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Mixed‐phase clouds (MPCs) have been identified as significant contributors to uncertainties in climate projections, attributable to model representation of processes controlling the formation and loss of supercooled water droplets and ice particles from the atmosphere. Arctic MPCs are commonly widespread and long‐lived, with sustained ice crystal formation processes that challenge current understanding. This study examines the ice‐nucleating particle (INP) reservoir dynamics governing immersion‐mode heterogeneous freezing in an observed case of Arctic MPCs using a simplified 1D aerosol‐cloud model. The model setup includes prescribed dynamical forcings and thermodynamic profiles, and represents INPs as multicomponent and polydisperse particle size distributions. Diagnostic and prognostic approaches to immersion freezing parameterization are compared, including time‐independent (singular) number‐ and surface area‐based descriptions and a time‐dependent description following classical nucleation theory (CNT). The choice of freezing parameterization defines the size of the INP reservoir. The CNT‐based description yields an orders of magnitude larger INP reservoir than the singular parameterizations, which is the dominant factor for sustained ice crystal formation. The efficiency of the freezing process and cloud cooling are of secondary importance. A diagnostic treatment neglecting INP loss is only accurate when the INP reservoir size is large and INP depletion weak. Since a larger INP reservoir sustains ice crystal formation substantially longer, and ice water path scales with ice crystal concentrations for the conditions considered, resolving the source of differences in INP reservoir dynamics due to model implementation is a high priority for advancing climate model physics. Plain Language Summary Knowledge gaps regarding long‐lived Arctic mixed‐phase clouds, wherein supercooled droplets and ice crystals coexist, lead to significant uncertainties when assessing Earth's surface warming from increasing greenhouse gases. The longevity of such clouds, sustaining both liquid and ice crystal formation over many hours, is poorly represented across global climate models. Application of a simplified column model shows that the underlying freezing parameterization defines the number of ice‐nucleating particles (INPs) available for ice formation, termed INP reservoir in this work. A time‐dependent freezing description yields a substantially greater INP reservoir than time‐independent approaches, and therefore greater ice formation over 10 hr, whereas other factors are less important. Future work will extend to additional environmental conditions and modeling approaches. Key Points A 1‐D model informed by a large‐eddy simulation allows detailed study of immersion ice‐nucleating particles (INPs) Stochastic immersion freezing yields a greater INP reservoir and more sustained ice formation than singular approaches The efficiency of the freezing process and cloud cooling are of secondary importance for the sustenance of ice crystal formation

Cloud ice particle effective radius in atmospheric models is usually parametrized. A widely‐used parametrization comprises a strong dependence on the temperature. Utilizing available satellite‐based estimates of both cloud ice particle effective radius and cloud‐top temperature we evaluate if a similar temperature‐dependence exists in these observations. We find that for very low cloud‐top temperatures the modeled cloud ice particle effective radius generally agrees on average with satellite observations. For high sub‐zero temperatures however, the modeled cloud ice particle effective radius becomes very large, which is not seen in the satellite observations. We conclude that the investigated parametrization for the cloud ice particle effective radius, and parametrizations with a similar temperature dependence, likely produce systematic biases at the cloud top. Supporting previous studies, our findings suggest that the vertical structure of clouds should be taken into account as factor in potential future updates of the parametrizations for cloud ice particle effective radius. Plain Language Summary Atmospheric models are often used to diagnose and predict the atmospheric state including clouds. One very important property of clouds that consist of ice particles is the cloud ice particle effective radius. This ice effective radius is based on assumptions about the size and shapes of the ice particles in clouds, and thus parametrized, and is one of the important variables needed for calculating the effect of clouds on electromagnetic radiation, in particular on the solar radiation that enters the Earth's atmosphere. In our study we found that the parametrized ice effective radius agrees well on average and global scale with the ice effective radius inferred from satellite observations for cold clouds. However, we also found that for warmer ice clouds the parametrized ice effective radius is much higher than in satellite observations. Our study suggests that parametrizations of the ice effective radius used in atmospheric models show potential for improvements. Key Points Comparisons of modeled cloud ice particle effective radius with satellite observations are presented For very low cloud temperatures the modeled cloud ice particle effective radius agrees on average with satellite observations Modeled large cloud ice particle effective radii for high sub‐zero temperatures are not found in satellite observations

