Global kilometer-scale models are the future of Earth system models as they can explicitly simulate organized convective storms and their associated extreme weather. Here, we comprehensively examined tropical mesoscale convective system (MCS) characteristics in the DYAMOND (DYnamics of the atmospheric general circulation modeled on non-hydrostatic domains) models for both summer and winter phases by applying eight different feature trackers to the simulations and satellite observations. Although different trackers produce substantial differences (a factor of 2-3) in observed MCS frequency and their contribution to total precipitation, model-observation differences in MCS statistics are more consistent among the trackers. DYAMOND models are generally skillful in simulating tropical mean MCS frequency, with multi-model mean biases of 2.9% over land and -0.5% over ocean. However, most models underestimate the MCS precipitation amount (23%) and their contribution to total precipitation (17%) relative to observations. These biases show large inter-model variability, but are generally smaller over land (13%) than over ocean (21%) on average. MCS diurnal cycle and cloud shield characteristics are better simulated than precipitation. Most models overestimate MCS precipitation intensity and underestimate stratiform rain contribution (up to a factor of 2), particularly over land. Models also predict a wide range of precipitable water in the tropics compared to reanalysis and satellite observations, and many models simulate a greater sensitivity of MCS precipitation intensity to precipitable water. The MCS metrics developed in this work provide process-oriented diagnostics for future model development efforts.

To address the effect of stratiform latent heating on meso- to large scale circulations, an enhanced implementation of the Multiscale Coherent Structure Parameterization (MCSP) is developed for the Met Office Unified Model. MCSP represents the top-heavy stratiform latent heating from under-resolved organized convection in general circulation models. We couple the MCSP with a mass-flux convection scheme (CoMorph-A) to improve storm lifecycle continuity. The improved MCSP trigger is specifically designed for mixed-phase deep convective cloud, combined with a background vertical wind shear, both known to be crucial for stratiform development. We also test a cloud top temperature dependent convective-stratiform heating partitioning, in contrast to the earlier fixed partitioning. Assessments from ensemble weather forecasts and decadal simulations demonstrate that MCSP directly reduces cloud deepening and precipitation areas by moderating mesoscale circulations. Indirectly, it amends tropical precipitation biases, notably correcting dry and wet biases over India and the Indian Ocean, respectively. Remarkably, the scheme outperforms a climate model ensemble by improving seasonal precipitation cycle predictions in these regions. This enhancement is partly due to the scheme’s refinement of Madden-Julian Oscillation (MJO) spectra, achieving better alignment with reanalysis data by intensifying MJO events and maintaining their eastward propagation after passing the Maritime Continent. However, the scheme also increases precipitation overestimation over the Western Pacific. Shifting from fixed to temperature-dependent convective-stratiform partitioning reduces the Pacific precipitation overestimation but also lessens the improvements of seasonal cycle in India. Spatially correlated biases highlight the necessity for advancements beyond deterministic approaches to align MCSP with environmental conditions.

This study evaluates convective cell properties and their relationships with convective and stratiform rainfall within a season-long convection-permitting simulation over central Argentina using measurements from the RELAMPAGO-CACTI field campaign. While the simulation reproduces the total observed rainfall, it underestimates stratiform rainfall by 46% and overestimates convective rainfall by 43%. As Convective Available Potential Energy (CAPE) increases, the overestimation of convective rainfall decreases, but the underestimation of stratiform rainfall increases such that the high bias in the contribution of convective rainfall to total rainfall remains approximately constant at 26% across all CAPE conditions. Overestimated convective rainfall arises from the simulation generating 2.6 times more convective cells than observed despite similar observed and simulated cell growth processes, with relatively wide cells contributing most to excessive convective rainfall. Relatively shallow cells, typically reaching heights of 4–7 km, contribute most to the cell number bias. This bias increases as CAPE decreases, potentially because cells and their updrafts become narrower and more under-resolved as CAPE decreases. The gross overproduction of shallow cells leads to overly efficient precipitation and inadequate detrainment of ice aloft, thereby diminishing the formation of robust stratiform rainfall regions. Decreasing the model’s horizontal grid spacing from 3 to 1 or 0.333 km for representative low and high CAPE cases results in minimal change to the cell number and depth biases, while the stratiform and convective rainfall biases also fail to improve. This suggests that improving prediction of deep convective system growth depends on factors beyond solely increasing model resolution.

