Topics covered in this session include, but are not limited to:
- Cloud and aerosol macro- and microphysical properties, precipitation formation mechanisms and their role in the
energy budget
- New constraints on aerosol, clouds or precipitation from EarthCARE
- Observational constraints on aerosol-cloud interactions
- Use of observational simulators to constrain aerosols, clouds and their radiative effects in models
- Explorations of cloud seeding or artificial cloud brightening techniques
- Experimental cloud and aerosol studies
- High-resolution modelling, including large-eddy simulation and cloud-resolving models
- Parameterization of cloud and aerosol microphysics/dynamics/radiation processes
Cloud–aerosol interactions are central to understanding the coupling between microphysical and dynamical cloud processes and precipitation. Our study focuses on shallow cumulus clouds, employing a high-resolution (10 m) large-eddy simulation using the System for Atmospheric Modeling (SAM) coupled with a Spectral Bin Microphysics (SBM) scheme. The model explicitly tracks aerosol evolution both in the air and within droplets, including activation, transport, growth through coalescence, and release back to the atmosphere via droplet evaporation, which is called the aerosol regeneration process.
Simulations of single clouds, under clean and polluted background conditions, show that droplet evaporation efficiently returns large CCN to the atmosphere, demonstrating that shallow convective clouds are an efficient source of these particles in the lower and middle atmosphere. Regeneration significantly modifies the aerosol size distribution and its vertical profile in the atmosphere, and also alters droplet number and size distributions, particularly in diluted cloud regions. Under clean conditions, including the regeneration process reduces surface precipitation by approximately 50%, highlighting a strong microphysical effect.
These findings underscore the importance of accurately representing aerosol regeneration in models to better quantify aerosol–cloud–precipitation interactions and their influence on the Earth’s radiation and water budgets.
How to cite: Arieli, Y., Khain, A., Gavze, E., Altaratz, O., Eytan, E., and Koren, I.: Effects of CCN Regeneration on Cumulus Cloud Microphysics and Aerosol Distribution, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-336, https://doi.org/10.5194/egusphere-egu26-336, 2026.
Stratocumulus clouds cover large parts of the subtropical oceans, and they dominate the net cooling effect of clouds in the Earth’s energy balance. Their non-linear response to anthropogenic aerosol forcings makes them a major source of uncertainty for climate projections. Part of this sensitivity arises from transitions between the two distinct states of stratocumulus (closed and open cells), which are associated with abrupt changes in the cloud’s radiative properties. These transitions can occur locally (pockets of open cells), or as a result of advection with the prevailing winds (stratocumulus-to-cumulus transition by drizzle). Here, we investigate the interaction of such transitions with aerosol perturbations and identify the perturbations that most strongly influence stratocumulus radiative properties.
The mesoscale evolution of stratocumulus decks is modeled using a data-driven, physics-informed stochastic dynamical system with time-dependent parameters. This description encapsulates the scales of cloud formation, mesoscale self-organization, and large-scale conditions through fluctuations, deterministic evolution, and slowly varying parameters, respectively. For relevant parameter conditions, the system features bistability, showcasing the coexistence of open and closed cells. This approach allows us to replicate previous LES results while efficiently extrapolating to a much wider range of parameters and initial conditions, enabling the study of regimes and transitions that LES cannot practically sample.
We find that aerosol-related processes, like rain-formation-washout feedback in open cells and slow aerosol accumulation in closed cells, lead to a lack of timescale separation. As a result, the system’s state is not equilibrated to the steady state prescribed by the large-scale parameters but instead strongly depends on its history. Combined with the system’s bistability, this results in mesoscale pollution plumes dominating the radiative response of stratocumulus, outweighing the effects of background aerosol forcing, cloud feedback, and small-scale fluctuations. It can also lead to delayed radiative responses to intermittent perturbations, such as ship-tracks. This strong mesoscale memory can complicate process attribution from satellite snapshot observations.
Our results highlight that mesoscale cloud organization needs to be considered in numerical modeling as well as in interpreting observations if we are to accurately constrain the response of stratocumulus to aerosol perturbations.
How to cite: Hernandez, B. and Glassmeier, F.: Mesoscale aerosol variability dominates stratocumulus-climate interactions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19943, https://doi.org/10.5194/egusphere-egu26-19943, 2026.
Climate change responds to, and in turn modifies, trends in the Earth’s top-of-atmosphere albedo.
These trends are caused by anthropogenic aerosol–radiation and aerosol–cloud interactions, as well as by non-aerosol feedbacks involving cloudiness and surface albedo.
To separate those contributions, we identify periods of higher and lower albedo and aerosol optical depth in CERES and MODIS satellite retrievals from 2002–2020 over Europe, Eastern North America, Northeastern Asia, and India. Albedo and aerosol optical depth decrease over 2002--2020 in all regions except India, where both increase. We then apply a Gradient Boosting regression to retrieval differences between periods to decompose regional albedo trends into aerosol, aerosol–cloud, and non-aerosol contributions. According to this regression, these trends are explained by changes in cloud fraction (partially aerosol-related), cloud droplet number, aerosol optical depth, and surface albedo, and by climate feedbacks as well. We also calculate sensitivities of top-of-atmosphere albedo to aerosol optical depth, cloud fraction, liquid water path, droplet number, surface albedo, and surface temperature regionally and seasonally. These sensitivities are compared to those obtained from the same Gradient Boosting regression applied to CMIP6 simulations highlighting limitations in aerosol-cloud representation in some CMIP6 models
How to cite: Clement, N.: Aerosol and Non-Aerosol Drivers of Regional Trends in Top-of-Atmosphere Albedo over 2002–2020 in Satellite Observations and Climate Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3756, https://doi.org/10.5194/egusphere-egu26-3756, 2026.
We introduce a new framework for defining marine stratocumulus cloud morphologies using a ternary diagram. The method is applied to one year of satellite observations of stratocumulus clouds and reveals the frequency of occurrence of different morphologies across the ternary space. Large-eddy simulations complement the satellite analysis and show that cloud evolution tends to follow preferred pathways across the ternary space, explaining why observations are concentrated within a limited range of morphologies. We further investigate the susceptibility of cloud liquid water path (LWP) and cloud albedo to variations in droplet number concentration, conditioned on cloud morphology. For the most frequently observed morphologies, LWP and cloud albedo susceptibilities largely offset each other, resulting in a net in-cloud albedo response close to zero. These findings have important implications for marine cloud brightening, whose effectiveness should be evaluated in a morphology-dependent framework, as well as for estimates of cloud radiative forcing due to aerosol–cloud interactions, which should be based on morphology-weighted averages.
How to cite: Goren, T., Choudhury, G., and Feingold, G.: A Ternary Framework for Marine Stratocumulus Morphology and Cloud Susceptibility, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7967, https://doi.org/10.5194/egusphere-egu26-7967, 2026.
Ship tracks serve as “natural laboratories” for investigating aerosol-cloud interactions, one of the largest sources of
uncertainty in climate change research. Observing ship tracks can help constrain the effect of anthropogenic aerosols on
cloud brightness and water content. The validity of these constraints relies, in part, on the accuracy of satellite retrieval
algorithms used to measure cloud properties. A known source of uncertainty in these algorithms is the representation of
the droplet size distribution. Standard operational retrievals (e.g. MODIS) assume a fixed effective variance (veff) for the
modified gamma distribution used to model cloud droplet dispersion. The introduction of aerosols into clouds produces not
only smaller droplets but also a narrower size distribution, contradicting this fixed assumption.
This study utilises a synthetic retrieval experiment to quantify the impact of this assumption. Top-of-atmosphere radiances
are forward-modelled for synthetic ship track scenes, ranging from clean to polluted regimes. These are then inverted using
standard retrieval logic, allowing us to compare retrieved products against a known “truth”, isolating the bias caused solely
by the fixed veff assumption.
Our results indicate that the fixed veff assumption causes a systemic overestimation of effective radius (r𝑒) of 3.31% in the
polluted regime, while optical depth (𝜏) is virtually unaffected. Consequently, liquid water path (LWP) is robustly retrieved
with a small bias of 2.85%, which is expected due to the linear dependence of LWP on r𝑒 and 𝜏. Cloud droplet number
concentration (N𝑑 ), however, suffers from a much larger overestimation of 23.92% in polluted clouds. This large error
arises due to the sensitivity of N𝑑 to the spectral width parameter 𝑘, which is a function of veff. This inflation of droplet
number in ship tracks may exaggerate cloud microphysical sensitivity to aerosols, potentially overstating the Twomey effect
in models constrained by observed N𝑑 and the efficacy of marine cloud brightening if monitored by satellite.
To address this, we introduce a physics-informed deep residual network (ResNet) correction model. This model does not
require prior knowledge of the true veff, and is trained on synthetic retrievals to map observable parameters to the underlying
bias. By leveraging the sensitivity of multi-angle scattering information implicit in the features, the network learns to
predict the veff and resulting correction factor. We demonstrate that the correction framework reduces the error in N𝑑 in
our synthetic retrieval experiment to less than 1% while preserving the accuracy of LWP.
How to cite: Boyce, I., Cicirello, A., and Gryspeerdt, E.: Assessment and correction of retrieval biases in ship tracks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19095, https://doi.org/10.5194/egusphere-egu26-19095, 2026.
The impact of anthropogenic aerosols on clouds remains the most uncertain driver of climate change, largely owing to the noise induced by the meteorological covariability between aerosols and clouds. Natural experiments of aerosol-cloud interactions at anthropogenic aerosol hot spots have recently emerged as a great possibility to overcome the noise of meteorological covariability and to quantify causal impacts of aerosols on clouds. Here, we present observational evidence for increases in cloud water path and coverage downwind of anthropogenic aerosol hot spots. Analysis of the temporal evolution of properties of liquid-water clouds in MODIS satellite data reveals gradual increases in cloud water path and coverage in response to increased cloud droplet numbers in precipitating cloud decks.
Importantly, we also identify a near-instantaneous decrease in cloud water path, which seems to be unphysical and is likely explained by satellite retrieval error. Such a satellite retrieval error has previously likely led to an underestimation of the average increase in cloud water path in response to aerosols and the associated climate cooling effect (e.g. in Toll et al 2019 Nature https://doi.org/10.1038/s41586-019-1423-9). Additionally, we find a stronger decrease in cloud water path downwind of industrial aerosol sources when liquid-water clouds are supercooled below -10 °C, suggesting a potential influence of ice-nucleating particles, consistent with recently discovered glaciation events at anthropogenic aerosol hot spots (Toll et al 2024 Science https://doi.org/10.1126/science.adl0303).
How to cite: Toll, V., Keernik, H., Virtanen, T., Luhamaa, A., Hyttinen, N., Aun, M., Kokkola, H., and Arola, A.: Strong increases in cloud water path and cloud fraction downwind of anthropogenic aerosol hotspots, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10079, https://doi.org/10.5194/egusphere-egu26-10079, 2026.
Mixed-phase cloud thinning (MCT) is an emerging climate intervention strategy that targets supercooled liquid clouds in polar regions during winter. In the absence of sunlight, these clouds exert a net warming effect by trapping outgoing longwave radiation. Seeding polar mixed-phase clouds with ice-nucleating particles (INPs) initiates glaciation, converting persistent, non-precipitating clouds into precipitating ones and reducing their optical thickness. This process enhances the emission of longwave radiation to space, leading to a net cooling of the polar atmosphere. By promoting this radiative cooling, MCT may help restore sea ice and counteract some of the expected warming over polar oceans due to climate change. Initial results suggest that MCT can offset roughly 25% of the expected increase in polar sea-surface temperature from a doubling of CO2. In this work, we apply different resolutions in ICON-HAM, recognising that model resolution is critical for realistically capturing mixed-phase clouds and their inherent phase heterogeneity. We show how the microphysical properties of mixed-phase clouds respond to varying INP concentrations, showing that their sensitivity is strongly resolution-dependent and highlighting the critical role of model scale in assessing the potential efficacy of MCT.
How to cite: Vella, R., Ferrachat, S., Lohmann, U., and Villanueva, D.: Sensitivity of mixed-phase cloud properties to ice-nucleating particles and model resolution, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1755, https://doi.org/10.5194/egusphere-egu26-1755, 2026.
The interactions among aerosol perturbations, cloud droplet freezing, and atmospheric dynamics are a critical source of uncertainty in our understanding of mixed-phase clouds. In the context of glaciogenic cloud seeding, for example, it is unclear whether the vertical transport of newly nucleated ice crystals is passively controlled by pre-existing turbulent flow or actively controlled by latent heat release associated with ice crystal growth. To address this question, we present a comprehensive analysis that bridges the gap between Eulerian field observations and Lagrangian process understanding. We use high-resolution large-eddy simulations coupled with the habit-resolving bin microphysics scheme SCALE-AMPS. These simulations are constrained by in situ measurements from a targeted seeding experiment in supercooled stratus during the CLOUDLAB campaign in Switzerland.
Our sensitivity analysis, which systematically varies the vertical wind conditions at the time of seeding, reveals a fundamental decoupling between the initial vertical wind speed and long-term plume evolution. Although the ambient vertical velocity determines the trajectory of the ice plume during the initial minutes, we identify a "crossover point" at which latent heat release begins to dominate. The growth of the seeded crystals through vigorous vapor deposition releases substantial latent heat, generating a localized buoyancy flux. This thermal perturbation is strong enough to terminate and eventually reverse the descent of plumes that form in downdrafts. Plumes seeded into updrafts rise rapidly, yet they are stopped vertically as they reach the cloud-top inversion layer. Conversely, plumes initiated in downdrafts undergo a delayed, buoyancy-driven ascent, resulting in a deeper vertical spread and enhanced mixing. Although downdraft plumes temporarily lose liquid water when approaching the drier cloud base, they recover and persist within the mixed-phase layer due to self-generated lift.
These results demonstrate that seeded ice plumes actively influence their development and always rise in our simulations independent from the vertical velocity within the cloud. This provides new constraints for modeling aerosol-cloud interactions in weakly forced stratiform systems.
How to cite: Ong, C. R., Zhang, H., Dipankar, A., Lohmann, U., and Henneberger, J.: Latent Heat Release Drives the Vertical Evolution of Seeded Ice Plumes in Supercooled Stratus Clouds, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14543, https://doi.org/10.5194/egusphere-egu26-14543, 2026.
Small-scale microphysical processes describe the interactions of cloud particles and the phase transitions of condensed water in the atmosphere. In numerical weather prediction and climate models, they are represented empirically by a parametrization scheme, which describes their impact on and coupling to the resolved scale. Incomplete process-level understanding of cloud microphysics contributes to the significant model uncertainties linked to the parameterization of sub-grid scale processes. However, progress in reducing these uncertainties is hindered by the lack of microphysical process rate data. Within the parameterization, microphysical process rates are computed at interim steps to update the prognostic cloud variables. Yet, despite their informative value, they are usually not included in the output of km-scale simulations due to resource limitations.
For this purpose, we developed PRecover (microphysical Process Rate recovery), a data-driven post-processing method to recover microphysical process rates in a two-moment microphysics scheme from high-resolution simulation output of the ICOsahedral Nonhydrostatic (ICON) model. Based on machine learning, PRecover emulates the computation of multiple warm-rain and ice microphysical process rates efficiently and flexibly, using a two-step classification-regression approach. Here, we use PRecover for a systematic evaluation of cloud microphysical processes. With a focus on instantaneous process rates, we demonstrate the functionality of PRecover. Additionally, we study the relevance of different microphysical processes and quantify their relative contribution to pathways of precipitation formation, e.g. the relative contributions of autoconversion and accretion to warm rain formation in different cloud regimes. In contrast to previous studies, which were often limited to idealized simulations, we are able to analyze the output of extensive high-resolution simulations in a regional and global configuration.
How to cite: Simm, M., Beucler, T., and Hoose, C.: Towards an improved understanding of cloud microphysics via data-driven process-rate diagnostics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13818, https://doi.org/10.5194/egusphere-egu26-13818, 2026.
With improvements to the global observational networks and model fidelity, climate models are getting increasingly good at producing an accurate cloud climatology. However, there is still significant variation in the response of their clouds to aerosol perturbations. This variation is magnified when considering intentional perturbations to clouds (such as marine cloud brightening), where a model not only needs to get aerosol-cloud interactions right 'on average', but in specific conditions. A similar challenge exists in developing observational constraints for models, where aerosol-cloud susceptibilities (relationships determined from temporal variability over long timescales) are harder to use to assess specific conditions. We need observations that can help constrain cloud processes, ensuring that a simulated cloud is 'right for the right reasons'.
While a simulated cloud might appear similar to an observed one, an external perturbation provides a unique opportunity to uncover the processes that have set the cloud properties. By 'poking' a cloud, they allow us to see if the cloud behaves like a real one, or it is just superficially similar (like a cake).
Here we show how the time evolution of clouds following inadvertent perturbations (so-called 'natural experiments') can be used to identify the role of different processes in setting cloud properties. The cloud response following these experiments can be used to identify model biases, improving the accuracy of aerosol-cloud processes. We link these natural experiments to the response in large-scale temporal cloud variation, highlighting how this can be used to isolate causal aerosol impacts on clouds and providing process-level constraints on climate model behaviour.
How to cite: Gryspeerdt, E., Driver, O., Marjani, S., Nair, V., Pugsley, G., and Tippett, A.: Is it cake (or a cloud)? Using time evolution and natural experiments to uncover aerosol impacts on cloud processes , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14943, https://doi.org/10.5194/egusphere-egu26-14943, 2026.
The effective radiative forcing from aerosol-cloud interactions (ERFaci) remains one of the most uncertain aspects of our understanding of the Earth’s sensitivity to greenhouse gases. This uncertainty is largely due to the uncertainties in the parameterizations used to represent subgrid-scale processes in global Earth system models (ESMs). Perturbed parameter ensembles (PPEs), which vary the values of multiple parameters simultaneously, can help address this parametric uncertainty; however, ERFaci estimates are also impacted by structural uncertainties in the design choices of different ESMs. An accurate estimate of ERFaci also hinges on the availability of observations that we can use to assess the fidelity of ESMs in simulating plausible aerosol-cloud interactions.