The representation of cloud microphysical processes in climate models continues to be a major challenge leading to uncertainty in climate simulations. The shape parameter (equivalent to relative dispersion) of gamma distribution for ice particles is assumed to be 0 in the Beijing Climate Center Atmospheric General Circulation Model (BCC‐AGCM). This study diagnoses the shape parameter by linking it to the ice volume‐mean diameter and analyzes the impact of the modified scheme on the performance of climate simulations. Results show that the modified scheme performs better in simulating global cloud fraction, cloud radiative forcing, and total precipitation compared to the control configuration, thereby significantly reducing simulation biases. The underlying physical mechanisms are driven by three key factors. First, the shape parameter in the modified scheme is greater than zero, narrowing the ice particle size distribution. This reduces the autoconversion of ice to snow and sedimentation processes while enhancing deposition growth, resulting in an increase in upper‐level ice clouds. The increase in ice‐clouds increases upper atmospheric temperatures, enhances atmospheric stability, and promotes the formation of lower‐level clouds. Second, the improvement in cloud fraction significantly mitigates the underestimation of longwave and shortwave cloud radiative forcing. Additionally, the overestimation of precipitation is improved, including both convective and large‐scale precipitation, particularly from an annual mean perspective. Increased atmospheric stability reduces convective precipitation, while weakened snow sources and enhanced sinks to reduce large‐scale precipitation. The study emphasizes the importance of ice particle spectral relative dispersion and provides valuable insights for improving cloud microphysics parameterization schemes. Plain Language Summary Clouds are a crucial part of the climate system, but accurately simulating them in global climate models remains challenging. In this study, we improved the Beijing Climate Center climate model by enhancing how ice particle sizes in clouds are represented. Our results show that this improvement reduces previous errors and leads to more accurate simulations of global cloud coverage, radiation, and rainfall. These advancements improve the reliability of climate projections and contribute to a better understanding of how the climate might change in the future. Key Points An improved ice particle spectral dispersion parameterization scheme is implemented in the BCC‐AGCM model The new scheme enhances the simulation performance of global cloud fraction, radiation, and precipitation in the BCC‐AGCM model The physical mechanisms driving the improved performance are clearly identified

From December 2020 to February 2022, 66 lava fountains (LF) occurred at Etna volcano (Italy). Despite their short duration (an average of about two hours), they produced a strong impact on human life, environment, and air traffic. In this work, the measurements collected from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument, on board Meteosat Second Generation (MSG) geostationary satellite, are processed every 15 min to characterize the volcanic clouds produced during the activities. In particular, a quantitative estimation of volcanic cloud top height (VCTH) and ash/ice/SO2 masses’ time series are obtained. VCTHs are computed by integrating three different retrieval approaches based on coldest pixel detection, plume tracking, and HYSPLIT models, while particles and gas retrievals are realized simultaneously by exploiting the Volcanic Plume Retrieval (VPR) real-time procedure. The discrimination between ashy and icy pixels is carried out by applying the Brightness Temperature Difference (BTD) method with thresholds obtained by making specific Radiative Transfer Model simulations. Results indicate a VCTH variation during the entire period between 4 and 13 km, while the SO2, ash, and ice total masses reach maximum values of about 50, 100, and 300 Gg, respectively. The cumulative ash, ice, and SO2 emitted from all the 2020–2022 LFs in the atmosphere are about 750, 2300, and 670 Gg, respectively. All the retrievals indicate that the overall activity can be grouped into 3 main periods in which it passes from high (December 2020 to March 2021), low (March to June 2021), and medium/high (June 2021 to February 2022). The different products have been validated by using TROPOspheric Monitoring Instrument (TROPOMI) polar satellite sensor, Volcano Observatory Notices for Aviation (VONA) bulletins, and by processing the SEVIRI data considering a different and more accurate retrieval approach. The products’ cross-comparison shows a generally good agreement, except for the SO2 total mass in case of high ash/ice content in the volcanic cloud.

