Stratocumulus (Sc) clouds have high impact on the climate system because of their strong cooling effect (albedo).
In the subtropical oceans they naturally break up into cumulus (Cu) as environmental conditions change, a phenomenon called Stratocumulus--Cumulus Transition (SCT). This results in significant loss cloudiness and thereby cooling.
To better understand the physical processes and environmental conditions influencing most this transition we develop a conceptual mixed layer model with dynamic cloudiness and sea surface temperature (SST).
In this model Sc and Cu states are two alternative attractors of the dynamics.
We tested various different parameterizations, and in most configurations the model broadly reproduces observed variability in cloud fraction, SST, heat fluxes, and moisture.
In this isolated system we robustly show that transitions between Sc and Cu are most sensitive to changes in circulation.
In climate change scenarios we show that SCT is enhanced due to direct radiative effects of increasing CO2 and due to weakening subtropical subsidence deepening the boundary layer. An accelerated SCT via weakening subisdence is a high strength positive feedback in the climate system based at its core on cloud-circulation coupling.
We close the talk by (1) highlighting the differences between linear and nonlinear response of clouds to weakening subsidence and (2) motivating the community to embrance high magnitude but low likely hood events as part of standard analyses of models.
How to cite: Datseris, G.: Interplay between decreasing subsidence and stratocumulus-cumulus transitions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2528, https://doi.org/10.5194/egusphere-egu26-2528, 2026.
Shallow cumulus clouds in trades present a unique challenge because they are both difficult to observe and describe. Recent studies have shown that mesoscale dynamics and synoptic scale cloud-controlling factors are important to describe them, but the interplay between these two scales is not well understood. Therefore, we attempt to disentangle their interplay to determine when mesoscale dynamics become important for the clouds. We have utilized data from the EUREC4A campaign aided with satellite and reanalysis data to extensively describe the cloudiness observed during the same period.
We find a bimodal distribution of cloud top height from WALES LIDAR, with peaks at 1 km and 2 km for low and high clouds, respectively. A multiple linear regression analysis of six cloud-controlling factors (CCF) explains 80% of the variance in projected cloud cover. Further analysis reveals that the wind shear in the cloud layer is the leading CCF for low cloud fraction and mesoscale vertical motion is not a strong control for such low clouds not under high clouds. For higher clouds, mesoscale vertical motion at 1900m is the leading factor with a 0.68 correlation to high cloud cover. Furthermore, rain co-varies with high cloud cover substantially, on many days, thus we conclude that mesoscale dynamics are more important for high clouds.
Alongside, ERA5 shows that variability in vertical motion is associated with high cloud variability only during the periods around rain. Thus, it becomes important to study high clouds in a cloud-circulation-rain framework. We propose a life cycle hypothesis to study various aspects of the coupling between mesoscale dynamics and trade wind clouds. We hypothesize that different stages of the life cycle of mesoscale cloud fraction of high clouds are related to growth by cloud-circulation coupling and decay by rain/cold pools driven circulations.
How to cite: Singh, C., Mieslinger, T., George, G., and Stevens, B.: When do the mesoscales matter for trade cumulus clouds? A EUREC4A Perspective, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15356, https://doi.org/10.5194/egusphere-egu26-15356, 2026.
The role of patterning of clouds in climate sensitivity is unknown. Smaller clouds grow due to the coupling with a shallow circulation, where the cloud resides on the ascending branch of the circulation. However, there seems to be a certain maximum size in large-eddy simulations (LESs) to this scale growth, which is smaller compared to observations. Therefore, we have conducted two 500 km-domain LESs: One (Control) with homogeneous initial conditions and one (“Fish”) with a 250 km initial moisture perturbation, created such that the domain-mean moisture is kept constant. The Fish simulation emulates the advection of large-scale moisture structures into the trades, and thus reminds us of “Fish-clouds”, which appear to form this way. This simulation indeed leads to a larger cloud, which has an elongated shape and develops a bimodal Total Water Path (TWP) distribution. Moreover, the moisture growth behaves differently as compared to previous research and the Control simulation. The upward motion of the circulation does not lead to moisture growth, but balances the moisture sink (rain). The moisture growth is caused by horizontal growth in the cloud top. Eventually, the Fish has a larger cloud fraction (26%) and a larger albedo (11%) than the Control simulation, leading to a larger daily short-wave cloud radiative effect (SW CRE) of 40%. In absolute numbers, this SW CRE is up to 40 W/m^2 larger in the Fish simulation compared to the Control simulation. Thus, the initial conditions seem to trigger a different pathway for moisture growth, which affects the cloud radiative effect. This study is a first step in unravelling this process, which might affect the climate sensitivity of patterning of clouds.
