Contrails - thin ice clouds formed by aircraft - are a major contributor to aviation-induced climate forcing, yet their observational characterization remains limited. We present a manually labeled contrail dataset derived from observations of the Meteosat Second Generation (MSG) SEVIRI instrument over Europe and the North Atlantic, comprising 140 scenes of 256 & times; 256 pixels at 3 km nominal resolution. The dataset covers the time period in which Meteosat-10 was the operational satellite (from January 2013 through February 2018 and from March 2023 through March 2024) and scenes are distributed randomly over the whole SEVIRI disk. Each scene was independently annotated by three labelers, with ground truth established via majority consensus. To provide additional context, the dataset includes outputs from two satellite retrievals: CiPS (Cirrus Properties from SEVIRI) and ProPS (Probabilistic Cloud Top Phase retrieval), offering information on cloud cover and cloud top phase, cirrus probability, ice optical thickness, and ice cloud top height. These complementary variables enable detailed investigations, such as factors influencing contrail visibility. The dataset supports analyses of contrail detection, contrail characteristics, cloud-contrail interactions, and environmental conditions affecting detection. By providing high-quality labeled data with auxiliary cloud information, this resource facilitates the development and evaluation of contrail studies, contributes to improved understanding of aviation-related cloud effects, and informs strategies for climate impact mitigation. The full dataset is available under: 10.5281/zenodo.17669443 with version v2 presented in this study.

The Alpine region experiences frequent deep convection during summer, driven by thermal and mechanical forcing associated with the complex terrain. Deep convection transports moisture into the upper troposphere and lower stratosphere, which affects the climate through its radiative interactions. It is poorly represented in models that rely on parameterized convection and lack adequate representation of boundary layer turbulence and microphysics. In this study, we investigate the evolution of moist deep convection observed on 8 July 2021 over the Alps using ICON simulations with explicitly resolved convection (horizontal resolution of 1 km). The simulations use two turbulence parameterizations: the default turbulence kinetic energy (TKE) and the newly developed two turbulence energies (2 TE) scheme combined with single moment (SM) and double moment (DM) microphysics parameterizations. The simulations are evaluated using cloud properties derived from MSG/SEVIRI satellite measurements. The sensitivity of cross tropopause transport to the choice of turbulence and microphysics scheme is examined. Although, the ICON simulations capture the observed diurnal cycle of convection and successfully simulate the overshooting cloud tops during peak convective activity, our results show that the choice of the turbulence scheme influences the temporal evolution and spatial extent of deep convection, while the microphysics parameterization has a larger impact on the hydrometeor distribution and on the cross tropopause transport.

Line-shaped ice clouds known as contrails are produced by aircraft and play a notable role in aviation's contribution to climate change. One promising and cost-effective approach to mitigating this impact is the operational avoidance of contrail formation. To enable the design and evaluation of such mitigation strategies, reliable automated detection of contrails using spaceborne geostationary sensors is essential. In this work, we present a contrail detection algorithm named COCOS (Contrail Confidence Score) for the Meteosat Second Generation (MSG) satellite. Contrail detection with MSG is challenging due to its moderate spatial resolution of 3 km at nadir. COCOS uses a combination of image processing techniques to identify line-shaped contrails. An adaptive thresholding technique as well as a new object separation method and advanced false alarm reduction procedures are implemented. Furthermore, instead of returning just a binary contrail mask as a result, COCOS returns a confidence score to indicate the degree of certainty of each contrail identification. COCOS is evaluated based on a human-labeled dataset. It comprises 140 images of 256 & times; 256 pixels from 2013-2024, about 60 % of which contain contrails according to human labelers, covering the entire MSG disk with a higher concentration over Europe and the North Atlantic flight corridor. COCOS outperforms the other known contrail detection algorithms in the literature for MSG. At similar recalls (the fraction of true positives correctly identified) it achieves precisions (the fraction of positive predictions that are correct) more than three times higher (0.65 for recall 0.25 and 0.3 for recall 0.5) than other MSG-based contrail detection algorithms, providing a significant improvement in contrail detection for MSG.