There are large uncertainties in the single-scattering (i.e., morphologies) and microphysical (i.e., concentrations) properties of ice particles whose size are less than ~100 µm. Insufficient resolutions of the most advanced cloud probes (e.g., cloud particle imager) cannot resolve the micrometer-scale morphologies of small ice particles. Further, the shattering of large ice particles on probes’ inlets or tips causes uncertainties in the measurement of the concentrations of small ice particles. These uncertainties have large impacts on the single-scattering and microphysical properties of small ice particles that are utilized to quantify the bulk radiative properties of cirrus. In this study, the impacts of uncertainties in the morphologies and concentrations of small ice particles on the bulk radiative properties of tropical cirrus were calculated using measurements acquired during the Tropical Warm Pool-International Cloud Experiment. Five different models (i.e., budding Buckyball, Chebyshev particle, droxtal, Gaussian random sphere, and sphere) that represent the shapes of small ice particles were used to calculate the single-scattering properties. The bulk radiative properties, average phase-function (P11¯), and average asymmetry parameter (g¯) were computed by combining the measured size/habit distributions and the calculated single-scattering properties of ice particles. The impacts of the selection of varying morphologies of small particles on the bulk radiative properties were quantified. For these calculations, the possible range of the concentrations of small ice particles which depend on the degree of shattered large particles were also used. The impacts of varying the single-scattering properties of small ice particles on the bulk radiative properties were the largest in the upper parts of cirrus (T < −60 °C), while they were the smallest in the lower parts of cirrus (−45 < T < −30 °C). The impacts of uncertainties in the concentrations of small ice particles on the bulk radiative properties were largest in the lower parts of cirrus (−45 < T < −30 °C), whereas they were smallest in the upper parts of cirrus (T < −60 °C). The effect of shattering was maximum in the lower parts of cirrus, whilst it was minimum in the upper parts of cirrus. The combined impacts of uncertainties in the single-scattering (i.e., morphologies) and microphysical (i.e., concentrations) properties of small ice particles revealed variations of up to 11.2% (127.1%; 67.3%) of the integrated intensity in the forward (sideward; backward) angles in P11¯ and a corresponding change in g¯ by up to 12.61%.

We present a novel Monte‐Carlo ice microphysics model, McSnow, to simulate the evolution of ice particles due to deposition, aggregation, riming, and sedimentation. The model is an application and extension of the super‐droplet method of Shima et al. (2009) to the more complex problem of rimed ice particles and aggregates. For each individual super‐particle, the ice mass, rime mass, rime volume, and the number of monomers are predicted establishing a four‐dimensional particle‐size distribution. The sensitivity of the model to various assumptions is discussed based on box model and one‐dimensional simulations. We show that the Monte‐Carlo method provides a feasible approach to tackle this high‐dimensional problem. The largest uncertainty seems to be related to the treatment of the riming processes. This calls for additional field and laboratory measurements of partially rimed snowflakes. Key Points Monte‐Carlo model of the growth of ice particles due to depositional growth, aggregation, and riming Large uncertainty of the riming process emphasizes the need for laboratory and field measurements

This study investigates the size distribution, the mean diameter, and the concentration of ice particles within stratiform clouds by using in situ observations from 29 flights in Hebei, China. Furthermore, it examines the empirical fitting of ice particle size distributions at different temperatures using Gamma and exponential functions. Without considering the first three bins of ice particles, the mean diameter of ice particles (size range 100–1550 µm) is found to increase with temperature from −15 to −9 °C but decrease with temperature from −9 to 0 °C. By considering the first three bins of ice particles using the empirical Gamma fitting relationship found in this study, the mean diameter of ice particles (size range 25–1550 µm) shows a similar variation trend with temperature, while the turning point changes from −9 to −10 °C. The ice particle number concentration increases from 13.37 to 50.23 L−1 with an average of 31.27 L−1 when temperature decreases from 0 to −9 °C. Differently, the ice concentration decreases from 50.23 to about 22.4 L−1 when temperature decreases from −9 to −12 °C. The largest mean diameter of ice particles at temperatures around −9 and −10 °C is most likely associated with the maximum difference of ice and water supersaturation at that temperature, making the ice particles grow the fastest. These findings provide valuable information for future physical parameterization development of ice crystals within stratiform clouds.