Here, we focus on a PPE run in the E3SMv3 model, where 25 parameters related to aerosols and microphysics are perturbed across an ensemble of 250 nudged two-year simulations with both preindustrial and present-day aerosol forcings. In the E3SMv3 PPE, we examine which variables and which locations around the globe have the strongest correlations with the global ERFaci response to determine which measurements would be the most useful in constraining ERFaci. We also consider structural uncertainty in ERFaci by examining an opportunistic multi-model PPE consisting of a set of preindustrial and present-day PPE simulations run in different ESMs. Using the multi-model PPE, we identify the regions with the greatest disagreement between ESMs, which indicate where there are large structural uncertainties in the simulation of aerosol-cloud interactions. Together, these results highlight which regions and variables are subject to the greatest parametric and structural uncertainty related to simulating ERFaci. We argue that additional observations of key variables from these regions would have the greatest impact on reducing uncertainty in ERFaci and thus would help narrow our estimates of future projected temperature changes. This work provides a starting point for a new deployment planning framework using PPEs as part of an observing system simulation experiment (OSSE) to help improve our process understanding and simulation of aerosol-cloud interactions simultaneously.
How to cite: Nugent, J. and McCoy, D.: What observations do we need to better constrain ERFaci in Earth system models?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8424, https://doi.org/10.5194/egusphere-egu26-8424, 2026.
Deep convective clouds (DCCs) are crucial in the hydrological cycle and Earth’s energy budget. However, even with same meteorological conditions, continental DCC remains more, higher, and stronger than that over ocean. Here, through nine years of full-cycle tracking on tropical DCCs, the land-ocean different effect of aerosols on DCC is quantified. our observations discovered that both fine aerosols (FA, radius<1 μm) and coarse sea salt aerosols (CSA, radius>1 μm) play significant but opposing roles in the DCC development. Adding fine aerosols significantly invigorate the DCC through delaying the rain formation, increasing in total area and rainfall amount of DCC by up to 5 times at the optimal concentration of 5 µg/cm3. The fine aerosol effect contributes to the intensive DCC, frequent lightning and heavy rain event over land. In contrast, adding coarse sea salt aerosol weakens the cloud vigor and lightning by producing fewer but larger cloud drops, which accelerate warm rain at the expense of mixed-phase precipitation. Adding CSS weaken the DCC, but expanded its area by 4 times. Corresponsdingly, the lightning density is reduced by up to 90% due to the additional CSS-enhanced warm rain process. The CSS effect contributes the moderate DCC, few lightning and expansive rain event over ocean. These findings indicate that the different aerosol effects on DCC explain the land-ocean contrast on intensity and frequency of DCC.
How to cite: Pan, Z., Yin, J., Liu, F., Zang, L., Mao, F., and Rosenfeld, D.: Opposite but comparable effects of fine and coarse aerosols on lifecycle properties of deep convective clouds, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9044, https://doi.org/10.5194/egusphere-egu26-9044, 2026.
Clouds and aerosols remain major sources of uncertainty in numerical weather prediction and climate applications. Reducing these uncertainties requires better observational constraints on key state variables. Visible satellite observations contain rich information on cloud and aerosol properties, yet they are still only marginally exploited in data assimilation systems due to complex and expensive radiative transfer simulations. This study explores the potential of visible reflectances to estimate and constrain cloud and aerosol parameters within the Integrated Forecasting System (IFS) using direct all-sky assimilation in a 4D-Var framework. We demonstrate the assimilation of visible imager observations from various satellite platforms. For clouds, the approach is close to operational readiness. For aerosols, we conducted a proof-of-concept study directly assimilating visible reflectances in cloud-cleared scenes within the IFS-COMPO configuration. We evaluate the impact of visible assimilation on cloud liquid water and ice, as well as on thermodynamic fields. The experiments indicate an improved fit of analyses and short-range forecasts to observed reflectances, several-percent changes in cloud water and ice path, and measurable impacts on temperature and humidity. A case studies of low-level maritime stratus highlight that visible observations can effectively constrain low-level clouds and correct model biases where information from other satellite observations is sparse. The analysis and forecast departures in reflectance space further reveal systematic model biases, offering diagnostic insight into deficiencies in current cloud and aerosol representations. These results represent one of the first demonstrations of direct visible reflectance assimilation in a global 4D-Var system for both clouds and aerosols. At this stage, clouds and aerosols are treated separately, providing an initial step toward broader exploitation of visible observations. Beyond forecasting, the approach offers strong potential for future reanalysis by improving the consistency and realism of long-term cloud records.
How to cite: Necker, T., Quesada Ruiz, S., Lupu, C., Firat, V., and Benedetti, A.: Exploiting visible reflectances for estimating cloud parameters and aerosols, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12128, https://doi.org/10.5194/egusphere-egu26-12128, 2026.
Arctic warm air mass intrusions, events characterised by the transport of strong heat and moisture anomalies from the mid-latitudes into the Arctic, have received increasing attention in recent years because of their pronounced impacts on the Arctic regional climate. However, the influence of anthropogenic aerosols transported within these intrusions on cloud properties and the Arctic radiative budget remains poorly constrained. In this study, we investigate a well-characterised warm air mass intrusion with exceptionally high aerosol loading observed during the MOSAiC expedition in spring 2020. Using the WRF-Chem-Polar model, we simulate the April 2020 event both with and without the observed anthropogenic aerosol transport, in order to isolate and quantify the aerosol impacts relative to those of the warm air mass itself.
We analyse the effects of the anthropogenic aerosols on simulated cloud microphysical and macrophysical properties, including cloud fraction, cloud droplet number concentration, droplet size, and cloud liquid water content, as well as the resulting cloud radiative effects at the surface. The presence of anthropogenic aerosols leads to enhanced cloud droplet formation within the plume, smaller droplet sizes, and suppressed precipitation. These changes produce a net surface cooling effect, most pronounced over dark, open ocean surfaces where shortwave radiative impacts dominate. Over ice-covered regions, however, the radiative response is substantially weaker, reflecting the high surface albedo and reduced sensitivity of the surface energy budget. We find that aerosol-driven longwave warming is small and generally offset by shortwave cooling. Furthermore, the aerosol-driven shortwave cooling associated with enhanced droplet numbers is spatially heterogeneous and confined to limited regions. The broader implications of these findings for the role of aerosol-cloud interactions in Arctic regional climate are discussed.
How to cite: Price, R., Marelle, L., Bastien, L., Lapere, R., Schmale, J., Heutte, B., and Thomas, J.: Isolating aerosol impacts on cloud properties and the surface radiative budget during an extreme Arctic warm air mass intrusion, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12328, https://doi.org/10.5194/egusphere-egu26-12328, 2026.
Natural and anthropogenic aerosols influence Earth’s climate through many different radiative and cloud microphysical processes; directly by scattering and absorbing radiation and indirectly by serving as cloud condensation and ice nuclei. They are thought to influence precipitation on global to local scale, but the mechanisms governing their effects and their relative importance remain highly uncertain, lowering the confidence in future projections on smaller scales. Understanding how aerosols affect extreme precipitation is especially important, given its potential large societal impacts, but while many Earth system models include complex aerosol-radiation-cloud processes and feedbacks, smaller scale processes are not explicitly resolved and instead parameterized. However, the newer generation of km-scale cloud-resolving Earth system models allow for these processes to be studied in much greater detail.
In this study we analyze results from 1-year global km-scale simulations run using ICON coupled to HAM-lite, a one-moment aerosol module derived from the two-moment module HAM. The simulations are run with prescribed pre-industrial and present-day aerosol emissions, allowing us to investigate the impacts of anthropogenic aerosols on extreme precipitation. Preliminary results indicate a strengthening of extreme precipitation rates in the tropics in the present-day simulation compared to the pre-industrial control, with regional differences that will be explored further to distinguish between large-scale dynamical changes and local convective changes.
How to cite: Berntell, E., Weiss, P., Bender, F., and Mauritsen, T.: Anthropogenic aerosol effects on extreme precipitation in the tropics in ICON HAM-lite global km-scale simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13184, https://doi.org/10.5194/egusphere-egu26-13184, 2026.
The top-of-atmosphere net radiative effect of convective anvils is estimated to be close to zero and arises from a balance of significant short-wave cooling and long-wave warming over a complex diurnal cycle. When anvils are optically thick, the cooling due to daytime scattering of shortwave solar radiation dominates. In contrast, optically thin anvils have weaker scattering of solar radiation, so longwave warming becomes the dominant effect. Hence, it is essential to understand the controls of anvil radiative properties over the convective lifecycle, which arises from a complex interplay of convective cloud dynamics and microphysics. The convective mass flux modulates anvil extent, and changes in ice crystal size and morphology affect anvil lifetime and radiative properties. Convective anvils have been proposed to respond to global warming (cloud feedbacks) and anthropogenic aerosols (aerosol-cloud interactions). However, the associated uncertainties remain large and key relevant processes are not represented in the current generation of climate models. Emerging kilometre-scale climate models present new opportunities to examine these effects at the process level.
In this work we bring together multiple research strands to quantify the controls of convective anvil clouds and associated radiative effects over the convective lifecycle towards understanding its sensitivity to climate and air pollution changes. We use the tobac cloud tracking framework to track convective cores and associated anvils in 4D across regional and global km-scale ICON model simulations which allows us to quantify the link between convective mass flux, anvil extent and anvil radiative properties. We apply this framework to regional high-resolution simulation of ICON coupled to HAM-lite, our reduced complexity aerosol model derived from the microphysical aerosol scheme HAM [Weiss et al., GMD, 2025], to explore the sensitivity of anvils and their radiative effects to aerosol perturbations in the context of the ORCHESTRA/EarthCARE Model Intercomparison Project (ECOMIP) as well as the TRACER campaign MIP. We find that an increase in aerosol increases cloud droplet numbers, suppresses warm rain formation, increases convective mass flux and thereby upper tropospheric ice water content and will discuss how these changes translate into anvil cloud radiative effects. Prototype next generation km-scale climate models are implicitly already including such anvil radiative effects; however, these currently remain unconstrained by observations. We develop novel observational constraints on the convective anvil cloud lifecycle through consistent tracking of convection using the tobac-flow cloud tracking framework [Jones et al., 2024] between MSG SEVIRI observations and forward simulated geostationary satellite radiances from ICON model output. This reveals that deep convective systems in ICON grow too fast and show a faster dissipation of thick to thin anvils than observations, which affects their radiative effects.
Our work provides novel approaches to improve our understanding of aerosol effects on convective clouds and climate.
How to cite: Stier, P., Jones, W., Ritman, M., Sela, M., and De, S.: Anthropogenic perturbations to anvil cloud radiative effects? , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23162, https://doi.org/10.5194/egusphere-egu26-23162, 2026.
The magnitude of aerosol–cloud radiative forcing remains one of the dominant uncertainties in climate projections. Emergent constraints are increasingly used to reduce this uncertainty by linking observable cloud properties to modelled aerosol–cloud interactions. Their physical validity, however, depends critically on whether models reproduce the same cloud–aerosol coupling mechanisms as the real atmosphere. Here we show that current Earth system models exhibit a systematic structural inconsistency in cloud microphysics that undermines the physical interpretability of emergent constraints on aerosol–cloud radiative forcing (ΔFaci).
Using perturbed-parameter ensembles (PPEs) of UKESM1, we analyse observed and modelled relationships between cloud droplet number concentration (Nd), liquid water path (LWP), and aerosol perturbations across key marine stratocumulus regimes. We show that the Nd–LWP sensitivity, which controls how strongly clouds brighten in response to aerosol, varies by a factor of ∼4 across model parameterisations but is tightly constrained by observations. As a result, models that reproduce present-day mean cloud properties can require physically implausible Nd–LWP responses to generate their aerosol forcing, leading to equally plausible yet physically incompatible ΔFaci estimates.
This structural degeneracy implies that conventional emergent constraints targeting mean cloud states cannot uniquely constrain aerosol forcing. Instead, physically meaningful constraints must explicitly account for the microphysical response pathways linking aerosols, cloud water, and radiation. Our results reveal a previously under-recognised structural source of uncertainty in aerosol–cloud interactions and provide a new physically grounded diagnostic for evaluating and constraining modelled aerosol–cloud radiative forcing.
How to cite: Ghosh, K., Regayre, L. A., Prévost, L. M. C., Johnson, J. S., Owen, J., Webb, I., Oakley, J., and Carslaw, K. S.: Structural inconsistency in cloud microphysics limits emergent constraints on aerosol-cloud radiative forcing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13798, https://doi.org/10.5194/egusphere-egu26-13798, 2026.
New observational datasets of atmospheric state and key atmospheric processes and quantifying observational uncertainties are essential to better understand different feedback mechanisms and increase the fidelity of models at different scales. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility aims to alleviate these and other deficiencies and needs by providing tools and comprehensive suites of advanced in-situ and remote-sensing ground-based and airborne observations. Here, we present new and updated retrievals and high-level data products developed at ARM, leveraging machine learning (ML) and other advanced techniques. These ML-augmented multi-instrument retrievals provide useful microphysical quantities, accompanied by uncertainty estimates, including ice precipitation microphysical properties in sub-cloud profiles, hydrometeor phase classification profiles, all-sky imager pixel segmentation, and aerosol size distributions spanning an extensive size spectrum. Finally, we also present a set of ARM-supported tools to bridge between ARM observations and model simulations, such as the Earth Model Column Collaboratory (EMC²).
How to cite: Silber, I., Comstock, J. M., Shilling, J. E., Tian, J., Zhang, D., Flynn, D. M., and Cromwell, E. L.: Advancements in ARM User Facility Products and Tools using Machine Learning to Support Atmospheric Research, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1915, https://doi.org/10.5194/egusphere-egu26-1915, 2026.
Oceans contribute to atmospheric aerosol concentration by directly emitting particles and releasing aerosol precursor gases which later reacts and condenses to form secondary aerosols. Marine aerosols modulate the Earth’s radiative budget by scattering and absorbing radiation and influencing the cloud microphysics. Aerosol–cloud interactions are one of the largest sources of uncertainty in climate projections, and understanding the natural, pre-industrial aerosol background is essential for constraining anthropogenic influences. Marine aerosols constitute a substantial fraction of this natural aerosol burden.
In this study, we assess the marine aerosols predictions and their effects on climate using the Norwegian Earth System Model version 2 (NorESM2). NorESM2 uses OsloAero6, a production tagged aerosol module to simulate aerosol size distribution, aerosol mass, detailed aerosol physical, chemical and optical properties. OsloAero6 includes marine aerosols as sea salt emissions driven by wind speed and sea surface temperature (SST), primary organic aerosols (POA) emissions linked to wind speed, SST and chlorophyll concentrations, secondary organic (SOA) and sulphate aerosols formed from the oxidation of dimethyl sulphide (DMS).
Ten-year simulations from 2009-2019, nudged to ERA-Interim reanalysis data are analyzed. Predicted total aerosol number concentration (Ntotal) and the number concentration of particles larger than 100 nm (N100) are evaluated against long-term surface observations from four marine sites: Ascension Island, Zeppelin, Graciosa Island, and La Réunion.
At Ascension Island, the model overestimates Ntotal by up to 500% and N100 by up to 200% compared to observation, largely due to long-range transport of aerosols from African continent. At Zeppelin, N100 is underestimated by up to 200% and Ntotal by up to 100% compared to observation, maybe due to underestimated long-range transport, missing aerosol sources, or an underrepresented condensation sink that limits particle growth. At Graciosa Island and La Réunion, predicted aerosol number concentrations agree with observations within 30%. Overall, predicted Ntotal agrees reasonably well with observations across sites but underestimates N100, while capturing the observed seasonal variability.
To quantify radiative impacts, sensitivity simulations were performed by removing marine aerosol sources to study the direct radiative effect (DRE) and aerosol indirect effect (AIE). The removal of sea salt results in a warmer climate, with decrease in the magnitude of globally averaged DRE and AIE by 0.013 Wm−2 and 0.003 Wm−2, respectively, relative to the control simulation. This demonstrates the net cooling effect of sea salt through radiative scattering and cloud interactions. Removing POA and DMS also leads to warming driven by decrease in the magnitude of AIE, with negligible changes in DRE, consistent with their weaker direct radiative influence. These results highlight the importance of prediction of marine aerosol size and composition, and their role in regulating Earth’s radiative balance and cloud properties.
Acknowledgements. We acknowledge the U.S. DOE ARM user facility for providing data from the Graciosa (ENA) and Ascension Island (ASI) sites. We also thank the NILU EBAS database and the contributing networks (ACTRIS, GAW-WDCA, EMEP) for the chemical composition, size distribution, and meteorological data from the Zeppelin (Ny-Ålesund) and Maïdo (La Réunion) observatories.
How to cite: Jadav, A., Singh, G., Yli-juuti, T., Blichner, S., and Sporre, M.: Evaluating representation of marine aerosols in Norwegian Earth System Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14651, https://doi.org/10.5194/egusphere-egu26-14651, 2026.
Aerosol properties, loading, trends, and variability in the upper troposphere are key to understanding the evolving state of the atmosphere and the role of aerosols in climate and cloud processes. However, long-term in-situ aerosol observations at high altitudes remain scarce worldwide, particularly in the Global South. This observational gap limits our ability to develop a global perspective on aerosol sources, processes, and impacts within the climate system.
Here we present 13 years (2012–2024) of continuous aerosol-related measurements conducted at the world’s highest Global Atmosphere Watch (GAW) station, located on Mount Chacaltaya (CHC) in the central Andes of Bolivia at an elevation of 5.2 km a.s.l. This dataset is one of the longest in existence on the South American continent and therefore provides a unique opportunity to evaluate trends in aerosol concentrations and properties. These trends and properties are influenced by, for example, biomass burning in the Amazon, the transport of pollution from the conurbation of La Paz and El Alto, located 18 km to the south, and the subsidence of air masses from the upper troposphere.
We focus on particle number size distributions (PNSD), equivalent black carbon (eBC), and related meteorological and chemical tracers, including water vapor mixing ratio (WVMR) and carbon monoxide (CO). We characterize aerosol properties and loading by combining traditional time-series analysis (e.g., separation by hour of day, season, and year) with an unsupervised k-means clustering approach that disentangles the dominant atmospheric regimes influencing aerosol properties at CHC. The clustering uses PNSD, eBC, and WVMR as input variables and identifies seven distinct categories of days, hereafter referred to as atmospheric regimes, which represent significantly different source regions and aerosol processing pathways (e.g., cloud processing, wet deposition, and new particle formation). The performance of the clustering is evaluated using independent tracers, namely CO concentrations and HYSPLIT back trajectories. For each regime, the individual days grouped within it exhibit internally consistent CO levels and air-mass provenance that are clearly distinct from those of other regimes. This result is particularly encouraging given that neither CO nor back trajectories were included as inputs to the clustering algorithm.
One regime is particularly noteworthy, representing a persistent free-tropospheric state characterized by extremely low WVMR, CO, and eBC, along with signatures of early-morning new particle formation. We find that the concentration of particles in this regime has significantly decreased over the 13-year period which indicates a declining upper-tropospheric particle concentration. A second notable regime is associated with biomass burning. We find that its occurrence has increased over time, from ~10% of days during the biomass-burning season (August–November) in the first years to ~50% in the last years. This suggests an increment on the number of biomass burning episodes measured at the station. Additional categories capture aerosol–cloud processing during Amazonian boundary-layer uplift, local eBC influence from the La Paz–El Alto metropolitan area, and strong nucleation under dry, coastal/Altiplano air masses. Overall, these results emphasize a region in rapid change and the importance and utility of long-term measurements in under sampled areas.