Abstract. Persistent contrails are a major contributor to the effective radiative forcing from aviation. Optimizing flight trajectories to avoid regions prone to the formation of warming contrails has therefore been proposed as a mitigation strategy to achieve the international climate targets for the aviation sector. However, the precise attribution of observed contrails to individual flights remains a challenge for both the evaluation of avoidance measures and the validation of contrail models. This study presents a fully automated evaluation framework designed for matching contrails to flights using observations from the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) instrument aboard the geostationary Meteosat Second Generation (MSG) satellite and flight trajectories. The method utilizes an automated contrail detection algorithm. Although such automated detections designed for MSG/SEVIRI often face a trade-off between high detection efficiency and low false alarm rate, the proposed matching process substantially improves detection reliability through a multi-step verification: contrails are preselected based on their spatial and temporal occurrence as well as their spatial orientation with respect to the flight trajectories, followed by temporal tracking. A new aspect is the development of a life cycle-based confidence score to derive a quantitative matching score. In contrast to previous approaches, the proposed method is entirely observation-driven, requires no model input, and enables rapid, large-scale application. The framework’s performance is demonstrated through two specific case studies. Future applications include the assessment of contrail avoidance trials across the Europe-African airspace and adaptation to other satellite configurations.

Abstract. The climate effect of aviation is significant and expected to increase. Reducing the sector’s environmental footprint to contribute to global temperature targets will require not only investments in airframe and engine technologies but also operational strategies such as eco-efficient flight routing, focusing on reducing non-CO2 effects. The D-KULT project (Demonstrator Climate and Environmentally Friendly Air Transport), funded under the German Federal Aviation Research Programme (LuFo), aims to demonstrate the feasibility of optimising flight trajectories with respect to climate effect. The project addresses a multi-objective optimisation problem in which flight trajectories minimise climate effects while maintaining operational and economic efficiency. Operational constraints such as meteorological hazards, regulatory requirements, airspace and airport capacity need to be incorporated to ensure real-world applicability. This work provides a comprehensive overview of the project, describing new developments and major challenges on implementation pathways and summarizes the key findings. D-KULT developed an end-to-end information chain integrating aviation weather forecasting, flight planning, air traffic control, and climate benefit assessment to enable eco-efficient flight routing for testing purposes. Achieving this complex operational and environmental objective required close collaboration across multiple disciplines and substantial upgrades to the majority of participating components. Novel aviation weather products were generated that estimate the climate sensitivity of emissions under prevailing meteorological conditions. Flight planning tools have been extended to take this information into account in addition to the standard data in the flight planning optimization algorithms. In this way, flight planning tools can calculate emissions and corresponding climate effects along flights, both as part of strategic (pre-departure) and tactical (pre-take-off and in-flight) eco-efficient flight optimisation. Developments within D-KULT were tested through a large-scale national contrail avoidance flight trial campaign, including enhanced satellite-based contrail detection methods and assessment and workflow implications in a high-fidelity simulator environment. Results demonstrate substantial progress toward operational climate-optimised aviation but also highlight remaining challenges, including uncertainties in weather forecast and non-CO2 climate effects, automation needs along the workflow and increased controller workload in dense airspaces. A key requirement for operational implementation is transparent information of prediction uncertainties, enabling informed decision-making when rerouting for climate benefit. These remaining research of achievable climate benefits. Further evaluation focused on operational integration, examining air traffic control procedures, and development needs form the basis for the planned successor programme to D-KULT.

