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This study compares the spread in climatological tropical cyclone (TC) precipitation across eight different reanalysis datasets: NCEP-CFSR, ERA-20C, ERA-40, ERA5, ERA-Interim, JRA-55, MERRA-2, and NOAA-20C. TC precipitation is assigned using manual tracking via a fixed 500-km radius from each TC center. The reanalyses capture similar general spatial patterns of TC precipitation and TC precipitation fraction, defined as the fraction of annual precipitation assigned to TCs, and the spread in TC precipitation is larger than the spread in total precipitation across reanalyses. The spread in TC precipitation relative to the inter-reanalysis mean TC precipitation, or relative spread, is larger in the east Pacific than in the west Pacific. Partitioned by reanalysis intensity, the largest relative spread across reanalyses in TC precipitation is from high-intensity TCs. In comparison with satellite observations, reanalyses show lower climatological mean annual TC precipitation over most areas. A comparison of area-averaged precipitation rate in TCs composited over reanalysis intensity shows the spread across reanalyses is larger for higher intensity TCs. Testing the sensitivity of TC precipitation assignment to tracking method shows that climatological mean annual TC precipitation is systematically larger when assigned via manual tracking versus objective tracking. However, this tendency is minimized when TC precipitation is normalized by TC density. Overall, TC precipitation in reanalyses is affected by not only horizontal output resolution or any TC preprocessing, but also data assimilation and parameterization schemes. The results indicate that improvements in the representation of TCs and their precipitation in reanalyses are needed to improve overall precipitation.

Here we explore the relationship between the global climatological characteristics of tropical cyclones (TCs) in climate models and the modeled large-scale environment across a large number of models. We consider the climatology of TCs in 30 climate models with a wide range of horizontal resolutions. We examine if there is a systematic relationship between the climatological diagnostics for the TC activity [number of tropical cyclones (NTC) and accumulated cyclone energy (ACE)] by hemisphere in the models and the environmental fields usually associated with TC activity, when examined across a large number of models. For low-resolution models, there is no association between a conducive environment and TC activity, when integrated over space (tropical hemisphere) and time (all years of the simulation). As the model resolution increases, for a couple of variables, in particular vertical wind shear, there is a statistically significant relationship in between the models’ TC characteristics and the environmental characteristics, but in most cases the relationship is either nonexistent or the opposite of what is expected based on observations. It is important to stress that these results do not imply that there is no relationship between individual models’ environmental fields and their TC activity by basin with respect to intraseasonal or interannual variability or due to climate change. However, it is clear that when examined across many models, the models’ mean state does not have a consistent relationship with the models’ mean TC activity. Therefore, other processes associated with the model physics, dynamical core, and resolution determine the climatological TC activity in climate models.

This review article summarizes recent (2014–2019) advances in our understanding of tropical cyclogenesis, stemming from activities at the ninth International Workshop on Tropical Cyclones. Tropical cyclogenesis involves the interaction of dynamic and thermodynamic processes at multiple spatio-temporal scales. Studies have furthered our understanding of how tropical cyclogenesis may be affected by external processes, such as intraseasonal oscillations, monsoon circulations, the intertropical convergence zone, and midlatitude troughs and cutoff lows. Additionally, studies have furthered our understanding of how tropical cyclogenesis may be affected by internal processes, such as the organization of deep convection; the evolution of the “pouch” structure; the role of friction; the development of the moist, warm core; the importance of surface fluxes; and the role of the mid-level vortex. A relatively recent class of idealized, numerical simulations of tropical cyclogenesis in radiative-convective equilibrium have highlighted the potential importance of radiative feedbacks on tropical cyclogenesis. We also offer some recommendations to the community on future directions for tropical cyclogenesis research.

The frequency with which tropical cyclones (TCs) occur controls all other aspects of tropical cyclone risk since a storm that does not occur can do no harm. Yet this frequency is poorly understood. There is no accepted theory that explains the average number of TCs that occur each year on the Earth, nor how that number will change with global warming. Arguments based on global budgets of heat or moisture do not yet appear helpful, nor does a detailed understanding of the physical processes of TC genesis. Empirical indices that predict TC frequency as a function of large‐scale environmental variables can explain some of its relative variations in space and time, but not its absolute value. Global numerical models with horizontal grid spacings on the order of 25–50 km have allowed much improved simulations of TC activity, however. Many such models project a decrease in frequency with warming, but some project an increase. Idealized simulations, including those at higher resolutions, offer promise by allowing a systematic, deductive investigation of the roles of individual environmental factors. In addition to the larger‐scale environmental modulation of genesis likelihood, precursor disturbances, or “seeds”, may exert an independent influence on TC frequency.

