Subscale forcing in a global atmospheric circulation model and stochastic parametrization

Abstract

SUMMARYA global atmospheric circulation model is used to derive the properties of the subscale forcing in the primitive equations. The study is based on a simulation with the model PUMA (Portable University Model of the Atmosphere), which represents a dynamical core with linear diabatic heating and friction. The subscale forcing is determined for a low wave number resolution T 21 (≈5 • × 5 • ) embedded in T 42 resolution (≈2.5 • × 2.5 • ) using the differences between the low wave number filtered T 42 model and the forcing by low wave numbers (T 21). The mean subscale forcing vanishes (besides a small heating contribution). The variance has largest values in the midlatitudes for vorticity (mid-troposphere), temperature (lower troposphere), and in the polar mid-troposphere for divergence. The temporal correlations reveal a slow decay in the first few hours followed by an exponential decay with an e-folding time of about one day. The correlation with hyperdiffusion (∼∇ 8 ) is below 0.4. Based on these results the design of stochastic parametrizations is suggested.

Statistical Dynamical Closures and Subgrid Modeling for Inhomogeneous QG and 3D Turbulence

“…In the last decade there has been increasing interest in exploring how parameterizations of stochastic backscatter may improve simulations and predictions of weather and climate [36,37]. Shutts [38], O'Kane and Frederiksen [16] and Berner et al [39] have considered the role of stochastic backscatter in weather prediction studies where it increases ensemble spread.…”

Subgrid modelling for geophysical flows

“…Stochastic modelling has had a long and successful history of application to problems in atmospheric science [38,39]. Recently, there has been increasing interest in exploring how parametrizations of stochastic backscatter may improve simulations and predictions of weather and climate [40][41][42]. Shutts [43], O'Kane & Frederiksen [44] and Berner et al [45] have considered the role of stochastic backscatter in weather prediction studies where it increases ensemble spread and improves forecasts in a manner similar to ensemble methods [46].…”

Sampling errors in estimation of the small scales of monthly mean climate

“…The dependence of climate simulations on the specified subgrid-scale processes such as dissipation has been an ongoing issue (see Koshyk and Boer (1995), Kaas et al (1999), Frederiksen et al (2003) and references therein). In recent years there has also been increasing interest in exploring how parameterizations of stochastic backscatter may improve weather and climate predictions and simulations (Frederiksen and Davies, 1997;Palmer, 2001;Shutts, 2005;Seiffert et al, 2006Seiffert et al, , 2008Berner et al, 2008;O'Kane and Frederiksen, 2008a). Frederiksen et al (2003) implemented a closure-based eddy viscosity parameterization into an atmospheric GCM demonstrating significant improvement in the circulation and energy spectra.…”