Surface albedo is a crucial component of accurate radiative transfer simulations of Earth's system, playing a key role in calculating the planet's energy budget. The MODIS Surface Reflectance dataset (MCD43C3, Version 6.1) provides detailed albedo maps across seven spectral bands, enabling the monitoring of daily and yearly changes in planetary surface albedo. However, a comprehensive set of albedo maps covering the entire wavelength range is essential for simulating radiance spectra and accurately retrieving atmospheric and cloud properties in Earth's remote sensing. Braghiere et al. (2023) highlighted the impact of simplistic assumptions on albedo maps in Earth System Models, estimating a 3.55 W m-2 divergence in radiative forcing when using hyperspectral albedo maps instead of the commonly employed two broadband albedo value approach. They find that omitting the hyperspectral nature of Earth’s surface causes deviation in many climatological patterns, such as precipitation and surface temperature, over regional scales. We average the MODIS datasets over a 10-years period for different times of the year, obtaining a MODIS climatological dataset. Thanks to both high spatial and temporal resolution, we study albedo seasonal and spatial variability in the seven MODIS bands, obtaining estimates of the surface reflectivity as a function of space and time. This MODIS climatological average is the starting point to generate hyperspectral albedo maps using a Principal Component Analysis (PCA) regression algorithm. Combining different datasets of hyperspectral reflectance laboratory measurements for various dry soils, vegetation surfaces, and mixtures of both, we reconstruct the albedo maps in the entire wavelength range from 400 to 2500 nm. We obtain hyperspectral albedo maps with a spatial resolution of 0.05° in latitude and longitude, a spectral resolution of 10 nm, and a temporal resolution of 8 days. The hyperspectral albedo maps are validated against SEVIRI and TROPOMI land surface products. Using the spectral dimension of our albedo maps, we select different land surface types such as forests, deserts, cities and icy surfaces, and we integrate their spectral profiles over entire regions. In this way, it is possible to reconstruct regional spectral patterns which are the combination of typical vegetation and surface spectral features, like the Vegetation Red Edge. In addition, we study the seasonal variability of every region averaging spatially integrated spectra over three months period. From these seasonal spectra, we clearly see the impact of snow cover over different regions, the difference between wet and dry seasons over boreal forests and the formation of lakes over Greenland during the boreal summer. This hyperspectral albedo dataset will lead to more refined calculations of Earth's energy budget, its seasonal variability, and could be used to improve climate simulations.

Knowledge of humidity in the upper troposphere and lower stratosphere (UTLS) is of special interest due to its importance for cirrus cloud formation and its climate impact. However, the UTLS water vapor distribution in current weather models is subject to large uncertainties. Here, we develop a dynamic-based humidity correction method using an artificial neural network (ANN) to improve the relative humidity over ice (RHi) in ECMWF numerical weather predictions. The model is trained with time-dependent thermodynamic and dynamical variables from ECMWF ERA5 and humidity measurements from the In-service Aircraft for a Global Observing System (IAGOS). Previous and current atmospheric variables within ±2 ERA5 pressure layers around the IAGOS flight altitude are used for ANN training. RHi, temperature, and geopotential exhibit the highest impact on ANN results, while other dynamical variables are of low to moderate or high importance. The ANN shows excellent performance, and the predicted RHi in the UT has a mean absolute error (MAE) of 5.7 % and a coefficient of determination (R2) of 0.95, which is significantly improved compared to ERA5 RHi (MAE of 15.8 %; R2 of 0.66). The ANN model also improves the prediction skill for all-sky UT/LS and cloudy UTLS and removes the peak at RHi = 100 %. The contrail predictions are in better agreement with Meteosat Second Generation (MSG) observations of ice optical thickness than the results without humidity correction for a contrail cirrus scene over the Atlantic. The ANN method can be applied to other weather models to improve humidity predictions and to support aviation and climate research applications.