How to cite: Aliaga, D., Velarde, F., Andrade, M., Laj, P., Uzu, G., Weinhold, K., Wiedensohler, A., Riipinen, I., and Krejci, R.: Aerosols in the Andes: Microphysical Properties and Long-Term Variability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8699, https://doi.org/10.5194/egusphere-egu26-8699, 2026.
Aerosols play a central role in regulating cloud microphysical processes and climate through their ability to act as ice-nucleating particles (INPs). Mineral dust is a dominant global INP source, with laboratory and field studies demonstrating that specific mineral phases—most notably K-feldspar and quartz—control ice formation in mixed-phase clouds. This motivates their explicit representation in Earth system models seeking to reduce uncertainties in aerosol–cloud interactions.
Building on the fundamental aerosol–cloud interaction framework implemented in EC-Earth3, we present recent advances in the representation of mineral dust emissions and heterogeneous ice nucleation in the OpenIFS 48r1 atmospheric model, as part of the development pathway towards EC-Earth4. We introduce a new mineral dust emission scheme that explicitly resolves dust mineralogy using global mineralogical atlases. The scheme calculates the atmospheric abundance of individual dust minerals and incorporates key land surface controls—vegetation, soil type, and potential sources—allowing more realistic dust simulations and hence potentially improving projections of future climate impacts.
The model allows flexible selection among state-of-the-art mineralogical datasets, including the new NASA EMIT mineral map, which enables sensitivity studies of mineral-specific INP activity. Model performance is evaluated and calibrated against long-term dust surface concentration measurements from global and regional observational networks, while simulated aerosol optical depth is compared with observations from dust-dominated ground-based stations to constrain dust loading and transport.
INP concentrations are further evaluated by applying mineral-specific laboratory-based ice-nucleation parameterizations to the simulated mineral dust fields over a range of temperatures. This enables direct assessment of how different mineral phases contribute to INP concentrations and provides a benchmark for future fully coupled aerosol–cloud simulations.
Together, these developments establish a more physically consistent and mineralogy-aware representation of dust–cloud interactions in EC-Earth4, supporting improved quantification of aerosol-driven uncertainties in cloud feedbacks and climate sensitivity.
How to cite: Chatziparaschos, M., Costa-Surós, M., Gonçalves Ageitos, M., Vacondio, S., Bergman, T., Holopainen, E., Huijnen, V., Kokkola, H., Laakso, A., Le Sager, P., van Noije, T., Wu, L., and Pérez García-Pando, C.: Linking Dust Mineralogy and Ice Nucleation in Mixed-Phase Clouds in EC-Earth4, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19555, https://doi.org/10.5194/egusphere-egu26-19555, 2026.
Understanding aerosol properties is essential for assessing their impacts on clouds, precipitation and climate. These interactions depend strongly on the aerosol levels present as well as the dynamical forcing (vertical velocity) that drive supersaturation development and droplet formation. Datasets that span the wide range of conditions found throughout the atmosphere are much needed to help constrain models and to characterize cloud susceptibility to aerosol.
High-altitude mountain stations, offer an exciting opportunity to study aerosol-cloud interactions because clouds often form at their peaks. The aerosol that acts as precursors of droplet formation can originate from near ground (i.e., within the planetary boundary layer) or long-range sources (i.e., through free-tropospheric transport). Being able to unravel the periods during which clouds are influenced by each air type can vastly expand the scientific value and relevance of aerosol-cloud studies at mountain tops.
The Demokritos Helmos Hellenic Atmospheric Aerosol and Climate Change ((HAC)²) station in Greece (2314 m a.s.l.) is the only high-altitude station in the eastern Mediterranean, a region highly sensitive to climate change. It is located at the crossroads of different air masses and is therefore very well-suited for aerosol-cloud interaction studies. To enhance understanding of the processes driving the formation and evolution of warm and mixed-phase clouds, the CALISHTO (Cloud-Aerosol InteractionS in the Helmos Background TropOsphere) and CHOPIN (Cleancloud Helmos OrograPhic sIte experiment) campaigns were conducted at Mount Helmos during the autumn-winter periods of 2021–2022 and 2024–2025, respectively. During these campaigns, in-situ and remote sensing measurements at a number of sites, located at the Kalavrita Ski Center and the (HAC)2 station, were used to characterize the influence of the PBL at the (HAC)2 and also the concentration of cloud droplets when a cloud forms at the station. We use these measurements, together with a state-of-the-art cloud droplet formation parameterization to predict the concentrations of CCN, and cloud droplet number that form throughout the year at the (HAC)2. Using established metrics, we separate the periods of BL and FT influence and thus determine the susceptibility of clouds to aerosol in each airmass type and class. The calculations are also confirmed using in-situ measurements of cloud droplet number obtained through a PVM-100.
How to cite: Zografou, O., Foskinis, R., Gini, M. I., Fetfatzis, P., Granakis, K., Mitsios, C., Molina, C., Kommpula, M., Papayannis, A., Eleftheriadis, K., and Nenes, A.: Understanding Cloud Formation in Eastern Mediterranean Mountainous Environments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19092, https://doi.org/10.5194/egusphere-egu26-19092, 2026.
Measurements of ambient ice nucleating particle (INP) composition, concentration, and ice activation properties in the cirrus regime are extremely sparse. However, such measurements are fundamental to advancing understanding of cirrus extent and sensitivities to varied sources, as well as the impacts from perturbations of natural and/or anthropogenic origin. Here we present a potential approach to providing cirrus-relevant INP observations in both fast-response and systematic measurement scenarios. We will leverage experience with an existing upper-tropospheric sampling network relying on small weather balloons to enable collection of INP for off-line analysis. We will present an overview of our scheme, provide status of new instrumentation, and identify the significant technical challenges to be overcome. Finally, we will discuss how we plan to use these measurements to improve cirrus modeling, including better capturing the effects of anthropogenic aerosols on cirrus properties, distribution, and overall radiative effect.
How to cite: Schwarz, J., Whale, T., Asher, E., Baron, A., Eastham, S., jensen, E., Kinney, N., Murray, B., rollins, A., rosenlof, K., smith, K., and Thornberry, T.: Balloon-borne measurements and off-line analyses to improve constraints on ice nucleating particles in the cirrus regime. , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8400, https://doi.org/10.5194/egusphere-egu26-8400, 2026.
In arid and semi-arid subtropical regions like the Arabian Peninsula, rainfall enhancement through glaciogenic cloud seeding represents a critical strategy for water resource management. However, quantifying the efficacy of these interventions remains a challenge due to high natural meteorological variability. This study investigates the physical "seeding signature" of silver iodide (AgI) aerosols within mixed-phase convective clouds over Saudi Arabia, utilizing an integrated approach that combines in-situ aircraft measurements, polarimetric Doppler radar observations, and high-resolution Weather Research and Forecasting (WRF) simulations.
Airborne observations using high-frequency microphysical probes reveal a distinct modification of the cloud droplet size distribution (DSD) following AgI release in the seeded clouds compared to the non-seeded clouds. A significant depletion of supercooled liquid water content (SLWC) was recorded, concurrent with a three-to-fivefold increase in ice crystal number concentrations (N_ice) within the target volume. These in-situ findings are corroborated by ground-based Doppler radar products, which identify a characteristic "reflectivity plume" downwind of the seeding track.
To isolate the seeding-induced perturbation from natural cloud evolution, high-resolution (3 km) WRF simulations were performed. By comparing "seeded" and "control" (non-seeded) model realizations, we establish a causal link between the introduction of artificial nuclei and the observed microphysical shifts. The model-derived baseline suggests that the observed SLWC depletion and subsequent reflectivity enhancement exceeded natural variability thresholds, providing a robust statistical signature of successful glaciation. This study underscores the necessity of a multi-platform framework linking microscopic particle-level changes to macroscopic radar signatures for the verification of weather modification efforts in subtropical climates.
How to cite: Boragapu, R., Abandeh, K., Albar, A., and Ghulam, A.: Cloud Seeding Signature over Saudi Arabia - Insights from airborne observations and numerical model simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16203, https://doi.org/10.5194/egusphere-egu26-16203, 2026.
Subgrid-scale vertical velocity variability (σw) plays a key role in aerosol activation, cloud droplet number concentration (CDNC), and cloud microphysical evolution. Despite its importance, σw remains one of the highly uncertain parameters in climate models, particularly under extreme atmospheric conditions. Recently developed machine-learning turbulence parameterizations, trained on global high-resolution climate model simulations, offer a promising alternative to traditional schemes. However, their ability to apply and generalize to real atmospheric conditions and to regimes that differ substantially from those represented in the training simulations and the resulting implications for cloud processes remain largely untested.
Here, we evaluate the deep-learning based σw parameterization, Wnet across two physically contrasting observational regimes that are highly relevant for aerosol–cloud interactions: (i) the ultra-stable Arctic boundary layer observed during the CLAVIER campaign at the Villum Research Station, and (ii) strong orographic turbulence associated with cloud formation during the CHOPIN campaign at Mt. Helmos in the Mediterranean. Using high-frequency observations, we drive Wnet in offline mode and compare its σw estimates against observed vertical-velocity variability, with a focus on conditions controlling cloud activation. We further assess the robustness of Wnet by diagnosing out-of-distribution (OOD) atmospheric states relative to its global training space and examining how such states are associated with systematic σw errors. This framework enables identification of distinct ML failure modes under different atmospheric conditions, thereby elucidating the physical boundaries of applicability of ML-based Wnet turbulence scheme. Finally, we investigate when and where σw errors translate into meaningful biases in cloud-relevant quantities, particularly CDNC, by linking σw discrepancies to observed cloud properties and activation regimes. By explicitly connecting ML-driven turbulence errors to cloud microphysical impacts, this study provides a physically grounded evaluation of ML turbulence parameterizations in regimes critical for aerosol–cloud interactions.
The results will inform the safe and interpretable use of ML-based σw schemes in Earth-system models and highlight key challenges for their application in extreme atmospheric environments.
How to cite: Irfan, M., Barahona, D., Holopainen, E., and Nenes, A.: A Deep-Learning Parameterization of Vertical Velocity Variability (Wnet) Tested Across Contrasting Atmospheric Regimes: From the Arctic to the Mediterranean, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17471, https://doi.org/10.5194/egusphere-egu26-17471, 2026.
Climate models cannot afford the computational cost of the meter-scale resolution needed to accurately resolve turbulence and convection in the boundary layer. Machine learning based Gaussian process emulators (GPEs) have been recently presented as an alternative to close the gap between meter-scale and kilometer-scale resolutions (Ahola et al., 2022, https://doi.org/10.5194/acp-22-4523-2022). An emulator offers an improved alternative for climate models to include turbulence effects in the boundary layer on the formation of stratocumulus clouds. Here we have trained a GPE using vertical winds from the large-eddy model UCLALES following the approach of Ahola et al. (2022). The training data of our updraft emulator includes a wide range of stratocumulus conditions both over land and sea. The predicted standard deviation of cloud base vertical wind can be used directly in the activation calculation of global models. We have additionally implemented our emulator to the OpenIFS global climate model. In this study, we present a comparison of different parametrizations for updraft velocities, including our emulator, and how these affect cloud droplet number concentration and aerosol radiative forcing in the global scale.
This project has received funding from Horizon Europe programme under Grant Agreement No 101137680 via project CERTAINTY (Cloud-aERosol inTeractions & their impActs IN The earth sYstem).
How to cite: Hyttinen, N., Calderón, S. M., Holopainen, E., Raatikainen, T., Mielonen, T., Romakkaniemi, S., and Kokkola, H.: UCLALES-based cloud base updraft emulator for global models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16768, https://doi.org/10.5194/egusphere-egu26-16768, 2026.
Previous studies have demonstrated that the susceptibility of clouds to aerosol loading, quantified by the aerosol–cloud interactions (ACI) index, is strongly modulated by environmental conditions. The South China Sea (SCS), alternately influenced by the southwest and northeast monsoons, provides a unique natural laboratory for examining ACI under contrasting thermodynamic and moisture conditions. Using long-term satellite observations and reanalysis datasets, we investigate ACI over the SCS with a focus on non-precipitating warm liquid clouds. Based on large-scale circulation patterns and moisture conditions, the SCS monsoon system is classified into three distinct phases: the southwest monsoon wet period (SWMW), the northeast monsoon wet period (NEMW), and the northeast monsoon dry period (NEMD). The robust Twomey effect was observed across all three periods. The ACI intensity strengthens progressively from SWMW to NEMW and further to NEMD, corresponding to the transition from moist, convectively active conditions to dry, stably stratified environments. This transition is governed by variations in water-vapor availability and lower-tropospheric stability (LTS), where stable conditions may enhance ACI through aerosol accumulation, while moist environments are likely to weaken it via enhanced condensational and coalescence growth.These findings demonstrate that thermodynamic stability and moisture availability play central roles in regulating ACI over the SCS.The coupled effects of aerosols, humidity, and atmospheric stability control marine warm-cloud microphysical processes in tropical monsoon regions, providing robust observational constraints for improving ACI parameterizations in climate models.
How to cite: Liu, Y., Jia, H., and Han, Y.: Contrasting Monsoon-Driven Susceptibility of Marine Warm Clouds to Aerosols over the South China Sea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2720, https://doi.org/10.5194/egusphere-egu26-2720, 2026.
For models to reliably predict future climate and air-quality scenarios, an accurate representation of the cloud condensation nuclei (CCN) budget is key. In this regard, the effect of precipitation on the particle number size distribution (PNSD) is important in at least two ways: 1) wet deposition, generally considered a dominant sink of CCN, and 2) CCN replenishing, which has been shown to frequently follow precipitation via the process of formation and growth of new particles, thereby buffering the loss process. Together, these effects illustrate the complexity of precipitation–PNSD interactions.
In this study, we use correlations between measured PNSD at three stations and precipitation rates along back trajectories to evaluate precipitation-PNSD interactions in three general circulation models (GCMs; NorESM, EC-Earth and ECHAM-SALSA). This approach allows us to focus on the size- and time-resolved effects of precipitation on the CCN budget. The long-term measurement sites used in the study are Zeppelin (Arctic), Hyytiälä. (boreal forest), and ATTO (Amazon rainforest). To investigate potential confounding factors, we further apply eXtreme Gradient Boosting (XGBoost) and build a separate regression model for each site and data source using a minimal set of physically relevant predictors.
For CCN replenishment following precipitation, the models tend to underestimate new particle formation (NPF) and particle growth to CCN sizes at the two high-latitude stations. In the Amazon (ATTO), by contrast, two models simulate an immediate CCN source after rainfall, whereas observations show a weaker response that takes time to grow to CCN sizes, indicating overly rapid aerosol growth in the models. Finally, observations suggest weaker wet deposition during cold periods than warm periods, likely due to phase dependency. The models are in general better at reproducing patterns during warm periods, while in cold periods one model (EC-Earth) has too strong positive correlations with precipitation, while another has strongly negative correlations (ECHAM-SALSA).
The XGBoost analysis largely confirms the key findings from the correlation evaluation, but also uncovers likely confounding influences, such as the correlation between emission regions and regions with strong precipitation. For example, a feature that appears as a precipitation-driven source of large particles in correlation analyses is instead attributed by the machine-learning model to shifts in air-mass origin. This approach shows potential for disentangling spurious correlations and controlling for confounding factors in model evaluation.
Overall, evaluating the size-resolved impacts of precipitation on particle number highlights model shortcomings in new particle formation and growth, and underscores the importance of disentangling these processes from the direct deposition effect of precipitation when improving models.
How to cite: Blichner, S. M., Khadir, T., Talvinen, S., Artaxo, P., Heikkinen, L., Kokkola, H., Krejci, R., Irfan, M., van Noije, T., Petäjä, T., Pöhlker, C., Seland, Ø., Svenhag, C., Vartiainen, A., and Riipinen, I.: Evaluating the precipitation impact on particle number size distribution in climate models based on correlation analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15430, https://doi.org/10.5194/egusphere-egu26-15430, 2026.
Marine cold-air outbreaks (MCAO) strongly impact the radiative effects of clouds, playing a crucial role in the high-latitude climate system. MCAO clouds initially begin as shallow stratiform cloud streets near the ice edge and evolve into broken cellular convection farther downstream. Although these clouds undergo substantial changes in both macro- and microphysical properties during their evolution, a comprehensive understanding of their relationship with cloud dynamics has been limited by observational constraints. The recent launch of the EarthCARE satellite on May 28, 2024, carrying the first-ever spaceborne Doppler radar (Cloud Profiling Radar, CPR), provides unprecedented opportunities to investigate vertical motion within clouds from
space. Here, we analyze the Lagrangian trajectories of cold air outbreaks since the time they leave the Arctic sea ice edge and observe MCAO clouds with the EarthCARE CPR as they form over the Norwegian and Barents Seas from December 2024 to May 2025 at 0.25 by 0.25 resolution. We show that the CPR observations successfully capture the distinct developmental stages of MCAO clouds. Notably, despite the inherent observational challenges from satellite platforms, we identify enhanced riming signatures associated with strong updrafts and abundant supercooled liquid water, which increases ice particle sedimentation velocities. Our results provide the first comprehensive view of the evolution of cloud structure, microphysical processes, and dynamic features in MCAO clouds over extended spatial and temporal scales. These insights advance our understanding of MCAO cloud processes and can inform future improvements in numerical climate models.
How to cite: Tan, I., Kim, J., Kollias, P., and Puigdomènech Treserras, B.: First-Light Observations from EarthCARE’s Cloud Profiling Radar Reveal Insights intoMicrophysics-Dynamics Coupling of Marine Cold-Air Outbreaks Clouds, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13918, https://doi.org/10.5194/egusphere-egu26-13918, 2026.
NASA and ESA launched the PACE and Earthcare satellites in 2024 to provide unique aerosol and cloud measurements. We use these measurements to constrain model uncertainty on aerosol Effective Radiative Forcing (ERF). Perturbed Parameter Ensembles (PPEs) are extremely powerful tools that offer an effective approach to evaluate and constrain the model uncertainty of aerosol using observations.