How to cite: van Driel, M., van Heerwaarden, C., and Janssens, M.: Heterogeneous initial conditions affect the pathway of moisture growth and the radiative effects of trade cumuli, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2585, https://doi.org/10.5194/egusphere-egu26-2585, 2026.
How to cite: Antary, N., Kazil, J., Ahlgrimm, M., Janssens, M., Raghunathan, G. N., Yanase, T., and Vogel, R.: CP-MIP: One cold pool in five models, ten runs, and one campaign, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6529, https://doi.org/10.5194/egusphere-egu26-6529, 2026.
The radiative effect of clouds is determined by their development, lifetime and microphysical characteristics. The processes influencing cloud properties are diverse and require further investigation to gain a more profound understanding. However, process studies cannot be conducted by modelling alone, as observations are rarely available, particularly over the oceans. This leads to uncertainties in global weather modelling and climate forecasts. In order to disentangle the effects of various influencing factors on clouds in the intertropical convergence zone (ITCZ), recent observations from the BOWTIE campaign are being analysed with regard to the effects of Saharan dust. During the BOWTIE (Beobachtung von Ozean und Wolken – das Trans-ITCZ Experiment) campaign, which was part of the ORCESTRA (Organized Convection and EarthCARE Studies over the Tropical Atlantic) campaign, the research vessel (RV) Meteor crossed the tropical Atlantic from Cape Verde to Barbados in August and September 2024. The proximity to the African continent and the Sahara led to some trajectories of the Saharan air layers (SAL) traversing the atmosphere above the RV Meteor. Three episodes of SAL lasting between two and three days were observed at altitudes spanning from one to five km. The dry and dust-loaden character of the SAL is determined by their origin in the Saharan desert. Analysis of the Raman lidar observations provides information about the time and altitude of the SAL. In synergy with other instruments, such as radiosondes and a motion-stabilised 94GHz cloud radar, the effect on clouds can be investigated. The stabilisation of the cloud radar, which was monitored by STARPAS (STAbilized Radar Platform Alignment Sensor), ensures reliable vertically pointing cloud observations. The findings indicate that SAL reduces cloud vertical development and suppresses weak convection. This behaviour is due to the thermodynamic structure of the SAL with low relative humidity, nearly dry adiabatic temperature gradients and inversions at the top and bottom. Particle-specific properties of acting as cloud condensation nuclei or ice nucleating particles are of secondary order.
How to cite: Trosits, A., Foth, A., Haarig, M., Witthuhn, J., Kötsche, A., Roschke, J., and Kalesse-Los, H.: Effects of Saharan air layers on clouds over the tropical Atlantic during BOWTIE, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3115, https://doi.org/10.5194/egusphere-egu26-3115, 2026.
One of the main sources of uncertainty in climate modeling is microphysics. While Lagrangian approaches are promising, their computational cost is not yet suited to global climate simulations, leaving global simulations to bulk schemes. Starting from simple one-moment microphysics formulations that prognose only the specific mass of each hydrometeor 𝑞x, physically richer approaches like double- and higher-moment bulk schemes were introduced to narrow model–observation gaps, but they deliver mixed, metric-dependent gains over simpler one-moment schemes [1,2]. This mixed record suggests that the link between microphysical processes and other atmospheric processes remains poorly understood.
Few studies have probed this link directly [3], and the intricacy of bulk schemes has hindered clear attribution of changes in climate statistics to specific microphysical processes. Here we aim to make microphysics more transparent by simplifying it to a Kessler-like three-category scheme. We thus collapse Lin’s six-category scheme [4] to three prognosed variables: vapor 𝑞v, condensates 𝑞ci (cloud water 𝑞c + ice 𝑞i ), and precipitates 𝑞rgs (rain 𝑞r + graupel 𝑞g + snow 𝑞s), where their precise category is defined by local thermodynamics (temperature 𝑇 and relative humidity RH) and updraft velocity 𝑤 (Fig. 1). Using a global 5-km atmosphere-only ICON simulation with a Lin-like microphysics scheme, we ask whether accurate mappings 𝑓x , 𝑓y exist such that
Condensates: 𝑞x ≈ 𝑞x* = 𝑓x (𝑞ci , 𝑇, RH, 𝑤), for x ∈ {c, i} ,
Precipitates: 𝑞y ≈ 𝑞y* = 𝑓y (𝑞rgs , 𝑇, RH, 𝑤), for y ∈ {r, g, s} ,
where 𝑞x,y are the specific masses from our ICON simulation, and 𝑞*x,y are the predicted specific masses from the partitioning functions 𝑓x,y , given 𝑞ci and 𝑞rgs (see Fig. 1). We construct histograms in the (𝑇, 𝑤, RH)-space and fit simple partition functions, like sigmoids, to build our partitioning functions.
