Comparing Convective Self‐Aggregation in Idealized Models to Observed Moist Static Energy Variability Near the Equator

Beucler, Tom; Abbott, Tristan H.; Cronin, Timothy W.; Pritchard, Michael S.

doi:10.1029/2019gl084130

Cited by 10 publications

(15 citation statements)

References 47 publications

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“…This effect, if it exists, may be related to the moisture-memory feedback as often discussed in attempt to interpret convective self-aggregation in numerical simulations (Held et al 1993;Tompkins 2001;Muller and Bony 2015). The importance of the advection term in the CMSE budget is in line with Bretherton and Khairoutdinov (2015), who showed that the advection accounts for the rapid (,4 days) growth of MSE at the earliest stage of aggregation, although at odds with Beucler et al (2019), who found that the advection damps the MSE variance at all spatial scales.…”

Section: Discussion and Summarysupporting

confidence: 59%

“…The lack of evidence for any distinct effect of Q R on the aggregation may be understood in the context of the scale dependence of the aggregation processes. Beucler and Cronin (2019) and Beucler et al (2019) showed that the longwave effects favor a growth of large-scale aggregation with horizontal wavelengths of ;1000 km or larger, which exceeds the typical size of cloud clusters studied in the current analysis. Shortwave radiation, on the other hand, has the effect of shrinking the aggregation to smaller scales (500-2000 km) (Beucler and Cronin 2019).…”

Section: Discussion and Summarycontrasting

confidence: 45%

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Transient Aggregation of Convection: Observed Behavior and Underlying Processes

et al. 2021

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Convective self-aggregation is among the most striking features emerging from radiative-convective equilibrium simulations, but its relevance to convective disturbances observed in the real atmosphere remains under debate. This work seeks the observational signals of convective aggregation intrinsic to the life cycle of cloud clusters. To this end, composite time series of the Simple Convective Aggregation Index (SCAI), a metric of aggregation, and other variables from satellite measurements are constructed around the temporal maxima of precipitation. All the parameters analyzed are large-scale means over 10°×10° domains. The composite evolution for heavy precipitation regimes shows that cloud clusters are gathered into fewer members during a period of ±12 h as precipitation picks up. The high-cloud cover per cluster expands as the number of clusters drops, suggesting a transient occurrence of convective aggregation. The sign of the transient aggregation is less evident or entirely absent in light precipitation regimes. An energy budget analysis is performed in search of the physical processes underlying the transient aggregation. The column moist static energy (MSE) accumulates before the precipitation peak and dissipates after, accounted for primarily by the horizontal MSE advection. The domain-averaged column radiative cooling is greater in a more aggregated composite than in a less aggregated one, although the role of radiative-convective feedback behind this remains unclear.

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Section: Discussion and Summarysupporting

confidence: 59%

Section: Discussion and Summarycontrasting

confidence: 45%

Transient Aggregation of Convection: Observed Behavior and Underlying Processes

et al. 2021

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“…It is important to note that Equations and are approximations, as in reality the tendencies of CWV may depend on more than just CWV itself. By assuming that the evolution of CWV is given by its mean value in CWV space, we successfully capture the time evolution of the spatial‐mean CWV field but reduce the spatial variance of the CWV time tendencies by a factor ∼100 (as estimated using the Beucler et al., 2019, framework, not shown). Finally, note that it is possible to choose column moist static energy (MSE) instead of CWV as the order variable in Equation : In that case, Equation can be applied to the MSE tendencies of individual physical processes, which helps link our framework to the MSE budget framework extensively used by the self‐aggregation community (see Appendix for more details).…”

Section: Theorymentioning

confidence: 99%

“…advection, convection, radiation, and microphysics on water vapor. This led to an Eulerian view of this bimodality, in which the formation of large-scale moist and dry regions happens within a week to a month at the ∼1,000to 10,000-km scale through the interaction between CWV and radiation in the slowly rotating tropics (e.g., Beucler et al, 2019;Holloway & Woolnough, 2016;C. Muller & Bony, 2015).…”

