Bulletin of the American Physical Society
70th Annual Meeting of the APS Division of Fluid Dynamics
Volume 62, Number 14
Sunday–Tuesday, November 19–21, 2017; Denver, Colorado
Session Q15: Minisymposium: Fluid Dynamics of Atmospheric CloudsGeophysical Mini-Symposium
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Chair: Raymond Shaw, Michigan Tecj Room: 601 |
Tuesday, November 21, 2017 12:50PM - 1:16PM |
Q15.00001: The fluid dynamics of atmospheric clouds David A. Randall Clouds of many types are of leading-order importance for Earth’s weather and climate. This importance is most often discussed in terms of the effects of clouds on radiative transfer, but the fluid dynamics of clouds are at least equally significant. Some very small-scale cloud fluid-dynamical processes have significant consequences on the global scale. These include viscous dissipation near falling rain drops, and ``buoyancy reversal” associated with the evaporation of liquid water. Major medium-scale cloud fluid-dynamical processes include cumulus convection and convective aggregation. Planetary-scale processes that depend in an essential way on cloud fluid dynamics include the Madden-Julian Oscillation, which is one of the largest and most consequential weather systems on Earth. I will attempt to give a coherent introductory overview of this broad range of phenomena. [Preview Abstract] |
Tuesday, November 21, 2017 1:16PM - 1:42PM |
Q15.00002: Cloud regimes as phase transitions Samuel Stechmann, Scott Hottovy Clouds are repeatedly identified as a leading source of uncertainty in future climate predictions. Of particular importance are stratocumulus clouds, which can appear as either (i) closed cells that reflect solar radiation back to space or (ii) open cells that allow solar radiation to reach the Earth's surface. Here we show that these clouds regimes -- open versus closed cells -- fit the paradigm of a phase transition. In addition, this paradigm characterizes pockets of open cells (POCs) as the interface between the open- and closed-cell regimes, and it identifies shallow cumulus clouds as a regime of higher variability. This behavior can be understood using an idealized model for the dynamics of atmospheric water as a stochastic diffusion process. Similar viewpoints of deep convection and self-organized criticality will also be discussed. With these new conceptual viewpoints, ideas from statistical mechanics could potentially be used for understanding uncertainties related to clouds in the climate system and climate predictions. [Preview Abstract] |
Tuesday, November 21, 2017 1:42PM - 2:08PM |
Q15.00003: Statistical steady states in turbulent droplet condensation Jeremie Bec, Giorgio Krstulovic, Christoph Siewert We investigate the general problem of turbulent condensation. Using direct numerical simulations we show that the fluctuations of the supersaturation field offer different conditions for the growth of droplets which evolve in time due to turbulent transport and mixing. This leads to propose a Lagrangian stochastic model consisting of a set of integro-differential equations for the joint evolution of the squared radius and the supersaturation along droplet trajectories. The model has two parameters fixed by the total amount of water and the thermodynamic properties, as well as the Lagrangian integral timescale of the turbulent supersaturation. The model reproduces very well the droplet size distributions obtained from direct numerical simulations and their time evolution. A noticeable result is that, after a stage where the squared radius simply diffuses, the system converges exponentially fast to a statistical steady state independent of the initial conditions. The main mechanism involved in this convergence is a loss of memory induced by a significant number of droplets undergoing a complete evaporation before growing again. The statistical steady state is characterised by an exponential tail in the droplet mass distribution. [Preview Abstract] |
Tuesday, November 21, 2017 2:08PM - 2:34PM |
Q15.00004: Direct Lagrangian tracking simulations of particles in vertically-developing atmospheric clouds Ryo Onishi, Yuichi Kunishima We have been developing the Lagrangian Cloud Simulator (LCS), which follows the so-called Euler-Lagrangian framework, where flow motion and scalar transportations (i.e., temperature and humidity) are computed with the Euler method and particle motion with the Lagrangian method. The LCS simulation considers the hydrodynamic interaction between approaching particles for robust collision detection. This leads to reliable simulations of collision growth of cloud droplets. Recently the activation process, in which aerosol particles become tiny liquid droplets, has been implemented in the LCS. The present LCS can therefore consider the whole warm-rain precipitation processes -activation, condensation, collision and drop precipitation. In this talk, after briefly introducing the LCS, we will show kinematic simulations using the LCS for quasi-one dimensional domain, i.e., vertically elongated 3D domain. They are compared with one-dimensional kinematic simulations using a spectral-bin cloud microphysics scheme, which is based on the Euler method. The comparisons show fairly good agreement with small discrepancies, the source of which will be presented. The Lagrangian statistics, obtained for the first time for the vertical domain, will be the center of discussion. [Preview Abstract] |
Tuesday, November 21, 2017 2:34PM - 3:00PM |
Q15.00005: Particles and snowflakes falling through turbulence Filippo Coletti The question of how turbulence affects the settling of small heavy particles has proven surprisingly hard to answer. Previous investigations identified several mechanisms by which turbulence may alter the particle settling rate as compared to the still-fluid terminal velocity, but different studies often show large discrepancies. This is especially problematic in meteorology, where an accurate knowledge of the precipitation fall speed is a prerequisite for reliable weather forecasting. I will present laboratory and field measurements that demonstrate how turbulence may lead to a multifold increase of the particle fall speed. In the laboratory, a large chamber is used where hundreds of randomly actuated jets create a large volume of homogeneous air turbulence, and in which flow tracers and inertial particles are simultaneously tracked by laser imaging. Clusters of particles are identified and described in their self-similar nature. In the field, snowflakes settling in the atmospheric surface layer are imaged and tracked during nighttime snowfalls, using stage lights and high speed cameras. The Lagrangian acceleration statistics show that small and compact snowflakes (graupel) follow the classic phenomenology of inertial particles in homogeneous turbulence. [Preview Abstract] |
Tuesday, November 21, 2017 3:00PM - 3:26PM |
Q15.00006: On the relevance of droplet sedimentation in stratocumulus-top mixing Juan Pedro Mellado, Alberto de Lozar The interaction between droplet sedimentation, turbulent mixing, evaporative cooling, and radiative cooling at the top of stratocumulus clouds has been studied using direct numerical simulations. This interaction is important to determine the mixing rate of the cloud and dry air above it, which eventually determines the cloud lifetime. By investigating the entrainment-rate equation, which is an analytical relationship between the contributions to cloud-top entrainment from the phenomena indicated above, we have found that the reduction of entrainment velocity by droplet sedimentation can be 2 to 3 times larger than previously conjectured. The reason is twofold. First, the reduction of evaporative cooling as droplets fall out of the inversion is stronger than previously observed in large-eddy simulations, where excessive mixing by turbulence models and numerical artifacts may have partially masked this effect of sedimentation on entrainment. Second, there is a non-negligible direct contribution from mass loading, as falling droplets leave behind more buoyant air in the inversion. This contribution is proportional to the fifth moment of the droplet-size distribution, which provides further evidence for the need to better understand the evolution of the droplet-size distribution. [Preview Abstract] |
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