Contrail cirrus represents the most significant warming component within the total aviation impact on climate, suspected to exceed even the effects of aviation CO2 emissions. It remains to be shown that regulating hydrogen and sulfur content in aviation kerosene could help to reduce the climate impact from CO2 and from contrails, in order to allow for a science-based jet fuel standardization. Hence, this study conducts a model-based scenario analysis of the climate impact of the European fleet in 2019, exploring different levels of aromatic and sulfur reductions in fossil fuel-based kerosene as short-term mitigation measures.Using the Lagrangian plume model CoCiP within the open-source pycontrails package, we simulate contrail properties and energy forcing (EF) for a reference fleet using Jet-A1 fuel (13.8% hydrogen content) over Europe in 2019. For scenarios with increased hydrogen content (13.8%–15.4%, in 0.2% increments), reductions in non-volatile particulate matter (nvPM) emissions and changes in contrail properties—such as initial ice particle number, persistent contrail formation, age, optical depth, contrail coverage, and EF—are quantified. In parallel, sulfur content scenarios—including high and ultralow levels with increased and reduced soot activation fractions, as well as zero sulfur—are analyzed, to estimate the impact of sulfur-mediated elevated or reduced activation of aerosol into water droplets.The reference simulations compare well to previous studies. Furthermore, results show that increasing hydrogen content from 13.8% to 15.2% (the theoretical maximum) enhances the potential for persistent contrail formation from 13% by 6%, but reduces nvPM emission index from 1.22 x 1015 kg-1 by 61%, contrail age from 2.37 h by 20%, contrail optical depth from 0.12 by 24%, and contrail cirrus coverage from 0.67% by 34%. This leads to a reduction in total EF by up to by 52%. The high sulfur scenario increases contrail EF by up to 10%, while the ultralow scenario reduces EF by up to 14%. The simulation of the zero-sulfur content scenario represents the potential lower limit and serves as a pre-study for hydrogen combustion. These findings, part of the European Fuel Standard project by the European Union Aviation Safety Agency, demonstrate how improving fuel composition can mitigate aviation climate impact. These results highlight the potential of hydroprocessed and ultra-low sulfur kerosene as near-term solutions, providing actionable insights and implications for the development of aviation fuel standardization.

High ice water content (HIWC) conditions are a concern for aviation as the ingestion of ice particles in the jet engines can induce ice crystal icing (ICI), which results in performance loss and damage. To constantly monitor these conditions, retrievals for the detection of ICI were recently developed based on geostationary satellite imagery, but their calibration is limited to targeted flight campaigns or scattered samplings from ICI events databases. In this work, we close this gap, using exclusively remote sensing data to develop and assess a new retrieval for potential ICI conditions. Cloud IWC measurements are provided from the synergy of radar and lidar (DARDAR) on board the polar-orbiting satellites CloudSat and CALIPSO. HIWC conditions (IWC≥0.5 g m−3) at typical cruise altitudes are used as the proxy for areas with potential ICI formation. The HIWC conditions predictors are taken from a combination of observations and retrievals of the geostationary satellite Meteosat Second Generation (MSG). A random forest is trained and tested based on the collocated dataset of active and passive measurements during the summer months of 2013 and 2015, covering the European domain. The input predictors are the brightness temperature difference between the MSG channels at 6.2 and 10.8 µm wavelengths, the visible channel at 0.6 µm wavelength, the cloud optical thickness at 0.6 µm wavelength, and four convection metrics related to the distance to the closest convective cell, area extent of the convective cells, and convection density in the pixel surroundings. Over Europe, 83 % of HIWC conditions measured in the DARDAR dataset are correctly detected. The associated false alarm rate is 51 %. The retrieval is further tested with the ICI events database reported by Lufthansa. Four out of seven events are correctly detected. In conclusion, the retrieval achieves performances comparable to previously developed retrievals. An operational application would enable aircraft rerouting around areas with high ICI probability.

