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Recent observations show that dust emitted within the Arctic (Arctic dust) has a remarkably high ice nucleating ability, especially between −20°C and −5°C, but its impacts on the number concentrations of ice nucleating particles (INPs) and radiative balance in the Arctic are not well understood. Here we incorporate an observation‐based ice‐nucleation parameterization indicating the high ice nucleating ability of Arctic dust into a global aerosol‐climate model. A simulation using this parameterization better reproduces INP observations in the Arctic and estimates >100 times higher dust INP number concentrations with ∼100% contribution from Arctic dust in the Arctic lower troposphere (>60°N and >700 hPa) during summer and fall (June–November) than a simulation applying a standard ice‐nucleation parameterization suitable for desert dust to Arctic dust. Our results demonstrate the importance of considering an ice‐nucleation parameterization suitable for Arctic dust when simulating INPs and their effects on aerosol‐cloud interactions in the Arctic. Plain Language Summary Dust is an important aerosol type acting as “ice nucleating particles,” which initiate the formation of ice crystals within mixed‐phase clouds (consisting of both supercooled water droplets and ice crystals) and influence the cloud lifetime and distribution. Recent observations show that dust is emitted from ice‐ and vegetation‐free areas in the Arctic region (hereafter Arctic dust), which has a remarkably high ice nucleating ability, compared with desert dust such as Asian dust and Saharan dust, because of the presence of certain organic matter. However, the impacts of Arctic dust with high ice nucleating ability on ice nucleating particles and mixed‐phase clouds in the Arctic are unknown. In this study, we investigate the importance of Arctic dust with high ice nucleating ability for ice nucleating particles in the Arctic using a global aerosol‐climate model. Our simulation results show that Arctic dust accounts for almost all dust ice nucleating particles in the Arctic lower troposphere (>60°N and about 0–3 km) during summer and fall (June–November). This study demonstrates the importance of considering the high ice nucleating ability of Arctic dust when simulating ice nucleating particles and their impacts on mixed‐phase clouds and radiative balance in the Arctic. Key Points Arctic dust, emitted within the Arctic, accounts for most of dust ice nucleating particles in the Arctic lower troposphere in summer to fall Importance of Arctic dust as ice nucleating particles in the Arctic strongly depends on its high ice nucleating ability at high temperatures Considering an ice‐nucleation parameterization suitable for Arctic dust is crucial for aerosol‐cloud‐climate simulations in the Arctic

Below 0°^{\\circ}$ C, cloud droplets can freeze, altering a cloud's optical and radiative properties and thereby affecting Earth's energy balance. The microphysical mechanisms that govern this process, known as glaciation, are expected to act on minute timescales. Nevertheless, stratiform clouds can persist in the mixed‐phase temperature range (from 0°^{\\circ}$ C to −38°^{\\circ}$ C) for hours, thus glaciation events remain poorly characterized. Here, we analyze satellite observations of individual cloud tops to track their temporal phase evolution and to quantify the extent of glaciation. We find that most glaciation events do not result in complete freezing; rather, they induce a sustained shift in cloud properties while the clouds remain in the mixed‐phase regime. Our results indicate that higher hemispheric and seasonal concentrations of ice‐nucleating particles correlate with glaciation occurrence rate. Future studies can utilize our phase‐evolution and glaciation data sets to evaluate how well weather and climate models simulate mixed‐phased cloud evolution and phase heterogeneity.

Postfrontal cloud regime transitions often result from strong aerosol‐cloud‐precipitation interaction. Using ACTIVATE measurements from 40 flights, we show a diurnal pattern of evolving aerosol particle size distributions (PSD) in the marine boundary layer upwind of clouds. A nucleation mode grows in size between 12 and 17 LT, likely from new particle formation (NPF), and an accumulation mode increases in concentration during daytime. Selecting the day of strongest size growth, we use quasi‐Lagrangian large‐eddy simulations to examine cloud impacts. With a morning PSD, moderate aerosol hygroscopicity aligns best with satellite and field campaign observational targets. Using a PSD near 17 LT, greater cloud droplet number concentrations delay the precipitation onset and keep albedo elevated for longer, primarily driven by increased accumulation mode concentrations, which facilitate an updraft‐limited regime and inhibit activation of smaller modes despite their growth after NPF. These results constitute a caveat for NPF‐related activation under polluted conditions.