Using dropsondes and advanced remote sensing instrumentation to configure the German High-Altitude LOng range research aircraft (HALO) is configured for use as an airborne cloud observatory. A configuration of HALO (the High-Altitude LOng-range research aircraft) as a remote sensing cloud observatory is described and its use is illustrated with results from the first and second NARVAL (Next-generation Aircraft Remote-sensing for VALidation) field studies. Measurements from NARVAL2 are used to highlight the ability of HALO, when configured in this fashion, to characterize not only the distribution of water condensate in the atmosphere, but also its impact on radiant energy transfer and the co-varying large-scale meteorological conditions – including the large-scale velocity field, and its vertical component. The NARVAL campaigns with HALO demonstrate the potential of airborne cloud observatories to address long-standing riddles in studies of the coupling between clouds and circulation, and are helping to motivate a new generation of field studies.

This paper reviews the self-aggregation of deep convection, its impact on the large-scale environment, its dependence on surface temperature, and its implications for climate. Self-aggregation generates significant humidity variability, dries the mean state, reduces high cloud cover, and increases the ability of the atmosphere to cool to space. Some studies find that convection is more self-aggregated at warmer temperatures but other studies, or other ways of measuring the degree of self-aggregation, disagree. There is not a simple, monotonic relationship between self-aggregation and surface temperature. Self-aggregation, through its effect on the humidity distribution and radiative budget, can affect climate. However, there is uncertainty over how strong the modulation of climate by self-aggregation is, in part because of the ambiguity over its temperature dependence. There are some indications that self-aggregation may modestly reduce climate sensitivity even without a dramatic temperature dependence, but more research is needed to understand this behavior.

Interactions between clouds, radiation, and circulations are fundamental to tropical climate, but until recently, the impact of these interactions on tropical cyclones (TCs) has been relatively unexplored. Simulations of rotating radiative-convective equilibrium confirm that radiative feedbacks are important for spontaneous TC genesis (in which a TC is allowed to form from random noise). While not strictly necessary, radiative feedbacks significantly accelerate TC genesis and especially contribute in the early stages of genesis. These radiative feedbacks arise from interactions between spatially and temporally varying radiative cooling (driven by the dependence of radiative cooling rate on clouds and water vapor) and the developing tropical cyclone (the circulation of which shapes the structure of clouds and water vapor). However, TCs in nature are generally observed to form from pre-existing disturbances, calling into question whether radiative feedbacks play a significant role.Here, I investigate the importance of radiative feedbacks in TC genesis and the mechanisms underlying their influence in a set of idealized cloud-resolving simulations in which a TC is allowed to develop after initialization from a mesoscale warm, saturated bubble on an f-plane, in an otherwise quiescent and moist neutral environment. TC genesis is delayed by a factor of two or three when radiative feedbacks are removed by prescribing a fixed cooling profile or spatially homogenizing the model-calculated cooling profiles. Further analysis and additional mechanism denial experiments pinpoint the longwave radiative feedback contributed by ice clouds as the strongest influence. These results are consistent with recently published case study simulations in which cloud-radiative effects accelerate TC formation and intensification in realistic scenarios. The important takeaway from the results presented here is that that cloud-longwave radiative feedbacks have a profound impact on TC genesis in a hierarchy of model simulations. Improving the representation of cloud-radiative feedbacks in forecast models therefore has the potential to yield critical advancements in TC prediction.

The deep convective clouds of developing tropical cyclones (TCs) are highly effective at trapping the infrared (or longwave) radiation welling up from the surface. This “cloud greenhouse effect” locally warms the lower–mid-troposphere relative to the TC’s surroundings – an effect that manifests in all stages of the TC lifecycle. While idealized studies suggest the importance of this feedback for TC formation, this issue has remained unexplored for TCs in nature, where non-zero background flow, wind shear, and synoptic-scale variability are known to greatly constrain TC development.To address this gap, we examine the potential role of this cloud–infrared (or longwave) radiation feedback in the context of two archetypal storms: Super Typhoon Haiyan (2013) and Hurricane Maria (2017). We conduct a set of numerical model experiments for both storms with a convection-resolving model (WRF-ARW) from the very early stages of TC development. We examine sensitivity experiments wherein this cloud–radiation feedback is removed at various lead-times prior to TC genesis and the onset of rapid intensification (RI). In both storms, removing this cloud–radiation feedback at a lead-time of ~1 day or less leads to delayed and/or weaker intensification than in the control case. When this feedback is removed with a lead-time of two days or longer, however, the storms altogether fail to development and intensify. This local cloud greenhouse effect strengthens the thermally direct transverse circulation of the incipient storm, in turn both promoting saturation within its core and accelerating the spin-up of its surface tangential circulation via angular momentum convergence. These findings indicate that the cloud greenhouse effect plays a critical role in accelerating and promoting TC development in nature. Progress in the prediction of TC formation and intensification has been very limited in recent decades. Cloud–radiation feedback represents a large source of uncertainty in models, which hence manifests as uncertainty in the prediction of TC development. Our findings highlight the pressing need to better constrain this feedback in models. Doing so holds promise for advancing our ability to forecast TCs.

