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Aircraft contrails are not just streaks in clear blue skies - they represent a significant source of warming from the aviation sector. Two new studies reveal that their climate impact is more complex than previously thought, as many contrails may form within existing cirrus clouds – a factor often overlooked in past assessments. Drawing on aircraft, satellite and meteorological data, Petzold et al. and Seelig et al. provide fresh insights into the occurrence frequency and the radiative properties of these often “hidden” contrails.

The cloud thermodynamic phase is a crucial parameter to understand the Earth’s radiation budget, the hydrological cycle, and atmospheric thermodynamic processes. Spaceborne active remote sensing such as the synergistic radar-lidar DARDAR product is considered the most reliable method to determine cloud phase; however, it lacks large-scale observations and high repetition rates. These can be provided by passive instruments such as SEVIRI aboard the geostationary Meteosat Second Generation (MSG) satellite, but passive remote sensing of the thermodynamic phase is challenging and confined to cloud top. Thus, it is necessary to understand to what extent passive sensors with the characteristics of SEVIRI are expected to provide a relevant contribution to cloud phase investigation. To reach this goal, we collect five years of DARDAR data to model the cloud top phase (CTP) for MSG/SEVIRI and create a SEVIRI-like CTP through an elaborate aggregation procedure. Thereby, we distinguish between ice (IC), mixed-phase (MP), supercooled (SC), and warm liquid (LQ). Overall, 65% of the resulting SEVIRI pixels are cloudy, consisting of 49% IC, 14% MP, 13% SC, and 24% LQ cloud tops. The spatial resolution has a significant effect on the occurrence of CTP, especially for MP cloud tops, which occur significantly more often at the lower SEVIRI resolution than at the higher DARDAR resolution (9%). We find that SC occurs most frequently at high southern latitudes, while MP is found mainly in both high southern and high northern latitudes. LQ dominates in the subsidence zones over the ocean, while IC occurrence dominates everywhere else. MP and SC show little seasonal variability apart from high latitudes, especially in the south. IC and LQ are affected by the shift of the Intertropical Convergence Zone. The peak of occurrence of SC is at −3 ∘C, followed by that for MP at −13 ∘C. Between 0 and −27 ∘C, the occurrence of SC and MP dominates IC, while below −27 ∘C, IC is the most frequent CTP. Finally, the occurrence of cloud top height (CTH) peaks lower over the ocean than over land, with MP, SC, and IC being undistinguishable in the tropics but with separated CTH peaks in the rest of the MSG disk. Finally, we test the ability of a state-of-the-art AI-based ice cloud detection algorithm for SEVIRI named CiPS (Cirrus Properties for SEVIRI) to detect cloud ice. We confirm previous evaluations with an ice detection probability of 77.1% and find a false alarm rate of 11.6%, of which 68% are due to misclassified cloud phases. CiPS is not sensitive to ice crystals in MP clouds and therefore not suitable for the detection of MP clouds but only for fully glaciated (i.e., IC) clouds. Our study demonstrates the need for the development of dedicated cloud phase distinction algorithms for all cloud phases (IC, LQ, MP, SC) from geostationary satellites.

Contrail cirrus account for the major share of aviation’s climate impact. Yet, the links between jet fuel composition, contrail microphysics and climate impact remain unresolved. Here we present unique observations from two DLR-NASA aircraft campaigns that measured exhaust and contrail characteristics of an Airbus A320 burning either standard jet fuels or low aromatic sustainable aviation fuel blends. Our results show that soot particles can regulate the number of contrail cirrus ice crystals for current emission levels. We provide experimental evidence that burning low aromatic sustainable aviation fuel can result in a 50 to 70% reduction in soot and ice number concentrations and an increase in ice crystal size. Reduced contrail ice numbers cause less energy deposition in the atmosphere and less warming. Meaningful reductions in aviation’s climate impact could therefore be obtained from the widespread adoptation of low aromatic fuels, and from regulations to lower the maximum aromatic fuel content.