Mixed‐phase clouds contribute to substantial uncertainties in global climate models due to their complex microphysical properties. Former model evaluations almost exclusively rely on satellite observations to assess cloud phase distributions globally. This study investigated mixed‐phase cloud properties using near global‐scale in situ observation data sets from 14 flight campaigns in combination with collocated output from a global climate model. The Southern Hemisphere (SH) shows significantly higher occurrence frequencies and higher mass fractions of supercooled liquid water than Northern Hemisphere (NH) based on observations at 0.2 and 100 km horizontal scales. Such hemispheric asymmetry is not captured by the model. The model also consistently overestimates liquid water content (LWC) in all cloud phases but shows ice water content (IWC) biases that vary with phase. Key processes contributing to model biases in phase partition can be identified through the combination of evaluation of phase frequency, liquid mass fraction, LWC and IWC.

Models assume that mixed‐phase clouds consist of uniformly mixed ice crystals and liquid cloud droplets when observations have shown that they consist of clusters, or “pockets,” of ice crystals and liquid cloud droplets. We characterize the spatial distribution of cloud phase over the Arctic and the Southern Ocean using active satellite observations and determine the relative importance of collocated meteorological parameters and aerosols from reanalysis to predict how uniformly mixed mixed‐phase clouds are for the first time. We performed a multi‐linear regression fit to the data set to predict the spatial distribution of the ice and liquid pockets. Contrary to what models suggest, mixed‐phase clouds are rarely perfectly homogeneous. Our results suggest that high temperatures are associated with homogeneously mixed ice and liquid pockets. We also find that a high mixing ratio of black carbon is associated with heterogeneously mixed ice and liquid pockets. Plain Language Summary The representation of clouds in numerical models remains one of the largest uncertainties in predicting our future climate. Clouds can consist solely of liquid droplets, ice crystals, or the coexistence of both hydrometeor types. The latter cloud type is referred to as mixed phase. Climate models assume that liquid droplets and ice crystals are uniformly mixed in space in mixed‐phase clouds, but observations show that mixed‐phase clouds are organized in separate pockets of clustered liquid droplets and ice crystals. This difference in representation has a large impact on the lifetime of clouds and on their role in climate change. Using satellite observations over the Arctic and the Southern Ocean, we quantify the spatial distribution of ice and liquid in clouds. We used a statistical method to determine the relationship between meteorology and aerosols and the spatial distribution of ice and liquid. Our results suggest that high temperatures are associated with homogeneously mixed mixed‐phase clouds and high concentrations of soot are associated with heterogeneously mixed mixed‐phase clouds. Furthermore, pockets of liquid within ice clouds are larger than pockets of ice within liquid clouds. These results will improve the representation of mixed‐phase clouds in large‐scale models. Key Points Space‐based observations and reanalysis are considered to determine the factors that control how mixed mixed‐phase clouds are Liquid dominated clouds contain small and isolated ice pockets whereas ice dominated clouds contain large and isolated liquid pockets Temperature and black carbon play an important role in controlling the cloud phase spatial distribution and increasing phase heterogeneity

The sizes and shapes of ice crystals influence the radiative properties of clouds, as well as precipitation initiation and aerosol scavenging. However, ice crystal growth mechanisms remain only partially characterized. We present the growth processes of two complex ice crystal habits observed in Arctic mixed‐phase clouds during the Ny‐Ålesund AeroSol Cloud ExperimeNT campaign. First, are capped‐columns with multiple columns growing out of the plates' corners that we define as columns on capped‐columns. These ice crystals originated from cycling through the columnar and plate temperature growth regimes, during their vertical transport by in‐cloud circulation. Second, is aged rime on the surface of ice crystals having grown into faceted columns or plates depending on the environmental conditions. Despite their complexity, the shapes of these ice crystals allow to infer their growth history and provide information about the in‐cloud conditions. Additionally, these ice crystals exhibit complex shapes and could enhance aggregation and secondary ice production. Plain Language Summary Snowflakes formed in the atmosphere have a wide variety of shapes and sizes and no two snowflakes are identical. The reason for this infinite number of shapes is that the environmental temperature and relative humidity prevailing during the snowflakes' growth determine their exact aspects. Thus, the prevailing environmental conditions can be determined from the shape of snowflakes, and become more complicated with increased shape complexity. During a measurement campaign in the Arctic, we identified two complex snowflake types and the history of environmental conditions in which they grew in. We inferred that some snowflakes were recirculating to higher or lower parts of the clouds and that others had collided with cloud droplets that froze on their surface at the early stage of their growth. These snowflakes may enhance the formation of new snowflakes and the initiation of precipitation. Key Points A large variety of ice crystal sizes and shapes were observed in Arctic mixed‐phase clouds with a holographic imager The growth history of two types of complex ice crystals was inferred from their shapes These ice crystals could enhance aggregation and secondary ice production

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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.

