Accurate cloud phase classification in the near-infrared is challenging due to the overlapping radiative properties of water, ice, and mixed-phase clouds. This study presents a new composite Phase Classification Index (PCINIR,DV) for near-infrared satellite measurements in a dual-viewing geometry. The index is defined as the product of two physically derived components: (1) a spectral ratio of top-of-atmosphere radiances at 1.61 mu m and 2.25 mu m, which exploits the differences in absorption between water and ice, and (2) a directional ratio of 0.87 mu m radiances from oblique and nadir views, which are influenced by scattering. Theoretical simulations using the SCIATRAN radiative transfer model demonstrate that the PCINIR,DV effectively distinguishes between pure water and ice clouds, enabling mixed-phase clouds to be identified. Sensitivities are analyzed for ranges of particle sizes, ice fractions, and surface types. Theoretical results show that water clouds, excluding thin clouds over snow surfaces, exhibit high PCINIR,DV values (above 3.5), ice clouds yield low values (below 2.75), and intermediate values correspond to mixed-phase clouds. Validation of PCINIR,DV derived from the Sea and Land Surface Temperature Radiometer (SLSTR) dual-view observations (onboard Sentinel-3A) against CloudSat-CALIPSO phase classifications confirms its applicability, yielding 86% classification accuracy, including over 63% for mixed-phase clouds. The results demonstrate that PCINIR,DV provides a robust physical framework for dual-view satellite missions, which aim to measure the cloud phase.

The photolysis of nitrous acid (HONO) produces hydroxyl radicals (OH), the most important cleaning agent of the troposphere. For decades, HONO has been measured in concentrations which exceed the photo-stationary concentration arising from its gas phase formation via the reaction NO + OH and destruction by photolysis. Several photochemical and heterogeneous formation mechanisms, including the photolysis of nitrate have been proposed which may explain this excess HONO. This study reports on airborne remote sensing measurements of the mini-DOAS instrument over continental Europe, Southeast Asia, and the tropical Atlantic. The observations form a C-shaped profile in the troposphere with maximum volume mixing ratios of approximately 150 ppt in the planetary boundary layer, about 10 ppt in the free troposphere and up to 100 ppt in the tropical upper troposphere. These measurements of HONO throughout the troposphere exceed model predictions by up to an order of magnitude. Together with a host of other measured species and parameters, various formation mechanisms are explored to investigate in situ HONO sources. Although a precise formation mechanism in the polluted boundary layer remains elusive, the photolysis of particulate nitrate may explain excess HONO in the marine boundary layer. The excess HONO observed in the upper troposphere requires a gas phase source with a formation rate of up to 300 ppt h(-1). The possible role of peroxynitrous acid (HOONO), formed by the reactions NO + HO2 + M and NO2 + OH + M, and further oxidation by reactions with NO or O-3, is explored.

This study investigates the sensitivity of satellite-based brightness temperature measurements at 3.74, 10.85, and 12.00 & micro;m with respect to the identification of water, ice, and mixed-phase clouds (MPC). Radiative transfer simulations computed by SCIATRAN reveal that the directional brightness temperature difference at 3.74 & micro;m (Delta BT3.74), which is dependent on scattering, enables water clouds and MPC separation from ice clouds. For water clouds and MPC, Delta BT3.74 typically exceeds 2 K, whereas for ice clouds it remains below 2 K. To separate MPC from water clouds, we introduce the Liquid Cloud Probability Index (LCPI) based on cloud top temperature and absorption differences between water and ice at 10.85 and 12.00 & micro;m. LCPI values generally exceed 0.4 for water clouds but fall below 0.4 for many MPC cases. The Delta BT3.74 and LCPI approach is validated using the Sea and Land Surface Temperature Radiometer (SLSTR) dual-view data collocated with the 2B-CLDCLASS-LIDAR cloud phase product, showing over 90% accuracy in water and ice phase classification, and approximately 60% for MPC. This dual-view, multi-channel method enhances the detection of cloud phases, offering improved results for brightness temperature measurements.

