How to cite: Schimmel, W., Senf, F., Stoll, J., Ohneiser, K., Seifert, P., Henneberger, J., Lohmann, U., Spirig, R., Ramelli, F., Fuchs, C., Miller, A., Zhang, H., and Omanovic, N.: Combined Remote-Sensing, In-Situ and Modelling of Cloud Microphysical Perturbations in Supercooled Stratus Clouds, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12739, https://doi.org/10.5194/egusphere-egu25-12739, 2025.

The ice phase is a major contributor to precipitation formation over continents, as ice hydrometeors efficiently grow to large enough sizes for them to sediment. Several mechanisms underlie the growth of ice crystals, with one being the growth through vapor deposition onto the ice crystal. The speed of this growth depends next to temperature also on the availability of water vapor, one source being cloud droplets in mixed-phase clouds that locally may experience water-subsaturated conditions. This is referred to as the Wegener-Bergeron-Findeisen (WBF) process and describes the ability of ice crystals to grow at the expense of cloud droplets. While the presence of the WBF process is established, the actual growth rates of ice crystals in such conditions remain ambiguous. We conducted field experiments within the CLOUDLAB project with the goal to infer ice crystal growth rates in naturally occurring supercooled clouds through local perturbations from cloud seeding. A unique dataset was collected describing the characteristics of cloud droplets and ice crystals in the probed clouds, including their sizes, number concentrations, and ice and water contents.

Here, we combine large-eddy simulations (LES) in 65 m horizontal resolution with online Lagrangian trajectories to achieve a more straightforward comparison to our observations. We show that the model simulations can reproduce the field experiments in terms of ice crystal number concentrations. However, both the simulated changes in the liquid phase as a consequence of the WBF process and the ice crystal growth rates are underestimated compared to the observations. We perform a series of sensitivity studies on the vapor depositional growth equation of ice crystals given the uncertainty and simplification of two parameters of that equation.  We find that an increase of the vapor deposition efficiency up to a factor of three achieves comparable growth rates. However, matching growth rates in the model and observations does not lead to coinciding changes in the liquid phase, i.e., the WBF process remains too slow. We identify two limitations of our approach: (i) the simulated and actual water vapor fields may differ and (ii) our LES are still too coarse to fully capture the small-scale interactions between the liquid and ice phases. This study highlights the synergy of high-resolution model simulations and field observations for investigating a fundamental cloud process. Our results provide insights for future mixed-phase cloud modeling studies. 

How to cite: Omanovic, N., Ferrachat, S., Fuchs, C., Ramelli, F., Henneberger, J., Miller, A. J., Spirig, R., Zhang, H., and Lohmann, U.: Chasing ice crystals: Lagrangian trajectories in ICON-LES for investigating liquid and ice phase interactions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6543, https://doi.org/10.5194/egusphere-egu25-6543, 2025.

Clouds remain a major source of uncertainty in climate projections, particularly due to complexities in aerosol-cloud interactions. To improve the representation of mixed-phase clouds in EC-Earth3, the model's heterogeneous ice nucleation scheme has been updated. The previous temperature-based parameterization has been replaced with aerosol- and temperature-sensitive immersion freezing schemes for mixed-phase clouds that consider ice-active desert dust minerals (K-feldspar and quartz) and marine organic aerosols, both explicitly tracked in EC-Earth3. Additionally, a secondary ice production scheme based on a random forest regressor further enhances the ice crystal concentrations.

The updated model is evaluated against an extensive observational dataset of ice-nucleating particle (INP) concentrations, satellite observations of cloud properties (MODIS and CALIPSO), and both Top of the Atmosphere (TOA) and surface radiative Cloud Radiative Effect (CRE) flux components from CERES-EBAF. The impact of the updates is analysed relative to the previous temperature dependent parameterization.

Results from 12-year (2009-2020) nudged simulations show improved agreement with INP observations using the updated aerosol-aware scheme compared to the earlier approach. The ice nucleation parameterization clearly links simulated ice crystal number concentrations with aerosol emission sources and transported pathways. Despite remaining biases largely attributed to other processes, this update improves consistency with MODIS and CALIPSO retrieved data, including total cloud cover, low/mid/high cloud area percentages, liquid and ice cloud fractions, and water paths. Sensitivity analyses reveal that the new scheme impacts global cloud cover, liquid and ice water content, temperature, and radiative balances. Evaluation with CERES-EBAF indicates that the new parameterization reduces surface net CRE bias at mid-to-high latitudes while slightly increasing bias at low latitudes, despite no specific model tuning for this configuration.

