considered here covers the pressure range from 300–100 hPa. Water vapor in this region is a challenging measurement because concentrations vary between 2–1000 parts per million volume with sharp changes in vertical gradients near the tropopause. The atmospheric temperature is also highly variable ranging from 180–250 K. The assessment of satellite measured humidity is based on coincident comparisons with frostpoint hygrometer sondes, multi month mapped comparisons, zonal mean time series comparisons and coincident satellite to satellite comparisons. While the satellite fields show similar features in maps and time series, quantitatively, they can differ by a factor of two in concentration, with strong dependencies on the amount of H2O. Additionally, time-lag response corrected Vaisala-RS92 radiosondes are compared to satellites and the frostpoint hygrometer measurements. In summary, most satellite data sets reviewed here show on average ~30 % agreement amongst themselves and frostpoint data but with an additional ~30 % variability about the mean. The Vaisala-RS92 sonde even with a time-lag correction shows poor behavior for pressure less than 200 hPa.

We present a Lagrangian framework for identifying mechanisms that control the isotopic composition of mid‐tropospheric water vapor in the Sahel region during the West African Monsoon 2016. In this region mixing between contrasting air masses, strong convective activity, as well as surface and rain evaporation lead to high variability in the distribution of stable water isotopologues. Using backward trajectories based on high‐resolution isotope‐enabled model data, we obtain information not only about the source regions of Sahelian air masses, but also about the evolution of H2O and its isotopologue HDO (expressed as δD) along the pathways of individual air parcels. We sort the full trajectory ensemble into groups with similar transport pathways and hydro‐meteorological properties, such as precipitation and relative humidity, and investigate the evolution of the corresponding paired {H2O, δD} distributions. The use of idealized process curves in the {H2O, δD} phase space allows us to attribute isotopic changes to contributions from (a) air mass mixing, (b) Rayleigh condensation during convection, and (c) microphysical processes depleting the vapor beyond the Rayleigh prediction, i.e., partial rain evaporation in unsaturated and isotopic equilibration in saturated conditions. Different combinations of these processes along the trajectory ensembles are found to determine the final isotopic composition in the Sahelian troposphere during the monsoon. The presented Lagrangian framework is a powerful tool for interpreting tropospheric water vapor distributions. In the future, it will be applied to satellite observations of {H2O, δD} over Africa and other regions in order to better quantify characteristics of the hydrological cycle., Key Points: New Lagrangian framework to attribute variability in {H2O, δD} distributions to air mass mixing and phase changes of water. Application to West African Monsoon season 2016 shows characteristic mixing and precipitation effects along trajectories. New framework can be used for the interpretation of satellite and in‐situ observations, and for model validation in future work., Deutsche Forschungsgemeinschaft (DFG) http://dx.doi.org/10.13039/501100001659, Swiss National Science Foundation, European Space Agency, Bundesministerium für Bildung und Forschung (BMBF) http://dx.doi.org/10.13039/501100002347, Ministerium für Wissenschaft, Forschung und Kunst Baden‐Württemberg (MWK) http://dx.doi.org/10.13039/501100003542

The strong coupling between atmospheric circulation, moisture pathways and atmospheric diabatic heating is responsible for most climate feedback mechanisms and controls the evolution of severe weather events. However, diabatic heating rates obtained from current meteorological reanalysis show significant inconsistencies. Here, we theoretically assess with an Observation System Simulation Experiment (OSSE) the potential of the MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water (MUSICA) Infrared Atmospheric Sounding interferometer (IASI) mid-tropospheric water isotopologue data for constraining uncertainties in meteorological analysis fields. For this purpose, we use the Isotope-incorporated General Spectral Model (IsoGSM) together with a Local Ensemble Transform Kalman Filter (LETKF) and assimilate synthetic MUSICA IASI isotopologue observations. We perform two experiments consisting each of two ensemble simulation runs, one ensemble simulation where we assimilate conventional observations (temperature, humidity and wind profiles obtained from radiosonde and satellite data) and a second one where we assimilate additionally to the conventional observations the synthetic IASI isotopologue data. In the second experiment, we perform one ensemble simulation where only synthetic IASI isotopologue data are assimilated and another one where no observational data at all are assimilated. The first experiment serves to assess the impact of the IASI isotopologue data additional to the conventional observations and the second one to assess the direct impact of the IASI isotopologue data on the meteorological variables, especially on the heating rates and vertical velocity. The assessment is performed for the tropics in the latitude range from 10° S to 10° N. When the synthetic isotopologue data are additionally assimilated, we derive in both experiments lower Root-Mean Square Deviations (RMSDs) and improved skills with respect to meteorological variables (improvement by about 8–13 %). However, heating rates and vertical motion can only be improved throughout the troposphere when additionally to IASI δD conventional observations are assimilated. When only IASI δD is assimilated the improvement in vertical velocity and heating rate is minor (up to a few percent) and restricted to the mid-troposphere. Nevertheless, these assimilation experiments indicate that IASI isotopologue observations have the potential to reduce the uncertainties of diabatic heating rates and meteorological variables in the tropics and in consequence offer potential for improving meteorological analysis, weather forecasts and climatepredictions in the tropical regions.

