Large-eddy simulations of mixed-phase Arctic clouds by 11 different models are analyzed with the goal of improving understanding and model representation of processes controlling the evolution of these clouds. In a case based on observations from the Indirect and Semi-Direct Aerosol Campaign (ISDAC), it is found that ice number concentration, N i , exerts significant influence on the cloud structure. Increasing N i leads to a substantial reduction in liquid water path (LWP), in agreement with earlier studies. In contrast to previous intercomparison studies, all models here use the same ice particle properties (i.e., mass-size, mass-fall speed, and mass-capacitance relationships) and a common radiation parameterization. The constrained setup exposes the importance of ice particle size distributions (PSDs) in influencing cloud evolution. A clear separation in LWP and IWP predicted by models with bin and bulk microphysical treatments is documented and attributed primarily to the assumed shape of ice PSD used in bulk schemes. Compared to the bin schemes that explicitly predict the PSD, schemes assuming exponential ice PSD underestimate ice growth by vapor deposition and overestimate mass-weighted fall speed leading to an underprediction of IWP by a factor of two in the considered case. Sensitivity tests indicate LWP and IWP are much closer to the bin model simulations when a modified shape factor which is similar to that predicted by bin model simulation is used in bulk scheme. These results demonstrate the importance of representation of ice PSD in determining the partitioning of liquid and ice and the longevity of mixed-phase clouds.

Abstract. The Arctic climate is changing; temperature changes in the Arctic are greater than at midlatitudes, and changing atmospheric conditions influence Arctic mixedphase clouds, which are important for the Arctic surface energy budget. These low-level clouds are frequently observed across the Arctic. They impact the turbulent and radiative heating of the open water, snow, and sea-ice-covered surfaces and influence the boundary layer structure. Therefore the processes that affect mixed-phase cloud life cycles are extremely important, yet relatively poorly understood. In this study, we present sensitivity studies using semi-idealized large eddy simulations (LESs) to identify processes contributing to the dissipation of Arctic mixed-phase clouds. We found that one potential main contributor to the dissipation of an observed Arctic mixed-phase cloud, during the Arctic Summer Cloud Ocean Study (ASCOS) field campaign, was a low cloud droplet number concentration (CDNC) of about 2 cm −3 . Introducing a high ice crystal concentration of 10 L −1 also resulted in cloud dissipation, but such high ice crystal concentrations were deemed unlikely for the present case. Sensitivity studies simulating the advection of dry air above the boundary layer inversion, as well as a modest increase in ice crystal concentration of 1 L −1 , did not lead to cloud dissipation. As a requirement for small droplet numbers, pristine aerosol conditions in the Arctic environment are therefore considered an important factor determining the lifetime of Arctic mixed-phase clouds.

While recent laboratory experiments have thoroughly quantified the ice nucleation efficiency of different aerosol species, the resulting ice nucleation parameterizations have not yet been extensively evaluated in models on different scales. Here the implementation of an immersion freezing parameterization based on laboratory measurements of the ice nucleation active surface site density of mineral dust and ice nucleation active bacteria, accounting for nucleation scavenging of ice nuclei, into a cloud‐resolving model with two‐moment cloud microphysics is presented. We simulated an Arctic mixed‐phase stratocumulus cloud observed during Flight 31 of the Indirect and Semi‐Direct Aerosol Campaign near Barrow, Alaska. Through different feedback cycles, the persistence of the cloud strongly depends on the ice number concentration. It is attempted to bring the observed cloud properties, assumptions on aerosol concentration, and composition and ice formation parameterized as a function of these aerosol properties into agreement. Depending on the aerosol concentration and on the ice crystal properties, the simulated clouds are classified as growing, dissipating, and quasi‐stable. In comparison to the default ice nucleation scheme, the new scheme requires higher aerosol concentrations to maintain a quasi‐stable cloud. The simulations suggest that in the temperature range of this specific case, mineral dust can only contribute to a minor part of the ice formation. The importance of ice nucleation active bacteria and possibly other ice formation modes than immersion freezing remains poorly constrained in the considered case, since knowledge on local variations in the emissions of ice nucleation active organic aerosols in the Arctic is scarce.

Abstract. A new heterogeneous ice nucleation parameterization that covers a wide temperature range (−36 to −78 °C) is presented. Developing and testing such an ice nucleation parameterization, which is constrained through identical experimental conditions, is important to accurately simulate the ice nucleation processes in cirrus clouds. The ice nucleation active surface-site density (ns) of hematite particles, used as a proxy for atmospheric dust particles, were derived from AIDA (Aerosol Interaction and Dynamics in the Atmosphere) cloud chamber measurements under water subsaturated conditions. These conditions were achieved by continuously changing the temperature (T) and relative humidity with respect to ice (RHice) in the chamber. Our measurements showed several different pathways to nucleate ice depending on T and RHice conditions. For instance, almost T-independent freezing was observed at −60 °C < T < −50 °C, where RHice explicitly controlled ice nucleation efficiency, while both T and RHice played roles in other two T regimes: −78 °C < T < −60 °C and −50 °C < T < −36 °C. More specifically, observations at T lower than −60 °C revealed that higher RHice was necessary to maintain a constant ns, whereas T may have played a significant role in ice nucleation at T higher than −50 °C. We implemented the new hematite-derived ns parameterization, which agrees well with previous AIDA measurements of desert dust, into two conceptual cloud models to investigate their sensitivity to the new parameterization in comparison to existing ice nucleation schemes for simulating cirrus cloud properties. Our results show that the new AIDA-based parameterization leads to an order of magnitude higher ice crystal concentrations and to an inhibition of homogeneous nucleation in lower-temperature regions. Our cloud simulation results suggest that atmospheric dust particles that form ice nuclei at lower temperatures, below −36 °C, can potentially have a stronger influence on cloud properties, such as cloud longevity and initiation, compared to previous parameterizations.

