Characterization of cumulus cloud fields using trajectories in the center of gravity versus water mass phase space: 1. Cloud tracking and phase space description

Heiblum, Reuven H.; Altaratz, Orit; Koren, Ilan; Feingold, Graham; Kostinski, A. B.; Хаин, А.; Ovchinnikov, Mikhail; Fredj, Erick; Dagan, Guy; Pinto, Lital; Yaish, Ricki; Chen, Qian

doi:10.1002/2015jd024186

Cited by 27 publications

(35 citation statements)

References 61 publications

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“…This case was initialized using the setup specified in Siebesma et al (2003). The setup includes surface fluxes and large-scale forcing (see details in Heiblum et al, 2016b). The horizontal resolution was set to 100 m, while the vertical resolution was set to 40 m. The domain size was 12.8 × 12.8 × 4.0 km 3 and the time step was 1 s. Due to computational limitations, we had to restrict the domain size to a scale that has a limited capability for capturing large-scale organization (Seifert and Heus, 2013).…”

Section: Methodsmentioning

confidence: 99%

Time-dependent, non-monotonic response of warm convective cloud fields to changes in aerosol loading

et al. 2017

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Abstract. Large eddy simulations (LESs) with bin microphysics are used here to study cloud fields' sensitivity to changes in aerosol loading and the time evolution of this response. Similarly to the known response of a single cloud, we show that the mean field properties change in a nonmonotonic trend, with an optimum aerosol concentration for which the field reaches its maximal water mass or rain yield. This trend is a result of competition between processes that encourage cloud development versus those that suppress it. However, another layer of complexity is added when considering clouds' impact on the field's thermodynamic properties and how this is dependent on aerosol loading. Under polluted conditions, rain is suppressed and the non-precipitating clouds act to increase atmospheric instability. This results in warming of the lower part of the cloudy layer (in which there is net condensation) and cooling of the upper part (net evaporation). Evaporation at the upper part of the cloudy layer in the polluted simulations raises humidity at these levels and thus amplifies the development of the next generation of clouds (preconditioning effect). On the other hand, under clean conditions, the precipitating clouds drive net warming of the cloudy layer and net cooling of the sub-cloud layer due to rain evaporation. These two effects act to stabilize the atmospheric boundary layer with time (consumption of the instability). The evolution of the field's thermodynamic properties affects the cloud properties in return, as shown by the migration of the optimal aerosol concentration toward higher values.

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Section: Methodsmentioning

confidence: 99%

Time-dependent, non-monotonic response of warm convective cloud fields to changes in aerosol loading

et al. 2017

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“…2. Under clean conditions, cloud amount increases in response to increasing aerosol; this includes deepening of clouds and an increase in cloud fraction [3,27,30,54,68,127,160]. The aerosol helps stabilize a system likely to precipitate (a colloidally unstable system).…”

Section: Translating Insights From Process-scale Studies Into Constramentioning

confidence: 99%

The Radiative Forcing of Aerosol–Cloud Interactions in Liquid Clouds: Wrestling and Embracing Uncertainty

Mülmenstädt

Feingold

2018

Curr Clim Change Rep

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This article discusses some of the challenges that have limited progress in quantifying the radiative forcing associated with aerosol-cloud interactions (ACI) in warm (liquid-water) clouds. It reviews recent progress and suggests new ways of viewing the problem that might accelerate progress. It calls for much greater attention to the scale problem, both in terms of aerosol-cloud process representation in models and comparison with observations. It suggests careful consideration of the balance in detail with which processes are represented, and proposes a merging of detailed process understanding and system-wide behavior. In this spirit, it advocates tackling the problem with models of varying complexity that consider both the depth and breadth of the complex dynamical system. Finally, it considers shifting attention from untangling aerosol and meteorological effects on cloud systems towards understanding the co-variability of key aerosol and meteorological drivers of cloud systems.

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“…Mergers are handled in a similar, but logically reversed, manner with one cloud whose identity "ends" at T N+1 receiving a ParentID. Coincidentally, Heiblum et al [14] recently used similar nomenclature and techniques for handling splits and mergers.…”

Section: Ramstracksmentioning

confidence: 99%

“…Thus, neither can capture the lifetime of all possible target clouds. To remedy this problem, clouds can be tracked in a cloud resolving model [11][12][13][14][15]. The problem with this method is that the computational expense of executing a model over which a reliable tracking code can be run offline is very high (see Methods) and the complexity and finality of running one online is daunting.…”

Section: Introductionmentioning

confidence: 99%

Lagrangian Cloud Tracking and the Precipitation-Column Humidity Relationship

Igel

2018

Atmosphere

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Abstract:The tropical, oceanic mean relationship between column relative humidity and precipitation is highly non-linear. Mean precipitation remains weak until it rapidly picks up and grows at high column humidity. To investigate the origin of this relationship, a Lagrangian cloud tracking code, RAMStracks, is developed, which can follow the evolution of clouds. RAMStracks can record the morphological properties of convective clouds, the meteorological environment of clouds, and their effects. RAMStracks is applied to a large-domain radiative convective equilibrium simulation, which produces a complex population of convective clouds. RAMStracks records the lifecycle of 501 clouds through growth, splits, mergers, and decay. The mean evolution of all these clouds is examined. It is shown that the column humidity evolves non-monotonically, but that lower-level and upper-level contributions to total moisture do evolve monotonically. The precipitation efficiency of tropical storms tends to increase with cloud age. This is confirmed using a prototype testing method. The same method reveals that different tracked clouds with similar initial conditions evolve in very different ways. This makes drawing general conclusions from individual storms difficult. Finally, the causality of the precipitation-column humidity relationship is examined. A Granger Causality test, as well as regressions, suggest that moisture and precipitation are causally linked, but that the direction of causality is ambiguous. Much of this link appears to come from the lower-level moisture's contribution to column humidity.

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Characterization of cumulus cloud fields using trajectories in the center of gravity versus water mass phase space: 1. Cloud tracking and phase space description

Cited by 27 publications

References 61 publications

Time-dependent, non-monotonic response of warm convective cloud fields to changes in aerosol loading

Time-dependent, non-monotonic response of warm convective cloud fields to changes in aerosol loading

The Radiative Forcing of Aerosol–Cloud Interactions in Liquid Clouds: Wrestling and Embracing Uncertainty

Lagrangian Cloud Tracking and the Precipitation-Column Humidity Relationship

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