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.

The objective is to derive methane (CH4) emissions of the metropolitan city Madrid Spain from the CH4 enhancements seen by the space-borne and the ground-based instruments. This study applies satellite-based measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) together with measurements from the ground-based COllaborative Carbon Column Observing Network (COCCON) instruments. In 2018, a two-week field campaign for measuring the atmospheric concentrations of greenhouse gases was performed in Madrid in the framework of Monitoring greenhousE Gas EmIssions of Madrid city (MEGEI-MAD) project. Five COCCON instruments were deployed at different locations around the Madrid city center enabling the observation of total column averaged CH4 mixing ratios (XCH4). Using available wind data, the differences between CH4 columns observed at these locations allow to estimate the emissions emerging from the surrounded area. In addition, based on the dominating wind direction in the Madrid region, we calculate the difference of the satellite data maps for two opposite wind regimes (northeast – southwest, NE – SW). In the following, we refer to the resultant signal as the wind-assigned anomaly. We use TROPOMI tropospheric nitrogen dioxide (NO2) observations as a test to verify our method of wind-assigned anomaly and its implementation, taking advantage of the much better detectability of the plume due to the short lifetime and low background concentrations of NO2. Pronounced bipolar plumes are found along NE and SW wind direction, which implies that our method of wind-assigned anomaly is working as expected. The wind-assigned TROPOMI XCH4 anomaly shows much weaker symmetric plumes than NO2 due to the long lifetime of CH4 and in consequence a high accumulated background of CH4 in the atmosphere. The wind-assigned plume method is also applied to the tropospheric and upper tropospheric/stratospheric column averaged CH4 mixing ratio products (in the following referred to as TXCH4 and UTSXCH4) derived from a-posteriori merged Infrared Atmospheric Sounding Interferometer (IASI) profile and TROPOMI total column data. Based on the NE and SW wind fields, we developed a simple plume model locating the source at three waste disposal sites east of Madrid for CH4. As CH4 emission strength we estimate 7.4 × 1025 ± 6.4 × 1024 molec s−1 from the TROPOMI XCH4 data and 7.1 × 1025 ± 1.0 × 1025 molec s−1 from the TROPOMI&IASI merged TXCH4 data. The COCCON observations indicate a weaker CH4 emission strength of around 3.7 × 1025 molec s−1 from local source (near to the Valdemingómez waste plant) in accordance with observations in a single day and. All emission rates estimated from the different observations are significantly larger than the emission rates provided via the official Spanish Register of Emissions and Pollutant Sources.

We present a global and multi-annual space-borne dataset of tropospheric {H2O, δD} pairs that is based on radiance measurements from the nadir thermal infrared sensor IASI (Infrared Atmospheric Sounding Interferometer) onboard the Metop satellites of EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites). This dataset is an a posteriori processed extension of the MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) IASI full product dataset as presented in Schneider et al. (2021b). From the independently retrieved H2O and δD proxy states, their a priori settings and constraints, and their error covariances provided by the IASI full product dataset we generate an optimal estimation product for pairs of H2O and δD. Here, this standard MUSICA method for deriving {H2O, δD} pairs is extended using an a posteriori reduction of the constraints for improving the retrieval sensitivity at dry conditions. By applying this improved water isotopologue post-processing for all cloud-free MUSICA IASI retrievals, this yields a {H2O, δD} pair dataset for the whole period from October 2014 to June 2019 with a global coverage twice per day (local morning and evening overpass times). In total, the dataset covers more than 1200 million individually processed observations. The retrievals are most sensitivity to variations of {H2O, δD} pairs within the free troposphere, with up to 30 % of all retrievals containing vertical profile information in the {H2O, δD} pair product. After applying appropriate quality filters, the largest number of reliable pair data arises for tropical and subtropical summer regions, but also for higher latitudes there is a considerable amount of reliable data. Exemplary time-series over the Tropical Atlantic and West Africa are chosen to illustrates the potential of the MUSICA IASI {H2O, δD} pair data for atmospheric moisture pathway studiess. Finally, the dataset is referenced with the DOI 10.35097/415 (Diekmann et al., 2021).

