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Abstract We examine the influence of convective organization on extreme tropical precipitation events using model simulation data from the Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP). At a given SST, simulations with convective organization have more intense precipitation extremes than those without it at all scales, including instantaneous precipitation at the grid resolution (3 km). Across large‐domain simulations with convective organization, models with explicit convection exhibit better agreement in the response of extreme precipitation rates to warming than those with parameterized convection. Among models with explicit convection, deviations from the Clausius‐Clapeyron scaling of precipitation extremes with warming are correlated with changes in organization, especially on large spatiotemporal scales. Though the RCEMIP ensemble is nearly evenly split between CRMs which become more and less organized with warming, most of the models which show increased organization with warming also allow super‐CC scaling of precipitation extremes. We also apply an established precipitation extremes scaling to understand changes in the extreme condensation events leading to extreme precipitation. Increased organization leads to greater increases in precipitation extremes by enhancing both the dynamic and implied efficiency contributions. We link these contributions to environmental variables modified by the presence of organization and suggest that increases in moisture in the aggregated region may be responsible for enhancing both convective updraft area fraction and precipitation efficiency. By leveraging a controlled intercomparison of models with both explicit and parameterized convection, this work provides strong evidence for the amplification of tropical precipitation extremes and their response to warming by convective organization. 

Abstract Interactions between clouds, water vapor, radiation, and circulation influence tropical cyclone (TC) development. Many of these interactions can be quantified by understanding tendencies of the spatial variance of moist static energy (MSE). Dropsondes from aircraft reconnaissance sample profiles needed to compute MSE at fine vertical resolution, and may be useful in analyzing these feedbacks on TCs in situ. However, dropsondes are spatially sparse, and sample limited column depths depending on the type of reconnaissance mission. We use idealized convection‐permitting simulations to examine how MSE variability, and the feedbacks that influence it, are resolved using selected patterns of grid points meant to resemble dropsonde launch points in reconnaissance flight patterns. We first examine the column depth necessary to capture the MSE variability of the full atmosphere. We then study how these simulated flight patterns depict MSE variance and its relevant diabatic feedbacks in TCs of varying structure and intensity. 

Abstract This study investigates Gulf Stream (GS) sea surface temperature (SST) anomalies associated with the extratropical transition (ET) of tropical cyclones (TCs) in the North Atlantic. Composites of western North Atlantic TCs indicate that GS SSTs are warmer, and both large‐ and fine‐scale SST gradients are weaker than average, for TCs that begin the ET process but do not complete it, compared with TCs that do. Further analysis suggests that the associated fine‐scale GS SST gradient anomalies are related to atmospheric processes but not the same as those that are typically associated with the onset of ET. As sensible heat flux gradients and surface diabatic frontogenesis are shown to generally scale with the local SST gradient strength, these results suggest that knowledge of the fine‐scale GS SST gradient in the weeks prior to the arrival of a TC might potentially provide additional information regarding the likelihood of that system completing ET. 

Abstract The Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) consists of simulations at three fixed sea‐surface temperatures (SSTs: 295, 300, and 305 K) and thus allows for a calculation of the climate feedback parameter based on the change of the top‐of‐atmosphere radiation imbalance. Climate feedback parameters range widely across RCEMIP, roughly from−6 to 3 W m⁻² K⁻¹, particularly across general‐circulation models (GCMs) as well as global and large‐domain cloud‐resolving models (CRMs). Small‐domain CRMs and large‐eddy simulations have a smaller range of climate feedback parameters due to the absence of convective self‐aggregation. More than 70–80% of the intermodel spread in the climate feedback parameter can be explained by the combined temperature dependencies of convective aggregation and shallow cloud fraction. Low climate sensitivities are associated with an increase of shallow cloud fraction (increasing the planetary albedo) and/or an increase in convective aggregation with warming. An increase in aggregation is associated with an increase in outgoing longwave radiation, caused primarily by mid‐tropospheric drying, and secondarily by an expansion of subsidence regions. Climate sensitivity is neither dependent on the average amount of aggregation nor on changes in deep/anvil cloud fraction. GCMs have a lower overall climate sensitivity than CRMs because in most GCMs convective aggregation increases with warming, whereas in CRMs, convective aggregation shows no consistent temperature trend. 

Abstract 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.