Cloud Resolving Modeling of the ARM Summer 1997 IOP: Model Formulation, Results, Uncertainties, and Sensitivities

Abstract

A new three-dimensional cloud resolving model (CRM) has been developed to study the statistical properties of cumulus convection. The model was applied to simulate a 28-day evolution of clouds over the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains site during the summer 1997 Intensive Observation Period. The model was forced by the large-scale advective tendencies and surface fluxes derived from the observations. The sensitivity of the results to the domain dimensionality and size, horizontal grid resolution, and parameterization of microphysics has been tested. In addition, the sensitivity to perturbed initial conditions has also been tested using a 20-member ensemble of runs. The model captures rather well the observed temporal evolution of the precipitable water and precipitation rate, although it severely underestimates the shaded cloud fraction possibly because of an inability to account for the lateral advection of clouds over the ARM site. The ensemble runs reveal that the uncertainty of the simulated precipitable water due to the fundamental uncertainty of the initial conditions can be as large as 25% of the mean values. Even though the precipitation rates averaged over the whole simulation period were virtually identical among the ensemble members, the timing uncertainty of the onset and reaching the precipitation maximum can be as long as one full day. Despite the predictability limitations, the mean simulation statistics are found to be almost insensitive to the uncertainty of the initial conditions. The overall effects of the third spatial dimension are found to be minor for simulated mean fields and scalar fluxes but are quite considerable for velocity and scalar variances. Neither changes in a rather wide range of the domain size nor the horizontal grid resolution have any significant impact on the simulations. Although a rather strong sensitivity of the mean hydrometeor profiles and, consequently, cloud fraction to the microphysics parameters is found, the effects on the predicted mean temperature and humidity profiles are shown to be modest. It is found that the spread among the time series of the simulated cloud fraction, precipitable water, and surface precipitation rate due to changes in the microphysics parameters is within the uncertainty of the ensemble runs. This suggests that correlation of the CRM simulations to the observed long time series of the aforementioned parameters cannot be generally used to validate the microphysics scheme.

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

“…The SAM (System for Atmospheric Modeling), nonhydrostatic, anelastic large eddy simulation (LES) model version 6.10.3 (Khairoutdinov and Randall, 2003) was used to simulate the well-studied trade cumulus case of the Barbados Oceanographic and Meteorological EXperiment (BOMEX; Holland and Rasmusson, 1973;Siebesma et al, 2003). The BOMEX case is an idealized trade cumulus cloud field that is based on observations made near Barbados during June 1969.…”

“…The condensationevaporation process is proportional to the total droplet surface area, which increases with the droplet number concentration (for a given total liquid water mass). Under given supersaturation conditions, the condensation in polluted clouds is more efficient (higher condensation rate or shorter consumption time of the supersaturation - Pinsky et al, 2013;Seiki and Nakajima, 2014;Koren et al, 2014;Kogan and Martin, 1994;Dagan et al, 2015a). However, under subsaturation conditions, for the same reason, it implies higher evaporation efficiency.…”

Modelling convective processes during the suppressed phase of a Madden–Julian oscillation: Comparing single‐column models with cloud‐resolving models

“…The domain size is 256 km × 20 km with x=500 m and a stretched grid in the vertical with z=500 m above 4.5 km. The System for Atmospheric Modelling (SAM) is described in detail in Khairoutdinov and Randall (2003). It has prognostic equations for liquid water-ice static energy, total water (vapour, cloud) and precipitating water that are integrated numerically using a positive-definite, monotonic advection scheme.…”

“…A single moment, bulk microphysics scheme is used, and phases of cloud condensate and precipitate are distinguished by a temperature diagnostic. Here, version 6.3 is used, and it differs from the description in Khairoutdinov and Randall (2003) mainly in the fall speed of cloud ice, which depends on ice water content following Heymsfield (2003). Radiative heating is calculated using the optional CAM3.0 scheme, and a Smagorinsky-Lilly subgrid parametrization is used.…”

Analytic upscaling of a local microphysics scheme. Part I: Derivation

“…The LES model that we use is the System for Atmospheric Modeling (SAM) (Khairoutdinov and Randall, 2003).…”