&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; The Arctic climate is changing; temperature changes in the Arctic are greater than at mid-latitudes, and changing atmospheric conditions influence Arctic mixed-phase clouds, which are important for the Arctic surface energy budget. These low-level clouds are frequently observed across the Arctic. They impact the turbulent and radiative heating of the open water, snow and sea-ice covered surfaces, and are influencing the boundary layer structure. Therefore the processes that affect mixed-phase cloud life cycles are extremely important, yet relatively poorly understood. In this study, we present sensitivity studies using semi-idealized large eddy simulations (LES) to identify processes contributing to the dissipation of Arctic mixed-phase clouds. We found that one potential main contributor to the dissipation of an observed Arctic mixed-phase cloud, during the Arctic Summer Cloud Ocean Study (ASCOS) field campaign, was a low cloud droplet number concentration (CDNC) of about 2 cm&lt;sup&gt;&amp;#8722;3&lt;/sup&gt;. Introducing a high ice crystal concentration of 10 l&lt;sup&gt;&amp;#8722;1&lt;/sup&gt; also resulted in cloud dissipation, but such high ice crystal concentrations were deemed unlikely for the present case. Sensitivity studies simulating the advection of dry-air above the boundary layer inversion, as well as a modest increase in ice crystal concentration of 1 l&lt;sup&gt;&amp;#8722;1&lt;/sup&gt;, did not lead to cloud dissipation. As a requirement for small droplet numbers, pristine aerosol conditions in the Arctic environment are therefore considered an important factor determining the lifetime of Arctic mixed-phase clouds.&lt;/p&gt;

A bulk three-moment representation for rain microphysics is developed and implemented in the Predicted Particle Properties (P3) microphysics scheme. In addition, a new parameterization for rain self-collection and collisional breakup (RSCB) is presented using a lookup table approach, based on the Spectral-Bin Model (SBM). To quantify the impacts of sedimentation, evaporation, and RSCB on drop size distributions (DSDs), a rain shaft model is applied to a wide range of atmospheric scenarios (i.e., initial conditions and regimes) and compared against results from the SBM. DSD shapes are mainly determined by both sedimentation and evaporation, except in heavy rain where the impact of RSCB on DSD shape becomes more important than evaporation. The new parameterization for RSCB has a considerable impact on the mean drop size, improving the agreement between P3 and SBM. Only 4% of the original two-moment rainshaft simulations have mean drop sizes and rain rates within ±20% of the SBM results, but this increases to more than 95% agreement when the three-moment rain representation is used together with the new parameterization for RSCB. Generally, the improvement is more significant for heavy rain than for light drizzle. Remaining differences between bin and bulk model are attributable to treatments of evaporation, and the restriction to gamma DSDs in P3. Plain Language SummaryWe improve the representation of rain for numerical models of the atmosphere. This is achieved by adding an additional predicted variable to allow for a more physically based prediction of raindrop sizes evolving from various microphysical processes, such as gravitational settling, evaporation, and growth and breakup upon drop-drop collisions.PAUKERT ET AL. 257

In the original Predicted Particle Properties (P3) bulk microphysics scheme, all ice-phase hydrometeors are represented by one or more “free” ice categories, where the physical properties evolve smoothly through changes to the four prognostic variables (per category,) and with a 2-moment representation of the particle size distribution. As such, the spectral dispersion cannot evolve independently, which thus results in limitations in representation of ice – in particular hail – due to necessary constraints in the scheme to prevent excessive gravitational size sorting. To overcome this, P3 has been modified to include a 3-moment representation of the size distribution of each ice category through the addition of a fifth prognostic variable, the sixth moment of the size distribution. The details of the 3-moment ice parameterization in P3 are provided. The behavior of the modified scheme, with the single-ice-category configuration, is illustrated through simulations in a simple 1D kinematic model framework as well as with near large-eddy-resolving (250-m grid spacing) 3D simulations of a hail-producing supercell. It is shown that the 3-moment ice configuration controls size sorting in a physically-based way and leads to an improved capacity to simulate large, heavily-rimed ice (hail), including mean and maximum sizes and reflectivity, and thus an overall improvement in the representation of ice-phase particles in the P3 scheme.

In model studies of aerosol‐dependent immersion freezing in clouds, a common assumption is that each ice nucleating aerosol particle corresponds to exactly one cloud droplet. In contrast, the immersion freezing of larger drops—“rain”—is usually represented by a liquid volume‐dependent approach, making the parameterizations of rain freezing independent of specific aerosol types and concentrations. This may lead to inconsistencies when aerosol effects on clouds and precipitation shall be investigated, since raindrops consist of the cloud droplets—and corresponding aerosol particles—that have been involved in drop‐drop‐collisions. Here we introduce an extension to a two‐moment microphysical scheme in order to account explicitly for particle accumulation in raindrops by tracking the rates of selfcollection, autoconversion, and accretion. This provides a direct link between ice nuclei and the primary formation of large precipitating ice particles. A new parameterization scheme of drop freezing is presented to consider multiple ice nuclei within one drop and effective drop cooling rates. In our test cases of deep convective clouds, we find that at altitudes which are most relevant for immersion freezing, the majority of potential ice nuclei have been converted from cloud droplets into raindrops. Compared to the standard treatment of freezing in our model, the less efficient mineral dust‐based freezing results in higher rainwater contents in the convective core, affecting both rain and hail precipitation. The aerosol‐dependent treatment of rain freezing can reverse the signs of simulated precipitation sensitivities to ice nuclei perturbations.