Circling in on Convective Organization

Haerter, Jan O.; Böing, Steven J.; Henneberg, Olga; Nissen, Silas Boye

doi:10.1029/2019gl082092

Cited by 37 publications

(68 citation statements)

References 62 publications

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“…4b), a finding in line with collision effects of multiple cold pool gust fronts. 13,28 These interiour cells could further act to deepen and cool the combined cold pool driving the MCS expansion. Together, MCSs hence act to excite new convection both within and around the combined cold pool area.…”

Section: Discussionmentioning

confidence: 99%

“…20 Mechanistically, cold pools (CPs), that is, denser air formed by rain evaporation under thunderstorm clouds, were long implicated in the organisation of convection, 21,22 both by thermodynamic and mechanical effects, [23][24][25][26][27] and were suggested to lead to clustering. 28,29 CPs can be long-lived when they arise in MCSs. Over ocean, Chen and Houze (1997) observed MCSs to undergo bidiurnal oscillations of local cloudiness and referred to this dynamics as "diurnal dancing."…”

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confidence: 99%

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Diurnal Self-Aggregation

Haerter¹,

Meyer²,

Nissen³

2020

Preprint

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Convective self-aggregation is a modelling paradigm for thunderstorm organisation over a constant-temperature tropical sea surface. This setup can give rise to cloud clusters over timescales of weeks. In reality, sea surface temperatures do oscillate diurnally, affecting the atmospheric state. Over land, surface temperatures vary more strongly, and rain rate is significantly influenced. Here, we carry out a substantial suite of cloud-resolving numerical experiments, and find that even weak surface temperature oscillations enable qualitatively different dynamics to emerge: the spatial distribution of rainfall is only homogeneous during the first day. Already on the second day, the rain field is firmly structured. In later days, the clustering becomes stronger and alternates from day-to-day. We show that these features are robust to changes in resolution, domain size, and surface temperature, but can be removed by a reduction of the amplitude of oscillation, suggesting a transition to a clustered state. Maximal clustering occurs at a scale of l max ≈ 180 km, a scale we relate to the emergence of mesoscale convective systems. At l max rainfall is strongly enhanced and far exceeds the rainfall expected at random. We explain the transition to clustering using simple conceptual modelling. Our results may help clarify how continental extremes build up and how cloud clustering over the tropical ocean could emerge much faster than through conventional self-aggregation alone. Currently, general circulation models cannot simulate organised deep convection as these models describe convection as a collection of non-interacting convective cells. Yet, the increase in tropical rainfall was stated to predominantly stem from organised deep convection. 1 Similarly, in midlatitudes, the majority of flood-producing rainfall was attributed to mesoscale convective systems (MCSs), 2,3 that is, long-lived complexes of thunderstorms spanning ∼ 100 km in diameter. 4 In self-aggregation studies pronounced clustering occurs on the timescale of several weeks. 5-7 There, the radiative convective equilibrium scheme (RCE) 8,9 is usually employed, assuming spatially and temporally constant surface temperature (∼ 300 K). In self-aggregation, radiation feedbacks have emerged as the "smoking gun" for sustaining and increasing clustering. 5 Still, factors such as sea surface feedbacks, 10 domain size, geometry, and resolution, 11 as well as cold pool effects, 12,13 all contribute.Prescribing constant boundary conditions is an elegant model simplification, but not always realistic. Especially under weak surface wind conditions and strong insolation, sea surface temperature amplitudes were observed to be as large as two 14,15 to five kelvin 16 and suggested to modify atmospheric properties. 16 Such temperature variations may even affect the Madden-Julian Oscillation arXiv:2001.04740v1 [physics.ao-ph]

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Diurnal Self-Aggregation

Haerter¹,

Meyer²,

Nissen³

2020

Preprint

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“…Different slice sizes were tested regarding the identification of a single cold pool, and the one yielding edges encircling the maximum radial cold pool velocity was chosen. The relatively small values of ϕ indicates considerable heterogeneity of the dynamical cold pool edge contour, which we mainly attribute to deformations from mostly circular shapes during the initial phase of spreading, to more Voronoi graph-type structures upon collisions between cold pools Haerter et al (2019).…”