We analyze the importance of cloud top temperature, dust aerosol, sea salt aerosol, and sea ice cover for the thermodynamic phase of low‐level, mid‐level, and mid to low‐level clouds observed by CloudSat/CALIPSO over the Arctic and the Southern Ocean using an explainable machine learning technique. As expected, the cloud top temperature is found to be the most important parameter for determining cloud phase. The results show also a predictive power of sea salt and sea ice on the phase of low‐level clouds, while in mid‐level clouds dust shows predictive power. Over the Southern Ocean, strong zonal winds coincide with the aerosol distribution. While they can produce high mixing ratios of sea spray at lower levels, the strong zonal winds may prevent the pole‐ward transport of dust. Sea ice may prevent the release of sea salt aerosols and marine organic aerosols leading to higher liquid fractions in clouds over sea ice. Plain Language Summary The cloud phase describes whether a cloud consists of ice particles, liquid droplets, or both. The representation of the cloud phase in climate and weather models is uncertain, leading to radiation biases over the Southern Ocean and the Arctic Ocean. To investigate the impact of four different parameters on the cloud phase, we use an explainable machine learning technique. The parameters studied are the temperature of the cloud top, the sea ice coverage, and the concentration of sea salt aerosols and dust aerosols, both of which can act as ice nucleating particles and contribute to the ice formation in clouds. We find that temperature seems to be the most important factor in determining the cloud phase. Sea salt aerosol seems to be more relevant for low‐level clouds closer to the ocean surface, the source of sea salt aerosol. Sea ice may prevent the release of sea salt aerosol by covering the ocean and our analysis supports this hypothesis. Dust is typically transported over long distances and our analysis shows that dust aerosol is more important for mid‐level clouds, but persistent strong winds surrounding Antarctica may have an influence on the dust concentration and thus on cloud phase. Key Points Cloud phase in polar regions can be predicted based on cloud top temperature, sea ice concentration, and sea salt and dust mixing ratios Cloud top temperature has the strongest impact, while sea salt/spray aerosol is relevant for low‐level, and dust for mid‐level cloud phase Sea ice coverage and Southern Ocean westerly winds may influence the aerosol distribution and thereby cloud phase

Ice nucleation in mixed‐phase clouds has been identified as a critical factor in projections of future climate. Here we explore how this process influences climate sensitivity using the Community Earth System Model 2 (CESM2). We find that ice nucleation affects simulated cloud feedbacks over most regions and levels of the troposphere, not just extratropical low clouds. However, with present‐day global mean cloud phase adjusted to replicate satellite retrievals, similar total cloud feedback is attained whether ice nucleation is simulated as aerosol‐sensitive, insensitive, or absent. These model experiments all result in a strongly positive total cloud feedback, as in the default CESM2. A microphysics update from CESM1 to CESM2 had substantially weakened ice nucleation, due partly to a model issue. Our findings indicate that this update reduced global cloud phase bias, with CESM2's high climate sensitivity reflecting improved mixed‐phase cloud representation. Plain Language Summary Simulations of Earth's climate have revealed that the extent of greenhouse gas warming depends on a microscopic process in cold clouds known as ice nucleation. Problematically, this process is poorly understood and crudely represented in projections of future climate. Here we assess why ice nucleation affects Earth's projected future temperature, and estimate the sensitivity to different simulated representations of this process. We find that ice nucleation influences warming through feedback mechanisms in clouds in all regions and heights of the troposphere that are at temperatures where either ice crystals or liquid droplets may exist. The primary link between ice nucleation and warming is revealed to be the role this process has in setting the global mean ratio of ice to liquid water within clouds. We also demonstrate that an issue that weakened ice nucleation in a widely used climate model reduced bias in this ratio. Our findings suggest that the reduced bias is responsible for this model's strong global warming projections, enhancing the possibility that such projections may be realistic. Key Points Ice nucleation representation is only found to sizably affect total cloud feedback when allowed to promote biased global mean cloud phase Community Earth System Model 2's strongly positive cloud feedback is consistent with realistic mixed‐phase cloud representation despite a known model issue Simulated relationships among ice nucleation, cloud phase, and feedback strength are partly set by mid‐level and tropical high clouds