While carbon dioxide emissions from aviation often dominate climate change discussions, non-CO2 effects such as contrails and contrail cirrus must also be considered. Despite varying estimates of their radiative forcing, avoiding contrails is a reasonable strategy for reducing aviation’s climate effects. This study examines temperature and humidity, key atmospheric parameters for contrail formation, across different ECHAM/MESSy (European Centre Hamburg General Circulation Model/Modular Earth Submodel System) Atmospheric Chemistry (EMAC) model setups. EMAC, a general circulation model, is evaluated with various vertical resolutions and two nudging methods across seven specified dynamics setups. A higher vertical resolution aims to capture steep water vapour gradients near the tropopause, crucial for accurate contrail prediction. Comparisons with reanalysis data (March–April 2014) indicate a systematic cold bias (approximately 3–5 K in mid-latitudes), particularly in setups without mean temperature nudging. In the upper troposphere and lower stratosphere, all simulations exhibit a wet bias, while lower altitudes display a dry bias, both affecting contrail formation estimates. Point-by-point comparisons along aircraft trajectories confirm similar biases. Sensitivity experiments with varying thresholds of relative humidity over ice illustrate trade-offs between achieving high hit rates and minimising false alarms in contrail detection. A single-day case study integrating aircraft and satellite observations demonstrates that EMAC’s predicted contrail coverage aligns well with the observed formation. These results suggest that, despite existing temperature and humidity biases, EMAC generally captures regions favourable for contrail formation across diverse atmospheric conditions. Addressing model biases by refining temperature and humidity representation could significantly improve contrail prediction accuracy, strengthening contrail-avoidance strategies and supporting climate-optimised flight routing to mitigate aviation's overall climate effect.

Accurately determining and reducing the climate impact of aviation and its uncertainties is one of the pressing challenges of our times. Contrail cirrus are estimated to contribute more than half of the total effective radiative forcing from aviation, yet the uncertainties in their optical and radiative properties are large. In contrast to midlatitude cirrus, high-latitude cirrus are less anthropogenically influenced; thus, they are more pristine. However, little is known about Arctic cirrus properties and their role in the amplified warming of this region. The Cirrus in High Latitudes (CIRRUS-HL) mission using the High Altitude and Long Range Research Aircraft (HALO) provides measurements in mid-and high latitudes during summer (June/July) 2021, exploiting HALO's capabilities and a comprehensive cloud-aerosol-trace gas and radiation instrumentation. The results of 24 HALO flights provide new insights into both natural cirrus and contrail cirrus properties in high (60 degrees-76 degrees N) and midlatitudes (38 degrees-60 degrees N). In particular, we find lower ice water content (-42%) and lower number concentrations (-88%) of cirrus particles with larger mean diameters (+22%) in high latitudes. Ice supersaturated regions were frequently observed in mid-and high latitudes, with median in-cloud relative humidity over ice between 105% and 122%. Mean aerosol number concentrations in the midlatitudes were reduced by up to 80% compared to pre-COVID-19 times. Less air traffic during the COVID-19 lockdowns, reduced contrail cirrus coverage, and lower ice nucleating particle concentrations in high latitudes help to explain the observed differences in cirrus properties. The extensive dataset will be used to improve weather and climate models. SIGNIFICANCE STATEMENT: In contrast to Arctic cirrus, midlatitude cirrus are more often modi-fied by human activities, of which air traffic is a significant contributor through the formation of contrails and contrail cirrus. These man-made cirrus warm Earth, but to constrain their effects on climate, in situ and remote sensing measurements were conducted with the German research aircraft High Altitude and Long Range Research Aircraft (HALO). During 24 flights, we used HALO's exceptional altitude and distance range to sample and contrast different cirrus types from the dense air traffic regions to the remote Arctic regions. We find that microphysical properties of high-and midlatitude cirrus differ substantially, related to their formation pathway, the abundance of air traffic, and the availability of ice nucleating particles. The measurements will help to validate contrail cirrus and climate models.

