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Cloud microphysical processes occur at the smallest end of scales among cloud-related processes and thus must be parameterized not only in large-scale global circulation models (GCMs) but also in various higher-resolution limited-area models such as cloud-resolving models (CRMs) and large-eddy simulation (LES) models. Instead of giving a comprehensive review of existing microphysical parameterizations that have been developed over the years, this study concentrates purposely on several topics that we believe are understudied but hold great potential for further advancing bulk microphysics parameterizations: multi-moment bulk microphysics parameterizations and the role of the spectral shape of hydrometeor size distributions; discrete vs “continuous” representation of hydrometeor types; turbulence-microphysics interactions including turbulent entrainment-mixing processes and stochastic condensation; theoretical foundations for the mathematical expressions used to describe hydrometeor size distributions and hydrometeor morphology; and approaches for developing bulk microphysics parameterizations. Also presented are the spectral bin scheme and particle-based scheme (especially, super-droplet method) for representing explicit microphysics. Their advantages and disadvantages are elucidated for constructing cloud models with detailed microphysics that are essential to developing processes understanding and bulk microphysics parameterizations. Particle-resolved direct numerical simulation (DNS) models are described as an emerging technique to investigate turbulence-microphysics interactions at the most fundamental level by tracking individual particles and resolving the smallest turbulent eddies in turbulent clouds. Outstanding challenges and future research directions are explored as well.

An overview of the Community Earth System Model Version 2 (CESM2) is provided, including a discussion of the challenges encountered during its development and how they were addressed. In addition, an evaluation of a pair of CESM2 long preindustrial control and historical ensemble simulations is presented. These simulations were performed using the nominal 1° horizontal resolution configuration of the coupled model with both the “low‐top” (40 km, with limited chemistry) and “high‐top” (130 km, with comprehensive chemistry) versions of the atmospheric component. CESM2 contains many substantial science and infrastructure improvements and new capabilities since its previous major release, CESM1, resulting in improved historical simulations in comparison to CESM1 and available observations. These include major reductions in low‐latitude precipitation and shortwave cloud forcing biases; better representation of the Madden‐Julian Oscillation; better El Niño‐Southern Oscillation‐related teleconnections; and a global land carbon accumulation trend that agrees well with observationally based estimates. Most tropospheric and surface features of the low‐ and high‐top simulations are very similar to each other, so these improvements are present in both configurations. CESM2 has an equilibrium climate sensitivity of 5.1–5.3 °C, larger than in CESM1, primarily due to a combination of relatively small changes to cloud microphysics and boundary layer parameters. In contrast, CESM2's transient climate response of 1.9–2.0 °C is comparable to that of CESM1. The model outputs from these and many other simulations are available to the research community, and they represent CESM2's contributions to the Coupled Model Intercomparison Project Phase 6. Plain Language Summary The Community Earth System Model (CESM) is an open‐source, comprehensive model used in simulations of the Earth's past, present, and future climates. The newest version, CESM2, has many new technical and scientific capabilities ranging from a more realistic representation of Greenland's evolving ice sheet, to the ability to model in detail how crops interact with the larger Earth system, to improved representation of clouds and rain, and to the addition of wind‐driven waves on the model's ocean surface. The data sets from a large set of simulations that include integrations for the preindustrial conditions (1850s) and for the 1850‐2014 historical period are available to the community, representing CESM2's contributions to the Coupled Model Intercomparison Project Phase 6 (CMIP6). Key Points Community Earth System Model Version 2 includes many substantial science and infrastructure improvements since its previous version Preindustrial control and historical simulations were performed with low‐top and high‐top with comprehensive chemistry atmospheric models Comparisons to observations are improved relative to previous versions, including major reductions in radiation and precipitation biases

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 two-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 three-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 three-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 three-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.

A method for the parameterization of ice-phase microphysics is proposed and used to develop a new bulk microphysics scheme. All ice-phase particles are represented by several physical properties that evolve freely in time and space. The scheme prognoses four ice mixing ratio variables, total mass, rime mass, rime volume, and number, allowing 4 degrees of freedom for representing the particle properties using a single category. This approach represents a significant departure from traditional microphysics schemes in which ice-phase hydrometeors are partitioned into various predefined categories (e.g., cloud ice, snow, and graupel) with prescribed characteristics. The liquid-phase component of the new scheme uses a standard two-moment, two-category approach. The proposed method and a complete description of the new predicted particle properties (P3) scheme are provided. Results from idealized model simulations of a two-dimensional squall line are presented that illustrate overall behavior of the scheme. Despite its use of a single ice-phase category, the scheme simulates a realistically wide range of particle characteristics in different regions of the squall line, consistent with observed ice particles in real squall lines. Sensitivity tests show that both the prediction of the rime mass fraction and the rime density are important for the simulation of the squall-line structure and precipitation.