We create a PPE for July 2024-August 2025 based on 250 simulations by the aerosol -climate model ECHAM-HAM and co-locate the 3-hourly output with aerosol and cloud products from PACE and Earthcare. We define regional monthly mean observations for 19 regions of fine- and coarse Aerosol Optical Depth (AOD), Aerosol Index (AI), Single Scattering Albedo (SSA), cloud droplet number concentration (Nd), Cloud Effective Radius (CER), and fraction of extinction below 2km altitude, resulting in almost 1600 observations. An emulator is used to extend the PPE to simulate these observations to 2 million PPE members and constrain the PPE by applying least-squares minimization. resulting in 0.2% of accepted ensemble members.
Both PACE and EarthCARE independently and consistently constrain several model parameters that affect ERFaci and RFari. These observations fundamentally and consistently change where and how ERF uncertainty is controlled and alter the global spatial ERF distribution. The constrained ensemble indicates a stronger negative global aerosol ERF than previous mean estimates, alongside a more positive forcing over Central Africa. The observations suggest a reduction of emission of DMS, Organic, and Black Carbon (anthropogenic and biomass burning), and accumulation mode sea salt. Also, the absorption capability (imaginary refractive index) of different aerosol species is reduced. Cloud observations constrain ‘activation’ and ‘vertical velocity’ parameters, resulting in smaller aerosol-Nd susceptibility. However, some parameter uncertainties, such as biomass burning emission particle size, remain mostly unchanged. These results demonstrate that new satellite observations can robustly and consistently constrain aerosol ERF uncertainty, while also identifying key processes where additional or complementary observations are required.
How to cite: Bhatti, Y., Regayre, L., Jia, H., Watson-Parris, D., Im, U., Schutgens, N., Nenes, A., van Diedenhoven, B., Arifi, A., Fu, G., Wang, X., van Zadelhoff, G.-J., and Hasekamp, O.: New aerosol and cloud satellite observations from PACE and EarthCARE consistently constrain model uncertainties, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14149, https://doi.org/10.5194/egusphere-egu26-14149, 2026.
Aerosol–cloud interactions (ACI) remain a major source of uncertainty in understanding the cloud microphysical and macrophysical responses to aerosol perturbations, with important implications for cloud evolution and climate processes. Within the Satellite observations to improve our understanding of aerosol-cloud interactions (SATACI) framework, we exploit synergistic satellite observations to derive robust observational constraints on aerosol effects on liquid clouds.
This study focuses on using high-frequency geostationary observations, relying on the heritage of the ESA aerosol and cloud CCI projects. Aerosol and cloud properties are collocated at the pixel level and harmonized in space and time to characterize aerosol loading, cloud droplet number concentration (CDNC), cloud fraction (CF), and cloud phase under controlled meteorological stratifications. The geostationary perspective enables systematic investigation of temporal offsets between aerosol and cloud observations, allowing assessment of time-lagged aerosol–cloud responses that are not accessible from polar-orbiting sensors alone.
We quantify CDNC and CF sensitivities to aerosol perturbations using large-sample, stratified analyses that explicitly account for spatial aggregation, aerosol loading regimes, surface type, and temporal co-variability. Aerosol loading is analysed using stepwise binning approaches to separate distinct loading regimes and to identify changes in aerosol–cloud sensitivities that are not well represented by a single linear relationship. Separate analyses over land and ocean reveal distinct sensitivity patterns in both magnitude and variability, highlighting the non-uniform nature of ACI across environments. The robustness of derived sensitivities is assessed across multiple aerosol proxies and independent cloud datasets, and uncertainty information is propagated throughout the analysis to support quantitative interpretation.
Additional stratifications are used to assess the influence of environmental factors such as relative humidity and precipitation occurrence on inferred ACI metrics. Comparisons with climate model simulations from NorESM, performed under matched spatial and temporal stratifications, provide a consistency check on observed aerosol–cloud sensitivities and support interpretation of the observational diagnostics.
The analysis underscores the importance of high-frequency observations, regime-aware stratification, and uncertainty-aware methodologies for constraining aerosol effects on liquid clouds. By providing statistically robust, observation-based diagnostics of aerosol–cloud interactions, SATACI contributes to the efforts of the ACI cluster (involving CERTAINTY, CleanCloud and AirSense) to improve process understanding and reduce observational uncertainty in aerosol–cloud studies.
How to cite: Luffarelli, M., Baltodano Martinez, A., Misk, N., Schulz, M., Haugvalstad, O., and Eisinger, M.: Observational constraints on aerosol–cloud interactions in liquid clouds from geostationary satellite observations., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10052, https://doi.org/10.5194/egusphere-egu26-10052, 2026.
Understanding Aerosol-Cloud Interactions (ACI) is today at the heart of our ability to accurately model the current and future states of Earth’s climate. High levels of aerosols lead to bright clouds with high albedo, effectively enhancing the overall cooling radiative forcing of aerosols up to –1.3 W.m⁻², rivalling the warming radiative forcing from greenhouse gases (+3.2 W.m⁻²) according to IPCC (2023). These interactions could be altered in unforeseen ways by changes in the global aerosol mix, either human-made or natural, with unforeseen consequences for climate. Improving our understanding of ACI is also the key to more accurate short-term weather predictions and severe weather alerts, such as hurricane events. Today, there is still no consensus on how aerosols radiative and microphysical effects impact the variations in precipitations, intensity, and structure of tropical cyclones. A good understanding of the properties and concentrations of aerosols that act as cloud condensation nuclei (CCN) and ice nucleating particles (INP) is required to better model and predict those extreme events.
We will present our investigation of ACIs in a case study: the intense tropical cyclone Humberto (category 5 at maximum intensity) that occurred in September 2025 in the Atlantic Ocean. During its cyclogenesis and intensification period, a layer of Saharan dust aerosols was transported to the cyclone, representing a meteorological system where multiple and specific ACIs occur. The study is based on vertically-resolved optical measurements from the ATLID space lidar aboard the ESA-JAXA satellite EarthCARE, from which the nature and properties of cloud-relevant particles are retrieved. The POLIPHON method is applied to estimate CCN and INP concentrations from level 2 lidar products as a function of aerosol subtypes and relative humidity of the atmosphere. In parallel, we will show results from the simulation of the Humberto cyclone using the Meso-NH mesoscale atmospheric model, which numerically integrates the chemical and microphysical processes of aerosols. With a horizontal resolution of the simulation below 5 km, the model explicitly resolves atmospheric dynamics such as convection. By combining observations with modeling, we will describe how interactions between clouds and aerosols affect the lifetime and properties of the Humberto cyclone, which interacts mainly with Saharan dust and marine aerosols.
How to cite: Fourmi, G. and Noël, V.: Investigation of aerosol-cloud interactions during Hurricane Humberto: a case study with EarthCARE ATLID data and MesoNH simulations., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10953, https://doi.org/10.5194/egusphere-egu26-10953, 2026.
Cloud radiative effects depend critically on microphysical properties, that in turn are influenced by aerosol-cloud interactions, which remain the dominant source of uncertainty in anthropogenic radiative forcing estimates. Cloud droplet number concentration, Nd, is a key parameter for constraining these phenomena using satellite observations. However, current satellite-based Nd retrievals suffer from substantial biases in comparison with in-situ measurements. A fundamental limitation is that even specialized satellites, such as MODIS, typically observe at scales of hundreds of meters to kilometers. At these resolutions, retrievals necessarily average over fine-scale cloud variability associated with differing cloud life-cycle stages, spatially varying dynamical forcing, and heterogeneity in aerosol conditions at cloud base. Averaging this sub-pixel heterogeneity limits our ability to understand aerosol-cloud interactions or to evaluate high-resolution cloud models.
The Clouds Decoded project, funded by the Advanced Research + Invention Agency (ARIA), retrieves cloud properties from Sentinel-2 (S2) imagery at ~60 m resolution. This resolution captures spatial structures at scales where key microphysical processes operate: cloud edges, gradients in optical depth and effective radius, and spatial heterogeneity patterns that inform sub-grid parameterizations in climate models. These high-resolution observations can also complement ground-based and aircraft measurements when S2 overpasses are available.
In this contribution, we present case studies demonstrating how S2 observations can characterize cloud heterogeneity at scales previously invisible to satellite sensors. Can observations at this spatial resolution help resolve discrepancies in satellite-derived aerosol-cloud relationships and reduce uncertainties in aerosol-cloud interaction estimates?
How to cite: Bertozzi, B., Campbell, J., Borne--Pons, P., Czerkawski, M., and Francis, A.: Resolving cloud microphysical heterogeneity with Sentinel-2: implications for aerosol-cloud interaction studies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18629, https://doi.org/10.5194/egusphere-egu26-18629, 2026.
Aerosol–cloud interactions (ACI), which substantially offset anthropogenic greenhouse warming, remain a major source of uncertainty in current climate assessments. Satellite-based estimates of ACI radiative forcing (RFaci) serve as a key benchmark for climate predictions and for evaluating improvements in climate models. However, these estimates remain poorly constrained. A critical limitation is that satellite assessments typically rely on column-integral aerosol proxies (e.g., AOD, AODf, AI, etc.), which may not accurately represent cloud-base cloud condensation nuclei (CCN)—the particles that actually form cloud droplets. This limitation has given rise to a puzzling phenomenon: over the Southern Hemisphere, cloud droplet concentrations have declined despite increases in column-integral aerosol proxies. While some previous studies have noted this droplet–aerosol trends discrepancy, they often relied on limited datasets and single aerosol proxies, without providing systematic validation, causal analysis, or quantification of its implications for RFaci. This gap has been a significant obstacle to reducing uncertainties in ACI forcing.
To address this challenge, we first combined multi-source observations to provide robust, quantitative evidence of the droplet–aerosol trends discrepancy across the Southern Hemisphere from 2003 to 2020. We then used a source–sink framework to explore the underlying physical mechanisms, finding that the discrepancy arises from elevated cloud bases systematically reducing CCN availability, while enhanced precipitation accelerates droplet removal. By explicitly accounting for these processes, this study provides a physically grounded estimate of aerosol–cloud radiative forcing, constraining RFaci to −1.37 W m⁻². Previous global assessments relying on column-integral variables are therefore biased by –35% to +26%, with discrepancies reaching +42% over the Southern Hemisphere.
This work reconciles a long-standing discrepancy between observed droplet and column-integral aerosol trends, highlighting the critical importance of considering cloud-base CCN in future ACI radiative forcing estimations. It provides a physically grounded constraint on aerosol forcing based on cloud-base CCN, supporting more precise estimates of climate sensitivity and guiding model development.
How to cite: Wang, H., Letu, H., Peng, Y., Jia, H., and Quaas, J.: Constraining Satellite Estimates of Aerosol–Cloud Interactions via the Droplet–Aerosol Trends Discrepancy , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2224, https://doi.org/10.5194/egusphere-egu26-2224, 2026.
One of the largest uncertainties in estimating the anthropogenic radiative forcing is related to the impact of atmospheric aerosols on cloud properties. The estimates of radiative forcing due to changes in cloud properties vary significantly between different global climate models, highlighting the need for constraining this forcing by using observations. Currently, one of the least well-understood aerosol components is secondary organic aerosols, most of which are of natural origin, i.e., biogenic SOA (BSOA). Here, we aim to quantify the effects of BSOA on cloud properties by combining field observations of aerosol concentrations and satellite observations of cloud properties. The aerosol particles were measured with a Scanning-Mobility Particle Sizer (SMPS) in Hyytiälä, Finland for the summers 2012-2023. Particles with diameter larger than 100 nm (N100) were considered as a proxy for cloud condensation nuclei. Cloud microphysical properties were obtained from the MODIS Collection 6 Level-2 cloud product (MYD06_L2) at 1 km resolution and averaged over a 1° × 1° region surrounding the site. Based on sensitivity tests and previous studies, the cloud droplet number concentration (CDNC) was derived for only low-level liquid warm clouds over the study region based on the retrieved cloud effective radius (CER) and cloud optical thickness (COT) values. The impact of different linear regression methods, measurement uncertainties, and sampling criteria on cloud susceptibility estimates was thoroughly analyzed. Based on meteorological conditions and aerosol size distributions observed in Hyytiälä, cloud parcel model simulations were performed for comparison. The simulated CDNC–aerosol susceptibility was found to be 0.68, while the observed value was somewhat smaller, 0.37. Using the observed temperature dependence of N100 and CDNC-aerosol susceptibility, we estimated temperature-driven cloud albedo feedback to be −0.68 W m⁻² °C⁻¹ (95 % confidence interval: −0.87 to −0.50 W m⁻² °C⁻¹). The magnitude of this feedback is approximately twice as large as reported in an earlier study which utilized MODIS Level-3 data. This difference highlights the strong sensitivity of estimated cloud susceptibility to satellite data sampling and filtering choices.
How to cite: Taneja, K., Calderon, S. M., Romakkaniemi, S., Arola, A., Lipponen, A., Kokkola, H., Yli-Juuti, T., Chellappan, S., and Mielonen, T.: How sensitive are clouds to biogenic aerosols? Insights from satellite observations and model simulations , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17155, https://doi.org/10.5194/egusphere-egu26-17155, 2026.
Aerosols and clouds play key roles in climate through their direct radiative effects (DREs) by modulating the Earth-atmosphere radiative energy budget. Satellite-based retrievals of DREs are essential for quantifying Earth’s radiative energy budget. They are, however, subject to much uncertainty due to difficulties in characterizing the spatiotemporal variability of aerosols and clouds and their optical properties. In addition, DRE quantification relies predominantly on one-dimensional (1D) radiative transfer (RT) simulations. According to Cole et al. (2023), differences between 1D and three-dimensional (3D) RT calculations of upwelling shortwave fluxes at 20 km altitude are expected to exceed EarthCARE’s scientific goal (differences between predicted and “observed” fluxes of less than ±10 W m⁻²) in at least 50% of cases. While several studies have quantified differences in cloud DREs between 1D and 3D RT simulations, to our knowledge no such studies exist for aerosol DREs.
The EarthCARE (EC) mission aims to improve our understanding of how aerosols and clouds modify radiative fluxes by providing collocated observations of aerosols, clouds, precipitation, and radiation, enabling a three-dimensional representation of the atmosphere. In this study, we use these novel datasets to quantify differences in aerosol shortwave DREs between 1D and 3D RT simulations under clear- and cloudy-sky conditions. Aerosol DREs are calculated using1D and 3D RT solvers from the libRadtran package (Mayer & Kylling, 2005; Emde et al., 2016; Mayer 2009) for selected scenes from pre-operational EC test frames. The scenes are chosen to represent a range of aerosol types and cloud conditions. The sensitivity of the 3D effect (defined as the difference between 3D and 1D calculations of the DRE) is investigated as a function of aerosol optical depth and solar zenith angle. To assess the influence of the relative vertical positioning of aerosols and clouds on 3D effects, artificial aerosol layers placed above and below cloud layers are examined. Overall, our analysis provides insights into how more realistic 3D representations of atmospheric constituents can improve understanding of the role of aerosols in modifying Earth’s radiative energy fluxes.
Acknowledgements:
This research was financially supported by the CERTAINTY (Cloud aERosol inTeractions & their impActs IN The earth sYstem ) project funded from Horizon Europe programme under Grant Agreement No 101137680, the project RACE-ECV, (SBFI-633.4-2021-2024/PMOD - EarthCARE 202/2) supported by SBFI (State Secretariat of Research and Innovation Switzerland), and the Obs3RvE (Optimising 3D RT EarthCARE product using geostationary observations and AI) project, funded from the European Space Agency under Contract No. 4000147848/25/I/AG. We would like also to acknowledge the COST Action HARMONIA, CA21119.
References:
Cole, J. N. S. et al, (2023) Broadband radiative quantities for the EarthCARE mission: the ACM-COM and ACM-RT products, Atmos. Meas. Tech., 16, 4271–4288.
Emde, C. et al, (2016) The libRadtran software package for radiative transfer calculations (version 2.0.1), Geoscientific Model Development, 9(5), 1647–1672.
Mayer, B., A. Kylling, (2005) Technical note: The libRadtran software package for radiative transfer calculations - description and examples of use. Atmos. Chem. Phys., 5(7), 1855–1877.
Mayer, B. (2009) Radiative transfer in the cloudy atmosphere, in: EPJ Web of Conferences, 75–99.
How to cite: Papachristopoulou, K., Tsekeri, A., Kouklaki, D., Gialitaki, A., Emde, C., Mayer, B., Barker, H. W., Cole, J. N. S., Qu, Z., Kacimi, M., Amiridis, V., Marinou, E., and Kazadzis, S.: Do 1D and 3D radiative transfer estimates of aerosol direct radiative effects differ? A sensitivity study using realistic cloudy EarthCARE scenes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10359, https://doi.org/10.5194/egusphere-egu26-10359, 2026.
Aerosol–cloud interactions (ACI) are a major source of uncertainty in climate science, critically affecting our ability to project near-term climate evolution and assess societal risks. ACI influence effective radiative forcing, cloud dynamics, and precipitation patterns, yet remain insufficiently constrained due to limitations in observations, modeling, and process understanding. Uncertainty from ACI hampers robust policy advice across multiple domains—from estimating remaining carbon budgets and climate sensitivity, to anticipating regional extreme events and evaluating climate interventions such as solar radiation modification. Despite these important issues, ACI is often underappreciated or excluded from decision-making frameworks due to its complexity and lack of quantification.
This talk outlines a path forward to overcome these barriers by leveraging emerging opportunities in satellite remote sensing, ground-based and airborne observations, high resolution climate modeling, and machine learning. We identify key areas where rapid progress is feasible, including improved retrievals of cloud microphysical properties, better representation of natural aerosols in a warming world, and enhanced integration of observational and modeling communities. Even as anthropogenic aerosol and its impacts on clouds is reducing owing to emissions controls, addressing ACI uncertainties remains essential for refining climate projections, supporting effective mitigation and adaptation strategies, and delivering actionable science to policymakers in a rapidly changing climate system.
How to cite: Nenes, A., Im, U., Samset, B. H., Thomas, J. L., Kokkola, H., Dubovik, O., Kahn, R. A., Storelvmo, T., and Tsigaridis, K. and the EC/ESA ACI Cluster: Aerosol-cloud Interactions: Overcoming a Barrier to Projecting Near-term Climate Evolution and Risk, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20984, https://doi.org/10.5194/egusphere-egu26-20984, 2026.
It is well known that aerosols can increase the number of droplets in clouds. Increased droplet numbers have been observed over oceans as ship tracks , and more recently over continents as industry tracks (Toll et al 2019 Nature https://doi.org/10.1038/s41586-019-1423-9). However, it is expected that the influence of aerosols on cloud properties is broader and exists in many more cases than those with high-contrast visible cloud tracks.