The migration of a waved-shape accumulation of ice particles under an ice cover (referred to as “ice wave” in this study) is a phenomenon of transport of ice particles during an ice accumulation process in rivers. The migration of an ice wave will affect the pier scour. On the other hand, the local scour at the pier will affect the migration of ice waves. The interaction between the migration of ice waves and local scour around a pier is a very complicated process since not only the channel bed deforms, but also the ice jam develops simultaneously. By conducting a series of flume experiments, the interaction between the local scour around bridge piers and the migration of ice waves was studied. By applying both continuity and momentum equations, an empirical equation has been derived for predicting the thickness of ice waves around the pier. The impacts of the scour hole on the thickness of ice waves around the pier have been studied. The thickness of the wave crest and the migration speed of ice waves have been investigated. Similar to a scour hole in a sand bed, an “ice scour hole” appeared at the bottom of the ice jam around the pier. The existence of the “ice scour hole” affects the development of ice waves. A formula for calculating ice transport capacity has been obtained. Results calculated using the derived formula are in good agreement with those of laboratory experiments.

The Arctic is warming faster than any other region on Earth. This warming affects Arctic clouds, both directly and through changes in aerosol concentrations, triggering feedbacks that may further amplify the warming. Here we present simulations of a wintertime cloud case from Ny‐Ålesund with parameterizations optimized for representing secondary ice production (SIP). We compare cloud phase and its impact on radiation in present‐day conditions with simulations where we perturb (a) ice‐nucleating particle concentrations (INPC) and (b) atmospheric and surface temperatures using a pseudo‐global‐warming approach. Increasing the INPC leads to cloud thinning and reduced downward longwave radiation at the surface (SDLR). Intriguingly, with warming we find an increase in cloud ice due to increased rime splintering (RS) and subsequent SIP, which also leads to reduced SDLR. This can be explained by an upward shift in the temperature region where RS is active to higher altitudes where more liquid water is present.

Various mechanisms of secondary ice production (SIP) cause multiplication of numbers of ice particle, after the onset of primary ice. A measure of SIP is the ice enhancement ratio (“IE ratio”) defined here as the ratio between number concentrations of total ice (excluding homogeneously nucleated ice) and active ice-nucleating particles (INPs). A convective line observed on 11 May 2011 over the Southern Great Plains in the Mesoscale Continental Convective Cloud Experiment (MC3E) campaign was simulated with the “Aerosol–Cloud” (AC) model. AC is validated against coincident MC3E observations by aircraft, ground-based instruments, and satellite. Four SIP mechanisms are represented in AC: the Hallett–Mossop (HM) process of rime splintering, and fragmentation during ice–ice collisions, raindrop freezing, and sublimation. The vertical profile of the IE ratio, averaged over the entire simulation, is almost uniform (102 to 103) because fragmentation in ice–ice collisions dominates at long time scales, driving the ice concentration toward a theoretical maximum. The IE ratio increases with both the updraft (HM process, fragmentation during raindrop freezing, and ice–ice collisions) and downdraft speed (fragmentation during ice–ice collisions and sublimation). As reported historically in aircraft sampling, IE ratios were predicted to peak near 103 for cloud-top temperatures close to the −12°C level, mostly due to the HM process in typically young clouds with their age less than 15 min. Here, at higher altitudes with temperatures of −20° to −30°C, the predicted IE ratios were smaller, ranging from 10 to 102, and mainly resulted from fragmentation in ice–ice collisions.