Governments and industries worldwide are seeking methods to accurately estimate their methane inventories, particularly in the open-cut coal mining sector, where quantifying facility-level emissions remains challenging and robust verification methods are not yet widespread. Here, we compare methane emission rates estimated from two aircraft-based measurement platforms with operator-reported emissions from an open-cut coal mine in the Bowen Basin (Queensland, Australia). Coarse-resolution satellite-based data identified the mine as a significant emitter, making it ideal for case studies using airborne in situ and remote sensing platforms that provide high-resolution measurements to isolate mine-scale emissions. Using airborne in situ measurements, we estimated methane emission rates of 14.0 +/- 3.3 (+/- 2 sigma) t h-1 during May and June 2022. In September 2023, airborne in situ and remote sensing measurements yielded consistent emission rate estimates of 9.6 +/- 1.9 (+/- 2 sigma) t h-1 and 11.3 +/- 5.3 (+/- 2 sigma) t h-1, respectively. If sustained, these rates would equate to annual emissions of 1.5-4.2 Mt of CO2 equivalents (CO2-e) year-1, 3-8 times higher than operator-reported annual Scope 1 emissions (0.53-0.54 Mt of CO2-e year-1). Beyond highlighting the potential for under-reporting of emissions at this mine, our results indicate that aircraft-based technologies are valuable tools for supporting accurate reporting of facility-scale methane emissions from open-cut coal mines.

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.

Nitrogen oxides (NOx = NO + NO2) are among the most important pollutants in the atmosphere. They impact tropospheric ozone chemistry, contribute to particle formation and adversely affect human health.The monitoring of NO2 is mainly performed by surface in-situ networks. Satellite observations can contribute by providing a large-scale picture and covering regions without in-situ observations. The satellite instruments traditionally used for NO2 retrieval (GOME, SCIAMACHY, OMI, TROPOMI) operate on low-earth orbiting platforms, providing global coverage but only one or two measurements per day. The Korean GEMS instrument, launched in February 2020, is the first in a series of geostationary observation platforms allowing hourly measurements of NO2 from space.Based on the work performed in preparation for the European S4 satellite, a tropospheric NO2 retrieval for GEMS has been developed at IUP-UB. This product focuses on achieving low noise and high accuracy by optimising the fitting window and including corrections for instrument polarisation sensitivity and scene inhomogeneity. Stratospheric correction is performed using different approaches to investigate the impact on the tropospheric columns. For the airmass factors, cloud correction is applied using cloud fractions derived after correction for calibration issues in GEMS irradiance measurements. The resulting tropospheric columns for the first three years of GEMS operation show excellent agreement with the operational TROPOMI NO2 product at the time of TROPOMI overpass. They also exhibit systematic and variable daily patterns, which depend on season and location.

The Geostationary Environment Monitoring Spectrometer (GEMS), launched in 2020, provides both temporally and spatially continuous air quality data from geostationary Earth orbit (GEO). This study first investigates the seasonal variations and diurnal behavior of nitrogen dioxide (NO2) tropospheric vertical column densities (TropVCDs) over the Seoul metropolitan area (SMA) using GEMS data, retrieved by the IUP-UB algorithm. We find that the magnitude of the NO2 TropVCDs and their diurnal behavior have significant seasonal dependences. In January, the highest NO2 TropVCD values in the range 27.5–28.9×1015 molec.cm-2 during the four seasons were observed at 15:00 local time (LT) and NO2 TropVCD increases from the first retrieved values at 10:00 LT. On the other hand, we find the lowest values (7.4–8.8×1015 molec.cm-2) are at ∼14:00 LT in July. The VCD values in July increased up to 10:00 LT and then decreased until 14:00 LT but then began to increase again. These different diurnal behaviors of the TropVCDs in the different seasons reflect the differences in photochemical and meteorological conditions as well as the emissions of NOx. Photochemical transformations are typically more rapid in July and slower in January. The absolute values and diurnal behavior of NO2 TropVCDs are significantly influenced by the wind speed, except in July. Moderate (wind speed ≥3 m s−1) or strong wind (wind speed >5 m s−1) reduced the magnitude of the diurnal behavior in January, implying that the NO2 plumes were transported downwind. Finally, we compared the retrieved NO2 TropVCDs by using different a priori NO2 data simulated by TM5 and WRF-Chem, calculated using the most recent emission inventories. Although simulated VCDs from WRF-Chem and TM5 show differences of up to a factor 2.75, retrieved NO2 TropVCDs using each a priori data have almost identical values and diurnal behaviors, except in July. Notably, the diurnal behavior of the retrieved NO2 TropVCDs is independent of that from the two chemical transport models, indicating that observations of slant column densities are the dominant factor in determining the diurnal behavior of NO2 TropVCDs. Changes in the model horizontal resolution and volatile organic compound (VOC) emission inventory do not significantly affect the retrieved NO2 TropVCDs in this study. However, when the a priori NO2 vertical profile was fixed as the values at 13:45 LT, the diurnal patterns of NO2 TropVCDs showed significant changes, with differences of up to −18.3 %.