Atmospheric moisture is a crucial factor for the redistribution of heat in the atmosphere, with a strong coupling between atmospheric circulation and moisture pathways responsible most climate feedback mechanisms. Conventional satellite and in situ measurements provide information on water vapour content and vertical distribution; however, observations of water isotopologues make a unique contribution to a better understanding of this coupling.In recent years, observations of water vapour isotopologue from satellites have become available from nadir thermal infrared measurements (TES, AIRS, IASI) which are sensitive to the free troposphere and from shortwave-infrared (SWIR) sensors (GOSAT, SCIAMACHY) that provide column-averaged concentrations including sensitivity to the boundary layer. The TROPOMI instrument on-board Sentinel 5P (S5p) measures SWIR radiance spectra that allow retrieval of water isotopologue columns but with much improved spatial and temporal coverage compared to other SWIR sensors promising a step-change for scientific and operational applications.Here we present the retrieval algorithm development for stable water isotopologues from TROPOMI as part of the ESA S5p Innovation programme. We also discuss the validation of these types of satellite products with fiducial in situ measurements, and challenges compared with other satellite measurements. Finally, we outline the roadmap for assessing the impact of TROPOMI data against state-of-the-art isotope enabled models.

The strong coupling between atmospheric circulation, moisture pathways and atmospheric diabatic heating is responsible for most climate feedback mechanisms and controls the evolution of severe weather events. However, diabatic heating rates obtained from current meteorological reanalyses show significant inconsistencies. Water isotopologue observations (e.g. H2O and HDO) assimilated into meteorological reanalyses can make an invaluable contribution since the isotopologue composition depends on the history of phase transition. Therefore, isotopologue observations can provide information that is closely linked to latent heating processes. Using the retrieval recipe of MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water), the free tropospheric water vapour isotopologue composition can be retrieved from IASI spectra measured for cloud free conditions.Here, we theoretically assess with an Observation Simulation Experiment (OSSE) the potential of the MUSICA IASI isotopologue data for constraining uncertainties in analyses fields. For this purpose, we use the isotopes-incorporated General Spectral Model (IsoGSM) and mock MUSICA IASI isotopologue observations. We use the Local Transform Ensemble Kalman Filter (LETKF) data assimilation method and perform two different experiments. In a first experiment we assimilate temperature, humidity and wind profiles obtained from radiosonde and satellite data. In a second experiment we assimilate additionally the mocked IASI isotopologue data. When mocked isotopologue data are additionally assimilated, we find reduced ensemble spreads with respect to meteorological variables and rain rates. This indicates that IASI isotopologue observations can indeed reduce the uncertainties of latent heating rates and meteorological analysis fields and in consequence offer potential for improving weather forecasts.

The thermal infrared nadir spectra of IASI (Infrared Atmospheric Sounding Interferometer) are successfully used for retrievals of different atmospheric trace gas profiles. However, these retrievals offer generally reduced information about the lowermost tropospheric layer due to the lack of thermal contrast close to the surface. Spectra of scattered solar radiation observed in the near and/or short wave infrared, for instance by TROPOMI (TROPOspheric Monitoring Instrument) offer higher sensitivity near ground and are used for the retrieval of total column averaged mixing ratios of a variety of atmospheric trace gases. Here we present a method for the synergetic use of IASI profile and TROPOMI total column data. Our method uses the output of the individual retrievals and consists of linear algebra a posteriori calculations (i.e. calculation after the individual retrievals). We show that this approach is largely equivalent to applying the spectra of the different sensors together in a single retrieval procedure, but with the substantial advantage of being applicable to data generated with different individual retrieval processors, of being very time efficient, and of directly benefiting from the high quality and most recent improvements of the individual retrieval processors. We demonstrate the method exemplarily for atmospheric methane (CH4). We perform a theoretical evaluation and show that the a posteriori combination method yields a total column averaged CH4 product (XCH4) that conserves the good sensitivity of the corresponding TROPOMI product while merging it with the upper tropospheric and lower stratospheric (UTLS) CH4 partial column information of the corresponding IASI product. As consequence, the combined product offers in addition sensitivity for the tropospheric CH4 partial column, which is not provided by the individual TROPOMI nor the individual IASI product. The theoretically predicted synergetic effects are verified by comparisons to CH4 reference data obtained from collocated XCH4 measurements at six globally distributed TCCON (Total Carbon Column Observing Network) stations, CH4 profile measurements made by 24 individual AirCore soundings, and lower tropospheric CH4 data derived from continuous ground-based in-situ observations made at two nearby Global Atmospheric Watch (GAW) mountain stations. The comparisons clearly demonstrate that the combined product can reliably detect XCH4 signals and allows to distinguish between tropospheric and UTLS CH4 partial column averaged mixing ratios, which is not possible by the individual TROPOMI and IASI products. We find indications of a weak positive bias of about +1 % of the combined lower tropospheric data product with respect to the references. For the UTLS CH4 partial columns we find no significant bias.

