Search

A direct numerical simulation (DNS) with the decaying turbulence setup has been carried out to study cloud-edge mixing and its impact on the droplet size distribution (DSD) applying thermodynamic conditions observed in monsoon convective clouds over Indian subcontinent during the Cloud Aerosol Interaction and Precipitation Enhancement EXperiment (CAIPEEX). Evaporation at the cloud-edges initiates mixing at small scale and gradually introduces larger-scale fluctuations of the temperature, moisture, and vertical velocity due to droplet evaporation. Our focus is on early evolution of simulated fields that show intriguing similarities to the CAIPEEX cloud observations. A strong dilution at the cloud edge, accompanied by significant spatial variations of the droplet concentration, mean radius, and spectral width, are found in both the DNS and in observations. In DNS, fluctuations of the mean radius and spectral width come from the impact of small-scale turbulence on the motion and evaporation of inertial droplets. These fluctuations decrease with the increase of the volume over which DNS data are averaged, as one might expect. In cloud observations, these fluctuations also come from other processes, such as entrainment/mixing below the observation level, secondary CCN activation, or variations of CCN activation at the cloud base. Despite large differences in the spatial and temporal scales, the mixing diagram often used in entrainment/mixing studies with aircraft data is remarkably similar for both DNS and cloud observations. We argue that the similarity questions applicability of heuristic ideas based on mixing between two air parcels (that the mixing diagram is designed to properly represent) to the evolution of microphysical properties during turbulent mixing between a cloud and its environment.

In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth's atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models is divided into two parts: (i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and (ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics. The recently developed Lagrangian particle‐based method is advocated as a way to address several conceptual and practical challenges of representing particle populations using traditional bulk and bin microphysics parameterization schemes. For addressing critical gaps in cloud physics knowledge, sustained investment for observational advances from laboratory experiments, new probe development, and next‐generation instruments in space is needed. Greater emphasis on laboratory work, which has apparently declined over the past several decades relative to other areas of cloud physics research, is argued to be an essential ingredient for improving process‐level understanding. More systematic use of natural cloud and precipitation observations to constrain microphysics schemes is also advocated. Because it is generally difficult to quantify individual microphysical process rates from these observations directly, this presents an inverse problem that can be viewed from the standpoint of Bayesian statistics. Following this idea, a probabilistic framework is proposed that combines elements from statistical and physical modeling. Besides providing rigorous constraint of schemes, there is an added benefit of quantifying uncertainty systematically. Finally, a broader hierarchical approach is proposed to accelerate improvements in microphysics schemes, leveraging the advances described in this paper related to process modeling (using Lagrangian particle‐based schemes), laboratory experimentation, cloud and precipitation observations, and statistical methods., Key Points Microphysics is an important component of weather and climate models, but its representation in current models is highly uncertainTwo critical challenges are identified: representing cloud and precipitation particle populations and knowledge gaps in cloud physicsA possible blueprint for addressing these challenges is proposed to accelerate progress in improving microphysics schemes

As a complement to measurements, numerical modeling facilitates improved understanding of the complex turbulent processes in the stratocumulus-topped boundary layer (STBL). Due to limited computational resources simulations are often run at too coarse resolutions to resolve details of cloud-top turbulence and potentially in computational domains too small to account for the largest scales of boundary layer circulations. The effects of such deficiencies are not fully understood. Here the influence of resolution/anisotropy of the computational grid and domain size in under-resolved implicit large-eddy simulation of the STBL is investigated. The performed simulations are based on data from the first research flight of the DYCOMS-II campaign. Regarding cloud cover and domain-averaged liquid water path, simulations with horizontal/vertical grid spacing of 35/5 m, 70/10 m, and 105/15 m are found to agree better with measurements than more computationally expensive simulations with isotropic grid boxes, e.g., with 10/10 m or 15/15 m grid spacing. While decreasing the vertical grid spacing allows more representative simulation of the thin, turbulent, stably stratified interfacial layer between the STBL and the free troposphere, coarsening the horizontal resolution dampens vertical velocity fluctuations in this region and mimics the observed anisotropy of stably stratified small-scale turbulence near the cloud top. The size of the computational domain is found to have almost no impact on mean cloud properties. However, increasing it from 3.5×3.5 km2 to 14×14 km2 does lead to the occurrence of larger coherent updraft structures. Increasing it further to 21×21 km2 shows little or no increase in the updraft size.

Anisotropy of turbulence near the top of the stratocumulus‐topped boundary layer (STBL) is studied using large‐eddy simulation (LES) and measurements from the POST and DYCOMS‐II field campaigns. Focusing on turbulence ∼100 m below the cloud top, we see remarkable similarity between daytime and nocturnal flight data covering different inversion strengths and free‐tropospheric conditions. With λ denoting wavelength and zt cloud‐top height, we find that turbulence at λ/zt≃0.01 is weakly dominated by horizontal fluctuations, while turbulence at λ/zt>1 becomes strongly dominated by horizontal fluctuations. Between are scales at which vertical fluctuations dominate. Typical‐resolution LES of the STBL (based on POST flight 13 and DYCOMS‐II flight 1) captures observed characteristics of below‐cloud‐top turbulence reasonably well. However, using a fixed vertical grid spacing of 5 m, decreasing the horizontal grid spacing and increasing the subgrid‐scale mixing length leads to increased dominance of vertical fluctuations, increased entrainment velocity, and decreased liquid water path. Our analysis supports the notion that entrainment parameterizations (e.g., in climate models) could potentially be improved by accounting more accurately for anisotropic deformation of turbulence in the cloud‐top region. While LES has the potential to facilitate improved understanding of anisotropic cloud‐top turbulence, sensitivity to grid spacing, grid‐box aspect ratio, and subgrid‐scale model needs to be addressed.