Over the past decade, the number of studies that investigate aerosol–cloud interactions has increased considerably. Although tremendous progress has been made to improve the understanding of basic physical mechanisms of aerosol–cloud interactions and reduce their uncertainties in climate forcing, there is still poor understanding of 1) some of the mechanisms that interact with each other over multiple spatial and temporal scales, 2) the feedbacks between microphysical and dynamical processes and between local-scale processes and large-scale circulations, and 3) the significance of cloud–aerosol interactions on weather systems as well as regional and global climate. This review focuses on recent theoretical studies and important mechanisms on aerosol–cloud interactions and discusses the significances of aerosol impacts on radiative forcing and precipitation extremes associated with different cloud systems. The authors summarize the main obstacles preventing the science from making a leap—for example, the lack of concurrent profile measurements of cloud dynamics, microphysics, and aerosols over a wide region on the observation side and the large variability of cloud microphysics parameterizations resulting in a large spread of modeling results on the modeling side. Therefore, large efforts are needed to escalate understanding. Future directions should focus on obtaining concurrent measurements of aerosol properties and cloud microphysical and dynamic properties over a range of temporal and spatial scales collected over typical climate regimes and closure studies, as well as improving understanding and parameterizations of cloud microphysics such as ice nucleation, mixed-phase properties, and hydrometeor size and fall speed.

Mechanisms of Arctic amplification and Arctic climate change are difficult to pinpoint, and current climate models do not represent the complex local processes and feedbacks at play, in particular for aerosol–climate interactions. This Perspective highlights the role of aerosols in contemporary Arctic climate change and stresses that the Arctic natural aerosol baseline is changing fast and its regional characteristics are very diverse. We argue that to improve understanding of present day and future Arctic, more detailed knowledge is needed on natural Arctic aerosol emissions, their evolution and transport, and the effects on cloud microphysics. In particular, observation and modelling work should focus on the sensitivity of aerosol–climate interactions to the rapidly evolving base state of the Arctic.Aerosol–climate interactions are important in the Arctic, but they exhibit large spatiotemporal variability. This Perspective argues for community-driven model and observational improvement, emphasizing the need to understand natural aerosol processes and quantify how their baseline is changing.

Recent in situ observations show that haze particles exist in a convection cloud chamber. The microphysics schemes previously used for large‐eddy simulations of the cloud chamber could not fully resolve haze particles and the associated processes, including their activation and deactivation. Specifically, cloud droplet activation was modeled based on Twomey‐type parameterizations, wherein cloud droplets were formed when a critical supersaturation for the available cloud condensation nuclei (CCN) was exceeded and haze particles were not explicitly resolved. Here, we develop and adapt haze‐capable bin and Lagrangian microphysics schemes to properly resolve the activation and deactivation processes. Results are compared with the Twomey‐type CCN‐based bin microphysics scheme in which haze particles are not fully resolved. We find that results from the haze‐capable bin microphysics scheme agree well with those from the Lagrangian microphysics scheme. However, both schemes significantly differ from those from a CCN‐based bin microphysics scheme unless CCN recycling is considered. Haze particles from the recycling of deactivated cloud droplets can strongly enhance cloud droplet number concentration due to a positive feedback in haze‐cloud interactions in the cloud chamber. Haze particle size distributions are more realistic when considering solute and curvature effects that enable representing the complete physics of the activation process. Our study suggests that haze particles and their interactions with cloud droplets may have a strong impact on cloud properties when supersaturation fluctuations are comparable to mean supersaturation, as is the case in the cloud chamber and likely is the case in the atmosphere, especially in polluted conditions. Plain Language Summary In atmospheric models, cloud droplet formation is usually simulated to occur when a submicron dry aerosol particle encounters supersaturated conditions. In reality, dry aerosol particles composed of water‐soluble compounds form aqueous haze particles first before they activate to cloud droplets. However, haze particles and the associated interactions with cloud droplets are usually not fully resolved in atmospheric models. In this study, we develop two types of microphysics schemes to explore haze‐cloud interactions in a convection cloud chamber. Our results show that recycling of deactivated cloud droplets through either dry aerosol or haze particles can significantly enhance the cloud droplet number concentration in the cloud chamber. Our study indicates that it is important to properly resolve haze particles and haze‐cloud interactions for cloud chamber simulations, which is likely also true for atmospheric cloud simulations, especially under polluted conditions. Key Points Bin and Lagrangian microphysics schemes with various levels of complexity are used to handle aerosol‐cloud interactions in a cloud chamber Simulations using haze‐capable bin and Lagrangian schemes capture the observed haze mode in the chamber Activation and deactivation rates are overestimated when using a CCN‐based bin scheme compared with haze‐capable schemes