Measurements of the pollution trace gases ethane (C2H6), ethyne (C2H2), formic acid (HCOOH), and peroxyacetyl nitrate (PAN) were performed in the North Atlantic upper troposphere and lowermost stratosphere (UTLS) region with the airborne limb imager GLORIA (Gimballed Limb Observer for Radiance Imaging of the Atmosphere) with high spatial resolution down to cloud top. Observations were made during flights with the German research aircraft HALO (High Altitude and LOng Range Research Aircraft) in the frame of the WISE (Wave-driven ISentropic Exchange) campaign, which was carried out in autumn 2017 from Shannon (Ireland) and Oberpfaffenhofen (Germany). Enhanced volume mixing ratios (VMR) of up to 2.2 ppbv C2H6, 0.2 ppbv C2H2, 0.9 ppbv HCOOH, and 0.4 ppbv PAN were detected during the flight on 13 September 2017 in the upper troposphere and around the tropopause above the British Isles. Since PAN has the longest lifetime of this foursome, elevated quantities of this molecule could be measured even in the lowermost stratosphere (locally up to 14 km). Backward trajectory calculations as well as global three-dimensional CLaMS simulations with artificial tracers of air mass origin have shown that the main sources of the observed pollutant species are forest fires in North America and anthropogenic pollution in South and Southeast Asia uplifted and moved within the Asian monsoon anticyclone (AMA) circulation system. After release from the AMA, these species or their precursor substances are transported by strong tropospheric winds over large distances, depending on their particular atmospheric lifetime of up to months. Observations are compared to simulations with the atmospheric models EMAC (ECHAM5/MESSy Atmospheric Chemistry) and CAMS (Copernicus Atmosphere Monitoring Service). These models are qualitatively able to reproduce the measured VMR enhancements but underestimate the absolute amount of the increase. Increasing the emissions in EMAC by a factor of 2 reduces the disagreement between simulated and measured results and illustrates the importance of the quality of emission databases used in chemical models.

In this paper, we compare column-averaged dry-air mole fractions of water vapor (XH2O) retrievals from COCCON (COllaborative Carbon Column Observing Network) with retrievals from two co-located high-resolution FTIR (Fourier transform infrared) spectrometers as references at two boreal sites, Kiruna, Swedenand Sodankylä, Finland. In the framework of the NDACC (Network for the Detection of Atmospheric Composition Change) an FTIR spectrometer is operated in Kiruna. The H2O product derived from these observations has been generated with the MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) processor. In Sodankylä, a TCCON (Total Carbon Column Observing Network) spectrometer is operated, and the official XH2O data as provided by TCCON are used for this study. The datasets are in good overall agreement, with COCCON data showing a wet bias of (49.20 ± 58.61) ppm ((3.33 ± 3.37) %, R2 = 0.9992) compared to MUSICA NDACC and (56.32 ± 45.63) ppm ((3.44 ± 1.77) %, R2 = 0.9997) compared to TCCON. Furthermore, the a priori H2O VMR (volume mixing ratio) profiles (MAP) used as a priori in the TCCON retrievals (also adopted for COCCON retrievals) are evaluated with respect to radiosonde (Vaisala RS41) profiles at Sodankylä. The MAP and radiosonde profiles show similar shapes and good correlation of integrated XH2O, indicating that MAP is a reasonable approximation for the true atmospheric state and an appropriate choice for the scaling retrieval methods as applied by COCCON and TCCON. COCCON shows a reduced dry bias (−1.66 %) in comparison to TCCON (−5.63 %) with respect to radiosonde XH2O and this small bias indicates that besides XCO2 and XCH4 COCCON is also able to serve as validation tool for space-borne XH2O measurements. Finally, we investigate the quality of satellite data at high latitudes. For this purpose, the COCCON XH2O is compared with retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) generated with the MUSICA processor (MUSICA IASI) and with retrievals from the TROPOspheric Monitoring Instrument (TROPOMI). Both paired datasets show generally good agreement and similar correlations at the two sites. COCCON measures 4.64 % less XH2O at Kiruna and 3.36 % at Sodankylä with respect to MUSICA IASI, while COCCON measures 9.71 % more XH2O at Kiruna and 7.75 % at Sodankylä compared with TROPOMI. Our study supports the assumption that COCCON also delivers a well-characterized XH2O data product. This emphasizes the approach of supplementing the TCCON network for satellite validation efforts. This is the first published study where COCCON XH2O is compared with MUSICA NDACC and TCCON retrievals, and for MUSICA IASI and TROPOMI validation.