Aerosol‐cloud interactions (ACI) pose the largest uncertainty for climate projection. Among many challenges of understanding ACI, the question of whether ACI can be deterministically predicted has not been explicitly answered. Here we attempt to answer this question by predicting cloud droplet number concentration Nc${N}_{c}$from aerosol number concentration Na${N}_{a}$and ambient conditions using a data‐driven framework. We use aerosol properties, vertical velocity fluctuations, and meteorological states from the ACTIVATE field observations (2020–2022) as predictors to estimate Nc${N}_{c}$ . We show that the campaign‐wide Nc${N}_{c}$can be successfully predicted using machine learning models despite the strongly nonlinear and multi‐scale nature of ACI. However, the observation‐trained machine learning model fails to predict Nc${N}_{c}$in individual cases while it successfully predicts Nc${N}_{c}$of randomly selected data points that cover a broad spatiotemporal scale. This suggests that, within a data‐driven framework, the Nc${N}_{c}$prediction is uncertain at fine spatiotemporal scales. Plain Language Summary Ambient aerosol particles act as seeds for ice crystals and cloud droplets that form clouds. Both aerosols and clouds regulate the energy and water budgets of the Earth via radiative and cloud micro/macro‐processes. This is the so‐called aerosol‐cloud interactions (ACI). ACI remains the source of the largest uncertainty for accurate climate projections, due to incomplete understanding of nonlinear multi‐scale processes, limited observations across various cloud regimes, and insufficient computational power to resolve them in models. Quantifying the relation between the cloud droplet Nc$\\left({N}_{c}\\right)$and aerosol Na$\\left({N}_{a}\\right)$number concentration has been a central challenge of understanding and representing ACI. In this work, we tackle this challenge by predicting Nc${N}_{c}$from observations made during the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) using machine learning models. We show that the climatological Nc${N}_{c}$can be successfully predicted despite the strongly nonlinear and multi‐scale nature of ACI. However, the observation‐trained machine learning model fails to predict Nc${N}_{c}$at fine spatiotemporal scales. Key Points Three‐year in situ measurements (179 flights) provide adequate data to train and validate a random forest model (RFM) to study aerosol‐cloud interactions The RFM can successfully predict cloud droplet number concentration Nc${N}_{c}$and identify importance of key predictors Data‐driven Nc${N}_{c}$prediction in individual cases shows strong dependency on sampling strategy

Ambient weather conditions strongly impact contrail formation and persistence. The implementation of contrail avoidance and mitigation strategies, therefore, requires regional and altitude-dependent information on the frequency of contrail occurrence. To this end, we have developed a method to quantify the potential contrail cover based on 10 years of high-resolution reanalysis of climatology and weather data from the European Center for Medium-Range Weather Forecast (ECMWF). We use the Schmidt–Appleman threshold temperature for contrail formation and additionally select thresholds for the relative humidity to evaluate the occurrence of persistent contrails and assess their regional and seasonal variation. We find a potential contrail cirrus cover of 10% to 20% above Europe at higher altitudes of 200 and 250 hPa in the 10-year climatology and a weak seasonal variation. At lower altitudes, near 300 hPa, a steep onset and a high potential contrail cirrus cover of 20% is found in late fall and in winter, decreasing to 2% potential contrail cirrus cover in summer. In comparison to ECMWF data, evaluations using data from the National Centers for Environmental Prediction (NCEP) show a significantly lower potential contrail cirrus cover. Our results help to investigate the seasonal and altitude dependence of contrail mitigation strategies, in particular for warming nighttime contrails that contribute strongly to the total climate impact from aviation.

Volcanic ash clouds are a threat to air traffic security and, thus, can have significant societal and financial impact. Therefore, the detection and monitoring of volcanic ash clouds to enhance the safety of air traffic is of central importance. This work presents the development of the new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) which is based on artificial neural networks, the thermal channels of the geostationary sensor MSG/SEVIRI and auxiliary data from a numerical weather prediction model. It derives a pixel classification as well as cloud top height, effective particle radius and, indirectly, the mass column concentration of volcanic ash clouds during day and night. A large set of realistic one-dimensional radiative transfer calculations for typical atmospheric conditions with and without generic volcanic ash clouds is performed to create the training dataset. The atmospheric states are derived from ECMWF data to cover the typical diurnal, annual and interannual variability. The dependence of the surface emissivity on surface type and viewing zenith angle is considered. An extensive dataset of volcanic ash optical properties is used, derived for a wide range of microphysical properties and refractive indices of various petrological compositions, including different silica contents and glass-to-crystal ratios; this constitutes a major innovation of this retrieval. The resulting ash-free radiative transfer calculations at a specific time compare well with corresponding SEVIRI measurements, considering the individual pixel deviations as well as the overall brightness temperature distributions. Atmospheric gas profiles and sea surface emissivities are reproduced with a high agreement, whereas cloudy cases can show large deviations on a single pixel basis (with 95th percentiles of the absolute deviations > 30 K), mostly due to different cloud properties in model and reality. Land surfaces lead to large deviations for both the single pixel comparison (with median absolute deviations > 3 K) and more importantly the brightness temperature distributions, most likely due to imprecise skin temperatures. The new method enables volcanic ash-related scientific investigations as well as aviation security-related applications.