We present here results from this mapping, as well as an evaluation of its performance. We measure the R², and we compare global instantaneous outputs from our ICON simulation to the predictions provided by our partitioning functions, such as LWP and IWP comparisons.
Fig.1: Collapse state of microphysics and its link to the predicted partitioning.
[1] Seiki, Kodama, Noda, Satoh, J. Climate, 28, 2405–2419 (2015).
[2] Song, Sunny Lim, Weather and Climate Extremes, 37, 2212-0947 (2022).
[3] Proske, Ferrachat, Neubauer, Staab, Lohmann, Atmos. Chem. Phys., 22, 4737–4762 (2022).
[4] Lin, Farley, Orville, J. Appl. Meteorol. Climatol., 22, 1065–1092 (1983).
How to cite: Downey, P.-O., Schmidt, H., and Stevens, B.: The path to simplification of cloud microphysics., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19016, https://doi.org/10.5194/egusphere-egu26-19016, 2026.
Shallow cumulus clouds in the trade-wind region are a major source of uncertainty in the global cloud feedback on climate. Although previous studies have investigated cloud feedback based on daily mean or even monthly data, the time of the day when clouds occur currently and in the future matters for their radiative effect. On top of that, with the availability of high-frequency data comes the opportunity to study time series data of cloud fields rather than relying on snapshots. To quantify the role of the diurnal cycle for the cloud feedback, we study the relationship between the daily cycle in cloudiness and in the large-scale environment. We compile a dataset of Lagrangian satellite observations together with cloud controlling factors (CCFs) along ~30000 ERA5 trajectories, obtained from 925hPa wind fields. The 6-day-long trajectories are centered at the tropical North Atlantic in the winter months (DJF), which is representative of the trade-cumulus regime. We utilize the high temporal resolution of GOES-16 (10-15 min), ERA5, and CERES (both hourly) to fully resolve sub-daily timescales. With this dataset, we explore correlations between the amplitudes and phases of cloudiness and CCFs. We examine which CCFs control the daily cycle of clouds and quantify response times between the drivers and their effects. Our goal is to develop a model that describes the daily cycle in cloudiness based on the most important CCFs and use time series data to constrain the trade cumulus cloud feedback.
How to cite: Meier, R., Alinaghi, P., Eastman, R., George, G., and Glassmeier, F.: Identifying large-scale drivers of the daily cycle of trade-wind cloudiness from geostationary satellite, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19193, https://doi.org/10.5194/egusphere-egu26-19193, 2026.
When cold, dry air from the high Arctic is advected southward over the open ocean, strong sensible and latent heat fluxes can cause rapid boundary-layer growth and clouds that are predominantly mixed-phase. These clouds in marine cold air outbreaks (MCAOs) are strongly controlled by ice nucleation processes and interactions between cloud ice and supercooled liquid droplets. The role of large-scale vertical motion in shaping the thermodynamic, microphysical, and convective evolution of MCAOs remains poorly constrained. This uncertainty largely reflects the scarcity of high-resolution observations in Arctic source regions. To address this gap, we investigate how mesoscale subsidence influences atmospheric boundary-layer (ABL) development, cloud phase transitions, and mixed-phase precipitation characteristics during a shallow MCAO observed over the Fram Strait in March 2022 as part of the HALO–(AC)³ campaign. During the campaign, mesoscale flight circles with regularly spaced dropsonde releases were conducted, allowing the estimation of subsidence following a method previously applied in the sub-tropics during the NARVAL2 and EUREC⁴A campaigns. Our analysis is based on quasi-Lagrangian large-eddy simulations (LES) that are initialised and forced exclusively with airborne in-situ and remote-sensing observations. The control LES realistically reproduces both the thermodynamic structure of the ABL and the temporal evolution of the air mass as it is advected from Arctic sea ice toward the open ocean. In particular, the simulated ABL depth, integrated water vapour, and cloud liquid and ice water paths agree well with observations. A set of sensitivity simulations with prescribed subsidence rates demonstrates that reduced mesoscale subsidence substantially alters cloud-phase evolution, resulting in a deeper boundary layer and a more rapid transition toward fully glaciated clouds. This response is closely linked to earlier development of internal ABL decoupling under weaker subsidence conditions. The earlier onset of decoupling promotes convective graupel production, thereby accelerating the conversion of liquid cloud droplets. The strong link between boundary-layer decoupling and cloud glaciation provides a plausible explanation for the frequently observed evolution of cloud liquid water path in MCAOs, and establishes a mechanistic understanding of how mesoscale subsidence governs Arctic air-mass transformation.