Our approach offers potential enhancements in future climate projections using EC-Earth3-AerChem and future generations of the model.

How to cite: Costa-Surós, M., Gonçalves, M., Chatziparaschos, M., Georgakaki, P., Myriokefalitakis, S., Van Noije, T., Le Sager, P., Kanakidou, M., Nenes, A., and Pérez García-Pando, C.: Aerosol-Driven Parameterization of Ice Nucleation and Secondary Ice Processes in EC-Earth3: Evaluation and Climate Impacts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16830, https://doi.org/10.5194/egusphere-egu25-16830, 2025.

Secondary ice production (SIP) refers to a series of physical mechanisms that significantly increase ice number concentrations above those from primary ice production (PIP) via ice nucleating particles (INP). In-cloud observations have provided increasing evidence of SIP in mixed-phase stratiform and convective clouds at different latitudes. The presence of fragmented frozen drops and small columnar particles in measurements from holographic particle imaging systems is consistent with findings of laboratory experiments focused on rime splintering (RS), droplet shattering (DS), and ice-ice collisional breakup (IIBR) mechanisms. Since SIP rates are driven by the relative size of interacting hydrometeors, there is a need to understand which microphysical conditions trigger which mechanism in realistic atmospheric conditions where cloud micro physics are constrained by aerosols. Without describing aerosol-hydrometeor interactions, the majority of cloud modelling tools are limited to prescribed size distributions and process rates that may fail giving proper description of ice formation via primary and secondary pathways missing important links to others such as secondary activation and aerosol invigoration.
In this study we offer insights on aerosol-induced effects on SIP rates by coupling results from aerosol-aware large-eddy- simulations and in-cloud observations of a deep convective cloud case studied in the SPICULE campaign in the Southern Great Plains (USA) on June 05 2021. We employed UCLALES-SALSA, an LES model with sectional representation of aerosol microphysics to resolve rates for PIP via immersion freezing with time evolving contact angle distribution and SIP via droplet shattering (DS), and ice-ice collisional breakup (IIBR). After model initialization with observed atmospheric soundings and aerosol concentrations, we were able to reproduce observed trends in cloud properties including boundaries and vertical profiles of droplet and ice particle size distributions. The model was able to emulate the observed ice multiplication in the rising cloud tower indicating a positive feedback between SIP-DS and SIP-IIBR processes which in turn increased convection intensity through mixed-phase invigoration at the upper level and finally lead to glaciation and precipitation via seeder-feeder mechanism. Both, the convective available energy (CAPE) and the level of neutral buoyancy (LNB), were adjusted to reach model closure in the cloud tower. We also compared simulations differing in the aerosol number concentration in the accumulation mode used for model initialization and found that increasing fine particle concentrations increase ice formation and updraft strength above freezing level suggesting that mixed-phase invigoration has an role in cloud phase structure and glaciation of convective clouds.

How to cite: Romakkaniemi, S., Raatikainen, T., Kokkola, H., Lawson, P., and Calderón, S.: Aerosol effects on Secondary Ice Production in Deep ConvectiveClouds: exploiting the synergistic benefit of observations andaerosol-aware cloud simulations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9155, https://doi.org/10.5194/egusphere-egu25-9155, 2025.

Warm conveyor belts (WBCs) are large-scale ascending airstreams found in extratopical cyclones. They constitute a major source of upper tropospheric/lower stratospheric (UTLS) water vapor—a potent greenhouse gas—and hydrometeors, which can form cirrus clouds. Therefore, WCBs play an important role for Earth’s radiative budget. Additionally, WCBs transport large amounts of heat across latitudes and can influence large scale upper tropospheric circulation after their dissipation, underscoring their significance for Earth’s weather and climate.

Recent studies using high-resolution, convection-permitting simulations have shown that convection is a prominent feature of WCBs, with convective air parcels transporting significantly more hydrometeors into the UTLS than their slower-ascending counterparts. Furthermore, the cloud and precipitation development in convective air parcels is dominated by different processes than in slower ascending air. However, the global numerical weather prediction and climate models commonly used to assess the climatological and future impacts of WCBs operate at coarse grid resolutions (15–50 km) that are not convection-permitting, relying instead on convection parametrization schemes. The widely used Tiedtke-Bechtold convection parameterization scheme is designed to simulate heat, moisture, and momentum transport in convective systems but includes only basic representations of cloud microphysics. This raises the question of whether low-resolution simulations fail to accurately represent the transport of hydrometeors into the UTLS by WCBs when compared to high-resolution, convection-permitting simulations.