Nitrogen dioxide (NO2) is one of the most important air pollutants in the troposphere. NO2 can be retrieved by differential optical absorption spectroscopy measurements, which can be performed from various platforms. Measurements from low earth satellites in sun-synchronous orbits provide a global overview and have already contributed valuable insights into understanding NO2. The latest instrument, TROPOMI, with its high spatial resolution of 3.5 x 5.5 km2, has given new opportunities to disentangle and analyze NOx sources. However, instruments in low-earth orbits usually provide only one measurement per day at each location. To achieve diurnal cycles of trace gases, instruments on geostationary satellites are needed. The Korean instrument GEMS on GK2B, launched in February 2020, is the first instrument in geostationary orbit, delivering hourly daytime observations of NO2 with a spatial resolution of 3.5 x 8 km2 over a large part of Asia. In this study, one year of tropospheric NO2 vertical column densities (VCDs) of the operational GEMS product are compared to the scientific GEMS IUP-UB NO2 VCD product, the operational TROPOMI NO2 VCD product, and ground-based DOAS measurements in Korea. The diurnal variation of NO2 observed by GEMS is compared to the diurnal variation observed at several ground-based MAX-DOAS stations located in different pollution regimes in Korea. The large variety of observed diurnal cycles are interpreted regarding potential influencing factors. In this respect, the ERA5 10 m wind data provide valuable insights into the influence of transport effects on the tropospheric NO2 VCD depending on station location and seasonality.

Wind and weather in Taiwan are strongly influenced by the monsoons. Taiwan is on the lee side of the Asian winter monsoon, originating on the Asian continent. It receives continental air masses transported by the monsoon, thus air pollutants originating in the eastern and northern parts of China. While polluted air reaches Taiwan during winter monsoon, clean air masses from the remote western North Pacific are predominant in summer, making Taiwan an ideal location to investigate variations in atmospheric composition due to the monsoons. In addition to that, the monsoons themselves are also subject to regional climate changes in the future. Since June 2023, a Multi-AXis-DOAS (MAX-DOAS) instrument has been installed at the Cape Fuguei Research Station (CAFE) at the northernmost point of Taiwan, measuring vertical and horizontal distributions of trace gases, including NO2, SO2, HCHO, and aerosols. The measurements aim to investigate local air pollution and study the impact of pollution export from mainland China on tropospheric composition and local air quality. The MAX-DOAS and the other measurements at the site will also be very valuable in the validation of the data products from the GEMS satellite. The measurements are part of the project “Investigation of Pollution Transport to Taiwan” (IPToT), which has been established as a cooperation between the Insitute of Environmental Physics (IUP) of the University of Bremen (Germany) and the Research Center for Environmental Changes of the Academia Sinica (Taiwan), funded by the DFG (Deutsche Forschungsgemeinschaft). Here, we present first MAX-DOAS observations and retrieved profiles of NO2, SO2, HCHO, and aerosols at this new station and compare them to collocated in-situ and (aerosol) LIDAR measurements. The data are evaluated for diurnal and weekly pollution signals and the dependency on the prevailing wind direction. A first comparison to satellite measurements is also shown.

Methane (CH4) is the second most important anthropogenic greenhouse gas (GHG), and its emissions reduction has been identified as an essential mitigation target to slow down climate change. According to inventories, fossil fuel production and usage account for roughly 17% of the global CH4 emissions, of which approximately 33% originate from coal mining. Accurate identification of coal mining-related CH4 sources and quantification of their annual emission rate is needed for corporate reporting requirements, national inventory verification, and the development of CH4 mitigation strategies.A previous study estimated CH4 emissions for six coal mines in the Bowen Basin in Queensland, Australia, using TROPOMI satellite measurements. It covered a sub-area of the Bowen Basin, where coal is mined at over 40 active mining locations distributed over 60,000 km2. The study showed a significant discrepancy compared to inventory estimates by a factor of 7 during 2018 and 2019.To further verify satellite estimates and improve knowledge of the distribution, persistence, and strength of emissions of this mining region, the Bowen Basin CH4 Mapping (BBCMap) Campaign was conducted in September-October 2023, funded by and performed in collaboration with UNEP's International Methane Emissions Observatory. During this campaign, two HK36 Eco-Dimona research aircraft carrying complementary sensing instrumentation were deployed. The MAMAP2D-Light (Methane Airborne MAPper 2D – Light) imaging spectrometer for estimating atmospheric CH4 and CO2 column anomalies and a lidar for topography scans were deployed on one DIMONA HK36 research aircraft, while the second identical aircraft was equipped with an in-situ payload consisting of an LGR OA-ICOS gas analyser for simultaneous measurements of atmospheric CH4, CO2, and water vapor concentrations, a turbulence probe for wind statistics, and a bag sampler for collecting multiple gas samples during each flight for later 13C isotope analyses in the laboratory. This two-aircraft strategy allowed coordinated measurements of CH4 emissions from different coal mines with both remote sensing and in-situ instruments and simultaneous wind measurements, which is essential for deriving a robust flux estimate.During the campaign, 39 flights were conducted, covering approximately 33 mines across roughly 20,000 km2, focussing on the northern part of the Bowen Basin. Preliminary MAMAP2D-Light measurements of atmospheric CH4 column anomalies and emission estimates for both open-cut and underground coal mines will be presented and discussed.