The trends in tropical cyclones point to more frequent strong storms near land

The science guiding the EUREC4A campaign and its measurements is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. Through its ability to characterize processes operating across a wide range of scales, EUREC4A marked a turning point in our ability to observationally study factors influencing clouds in the trades, how they will respond to warming, and their link to other components of the earth system, such as upper-ocean processes or the life cycle of particulate matter. This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso- (200 km) and larger (500 km) scales, roughly 400 h of flight time by four heavily instrumented research aircraft; four global-class research vessels; an advanced ground-based cloud observatory; scores of autonomous observing platforms operating in the upper ocean (nearly 10 000 profiles), lower atmosphere (continuous profiling), and along the air–sea interface; a network of water stable isotopologue measurements; targeted tasking of satellite remote sensing; and modeling with a new generation of weather and climate models. In addition to providing an outline of the novel measurements and their composition into a unified and coordinated campaign, the six distinct scientific facets that EUREC4A explored – from North Brazil Current rings to turbulence-induced clustering of cloud droplets and its influence on warm-rain formation – are presented along with an overview of EUREC4A's outreach activities, environmental impact, and guidelines for scientific practice. Track data for all platforms are standardized and accessible at https://doi.org/10.25326/165 (Stevens, 2021), and a film documenting the campaign is provided as a video supplement.

The tall clouds that comprise tropical storms, hurricanes, and typhoons—or more generally, tropical cyclones (TCs)—are highly effective at trapping the infrared radiation welling up from the surface. This cloud–infrared radiation feedback, referred to as the “cloud greenhouse effect,” locally warms the lower–middle troposphere relative to a TC’s surroundings through all stages of its life cycle. Here, we show that this effect is essential to promoting and accelerating TC development in the context of two archetypal storms—Super Typhoon Haiyan (2013) and Hurricane Maria (2017). Namely, this feedback strengthens the thermally direct transverse circulation of the developing storm, in turn both promoting saturation within its core and accelerating the spin-up of its surface tangential circulation through angular momentum convergence. This feedback therefore shortens the storm’s gestation period prior to its rapid intensification into a strong hurricane or typhoon. Further research into this subject holds the potential for key progress in TC prediction, which remains a critical societal challenge.

In a modeled environment of rotating radiative‐convective equilibrium (RCE), convective self‐aggregation may take the form of spontaneous tropical cyclogenesis. We investigate the processes leading to tropical cyclogenesis in idealized simulations with a three‐dimensional cloud‐permitting model configured in rotating RCE, in which the background planetary vorticity is varied across f‐plane cases to represent a range of deep tropical and near‐equatorial environments. Convection is initialized randomly in an otherwise homogeneous environment, with no background wind, precursor disturbance, or other synoptic‐scale forcing. We examine the dynamic and thermodynamic evolution of cyclogenesis in these experiments and compare the physical mechanisms to current theories. All simulations with planetary vorticity corresponding to latitudes from 10°–20° generate intense tropical cyclones, with maximum wind speeds of 80 m s−1 or above. Time to genesis varies widely, even within a five‐member ensemble of 20° simulations, indicating large stochastic variability. Shared across the 10°–20° group is the emergence of a midlevel vortex in the days leading to genesis, which has dynamic and thermodynamic implications on its environment that facilitate the spin‐up of a low‐level vortex. Tropical cyclogenesis is possible in this model at values of Coriolis parameter as low as that representative of 1°. In these experiments, convection self‐aggregates into a quasicircular cluster, which then begins to rotate and gradually strengthen into a tropical storm, aided by strong near‐surface inflow that is already established days prior. Other experiments at these lower Coriolis parameters instead self‐aggregate into a nonrotating elongated band and fail to undergo cyclogenesis over the 100‐day simulation.