To gain a better understanding of the pollution's effect on clouds, we analyzed cloud properties around hundreds of industrial sites over a 20-year period using MODIS satellite data. We simulated aerosol dispersion using the HYSPLIT model and reanalysis winds, and compared the properties of aerosol-polluted cloud areas to the properties of nearby unpolluted clouds. Importantly, we conducted several null experiments to rule out that systematic differences between polluted and unpolluted areas arise from differences in orography or land cover, prevailing weather patterns, or other surrounding pollution sources.
Preliminary results suggest that aerosol-dispersion modelling allows to successfully identify anthropogenic aerosol point sources that lead to increased cloud droplet numbers
How to cite: Luhamaa, A., Aun, M., Keernik, H., and Toll, V.: Anthropogenic aerosol point sources exposed through satellite-based identification of polluted cloudsLong-term impact of industrial pollution sources on cloud properties, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9322, https://doi.org/10.5194/egusphere-egu26-9322, 2026.
Black carbon (BC) strongly absorbs solar radiation, while its warming effect on climate is poorly quantified. A key challenge is to accurately assess BC light absorption after being mixed with non-BC components. However, there has consistently been a large observation-modeling gap in BC light absorption estimation, reflecting the insufficient understanding of realistic BC complexity. Here we conduct comprehensive in-situ measurements of BC single-particle microphysics, e.g., size, coating amounts, density, and shape, along with optical closure calculation. Specifically, the observed particle-to-particle heterogeneities in size and coating, and the non-spherical BC shape only explain the lower observed BC absorption by ~20% and ~30%, respectively. A remaining gap for fully aged spherical BC-containing particles is related to the off-center BC core position. The global climate model assessment shows that fully accounting for the observed BC complexity in the aerosol microphysical representation reduces the global BC direct radiative forcing by up to 23%.
How to cite: Peng, Y., Huang, X.-F., Wei, J., Peng, J., He, L.-Y., Seinfeld, J. H., and Wang, Y.: Microphysical complexity of black carbon particles restricts their warming potential, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4732, https://doi.org/10.5194/egusphere-egu26-4732, 2026.
Clouds exert first-order controls on Earth’s radiation budget and hydrological cycle, and cloud-base height (CBH) is a key parameter for climate modeling and aviation applications. Using a ceilometer (measurement range 15 m to 12 000 m) deployed in Guyuan, China (35°29′40″ N, 106°18′31″ E; 1984.2 m a.s.l.), we investigated CBH variability from 2020 to 2023. The cloud occurrence frequency was lowest in winter (62.5%), followed by spring (70.4%), summer (71.2%), and autumn (72.4%). Single-layer clouds dominated all year round (∼50%), whereas multilayer clouds were more frequent in summer. A pronounced diurnal cycle was observed in all seasons: daytime cloud occurrence exceeded nighttime values except in spring, where the difference was small. CBH showed distinct seasonal behavior: daytime CBH was lower than nighttime in all seasons; mean CBH was lowest in autumn with the smallest diurnal amplitude, and highest in spring with the largest amplitude, with daily minima at 14:00 in spring and at 11:00 and 12:00 in winter and summer, respectively. Layered statistical data indicated a persistent multilayer cloud structure over the study region. After classifying clouds by CBH, low-level clouds and mid-level clouds comprised the majority of occurrences. Histograms using 500-m bins revealed that low clouds below 500 m were most common in autumn; over the full year, clouds with CBH < 2000 m occurred far more frequently than those with CBH between 2000 and 6000 m, whereas CBH > 7000 m clouds were rare. In spring, high-level clouds (> 7000 m) exhibited a clear diurnal cycle with a midday minimum. Both spring and winter displayed a bimodal distribution of CBH. These results provided an observational baseline for the Guyuan region and offer actionable information for weather forecasting, climate model evaluation, and photovoltaic nowcasting and operations.
How to cite: Chang, Z., Cui, Y., and Tian, L.: Observing Changes in Cloud Base Height Using a Ceilometer in Guyuan City, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2177, https://doi.org/10.5194/egusphere-egu26-2177, 2026.
How to cite: Rowland, Z. C., Hoffmann, F., Glassmeier, F., Steinke, I., and Russchenberg, H.: The optimal size distribution of seeding aerosol for marine cloud brightening: insights from emulating a Lagrangian cloud model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3155, https://doi.org/10.5194/egusphere-egu26-3155, 2026.
The large horizontal grid size of current atmospheric models means that subgrid heterogeneity in cloud properties must be parameterised. A number of studies have suggested that this heterogeneity may have significant negative consequences for the representation of mixed phase clouds, marine boundary layer clouds and stratocumulus cloud decks in models. This study details work which uses high temporal (10 minute) and spatial resolution (4 km) Advanced Himawari Imager data collected by the Himawari-9 geostationary satellite to identify and track coherent cloud objects. In particular, we track cloud data in the South West Pacific centred on New Zealand during the HALO-South and ACADIA field campaigns. HALO-South is an airborne campaign using the HALO research aircraft deployed from New Zealand in September to October 2025 to study Southern Ocean clouds and aerosol-cloud interactions, while the ACADIA campaign is a longer term deployment of ground-based remote sensing instrumentation to sample the cloud and aerosol environment at two sites in New Zealand.
This work details the derivation of coherent cloud objects by identifying features using cloud top temperatures and the application of a watershedding segmentation scheme to identify coherent regions. These coherent cloud objects are then tracked between individual images using the “tobac” tracking scheme. The tracked Himiwari-9 cloud information is then used to examine the heterogeneity of cloud properties, particularly cloud phase, inside and across these coherent cloud objects in the South West Pacific. With a normalised version of the largest length of conistent cloud properties within a coherent cloud object being used as our measure of spatial heterogenity. Results of cloud tracking are also used in an effort to analyse heterogeneity as a function of cloud lifetime by associating cloud properties with individual coherent cloud objects across their evolution. Coherent cloud objects identified from Himawari-9 satellite imagery are then compared with remotely sensed cloud data at the ACADIA field sites to examine the spatial and temporal consistency between ground-based and satellite-based remote sensing perspectives. We also examine the potential to identify coherent cloud regions along flight transects completed during the HALO-South campaign.
How to cite: McDonald, A., Kalesse-Los, H., Seifert, P., Schuddeboom, A., and Morrish, D.: Using Himiwari-9 cloud tracking to support the analysis of measurements from the ACADIA and HALO-South field campaigns, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15006, https://doi.org/10.5194/egusphere-egu26-15006, 2026.
Instantaneous Radiative Forcings due to Aerosol-Cloud Interactions (IRFaci) occur due to anthropogenic aerosols' first indirect effect on the global radiative balance. These forcings represent a significant source of uncertainty regarding human impact on climate, mainly when warm clouds act as the mediating element [1]. Studies quantifying IRFaci have focused primarily on stratiform clouds over the oceans (e.g. [2]). It is particularly noteworthy the lack of studies about the first indirect effect (i.e., Twomey effect) over the Amazon. This study uses datasets from the GoAmazon2014/5 campaign (collected both in situ and via ground-based remote sensing) to configure warm cloud models that serve as inputs to a radiative transfer code (libRadtran). This allows the calculation of daily values of ascending irradiance at the Top of Atmosphere (TOA) for 2014 and 2015. Given that a detailed evaluation of aerosol conditions in the reference atmosphere can reduce the uncertainties associated with RFaci estimates [3], the IRFaci values were calculated based on two clean atmospheric reference states. The annual distributions of IRFaci derived from these references show interannual variation, with the 2014 forcings being more negative than in 2015. The average IRFaci values (and the average values of the 25th and 75th percentiles in the brackets) for the entire duration of the GoAmazon2014/5 campaign relative to the two reference states were -11.8 [-23.0; -2.4] W/m² and -1.3 [-5.8; 0.3] W/m², respectively. These values align with the maximum IRFaci amounts per aerosol optical depth (AOD) unit documented in the literature [4] for the Amazon region. The value obtained for the second reference state corresponds to the most recent estimate provided by the IPCC, which is -0.7 ± 0.5 W/m² on a global scale. Sensitivity tests of IRFaci revealed a strong dependence on aerosol load for clean background conditions. Further increases in aerosol load reduced the sensitivity. The techniques and results presented here offer a unique approach to calculating indirect radiative forcings related to the Twomey effect of warm clouds over the Amazon, contributing to a better understanding of human impact on the region's climate.
[1] Mülmenstädt, J. and Feingold, G.: The Radiative Forcing of Aerosol–Cloud Interactions in Liquid Clouds: Wrestling and Embracing Uncertainty, Curr Clim Change Rep, 4, 23–40, https://doi.org/10.1007/s40641-018-0089-y, 2018.
[2] Wall, C. J., Storelvmo, T., and Possner, A.: Global observations of aerosol indirect effects from marine liquid clouds, Atmospheric Chemistry and Physics, 23, 13125–13141, https://doi.org/10.5194/acp-23-13125-2023, 2023.
[3] Gryspeerdt, E., Povey, A. C., Grainger, R. G., Hasekamp, O., Hsu, N. C., Mulcahy, J. P., Sayer, A. M., and Sorooshian, A.: Uncertainty in aerosol–cloud radiative forcing is driven by clean conditions, Atmospheric Chemistry and Physics, 23, 4115–4122, https://doi.org/10.5194/acp-23-4115-2023, 2023.
[4] Herbert, R. and Stier, P.: Satellite observations of smoke–cloud–radiation interactions over the Amazon rainforest, Atmospheric Chemistry and Physics, 23, 4595–4616, https://doi.org/10.5194/acp-23-4595-2023, 2023.
How to cite: Pugliesi Silva, A. C., Correia, A. L., and Cecchini, M. A.: Instantaneous radiative forcings due to the first indirect effect of aerosols linked to warm clouds in the Amazon, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2330, https://doi.org/10.5194/egusphere-egu26-2330, 2026.
Aerosol parametric uncertainty in global climate models can be as large as intermodal uncertainty, posing a significant challenge for reliable climate projections. This study investigates the magnitude and drivers of aerosol-related uncertainty in the TM5 chemical transport model (CTM), the atmospheric chemistry and transport component of the EC-Earth3 Earth System Model (ESM), and a contributor to the Coupled Model Intercomparison Project (CMIP).
Aerosol parameters in TM5 describe characteristics of emissions, removal, transformations, physical, chemical, and optical properties. To assess aerosol representation in TM5, we have performed one-at-a-time sensitivity studies, where individual aerosol parameters were perturbed to the minimum and maximum of their respective uncertainty ranges. The resulting impacts were evaluated using climate-relevant outputs, cloud condensation nuclei number (CCN) concentrations at supersaturations of 0.2% and 1% averaged over the lowest five model levels, and aerosol optical depth (AOD) at 550 nm. Model responses were analyzed on seasonal timescales over a two-year period (2017–2018).
The results indicate that sea-salt and dimethyl sulfide (DMS) emissions, particle size of biomass-burning emissions, and dry-deposition rates exert the strongest influence on CCN number concentrations and aerosol optical depth (AOD). Simulated AOD from each sensitivity experiment was constrained using the AOD merged product for 2017 by Sogacheva et al. (2020). Seasonal comparisons were performed for 2017 across four regions: West Asia and North Africa (WANA), India and South Asia (ISA), Southern Africa (SA), and Mexico to Colombia (MC). Across all seasons, high AOD regions, TM5 exhibits minimal sensitivity to the perturbed simulations and accurately captures high AOD values. In contrast, the model shows more variation in low AOD values than the observations, where sensitivity to parameter perturbations, particularly emission-related parameters, is most pronounced. Increased dry deposition and reduced SO₂ emissions consistently improve low-AOD predictions with respect to the satellite product, especially in WANA and ISA, while the default setup performs best in South Asia and during JJA.
These findings identify the aerosol parameters that contribute most significantly to uncertainty in TM5 and highlight the key sensitivities that will inform future work involving emulated perturbed-parameter ensemble (PPE) experiments. The PPEs will vary these parameters over their uncertainty range simultaneously to study their combined effect on TM5.
How to cite: Bouchahmoud, M., Bergman, T., Makkonen, R., Chandrasekharan, A., Luoma, K., Tukiainen, S., Kokkola, H., and Williamson, C.: Evaluation of aerosol representation in TM5 using a one-at-a-time sensitivity analysis constrained by aerosol optical depth , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7158, https://doi.org/10.5194/egusphere-egu26-7158, 2026.
Clouds cover a large fraction of the Earth’s surface and play a central role in regulating Earth’s radiative balance, precipitation, and the global water cycle. Aerosols influence cloud formation by acting as cloud condensation nuclei (CCN), thereby modifying cloud microphysical and dynamical processes. However, the extent to which aerosol perturbations and dynamical factors influence cloud susceptibility (β = ∂lnNd/∂lnNa , where Nd is cloud droplet number concentration and Na is aerosol number concentration) across different cloud types remains uncertain. In this study, we employ Large Eddy Simulations (LES) to quantify aerosol susceptibility in marine liquid phase stratocumulus and mixed-phase clouds. Two sets of simulations are performed: (i) simulations with increasing aerosol number concentrations (65, 100, 500, 1000, and 10 000 cm⁻³) as reference case, and (ii) simulations with enhanced updraft velocities for the same range of aerosol concentrations. For liquid-phase clouds, the susceptibility decreases from 1 to 0.78, 0.69, and 0.2 with increasing aerosol concentration, indicating a transition from an aerosol-limited to an updraft-limited regime. For enhanced updraft cases, the susceptibility decreases from 1, 0.84, 0.71, and 0.34. Increasing the updraft velocity enhances supersaturation, leading to increased activation of aerosols into cloud droplets compared to the reference case. As a result, at higher aerosol concentrations, the susceptibility is higher than in the reference case. Thus, the comparison between the reference and enhanced-updraft simulations indicates a transition from updraft-limited to aerosol-limited behaviour at high aerosol concentrations. Ongoing simulations of mixed-phase clouds also aim to quantify aerosol susceptibility in different dynamical regimes and assess how cloud phase influences aerosol-cloud interactions.
How to cite: Dogra, G., Boucher, O., and Bellouin, N.: Quantifying Susceptibility in aerosol and updraft limited regimes for Warm and Mixed-Phase Clouds Using LES, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15937, https://doi.org/10.5194/egusphere-egu26-15937, 2026.
Radiative transfer (RT) calculations are essential in climate and weather models. At high spatial resolution, three-dimensional (3D) radiative effects can no longer be neglected, but multi-dimensional RT is computationally expensive. One of the direct deterministic ways to treat the radiance field is to use the discrete ordinates method (DOM), which reduces RT to a large linear system. However, extending such deterministic solvers to fully 3D RT is computationally prohibitive, making the approach impractical for current models. Here we explore an approach based on quantum computing, which is expected to outperform classical computers for certain problems by exploiting quantum properties.
As the first step, we study stationary radiative transfer in a two-dimensional cloudy atmosphere and discretize the boundary-value problem with DOM. We design a quantum algorithm that combines a block-encoding of the DOM coefficient matrix and quantum singular value transformation (QSVT). This approach enables implementation of the inverse operation that is required to solve the linear system under the fault-tolerant quantum computation. We encode a group of wavelengths in a superposition state to process them in parallel. By targeting integrated quantities such as heating rates, we avoid reconstructing full radiance-field while still keeping the advantages of this wavelength-level parallelism.
We estimate the quantum resources required for our quantum algorithm and examine their dependence on the number of spatial grid points N, the number of discrete angles Nang, and the number of wavelength bins Nλ. We count the number of quantum gates to measure the computational cost. The gate count increases with the condition number of the DOM coefficient matrix, which increases roughly linearly with N. The gate count also increases with the block-encoding overhead, which increases roughly quadratically with Nang. On the other hand, the dependence on Nλ can be kept nearly constant under a low-parameter approximation of the scattering phase function. Our results suggest that quantum computing is a promising approach for the DOM-based radiative transfer in a cloudy atmosphere, especially in fully 3D settings.
How to cite: Ueno, K. and Miura, H.: Quantum Algorithm for Two-Dimensional Radiative Transfer in a Cloudy Atmosphere, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15233, https://doi.org/10.5194/egusphere-egu26-15233, 2026.
Marine stratocumulus clouds play a pivotal role in Earth’s climate system, reflecting much of the incoming solar radiation back to space. One important aspect of stratocumulus clouds is their mesoscale organization, e.g. closed or open cell structures. A transition from closed to open cells usually leads to a drop in cloud albedo and consequently the clouds’ cooling effect. It is therefore important to understand when and why transitions between these cloud structures happen.
The EarthCARE satellite enables for the first time simultaneous spaceborne measurement of cloud mesoscale structure, and detailed observations below cloud top. EarthCARE’s active sensors (ATLID and CPR) can resolve the vertical profiles of marine stratocumulus, overcoming previous CloudSat limitations caused by ground clutter, and allow observations of microphysics, such as precipitation, liquid water content and droplet size. The multi-spectral imager (MSI) adds spatial context, capturing the mesoscale structure of clouds.
We use a convolutional neural network (CNN) with MSI data to identify cloud structures and analyze their microphysics using EarthCARE’s active sensors. Initial analysis shows open and closed cells have similar vertical extents and surface coupling, but open cells produce heavier and more frequent rain.
To understand the drivers for transitions from closed to open cells, we use data from the geostationary GOES-19 satellite and ERA5 wind trajectories to track the clouds measured by EarthCARE over time. This enables us to determine whether clouds observed by EarthCARE will transition to a different structure, and the timing of this transition. Combining this information with EarthCARE, we present how microphysics changes around transitions from closed to open cells. Our findings suggest that these transitions are mainly driven by rain: before transitioning, closed cells show increased rain but no significant changes in other cloud properties, like cloud top height or surface decoupling.
This study offers important insights into the cloud processes responsible for transitions between different cloud structures. A comprehensive understanding of these mechanisms is essential for assessing how the cooling effects of clouds may change in response to our changing climate.
How to cite: Mayer, J., Piskala Gvozdikova, B., Malina, E., Gasbarra, D., and Mason, S.: EarthCARE Reveals Rain as Dominant Factor in Closed-to-Open Cell Transitions in Marine Stratocumulus, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9881, https://doi.org/10.5194/egusphere-egu26-9881, 2026.
Aerosol–cloud interactions (ACI) represent the main source of uncertainty in estimating global radiative forcing, and the processes driving these interactions are still poorly understood. This is particularly relevant in the context of extreme precipitation events, where ACI can have a decisive impact, especially over polluted regions.
The Po Valley is one of the most polluted regions in Europe, due to its intense anthropogenic emissions combined with unique topography (flat terrain enclosed by the Alps and Apennines mountain chains), which promotes aerosol accumulation due to low dispersion capabilities.