How to cite: Paulus, F., Müller, J., Kirbus, B., Mech, M., Sodemann, H., van Gelder, L., Walbröl, A., Wendisch, M., and Neggers, R.: Impacts of mesoscale atmospheric subsidence on cloud glaciation and decoupling in Arctic marine cold air outbreaks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11331, https://doi.org/10.5194/egusphere-egu26-11331, 2026.
Cloud regimes provide a useful observational framework to link cloud properties, organisation, and variability to large-scale circulation across spatial and temporal scales. We present CRAAS-3, a cloud regime dataset over Europe derived from 20 years of daytime MSG-SEVIRI observations from the CLAAS-3 cloud property dataset. Cloud regimes are defined from joint histograms of cloud optical thickness, cloud top pressure, and thermodynamic phase, clustered with a refined k-means method into eight prototypical regimes. The high temporal resolution of SEVIRI enables analyses of regime persistence, transitions, and regional variability, which can be related to large-scale circulation and synoptic conditions. We further combine CRAAS-3 with high-resolution cloud properties from polar-orbiting satellites to investigate how cloud structural characteristics vary across regimes, and with vertical profiles from ACTRIS stations to characterise typical regime-specific cloud profiles. This integrated approach exploits the high temporal resolution of geostationary observations together with detailed cloud structural information to enable process-oriented analyses of cloud evolution across regimes and circulation contexts.
How to cite: Happich, J. and Deneke, H.: CRAAS-3: A 20-year cloud regime dataset over Europe linking cloud structure and circulation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19268, https://doi.org/10.5194/egusphere-egu26-19268, 2026.
We report the first results of an intercomparison of nine atmospheric models with horizontal resolutions between 150 m and 2.5 km: ICON, DALES, MesoNH, MicroHH, MetOffice UM, HARMONIE-AROME, AROME, WRF, and COSMO. These models all simulated shallow cumulus convection over the subtropical Atlantic Ocean during the period of the EUREC4A field campaign (Jan-Feb 2020), most of them with open boundary conditions and on large (>500 km) domains. Our "EUREC4A-MIP" seeks to answer how consistently such models reproduce i) statistics of the observed atmospheric state, clouds and energy/water budgets over large (200 km) areas, ii) the horizontal cloud organization that set the regions' contribution to the planetary albedo and iii) the coupling between clouds and circulations at mesoscales. We extend an open invitation for further exploration of the simulation data, which we publish through the EUREC4A intake catalog.
How to cite: Janssens, M., Siebesma, P., Beucher, F., Brient, F., Chen, J., Couvreux, F., Heus, T., Jansson, F., Liqui-Lung, F., Lock, A., Raghunathan, G., De Rooy, W., Schulz, H., Van Stratum, B., Torres, A., and Van Ulft, B.: The EUREC4A Model Intercomparison Project: A first look at our ability to simulate trade cumuli at hectometre- and storm-resolving resolutions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6271, https://doi.org/10.5194/egusphere-egu26-6271, 2026.
Satellite observations with passive sensors generally classify cloud phase – liquid water, mixed-phase, or ice – based on snap-shots of polar-orbiting observations or individual time steps of geostationary observations. In that context, mixed-phase clouds are defined as cloud objects that contain both pixels that are classified as liquid water and as ice.
In contrast to polar-orbiting satellites, observations with geostationary satellites provide the data needed for tracking clouds over their lifetime. This Lagrangian perspective allows for quantifying the evolution of cloud physical properties, including cloud phase. The temporally-resolved view of a cloud as a sequence of subsequent observations provides a refined perspective of mixed-phase clouds as objects that evolve over time. There are three straightforward options. First, a cloud starts as all-liquid pixels, contains at least one time step that feature both liquid and ice pixels, and ends as ice-only pixels. Second, the cloud features only time steps that are all liquid or all ice pixels (the former option without the mixed-phase state as defined in snap-shots). Third, the cloud contains both liquid and ice pixels at any time step throughout its lifetime. This revised perspective of mixed-phase clouds challenges the static snap-shot view which would identify a cloud as mixed-phase only if it was at the mid-phase of option one or an option-three cloud.
The purpose of this poster is to stimulate a discussion on the need for a time-resolved definition of mixed-phase clouds, on how to reconcile such a definition with snap-shot-based observations, and on what can be learned from the time-resolved definition of mixed-phase clouds.
How to cite: Tesche, M., Alexandri, F., Müller, F., and Seelig, T.: Revisiting the definition of mixed-phase clouds in satellite remote sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11463, https://doi.org/10.5194/egusphere-egu26-11463, 2026.
During field campaigns, unique and often irreplaceable datasets essential for advancing Earth system science are collected. Experience from previous campaigns has shown that the long-term scientific value of such data critically depends on proper data management, because data that are difficult to find or access are often underused, despite their high scientific value.
