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The Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative‐convective equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climate sensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique among intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud‐resolving models (CRMs), large eddy simulations (LES), and global cloud‐resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self‐aggregation in large domains and agree that self‐aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self‐aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than unaggregated simulations. Plain Language Summary This study investigates tropical clouds and climate using results from more than 30 different numerical models set up in a simplified framework. The data set of model simulations is unique in that it includes a wide range of model types configured in a consistent manner. We address some of the biggest open questions in climate science, including how cloud properties change with warming and the role that the tendency of clouds to form clusters plays in determining the average climate and how climate changes. While there are large differences in how the different models simulate average temperature, humidity, and cloudiness, in a majority of models, the amount of high clouds decreases as climate warms. Nearly all models simulate a tendency for clouds to cluster together. There is agreement that when the clouds are clustered, the atmosphere is drier with fewer clouds overall. We do not find a conclusive result for how cloud clustering changes as the climate warms. Key Points Temperature, humidity, and clouds in radiative‐convective equilibrium vary substantially across models Models agree that self‐aggregation dries the atmosphere and reduces high cloudiness There is no consistency in how self‐aggregation depends on warming

Organized convection in the tropics occurs across a range of spatial and temporal scales and strongly influences cloud cover and humidity. One mode of organization found is “self-aggregation,” in which moist convection spontaneously organizes into one or several isolated clusters despite spatially homogeneous boundary conditions and forcing. Self-aggregation is driven by interactions between clouds, moisture, radiation, surface fluxes, and circulation, and occurs in a wide variety of idealized simulations of radiative–convective equilibrium. Here we provide a review of convective self-aggregation in numerical simulations, including its character, causes, and effects. We describe the evolution of self-aggregation including its time and length scales and the physical mechanisms leading to its triggering and maintenance, and we also discuss possible links to climate and climate change.

Severe convective storms are among the most dangerous weather phenomena and accurate forecasts mitigate their impacts. The recently released suite of AI‐based weather models produces medium‐range forecasts within seconds, with a skill similar to state‐of‐the‐art operational forecasts for variables on single levels. However, predicting severe thunderstorm environments requires accurate combinations of dynamic and thermodynamic variables and the vertical structure of the atmosphere. Advancing the assessment of AI‐models toward process‐based evaluations lays the foundation for hazard‐driven applications. We assess the forecast skill of the top‐performing AI‐models GraphCast, Pangu‐Weather and FourCastNet for convective parameters at lead‐times up to 10 days against reanalysis and ECMWF's operational numerical weather prediction model IFS. In a case study and seasonal analyses, we see the best performance by GraphCast and Pangu‐Weather: these models match or even exceed the performance of IFS for instability and shear. This opens opportunities for fast and inexpensive predictions of severe weather environments. Plain Language Summary Over the past year, several global AI‐based weather models were released and produce a similar quality of forecasts as traditional weather models. AI‐models are very fast and computationally cheap to produce forecasts. The evaluation of AI‐models has largely focused on single atmospheric variables at certain heights. To forecast specific phenomena, such as thunderstorms, a combination of variables must be accurate at multiple heights. Here we use the output of AI‐models to derive thunderstorm‐related ingredients. We compare 10‐day‐forecasts between the AI‐models GraphCast, Pangu‐Weather and FourCastNet and a state‐of‐the‐art traditional weather model while using a reanalysis data set as the reference. The example of a tornado outbreak in the southern United States shows that all models are capable of forecasting thunderstorm ingredients multiple days in advance. To obtain a robust assessment, we evaluate the entire thunderstorm season in 2020 in North America, Europe, Argentina, and Australia, where severe thunderstorms occur frequently. Two of the three AI‐models achieve similar or even better results than the traditional weather model while being much cheaper to operate computationally. Forecasting thunderstorm parameters directly, instead of calculating them afterward, is likely to produce even better results. This opens opportunities for rapid and accessible forecasts for severe thunderstorm phenomena. Key Points AI‐based global weather models produce forecasts with sufficient accuracy to derive instability and shear metrics skillfully The best AI‐based weather models are capable of competing with state‐of‐the‐art numerical weather predictions of instability and shear This is a major step toward computationally inexpensive and fast convective outlooks