Contrail cirrus is estimated to be the largest contributor to global effective radiative forcing from aviation, surpassing even aviation CO2 emissions in their impact. One promising mitigation strategy is contrail avoidance by rerouting flights to avoid regions where warming contrails can persist. These regions are forecast with numerical contrail models fed with weather prediction model output. Satellite imagery presents a good opportunity to evaluate both the models and the success of the mitigation strategy.An automatic contrail detection algorithm was implemented by Mannstein et al. (1999) for a polar orbiting satellite with high spatial resolution (≈ 1 km and used two thermal channels in the atmospheric window). Since then, it has been adapted to the Meteosat Second Generation (MSG) satellite because geostationary satellites have the big advantage of high temporal coverage of a large area. However, its lower spatial resolution (≥ 3 km) is a challenge for contrail detection. In recent years AI algorithms have been presented for the American geostationary GOES-R/S satellites (spatial resolution ≥ 2 km). In this study, a new improved contrail detection algorithm for MSG is proposed based on image processing.To establish a new detection algorithm a labeled dataset was compiled. This labeled dataset contains 140 MSG images with data from the years 2013-2018 and 2023-2024. This data volume is very suitable to develop and evaluate an algorithm with a classical approach, would however not be sufficient to train an AI based algorithm. The data covers the whole MSG disk with a wide distribution of satellite viewing angles, cloud cover, number of contrails in the image and other properties. Each image was labeled by three individuals, and a common contrail mask was established as the consensus of the labelers. Based on this dataset, a new detection algorithm was developed. Making use of the spectral information of MSG, an image is created as input for the algorithm where contrail visibility is enhanced. The algorithm takes advantage of several new techniques compared to Mannstein et al. (1999). In addition to the contrail mask, uncertainty information is provided.This new algorithm for contrail detection in MSG images demonstrates superior performance compared to the previous algorithm for MSG based on the Mannstein approach with a probability of detection higher than 70%. The contrail detection algorithm can now be employed for generating datasets to evaluate contrail models as well as to assess the success of contrail mitigation strategies such as flight path alteration. Thanks to this classical image processing approach, in the future the algorithm can be adapted to other satellites like Meteosat Third Generation.

Surface albedo is an important parameter in radiative-transfer simulations of the Earth's system as it is fundamental for correctly calculating the energy budget of the planet. The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on NASA's Terra and Aqua satellites continuously monitor daily and yearly changes in reflection at the planetary surface. The MODIS Surface Reflectance Black-Sky Albedo dataset (version 6.1 of MCD43D) provides detailed albedo maps for seven spectral bands in the visible and near-infrared range. These albedo maps allow us to classify different Lambertian surface types and their seasonal and yearly variability and change, albeit only into seven spectral bands. However, a complete set of albedo maps covering the entire wavelength range is required to simulate radiance spectra and correctly retrieve atmospheric and cloud properties from remote sensing observations of the Earth. We use a principal component analysis (PCA) regression algorithm to generate hyperspectral albedo maps of the Earth. By combining different datasets containing laboratory measurements of hyperspectral reflectance for various dry soils, vegetation surfaces, and mixtures of both, we reconstruct albedo maps across the entire wavelength range from 400 to 2500 nm. The PCA method is trained with a 10-year average of MODIS data for each day of the year. We obtain hyperspectral albedo maps with a spatial resolution of 0.05° in latitude and longitude, a spectral resolution of 10 nm, and a temporal resolution of 1 d (day). Using the hyperspectral albedo maps, we estimate the spectral profiles of different land surfaces, such as forests, deserts, cities, and icy surfaces, and study their seasonal variability. These albedo maps will enable us to refine calculations of the Earth's energy budget and its seasonal variability and improve climate simulations.