The strong coupling between atmospheric circulation, moisture pathways and atmospheric diabatic heating is a great challenge in atmospheric research since this coupling is responsible for most climate feedback mechanisms and controls the evolution of severe weather events. Although diabatic heating rates are the major driving force of atmospheric circulation on weather and climate time scales, the diabatic heating rates obtained from current meteorological reanalyses show significant inconsistencies. This is mainly indebted to the fact that diabatic heating rates cannot be directly observed. Isotopologue observations assimilated into meteorological reanalyses can make an invaluable contribution since the isotopologue composition depends on the history of phase transition. Therefore, isotopologue observations can provide information that is closely linked to latent heating processes. Here, we analyse idealized experiments performed with the isotopes-incorporated General Spectral Model (IsoGSM) to investigate whether the additional assimilation of isotopologue observations can improve the diabatic heating rates. To do so, we use a Local Transform Ensemble Kalman Filter (LETKF) for data assimilation, and mock the high-density isotopologue MUSICA IASI observational data. The MUSICA IASI data apply the retrieval recipe of MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) to the thermal nadir spectra recorded by the IASI (Infrared Atmospheric Sounding Interferometer) satellite instrument. The mocked isotopologue observations are then assimilated into the model in addition to temperature, humidity and wind profiles obtained from radiosonde and satellite data. By comparing the ensemble runs with and without the additional assimilation of the isotopologue data we can reveal the potential of MUSICA IASI isotopologue data for constraining uncertainties in diabatic heating rates.

The Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA) is an imaging Fourier transform spectrometer (iFTS) using a 2-dimensional detector array to record emission spectra in the mid-infrared region with high spatial resolution. GLORIA is operated on high altitude research aircraft, mainly in the limb observational geometry to measure vertical profiles of temperature and atmospheric trace species with high vertical resolution.In autumn 2017, the Wave-driven ISentropic Exchange (WISE) aircraft campaign took place from Shannon (Ireland). Sixteen flights with the High Altitude and Long Range Research Aircraft (HALO) were performed between 31 August and 21 October 2017 over the eastern North Atlantic region.GLORIA observations were analysed with regard to pollutant species like C2H6, C2H2, HCOOH, and PAN, which are produced at distinct source regions near the ground and transported to remote regions due to their atmospheric lifetime of several weeks. Enhanced volume mixing ratios of these molecules were detected along some parts of the flight track in the upper troposphere and lowermost stratosphere (UTLS).Measured profiles of these species are compared to simulations from the ECHAM/MESSy Atmospheric Chemistry (EMAC) model and reanalysis data from the Copernicus Atmosphere Monitoring Service (CAMS). Furthermore, emission tracers and back-trajectories from the Chemical Lagrangian Model of the Stratosphere (CLaMS) are used to analyse the source regions of these pollution events.

We present model simulations with the atmospheric chemistry–climate model ECHAM5/MESSy Atmospheric Chemistry (EMAC) nudged toward European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalyses for the Arctic winters 2009/2010 and 2010/2011. This study is the first to perform an extensive assessment of the performance of the EMAC model for Arctic winters as previous studies have only made limited evaluations of EMAC simulations which also were mainly focused on the Antarctic winter stratosphere. We have chosen the two extreme Arctic winters 2009/2010 and 2010/2011 to evaluate the formation of polar stratospheric clouds (PSCs) and the representation of the chemistry and dynamics of the polar winter stratosphere in EMAC. The EMAC simulations are compared to observations by the Michelson Interferometer for Passive Atmospheric Soundings (Envisat/MIPAS) and the observations from the Aura Microwave Limb Sounder (Aura/MLS). The Arctic winter 2010/2011 was one of the coldest stratospheric winters on record, leading to the strongest depletion of ozone measured in the Arctic. The Arctic winter 2009/2010 was, from the climatological perspective, one of the warmest stratospheric winters on record. However, it was distinguished by an exceptionally cold stratosphere (colder than the climatological mean) from mid-December 2009 to mid-January 2010, leading to prolonged PSC formation and existence. Significant denitrification, the removal of HNO3 from the stratosphere by sedimentation of HNO3-containing polar stratospheric cloud particles, occurred in that winter. In our comparison, we focus on PSC formation and denitrification. The comparisons between EMAC simulations and satellite observations show that model and measurements compare well for these two Arctic winters (differences for HNO3 generally within ±20 %) and thus that EMAC nudged toward ECMWF ERA-Interim reanalyses is capable of giving a realistic representation of the evolution of PSCs and associated sequestration of gas-phase HNO3 in the polar winter stratosphere. However, simulated PSC volume densities are smaller than the ones derived from Envisat/MIPAS observations by a factor of 3–7. Further, PSCs in EMAC are not simulated as high up (in altitude) as they are observed. This underestimation of PSC volume density and vertical extension of the PSCs results in an underestimation of the vertical redistribution of HNO3 due to denitrification/re-nitrification. The differences found here between model simulations and observations stipulate further improvements in the EMAC set-up for simulating PSCs.