In May 2023, Northern Italy experienced two extreme precipitation events, occurring in close succession, both characterized by exceptionally heavy precipitation exceeding 200 mm within 48 hours over the Apennines slopes. More than 21 rivers flooded in the Po Valley, causing over €8.5 billion in damages and widespread landslides and flooding, resulting in several deaths. The present work seeks to estimate the role of aerosols in this extreme precipitation event.
In this study, we have performed high-resolution regional chemical transport model simulations with the WRF-CHIMERE model to evaluate the impact of ACI during the 2–3 May 2023 and 16–17 May 2023 precipitation events. Both events were characterized by strong water vapour advection from the southern Mediterranean, North Africa and the Adriatic Sea, increasing moisture availability over the region. Simulations including online ACI were conducted to assess the aerosol impact on precipitation. Precipitation patterns were then compared to rain gauges, radar, and satellite observations to accurately evaluate the simulated spatial variability and intensity during the events. Sensitivity tests reveal that ACI from anthropogenic emissions resulted in significant reductions in precipitation of up to 30–40 mm locally, and 10 mm regionally, accompanied by a temporal shift of the precipitation peak by approximately 3 hours.
This work demonstrates that aerosols can play an important role in extreme precipitation events, and need to be taken into account to better forecast the intensity and timing of such events.
Keywords: aerosol-cloud interactions, anthropogenic aerosols, regional modelling
How to cite: Di Antonio, L., Marelle, L., Davolio, S., Chyhareva, A., Ciarelli, G., Krakovska, S., Lapere, R., Lombardi, A., Montopoli, M., Pysarenko, L., Savenets, M., Siour, G., Tomassetti, B., Tuccella, P., Kokkola, H., Thomas, J., and Raut, J.-C.: Regional modelling of aerosol-cloud interactions (ACI) in extreme precipitation events: the Emilia-Romagna May 2023 floods case study, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12731, https://doi.org/10.5194/egusphere-egu26-12731, 2026.
While aerosol-cloud interactions (ACI) are represented with a high degree of detail in small-scale high-resolution models (high-res), they are not taken into account in the parameterization of convection, which means that there is no representation of the aerosol load in e.g. the Tiedtke-Bechtold convection scheme. The aim of this study is to use the results of high-res experiments as input data for the training of an emulator predicting the ACI under different environmental conditions. This emulator will be embedded in the convection parameterization of the ICON model configured as general circulation model (GCM).
We will present the creation of the training data set as well as the emulator itself. The training data set consists of 206 experiments in the high-res setting of ICON (300 m grid spacing, torus grid, two-moment cloud scheme, 3D turbulence). An adaptation of idealized Weisman-Klemp profiles representing the environmental conditions of convective cells in several GCM experiments was used as input data, combined with perturbations in various input variables. The emulator is trained on the CCN-sensitivity for precipitation. In order to calculate these sensitivities, every experiment is run twice: once with a specific amount of aerosol and a second time with half the amount of aerosols. The training inputs are derived features like CAPE, relative humidity and mean updraft speed, which can also be extracted from the GCM setting later on. As validation we will present R2 scores, RMSE and SHAP values. Due to the high variability of the investigated convective systems, the sensitivities to CCN, which contributes only to a small part of the total variability, is very hard to predict and varies a lot – even under very similar environmental conditions. Therefore, a positive correlation of R2 ~ 0.4 (depending on the configuration) is seen as a success.
A first version of the emulator embedded in the convective scheme of the GCM will also be presented.
How to cite: Dehnen, F. and Hoose, C.: Towards the Emulation of Aerosol Effects in Convective Precipitation in a General Circulation Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10969, https://doi.org/10.5194/egusphere-egu26-10969, 2026.
Aerosol-cloud interactions are currently the largest uncertainty in climate simulations of how Earth's radiation budget is changing. Consequently, significant effort has gone into improving the representation of these interactions in weather and climate models alike. One of the most critical controlling variables for aerosol-cloud interactions is cloud droplet number concentration (CDNC). Here we develop the representation of CDNC in the UK Met Office Unified Model (UM) global model and explore how simulated CDNC should be evaluated using satellite retrievals.
In unified weather and climate prediction systems, it is desirable to represent cloud microphysics using consistent code across scales. Consequently, we implement the UM’s regional double-moment microphysics scheme into the global model, which by default uses a single-moment scheme. Although this increases computational cost, the double-moment scheme can be evaluated more precisely against pixel-level satellite retrievals in high-resolution regional simulations, and it enables a more robust treatment of cloud droplets. Therefore, we describe how we develop and assess it in the global UM.
However, the evaluation of cloud droplets in a global model is difficult as comparisons must be made with satellite-derived, and typically masked, cloud top CDNC. We address this issue through the analysis of various masking strategies, along with evaluating the performance of the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) satellite simulator as implemented in the UM. In doing so, we improve protocols for global model-satellite comparisons of CDNC.
Through the implementation of double-moment cloud microphysics into the UM’s global model, we find systematic improvements in simulated cloud droplet representation. The annual root mean squared error (RMSE) decreases by 4 cm-3 globally, with a substantially larger reduction of 16 cm-3 in the tropics, as the enhanced representation of microphysical processes (e.g., accretion and autoconversion) is particularly beneficial for convective systems. Outside of the tropics, a low droplet bias exists regardless of the microphysics scheme. We find this bias is partially explained by a ~ 50% underprediction of simulated aerosol concentration when compared with in situ measurements from the NASA Atmospheric Tomography (ATom) Mission. Applying an aerosol scaling factor reduces this droplet bias by half, showing that errors in aerosol and activation are comparable.
We further find that COSP improves model agreement with remotely sensed cloud optical depth (τc) and effective radius (re). However, COSP-derived CDNC has an RMSE 16 cm-3 higher than that of CDNC calculated directly from the microphysics scheme, as biases in τc and re propagate through the simulator. Overall, we expect that our improvements to the representation of CDNC in the UM’s global model can meaningfully reduce the uncertainty in simulated aerosol-cloud interactions, and by extension, improve radiative forcing estimates.
How to cite: Asch, N., Field, P., Ghosh, P., Mahajan, S., Zhang, W., Kang, H.-G., Xu, M., Evans, K. J., and Gordon, H.: Enhancing Cloud Droplet Number Concentration Representation in Global Climate Simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15832, https://doi.org/10.5194/egusphere-egu26-15832, 2026.
Marine cloud brightening (MCB), rooted in aerosol-cloud interactions, is proposed as a rapid cooling strategy to combat global warming. However, uncertainties remain regarding its applicability across different cloud regimes and the potential counteracting effects of unfiltered coarse marine particles. Combining nearly a decade (2004-2013) of geostationally satellite observations and global reanalysis data, we compared the responses of three warm cloud regimes—marine stratocumulus (MSC), trade wind cumulus (Cu), and equatorial convection (Convection)—to fine aerosols (FA) and coarse sea salt aerosols (CSA). FA consistently strengthens cloud cooling in whatever cloud regimes, whereas CSA enhances cooling in dry MSC and Cu but induces warming in humid Cv, where precipitation dominates the cloud water loss. Further, a 50% FA increase induces a significant cooling of -14.7 W·m-2 in MSC, as well as a moderate cooling of -5.1 W·m-2 and -4.1 W·m-2 in Cu and Cv regions, far exceeding CSA effects (e.g., +1.5 W·m-2 in Cv). These results confirm that combining FA and CSA injections enhances cooling in MSC and Cu, whereas CSA injection in Cv should be avoided to prevent reduced radiative cooling. The cloud physics behind these aerosol-cloud interactions involves the balance between cloud droplet formation, cloud cover, albedo (reflectivity), precipitation, and evaporation processes, all of which are shaped by the aerosol size and the moisture content above the cloud. These findings offer key insights for climate modeling and intervention strategies.
How to cite: Zang, L., Liu, F., Pan, Z., Rosenfeld, D., and Mao, F.: Observed feasibility of fine and coarse aerosol-driven marine cloud brightening across different cloud regimes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18075, https://doi.org/10.5194/egusphere-egu26-18075, 2026.
Aerosol-Cloud Interactions (ACIs) remain one of the largest sources of uncertainty in climate projections. Satellite observations provide essential constraints to estimate ACI-induced radiative forcing (e.g., Twomey, 1974; Albrecht, 1989), yet large discrepancies among studies persist due to measurement limitations. Passive sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) cannot simultaneously retrieve aerosol and cloud properties, leading to biases when absorbing aerosols above clouds (AACs) alter cloud optical retrievals. These biases become particularly pronounced during extreme aerosol events - such as wildfires, dust outbreaks, or volcanic eruptions - when AACs distort satellite-derived cloud effective radius (CER) and cloud optical thickness (COT). Previous studies over the Southeast Atlantic and Saharan regions have shown that AACs can lead to underestimated COT and either over- or underestimated CER (Haywood et al., 2004; Alfaros and Contreras, 2013; Costantino and Bréon, 2010, 2013).
To address these issues, we develop a new methodology combining data from MODIS (and VIIRS) with TROPOMI to construct a high-resolution aerosol–cloud joint dataset. This synergy enables separation of distinct aerosol–cloud configurations - (i) aerosol below cloud top (BCT), (ii) aerosol above cloud and attached (ACTa), and (iii) aerosol above cloud top and separated (ACTs) - facilitating a clearer quantification of their respective influences on cloud properties hence, radiative forcing. The dataset provides global coverage from 2019 to the present, and is applied here to three case studies: the 2019/2020 Australian fires, the 2020 California fires, and the recurrent Namibia/Angola fire season (July-October).
Our results highlight that accounting for aerosol-cloud vertical configuration substantially improves the quantitative evaluation of ACIs, with cloud droplet number concentration (Nd) exhibiting distinct responses across scenarios. Additionally, we use the Successive Order of Scattering (SOS) radiative transfer model (Lenoble et al., 2007) to simulate aerosol-cloud radiative effects, generate lookup tables (LUTs) to correct cloud retrieval biases in MODIS and other passive sensors and generate aerosol index to better understand its dependency on aerosol layer height and cloud cover.
How to cite: Devigne, E., Sourdeval, O., Waquet, F., De Graaf, M., and Jia, H.: Investigating Aerosol-Cloud-Interactions Radiative Impacts combining a New Global Satellite Joint-Dataset and Radiative Transfer Model., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4029, https://doi.org/10.5194/egusphere-egu26-4029, 2026.
Aerosols affect radiation, cloud properties, convection, air temperature, and large-scale circulation, yet their influence on precipitation distribution over the Maritime Continent (MC), a complex tropical region composed of islands interspersed with shallow seas, remains poorly understood. Using high-resolution cloud-system resolving model simulations, satellite observations, and reanalysis data, we demonstrate that rising aerosol concentrations amplify oceanic precipitation more than they suppress land precipitation, thereby increasing the sea-to-land precipitation ratio over the MC. This shift is supported by observations and contrasts with the land-enhanced precipitation distribution seen in pristine simulations or those without aerosol radiative effects. Our results underscore that aerosol-induced radiative cooling stabilizes the lower troposphere more over land than over the ocean, enhancing low-level convergence and convection over the sea. Moreover, high aerosol concentrations delay the diurnal precipitation peak over land from late afternoon to midnight, driven by diminished daytime heating and subsequent nighttime increases in moist static energy—an interesting pattern evident in some observed high-aerosol days.
How to cite: Seo, K.-H.: Aerosol effects on Maritime Continent precipitation: Oceanic intensification and land diurnal cycle delay, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4658, https://doi.org/10.5194/egusphere-egu26-4658, 2026.
Aerosol-cloud interactions (ACI) remain a major source of uncertainty in anthropogenic radiative forcing, primarily due to the challenge of simultaneously observing aerosols acting as cloud condensation nuclei (CCN) and their impact on cloud microphysics. This study leverages the synergy between ground-based measurements at the ATOLL peri-urban site (Lille, Northern France) and satellite observations (CLAAS-3 SEVIRI) to quantify how variations in boundary-layer aerosol loading influence cloud droplet number concentration (nd) and effective radius (re).
For the period between 2020 and 2024, collocated datasets of space- and ground-based instruments, in-situ and remote sensing, were analyzed under different conditions: filters have been applied to isolate CCN-relevant aerosols, low-level clouds, and stable atmospheric layers. Results reveal a relationship between aerosol scattering coefficient (σsp) related to aerosol concentration and cloud microphysical properties: nd increases with σsp, while re decreases, in line with CCN impact on liquid clouds. The aerosol-cloud interaction indices related to nd and re range from 0.11 to 0.36 and 0.07 to 0.13, respectively, depending on liquid water path (LWP) bins. These values align with previous field and satellite studies but are slightly lower, likely due to the coarse spatial resolution of SEVIRI and the predominance of winter conditions in the dataset.
This work highlights the measurable sensitivity of stratiform clouds to boundary-layer aerosol loadings in northern France and underscores the value of combining ground- and space-based observations. Future research will expand this methodology to sites with contrasting aerosol regimes and incorporate aerosol chemical composition data to further disentangle the influence of hygroscopicity and mixing state on cloud microphysical responses.
How to cite: Crumeyrolle, S., Coopman, Q., Bourrianne, E., Lapointe, C., Delbarre, E., Devigne, E., Pujol, O., and Garrett, T.: Assessing Aerosol-Cloud Interactions Using Ground- and Space-Based Observations: Insights from Northern France, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5642, https://doi.org/10.5194/egusphere-egu26-5642, 2026.
Large-scale biomass burning (BB) events in South America constitute a major source of atmospheric aerosols, with profound implications for regional radiative budgets, cloud microphysics, and precipitation processes. However, most operational and regional weather prediction models still neglect interactive atmospheric chemistry, limiting their ability to realistically represent aerosol radiative effects and associated feedbacks on clouds and precipitation during extreme BB episodes. Despite extensive observational evidence, the representation of aerosol–meteorology interactions linked to intense biomass burning remains a major source of uncertainty in regional climate and weather simulations.
In this study, we investigate the atmospheric impacts of an intense and persistent BB episode that affected South America during September 2022, using the fully coupled Weather Research and Forecasting model with online chemistry (WRF-Chem). The event was identified based on active fire detections from the FIRMS web-portal, consistently observed through enhanced Aerosol Optical Depth (AOD) from MODIS and elevated carbon monoxide (CO) columns retrieved from IASI. Model simulations were conducted for the period from 25 August to 12 September 2022, employing the MOZCART chemical/aerosol mechanism (MOZART and GOCART), Morrison double-moment microphysics, and the RRTMG radiation scheme. To isolate aerosol-driven perturbations from the large-scale meteorological forcing, a control experiment without interactive chemistry was performed and used as a baseline. The analysis focuses on aerosol-induced modifications to cloud microphysical properties and precipitation, evaluated over four distinct geographical subregions representative of the most affected areas.
Model performance was assessed through a comprehensive comparison with observations. The chemical component was evaluated by analyzing the spatial and temporal evolution of simulated AOD and CO against MODIS and IASI satellite products. The meteorological consistency of the simulations was independently verified using surface observations, with statistical metrics computed for near-surface temperature, wind speed, and relative humidity across the domain. The results highlight the critical role of interactive aerosol–radiation and aerosol–cloud processes in shaping the atmospheric response to extreme biomass burning events. This study demonstrates the added value of fully coupled chemistry–meteorology modeling and spatially resolved diagnostics for improving the representation of biomass burning impacts in regional weather and climate simulations over South America.
How to cite: de Bem, D. L., Anabor, V., Puhales, F. S., Steffenel, L. A., Brenner, L., Morichetti, M., Grasso, F., and Rizza, U.: Interactive aerosol effects during an extreme biomass burning episode over South America simulated with WRF-Chem, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7461, https://doi.org/10.5194/egusphere-egu26-7461, 2026.
The Arctic is warming up to 4 times faster than the global average, leading to rapid ice melting and consequently, a drastic change of the sources and processing of aerosols and their impact on clouds. Monitoring of these changes over the Arctic is extremely sparse, especially in the most remote regions where harsh conditions make it difficult to carry out even simple measurements. To address these knowledge gaps and develop better and new methods of remote sensing of aerosols and clouds, the CleanCloud project carried out the field campaign CLeancloud Arctic VIllum ExpeRiment (CLAVIER) at Villum Research Station (VRS) in northeast Greenland to study aerosol-cloud interaction (ACI) using in-situ surface and remote sensing as well as airborne measurements.
CLAVIER covered two phase; spring (April) and summer (July/August) 2024, each lasting for one month. We have employed the existing in-situ surface aerosol monitoring at VRS, which includes a Scanning Mobility Particle Sizer (SMPS), a Cloud Condensation Nuclei Counter (CCNC), a High-Volume Sampler (HVS), a Nephelometer, an Aethalometer, a Neutral cluster and Air Ion Spectrometer (NAIS), a wind lidar and a ceilometer. During CLAVIER, the site was additionally equipped with a AeRosol aerosol-cloud lIdar System (ARIS lidar) and a Wideband Integrated Bioaerosol Sensor (WIBS-5/NEO) to provide realtime measurement of aerosols and fluorescent particles to infer the presence of bioaerosols and their potential contribution to Ice Nucelating Particles (INP). In addition, a W-band Cloud Doppler Radar (WProf) and a tethered balloon (Helikite) was operated during the spring phase. The helikite was equipped with aerosol and cloud instrumentation, including a Portable Optical Particle Spectrometer (POPS), a Miniaturized Scanning Electrical Mobility Sizer (mSEMS), a Single-channel tricolor absorption photometer (STAP) and a miniaturized Cloud Droplet Analyzer (miniCDA), and a filter sampler with the new nano-electromechanical membrane FTIR (NEMS-FTIR) technique. A second tethered balloon was also employed for meteorological and flux measurements. In the summer phase, a Proton-Transfer-Reaction Mass Spectrometry (PTR-MS) was used to measure VOCs online and cartridge sampling was performed for offline sampling of VOCs, as well as a WELAS (white-light aerosol spectrometer) for size distribution of larger sizes and the newest aethalometer AE36s. Finally, summertime measurements were also coordinated with the NASA ARCSIX aircraft mission for clousure experiments.
In order to get a better understanding of the processes related to aerosol-cloud interactions, several modelling activities were and are being carried out for the CLAVIER period. These include the Flexible Particle Dispersion Model (FLEXPART), the WRF-SIP model to study in detail the secondary ice production in clouds, OpenIFSv48 global model to simulate the aerosol composition and forcing during the campaign, and finally, the FLEXPART-SOSAA framework and the ADCHEM model to study in detail the aerosol chemistry and impacts on CCN.
This presentation will provide an overview of these activities and some preliminary results.