Volcanic ash clouds can damage aircrafts during flight and, thus, have the potential to disrupt air traffic on a large scale, making their detection and monitoring necessary. The new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) using the geostationary instrument MSG/SEVIRI and artificial neural networks is introduced in a companion paper. It performs pixelwise classifications and retrieves (indirectly) the mass column concentration, the cloud top height and the effective particle radius. VACOS is comprehensively validated using simulated test data, CALIOP retrievals, lidar and in situ data from aircraft campaigns of the DLR and the FAAM, as well as volcanic ash transport and dispersion multi model multi source term ensemble predictions. Specifically, emissions of the eruptions of Eyjafjallajökull (2010) and Puyehue-Cordón Caulle (2011) are considered. For ash loads larger than 0.2 g m−2 and a mass column concentration-based detection procedure, the different evaluations give probabilities of detection between 70% and more than 90% at false alarm rates of the order of 0.3–3%. For the simulated test data, the retrieval of the mass load has a mean absolute percentage error of ~40% or less for ash layers with an optical thickness at 10.8 μm of 0.1 (i.e., a mass load of about 0.3–0.7 g m−2, depending on the ash type) or more, the ash cloud top height has an error of up to 10% for ash layers above 5 km, and the effective radius has an error of up to 35% for radii of 0.6–6 μm. The retrieval error increases with decreasing ash cloud thickness and top height. VACOS is applicable even for overlaying meteorological clouds, for example, the mean absolute percentage error of the optical depth at 10.8 μm increases by only up to ~30%. Viewing zenith angles >60° increase the mean percentage error by up to ~20%. Desert surfaces are another source of error. Varying geometrical ash layer thicknesses and the occurrence of multiple layers can introduce an additional error of about 30% for the mass load and 5% for the cloud top height. For the CALIOP data, comparisons with its predecessor VADUGS (operationally used by the DWD) show that VACOS is more robust, with retrieval errors of mass load and ash cloud top height reduced by >10% and >50%, respectively. Using the model data indicates an increase in detection rate in the order of 30% and more. The reliability under a wide spectrum of atmospheric conditions and volcanic ash types make VACOS a suitable tool for scientific studies and air traffic applications related to volcanic ash clouds.

This study places HALO research aircraft observations in the upper-tropospheric Asian summer monsoon anticyclone (ASMA) into the context of regional, intra-annual variability by hindcasts with the ECHAM/MESSy Atmospheric Chemistry (EMAC) model. The observations were obtained during the Earth System Model Validation (ESMVal) campaign in September 2012. Observed and simulated tracer–tracer relations reflect photochemical O3 production as well as in-mixing from the lower troposphere and the tropopause layer. The simulations demonstrate that tropospheric trace gas profiles in the monsoon season are distinct from those in the rest of the year, and the measurements reflect the main processes acting throughout the monsoon season. Net photochemical O3 production is significantly enhanced in the ASMA, where uplifted precursors meet increased NOx, mainly produced by lightning. An analysis of multiple monsoon seasons in the simulation shows that stratospherically influenced tropopause layer air is regularly entrained at the eastern ASMA flank and then transported in the southern fringe around the interior region. Radial transport barriers of the circulation are effectively overcome by subseasonal dynamical instabilities of the anticyclone, which occur quite frequently and are of paramount importance for the trace gas composition of the ASMA. Both the isentropic entrainment of O3-rich air and the photochemical conversion of uplifted O3-poor air tend to increase O3 in the ASMA outflow.

Two airborne field campaigns focusing on observations of Arctic mixed-phase clouds and boundary layer processes and their role with respect to Arctic amplification have been carried out in spring 2019 and late summer 2020 over the Fram Strait northwest of Svalbard. The latter campaign was closely connected to the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Comprehensive datasets of the cloudy Arctic atmosphere have been collected by operating remote sensing instruments, in-situ probes, instruments for the measurement of turbulent fluxes of energy and momentum, and dropsondes on board the AWI research aircraft Polar 5. In total, 24 flights with 111 flight hours have been performed over open ocean, the marginal sea ice zone, and sea ice. The datasets follow documented methods and quality assurance and are suited for studies on Arctic mixed-phase clouds and their transformation processes, for studies with a focus on Arctic boundary layer processes, and for satellite validation applications. All datasets are freely available via the world data center PANGAEA.Measurement(s)navigation data • temperature of air • atmospheric humidity • radar reflectivity • brightness temperature • cloud properties • radiation • atmospheric windTechnology Type(s)GPS navigation system • dropsondes • radar • microwave radiometer • particle count and size analyzer • imager • noseboom sensorsFactor Type(s)temporal • locationSample Characteristic - EnvironmentatmosphereSample Characteristic - LocationArctic Ocean region

The Future Fuels project combines research in several institutes of the German Aerospace Center (DLR) on the production and use of synthetic fuels for space, energy, transportation, and aviation. This article gives an overview of the research questions considered and results achieved so far and also provides insight into the multidimensional and interdisciplinary project approach. Various methods and models were used which are embedded in the research context and based on established approaches. The prospects for large-scale fuel production using renewable electricity and solar radiation played a key role in the project. Empirical and model-based investigations of the technological and cost-related aspects were supplemented by modelling of the integration into a future electricity system. The composition, properties, and the related performance and emissions of synthetic fuels play an important role both for potential oxygenated drop-in fuels in road transport and for the design and certification of alternative aviation fuels. In addition, possible green synthetic fuels as an alternative to highly toxic hydrazine were investigated with different tools and experiments using combustion chambers. The results provide new answers to many research questions. The experiences with the interdisciplinary approach of Future Fuels are relevant for the further development of research topics and co-operations in this field.