The annual variation of δD in the tropical lower stratosphere is a critical indicator for the relative importance of different processes contributing to the transport of water vapour through the cold tropical tropopause region into the stratosphere. Distinct observational discrepancies of the δD annual variation were visible in the works of Steinwagner et al. (2010) and Randel et al. (2012). Steinwagner et al. (2010) analysed MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) observations retrieved with the IMK/IAA (Institut für Meteorologie und Klimaforschung in Karlsruhe, Germany, in collaboration with the Instituto de Astrofísica de Andalucía in Granada, Spain) processor, while Randel et al. (2012) focused on ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) observations. Here we reassess the discrepancies based on newer MIPAS (IMK/IAA) and ACE-FTS data sets, also showing for completeness results from SMR (Sub-Millimetre Radiometer) observations and a ECHAM/MESSy (European Centre for Medium-Range Weather Forecasts Hamburg and Modular Earth Submodel System) Atmospheric Chemistry (EMAC) simulation (Eichinger et al., 2015b). Similar to the old analyses, the MIPAS data set yields a pronounced annual variation (maximum about 75 ‰), while that derived from the ACE-FTS data set is rather weak (maximum about 25 ‰). While all data sets exhibit the phase progression typical for the tape recorder, the annual maximum in the ACE-FTS data set precedes that in the MIPAS data set by 2 to 3 months. We critically consider several possible reasons for the observed discrepancies, focusing primarily on the MIPAS data set. We show that the δD annual variation in the MIPAS data up to an altitude of 40 hPa is substantially impacted by a “start altitude effect”, i.e. dependency between the lowermost altitude where MIPAS retrievals are possible and retrieved data at higher altitudes. In itself this effect does not explain the differences with the ACE-FTS data. In addition, there is a mismatch in the vertical resolution of the MIPAS HDO and H2O data (being consistently better for HDO), which actually results in an artificial tape-recorder-like signal in δD. Considering these MIPAS characteristics largely removes any discrepancies between the MIPAS and ACE-FTS data sets and shows that the MIPAS data are consistent with a δD tape recorder signal with an amplitude of about 25 ‰ in the lowermost stratosphere.

The Arctic winter 2015/16 was characterized by cold stratospheric temperatures. Here we present a comprehensive view of the temporal evolution of chlorine in the lowermost stratosphere over the course of the studied winter. We utilize two-dimensional vertical cross sections of ozone (O3) and chlorine nitrate (ClONO2), measured by the airborne limb imager GLORIA (Gimballed Limb Observer for Radiance Imaging of the Atmosphere) during the POLSTRACC/GW-LCYCLE II/GWEX/SALSA campaigns, to investigate the tropopause region in detail. Observations from three long-distance flights in January, February, and March 2016 are discussed. ClONO2 volume mixing ratios up to 1100 pptv were measured at 380 K potential temperature in mesoscale structures. Similar mesoscale structures are also visible in O3 measurements. Both trace gas measurements are applied to evaluate simulation results from the chemistry transport model CLaMS (Chemical Lagrangian Model of the Stratosphere) and the chemistry–climate model EMAC (ECHAM5/MESSy Atmospheric Chemistry). These comparisons show agreement within the expected performance of these models. Satellite measurements from Aura/MLS (Microwave Limb Sounder) and SCISAT/ACE-FTS (Atmospheric Chemistry Experiment – Fourier Transform Spectrometer) provide an overview over the whole winter and information about the stratospheric situation above the flight altitude. Time series of these satellite measurements reveal unusually low hydrochloric acid (HCl) and ClONO2 at 380 K from the beginning of January to the end of February 2016, while chlorine monoxide (ClO) is strongly enhanced. In March 2016, unusually rapid chlorine deactivation into HCl is observed instead of deactivation into ClONO2, the more typical pathway for deactivation in the Arctic. Chlorine deactivation observed in the satellite time series is well reproduced by CLaMS. Sensitivity simulations with CLaMS demonstrate the influence of low abundances of O3 and reactive nitrogen (NOy) due to ozone depletion and sedimentation of NOy-containing particles, respectively. On the basis of the different altitude and time ranges of these effects, we conclude that the substantial chlorine deactivation into HCl at 380 K arose as a result of very low ozone abundances together with low temperatures. Additionally, CLaMS estimates ozone depletion of at least 0.4 ppmv at 380 K and 1.75 ppmv at 490 K, which is comparable to other extremely cold Arctic winters. We have used CLaMS trajectories to analyze the history of enhanced ClONO2 measured by GLORIA. In February, most of the enhanced ClONO2 is traced back to chlorine deactivation that had occurred within the past few days prior to the GLORIA measurement. In March, after the final warming, air masses in which chlorine has previously been deactivated into ClONO2 have been transported in the remnants of the polar vortex towards the location of measurement for at least 11 d.