Apart from the loss of life and property, the hostilities, which started on February 24, 2022, with the invasion by Russian armed forces into Ukraine, are altering Ukraine’s environment. In this study, we will present the major findings of Malytska et al., 2024, discussing the changes in tropospheric pollution, specifically nitrogen dioxide (NO2), as a consequence of military activities. This study uses the TROPOspheric Monitoring Instrument (TROPOMI) and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite observations, Global Fire Assimilation System (GFAS) wildfire emission inventory, European Centre for Medium-Range Weather Forecasts (ECMWF) daily ERA5 reanalysis data, and the Hybrid Single-Particle Lagrangeian Integrated Trajectory analysis model (HYSPLIT) 5.2 to quantify the spatiotemporal distribution of tropospheric NO2, its changes and transport during the first three months of the armed conflict in Ukraine. This provides insights into the impact of hostilities on local air quality, as well as regional and transborder pollution.We discuss NO2 variability with a particular emphasis on comparing periods before and during hostilities to differentiate the effects of COVID-19 restrictions and the hostilities on NO2 emissions over Ukraine. The retrieved emissions show a temporal reduction in NO2 emission in industrial areas, comparable to the COVID-19 pandemic lockdown, whereas the NO2 tropospheric vertical column locally increased in the areas of conflict. Based on the TROPOMI and VIIRS data, we linked the major fires caused by the conflict to air pollution and found that hostilities led to more frequent and intense fires in conflict zones deteriorating air quality in the region. To investigate the impact of the hostilities on atmospheric pollution, we analysed nitrogen oxide (NOx) emission and injection altitude of fire, using the GFAS data. It was found that fires in conflict-affected areas exhibit greater intensity, characterized by larger plume top heights and higher rates of emission, in comparison to fires located far from the front line or resulting from isolated strikes. To demonstrate that increased fire activities contribute to pollution at both local and regional levels, we provide a case study of the fire episode of March 19-23, 2022, in the Kyiv region, coinciding with the active stage of the conflict and the defense of the capital, Kyiv. The simulations of HYSPLIT version 5.2 forward trajectories model showed that a smoke-particle-included air mass, related to the fires in the Chornobyl Exclusion Zone, was transported to Poland and countries of the Baltic region at the height of 1.5-3 km within 72 hours. A plume of NO2, which originated from fires in the Chornobyl Exclusion Zone on March 20, 2022, was observed in Poland the following day.References:Malytska L., Ladstätter-Weißenmayer A., Galytska E, and. Burrows J.P. Assessment of environmental consequences of hostilities: Tropospheric NO2 vertical column amounts in the atmosphere over Ukraine in 2019–2022. Atmospheric Environment 318 (2024) 120281, DOI: https://doi.org/10.1016/j.atmosenv.2023.12028