To comprehensively characterize convective precipitation in the central Amazon region, we utilize the Python FLEXible object TRacKeR (PyFLEXTRKR) to track mesoscale convective systems (MCSs) observed through satellite measurements and simulated by the Weather Research and Forecasting (WRF) model at convection-permitting resolution. This study spans a two-month period during the wet seasons of 2014 and 2015. We observe a strong correlation between MCS track density and accumulated precipitation in the Amazon basin. Key factors contributing to precipitation, such as MCS properties (number, size, rainfall intensity, and movement), are thoroughly examined. Our analysis reveals that while the overall model produces fewer MCSs with smaller mean sizes compared to observations, it tends to overpredict total precipitation due to excessive rainfall intensity for heavy rainfall events (≥ 10 mm h-1) and longer traveled distances than observed. These biases in simulated MCS properties vary with the strength of constraints on convective background environment. Moreover, while the wet bias from heavy (convective) rainfall outweighs the dry bias in light (stratiform) rainfall, the latter can be crucial, particularly when MCS cloud cover is significantly underestimated. A relevant case study for April 1, 2014 highlights the influence of environmental conditions on the MCS lifecycle and identifies an unrealistic model representation in convective precipitation features.

Mesoscale convective systems (MCSs) play an important role in modulating the global hydrological cycle, general circulation, and radiative energy budget. In this study, we evaluate MCS simulations in the second version of U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SMv2). E3SMv2 atmosphere model (EAMv2) is run at the uniform 0.25° horizontal resolution. We track MCSs consistently in the model and observations using the PyFLEXTRKR algorithm, which defines MCS based on both cloud-top brightness temperature (Tb) and surface precipitation. Results from using Tb only to define MCS, commonly used in previous studies, are also discussed. Furthermore, sensitivity experiments are performed to examine the impact of new cloud and convection parameterizations developed for EAMv3 on simulated MCSs. Our results show that EAMv2 simulated MCS precipitation is largely underestimated in the tropics and contiguous United States. This is mainly attributed to the underestimated precipitation intensity in EAMv2. In contrast, the simulated MCS frequency becomes more comparable to observations if MCSs are defined only based on cloud-top Tb. The Tb-based MCS tracking method, however, includes many cloud systems with very weak precipitation which conflicts with the MCS definition. This result illustrates the importance of accounting for precipitation in evaluating simulated MCSs. We also find that the new physics parameterizations help increase the relative contribution of convective precipitation to total precipitation in the tropics, but the simulated MCS properties are overall not significantly improved. This suggests that simulating MCSs will remain a challenge for the next version of E3SM.

Mesoscale convective systems (MCSs) produce over 50% of tropical precipitation and account for the majority of extreme rainfall and flooding events. MCSs are considered the building blocks of larger-scale convectively coupled equatorial waves (CCEWs). While CCEWs can provide favorable environments for convection, how CCEWs can systematically impact organized convection and thereby MCS characteristics is less clear. We examine this question by analyzing a global MCS tracking dataset. During the active phase of CCEWs, MCS frequency increases and MCSs rain harder, produce more lifetime total rain, and grow larger in size. The probability of extreme MCSs also elevates. These changes are most pronounced when MCSs are associated with Kelvin waves and tropical depression-type waves while least so with the Madden-Julian Oscillation. These results can be benchmarks to improve model representation of MCS interactions with large-scale circulations and can be leveraged operationally for extended forecasts of high-impact MCSs.

Mesoscale convective systems (MCSs) are clusters of thunderstorms that are important in Earth’s water and energy cycle. Additionally, they are responsible for extreme events such as large hail, strong winds, and extreme precipitation. Automated object-based analyses that track MCSs have become popular since they allow us to identify and follow MCSs over their entire life cycle in a Lagrangian framework. This rise in popularity was accompanied by an increasing number of MCS tracking algorithms, however, little is known about how sensitive analyses are concerning the MCS tracker formulation. Here, we assess differences between six MCS tracking algorithms on South American MCS characteristics and evaluating MCSs in kilometer-scale simulations with observational-based MCSs over three years. All trackers are run with a common set of MCS classification criteria to isolate tracker formulation differences. The tracker formulation substantially impacts MCS characteristics such as frequency, size, duration, and contribution to total precipitation. The evaluation of simulated MCS characteristics is less sensitive to the tracker formulation and all trackers agree that the model can capture MCS characteristics well across different South American climate zones. Dominant sources of uncertainty are the segmentation of cloud systems and the treatment of splitting and merging of storms in MCS trackers. Our results highlight that comparing MCS analyses that use different tracking algorithms is challenging. We provide general guidelines on how MCS characteristics compare between trackers to facilitate a more robust assessment of MCS statistics in future studies.

