Brian M. Griffin

Elucidating Model Inadequacies in a Cloud Parameterization by Use of an Ensemble-Based Calibration Framework

Every cloud parameterization contains structural model errors. The source of these errors is difficult to pinpoint because cloud parameterizations contain nonlinearities and feedbacks. To elucidate these model inadequacies, this paper uses a general-purpose ensemble parameter estimation technique. In principle, the technique is applicable to any parameterization that contains a number of adjustable coefficients. It optimizes or calibrates parameter values by attempting to match predicted fields to reference datasets. Rather than striving to find the single best set of parameter values, the output is instead an ensemble of parameter sets. This ensemble provides a wealth of information. In particular, it can help uncover model deficiencies and structural errors that might not otherwise be easily revealed. The calibration technique is applied to an existing single-column model (SCM) that parameterizes boundary layer clouds. The SCM is a higher-order turbulence closure model. It is closed using a multivariate probability density function (PDF) that represents subgrid-scale variability. Reference datasets are provided by large-eddy simulations (LES) of a variety of cloudy boundary layers. The calibration technique locates some model errors in the SCM. As a result, empirical modifications are suggested. These modifications are evaluated with independent datasets and found to lead to an overall improvement in the SCM’s performance.

A single-column model intercomparison of a heavily drizzling stratocumulus-topped boundary layer

Received 12 February 2007; revised 11 July 2007; accepted 2 August 2007; published 27 December 2007. [1] This study presents an intercomparison of single-column model simulations of a nocturnal heavily drizzling marine stratocumulus-topped boundary layer. Initial conditions and forcings are based on nocturnal flight observations off the coast of California during the DYCOMS-II field experiment. Differences in turbulent and microphysical parameterizations between models were isolated by slightly idealizing and standardizing the specification of surface and radiative fluxes. For most participating models, the case was run at both typical operational vertical resolution of about 100 m and also at high vertical resolution of about 10 m. As in prior stratocumulus intercomparisons, the simulations quickly develop considerable scatter in liquid water path (LWP) between models. However, the simulated dependence of cloud base drizzle fluxes on LWP in most models is broadly consistent with recent observations. Sensitivity tests with drizzle turned off show that drizzle substantially decreases LWP for many models. The sensitivity of entrainment rate to drizzle is more muted. Simulated LWP and entrainment are also sensitive to the inclusion of cloud droplet sedimentation. Many models underestimate the fraction of drizzle that evaporates below cloud base, which may distort the simulated feedbacks of drizzle on turbulence, entrainment, and LWP.