Large‐Eddy Simulation of Maritime Deep Tropical Convection

By M. Khairoutdinov, S. Krueger, C. Moeng, P. Bogenschutz, David A. Randall. 2009

167 citations recorded by the source corpus.

This study represents an attempt to apply Large‐Eddy Simulation (LES) resolution to simulate deep tropical convection in near equilibrium for 24 hours over an area of about 205 × 205 km2, which is comparable to that of a typical horizontal grid cell in a global climate model. The simulation is driven by large‐scale thermodynamic tendencies derived from mean conditions during the GATE Phase III field experiment. The LES uses 2048 × 2048 × 256 grid points with horizontal grid spacing of 100 m and vertical grid spacing ranging from 50 m in the boundary layer to 100 m in the free troposphere. The simulation reaches a near equilibrium deep convection regime in 12 hours. The simulated vertical cloud distribution exhibits a tri‐modal vertical distribution of deep, middle and shallow clouds similar to that often observed in Tropics. A sensitivity experiment in which cold pools are suppressed by switching off the evaporation of precipitation results in much lower amounts of shallow and congestus clouds. Unlike the benchmark LES where the new deep clouds tend to appear along the edges of spreading cold pools, the deep clouds in the no‐cold‐pool experiment tend to reappear at the sites of the previous deep clouds and tend to be surrounded by extensive areas of sporadic shallow clouds. The vertical velocity statistics of updraft and downdraft cores below 6 km height are compared to aircraft observations made during GATE. The comparison shows generally good agreement, and strongly suggests that the LES simulation can be used as a benchmark to represent the dynamics of tropical deep convection on scales ranging from large turbulent eddies to mesoscale convective systems. The effect of horizontal grid resolution is examined by running the same case with progressively larger grid sizes of 200, 400, 800, and 1600 m. These runs show a reasonable agreement with the benchmark LES in statistics such as convective available potential energy, convective inhibition, cloud fraction, precipitation rates, and surface latent and sensible fluxes. All runs reveal a tri‐model cloud distribution in the vertical. However, there are differences in the updraft‐core cloud statistics, and convergence of statistical properties is found only between the LES benchmark and the run with 200 m grid size. The effect of vertical grid resolution is also investigated with another run that uses a typical cloud‐resolving model (CRM) horizontal grid size on the order of 1 km and only 64 vertical levels. A comparison to the run with 256 vertical levels shows different vertical cloud distributions. It is concluded that representation of the often observed tri‐modal vertical distribution of clouds requires a vertical grid spacing in the range of 50‐100 m in mid‐to‐low troposphere.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/5b586b99a9e8faaa73f5f075081549a5396892a1

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Citations in the corpus

A review on regional convection‐permitting climate modeling: Demonstrations, prospects, and challenges (2015, background): “In the limit of extremely fine horizontal grid spacings, CPMs are thought to converge to large-eddy simulations [Bryan et al., 2003; Cullen and Brown, 2009; Khairoutdinov et al., 2009].”
Large‐eddy simulations over Germany using ICON: a comprehensive evaluation (2017, background): “Thanks to increasing computational resources, increasingly larger domain cloud-resolving simulations are now possible (e.g. Hohenegger et al., 2008; Khairoutdinov et al., 2009; Love et al., 2011; Schlemmer and Hohenegger, 2014; Schalkwijk et al., 2015).”
Mechanisms of convective cloud organization by cold pools over tropical warm ocean during the AMIE/DYNAMO field campaign (2015, methodology): “Previous studies using CRM or LES models were conducted over relatively small horizontal regions with domain sizes ranging from 60 to 200 km, [e.g., B€ oing et al., 2012; Khairoutdinov et al., 2009; Khairoutdinov and Randall, 2006; Tompkins, 2001], and therefore their background value can be defined”
Evaluation of cloud‐resolving and limited area model intercomparison simulations using TWP‐ICE observations: 1. Deep convective updraft properties (2014, background): “[2003] and Craig and Dörnbrack [2008] conclude that a 1-km horizontal mesh in simulations is insufficient to resolve buoyancy in convective clouds, while several other studies [Petch and Gray, 2001; Adlerman and Droegemeier, 2002; Petch et al., 2002; Khairoutdinov et al., 2009; Dawson et al., 2010; ”
Large-Eddy Simulation of the Atmospheric Boundary Layer (2020, background): “…an early example comes from the cloud modeling community where very large domain simulations have enabled the study of deep tropical convection and its impact on cloud formation, a critical component toward improving the representation of clouds in global climate models (Khairoutdinov et al. 2009).”
Could Machine Learning Break the Convection Parameterization Deadlock? (2018, background): “For instance, the transition between shallow and deep convection can be correctly captured at convective permitting scale [ Khairoutdinov and Randall , 2006; Khairoutdinov et al. , 2009].”
The Non-hydrostatic Icosahedral Atmospheric Model: description and development (2014, background): “For a global non-hydrostatic model, boundary-layer scale turbulence becomes explicitly and partially calculatable around this resolution, but will not be sufficiently resolved until the mesh size is less than 100 m (Khairoutdinov et al. 2009; Moeng et al. 2009).”
What favors convective aggregation and why? (2015, background): “This is consistent with larger cloud condensate amounts on larger domains and at coarser resolutions [Khairoutdinov et al., 2009], leading to larger spatial variability of radiative cooling rates between regions with clouds and regions without.”
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations (2017, background): “For example, the turbulent dynamics (though currently not the microphysics) of clouds can be computed with high fidelity in limited domains in largeeddy simulations (LES) with grid spacings of O(10 m) [Siebesma et al., 2003; Stevens et al., 2005; Khairoutdinov et al., 2009; Matheou and Chung, 2014; ”
Entrainment and detrainment in cumulus convection: an overview (2013, methodology): “…to be an extremely useful tool in determining entrainment and detrainment rates in cumulus clouds, initially for shallow cumulus (e.g. Siebesma and Cuijpers, 1995; Siebesma et al., 2003), but more recently also for deep convection (e.g. Kuang and Bretherton, 2006; Khairoutdinov et al., 2009).”
Detailed Investigation of the Self-Aggregation of Convection in Cloud-Resolving Simulations (2012, background): “This is consistent with Khairoutdinov et al. (2009), who also find an increase in low-cloud fractional area and low-cloud water with coarser horizontal resolutions.”
Organization of tropical convection in low vertical wind shears: Role of updraft entrainment (2017, background): “Interestingly, there is a distinct moist mode in the smag3dpbl integration in the 100 hPa layer below the freezing level; a possible indication of the tri-modal detrainment level [Johnson et al., 1999; Tompkins and Emanuel , 2000; Khairoutdinov et al., 2009].”