To address this, we analyze two simulations of the same WCB—one convection-permitting and one using convection parameterization—with a specific focus on vapor and hydrometeor transport. Our results show that the WCB in the high-resolution simulation transports substantially larger amounts of hydrometeors into the UTLS, while UTLS vapor conditions remain comparable between the two simulations. Microphysics processes also shift from liquid-dominated to frozen-dominated depending on the grid-scale.

How to cite: Schwenk, C. and Miltenberger, A.: How does Model Grid Resolution Influence Mixed-Phase Processes and UTLS Moisture Transport by a WCB?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4261, https://doi.org/10.5194/egusphere-egu25-4261, 2025.

Representing the glaciation of mixed-phase clouds in terms of the Wegener-Bergeron-Findeisen process is a challenge for many weather and climate models, which tend to overestimate this process because cloud dynamics and microphysics are not accurately represented. As turbulence is essential for the transport of water vapour from evaporating liquid droplets to ice crystals, we developed a statistical model using established closures to assess the role of small-scale turbulence. The model successfully captures results of direct numerical simulations, and we use it to assess the role of small-scale turbulence. We find that small-scale turbulence broadens the droplet-size distribution somewhat, but it does not significantly affect the glaciation time on submetre scales. However, our analysis indicates that  turbulence on larger spatial scales is likely to affect ice growth. While the model must be amended to describe larger scales, the present work facilitates a path forward to understanding the role of turbulence in the Wegener-Bergeron-Findeisen process. This talk is based on  arXiv:2410.06724 which is joint work with G. Sarnitsky, G. Sardina, G. Svensson, A. Pumir, and F. Hoffmann.

How to cite: Mehlig, B., Sarnitsky, G., Sardina, G., Svensson, G., Pumir, A., and Hoffmann, F.: Does small-scale turbulence matter for ice growth in mixed-phase clouds?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15500, https://doi.org/10.5194/egusphere-egu25-15500, 2025.

Ice crystals in mixed-phase clouds (MPCs) can grow rapidly to large sizes by vapor deposition via the Wegener-Bergeron-Findeisen (WBF) process, i.e., growth of ice crystals at the expense of cloud droplets. This rapid growth can trigger subsequent processes such as riming and aggregation, which often initiate precipitation, making MPCs the major source of precipitation over continents. The growth of ice crystals has been thoroughly studied in the laboratory for many decades and several theoretical models were developed on their basis. However, in situ measurements of growth rates to confirm laboratory studies are still sparse due to the lack of controllability of experiments in natural clouds.

In the CLOUDLAB project, we conducted confined, controlled, and repeatable glaciogenic cloud seeding experiments to study ice crystal growth in natural clouds. A drone released seeding particles in supercooled low stratus clouds to initiate the formation of ice crystals. The freshly formed ice crystals were observed 5-10 minutes downwind of the seeding location using cloud radars and holographic imager for in situ observations. The holographic imager obtains phase-resolved information on cloud droplets > 6 µm and ice crystals > 25 µm with a high spatio-temporal resolution, which allows us to quantify accurate ice crystal growth rates.

In this study, we present ice crystal growth rates obtained from in-situ observations from 14 seeding experiments in the temperature range between -5.1°C and -8.3°C. During the seeding experiments, ice crystal number concentrations (ICNC) increased by several orders of magnitude, accompanied by a strong reduction in cloud droplet number concentration, a clear indicator of the WBF process.  We also observed that high ICNCs limit or inhibit ice growth due to the competition of the ice crystals for the available water vapor.  The obtained ice crystal growth rates are compared with laboratory studies and show on average slightly lower values. We also observed the expected variation in growth rates across our temperature range agreeing with laboratory findings. These findings can connect laboratory studies and in situ observations and provide valuable insights into vapor depositional growth of ice crystals in natural clouds.

How to cite: Fuchs, C., Ramelli, F., Lohmann, U., Miller, A. J., Omanovic, N., Spirig, R., Zhang, H., and Henneberger, J.: Quantifying ice crystal growth rates in natural clouds from glaciogenic cloud seeding experiments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8032, https://doi.org/10.5194/egusphere-egu25-8032, 2025.

