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Global cloud-resolving models (GCRMs) are a new category of atmospheric global models designed to solve different flavors of the nonhydrostatic equations through the use of kilometer-scale global meshes. GCRMs make it possible to explicitly simulate deep convection, thereby avoiding the need for cumulus parameterization and allowing for clouds to be “resolved” by microphysical models responding to grid-scale forcing. GCRMs require high-resolution discretization over the globe, for which a variety of mesh structures have been proposed and employed. The first GCRM was constructed 15 years ago, and in recent years, other groups have also begun adopting this approach, enabling the first intercomparison studies of such models. Because conventional general circulation models (GCMs) suffer from large biases associated with cumulus parameterization, GCRMs are attractive tools for researchers studying global weather and climate. In this review, GCRMs are described, with some emphasis on their historical development and the associated literature documenting their use. The advantages of GCRMs are presented, and currently existing GCRMs are listed and described. Future prospects for GCRMs are also presented in the final section.

A comparison between the spectral microphysics cloud scheme (MiStra) and the parametrized cloud scheme (PaStra) is presented. The main feature of MiStra consists of the treatment of aerosol particles, cloud droplets, and drizzle particles in a joint two-dimensional particle size distribution, whereas PaStra consists of a two-moment scheme for cloud droplets combined with a one-moment scheme for drizzle. Both cloud schemes have been implemented in a single-column model of the cloud-topped marine boundary layer. Numerical sensitivity studies are presented demonstrating that MiStra is capable of simulating in great detail the major cloud microphysical processes occurring in low-level stratiform clouds. While in MiStra no empirical parameter is available to tune the model, the empirical model parameters of PaStra have been tuned by means of the MiStra model results. By comparing the numerical results of PaStra with those of MiStra it is found that PaStra simulates the overall characteristics of the cloud-topped marine boundary layer quite well. At the same time, however, the effects of single cloud microphysical processes differ substantially from those of MiStra. Finally, it is shown that even in PaStra the inclusion of all major cloud microphysical processes is mandatory in order to obtain appropriate results.

Selecting proper parameterization scheme combinations for a particular application is of great interest to Weather Research and Forecasting (WRF) model users. The goal of this research is to create an objective method for identifying a set of scheme combinations to form a Multi-Physics Ensemble (MPE) suitable for short-term precipitation forecasting over Odisha, India’s east coast state. In this study, five member ensembles for Cloud Microphysics (CMP) and Land Surface Model (LSM, conventional ensemble) are created, as well as an ensemble of the top five performing members (optimized ensemble) for 13 Monsoon Depressions (MD) and 8 Deep Depression (DD) cases. There are a total of 30 combinations (5 PBL * 5 CMP, 5 LSM with best PBL and CMP, and one with ISRO Land Use Land Cover data). WRF 4.1 is used to carry out simulations, which are initialized with ERA5 reanalysis data and have a 72-h lead time. Rainfall verification skill scores indicate that ensemble members perform significantly better than any deterministic model. Rainfall characteristics such as location, intensity, and time of occurrence are well predicted in ensemble members as measured by a higher correlation coefficient and a lower RMSE. Neighbourhood ensemble probability also demonstrates that ensemble members have a higher chance of detecting heavy to very heavy rainfall events with more spatial accuracy. The study also concludes that choice of parameterization also affects large-scale dynamical parameters (temperature, humidity, wind, hydrometeors) and thus associated rainfall. Ensemble members exhibited less bias in the composite analysis of large-scale parameters. Furthermore, a composite analysis of moisture budget components revealed that the convergence term is the most important component of moisture accumulation, resulting in rainfall during the monsoon low-pressure system. These findings indicate that the proposed method is an effective method for reducing bias in rainfall forecasts.

The representation of cloud microphysical processes in climate models continues to be a major challenge leading to uncertainty in climate simulations. The shape parameter (equivalent to relative dispersion) of gamma distribution for ice particles is assumed to be 0 in the Beijing Climate Center Atmospheric General Circulation Model (BCC‐AGCM). This study diagnoses the shape parameter by linking it to the ice volume‐mean diameter and analyzes the impact of the modified scheme on the performance of climate simulations. Results show that the modified scheme performs better in simulating global cloud fraction, cloud radiative forcing, and total precipitation compared to the control configuration, thereby significantly reducing simulation biases. The underlying physical mechanisms are driven by three key factors. First, the shape parameter in the modified scheme is greater than zero, narrowing the ice particle size distribution. This reduces the autoconversion of ice to snow and sedimentation processes while enhancing deposition growth, resulting in an increase in upper‐level ice clouds. The increase in ice‐clouds increases upper atmospheric temperatures, enhances atmospheric stability, and promotes the formation of lower‐level clouds. Second, the improvement in cloud fraction significantly mitigates the underestimation of longwave and shortwave cloud radiative forcing. Additionally, the overestimation of precipitation is improved, including both convective and large‐scale precipitation, particularly from an annual mean perspective. Increased atmospheric stability reduces convective precipitation, while weakened snow sources and enhanced sinks to reduce large‐scale precipitation. The study emphasizes the importance of ice particle spectral relative dispersion and provides valuable insights for improving cloud microphysics parameterization schemes. Plain Language Summary Clouds are a crucial part of the climate system, but accurately simulating them in global climate models remains challenging. In this study, we improved the Beijing Climate Center climate model by enhancing how ice particle sizes in clouds are represented. Our results show that this improvement reduces previous errors and leads to more accurate simulations of global cloud coverage, radiation, and rainfall. These advancements improve the reliability of climate projections and contribute to a better understanding of how the climate might change in the future. Key Points An improved ice particle spectral dispersion parameterization scheme is implemented in the BCC‐AGCM model The new scheme enhances the simulation performance of global cloud fraction, radiation, and precipitation in the BCC‐AGCM model The physical mechanisms driving the improved performance are clearly identified