Characteristics of tropical cyclones (TCs) in global climate models (GCMs) are known to be influenced by details of the model configurations, including horizontal resolution and parameterization schemes. Understanding model-to-model differences in TC characteristics is a prerequisite for reducing uncertainty in future TC activity projections by GCMs. This study performs a process-level examination of TC structures in eight GCM simulations that span a range of horizontal resolutions from 1° to 0.25°. A recently developed set of process-oriented diagnostics is used to examine the azimuthally averaged wind and thermodynamic structures of the GCM-simulated TCs. Results indicate that the inner-core wind structures of simulated TCs are more strongly constrained by the horizontal resolutions of the models than are the thermodynamic structures of those TCs. As expected, the structures of TC circulations become more realistic with smaller horizontal grid spacing, such that the radii of maximum wind (RMW) become smaller, and the maximum vertical velocities occur off the center. However, the RMWs are still too large, especially at higher intensities, and there are rising motions occurring at the storm centers, inconsistently with observations. The distributions of precipitation, moisture, and radiative and surface turbulent heat fluxes around TCs are diverse, even across models with similar horizontal resolutions. At the same horizontal resolution, models that produce greater rainfall in the inner-core regions tend to simulate stronger TCs. When TCs are weak, the radial gradient of net column radiative flux convergence is comparable to that of surface turbulent heat fluxes, emphasizing the importance of cloud–radiative feedbacks during the early developmental phases of TCs.

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., Key Points Temperature, humidity, and clouds in radiative‐convective equilibrium vary substantially across modelsModels agree that self‐aggregation dries the atmosphere and reduces high cloudinessThere is no consistency in how self‐aggregation depends on warming

The Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) consists ofsimulations at three fixed sea-surface temperatures (SSTs: 295, 300, and 305 K) and thus allows for acalculation of the climate feedback parameter based on the change of the top-of-atmosphere radiationimbalance. Climate feedback parameters range widely across RCEMIP, roughly from−6to3Wm−2K−1,particularly across general-circulation models (GCMs) as well as global and large-domain cloud-resolvingmodels (CRMs). Small-domain CRMs and large-eddy simulations have a smaller range of climate feedbackparameters due to the absence of convective self-aggregation. More than 70–80% of the intermodel spreadin the climate feedback parameter can be explained by the combined temperature dependencies ofconvective aggregation and shallow cloud fraction. Low climate sensitivities are associated withan increase of shallow cloud fraction (increasing the planetary albedo) and/or an increase in convectiveaggregation with warming. An increase in aggregation is associated with an increase in outgoing longwaveradiation, caused primarily by mid-tropospheric drying, and secondarily by an expansion of subsidenceregions. Climate sensitivity is neither dependent on the average amount of aggregation nor on changes indeep/anvil cloud fraction. GCMs have a lower overall climate sensitivity than CRMs because in mostGCMs convective aggregation increases with warming, whereas in CRMs, convective aggregation shows noconsistent temperature trend.

A recent observational analysis has reported significant repeating diurnal signals propagating outward at cloud-top height from tropical cyclone centers. Modeling studies suggest that the visible upper-level impacts reflect a diurnal cycle through the depth of the troposphere. In this study, the possibility of wavelike diurnal responses in tropical cyclones is characterized using 3D cloud-resolving numerical simulations with and without a diurnal cycle. Diurnal waves can only begin to propagate well beyond the storm core, and the outflow region is most receptive to near-core diurnal propagation because of its anticyclonic flow. The tropical cyclone structure appears particularly hostile to diurnal wave propagation during periods when the eyewall experiences a temporary breakdown similar to an eyewall replacement cycle.

Tropical cyclone intensification processes are explored in six high-resolution climate models. The analysis framework employs process-oriented diagnostics that focus on how convection, moisture, clouds, and related processes are coupled. These diagnostics include budgets of column moist static energy and the spatial variance of column moist static energy, where the column integral is performed between fixed pressure levels. The latter allows for the quantification of the different feedback processes responsible for the amplification of moist static energy anomalies associated with the organization of convection and cyclone spinup, including surface flux feedbacks and cloud-radiative feedbacks. Tropical cyclones (TCs) are tracked in the climate model simulations and the analysis is applied along the individual tracks and composited over many TCs. Two methods of compositing are employed: a composite over all TC snapshots in a given intensity range, and a composite over all TC snapshots at the same stage in the TC life cycle (same time relative to the time of lifetime maximum intensity for each storm). The radiative feedback contributes to TC development in all models, especially in storms of weaker intensity or earlier stages of development. Notably, the surface flux feedback is stronger in models that simulate more intense TCs. This indicates that the representation of the interaction between spatially varying surface fluxes and the developing TC is responsible for at least part of the intermodel spread in TC simulation.