About 10% of the total amount of ozone resides in the troposphere, which acts as a potent greenhouse gas. Anthropogenic emissions and biomass burning are the main sources of ozone in the troposphere, and overexposure to this pollutant causes health problems and damages vegetation. A combination of space-borne limb and nadir measurements in the UV-visible spectral range (so-called limb-nadir matching, LNM) provides valuable information on tropospheric ozone. This study uses data from the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) (2002-2012) and Ozone Mapping and Profiler Suite on board of Suomi National Polar-Orbiting Partnership (OMPS/NPP, since 2012). Both instruments observe the atmosphere in both limb and nadir geometry. Tropospheric ozone columns are retrieved globally by subtracting the stratospheric ozone column calculated from limb observations from the total ozone column derived from the nadir measurements. Tropospheric ozone retrievals use different upper altitude limits to calculate the tropospheric ozone column. In the case of the LNM technique, the upper limit is defined by the thermal and/or dynamical tropopause. The Convective Clouds Differential technique (CCD) calculates the tropospheric ozone column up to 270 hPa. Phase II of the Tropospheric Ozone Assessment Report (TOAR-II) uses different pressure levels for different latitudes as an upper limit for the tropospheric column. After updating and improving the SCIAMACHY-LNM and the OMPS/NPP-LNM datasets, we obtained a long-term dataset of tropospheric ozone (2002-2023) by merging them. Here, we present this new long-term LNM tropospheric ozone column dataset, which has been converted to the different definitions of column heights as prescribed in TOAR II. The datasets are validated using ozonesondes, and the results for the different column definitions are evaluated and discussed.

The Arctic is experiencing heightened precipitation, affected by aerosols impacting rainfall and snowfall. However, sparse aerosol observations in the central Arctic cryosphere contribute to uncertainties in simulating aerosol-precipitation two-way interaction. This study examines aerosol-precipitation co-variation in various climate models during the Arctic spring and summer seasons from 2003 to 2011, leveraging satellite-based aerosol data and various CMIP6 climate models. Findings reveal significant spatio-temporal biases between models and observations. Snowfall dominance occurs in models where total AOD surpasses the observation by 121% (57–186%, confidence interval), intensifying simulated snowfall by two times compared to rainfall during summer. Consequently, climate models tend to underestimate central Arctic rainfall to the total precipitation ratio, suggesting a positive bias towards snowfall dominance. This highlights the importance of constraining total AOD and associated aerosol schemes in climate models using satellite measurements, which potentially could lead to a substantial reduction in snowfall contribution to the total precipitation ratio in the central Arctic, contrary to current multi-model simulations across various spatiotemporal scales.

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 Arctic is currently warming rapidly, at a rate four times higher than the global average. This warming has significant consequences, leading to increased precipitation in the Arctic. Aerosols play a crucial role in cloud formation, cloud condensation nuclei (CCNs) and ice-nucleating particles (INPs), influencing rain and snowfall. However, uncertainties remain in the modelling of aerosols and their impact on precipitation due to a lack of high-resolution spatio-temporal observations. This is particularly the case in the central Arctic cryosphere due to the presence of extensive cold, bright snow and ice surfaces coupled with widespread cloud cover. This study addresses the observational data gap and provides an opportunity to refine model simulations at different spatio-temporal scales. We achieve this by using total aerosol optical depth (AOD) datasets generated by the AEROSNOW algorithm over the extensive central Arctic cryosphere. AEROSNOW retrieves AOD data using top-of-atmosphere reflectance measurements obtained through the Advanced Along-Track Scanning Radiometer (AATSR) aboard the ENVISAT satellite, spanning from 2003 to 2011. AEROSNOW integrates an aerosol retrieval algorithm with a rigorous cloud masking scheme and intro-duces a novel quality flagging methodology tailored for the central Arctic region (≥ 72°N). Using the AEROSNOW retrieved dataset for the central Arctic, we evaluate different models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Our results show significant differences in the spatio-temporal aerosol load and its annual and seasonal variations with precipitation. In particular, there is a decrease in aerosol loading that coincides with increased precipitation along the northern periphery of Alaska and the Bering Sea. Significant discrepancies and variations of up to 6.2 mm/day in precipitation are observed between models, with higher aerosol loading leading to lower precipitation and vice versa. Furthermore, the spatially averaged multi-model mean overestimates aerosol concentrations in spring and underestimates them in summer compared to satellite observations. The CMIP6 models do not reproduce the seasonal variations in aerosol distribution seen with AEROSNOW, particularly an increase in aerosol loading during the summer coinciding with the sea ice retreat cycle. These discrepancies may be due to the lack of advanced natural aerosol formation mechanisms in the models, as a consequence of Arctic warming, and exposure to openocean emissions. In summary, our study has led us to speculate that as model sophistication increases, modelled aerosol processes become increasingly uncertain. Ultimately, this investigation has the potential to elucidate the critical link between aerosols and the prevailing rain-dominated Arctic conditions under ongoing Arctic warming in future CMIP projects.