For the ORCESTRA campaign, these experiences informed the design of a data infrastructure that prioritizes global visibility, standardized metadata, and resilient access from the beginning. ORCESTRA datasets are stored and made available using the InterPlanetary File System (IPFS), with the central data node hosted at the Deutsches Klimarechenzentrum (DKRZ), which ensures 24/7 operational stability. The approach using IPFS improves data redundancy and resilience, addressing common risks identified in earlier campaigns where data availability depended on single hosting locations.
To increase and ensure findability and reuse of ORCESTRA campaign data, we implemented a dynamic catalog following the SpatioTemporal Asset Catalog (STAC) specification. The catalog feeds a public browser (https://orcestra.cloud.dkrz.de/), enabling intuitive exploration and direct access to datasets. Currently, data from the PERCUSION, MAESTRO, BOW-TIE sub-campaigns and the Barbados Cloud Observatory (BCO) are available. Further datasets are planned to complement this collection in the near future. Further, we configure the ORCESTRA STAC catalog according to the FAIR Digital Object
(FDO) specifications to enable real interdisciplinary findabiliy and reusability.
In our contribution, we will dive into the technical details of our implementation as well as emphasise that providing heterogenous field campaign data via dynamic STAC catalogs configure as FDOs enables interoperability with existing and emerging data spaces, e.g. the Destination Earth Data Lake or upcoming federated data infrastructure focused on climate science, e.g. FUTURA. In summary this approach reflects lessons learned from earlier campaigns and supports sustainable, federated data sharing to maximize scientific reuse.
How to cite: Ehbrecht, C., Modali, K. R., Kulüke, M., Kölling, T., Kluft, L., and Peters-von Gehlen, K.: ORCESTRAting IPFS STAC and FDO: An approach to enhance the FAIRness and global availability of campaign data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22680, https://doi.org/10.5194/egusphere-egu26-22680, 2026.
Accurate cloud detection is fundamental for atmospheric remote sensing, particularly for airborne observations where high spatial resolution enables detailed characterization of cloud fields. We present a cloud mask algorithm for the spectrometer of the Munich Aerosol Cloud Scanner (specMACS), a hyperspectral polarimetric imaging system operated aboard the German research aircraft HALO.
specMACS features four special RGB cameras that simultaneously provide polarization measurements at high spatial resolution. This enables novel cloud detection approaches by exploiting the difference in polarization signatures between clouds and cloudless-sky ocean surfaces. Specular reflection from the ocean surface produces polarized signals, while cloud droplets depolarize incoming radiation through multiple scattering, creating a clear physical contrast for cloud identification.
Our algorithm uses radiative transfer simulations with the libRadtran package to generate reference radiances for cloudless sky atmospheric conditions. We systematically vary solar geometry, aerosol properties, and surface conditions in the simulations to establish classification criteria for cloud detection. Expected surface values from radiative transfer simulations are interpolated across the full high-resolution field-of-view and compared against specMACS observations to identify cloud presence.
The algorithm was developed and tested using data from the Persistent EarthCARE Underflight Studies of the ITCZ and Organized Convection (PERCUSION) campaign over the tropical Atlantic in 2024. Initial results demonstrate reliable cloud detection across a range of optical thicknesses, providing robust cloud masks for subsequent retrieval applications. This work establishes a foundation for improved quality in ocean and atmospheric retrievals from high-resolution polarimetric aircraft observations.
How to cite: Demiralay, Z., Volkmer, L., Zinner, T., and Mayer, B.: Development of a Cloud Mask from High-Resolution Polarized Aircraft Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20617, https://doi.org/10.5194/egusphere-egu26-20617, 2026.
Known mechanisms causing Arctic amplification of global warming are lapse-rate and albedo feedbacks, as well as increased latent heat release due to enhanced moisture transport into the Arctic [1]. This classical understanding is based on the assumption of quasi-steady mean temperature and humidity states, described as a balance between advective transport from lower latitudes, surface fluxes, radiative exchange at the upper atmosphere, and related feedbacks [2]. While this integral, steady-state box-model framework has been instrumental in developing our current understanding, its inherent limitations hinder further progress in explaining Arctic amplification. For example, the box model cannot distinguish whether changes in heat or moisture transport are of fluid-dynamical or thermodynamical origin, nor can it describe how air masses entering the Arctic are transformed as they cool, mix, and exchange energy and moisture with surfaces and clouds. Yet Arctic air-mass transformations must be understood at the fundamental process level, as they are key to explaining, for instance, the necessary thermal conditions underlying the lapse-rate feedback.