Mesoscale convective systems (MCSs) are an important component of our hydrologic cycle as they produce prolific rainfall in the tropics and mid-latitudes. Recent advancements in high-resolution modeling show promise in representing MCSs in regional climate simulations. However, how well do these models represent the complex interactions between convective dynamics and microphysics in MCSs remain unknown. In this study, we take advantage of observations collected during the Midlatitude Continental Convective Cloud (MC3E) experiment to evaluate multi-scale aspects of MCSs in convection-permitting WRF model. We conducted three sets of month-long simulations with Morrison and P3 (1-ice and 2-ice categories) microphysics, respectively, at 1.8 km grid-spacing over the Southern Great Plains. MCSs in observations and simulations were tracked using a newly developed FLEXTRKR algorithm. About 15-20 MCSs were identified in the simulations, consistent with observations. All three simulations underestimate observed monthly total precipitation which are primarily from MCSs, suggesting the biases might be caused by large-scale forcings rather than microphysics. All simulated MCSs overestimate convective area and precipitation amount but underestimate stratiform rain area and precipitation. Simulated MCS convective updraft intensities are comparable with radar retrievals for moderate depths of convective cores, but are too strong for deep cores. The two P3 simulations have smaller mean ice mass aloft but more frequent heavy convective rain rate at the surface than the simulation with Morrison, agreeing better with observations (Figure 1). Simulated stratiform area ice mass in the upper troposphere are generally larger than radar retrievals, but the P3 2-ice category has relatively smaller bias. We will also use polarimetric radar 3-D rain water retrieval to further evaluate the vertical evolution of rainfall to explain differences in simulated surface precipitation.

The ability of an observationally-constrained cloud-system resolving model (Weather Research and Forecasting; WRF, 4-km grid spacing) and a global climate model (Energy Exascale Earth System Model; E3SM, 1-degree grid spacing) to represent the precipitation diurnal cycle over the Amazon basin during the 2014 wet season is assessed. The month-long period is divided into days with and without the presence of observed propagating mesoscale convective systems (MCSs) over the central Amazon. The MCSs are strongly associated with rain amounts over the basin and also control the observed spatial variability of the diurnal rain rate. WRF model coupled with a 3-D variational data assimilation scheme reproduces the spatial variability of the precipitation diurnal cycle over the basin and the lifecycle of westward propagating MCSs initiated by the coastal sea-breeze front. In contrast, a single morning peak in rainfall is produced by E3SM for simulations with and without nudging the large-scale winds towards global reanalysis, indicating precipitation in E3SM is largely controlled by local convection associated with diurnal heating. Both models produce contrast in easterly wind profiles between days with and without MCS that are similar to data collected by U.S. DOE Atmospheric Radiation Measurement (ARM) facility during the Green Ocean Amazon (GoAmazon2014/5) campaign and other operational radiosondes. A multivariate perturbation analysis indicates the dryness of low-level air transported from ocean to inland has higher impact on the formation and maintenance of MCS in the Amazon than other processes.

This study aims at improving understanding of the environments supporting summer MCS initiation in the U.S. Great Plains. A self-organizing map analysis is conducted to identify four types of summer MCS initiation environments during 2004-2017: Type-1 and Type-2 feature favorable large-scale environments, Type-3 has favorable lower-level and surface conditions but unfavorable upper-level circulation, while Type-4 features the most unfavorable large-scale environments. Despite the unfavorable large-scale environment, convection-centered composites reveal the presence of favorable sub-synoptic scale environments for MCS initiation in Type-3 and Type-4. All four types of MCS initiation environments delineate a clear eastward propagating feature in many meteorological fields, such as potential vorticity, surface pressure and equivalent potential temperature, upstream up to 25 west of and ~36 hours before MCS initiation. While the propagating environments and local, non-propagating low-level moisture are important to MCS initiation at the foothill of the Rocky Mountains, MCS initiation in the Great Plains is supported by the coupled dynamical and moisture anomalies, both associated with eastward propagating waves. Hence, the MCSs initiated at the plains can produce more rainfall than those initiated at the foothill due to more abundant moisture supply. By tracking MCSs and mid-tropospheric perturbations (MPs), a unique type of sub-synoptic disturbances with Rocky Mountains origin, it is shown that ~30% of MPs is associated with MCS initiation, mostly in Type-4. Although MPs are related to a small fraction of MCS initiation, MCSs that are associated with MPs tend to produce more rainfall in a larger area with a stronger convective intensity.