The formation of the first ice particles in developing convective clouds is a critical event in cloud evolution. The timing, location and amount of the initial ice has potentially significant implications for the eventual micro and macro scale properties as the cloud evolves. Here we present in-situ measurements of the first ice particles observed in growing convective clouds during the DCMEX project over the Magdalena mountains in New Mexico, USA. The Facility for Airborne Atmospheric (FAAM) Bae-146 research aircraft was used to make penetrations of the convective clouds below the freezing level, following the cloud top upwards. We used an Optical Array probe with a large sample volume suitable for detecting the earliest ice particles as they developed. We often observed no ice particles initially at only slightly supercooled temperatures before the eventual appearance of small irregular ice particles in low concentrations around the -8°C level. We will present the characteristics of the particles observed and compare their concentrations with Ice Nucleating Particle (INP) data analysed during the same project.

How to cite: Lloyd, G., Blyth, A., Gallagher, M., Choularton, T., Bower, K., Hu, K., Murray, B., and Daily, M.: Early Ice Particles in Growing Convective Clouds over a New Mexico Mountain Range during DCMEX, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18152, https://doi.org/10.5194/egusphere-egu25-18152, 2025.

In-situ based information on the distribution of RH_ice inside cirrus clouds is still retrieved mainly from focused research aircraft campaigns for dedicated regions and seasons, but a long-term and seasonal perspective is missing. We report on the distribution of RH_ice in clear sky as well as inside thin and thick cirrus clouds for the North Atlantic region and over subtropical Southeast Asia, with the focus on the occurrence of ice-supersaturated air masses.

The underlying data base builds on more than 7 years of continuous in-situ observations by the European research infrastructure IAGOS (www.iagos.org) which measures, among others, temperature, RH_ice and ice cloud particles, on instrumented passenger aircraft. Information on cloud coverage and cloud thickness were taken from ERA5 global reanalysis by means of the cloud ice water content (CIWC). The separation of clear-sky and in-cloud flight sequences was achieved by applying a novel ERA5 CIWC based cloud index validated by IAGOS in-situ RH_ice observations.

The analysed data set covers the period from June 2014 to December 2021. Four regions were identified for in-depth statistical analyses, with three of them located in the Northern midlatitudes (30–60°N), namely Eastern North America (105–65°W), the North Atlantic flight corridor (65–5°W), and Western Europe (5°W–30°E), and one in the Southeast Asian subtropics (0–30°N, 45–120°E).

We will present the novel cloud index and discuss the features of the resulting RH_ice probability distribution functions of the analysed regions, including seasonal variations, and potential implications for the underlying cirrus cloud formation processes.

How to cite: Petzold, A., Khan, N. F., Li, Y., Rohs, S., Crewell, S., and Krämer, M.: Distribution of RHice inside thin and thick cirrus clouds over the Northern Mid-latitudes and in a Subtropical Region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12954, https://doi.org/10.5194/egusphere-egu25-12954, 2025.

The climate impact of aviation can be separated into CO₂ and non-CO₂ effects, with the latter being potentially larger than the former. In this context we are more specifically interested in condensation trails (hereafter contrails) and induced cirrus. Monitoring contrail formation and evolution is necessary to understand their radiative effects and help the aviation industry to transition towards a more sustainable activity. 
Current research aimed at detecting contrails is mostly based on geostationary satellite images because they allow to follow the contrail over a long period of time. However a major shortcoming of this approach, due to the current spatial resolution of geostationary imagers, is that the contrail formation phase cannot be detected and larger, but older, contrails cannot always be attributed to the flights that produced them. To circumvent this problem and observe the contrail formation phase, we use a ground-based hemispheric camera with a two-minute sampling rate as a complementary source of information. 
As a first step, we have developed a traditional morphological algorithm that will help preparing a sufficiently large labelled database as required to train a deep-learning algorithm. This algorithm aims to detect whether each aircraft that passes in the field of view of the camera (as monitored from an ADSB radar) produces a contrail or not and, whenever possible, track the contrail across successive images. 
We are thus able to relate contrail formation and evolution with aircraft type, flight altitude and weather conditions.  We consider all weather conditions except completely cloudy conditions that prevent contrails from being observed. The performance of this algorithm is evaluated against a database that was manually annotated consisting of 400 images with 407 contrails. We find a specificity of 97\%, i.e. there are few false detections, but a sensitivity of about 55\%, i.e. it is missing a significant fraction of contrail that were annotated manually. An analysis of several years of contrail detection will be presented to determine precisely the fraction of contrail-producing flights and the  weather conditions associated with short-lived (less than 2 min) and longer-lived(more than 2 min) contrails. 
Additionally to this approach, which misses part of the young contrails and does not detect contrails formed outside the field of view of the camera, we have trained deep neural networks such as Unet and DeeplabV3, on a database of 1600 images in order to overcome those limits. Our preliminary results show a good performance on young contrails, with an improved detection capability, in particular for contrails formed outside of the camera field of view. The deep neural networks also work better for old contrails but may confuse very old contrails with background cloud features. 