How to cite: Im, U., Berne, A., Calmer, R. F. A., Favre, L., Foskinis, R., Massling, A. H., Nenes, A., Papayannis, A., Schmale, J., Zhang, L., Boy, M. H., Navale, K. V., Svenhag, C. A., Rosati, B., de Jonge, R. W., Koyyka, J., Teng, Z., Evangeliou, N., Dorff, H., and Skov, H. and the CLAVIER Team: Aerosol-cloud interactions over the high Arctic: CLeancloud Arctic VIllum ExpeRiment (CLAVIER) overview, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9201, https://doi.org/10.5194/egusphere-egu26-9201, 2026.
Running climate simulations with ICON (Sapphire configuration) using the 1-moment microphysical scheme so far relied on very simplistic assumptions about cloud droplet number concentrations (CDNC) profiles, which do not evolve in time and space. This has direct implications for cloud radiative effects and precipitation rates. Recent developments in km-scale modeling within the WarmWorld project, however, have introduced important advances in this area.
In this presentation, I introduce a new droplet parameterization developed for ICON-AES physics. It makes use of cloud condensation nuclei (CCN) derived from Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA; Block et al., ESSD, 2024). This approach provides observationally constrained input for computing CDNC fields without the need to couple ICON to a full aerosol model.
This new module is currently implemented for the 1-moment scheme, in which cloud water mass is predicted while CDNC are prescribed as time-dependent boundary conditions. CDNC are computed using a diagnostic, fitted droplet parameterization that depends on CCN and vertical velocity, adapted from Kuba and Fujiyoshi (ACP, 2006). This scheme is therefore computationally efficient and well suited for non-hydrostatic models. To fully exploit this parameterization, a CCN-supersaturation spectrum is constructed using an adaptation of Twomey’s power law when reading CCN of reduced complexity into ICON. This ensures computational efficiency and helps to correct biases recently identified in CCN evaluations.
I will discuss the scientific features of this scheme, its computational feasability, and present preliminary results.
How to cite: Block, K.: A new droplet parameterization for ICON-AES physics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9487, https://doi.org/10.5194/egusphere-egu26-9487, 2026.
To quantify microphysics-related uncertainties in cloud seeding modeling, parameterizations of silver iodide ice nucleation were implemented as a standalone physics module in the Weather Research and Forecasting (WRF) model. The module is compatible with most bulk microphysics schemes, enabling ensemble cloud seeding simulations using multiple microphysics schemes. Twin ensemble forecast experiments—a control (unseeded) and a seeded ensemble—were conducted for a post-frontal stratiform snowfall event in central China. The control ensemble reproduced the observed precipitation pattern, while the seeded ensemble predicted predominantly positive precipitation enhancement over the target area.
Both ensembles employed multiple initial and lateral boundary conditions (IC/LBCs) and microphysics schemes to assess their respective contributions to uncertainties. For the control ensemble, IC/LBCs and microphysics schemes exerted comparable overall influences on the variability of supercooled liquid water and precipitation. IC/LBCs primarily affected the spatial distribution of precipitation, whereas microphysics schemes had a stronger influence on intensity. For the seeded ensemble, microphysics schemes dominated the uncertainty in cloud-seeding-induced changes in microphysical properties and precipitation. These results underscore the importance of incorporating multiple microphysics schemes in ensemble cloud seeding modeling to robustly represent uncertainty.
How to cite: Zhu, J.: Ensemble Modeling of Cloud Seeding with Multiple Microphysics Schemes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9541, https://doi.org/10.5194/egusphere-egu26-9541, 2026.
One of the biggest challenges in working with satellite data is the vast volume of data. It makes downloading larger chunks slow, and keeping a local copy for infrequent analysis is often impractical. This is a well-known issue, and several institutions are creating cloud-based solutions. In addition to moving data to the cloud, new file formats and processing tools are emerging. However, there are data which are stored in the cloud in non-cloud-friendly file formats. For example, MODIS cloud optical properties are stored in HDF4 file format in the AWS cloud, but effective software tools for processing such data in the compute cloud are limited.
In this presentation, we discuss planned workflows within CloudTracker Observatory for efficient processing of MODIS data in HDF4 format in the AWS cloud. We use detection and analysis of ship-track-like aerosol-polluted cloud tracks (Toll et al 2019 Nature https://doi.org/10.1038/s41586-019-1423-9) as the main use case. We study both strong visible tracks and weak tracks invisible to the naked eye in the satellite images. We analyse existing software tools and how they could be improved, together with available architectural options in the AWS compute cloud. The Observatory is planned within an ERC-funded project CloudTracker - Tracking Polluted Clouds: the Plausibility of a Strong Aerosol Cooling Effect on Earth’s Climate. Shared cloud-based workflows close to the used satellite data that can be easily extended by any interested research group are likely to foster international collaboration.
How to cite: Keernik, H., Luhamaa, A., Aun, M., and Toll, V.: CloudTracker Observatory: analysing a large number of aerosol-polluted cloud tracks in the AWS compute cloud, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10331, https://doi.org/10.5194/egusphere-egu26-10331, 2026.
In this contribution, we analyze 14 years of global spaceborne lidar observations for trends in the occurrence of low-level clouds and their cloud-top height, and derive their sensitivities to controlling factors.
Low clouds play an important role in the Earth's energy budget because of their capability of reflecting large amounts of incoming sunlight. However, there is some ambiguity in the detection of low-level clouds in satellite observations which are mostly performed with passive sensors. Only active remote sensing with spaceborne lidar or radar can provide direct measurements of cloud-top height and, thus, lead to straightforward detection of low-level clouds. Here, we analyze 14 years of spaceborne lidar observations for trends in the occurrence of low-level clouds and their cloud-top height. We find that spatial trend patterns in low-level cloud cover and low-level cloud top height are negatively correlated, i.e., regions with a decrease in low-cloud cover tend to show an increase in cloud-top height. We find that spatial trend patterns of both parameters can be well explained by sea-surface temperature and estimated inversion strength trends. Low-level clouds in climatological stratocumulus regions are particularly sensitive to changes in sea-surface temperature (-3.4 to -3.7% K-1 in cloud cover and 48.8-52.3mK-1 in cloud-top height) and estimated inversion strength (3.3%K-1 in cloud cover and from -69.3 to -69.6mK-1 in cloud-top height).
How to cite: Andersen, H., Tesche, M., Goren, T., and Choudhury, G.: Trends and sensitivities of low-cloud cover and top height from CALIPSO observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11162, https://doi.org/10.5194/egusphere-egu26-11162, 2026.
Aerosols play an important role in cloud formation, radiative forcing and precipitation formation. However, the representation of aerosol-cloud interactions in climate models still causes one of the largest uncertainties in future climate projections. Large Eddy Simulation (LES) models have become useful tools for bridging our understanding of small-scale processes with parametrization development for Earth System Models (ESMs). Previous studies have exposed substantial inter-model variability in reproducing the susceptibility of cloud droplet number concentrations (CDNC) to cloud condensation nuclei (CCN) concentrations across aerosol- and updraft-limited regimes. Our work will ultimately contribute to the development of ESMs for reliable future projections under scenarios of changing aerosol emissions. We present preliminary results from the evaluation of LES model output against in-situ observations of aerosol and clouds microphysics and chemistry, from observation sites representing different ranges within the aerosol- and updraft-limited susceptibility regimes. To develop new process-based constraints for LES models, we will utilize data collected during the ARTofMELT expedition in the Arctic, the FAIRARI campaign in the Po Valley and the NOMODODO campaign at the Maïdo Observatory in La Réunion. The diverse settings of the observations give the chance of investigating case studies with varying aerosol loadings and land-atmosphere interactions. As we are interested in cloud susceptibility regimes and aerosol indirect effects on clouds, we mainly focus on liquid-phase processes. We analyze liquid water content, CDNC, CCN concentrations, and aerosol chemical composition. We also examine the shape of the aerosol and cloud droplet size distributions. This study serves as a benchmark to build more consistent representations of aerosol-cloud interactions in numerical models. Our results will be used to inform further research on the sensitivity of cloud properties to aerosol and cloud microphysical processes.
How to cite: Bellagente, I. E., Riipinen, I., Zieger, P., Heikkinen, L., Blichner, S., Ekman, A. M. L., Haberstock, L., Kojoj, J., Decesari, S., and Magand, O.: Evaluating LES models across aerosol- and updraft-limited susceptibility regimes , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11879, https://doi.org/10.5194/egusphere-egu26-11879, 2026.
Aerosol–cloud interactions (ACI) remain a major source of uncertainty in climate projections. Within the CleanCloud Project, we quantify present-day ACI radiative forcing and its evolution toward a post-fossil energy regime, including associated variability and rapid adjustments. Using improved Earth System Models (EC-Earth and ICON-HAM), updated with refined parameterizations for mineral dust emissions, wildfire smoke, and Arctic marine aerosols, we perform 30-year fixed-SST simulations to assess the radiative impacts of both anthropogenic and natural aerosol sources. Our results provide key insights into the role of ACI in climate feedbacks and establish a baseline for near-term projections of high-impact weather events, improving the reliability of climate model predictions under changing emission scenarios.
How to cite: Cremer, R. and Tegen, I.: From Fossil to Clean: Aerosol–Cloud Radiative Forcing in Transition, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12095, https://doi.org/10.5194/egusphere-egu26-12095, 2026.
The clouds of the Intertropical Convergence Zone (ITCZ), particularly deep convective clouds, play a pivotal role in the atmospheric circulation of energy and moisture as well as associated feedback processes. The Persistent EarthCARE underflight studies of the ITCZ and organized convection (PERCUSION) airborne campaign in 2024 sought to investigate the organization of convection in the ITCZ region across the Atlantic Ocean. The flight strategy of PERCUSION enabled the characterization of the cloud field across the northern and southern boundaries of the ITCZ as well as the eastern and western ends of the Atlantic basin, each of which is distinct in its aerosol and dynamic conditions.
The asymmetric and dynamic structure of the ITCZ implies differences in cloudiness and cloud properties at the northern and southern boundaries of the ITCZ. To assess the impacts that these differences have on the radiative energy budget of the Tropics, we provide a statistical characterization of the radiative effects (CRE) of these clouds. Airborne irradiance observations at flight altitude from the Broadband AirCrAft RaDiometer Instrumentation (BACARDI) on the research aircraft HALO are used to calculate the CRE, which is separated into its solar and thermal infrared components. To assess the representativeness of the airborne observations, satellite observations (e.g. GOES, EarthCARE) and reanalysis data from the same period and beyond will be used.
Additionally, the synergy of these datasets allows for the characterization of the CRE drivers, e.g. macro- or microphysical cloud properties. Observations during the 2020 Elucidating the Role of Cloud-Circulation Coupling and Climate (EUREC4A) campaign already demonstrated that the macrophysical properties of shallow cumulus clouds were the main driver of the solar and thermal-infrared CRE when cloud fractions were low, while microphysical properties became more relevant at higher cloud fractions. The PERCUSION campaign provides the opportunity to extend these results to deeper convection and more complex cloud systems.
How to cite: Luebke, A., Ehrlich, A., Rosenburg, S., and Wendisch, M.: The quantification of large-scale cloud radiative effects across the Atlantic ITCZ, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12130, https://doi.org/10.5194/egusphere-egu26-12130, 2026.
Anvil clouds form when ice particles detrained from deep convective updrafts spread horizontally near the tropopause, covering areas far larger than their parent convective cores and thereby strongly influencing the tropical cloud radiative effect. Atmospheric aerosol particles can modify anvil cloud development through their impacts on cloud microphysical and macrophysical processes and associated latent and radiative heating. As a result, aerosol effects on anvil clouds may have important implications for Earth’s radiation budget and radiative forcing. However, quantifying aerosol effects on anvil clouds remains challenging due to limited understanding of the processes that control anvil cloud extent.
Here, we use a cloud-resolving System for Atmospheric Modeling (SAM) to investigate how aerosol perturbations affect anvil cloud evolution. A series of warm-bubble–triggered isolated convection simulations is performed to capture the full life cycle of anvil clouds. Aerosol perturbations are represented through prescribed cloud droplet number concentrations, following the RCEMIP aerosol–cloud interaction protocol (Dagan et al., 2025). To quantify anvil evolution, we apply a passive tracer diagnostic that approximates cloud age after detrainment, enabling the examination of cloud properties as a function of time since convective origin (Gasparini et al., 2025). Our results provide new insight into how aerosol pollution influences anvil cloud evolution, persistence, and associated radiative effects, with implications for representing aerosol–cloud–radiation interactions in climate models.
How to cite: Song, C., Wall, C., Gasparini, B., and Lutsko, N.: Understanding aerosol–cloud interactions in tropical anvil clouds with cloud-resolving simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12941, https://doi.org/10.5194/egusphere-egu26-12941, 2026.
Extensive cloud cover over the Southern Ocean is a key contributor to global cloud radiative forcing. The region hosts some of the most pristine clouds on Earth, as its air masses originate largely over the open ocean and Antarctica with minimal influence from continental emissions. This provides an opportunity to investigate aerosol–cloud interactions under near-preindustrial aerosol conditions. However, the scarcity of in-situ measurements in this region leads to a misrepresentation of the Southern Ocean cloud properties in climate models, resulting in biases of simulated shortwave radiation and near-surface temperatures. To address these points, the HALO-South airborne campaign was conducted in September and October 2025, based out of Christchurch, New Zealand. Using DLR’s High Altitude and Long Range Research Aircraft (HALO), 20 research flights were carried out over the Southern Ocean in the vicinity of New Zealand, extending into the Antarctic marginal sea ice zone. The campaign targeted a broad suite of cloud regimes, from boundary-layer clouds to multilayer mixed-phase systems and high-level cirrus clouds. In addition, the flights sampled clouds embedded in a variety of synoptic weather systems, including cold-air outbreaks and convective systems. Flights were planned in synergy with satellite overpasses and with support from weather prediction models to ensure coverage of representative conditions. Here, we present a statistical overview of over 20 hours of cloud dataset collected by underwing probes during the HALO-South campaign, including cloud microphysical properties such as particle number concentration, liquid and ice water content, and particle size distributions. This dataset enables a deeper understanding of aerosol–cloud interactions, mixed-phase processes, and cloud radiative effects in the Southern Ocean. It provides critical observational constraints for evaluating satellite retrievals, assessing weather and climate model performance, and informing model development aimed at reducing long-standing regional radiation biases. Comparisons with Northern Hemisphere field campaigns further highlight hemispheric contrasts in cloud–aerosol coupling, offering new opportunities to investigate how differing aerosol environments shape cloud properties and climate feedbacks.
How to cite: Menekay, D., Kirschler, S., Voigt, C., Wang, Z., Pöhlker, M., Hanstein, I., Kleinbek, T., Sauer, D., De La Torre Castro, E., Jurkat-Witschas, T., and Afchine, A.: Cloud microphysical properties over the Southern Ocean: First results from the HALO-South airborne campaign, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13147, https://doi.org/10.5194/egusphere-egu26-13147, 2026.
Dust is a dominant aerosol type over Cabo Verde, sourced via long-range transport from Sahara and Sahel. Understanding the effects of the dust on surface energy balance, and thus temperature and precipitation has important implications for predicting the weather and climate in the region. Although such studies have been conducted over west Africa, their findings cannot be applied to Cabo Verde, where the meteorological regime is dominated by the Atlantic Ocean, with local effects of island topography and a distinct interaction between ITCZ migration and large-scale dust transport compared to that over continental Africa. Here, we analyze how the interaction between dust and radiation influences the radiative energy at the Top of Atmosphere (TOA) and at the surface, and how it affects cloudiness over Cabo Verde from 2018 to 2022. The Aerosol Optical Depth (AOD) data are from a ground-based sunphotometer AERONET and from the spaceborne CERES instruments. Saharan dust loading peaks in the Summer. June shows the highest AOD values with monthly mean reaching 1.4. Direct radiative effect (DRE) and indirect radiative effect (IRE) are calculated from CERES CRS1deg-Hour Ed4A, which includes clear sky, pristine and all sky conditions. Results showed that DRE in all seasons presents a net cooling at surface and at the TOA. The IRE shows net cooling on the surface and warming at the TOA. We also investigate how dust events are associated with the mean temperature (reanalysis data), liquid water path, precipitable water and the cloud amount (all three datasets are from CERES and geostationary satellite) combined with the height at which the dust is present (vertical profiles from spaceborne lidar). Further research aims to understand the changes in precipitation over Cabo Verde associated with large-scale dust transport.
How to cite: Lopes Mendes Goncalves, S. K., George, G., and Russchenberg, H.: Analyzing radiative effect of dust and its impact on energy budget and cloudiness over Cabo Verde, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13677, https://doi.org/10.5194/egusphere-egu26-13677, 2026.
This study addresses the persistent challenge of understanding precipitation inefficiency in the arid and semi-arid environment of the Arabian Peninsula. Despite frequent deep convective development, surface rainfall is often limited by sub-cloud evaporation and virga. To explain this disconnect, we propose a framework based on competing physical timescales, where precipitation formation is viewed as a race between warm-rain growth, mixed-phase (ice) growth, and evaporation/dilution "clocks."
We analyze thermodynamic and microphysical data by integrating in-situ radiosonde observations from the Integrated Global Radiosonde Archive (IGRA) with MERRA-2 reanalysis cloud diagnostics. Our analysis focuses on two climatically distinct regions: the elevated, orographic terrain of Abha and the continental interior of Hail.
Our results demonstrate that the warm-rain clock is systematically truncated across the region due to high lifting condensation levels (LCLs) and shallow warm-cloud layers (typically <1 km). We find a notable "CAPE paradox": high instability, which usually favors storm intensity, actually suppresses the warm-rain pathway by lofting droplets into the freezing zone before they can grow via collision-coalescence. Consequently, the mixed-phase clock dominates precipitation production. In the elevated Abha region, lower cloud bases and higher moisture availability allow growth processes to complete, whereas in Hail, the evaporation clock consistently wins the race, leading to significant rainfall loss. By framing these processes through competing timescales, we provide a physically consistent mechanism to explain the regional and seasonal variations in precipitation efficiency across the peninsula.
How to cite: Raju, S., Gunturu, U. B., Boragapu, R., Aldhaif, A., and Abandah, K.: Competing Timescales in Deep Convection: A Framework for Assessing Warm-Rain vs. Mixed-Phase Pathways over the Arabian Peninsula, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13815, https://doi.org/10.5194/egusphere-egu26-13815, 2026.
Aerosol–cloud interactions (ACI) remain the largest uncertainty in anthropogenic climate forcings. Observation-based estimates of instantaneous radiative forcing from ACI (RFaci; the Twomey effect) rely on the choice of aerosol quantities as proxies for cloud condensation nuclei (CCN) concentrations, which differ in their ability to represent cloud-base CCN and data accuracy. Using diverse observations and aerosol–climate models, we evaluate the utility of different proxies with two independent approaches. Both approaches reveal that surface CCN exhibits the smallest bias in predicting RFaci (+5 %), followed by aerosol index, surface sulfate and column CCN with similar biases of +25 %, while aerosol optical depth and column sulfate show the largest biases (–60 % and +92 %). Constraining RFaci with the optimal proxy reduces uncertainty from 66 % to 43 %, yielding a less negative RFaci (–1.0 W m−2) than the unconstrained case (–1.2 W m−2). Our findings highlight the crucial role of proxy constraint in reconciling and improving RFaci estimates.
