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. In situ measurements of Arctic clouds frequently show that ice crystal number concentrations (ICNCs) are much higher than the number of available ice-nucleating particles (INPs), suggesting that secondary ice production (SIP) may be active. Here we use a Lagrangian parcel model (LPM) and a large-eddy simulation (LES) to investigate the impact of three SIP mechanisms (rime splintering, break-up from ice–ice collisions and drop shattering) on a summer Arctic stratocumulus case observed during the Aerosol-Cloud Coupling And Climate Interactions in the Arctic (ACCACIA) campaign. Primary ice alone cannot explain the observed ICNCs, and drop shattering is ineffective in the examined conditions. Only the combination of both rime splintering (RS) and collisional break-up (BR) can explain the observed ICNCs, since both of these mechanisms are weak when activated alone. In contrast to RS, BR is currently not represented in large-scale models; however our results indicate that this may also be a critical ice-multiplication mechanism. In general, low sensitivity of the ICNCs to the assumed INP, to the cloud condensation nuclei (CCN) conditions and also to the choice of BR parameterization is found. Finally, we show that a simplified treatment of SIP, using a LPM constrained by a LES and/or observations, provides a realistic yet computationally efficient way to study SIP effects on clouds. This method can eventually serve as a way to parameterize SIP processes in large-scale models.

In large-eddy simulation (LES), large-scale turbulent structures are explicitly resolved on the numerical grid while the dissipative turbulent eddies, typically smaller than the grid size, must be modeled. Because in the atmospheric boundary layer a large disparity of turbulent scales exists (about 9 orders of magnitude separate the largest and smallest scales), LES is considered as an essential modeling approach to capture the physics and dynamics of boundary layer clouds. A new LES solver developed at Stockholm University is presented here for the first time. The model solves for nonhydrostatic anelastic equations using high-order low-dissipative numerical schemes for the advection of scalars and momentum. A two-moment bulk microphysics scheme is implemented representing five types of hydrometeors including ice crystals and snow. The LES is evaluated based on simulations of two well-documented stratiform cloud events that were previously used for LES intercomparisons. In the first one, a marine drizzling stratocumulus observed during DYCOMS-II, the model is shown to predict bulk cloud microphysical and dynamical properties within the range of the intercomparison model results. In the second case, based on a monolayer Arctic mixedphase cloud observed during ISDAC, we found that when using fast-falling crystals, ice quickly precipitates out of the cloud without significant growth, resulting in very low ice water paths. The simulated clouds are also found to be very sensitive to the prescribed ice crystal number concentration: multiplying the ice concentration by a factor 2.5 results in rapid cloud dissipation in the most extreme case. Overall, these results are found to be consistent with former studies of Arctic mixed-phase clouds as well as in situ measurements. More specifically, when the ice number concentration and parameterized ice habit are constrained by measurements, simulated microphysical properties such as the ice water path and ice crystal size distribution are found to agree well with observations.

Cold-pool-driven convective initiation is investigated in high-resolution, convection-permitting simulations with a focus on the diurnal cycle and organization of convection and the sensitivity to grid size. Simulations of four different days over Germany were performed using the ICON-LEM model with grid sizes from 156 to 625 m. In these simulations, we identify cold pools, cold-pool boundaries and initiated convection. Convection is triggered much more efficiently in the vicinity of cold pools than in other regions and can provide as much as 50% of total convective initiation, in particular in the late afternoon. By comparing different model resolutions, we find that cold pools are more frequent, smaller and less intense in lower-resolution simulations. Furthermore, their gust fronts are weaker and less likely to trigger new convection. To identify how model resolution affects this triggering probability, we use a linear causal graph analysis.In doing so, we postulate a graph structure with potential causal pathways and then apply multi-linear regression accordingly. We find a dominant, systematic effect: reducing grid sizes directly reduces upward mass flux at the gust front, which causes weaker triggering probabilities. These findings are expected to be even more relevant for km-scale, numerical weather prediction models. We thus expect that a better representation of cold-pool-driven convective initiation will improve forecasts of convective precipitation. K E Y W O R D S causal effect estimation, numerical weather prediction, convection organization INTRODUCTIONConvection-permitting models have become increasingly prominent for numerical weather prediction in recent years (Baldauf et al., 2011;Clark et al., 2016). Such models have a grid size of several hectometres to a few kilometres which is sufficient to run without the use of a convection parametrization. Due to the many underlying approximations and systematic biases of convection schemes (e.g., Gentine et al., 2018), being able to simulateThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.