Using multi‐satellite observations, the cloud dynamic and microphysical characteristics were revealed during the rapid intensification (RI) of super‐typhoon Nanmadol (2022). As the storm intensifies, the eyewall contracts, the upper‐level divergence strengthens, and the cirrus cloud increases, leading to stronger upper‐level radial outflow and the vertical updraft. Meanwhile, it is found that there exists a dynamically attractive area in the outer rainbands, where particles grow effectively and form “a small amount of large particles” around 300 km from the eye. A theory of cloud dynamics‐microphysics interaction, called “tunnel theory,” is further proposed to explain the generation, accumulation, and concentrated downflow of large particles in the outer rainbands during RI. Results suggest the unique feature of particle distribution in the outer rainbands could be a potential indicator for RI. Plain Language Summary The rapid intensification (RI) of tropical cyclones (TCs) becomes more frequent in recent years, but the TC RI forecasts still remain challenging. Better understanding of the physical processes associated with RI of TCs would essentially improve its forecasting capability. The cloud dynamical and microphysical processes, especially their interactions that respond to RI are not well explored. In this study, the cloud macro and micro characteristics associated with RI of a super‐typhoon Nanmadol (2022) over the western Pacific are investigated using multiple satellites observations. The storm underwent RI during 15–16 September 2022, and it has wreaked havoc on Japan's most cities as it moved across the Japanese island afterward with a track length of about 1,120 km. It is found inside Nanmadol as well as other typhoons that a few large particles tend to occur in the outer rainbands during RI, due to the interaction of cloud dynamical and microphysical processes. Such unique feature of particle distribution in the outer rainbands could be a potential indicator for RI, and should also be paid attention to in model forecasting of typhoon precipitation. Key Points First satellite‐based observational study on the interaction of cloud dynamics and microphysics during typhoon rapid intensification (RI) The eyewall contracts, the upper‐level divergence strengthens, and the convection column increases, providing kinetic energy for typhoon RI A “tunnel theory” is proposed for the generation, accumulation, and downflow of large particles in the outer rainbands during typhoon RI

Prognostic precipitation is added to a cloud microphysical scheme for global climate models. Results indicate very similar performance to other commonly used mesoscale schemes in an offline driver for idealized warm rain cases, better than the previous version of the global model microphysics scheme with diagnostic precipitation. In the mixed phase regime, there is significantly more water and less ice, which may address a common bias seen with the scheme in climate simulations in the Arctic. For steady forcing cases, the scheme has limited sensitivity to time step out to the ∼15-min time steps typical of global models. The scheme is similar to other schemes with moderate sensitivity to vertical resolution. The limited time step sensitivity bodes well for use of the scheme in multiscale models from the mesoscale to the large scale. The scheme is sensitive to idealized perturbations of cloud drop and crystal number. Precipitation decreases and condensate increases with increasing drop number, indicating substantial decreases in precipitation efficiency. The sensitivity is less than with the previous version of the scheme for low drop number concentrations (Nc < 100 cm−3). Ice condensate increases with ice number, with large decreases in liquid condensate as well for a mixed phase case. As expected with prognostic precipitation, accretion is stronger than with diagnostic precipitation and the accretion to autoconversion ratio increases faster with liquid water path (LWP), in better agreement with idealized models and earlier studies than the previous version.

The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a new variable-resolution global model with the ability to represent convective-scale features that serves as a prototype of the Next Generation Global Prediction System (NGGPS). The goal of this prediction system is to maintain the skill in large-scale features while simultaneously improving the prediction skill of convectively driven mesoscale phenomena. This paper demonstrates the new capability of this model in convective-scale prediction relative to the current operational Global Forecast System (GFS). This model uses the stretched-grid functionality of the Finite-Volume Cubed-Sphere Dynamical Core (FV3) to refine the global 13-km uniform-resolution model down to 4-km convection-permitting resolution over the contiguous United States (CONUS), and implements the GFDL single-moment 6-category cloud microphysics to improve the representation of moist processes. Statistics gathered from two years of simulations by the GFS and select configurations of the FV3-based model are carefully examined. The variable-resolution FV3-based model is shown to possess global forecast skill comparable with that of the operational GFS while quantitatively improving skill and better representing the diurnal cycle within the high-resolution area compared to the uniform mesh simulations. Forecasts of the occurrence of extreme precipitation rates over the southern Great Plains are also shown to improve with the variable-resolution model. Case studies are provided of a squall line and a hurricane to demonstrate the effectiveness of the variable-resolution model to simulate convective-scale phenomena.