Here we adopt an air-mass-centred framework that enables a physically consistent link between large-scale Arctic amplification of temperature and precipitation changes and the small-scale turbulent and microphysical processes governing the Arctic atmospheric boundary layer and its clouds. We combine air-mass-following balloon observations with large-eddy simulations (LES) to investigate the Lagrangian evolution of boundary-layer clouds during Arctic air-mass transformation events. Four such events have been tracked using CMET balloons [3] launched from Ny-Ålesund, Svalbard, providing unprecedented in situ measurements of thermodynamic quantities at controlled heights along air-mass trajectories. An additional campaign is planned for March 2026, during which up to twelve balloons will be launched from Station Nord, Greenland. The observational data are integrated into the LES code DALES [4] via time-dependent forcings and boundary conditions, yielding spatio-temporally resolved information on local thermo-fluid-dynamical processes and mixed-phase cloud microphysics along the air-mass pathways.
At the conference, we will present our collected field data and first LES results for one representative case. The focus of the current contribution is on establishing a robust Lagrangian LES framework, including domain-size sensitivity, grid-convergence behaviour, and basic physical plausibility checks against observations.
In perspective, this approach will allow us to compute vertical fluxes of energy and moisture during different transformation events and to analyse how the mean and final states of air masses, as well as their energy and moisture budgets, respond to varying climate conditions. Ultimately, we expect that this hybrid field–model study will enable us to test the following hypotheses: (i) liquid water path controls cloud persistence through cloud-top radiative cooling; and (ii) radiative cooling at cloud top (or in clear sky) drives air-mass transformation during both cloudy and clear states.
[1] M. Previdi, K.L. Smith, L.M. Polvani. Environ. Res. Lett., 2021, doi:10.1088/1748-9326/ac1c29.
[2] M. Cai. Geo. Res. Lett., 2005, doi:10.1029/2005gl024481.
[3] P. B. Voss. AIAA Balloon Systems Conference, 2009, doi:10.2514/6.2009-2810.
[4] T. Heus et al. Geosci. Model Dev., 2010, doi:10.5194/gmd-3-415-2010.
How to cite: Feldmann, D., Graßmel, L., and Pithan, F.: Combining air-mass-following balloon observations and large-eddy simulations to build process-level understanding of arctic amplification, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20220, https://doi.org/10.5194/egusphere-egu26-20220, 2026.
Understanding the vertical structure of deep convective systems is essential for assessing their
impacts on the atmospheric energy budget and hydrological cycle and for evaluating their repre-
sentation in models. However, because of limitations of current satellite observations, the vertical
structure of these systems remains poorly constrained. We use a novel ice cloud dataset called
IceCloudNet to study the temporal evolution of the vertical structure of tropical deep convective
systems on the basis of ice water content as a marker for convective intensity and anvil devel-
opment. IceCloudNet is the first 4D-consistent semi-observational ice cloud dataset covering the
tropical belt between 30°S–30°N and 30°W–30°E, developed by Jeggle et al. (2025). The spatial
resolution is 3 km in the horizontal and 240 m in the vertical. The temporal resolution is 15 min.
The dataset is constructed by filling observational gaps using machine learning. By applying the
Tobac cloud tracking algorithm to the vertically integrated ice water content over the course of the
year 2010, we identify and track deep convective systems to diagnose systematic changes in the
vertical distribution of ice water content during their lifecycle. We also assess the suitability of
IceCloudNet for a robust and physically coherent tracking and analysis of vertically resolved cloud
properties. This allows us to highlight both its limitations and its potential to enable, for the first
time, a comprehensive four-dimensional analysis of the evolution of tropical ice clouds.
How to cite: Hobiger, J., Mol, W., Gasparini, B., and Voigt, A.: Temporal evolution of the vertical structure of tropical deep convective systems in IceCloudNet, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17004, https://doi.org/10.5194/egusphere-egu26-17004, 2026.
Tropical anvil clouds exert a twofold impact on Earth’s radiation budget. The thickness of anvil clouds, and with it their radiative effects, change significantly throughout the cloud’s lifetime. Fresh anvils are initially thick, as the cloud ages and spreads out, the cloud loses mass. This leads to a rapid decrease in its cooling effect due to reflection of shortwave radiation until eventually the longwave warming effect dominates, resulting in a near neutral net radiative effect over the whole anvil lifecycle. However, our knowledge of anvil cloud lifetime and evolution in a warmer climate remains insufficient. A recent hypothesis suggests a thinning of anvil clouds in a warmer climate. However, it is currently not known whether this applies to all stages of the anvil lifecycle or only parts of it.