How to cite: Gourgue, N., Boucher, O., and Barthes, L.: Detection of Linear Contrails with a Morphological Algorithm and with Deep Neural Networks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6792, https://doi.org/10.5194/egusphere-egu25-6792, 2025.

Tropical tropopause layer (TTL) cirrus clouds play a key role in the Earth climate system, yet the relative role of the various processes shaping them remains poorly known. Characterizing the temporal evolution of cloudy structures from observations is essential to address this issue, but represents a challenge. Indeed, space- and air-borne platform are not well-suited for this task: moving much faster than the air, they only provide instantaneous snapshots. In boreal winter 2021-2022, two balloon-borne lidars flew over the Equatorial Pacific Ocean, slowly drifting above the clouds. We use those unique observations of truncated (nighttime only) lifetime distribution to quantify the underlying continuous distribution of cloud lifetime above this homogeneous region. While most clouds are short-lived (mean lifetime estimated at about 6 h, a median value of 1 h), the temporal cloud cover is still dominated by the few long-lived ones (24 h or more). These results are compared to cirrus lifetimes in ERA5 reanalysis, showing a fair agreement between the reanalysis and the observations, and demonstrating the value of our approach to evaluate cirrus representation in global models.

How to cite: Lesigne, T., Podglajen, A., and Ravetta, F.: First estimates of Tropical Tropopause Cirrus lifetimes using balloon-borne lidar observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17170, https://doi.org/10.5194/egusphere-egu25-17170, 2025.

The coexistence of liquid water and ice crystals in mixed phase clouds allows for collisions and subsequent freezing of droplets on ice crystals and aggregates, which is called riming. Because riming fills air cavities in aggregates and makes particles more round, rimed snow has a higher fall speed compared to unrimed crystals and aggregates. Therefore, a reliable method to detect riming are fall speed measurements based on the Doppler shift of cloud radar echos. For this, the cloud radar has to be oriented vertically, otherwise the Doppler shift is dominated by the horizontal wind advection of the hydrometeors. Limited to vertical observations, one of the key strengths of atmospheric radar is missing: To probe the atmosphere in 3D. In this contribution, we present the results from a winter measurement campaign, where we performed elevation scans through strong riming events. Assuming a model for the horizontal wind speed, we can remove the horizontal wind contribution from the Doppler signal. This reveals the underlying riming signatures from the measurements, allowing us to create snapshots in time of the actual spatial organization of strongly rimed particles in mixed phase clouds. By choosing the scanning plane into the main wind direction, we are able to track spatial features over multiple snapshots. A better characterization of the spatial picture can enhance our conceptual understanding of the structure and organization of strong riming in mixed phase clouds. Since supercooled liquid water is a precondition for riming and aircraft icing alike, our results could also proof helpful to design aircraft icing hazard warning products.

How to cite: Ockenfuss, P., Köcher, G., and Kneifel, S.: Visualizing the Spatial Structure of Strong Riming Events using Scanning Cloud Radars, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18125, https://doi.org/10.5194/egusphere-egu25-18125, 2025.

In the spring of 2024, the US National Science Foundation sponsored the Cold-Air outbrEaks in the Sub-Arctic Region (CAESAR) aircraft campaign, with the simple goal of characterizing cold-air outbreak (CAO) clouds coming off of the Arctic sea ice as comprehensively as possible. A strength of the CAESAR strategy is a comprehensive aerosol, cloud and remote sensing instrumentation suite and early development of a close connection to modeling spanning a range of scales, in part by building on prior DOE-sponsored activity through the Cold-Air Outbreaks in the Marine Boundary Layer (COMBLE) campaign. The higher-level motivation for CAESAR is to better understand how clouds participate and feedback upon the changing Arctic. New technologies, improved data integration and modeling frameworks that are increasingly comparable to the observations hold promise that both the numerical weather prediction and  global modeling of the super-cooled liquid, mixed-phase and ice clouds can be improved through the focus provided by the field campaign. In this presentation we provide an overview of the NCAR C-130 aircraft campaign, and its approach to the problem of improving understanding of the cold-air outbreak cloud evolution, microphysical processes including their relationship to aerosol, and cloud mesoscale organization including the development of CAO clouds into polar lows. Initial highlights will be included.