How to cite: Jia, H., Quaas, J., Kroese, W., van Diedenhoven, B., Gryspeerdt, E., Böhm, C., Block, K., and Hasekamp, O.: Optimal choice of proxy for cloud condensation nuclei reduces uncertainty in aerosol–cloud–climate forcing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16046, https://doi.org/10.5194/egusphere-egu26-16046, 2026.
Aerosol-Cloud Interactions (ACI) contribution to the Earth’s radiative budget remains as a major uncertainty in future climate projections. Clouds constantly interact with the surrounding non-saturated environment, forming cloud-aerosol transition zones (TZs). These suspensions are not fully assessed by cloud-cloudless distinction methodologies and have a non-negligible role in the radiative budget, making the lack of large-scale TZ observations a challenge for full comprehension of the climate system.
In this study, two complementary ground-based and spaceborne lidar TZ observation techniques are integrated to enhance knowledge on TZ conditions, including their detection occurrence, distribution, and optical characteristics, while highlighting the advantages and limitations of each methodology used. Ground-based Automatic Low-Power Lidars and Ceilometers (ALC) located at Burjassot (Spain), Gruenow (Germany), Girona (Spain) and the Cloudnet network are used, along with CALIOP observations over the region between coordinates 30º–80ºN and 7ºW–35ºE, covering Europe. The ALC method relies on varying the set of thresholds for cloud detection of the Cloudnetpy algorithm from ACTRIS Cloudnet. In contrast, the method for the CALIOP data applies several filters to avoid artifacts, and uses the CAD score values to identify clouds, aerosols, and TZ conditions.
Results show that the transition from cloud to cloud-free is gradual, and cloud detection depends on the thresholds used in the methods, as well as the local climatology. To properly assess the synergy between the methods, case studies of coincidental observations are presented, where the distance between the CALIOP overpass and the ALC site is less than 4 km. These cases represent various atmospheric patterns, such as cloud-free and boundary layer aerosols, Cirrus, low-level clouds, dense tropospheric clouds, and multi-layer cloud structures. Overall, ground-based ALC provide high temporal and vertical resolution, and are particularly effective at detecting TZ at low altitudes. In contrast, CALIOP offers global coverage and is especially useful for detecting TZ located at high altitudes. Although each approach has individual limitations, integrating spaceborne downward-looking and ground-based upward-looking lidar observations can provide a more comprehensive characterization of cloud-TZ-aerosol distribution.
How to cite: Ruiz de Morales, J., Calbó, J., González, J.-A., Andersen, H., Cermak, J., Fuchs, J., Sola, Y., and Gómez, J.-L.: The Characterization of the Cloud-Aerosol Transition Zone Using Ground-Based and Spaceborne LiDAR, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17400, https://doi.org/10.5194/egusphere-egu26-17400, 2026.
The few studies that considered aerosol scattering in the long-wave (LW) typically relied on using simple corrective factors instead of including it in the radiative code. To analyse the climatic effects of physically accounting for this process, simulations have been performed with the ARPEGE-Climat atmospheric global climate model over the 1985–2014 period using the ecRad radiation scheme and updated optical properties of coarse aerosols, particularly dust. The evaluation of the model coarse-aerosol optical depth (AOD) against AERONET data over North Africa and the Arabian Peninsula shows the ability of ARPEGE-Climat to capture spatio-temporal variations in coarse AOD despite regional biases. The comparison of simulations with and without LW aerosol scattering shows that this process leads to a significant increase in downwelling surface LW radiation in dust-emitting regions, correlated with the largest coarse AOD. This increase results in a rise in minimum near-surface temperatures of up to +1 °C. It is also associated with an outgoing LW radiation decrease at the top of the atmosphere (TOA). However, during certain months and in certain regions, near-surface temperatures can be significantly reduced due to short-wave surface radiation decreases related to increases in low-level clouds. A precipitation increase over Sahel during September, linked to wetter atmospheric layers, is also simulated. Neglecting LW aerosol scattering in climate simulations therefore has significant impacts on climate, notably in dust-emitting regions. Globally, the LW aerosol-scattering contribution to radiation is 0.4 W m−2 at both the surface and TOA.
How to cite: Druge, T.: Radiative and climate effects of aerosol scattering in long-wave radiation based on global climate modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18112, https://doi.org/10.5194/egusphere-egu26-18112, 2026.
The three-dimensional nature of clouds modifies incoming radiation and the representation of cloud-radiative effects is simplified in climate models. Radiative transfer thus assumes clouds to be plane-parallel and homogeneous, commonly known in the community as the independent column approximation method. Studies have shown that neglecting the 3D radiative transport effect can introduce substantial biases in simulated mean radiation fields. The C3SAR (Cloud Structure & Climate – Closing the 3D Gap) research unit was established, bringing together advanced remote sensing techniques and high-resolution modeling to investigate the 3D radiative effects of clouds. In our subproject, we use hectometer-scale simulations which are essential for resolving clouds, to investigate biases in 3D cloud-radiative effects.
To realize 3D cloud observations and make them available for different cloud scenarios is essential for our studies. We use the hectometer-scale simulations as a virtual testbed to test and evaluate potential 3D reconstruction algorithms, which could then be applied to satellite and ground-based products. While 3D reconstruction from observational data faces many challenges, these simulations provide a consistent framework to evaluate and estimate potential uncertainties.
For our simulations we apply the ICON model centered around Lindenberg, Germany, with a starting resolution of 600 m and a 100 km domain. The resolution is refined through several nests—potentially up to 75 m—forced by operational weather forecasts at 2.2 km resolution. We will show first case study results of the application of 3D cloud reconstruction within our virtual testbed by applying instrument simulators.
How to cite: Krishnan, P. S. and Schemann, V.: 3D reconstruction of cloud fields using cloud resolving modelling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18231, https://doi.org/10.5194/egusphere-egu26-18231, 2026.
Automatized image analysis with a broad spectrum of different approaches, e.g. pixel-wise statistical evaluation or machine learning methods, is ever more emerging to deal with a growing amount of data or to assist humans in classifying images. Due to high monetary and personnel expenses some of these automatized methods are even supposed to replace human annotators. Fields where such methods can be utilized are for example medical image analysis or the classification of clouds in the sky. Many studies introducing methods for automatized image analysis use human annotations as ground truth. However, assessments of the reliability and accuracy of those are rare.
In our work, we investigate the agreement of human cloud classifications conducted according to the WMO SYNOP coding scheme for operational cloud type observations where clouds are classified at every instance into one out of ten classes in each of three altitude levels. We base our analysis on three experiments, where we compare: a) non-simultaneous observations of seven observers at the same weather station in Vienna, b) simultaneous observations at three close together stations over the course of more than 50 years, and c) independent reports of five meteorologists, who classified clouds from over 350 ground-based RGB images. Experiments a) and b) are designed to find systematical biases in operational on-site observations of single observers or weather stations and experiment c) directly targets the subjectivity of human cloud classifications. Results indicate, that human cloud observations of both single observers at the same station and also at different stations are biased towards specific cloud types, which can only partly be assigned to environmental or meteorological influence. Even for classifications based on the exact same information, i.e. an identical set of images in experiment c), disagreement could be found. The accuracy of single observers is around 55 – 65% when their reports are compared with a gold standard ground truth and inter-observer agreement shows similar values. An accuracy of close to 70% can be reached if the reports of four observers are combined via a majority voting approach and similar cloud categories are merged during post-processing. It can thus be hypothesized that a fraction of false classifications is due to the confusion of visually similar categories, which is a consequence of the very complex WMO SYNOP classification scheme. On the other hand, with respect to operational human on-site observations, the annotation of all-sky images was correct in only 30–40% of cases. Therefore, the accuracy of image classifications with respect to the ground truth is highly dependent on the used data set.
Although the WMO classification scheme is well-defined, it can be summarized that cloud classification is subjective to some extent because of e.g. the occurrence of clouds in transitional stages. Also, if the quality of the ground truth is not assessed in future studies a reliable determination of the accuracy of a newly presented automatized method would be impossible since both the new method but also the ground truth could be erroneous.
How to cite: Rosenberger, M., Dorninger, M., and Weissmann, M.: Uncertainties of human SYNOP cloud classifications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18898, https://doi.org/10.5194/egusphere-egu26-18898, 2026.
Changes in atmospheric aerosol concentrations have the potential to reorganize global precipitation patterns, yet the downstream implications for river systems are not fully understood. This study examines the sensitivity of global river runoff and discharge to anthropogenic aerosol forcing, asking how hydrological regimes differ in an atmosphere with reduced aerosol burdens compared to historical conditions.
We analyze multi-model simulations from the CMIP6 Detection and Attribution Model Intercomparison Project (DAMIP). The analysis focuses on the 1950–1980 era, a period of substantial aerosol emissions, to maximize the potential detection of aerosol-driven hydrological changes. Total runoff outputs from simulations, including and excluding anthropogenic aerosols, are used to force the TRIPpy river-routing model. This offline routing approach allows for a spatially consistent assessment of discharge variability across major global river basins, independent of the coarse resolution of GCM native routing.
We present the study design and preliminary insights into the spatial heterogeneity of aerosol impacts. By isolating the aerosol signature in river discharge, this research contributes to a more integrated understanding of the interplay of aerosol, climate, and hydrology.
How to cite: Pomnitz, N. and Quaas, J.: From Atmosphere to River Catchment: Modeled Global River Runoff Responses to Anthropogenic Aerosol Forcing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14180, https://doi.org/10.5194/egusphere-egu26-14180, 2026.
At kilometer-scale resolution, convective systems start to be explicitly resolved in atmospheric models, albeit coarsely. This allows a more process-based analysis of certain aspects of aerosol–cloud interactions in tropical regions. Convective clouds are a ubiquitous feature above the Amazon rainforest and develop under strongly contrasting aerosol conditions, with particle number concentrations during the dry season often exceeding those in the wet season by an order of magnitude.
In this context, we explore aerosol and convective cloud processes over the Amazon rainforest by analyzing case studies that combine observations and km-scale cloud-resolving simulations with interactive aerosols in a limited-area configuration. Regional simulations are performed at approximately 1.6 km horizontal resolution using the Icosahedral Nonhydrostatic (ICON) model coupled to the one-moment aerosol scheme HAM-lite. The realism of the simulations is evaluated through comparison with a combination of ground-based, satellite, and aircraft observations.
For the wet season, we analyze a case study based on flight RF15, conducted with the German research aircraft HALO during the CAFE-Brazil (Chemistry of the Atmosphere: Field Experiment in Brazil; CAFE-BR) campaign in 2022–2023. Three simulations are presented for this case: a best-estimate factual simulation and two counterfactual sensitivity experiments representing background “green ocean” conditions and heavy aerosol loading associated with biomass burning during dry season periods.
For the dry season, we also revisit two research flights from the ACRIDICON-CHUVA 2014 campaign, representing one clean and one polluted case, to further assess the representation of aerosol–cloud processes under different convective regimes. Combining these cases, we discuss the impact of changing aerosol environments on convective systems and draw conclusions relevant to a transition toward a post-fossil aerosol regime.
How to cite: Henkes, A., Quaas, J., Romshoo, B., Pöhlker, M., Weiss, P., Heinold, B., De, S., Kubin, A., Toledo Machado, L. A., Pöhlker, C., Stier, P., Lloyd, P., Kretzschmar, J., Jia, H., Senf, F., and Tegen, I.: Linking observed aerosol–cloud processes and kilometer-scale cloud-resolving simulations over the Amazon rainforest, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15116, https://doi.org/10.5194/egusphere-egu26-15116, 2026.
This study presents a cloud climatology over Aotearoa New Zealand and the surrounding Southwest Pacific Ocean region (140-210°E, 10-70°S) using satellite observations, ground measurements and reanalysis data. Three cloud satellite datasets alongside a network of ceilometers operated by the New Zealand Metservice are used to observe cloud properties.
Sixteen ceilometer sites, nine sites in the North Island, six sites in the South Island and one on Chatham Island, makes up the ground measurement network with data from 2021 to 2023. Cloud occurrence data at each site are compared with the ERA5, MERRA-2 and JRA55 reanalyses using a ground-based instrument simulator. Initial results show that both cloud occurrence is better represented in the models at east coast sites, with ERA5 performing the best of the three reanalyses.
Satellite datasets include MODIS Aqua/Terra cloud properties (2003–2025), Himawari-9 Advanced Himawari Imager cloud products (2023–2025), and observations from Cloudsat/CALIOP (2007–2010). We compare cloud fraction data from these satellite datasets with ERA5 reanalyses processed using the COSP instrument simulator to aid comparability. We then compare cloud top pressure/cloud top height/cloud top temperature distributions from the MODIS and Himawari-9 datasets with vertical profiles of cloud occurrence statistics from the CloudSat/CALIOP 2BCL5 dataset and from ERA5.
Cloud regimes are identified using MODIS cloud-top pressure–optical depth histograms, and their occurrence statistics are compared with CloudSat/CALIOP 2B-CLDCL5 cloud-type classifications.
Synoptic drivers for cloud fraction and other cloud properties are also examined via comparison with the Kidson weather types, a set of 12 objectively classified daily weather patterns centred over New Zealand, derived from cluster analysis of 1000 hPa geopotential height fields to form a synoptic climatology.
How to cite: Morrish, D., McDonald, A., Schuddeboom, A., Venugopal, A., Katurji, M., and Coulson, G.: A baseline cloud climatology for Aotearoa New Zealand and the Southwest Pacific region , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15214, https://doi.org/10.5194/egusphere-egu26-15214, 2026.
The remote-sensing equipment of LACROS (Leipzig Aerosol and Cloud Remote Observations System) was installed in Eriswil, Switzerland during the winter campaign of 2023 / 2024. We utilize a big dataset of in situ (especially ice-nucleating particle (INP) sampler; low volume sampler) and remote-sensing (especially cloud radar and Raman lidar) equipment. In addition, INP measurements were available as well in Hohenpeißenberg, Germany and Melpitz, Germany.
We evaluate the regional variability of the number concentration of INPs between the two pre-Alpine central-European sites of Eriswil and Hohenpeißenberg, supported by INP measurements from Melpitz during the winter months of 2024. The aim of the study is to spatially and temporally evaluate INP availability and removal within the planetary boundary layer (PBL) during Bise situations because reasons for the lack of ice and precipitation in the supercooled clouds observed over the Swiss Plateau remain unclear and may be caused by the lack of INPs. Target scenario of the study were situations when northeasterly winds (so-called Bise winds) prevailed and layers of stratus clouds formed at the top of the PBL at temperatures down to −10 °C. In these situations, it is expected that INPs are depleted along the transport path.
We will present our main insights from our measurements:
1) During the cold-Bise (cloud minimum temperatures as low as −10 °C) and warm-Bise (cloud minimum temperatures above 0 °C), almost no INP contrast was found between Hohenpeißenberg and Eriswil if both were within the PBL. Also, the INP concentration was overall found to be much lower during the cold-Bise than during the later warm-Bise situation.
2) When the Hohenpeißenberg site was located in the free troposphere during the cold-Bise situation, INP concentrations were much higher compared to Eriswil (still within the PBL) but similar to cloud-free Melpitz. These observations led to the conclusion that during cold-Bise situations the INP reservoir within the PBL is depleted, likely by the presence of supercooled stratus. The inversion-capped wintertime PBL, especially during periods of widespread snow cover, is apparently not capable to replenish the INP reservoir from the free troposphere.
3) INP observations of around 10^−3 L^−1 at Hohenpeißenberg, when this site was above the PBL were on a similar order as the ice crystal number concentrations (ICNC) observed during the same period at Eriswil. This supports the hypothesis that INPs are entrained from the free troposphere via turbulence and afterwards immediately removed as they interact with the Bise cloud layer, leading to reduced availability of INPs downwind. It must be noted that an ICNC concentration which is higher than the observed INP concentration can in principle also be a result of secondary ice formation processes. Nevertheless, secondary ice formation processes generally lead to orders of magnitudes of increase in ICNC, which was, besides occasional peaks in the ICNC, not observed in the average ICNC values during the investigated time periods.
How to cite: Ohneiser, K., Hartmann, M., Wex, H., Seifert, P., Hardt, A., Miller, A., Baudrexl, K., Thomas, W., Ettrichrätz, V., Maahn, M., Gaudek, T., Schimmel, W., Senf, F., Griesche, H., Radenz, M., and Henneberger, J.: Ice-nucleating particle depletion in the wintertime boundary layer in the pre-Alpine region during stratus cloud conditions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2302, https://doi.org/10.5194/egusphere-egu26-2302, 2026.
Posters virtual: Tue, 5 May, 14:00–18:00 | vPoster spot 5
EGU26-323 | ECS | Posters virtual | VPS3
Aerosol-cloud interactions under fine-mode and coarse-mode aerosol conditions over the monsoon region of PakistanTue, 05 May, 14:15–14:18 (CEST) vPoster spot 5
Abstract
The densely populated monsoon region of Pakistan, influenced by a diverse mix of natural and anthropogenic aerosols, provides a natural laboratory to investigate aerosol impacts on cloud properties. Using a decade-long (2015–2024) dataset from MODIS, MERRA-2, and ERA5, we examine the response of non-precipitating warm clouds to fine-mode and coarse-mode aerosols. We find positive correlations between aerosol optical depth (AOD) and cloud effective radius (CER), with stronger sensitivity to fine-mode AOD. The relationships of AOD with cloud optical thickness (COT) and liquid water path (LWP) are generally negative, but more pronounced for coarse-mode AOD. These aerosol-cloud relationships are strongly modulated by meteorological conditions: low relative humidity and low lower-tropospheric stability enhance the negative AOD–COT and AOD–LWP responses. Additionally, the sensitivity of aerosol-cloud relationships to meteorology is greater for fine-mode AOD than coarse-mode. These results highlight the importance of aerosol size and ambient meteorology in determining cloud microphysical responses, providing insight into aerosol cloud interactions in a region critical for South Asian climate.
How to cite: Anwar, K., Liu, Y., Labban, A., and Zeydan, Ö.: Aerosol-cloud interactions under fine-mode and coarse-mode aerosol conditions over the monsoon region of Pakistan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-323, https://doi.org/10.5194/egusphere-egu26-323, 2026.
