Representing the glaciation of mixed-phase clouds in terms of the Wegener-Bergeron-Findeisen process is a challenge for many weather and climate models, which tend to overestimate this process because cloud dynamics and microphysics are not accurately represented. As turbulence is essential for the transport of water vapor from evaporating liquid droplets to ice crystals, we developed a statistical model using established closures to assess the role of small-scale turbulence. The model successfully captures results of direct numerical simulations and we use it to assess the role of small-scale turbulence. We find that small-scale turbulence broadens the droplet-size distribution somewhat, but it does not significantly affect the glaciation time on submeter scales. However, our analysis indicates that turbulence on larger spatial scales is likely to affect ice growth. While the model must be amended to describe larger scales, the present work facilitates a path forward to understanding the role of turbulence in the Wegener-Bergeron-Findeisen process.

In Arctic warm-Air intrusions, air masses undergo a series of radiative, turbulent, cloud, and precipitation processes, the sum of which constitutes the air mass transformation. During the Arctic air mass transformation, heat and moisture are transferred from the air mass to the Arctic environment, melting the sea ice and potentially reinforcing feedback mechanisms responsible for the amplified Arctic warming. We tackle this complex, poorly understood phenomenon from a Lagrangian perspective using the warm-Air intrusion event on 12-14 March captured by the 2022 HALO-(AC)³ campaign. Our trajectory analysis of the event suggests that the intruding air mass can be treated as a cohesive air column, therefore justifying the use of a single-column model. In this study, we test this hypothesis using the Atmosphere-Ocean Single-Column Model (AOSCM). The rates of heat and moisture depletion vary along the advection path due to the changing surface properties and large-scale vertical motion. Cloud radiative cooling and turbulent mixing in the stably stratified boundary layer are constant sinks of heat throughout the air mass transformation. Boundary layer cooling intensifies over the marginal ice zone and forces the development of a low-level cloud underneath the advected one. As the air mass flows past the marginal ice zone, large-scale updrafts dominate the temperature and moisture changes through adiabatic cooling and condensation. The ability of the Lagrangian AOSCM framework to simulate elements of the air mass transformation seen in aircraft observations, reanalysis, and operational forecast data makes it an attractive tool for future model analysis and diagnostics development. Our findings can benefit the understanding of the timescales and driving mechanisms of Arctic air mass transformation and help determine the contribution of warm-Air intrusions in Arctic amplification.

Simulation of turbulence in the atmospheric boundary layer (ABL) is challenging due to the wide range of turbulent scales in the flow. To leverage the currently available computational power for high-resolution simulation of atmospheric turbulence, fluid solvers that scale well on large compute clusters are required. We present a new large eddy simulation (LES) framework based on the open-source solver Nek5000, which uses the highly parallelizable spectral element method (SEM) for spatial discretization. We document the Nek5000 framework for LES of thermally stratified atmospheric boundary layers and present results from the solver for neutral, convective, and stably stratified boundary layers. To verify that the solver is capable of accurately representing important features of the ABL, we compare our results to an established LES solver and find very good agreement in statistics as well as coherent structures. We also compare results with two different subgrid-scale models and conclude that one based on the subgrid-scale turbulent kinetic energy performs better together with the SEM.

Boundary layer cloud transformations at high latitudes play a key role for the Arctic climate and are partially controlled by large-scale dynamics such as subsidence. While measuring large-scale and mesoscale divergence on spatial scales on the order of 100 km has proven notoriously difficult, recent airborne campaigns in the subtropics have successfully applied measurement techniques using multiple dropsonde releases in circular flight patterns. In this paper, it is shown that this method can also be effectively applied at high latitudes, in spite of the considerable differences in atmospheric dynamics compared to the subtropics. To show the applicability, data collected during the airborne High Altitude and Long Range Research Aircraft–Transregional Collaborative Research Center TRR 172-Arctic Amplification: Climate Relevant Atmospheric and Surface Processes and Feedback Mechanisms [HALO–(AC)3] field campaign near Svalbard in spring 2022 were analyzed, where several flight patterns involving multiple dropsonde launches were realized by two aircraft. This study presents a first overview of the results. We find that the method indeed yields reliable estimates of mesoscale gradients in the Arctic, producing robust vertical profiles of horizontal divergence and, consequently, subsidence. Sensitivity to aspects of the method is investigated, including dependence on sampling area and the divergence calculation.