Several warm‐core cyclones in the Mediterranean, which were analyzed in the literature, are studied using ERA5 reanalysis, to identify the environment where they develop and distinguish tropical‐like cyclones from non‐tropical warm‐core cyclones. Initially, the cyclone phase space is analyzed to distinguish the cyclones that have a symmetrical deep warm core. Subsequently, the temporal evolution of several parameters is considered, including the distance between the area of maximum tangential wind speed and the cyclone center. Some differences are observed between the cyclones analyzed: one category of cyclones develops in areas of moderate‐low baroclinicity and intense convective processes, as occurs in tropical cyclones. Another group of cyclones develops in a strongly baroclinic environment with weak convective processes and intense vertical wind shear, as occurs in warm seclusions. Two cyclones, showing similarities with polar lows, are also identified. Plain Language Summary Mediterranean tropical‐like cyclones (TLCs) are damaging weather systems, which form over the Mediterranean Sea, resembling tropical cyclones. These cyclones can drive important socio‐economic losses in coastal areas. However, due to their small size and the relatively recent investigation of these cyclones, there is currently no robust categorization of which Mediterranean cyclones can be considered TLC. Therefore, in this work, we propose a method to differentiate cyclones that attain actual tropical‐like characteristics in part of their lifetime, as they develop a warm core through intense convective processes. The main results of this study show that part of the analyzed cyclones have features similar to tropical cyclones. Another group of cyclones has a behavior closer to extratropical cyclones with weak convective processes in an environment with intense vertical wind shear, as occurs in warm seclusions or polar lows. The results of this study propose a key to identify the Mediterranean cyclones that have tropical‐like characteristics. Key Points A new method to detect cyclones with tropical‐like characteristics in the Mediterranean has been developed Part of the cyclones with deep warm core developed in low baroclinicity and with intense convective processes, as tropical cyclones Some cyclones have weak convective processes and intense vertical wind shear environments, such as warm seclusions or polar lows

A stochastic horizontal subgrid‐scale mixing scheme is evaluated in ensemble simulations of a tropical oceanic deep convection case using a horizontal grid spacing (Δh) of 3 km. The stochastic scheme, which perturbs the horizontal mixing coefficient according to a prescribed spatiotemporal autocorrelation scale, is found to generally increase mesoscale organization and convective intensity relative to a non‐stochastic control simulation. Perturbations applied at relatively short autocorrelation scales induce differences relative to the control that are more systematic than those from perturbations applied at relatively long scales that yield more variable outcomes. A simulation with mixing enhanced by a constant factor of 4 significantly increases mesoscale organization and convective intensity, while turning off horizontal subgrid‐scale mixing decreases both. Total rainfall is modulated by a combination of mesoscale organization, areal coverage of convection, and convective intensity. The stochastic simulations tend to behave more similarly to the constant enhanced mixing simulation owing to greater impacts from enhanced mixing as compared to reduced mixing. The impacts of stochastic mixing are robust, ascertained by comparing the stochastic mixing ensembles with a non‐stochastic mixing ensemble that has grid‐scale noise added to the initial thermodynamic field. Compared to radar observations and a higher resolution Δh = 1 km simulation, stochastic mixing seemingly degrades the simulation performance. These results imply that stochastic mixing produces non‐negligible impacts on convective system properties and evolution but does not lead to an improved representation of convective cloud characteristics in the case studied here. Plain Language Summary Regional weather prediction and climate models commonly have horizontal grid lengths of 2–4 km that cannot resolve mixing of air in cumulonimbus clouds with surrounding cooler, drier environmental air, a key process that modulates cloud and storm properties. This study evaluates a method to represent such mixing in models that induces random variability in the magnitude of mixing for a tropical oceanic deep convection case. This approach is found to alter both the intensity and areal coverage of precipitation. As space and time scales of the variability are increased, changes to precipitation coverage and intensity become greater and less systematic relative to a control simulation. Altering mixing also changes the sizes of convective clouds and the degree to which they cluster around one another. Ultimately, the new mixing approach induces variable responses in simulated convective clouds but generally makes them more intense with wider cloud cores, which does not improve upon the control simulation relative to radar observations and a higher resolution simulation. Key Points A stochastic subgrid‐scale mixing scheme was evaluated in a tropical oceanic deep convection case using 3‐km horizontal grid spacing Stochastic mixing modulates the area and intensity of convection, producing more organization and less dilute updrafts relative to a control More systematic impacts on convection properties occur for relatively short spatial and time scale variability of stochastic mixing