Open access. © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License., Time series of stratospheric and lower mesospheric water vapour using 33 data sets from 15 different satellite instruments were compared in the framework of the second SPARC (Stratosphere-troposphere Processes And their Role in Climate) water vapour assessment (WAVAS-II). This comparison aimed to provide a comprehensive overview of the typical uncertainties in the observational database that can be considered in the future in observational and modelling studies, e.g addressing stratospheric water vapour trends. The time series comparisons are presented for the three latitude bands, the Antarctic (80°-70°S), the tropics (15°S-15°N) and the Northern Hemisphere mid-latitudes (50°-60°N) at four different altitudes (0.1, 3, 10 and 80hPa) covering the stratosphere and lower mesosphere. The combined temporal coverage of observations from the 15 satellite instruments allowed the consideration of the time period 1986-2014. In addition to the qualitative comparison of the time series, the agreement of the data sets is assessed quantitatively in the form of the spread (i.e. the difference between the maximum and minimum volume mixing ratios among the data sets), the (Pearson) correlation coefficient and the drift (i.e. linear changes of the difference between time series over time). Generally, good agreement between the time series was found in the middle stratosphere while larger differences were found in the lower mesosphere and near the tropopause. Concerning the latitude bands, the largest differences were found in the Antarctic while the best agreement was found for the tropics. From our assessment we find that most data sets can be considered in future observational and modelling studies, e.g. addressing stratospheric and lower mesospheric water vapour variability and trends, if data set specific characteristics (e.g. drift) and restrictions (e.g. temporal and spatial coverage) are taken into account. © Author(s) 2018., The Atmospheric Chemistry Experiment (ACE), also known as SCISAT, is a Canadian-led mission mainly supported by the Canadian Space Agency and the Natural Sciences and Engineering Research Council of Canada. We would like to thank the European Space Agency (ESA) for making the MIPAS level-1b data set available. MLS data were obtained from the NASA Goddard Earth Sciences and Information Center. Work at the Jet Propulsion Laboratory, California Institute of Technology, was done under contract with the National Aeronautics and Space Administration. SCIAMACHY spectral data have been provided by ESA. The work on the SCIAMACHY water vapour data products has been funded by DLR (German Aerospace Center) and the University of Bremen. The SCIAMACHY limb water vapour data set v3.01 is a result of the DFG (German Research Council) Research Unit "Stratospheric Change and its Role for Climate Prediction" (SHARP) and the ESA SPIN (ESA SPARC Initiative) project and were partly calculated using resources of the German HLRN (High-Performance Computer Center North). We would like to thank Mark Hervig for providing the SOFIE data and Takafumi Sugita for providing the ILAS-II data. We acknowledge the HALOE science team and the many members of the HALOE project for producing and characterising the high-quality HALOE data set. Further, we would like to thank Ellis Remsberg for valuable comments on the manuscript. Stefan Lossow was funded by the SHARP project under contract STI 210/9-2. We want to express our gratitude to SPARC and WCRP (World Climate Research Programme) for their guidance, sponsorship and support of the WAVAS-II programme. We acknowledge support by Deutsche Forschungsgemeinschaft and Open Access Publishing Fund of Karlsruhe Institute of Technology.