Evaporation of cloud droplets accelerates when turbulence mixes dry air into the cloud, affecting droplet-size distributions in atmospheric clouds, combustion sprays, and jets of exhaled droplets. The challenge is to model local correlations between droplet numbers, sizes, and supersaturation, which determine supersaturation fluctuations along droplet paths (Lagrangian fluctuations). We derived a statistical model that accounts for these correlations. Its predictions are in quantitative agreement with results of direct numerical simulations, and explain the key mechanisms at play.

In the spring period of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, an initiative was in place to increase the radiosounding frequency during warm air intrusions in the Atlantic Arctic sector. Two episodes with increased surface temperatures were captured during April 12–22, 2020, during a targeted observing period (TOP).The large-scale circulation efficiently guided the pulses of warm air into the Arctic and the observed surface temperature increased from -30◦C to near melting conditions marking the transition to spring, as the temperatures did not return to values below -20◦C. Back-trajectory analysis identifies 3 pathways for the transport. For the first temperature maximum, the circulation guided the airmass over the Atlantic to the northern Norwegian coast and then to the MOSAiC site.The second pathway was from the south, and it passed over the Greenland ice sheet and arrived at the observational site as a warm but dry airmass due to precipitation on the windward side.The third pathway was along the Greenland coast and the arriving airmass was both warm and moist. The back trajectories originating from pressure levels between 700 and 900 hPa line up vertically, which is somewhat surprising in this dynamically active environment. The processes acting along the trajectory originating from 800 hPa at the MOSAIC site are analyzed. Vertical profiles and surface energy exchange are presented to depict the airmass transformation based on ERA5 reanalysis fields. The TOP could be used for model evaluation and Lagrangian model studies to improve the representation of the small-scale physical processes that are important for airmass transformation. A comparison between MOSAiC observations and ERA5 reanalysis demonstrates challenges in the representation of small-scale processes, such as turbulence and the contributions to various terms of the surface energy budget, that are often misrepresented in numerical weather prediction and climate models.

A set of CMIP6 models is evaluated for the turning of the wind over the planetary boundary layer (PBL) and the corresponding cross-isobaric mass flux. The bulk Richardson number method is used to calculate the height of the PBL to allow for comparisons with a climatology of observed wind-turning angles documented by Lindvall and Svensson based on more than 800 stations in the Integrated Global Radiosonde Archive. Wind-turning angles are found to be under-estimated in all models, with the GFDL CM4 model having the closest distribution to the observations. Large, negative cross-isobaric mass fluxes (flow toward higher pressure) are found over high-terrain areas and the North Atlantic storm -track region in all models and the ERA-Interim reanalysis. Bulk Richardson number-derived PBLs are particularly shal-low in the Norwegian Earth System Model, medium atmosphere-medium ocean resolution (NorESM2-MM), likely caused by a change in the turbulence and cloud scheme as compared to the CESM2 model that uses the same atmospheric model, leading to small wind-turning angles and cross-isobaric mass fluxes. Using the 850-hPa level as the upper boundary broad-ens the wind-turning angle distribution and increases the amount of cross-isobaric mass flux for all models. This makes the models closer to the observations, although substantial differences are still present. The assumption of a constant geo-strophic wind throughout the PBL possibly affects the calculated mass fluxes.

The distribution of liquid water in ice-free clouds determines their radiative properties, a significant source of uncertainty in weather and climate models. Evaporation and turbulent mixing cause a cloud to display large variations in droplet number density, but quite small variations in droplet size (Beals et al., Science, 2015, vol. 350, pp. 87-90). However, direct numerical simulations of the joint effect of evaporation and mixing near the cloud edge predict quite different behaviours, and how to reconcile these results with the experimental findings remains an open question. To infer the history of mixing and evaporation from observational snapshots of droplets in clouds is challenging, because clouds are transient systems. We formulated a statistical model that provides a reliable description of the evaporation-mixing process as seen in direct numerical simulations and allows us to infer important aspects of the history of observed droplet populations, highlighting the key mechanisms at work and explaining the differences between observations and simulations.