Section: Introductionmentioning

confidence: 99%

Quantifying Convective Aggregation Using the Tropical Moist Margin's Length

Beucler

Leutwyler

Windmiller

2020

J Adv Model Earth Syst

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On small scales, the tropical atmosphere tends to be either moist or very dry. This defines two states that, on large scales, are separated by a sharp margin, well identified by the antimode of the bimodal tropical column water vapor distribution. Despite recent progress in understanding physical processes governing the spatiotemporal variability of tropical water vapor, the behavior of this margin remains elusive, and we lack a simple framework to understand the bimodality of tropical water vapor in observations. Motivated by the success of coarsening theory in explaining bimodal distributions, we leverage its methodology to relate the moisture field's spatial organization to its time evolution. This results in a new diagnostic framework for the bimodality of tropical water vapor, from which we argue that the length of the margin separating moist from dry regions should evolve toward a minimum in equilibrium. As the spatial organization of moisture is closely related to the organization of tropical convection, we hereby introduce a new convective organization index (BLW) measuring the ratio of the margin's length to the circumference of a well-defined equilibrium shape. Using BLW, we assess the evolution of self-aggregation in idealized cloud-resolving simulations of radiative-convective equilibrium and contrast it to the time evolution of the Atlantic Intertropical Convergence Zone (ITCZ) in the ERA5 meteorological reanalysis product. We find that BLW successfully captures aspects of convective organization ignored by more traditional metrics, while offering a new perspective on the seasonal cycle of convective organization in the Atlantic ITCZ.

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“…External forcing such as spatially varying SSTs, large-scale low-level convergence of trade winds (Back & Bretherton, 2009;Bretherton & Sobel, 2002;Lindzen & Nigam, 1987), and wind shear (Rotunno et al, 1988), which are not considered in the RCE framework, are known to promote the organization of convection in the tropics and may mask the effects of self-aggregation. Consequently, observational evidence of self-aggregation of convection or of its expected effects on climate has been sought (Beucler et al, 2019;Holloway et al, 2017;Tobin et al, 2012), but the importance of self-aggregation of convection in the presence of external forcing remains unclear.…”

Section: Introductionmentioning

confidence: 99%

Self‐Aggregation of Convection in Spatially Varying Sea Surface Temperatures

Müller

Hohenegger

2020

J Adv Model Earth Syst

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The phenomenon of self-aggregation of convection was first identified in convection-permitting simulations of radiative convective equilibrium, characterized by homogeneous boundary conditions and in the absence of planetary rotation. In this study, we expose self-aggregation of convection to more complex, nonhomogeneous boundary conditions and investigate its interaction with convective aggregation, as forced by large-scale variations in sea surface temperatures (SSTs). We do this by conducting radiative convective equilibrium simulations on a spherical domain, with SST patterns that are zonally homogeneous but meridionally varying. Due to the meridional contrast in SST, a convergence line first forms, mimicking the Intertropical Convergence Zone. We nevertheless find that the convergence line breaks up and contracts zonally as a result of the self-aggregation of convection. The contraction is significant, being here more than 50% of the original extent. The stability of the convergence line is controlled by the strength of the meridional circulation, which depends upon the imposed SST contrast. However, the process of self-aggregation, once it is initiated, is insensitive to the strength of the SST contrast. The zonal contraction is accompanied by a slight meridional expansion and a moistening of the high latitudes, where SSTs are low. The moistening of the high latitudes can be understood from the fact that the convective cluster intensifies and expands its moist meridional low-level outflow when it self-aggregates zonally. Overall, our results suggest that the Intertropical Convergence Zone may be unstable to the self-aggregation of convection, that self-aggregation may serve as a precursor to the formation of atmospheric rivers, and that longer convergence lines are more likely to exist in regimes with strong SST gradients.Plain Language Summary Storm clouds over the tropical oceans are found to organize in large systems. They tend to form where the temperatures of the sea surface (SSTs) are highest and that is most often along the equator. With idealized computer model experiments we investigate how different sea surface temperature patterns, along with the natural tendency of clouds to aggregate, control the properties of the storm cloud system. We find that a contrast in SSTs immediately acts to organize the storm clouds. In our simulations, this means the formation of a long line of clouds, oriented along the equator. With time however, this line breaks up and then contracts just by itself. Here we find that the spatial difference in SSTs controls the stability of the cloud system against the break up and contraction: The stronger the difference in SST, the more stable is the line of clouds. Also we find that the storm clouds export moisture only in one direction: from the equator to the higher latitudes.

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Comparing Convective Self‐Aggregation in Idealized Models to Observed Moist Static Energy Variability Near the Equator

Cited by 10 publications

References 47 publications

Transient Aggregation of Convection: Observed Behavior and Underlying Processes

Transient Aggregation of Convection: Observed Behavior and Underlying Processes

Quantifying Convective Aggregation Using the Tropical Moist Margin's Length

Self‐Aggregation of Convection in Spatially Varying Sea Surface Temperatures

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