The Arctic winter 2015/2016 was one of the coldest stratospheric winters in recent years. A stable vortex formed by early December and the early winter was exceptionally cold. Cold pool temperatures dropped below the Nitric Acid Trihydrate (NAT) existence temperature of about 195 K, thus allowing Polar Stratospheric Clouds (PSCs) to form. The low temperatures in the polar stratosphere persisted until early March allowing chlorine activation and catalytic ozone destruction. Satellite observations indicate that sedimentation of PSC particles led to denitrification as well as dehydration of stratospheric layers. Model simulations of the Arctic winter 2015/2016 nudged toward European Center for Medium-Range Weather Forecasts (ECMWF) analyses data were performed with the atmospheric chemistry–climate model ECHAM5/MESSy Atmospheric Chemistry (EMAC) for the Polar Stratosphere in a Changing Climate (POLSTRACC) campaign. POLSTRACC is a High Altitude and LOng Range Research Aircraft (HALO) mission aimed at the investigation of the structure, composition and evolution of the Arctic Upper Troposphere and Lower Stratosphere (UTLS). The chemical and physical processes involved in Arctic stratospheric ozone depletion, transport and mixing processes in the UTLS at high latitudes, polar stratospheric clouds as well as cirrus clouds are investigated. In this study an overview of the chemistry and dynamics of the Arctic winter 2015/2016 as simulated with EMAC is given. Further, chemical-dynamical processes such as denitrification, dehydration and ozone loss during the Arctic winter 2015/2016 are investigated. Comparisons to satellite observations by the Aura Microwave Limb Sounder (Aura/MLS) as well as to airborne measurements with the Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA) performed on board of HALO during the POLSTRACC campaign show that the EMAC simulations are in fairly good agreement with observations. We derive a maximum polar stratospheric O3 loss of ~ 2 ppmv or 100 DU in terms of column in mid March. The stratosphere was denitrified by about 8 ppbv HNO3 and dehydrated by about 1 ppmv H2O in mid to end of February. While ozone loss was quite strong, but not as strong as in 2010/2011, denitrification and dehydration were so far the strongest observed in the Arctic stratosphere in the at least past 10 years.

In the framework of the second SPARC (Stratosphere-troposphere Processes And their Role in Climate) water assessment (WAVAS-II), the amplitudes and phases of the annual, semi-annual and quasi-biennial variation in stratospheric and lower mesospheric water were compared considering 32 data sets from 13 different satellite instruments. These comparisons aimed to provide a comprehensive overview of the typical uncertainties in the observational database which can be considered in subsequent observational and modelling studies. For the amplitudes, a good agreement of their latitude and altitude distribution was found. Quantitatively there were differences in particular at high latitudes, close to the tropopause and in the lower mesosphere. Here the standard deviation over all data sets typically exceeded 0.2 ppmv for the annual variation and 0.1 ppmv for the semi-annual and quasi-biennial variation. For the phase, larger differences between the data sets were found in the lower mesosphere. Generally the smallest phase uncertainties can be observed in regions where the amplitude of the variability is large. The standard deviations over all data sets were typically smaller than a month for the annual and semi-annual variation and smaller than 5 months for the quasi-biennial variation. The amplitude and phase differences among the data sets could be explained by a combination of reasons. An important role play temporal variations of systematic errors and differences in the temporal and spatial sampling. In addition, differences in the considered time periods, the vertical resolution of the data, influences of clouds, aerosols as well as non local thermodynamic equilibrium (NLTE) effects cause differences between the individual data sets.

Enhancement of the binary homogeneous nucleation rate of H2SO4 and H2O and condensation of H2O and HNO3 on liquid sulphate aerosol particles due to mixing processes in the tropopause region were investigated by conducting theoretical studies and by using a section aerosol box model. If two air parcels with a large initial temperature and humidity difference are mixed with each other the nucleation rate within the resulting air parcel will be enhanced. This is due to the curvature of the saturation vapour pressure curve. The theoretical studies show that in the tropopause region enhancements of the nucleation rate of up to five orders of magnitude can occur. Since the mixing causes strong supersaturations also the condensation rate will be enhanced, especially for HNO3. The simulation with the sectional aerosol box model shows that after the mixing of two air parcels up to 40 wt % of HNO3 is taken up by the smallest aerosol particles. Since the initial air parcels are initialized with 0 wt % HNO3 this corresponds to a very strong enhancement. Measurements during the STREAM 1998 campaign show an entrainment of stratospheric air into the troposphere during the flight on 15 July leading to a production of new particles. Box model studies reproduce fairly well the measured number of newly formed particles.

Measurements in the vicinity of the polar jet stream during the STREAM 1998 campaign in Timmins, Canada, show that during the flight on 15 July, a deep intrusion of stratospheric air into the troposphere occurred. At the edge of the deep intrusion dehydration was observed. The dehydration can be identified in tracer–tracer correlations of H2O and O3 and by the comparison of these correlations with correlations of H2O and O3 derived from two other flights of the STREAM 1998 campaign. Trajectories, calculated backwards for 10 days starting at each point of the measurement for the flight on 15 July, show that the saturation ratios required for homogeneous freezing are reached. However, box model simulations along the trajectories indicate no substantial growth of H2SO4/H2O particles due to H2O uptake. Since ice nuclei were not measured during the campaign, it cannot be precisely determined which freezing process, heterogeneous or homogeneous, is responsible for the formation of the ice particles. Most likely, both processes were involved in the formation of ice particles that led to the observed dehydration on 15 July 1998.DOI: 10.1111/j.1600-0889.2006.00182.x