In this study, we aim to quantify changes in the cloud radiative effect of anvil cirrus across their lifecycle in a warmer climate. To this end, we use passive tracers implemented in the ICON model coupled to the one-moment aerosol module HAM-lite at 5 km resolution. This allows us to track the origin and evolution of individual anvils and to determine a time after detrainment. We run global 40-day simulations, a control present climate, and a warming simulation in which we increase the sea surface temperature by +4 K. We aim to detect changes in anvil lifecycle and thickness that modulate radiative effects. We hypothesize a disproportionate reduction in the thick, cooling phase of anvils relative to their thin warming phase, ultimately resulting in a net positive anvil radiative effect.
How to cite: Hamzeh Marand, S., Gasparini, B., and Voigt, A.: Lifecycle of anvil clouds in a warmer climate as seen by passive tracers in km-scale ICON simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13559, https://doi.org/10.5194/egusphere-egu26-13559, 2026.
Shallow oceanic clouds strongly influence the global energy balance by cooling the planet and mediating exchanges of heat and moisture between the ocean and the atmosphere. A key challenge arises from the fact that these shallow clouds frequently organize into a range of spatial structures that are unresolved in global climate models and are heavily parameterized based on the meteorological conditions. Understanding the coupling of these clouds to meteorology is therefore essential to improve their representation in the models and for reducing uncertainties in future climate projections related to their feedbacks.
Using one year of SEVIRI/MSG data at 15-min temporal resolution, this study first explores the potential of deep machine learning (ML) to detect and classify mesoscale low-level cloud patterns. Using a supervised convolutional neural network, the shallow clouds are then classified into the dominant spatial patterns. The associated cloud properties and underlying meteorological conditions are further analysed using joint histograms based on the CM SAF CLAAS3 cloud climate data record and ERA5 reanalysis datasets to investigate if such information can be useful to evaluate and to better represent these clouds in global climate models.
How to cite: Thomas, M. A., Khalaj, P., and Devasthale, A.: Meteorological conditions associated with the shallow mesoscale clouds in the southern Atlantic, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9723, https://doi.org/10.5194/egusphere-egu26-9723, 2026.
Stratocumulus decks (Sc) are semi-persistent over the eastern subtropical oceans. They are hypothesized to stay there because of cooler ocean and subsidence warming, which together create strong lower-tropospheric stability. It is therefore naturally assumed that the variability of Sc is controlled by subsidence and local SST as well. However, our study finds this assumption is not supported by observations. In this study, we decompose the circulation coupled with lower-tropospheric stability (represented by estimated inversion strength, EIS) and Sc (represented by low-cloud cover, LCC), respectively, from synoptic to interannual timescales. The signals show that local subsidence doesn't dominate EIS variability. Instead, EIS variability is controlled by extratropical Rossby waves. SST only influences EIS on long timescales. More interestingly, LCC is associated with circulation patterns similar to those of EIS but shifted about 10 degrees upstream. Local SST cooling appears to be no more important than upstream warming for Sc. Since Klein et al. (1995), studies have observed that the conditions on the Lagrangian trajectory of Sc are important for its growth. Our results are consistent with them and further emphasize the importance of the upstream Rossby ridge across timescales. It suggests that an upstream warming with a relatively unchanged local Sc condition can also cause an Sc increase.
How to cite: Ding, H., Stevens, B., Lunkeit, F., and Žagar, N.: Rossby modes leading stratocumulus and lower-tropospheric stability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1939, https://doi.org/10.5194/egusphere-egu26-1939, 2026.
Object-based studies provide valuable insights into cloud life cycles in atmospheric circulation models. Cloud tracking tools are therefore widely applied but are often limited by data availability and temporal resolution, ranging from minutes to several hours depending on the model application. To address these limitations, we developed TARGO (Targeted Output), an approach that enables cloud detection during the atmospheric model runtime.
TARGO is implemented as a plugin for the ICON model and coupled via the ComIn interface. It employs the TOBAC tracking algorithm for cloud detection and temporal linking at every model time step and allows the targeted output of arbitrary model variables at cloud positions. Alongside cloud number, lifetime, and location, the targeted output enables the analysis of cloud-centred variables such as vertical profiles of mixing ratios, temperature, and wind velocity.
The capabilities of TARGO are illustrated using s set of realistic limited-area simulations of deep convective development, highlighting the benefits of cloud and object tracking on the model time-step level for analysing cloud evolution and sensitivities in ICON.
How to cite: Faust, M., Cremer, R., and Senf, F.: Online Cloud Tracking with ICON-TARGO, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10993, https://doi.org/10.5194/egusphere-egu26-10993, 2026.
Shallow cumulus clouds critically influence Earth's climate and hydrological cycles. Yet, their simulation remains uncertain in climate models, partly due to idealized geometric assumptions that neglect turbulent boundary irregularities.