This study examines how the congestus mode of tropical convection is expressed in numerical simulations of radiative‐convective equilibrium (RCE). We draw insights from the ensemble of cloud‐resolving models participating in the RCE Model Intercomparison Project (RCEMIP) and from a new ensemble of two‐dimensional RCE simulations. About half of the RCEMIP models produce a congestus circulation that is distinct from the deep and shallow modes. In both ensembles, the congestus circulation strengthens with large‐scale convective aggregation, and in the 2D ensemble this comes at the expense of the shallow circulation centered at the top of the boundary layer. Congestus invigoration occurs because aggregation dries out the upper troposphere, which allows moist congestus outflow to undergo strong radiative cooling. The cooling generates divergence that promotes continued congestus overturning (a positive feedback). This mechanism is fundamentally similar to the driving of shallow circulations by radiative cooling at the top of the surface boundary layer. Aggregation and congestus invigoration are also associated with enhanced static stability throughout the troposphere, but a modeling experiment shows that enhanced stability is not necessary for congestus invigoration; rather, invigoration itself contributes to the stability increase via its impact on the vertical profile of radiative cooling. Changes in entrainment cooling are also found to play an important role in stability enhancement, as has been suggested previously. When present, congestus circulations have a large impact on the mean RCE atmospheric state; for this reason, their inconsistent representation in models and their impact on the real tropical atmosphere warrant further scrutiny. Plain Language Summary Atmospheric convection over tropical oceans has three distinct types that differ in their vertical reach: a deep mode typically associated with towering cumulonimbus clouds, a shallow mode restricted to the lowest ∼2 km of the atmosphere, and a congestus mode that falls somewhere in between. This study focuses on the congestus mode, which has received comparatively little attention in the past. We investigate the sources of congestus mode variability in simple simulations of the tropical atmosphere. The congestus mode is expressed very strongly in some models but is absent in others. We find that it is stronger when convection is clumped into a limited portion of the model domain rather than dispersed. When present, the congestus mode has a big impact on the distribution of temperature and moisture throughout the atmosphere. These results are important because they help us better understand the nature of the congestus mode and the climates produced by commonly used atmospheric models. Key Points Representation of the congestus mode in RCE varies greatly across models The congestus mode is invigorated by large‐scale convective aggregation Tropospheric stability increases with aggregation due to congestus invigoration and reduced entrainment cooling

During the Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations-Cloud, Aerosol, and Complex Terrain Interactions (RELAMPAGO-CACTI) field experiments in 2018–19, an unprecedented number of balloon-borne soundings were collected in Argentina. Radiosondes were launched from both fixed and mobile platforms, yielding 2712 soundings during the period 15 October 2018–30 April 2019. Approximately 20% of these soundings were collected by highly mobile platforms, strategically positioned for each intensive observing period, and launching approximately once per hour. The combination of fixed and mobile soundings capture both the overall conditions characterizing the RELAMPAGO-CACTI campaign, as well as the detailed evolution of environments supporting the initiation and upscale growth of deep convective storms, including some that produced hazardous hail and heavy rainfall. Episodes of frequent convection were characterized by sufficient quantities of moisture and instability for deep convection, along with deep-layer vertical wind shear supportive of organized or rotating storms. A total of 11 soundings showed most unstable convective available potential energy (MUCAPE) exceeding 6000 J kg −1 , comparable to the extreme instability observed in other parts of the world with intense deep convection. Parameters used to diagnose severe-storm potential showed that conditions were often favorable for supercells and severe hail, but not for tornadoes, primarily because of insufficient low-level wind shear. High-frequency soundings also revealed the structure and evolution of the boundary layer leading up to convection initiation, convectively generated cold pools, the South American low-level jet (SALLJ), and elevated nocturnal convection. This sounding dataset will enable improved understanding and prediction of convective storms and their surroundings in subtropical South America, as well as comparisons with other heavily studied regions such as the central United States that have not previously been possible.

Using an index of tropical easterly wave (TEW) activity derived from spacetime‐filtered outgoing longwave radiation, we construct composites of long‐term hourly surface meteorological observations and morningtime sounding data collected near San José, Costa Rica to investigate how TEWs affect the diurnal cycle of rainfall over land. Our results indicate that TEWs enhance the frequency of occurrence of rain during convectively active conditions over the course of the diurnal cycle. By contrast, rainfall conditional intensity sensitivity to TEW phase appears more nuanced, with indications that active conditions induce a slight delay in the timing of the diurnal peak intensity but a longer duration of heavier rainfall. Analysis of associated hourly surface meteorology along with sounding profiles and derived thermodynamic parameters points to both initial vertical and time‐evolving surface conditions regulating diurnal behavior, such as greater instability and higher precipitable water in morningtime profiles under active phase conditions. Plain Language Summary Over tropical land, rainfall often follows a characteristic daily (or diurnal) cycle, with a peak in the late afternoon. This study explores how tropical easterly waves, westward‐propagating weather disturbances known to impact rainfall over many parts of the Tropics, affect the rainfall diurnal cycle at a long‐term observation site near San José, Costa Rica. Using an index of tropical easterly wave activity, and considering the diurnal cycle in terms of rainfall occurrence frequency and intensity, the results presented here indicate generally enhanced occurrence frequency over the course of the diurnal cycle during active conditions of tropical easterly waves, while intensity shows a more nuanced behavior, with active phases showing a slight lag in peak diurnal intensity with an overall lengthening of the highest intensities. Such diurnal rainfall sensitivity to tropical easterly waves is interpreted in terms of hourly surface meteorological observations and vertical profiles from morningtime soundings. Key Points Tropical easterly wave (TEW) impact on the rainfall diurnal cycle over Costa Rica is documented in surface meteorological and sounding data Convectively active phases of tropical easterly waves are associated with increased hourly rainfall frequency of occurrence Rainfall intensity sensitivity to TEW phase is more subtle, with indications of delayed active phase peak intensity