Section: Identification Of Cold Pool Edges Based On the Minimum In ∂Vmentioning

confidence: 94%

“…The relatively small values of ϕ indicates considerable heterogeneity of the dynamical cold pool edge contour, which we mainly attribute to deformations from mostly circular shapes during the initial phase of spreading, to more Voronoi graph‐type structures upon collisions between cold pools Haerter et al. ().…”

Section: Cold Pool Tracking Algorithmmentioning

confidence: 99%

Tracking the Gust Fronts of Convective Cold Pools

Fournier

Haerter

2019

JGR Atmospheres

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It is increasingly acknowledged that cold pools can influence the initiation of new convective cells. Yet the full complexity of convective organization through cold pool interaction is poorly understood. This lack of understanding may partially be due to the intricacy of the dynamical pattern formed by precipitation cells and their cold pools. Additionally, how exactly cold pools interact is insufficiently known. To better understand this dynamics, we develop a tracking algorithm for cold pool gust fronts. Rather than tracking thermodynamic anomalies, which do not generally coincide with the gust front boundaries, our approach tracks the dynamical cold pool outflow. Our algorithm first determines the locus of the precipitation event. Second, relative to this origin and for each azimuthal bin, the steepest gradient in the near‐surface horizontal radial velocity vr is employed to determine the respective locus of the cold pool gust front edge. Steepest vr gradients imply largest updraft velocities, hence strongest dynamical triggering. Results are compared to a previous algorithm based on the steepest gradient in temperature—highlighting the benefit of the method described here in determining dynamically active gust front regions. Applying the method to a range of numerical experiments, the algorithm successfully tracks an ensemble of cold pools. A linear relation emerges between the peak rain intensity of a given event and maximal vr for its associated cold pool gust front—a relation found to be nearly independent of the specific sensitivity experiment.

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“…A higher degree of deep convective organization is often associated with the term "cluster" (e.g., Leary & Houze, 1979;Hohenegger & Stevens, 2016;Weger et al, 1992), indicating that several convective objects exist in close proximity or form coherent structures. Clustering of convection can occur through processes internal to the convection, such as cold pools (e.g., Haerter et al, 2019) and radiative feedbacks (e.g., Coppin & Bony, 2015), or they can be external, such as land-sea-breezes (e.g., Dauhut et al, 2016) or wind shear (e.g., Rotunno et al, 1988). Many of the clustering mechanisms result in spatially extensive convective systems.…”

Section: Introductionmentioning

confidence: 99%

Assessing Convective Organization in Tropical Radar Observations

2020

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The spatial organization of deep moist convection is known to be an important determinant of the impacts of severe weather, while future changes to convective organization have been linked to various radiative feedbacks under climate warming. Yet there is no unanimously agreed upon definition of convective organization and so there is also no obvious way to objectively define it. In this work, we set out to define a metric for convective organization based on the size and proximity of convectively active regions. The metric is developed based upon tropical radar observations and takes two‐dimensional convective objects, which are predefined in a horizontal plane, as input. We call the metric Radar Organization Metric (ROME). In addition to the proximity of different convective objects, which is used in other organization metrics, ROME is also sensitive to object size. As a result, ROME is also defined for the case of only one convective object. Thus, ROME provides a smoothly evolving measure of the degree of convective organization, which compares well to a visual assessment of the convective objects. ROME is found to be sensitive to different regimes of the North Australian monsoon, and its average diurnal cycle is coherent with the daily evolution of tropical rainfall. Through its dependence on area, ROME adds new capabilities that other metrics lack in measuring the degree of convective organization. In particular, ROME permits quantification of the individual contributions of the object size distribution and the spatial clustering of objects to the overall degree of convective organization.

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Circling in on Convective Organization

Cited by 37 publications

References 62 publications

Diurnal Self-Aggregation

Diurnal Self-Aggregation

Tracking the Gust Fronts of Convective Cold Pools

Assessing Convective Organization in Tropical Radar Observations

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