To overcome this limitation, this study explores irregularity of cloud lateral ‑boundary using fractal dimension. The fractal dimensions were calculated using the Area‑Perimeter method (characterizing statistical self‑similarity of the cloud field) and the Box‑Counting method (capturing the irregularity of individual clouds). The results show that the fractal dimension derived from the Box‑Counting method is consistently higher than that from the Area‑Perimeter method, indicating that most clouds are not strictly self‑similar structures. A significant positive correlation is found between fractal dimension and precipitation intensity suggesting relationships between cloud morphology and cloud processes. Furthermore, the adiabaticity of cloud was quantified by computing the distribution differences of conserved quantities between the cloud interior and its surroundings.
These findings highlight that incorporating realistic cloud‑boundary geometry into parameterizations can better represent turbulent mixing and cloud‑environment interactions, ultimately contributing to more accurate simulations of shallow cumulus evolution.
How to cite: Song, Q., Chen, J., Janssens, M., and Lu, C.: The characteristics of cloud fractal dimension in shallow cumulus clouds and their implications to cloud processes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16131, https://doi.org/10.5194/egusphere-egu26-16131, 2026.
Posters virtual: Mon, 4 May, 14:00–18:00 | vPoster spot 5
EGU26-14298 | Posters virtual | VPS2
Characterizing cold pools in the ITCZ using soundings from the ORCESTRA campaignMon, 04 May, 14:24–14:27 (CEST) vPoster spot 5
In this study we analyze the occurrence of cold pools in the tropical Atlantic during the ORCESTRA field campaign (August to September 2024), using data collected from over 2000 soundings. We first tested whether the method to detect cold pools based on the mixed-layer height, developed for shallow convective cold pools in the winter trades during EUREC4A, is applicable to the deep convective regime of the ITCZ. The validation by a surface-based detection method and the investigated recovery behaviour of the mixed layer after a cold pool demonstrates its applicability to this environment. On this basis, we examine the distribution and properties of cold pools within the tropical Atlantic. A total of 26% of all ORCESTRA soundings detected cold pools, compared to only 7% during the EUREC4A campaign in the winter trades. The ITCZ region with the highest moisture content and presumably deepest convection features the largest number of cold pools. This presentation will further discuss how the cold pool strength and frequency correlate with wind, moisture and stability.
How to cite: Vogel, R., Mann, L., Robbins-Blanch, N., and Rochetin, N.: Characterizing cold pools in the ITCZ using soundings from the ORCESTRA campaign, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14298, https://doi.org/10.5194/egusphere-egu26-14298, 2026.
EGU26-15029 | Posters virtual | VPS2
All-Sky Camera Upward-looking Thermal Infrared Cloud CharacteristicsMon, 04 May, 14:27–14:30 (CEST) vPoster spot 5
Satellite data sets are the primary source of observations of cloud characteristics, but downward-looking passive sensors cannot see lower-level clouds obscured by higher clouds nor cloud bases. Observations of low clouds with downward-looking satellite IR are hampered by the small brightness temperature differences between the low cloud top and the underlying surface. In contrast, upward-looking thermal IR can readily distinguish warm clouds against the cold sky. By sampling thermal IR cloud characteristics across the diurnal cycle, upward looking thermal IR observations have the potential to yield improved understanding of transitions in cloudiness at sunrise and sunset and differences in the relative importance of different cloud processes with and without SW fluxes.
Our thermal IR all-sky camera was assembled from commercially available, off-the-shelf parts. The key components are a FLIR Boson thermal IR camera and a FLIR PTU-5 pan-tilt mount. The IR camera has a 50° field of view and a resolution of 640x500 pixels. To obtain imagery of the entire sky, the pan-tilt mount points the camera at 14 different directions, each varying in azimuth and elevation. The volume coverage pattern is executed once per minute, and the entire sky is sampled in less than 30 seconds. The images are then stitched together in software to yield a hemispherical array of IR brightnesses from horizon to horizon.
From the imagery we can infer cloud fraction, cloud coverage characteristics relating to the size and shapes of cloud elements, and estimate the altitude of cloud bases at all times of day. Sequences of images reveal the evolution of individual cloud elements and provide information on the phase space of cloud properties across the diurnal cycle and to related to air mass changes, such as the passage of fronts. Combined with other data from lidar and visible all sky cameras, the upward-looking thermal IR data on cloud outer surface temperature details at small spatial scale (10s of meters) and few minute time scale have high potential to yield new insights on cloud initiation and dissipation.
We will detail the performance of the thermal IR all-sky camera and analyze derived cloud characteristics in the context of data from visible wavelength all-sky imagery and additional atmospheric observations.
How to cite: Miller, M. and Yuter, S.: All-Sky Camera Upward-looking Thermal Infrared Cloud Characteristics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15029, https://doi.org/10.5194/egusphere-egu26-15029, 2026.