In this study, we have examined the role of implicit and explicit representation of cloud microphysics on the simulation of a monsoon depression formed over the Bay of Bengal and the associated rainfall from 0000 UTC of 13 August to 0000 UTC of 17 August 2018 using the Weather Research and Forecast model. Five different WRF model simulations are performed by changing the Cloud Micro Physics (CMP) schemes: WSM6, Goddard, Thompson, Morrison, and Thompson Scheme with Aerosol aware options in both explicit and implicit cloud models. WRF simulations are conducted by initializing the NCEP GFS analysis at 0000 UTC of 13 August 2018 and integrated up to 96-h. The boundary conditions are updated at 6-hourly intervals with the respective GFS forecasts. Our results of sensitivity simulations suggest that the Thompson Scheme with Aerosol aware scheme, followed by Goddard microphysics, captured the features of monsoon depression and associated rainfall. Microphysics schemes have an influence on the simulation of low level westerly jet, and upper level easterly jet. Implicit and explicit cloud microphysics options are able to reproduce the convection over the west-coast, but the implicit option failed in producing the prolonged convection over the east coast. The comparison of model rainfall with rain-gauge, and satellite merged rainfall estimates reveals that the large scale off-shore precipitation is better captured in CMP with the inclusion of explicit cumulus parameterization. The orographic rainfall over the wind-ward and lee-ward sides of the Eastern and Western Ghats is well predicted in the implicit CMP. The vertical distribution of the hydrometeors and rainfall analysis suggest that the Thompson Scheme with Aerosol aware scheme with the cloud-resolving explicit mode is suitable for simulating the monsoon depressions formed over the Bay of Bengal and the associated heavy rainfall over the east coast of India.

Aerosol-cloud-radiation interactions represent one of the largest uncertainties in the current climate assessment. Much of the complexity arises from the non-monotonic responses of clouds, precipitation and radiative fluxes to aerosol perturbations under various meteorological conditions. In this study, an aerosol-aware WRF model is used to investigate the microphysical and radiative effects of aerosols in three weather systems during the March 2000 Cloud Intensive Observational Period campaign at the US Southern Great Plains. Three simulated cloud ensembles include a low-pressure deep convective cloud system, a collection of less-precipitating stratus and shallow cumulus, and a cold frontal passage. The WRF simulations are evaluated by several ground-based measurements. The microphysical properties of cloud hydrometeors, such as their mass and number concentrations, generally show monotonic trends as a function of cloud condensation nuclei concentrations. Aerosol radiative effects do not influence the trends of cloud microphysics, except for the stratus and shallow cumulus cases where aerosol semi-direct effects are identified. The precipitation changes by aerosols vary with the cloud types and their evolving stages, with a prominent aerosol invigoration effect and associated enhanced precipitation from the convective sources. The simulated aerosol direct effect suppresses precipitation in all three cases but does not overturn the aerosol indirect effect. Cloud fraction exhibits much smaller sensitivity （typically less than 2%） to aerosol perturbations, and the responses vary with aerosol concentrations and cloud regimes. The surface shortwave radiation shows a monotonic decrease by increasing aerosols, while the magnitude of the decrease depends on the cloud type.

The present study explores the cloud microphysics (MPs) impact on the simulation of two convective rainfall events (CREs) over the complex topography of Andes mountains, using the Weather Research and Forecasting- Advanced Research (WRF-ARW) model. The events occurred on December 29 2015 (CRE1) and January 7 2016 (CRE2). Six microphysical parameterizations (MPPs) (Thompson, WSM6, Morrison, Goddard, Milbrandt and Lin) were tested, which had been previously applied in complex orography areas. The one-way nesting technique was applied to four domains, with horizontal resolutions of 18, 6, and 3 km for the outer ones, in which cumulus and MP parameterizations were applied, while for the innermost domain, with a resolution of 0.75 km, only MP parameterization was used. It was integrated for 36 h with National Centers for Environmental Prediction (NCEP Final Operational Global Analysis (NFL) initial conditions at 00:00 UTC (Coordinated Universal Time). The simulations were verified using Geostationary Operational Environmental Satellites (GOES) brightness temperature, Ka band cloud radar, and surface meteorology variables observed at the Huancayo Observatory. All the MPPs detected the surface temperature signature of the CREs, but for CRE2, it was underestimated during its lifetime in its vicinity, matching well after the simulated event. For CRE1, all the schemes gave good estimations of 24 h precipitation, but for CRE2, Goddard and Milbrandt underestimated the 24 h precipitation in the inner domain. The Morrison and Lin configurations reproduced the general dynamics of the development of cloud systems for the two case studies. The vertical profiles of the hydrometeors simulated by different schemes showed significant differences. The best performance of the Morrison scheme for both case studies may be related to its ability to simulate the role of graupel in precipitation formation. The analysis of the maximum reflectivity field, cloud top distribution, and vertical structure of the simulated cloud field also shows that the Morrison parameterization reproduced the convective systems consistently with observations.