Realistic climate and weather prediction models are necessary to produce confidence in projections of future climate over many decades and predictions for days to seasons. These models must be physically justified and validated for multiple weather and climate processes. A key opportunity to accelerate model improvement is greater incorporation of process-oriented diagnostics (PODs) into standard packages that can be applied during the model development process, allowing the application of diagnostics to be repeatable across multiple model versions and used as a benchmark for model improvement. A POD characterizes a specific physical process or emergent behavior that is related to the ability to simulate an observed phenomenon. This paper describes the outcomes of activities by the Model Diagnostics Task Force (MDTF) under the NOAA Climate Program Office (CPO) Modeling, Analysis, Predictions and Projections (MAPP) program to promote development of PODs and their application to climate and weather prediction models. MDTF and modeling center perspectives on the need for expanded process-oriented diagnosis of models are presented. Multiple PODs developed by the MDTF are summarized, and an open-source software framework developed by the MDTF to aid application of PODs to centers’ model development is presented in the context of other relevant community activities. The paper closes by discussing paths forward for the MDTF effort and for community process-oriented diagnosis.

RCEMIP, an intercomparison of multiple types of models configured in radiative–convective equilibrium (RCE), is proposed. RCE is an idealization of the climate system in which there is a balance between radiative cooling of the atmosphere and heating by convection. The scientific objectives of RCEMIP are three-fold. First, clouds and climate sensitivity will be investigated in the RCE setting. This includes determining how cloud fraction changes with warming and the role of self-aggregation of convection in climate sensitivity. Second, RCEMIP will quantify the dependence of the degree of convective aggregation and tropical circulation regimes on temperature. Finally, by providing a common baseline, RCEMIP will allow the robustness of the RCE state across the spectrum of models to be assessed, which is essential for interpreting the results found regarding clouds, climate sensitivity, and aggregation, and more generally, determining which features of tropical climate a RCE framework is useful for. A novel aspect and major advantage of RCEMIP is the accessibility of the RCE framework to a variety of models, including cloud-resolving models, general circulation models, global cloud-resolving models, single-column models, and large-eddy simulation models.

Tropical cloud and circulation changes are large sources of uncertainty in future climate change. This problem owes partly to the scale separation between large-scale tropical dynamics (~104km) and convective dynamics (~7 km), which generally requires parameterizing convection in models that resolve large-scale dynamics, or parameterizing (or omitting) large-scale dynamics in models that permit convection. Here we discuss simulations of radiative-convective equilibrium (RCE) across a wide range of surface temperatures in long-channel geometry—where the domain size and resolution marginally resolve both large-scale dynamics and convection. Self-aggregation of convection in these simulations spontaneously produces realistic dynamical regimes of large-scale vertical motion. The circulation weakens with surface warming but changes in the degree of self-aggregation depend on the metric that is used; there is no obvious trend in aggregation with warming. Surface warming causes an upward shift and decrease in area of high clouds, and a sharp decline in midlevel clouds, but no systematic trend in low cloud cover. We introduce a method for approximate radiative kernel feedback analysis in RCE, and apply it to both simulations in long-channel geometry and in a smaller square domain. The kernel-corrected cloud feedback is positive but its magnitude varies across temperatures. Compared to simulations that do not have aggregation, there is a more negative net feedback due to the effects of aggregation on relative humidity and cloud cover. These results are consistent with the hypothesis that self-aggregation moderately reduces climate sensitivity., National Science Foundation (U.S.) (Grant AGS-1623218), National Science Foundation (U.S.) (Grant AGS-1433251)

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.

International audience; 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.

[1] We elucidate the physics of self-aggregation by applying a new diagnostic technique to the output of a cloud resolving model. Specifically, the System for Atmospheric Modeling is used to perform 3- D cloud system resolving simulations of radiative-convective equilibrium in a nonrotating framework, with interactive radiation and surface fluxes and fixed sea surface temperature (SST). We note that self-aggregation begins as a dry patch that expands, eventually forcing all the convection into a single clump. Thus, when examining the initiation of self-aggregation, we focus on processes that can amplify this initial dry patch. We introduce a novel method to quantify the magnitudes of the various feedbacks that control self-aggregation within the framework of the budget for the spatial variance of column-integrated frozen moist static energy. The absorption of shortwave radiation by atmospheric water vapor is found to be a key positive feedback in the evolution of aggregation. In addition, we find a positive wind speed-surface flux feedback whose role is to counteract a negative feedback due to the effect of air-sea enthalpy disequilibrium on surface fluxes. The longwave radiation feedback can be either positive or negative in the early and intermediate stages of aggregation; however, it is the dominant positive feedback that maintains the aggregated state once it develops. Importantly, the mechanisms that maintain the aggregate state are distinct from those that instigate the evolution of self-aggregation.