How to cite: Zuidema, P., Geerts, B., and McFarquhar, G.: An Overview of the Cold-Air outbrEaks over the Sub-Arctic Region (CAESAR) campaign, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20471, https://doi.org/10.5194/egusphere-egu25-20471, 2025.

Cirrus clouds permit much of the incoming solar shortwave radiation to pass through while trapping and reemitting the Earth's outgoing longwave thermal radiation. This results in a net warming effect globally at the top of the atmosphere. They are found almost over every region, but their impact can differ depending on latitude. The arctic is a unique and fascinating area. Over the last few decades, scientists have shown that its average temperature is increasing at an accelerated rate compared to global warming. This phenomenon has been labeled Arctic Amplification and cirrus clouds are considered a potential contributor. Another arctic-specific phenomenon linked to arctic amplification are Warm Air Intrusions (WAI). During such events, warm, water-vapor- and aerosol-rich airmasses are meridionally transported into the arctic from the midlatitudes. Apart from the transport of sensible heat and water vapor, a strong greenhouse gas, these events can potentially alter properties and effects of the cirrus clouds that form in the arctic. The positive trend found in the frequency and longevity of these events further highlights the importance to understand how they affect the macrophysical and optical properties of cirrus clouds in the arctic.

In March and April of 2022, the HALO-(AC)³ field campaign was conducted. The main goal of this campaign was to investigate WAI events and airmass transformations in the arctic. One of the platforms employed during this campaign was the German research aircraft HALO. It was used to perform remote sensing measurements at high altitudes over cirrus clouds. Among the instruments aboard HALO, were the combined water vapor differential absorption and high spectral resolution lidar system WALES and the HAMP package including a cloud radar and radiometers. Measurements from these two instruments form the basic dataset analyzed in this study. The cirrus clouds detected during this campaign are classified as either WAI cirrus or AC cirrus depending on if they were measured during an active WAI or during undisturbed arctic conditions. In order to better classify the clouds and provide a more in-depth analysis their backwards trajectories were calculated using the Lagrangian analysis tool LAGRANTO and the CLaMS-Ice model, which combines the Chemical Lagrangian Model of the Stratosphere (CLaMS) with two-moment ice microphysics.

In this presentation we are comparing the geometrical and optical depths of WAI and AC cirrus as measured by WALES. From the same instrument we also calculate the supersaturations with respect to ice and get a first insight into the probable nucleation processes. The backwards trajectories reveal more details regarding the origin, formation process, nucleation pathway and microphysical properties of the two cloud types. The analysis of the microphysical properties is further strengthened by analyzing the combined radar-lidar products.

How to cite: Dekoutsidis, G., Groß, S., Wirth, M., Rolf, C., Krämer, M., Schäfler, A., and Ewald, F.: Observations and Analysis of Cirrus Clouds in the Arctic during Warm Air Intrusions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6928, https://doi.org/10.5194/egusphere-egu25-6928, 2025.

The 50˚–65˚ latitude band exhibits the largest hemispheric asymmetry of cloud albedo over the oceans as well as the largest negative Southern Ocean (SO) cloud albedo biases in CMIP models. In this study, we show that cloud albedo regressed against sea-surface temperatures (SSTs) highlights essential differences between the observed Northern and Southern hemisphere climatologies, and between the SO’s simulated and observed albedos. The threshold 4˚–5˚C stands out as a regime separator in both comparisons.

By linking our empirical findings with the extensive evidence that model errors are related to the unique microphysical characteristics of the SO environment, we hypothesize that cloud albedo as a function of SST may act as a predictor of the presence/absence of supercooled liquid water cloud content.

Using satellite-retrieved cloud optical thickness (COT) and cloud top temperature (CTT), we verify that a regime separation of COT as a function of CTT exists between the Northern and Southern hemispheres (for CTT< -12˚C), which becomes more noticeable under midlevel subsidence conditions (i.e., low, boundary layer clouds).