Forced mechanical lifting through cold pool gust fronts can trigger new convection and, as previous work highlights, is enhanced when cold pools collide. However, as shown by conceptual models, the organization of the convective cloud field emerging from two versus three colliding cold pools differs strongly. In idealized dry large‐eddy simulations we therefore compare collisions between two and three cold pools. The triggering likelihood is quantified in terms of the cumulative vertical mass flux of boundary layer air and the instantaneous updraft strength, generated at the cold pool gust fronts. We find that cold pool expansion can be well described by initial potential energy alone. Cold pool expansion monotonically slows but shows an abrupt transition between an axisymmetric and a broken‐symmetric state mirrored by a sudden drop in expansion speed. We characterize these two dynamic regimes by two distinct power law exponents and explain the transition by the onset of “lobe‐and‐cleft” instabilities at the cold pool head. Two‐cold pool collisions produce the strongest instantaneous updrafts in the lower boundary layer, which we expect to be important in environments with strong convective inhibition. Three‐cold pool collisions generate weaker but deeper updrafts and the strongest cumulative mass flux and are thus predicted to induce the largest midlevel moistening, which has been identified as a precursor for the transition from shallow to deep convection. Combined, our findings may help decipher the role of cold pools in spatially organizing convection and precipitation. Plain Language Summary The arrival of a convective thunderstorm is often announced by strong and cold wind gusts that can be felt by an observer at the surface. These gust fronts constitute the outer edge of cold pools, which are formed underneath clouds when part of the rain reevaporates before reaching the surface, thereby cooling the air. These cold pools have received increasing attention due to their contribution in the generation of new convective rain events, thereby affecting the spatial pattern of the cloud field. In this study we use a high‐resolution numerical model to study the life cycle of single cold pools and their collision with other cold pools. We assume that the likelihood that a cold pool causes a new rain event depends on (i) the vertical velocity of the wind gusts produced at its gust front and where it collides and (ii) how much moisture it can transport upward to a height where the water condenses and forms clouds. We show that both these factors are strongly increased where two or more cold pools collide,highlighting the importance of the representation of cold pool collisions in climate models to achieve a more realistic representation of clouds and rain. Key Points A cold pool's initial potential energy is a good predictor of its spreading dynamics Cold pool radii evolve as a combinationof two power laws, highlighting a dynamic transition induced by “lobe‐and‐cleft” instabilities Updrafts and mass flux are strongly enhanced in multicold pool collisions compared to single cold pool gust fronts.

The Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign was designed to improve understanding of orographic cloud life cycles in relation to surrounding atmospheric thermodynamic, flow, and aerosol conditions. The deployment to the Sierras de Córdoba range in north-central Argentina was chosen because of very frequent cumulus congestus, deep convection initiation, and mesoscale convective organization uniquely observable from a fixed site. The C-band Scanning Atmospheric Radiation Measurement (ARM) Precipitation Radar was deployed for the first time with over 50 ARM Mobile Facility atmospheric state, surface, aerosol, radiation, cloud, and precipitation instruments between October 2018 and April 2019. An intensive observing period (IOP) coincident with the RELAMPAGO field campaign was held between 1 November and 15 December during which 22 flights were performed by the ARM Gulfstream-1 aircraft. A multitude of atmospheric processes and cloud conditions were observed over the 7-month campaign, including numerous orographic cumulus and stratocumulus events; new particle formation and growth producing high aerosol concentrations; drizzle formation in fog and shallow liquid clouds; very low aerosol conditions following wet deposition in heavy rainfall; initiation of ice in congestus clouds across a range of temperatures; extreme deep convection reaching 21-km altitudes; and organization of intense, hail-containing supercells and mesoscale convective systems. These comprehensive datasets include many of the first ever collected in this region and provide new opportunities to study orographic cloud evolution and interactions with meteorological conditions, aerosols, surface conditions, and radiation in mountainous terrain.