This study examined a typical case of deep convective storm that formed over southwest India on October 12, 2011, using ground-based X-band radar measurements and Weather Research and Forecasting (WRF) model simulations. The radar observation showed isolated pockets of convective storm, which merged later to form a convective cluster. The observed storms were tall, extending well into the mixed-phase region. Few storms even extended up to the tropopause height. Three different WRF cloud microphysics schemes (WRF Double-Moment 6-Class, Morrison Double-Moment, and Milbrandt–Yau Double-Moment) were used to simulate the observed deep convective storm to examine the vertical structure of hydrometeors. All the cloud microphysics schemes were able to reproduce the convective storm event with a lag time of almost two and a half hours. The WRF Double-Moment 6-Class scheme better simulates the vertical structure of storm compared to the other two microphysics schemes. The WRF model reasonably simulated the observed patterns of convective storm when the WRF cloud microphysics scheme better simulate the graupel and snow. The differences in simulated storm structure obtained by different microphysics schemes compared to observation highlight the deficiency involved in the simulations in capturing the microphysics that is guiding the intensity of convective storms. The present study thus underscores the importance of microphysics in different parameterization schemes of WRF simulation over southwest India, which has an implication in the forecasting of convective storms.

In estimates of climate sensitivity obtained from global models, the need to represent clouds introduces a great deal of uncertainty. To address this issue, approaches using a high-resolution global non-hydrostatic model are promising: the model captures cloud structure by explicitly simulating meso-scale convective systems, and the results compare reasonably well with satellite observations. We review the outcomes of a 5-year project aimed at reducing the uncertainty in climate models due to cloud processes using a global non-hydrostatic model. In our project, which was conducted as a subgroup of the Program for Risk Information on Climate Change, or SOUSEI, we use the non-hydrostatic icosahedral atmospheric model (NICAM) to study cloud processes related to climate change. NICAM performs numerical simulations with much higher resolution (about 7 km or 14 km mesh) than conventional global climate models (GCMs) using cloud microphysics schemes without a cumulus parameterization scheme, which causes uncertainties in climate projection.The subgroup had three research targets: analyzing cloud changes in global warming simulations with NICAM with the time-slice approach, sensitivity of the results to the cloud microphysics scheme employed, and evaluating circulation changes due to global warming. The research project also implemented a double-moment bulk cloud microphysics scheme and evaluated its results using satellite observation, as well as comparing it with a bin cloud microphysics scheme. The future projection simulations show in general increase in high cloud coverage, contrary to results with other GCMs. Changes in cloud horizontal-size distribution size and structures of tropical/extratropical cyclones can be discussed with high resolution simulations. At the conclusion of our review, we also describe the future prospects of research for global warming using NICAM in the program that followed SOUSEI, known as TOUGOU.

At the same temperature, below 0 °C, the saturation vapor pressure (SVP) over ice is slightly less than the SVP over liquid water. Numerical models use the Clausius–Clapeyron relation to calculate the SVP and relative humidity, but there is not a consistent method for the treatment of saturation above the freezing level where ice and mixed-phase clouds may be present. In the context of current challenges presented by cloud microphysics in climate models, we argue that a better understanding of the impact that this treatment has on saturation-related processes like cloud formation and precipitation, is needed. This study explores the importance of the SVP calculation through model simulations of the Indian summer monsoon (ISM) using the regional spectral model (RSM) at 15 km grid spacing. A combination of seasonal and multiyear simulations is conducted with two saturation parameterizations. In one, the SVP over liquid water is prescribed through the entire atmospheric column (woIce), and in another the SVP over ice is used above the freezing level (wIce). When SVP over ice is prescribed, a thermodynamic drying of the middle and upper troposphere above the freezing level occurs due to increased condensation. In the wIce runs, the model responds to the slight decrease in the saturation condition by increasing, relative to the SVP over liquid water only run, grid-scale condensation of water. Increased grid-scale mean seasonal precipitation is noted across the ISM region in the simulation with SVP over ice prescribed. Modification of the middle and upper troposphere moisture results in a decrease in mean seasonal mid-level cloud amount and an increase in high cloud amount when SVP over ice is prescribed. Multiyear simulations strongly corroborate the qualitative results found in the seasonal simulations regarding the impact of ice versus liquid water SVP on the ISM’s mean precipitation and moisture field. The mean seasonal rainfall difference over All India between wIce and woIce is around 10% of the observed interannual variability of seasonal All India rainfall.