Our simple and straightforward method using macrophysical variables can be easily applied in model evaluation with an insight in microphysics performance, especially given the scarcity of archived cloud-specific variables by the participating CMIP models. For example, it is well known that models tend to produce glaciated rather than supercooled liquid water clouds, and we show that in many cases models are simulating Northern Hemisphere clouds for the SO. We also detect that some of the CMIP models produce the right climatological cloud albedo over the SO but for the wrong reasons.

How to cite: Blanco, J., Caballero, R., Sherwood, S., and Alexander, L.: Using SST as a proxy for cloud phase biases over the Southern Ocean, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2195, https://doi.org/10.5194/egusphere-egu25-2195, 2025.

As part of the NWCSAF project (Nowcasting Satellite Application Facility), the CNRM participates in the retrieval of cloud properties from geostationary satellite observations. These retrievals include the Cloud Mask and Cloud Types classification, thermodynamics properties at the macroscopic scales (Cloud Top Temperature and Height) as well as microphysical cloud properties (effective radius, optical thickness, liquid and ice water path). Radiative transfer simulations are mandatory to retrieve these properties. In this study, I performed simulations of observations from the Meteosat Second Generation satellite based on in-situ measurements taken on board an airborne campaign and mesoscale models using the radiative transfer model RTTOV.In order to compare the differences between simulation and observations for the case of ice clouds formed by deep convective systems, in the infrared and visible. Then discuss the sensitivity of the simulations to the physical and optical properties of the clouds, for example, how a misrepresentation of the ice water content at the top of clouds can be highlighted using simulations.

How to cite: Joseph, R., Fontaine, E., Schwarzenboeck, A., Delanoe, J., Kerdraon, G., Le Bastard, T., Gonthier, H., Tulet, P., Barthe, C., and Vidot, J.: Simulation of satellite observations with RTTOV for ice clouds from deep convection using in-situ observations and a mesoscale model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4385, https://doi.org/10.5194/egusphere-egu25-4385, 2025.

Clouds and their radiative effects remain one of the most pernicious unknowns in  our predictions of the climate. Climate models are particularly affected by uncertainties around mixed-phase clouds (MPCs) consisting of both super-cooled liquid droplets and ice crystals. Inaccurate measurements of the liquid water content can lead to an under or overestimation of the warming properties of these clouds. Moreover, we lack adequate constraints and parameterisations of the spatial heterogeneity of water/ice mixtures, and the distribution of ice crystal sizes within MPCs. 

Traditional cloud-monitoring satellites are able to retrieve physical cloud properties pertinent to these unknowns via, for example, LIDAR and radar sensors, but must necessarily treat MPCs as homogeneously mixed at scales below their resolution, which is on the order of 100s of metres to kilometres per pixel. Meanwhile, cloudy imagery from multispectral satellites with high spatial resolution, such as Sentinel-2, is treated as a waste product, with the ~60% of cloudy pixels left to gather dust in the archive. Multiple barriers exist that make these multispectral satellites difficult to use for ice cloud property retrievals, including their lack of thermal information, their tendency to mostly observe over land and not the ocean, their infrequent revisit times, and their sun-synchronous orbits which mean they only observe at 10 am local time. Nevertheless, these sensors offer a unique angle from which to study clouds, which can complement existing and future cloud-specialised sensors.

Here, we present early results of the Clouds Decoded project, funded by the Advanced Research + Invention Agency (ARIA), which seeks to help the community to tackle some of the key unknowns related to MPCs and ice clouds. Clouds Decoded aims to retrieve several physical cloud properties including cloud top height, optical depth (for optically thin clouds), ice/water ratio and ice particle effective radius, all at very fine resolution. This is being done at a massive scale, with the goal of processing several hundred terabytes of Sentinel-2 data from across the globe during the project. In this presentation, we will focus on a handful of case-studies which demonstrate how our data can be of use to the community. In particular, statistical relationships between cloud top temperature (inferred from height) and ice properties, alongside spatial frequency analyses, will be leveraged to describe the complex processes occurring in MPCs.

How to cite: Francis, A., Campbell, J., and Czerkawski, M.: Clouds Decoded: Understanding Mixed-Phase Clouds with High Resolution Multispectral Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17522, https://doi.org/10.5194/egusphere-egu25-17522, 2025.