Abstract. Surface albedo is an important parameter in radiative transfer simulations of the Earth's system, as it is fundamental to correctly calculate the energy budget of the planet. The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on NASA's Terra and Aqua satellites continuously monitor daily and yearly changes in reflection at the planetary surface. The MODIS Surface Reflectance dataset (MCD43C3, Version 6.1) gives detailed albedo maps in seven different spectral bands in the visible and near-infrared range. These albedo maps allow us to classify different Lambertian surface types and their seasonal and yearly variability and change, albeit only in seven spectral bands. However, a complete set of albedo maps covering the entire wavelength range is required to simulate radiance spectra, and to correctly retrieve atmospheric and cloud properties from Earth's remote sensing. We use a Principal Component Analysis (PCA) regression algorithm to generate hyperspectral albedo maps of Earth. Combining different datasets of hyperspectral reflectance laboratory measurements for various dry soils, vegetation surfaces, and mixtures of both, we reconstruct the albedo maps in the entire wavelength range from 400 to 2500 nm. The PCA method is trained with a 10-years average of MODIS data for different times of the year. We obtain hyperspectral albedo maps with a spatial resolution of 0.05° in latitude and longitude, a spectral resolution of 10 nm, and a temporal resolution of 8 days. Using the hyperspectral albedo maps, we estimate the spectral profiles of different land surfaces, such as forests, deserts, cities and icy surfaces, and study their seasonal variability. These albedo maps shall enable to refine calculations of Earth's energy budget, its seasonal variability, and improve climate simulations.

This study investigates the sensitivity of two brightness temperature differences (BTDs) in the infrared (IR) window of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) to various cloud parameters in order to understand their information content, with a focus on cloud thermodynamic phase. To this end, this study presents radiative transfer calculations, providing an overview of the relative importance of all radiatively relevant cloud parameters, including thermodynamic phase, cloud-top temperature (CTT), optical thickness (τ), effective radius (Reff), and ice crystal habit. By disentangling the roles of cloud absorption and scattering, we are able to explain the relationships of the BTDs to the cloud parameters through spectral differences in the cloud optical properties. In addition, an effect due to the nonlinear transformation from radiances to brightness temperatures contributes to the specific characteristics of the BTDs and their dependence on τ and CTT. We find that the dependence of the BTDs on phase is more complex than sometimes assumed. Although both BTDs are directly sensitive to phase, this sensitivity is comparatively small in contrast to other cloud parameters. Instead, the primary link between phase and the BTDs lies in their sensitivity to CTT (or more generally the surface–cloud temperature contrast), which is associated with phase. One consequence is that distinguishing high ice clouds from low liquid clouds is straightforward, but distinguishing mid-level ice clouds from mid-level liquid clouds is challenging. These findings help to better understand and improve the working principles of phase retrieval algorithms.

Abstract. While carbon dioxide emissions from aviation often dominate climate change discussions, the significant impact of non-CO2 effects like contrails and contrail-cirrus must not be overlooked, particularly for the mitigation of climate effects. This study evaluates key atmospheric parameters influencing contrail formation, specifically temperature and humidity, using various model setups of a general circulation model (GCM) with different vertical resolutions and two nudging methods for specified dynamics setups. Comparing simulation results with reanalysis data for March and April 2014 reveals a systematic cold bias in mean temperatures across all altitudes and latitudes, particularly in the mid-latitudes where the bias is about 3–5 K, unless mean temperature nudging is applied. In the upper-troposphere/lower stratosphere, the humidity of the nudged GCM simulations shows a wet bias, while a dry bias is observed at lower altitudes. These biases result in overestimated regions for contrail formation in GCM simulations compared to reanalysis data. A point-by-point comparison along flown trajectories with measurement aircraft data shows similar biases. Exploring relative humidity over ice (RHice) threshold values for identifying ice-supersaturation regions provides insights into the risks of false alarms for contrail formation, together with information on hit rates. Accepting a false alarm rate of 16 % results in a hit rate of about 40 % (RHice threshold 99 %), while aiming for an 80 % hit rate increases the false alarm rate to at least 35 % (RHice threshold 91–94 %). A comprehensive one-day case study, involving aircraft-based observations and satellite data, confirms contrail detection in regions identified as potential contrail coverage areas by the GCM.