Abstract. The cryosphere plays a crucial role in global climate change. To accurately quantify impacts of typical cryospheric surface types, such as snow, ice, and melt ponds on the radiative processes both in the atmosphere and at the surface, new developments in the radiative transfer modeling are necessary. This paper summarizes recent developments in the coupled atmosphere-snow(water)-ice-water radiative transfer model SCIATRAN, which are essential for cryospheric science applications. Novel implementations include a polarized treatment of the coupled ocean-atmosphere, support for multi-layer ice with an ice crust, a flexible interface for incorporating diverse total suspended matter, and an improved cloud parameter input for mixed clouds. We also introduce new surface reflection models and expanded databases of inherent optical properties for snow and ice. Furthermore, it includes selected verification and validation results obtained by comparing SCIATRAN simulations with benchmark data and with measurements from various campaigns. The SCIATRAN software package is freely distributed via the homepage of the Institute of Environmental Physics (IUP), University of Bremen: https://www.iup.uni-bremen.de/sciatran/.

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.

To reduce and mitigate anthropogenic greenhouse gas surface fluxes from industrial sites, their sources must be, firstly, identified or localized and, secondly, accurately quantified. For methane (CH4), the second most important anthropogenic greenhouse gas, the quantification of its diverse emitters is still a challenge. Due to their nature, these emitters can reach dimensions from point sources to hundreds of square kilometres for fossil fuel (gas, oil, coal) exploitation sites or up to several square kilometres in case of waste disposal sites. Although, CH4 emissions from, e.g., waste disposal sites can be computed from activity data combined with landfill models, a high potential for unintended and poorly quantified leakages remain due to, e.g., potential ruptures in the landfill cover. Consequently, the exact localization and quantification of those leakages is a necessary step towards reducing CH4 emissions from waste disposal sites. To have better knowledge and insights into anthropogenic and natural greenhouse gas emissions, a team of scientists has assembled a comprehensive suite of instruments aboard the German Research aircraft HALO (High Altitude and Long Range Research Aircraft) during the CoMet 2.0 Arctic mission conducted in Canada in August and September 2022. Although the campaign was primarily intended to observe and quantify CH4 and CO2 emissions and disentangle anthropogenic from natural sources at the high northern latitudes of Canada, a test flight over Spain revealed unexpectedly high and still persistent emissions from two landfills in Madrid - Valdemingomez and Pinto, previously also pointed out in an ESA story based on satellite observations. Both were investigated by means of passive and active remote sensing, as well as in situ airborne techniques. The measurements of the passive airborne remote sensing instrument MAMAP2D-Light, developed at the University of Bremen, delivers atmospheric concentration anomaly maps of CH4 and CO2. Here, its imaging capabilities are used to pin-point the origin of the CH4 emissions across the targeted landfills and to quantify their emissions. MAMAP2D-Light’s concentration maps are combined with highly accurate CH4 column concentration measurements from the integrated-path differential-absorption lidar CHARM-F (CO2 and CH4 Remote Monitoring-Flugzeug), developed by German Aerospace Center (DLR) in Oberpfaffenhofen. Additionally, airborne CH4 in situ mole fractions were measured by the Jena Instrument for Greenhouse Gases (JIG) and supplemented with wind data within the emission plume in order to complement the remote sensing observations. This contribution will present top-down emission estimates from measurements of all aforementioned instruments, operated quasi-simultaneously, i.e. within a time span of approximate 2 hours, over the targeted area in Madrid in August 2022.

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.

Observations have demonstrated the ubiquity of short-lived halogens (SLHs)-defined as organic and inorganic chlorine, bromine and iodine compounds with an overall atmospheric lifetime of less than 6 months-in the global atmosphere. They are primarily emitted naturally from the ocean, cryosphere, volcanoes, salt lakes and the biosphere. However, unregulated anthropogenic sources are increasingly contributing to their atmospheric loading. Some of their natural emissions have increased over time due to anthropogenic pollution, for example, the increased oceanic emissions of iodine compounds due to the deposition of ozone on the sea surface. SLHs affect chemical processes, such as ozone and methane chemistry, and therefore influence air quality and climate. Nevertheless, some of their sources and chemistry are not included in air-quality and climate models used in international assessment reports. Here we describe in detail the various impacts of SLHs on air quality and climate, and make a case for the inclusion of more comprehensive SLH chemistry in future atmospheric, air-quality and climate assessments. In doing so, we also identify gaps in our knowledge of SLH emissions, chemistry, and environmental and climate impacts.