The supercooled liquid fraction (SLF) in mixed-phase clouds (MPCs) is an essential variable of cloud microphysical processes and climate sensitivity. However, the SLF is currently calculated in spaceborne remote sensing only as the cloud phase–frequency ratio of adjacent pixels, which results in a loss of the original resolution in observations of cloud liquid or ice content within MPCs. Here, we present a novel method for retrieving the SLF in MPCs based on the differences in radiative properties of supercooled liquid droplets and ice particles at visible (VIS) and shortwave infrared (SWI) channels of the geostationary Himawari-8. Liquid and ice water paths are inferred by assuming that clouds are composed of only liquid or ice, with the real cloud water path (CWP) expressed as a combination of these two water paths (SLF and 1-SLF as coefficients), and the SLF is determined by referring to the CWP from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The statistically relatively small cloud phase spatial inhomogeneity at a Himawari-8 pixel level indicates an optimal scene for cloud retrieval. The SLF results are comparable to global SLF distributions observed by active instruments, particularly for single-layered cloud systems. While accessing the method's feasibility, SLF averages are estimated between 74 % and 78 % in Southern Ocean (SO) stratocumulus across seasons, contrasting with a range of 29 % to 32 % in northeastern Asia. The former exhibits a minimum SLF around midday in summer and a maximum in winter, while the latter trend differs. This novel algorithm will be valuable for research to track the evolution of MPCs and constrain the related climate impact.

Abstract. This study investigates the sensitivity of two brightness temperature differences (BTDs) in the infrared (IR) window of the SEVIRI imager to various cloud parameters in order to better understand their information content, with a focus on cloud thermodynamic phase. To this end, this study presents radiative transfer calculations, providing an overview of the relative importance of all radiatively relevant cloud parameters, including thermodynamic phase, cloud top temperature (CTT), optical thickness (τ), effective radius (Reff) and ice crystal habit. By disentangling the roles of cloud absorption and scattering, we are able to explain the relationships of the BTDs to the cloud parameters on the one hand by spectral differences in the cloud optical properties. In addition, an effect due to the nonlinear transformation from radiances to brightness temperatures contributes to the specific characteristics of the BTDs and their dependence on τ and CTT. We find that the dependence of the BTDs on phase is more complex than sometimes assumed. Although both BTDs are directly sensitive to phase, this sensitivity is comparatively small in contrast to other cloud parameters. Instead, the primary link between phase and the BTDs lies in their sensitivity to CTT, which is associated with phase. One consequence is that distinguishing high ice clouds from low liquid clouds is straightforward, but distinguishing mid-level ice clouds from mid-level liquid clouds is challenging. These findings help to better understand and improve the working principles of phase retrieval algorithms.

A comprehensive understanding of the cloud thermodynamic phase is crucial for assessing the cloud radiative effect and is a prerequisite for remote sensing retrievals of microphysical cloud properties. While previous algorithms mainly detected ice and liquid phases, there is now a growing awareness for the need to further distinguish between warm liquid, supercooled and mixed-phase clouds. To address this need, we introduce a novel method named ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI), which enables cloud detection and the determination of cloud-top phase using SEVIRI (Spinning Enhanced Visible and Infrared Imager), the geostationary passive imager aboard Meteosat Second Generation. ProPS discriminates between clear sky, optically thin ice (TI) cloud, optically thick ice (IC) cloud, mixed-phase (MP) cloud, supercooled liquid (SC) cloud and warm liquid (LQ) cloud. Our method uses a Bayesian approach based on the cloud mask and cloud phase from the lidar-radar cloud product DARDAR (liDAR/raDAR). The validation of ProPS using 6 months of independent DARDAR data shows promising results: the daytime algorithm successfully detects 93 % of clouds and 86 % of clear-sky pixels. In addition, for phase determination, ProPS accurately classifies 91 % of IC, 78 % of TI, 52 % of MP, 58 % of SC and 86 % of LQ clouds, providing a significant improvement in accurate cloud-top phase discrimination compared to traditional retrieval methods.