Abstract: A basic question in turbulence theory is whether Markov models produce statistics that differ systematically from dynamical systems. The conventional wisdom is that Markov models are problematic at short time intervals, ...

Abstract: A stochastic model for shear-flow turbulence is constructed under the constraint that the parameterized nonlinear eddy?eddy interactions conserve energy but dissipate potential enstrophy. This parameterization is appropriate ...

Abstract: Numerically generated data of quasigeostrophic turbulence in an equilibrated shear flow are analyzed to determine the extent to which they can be modeled by a Markov model. The time lagged covariances are collected into a ...

Abstract: A two-layer quasigeostrophic model is used to investigate whether dissipation can induce absolute instability in otherwise convectively unstable or stable background states. It is shown that dissipation of either temperature ...

Abstract: This paper tests the hypothesis that optimal perturbations in quasigeostrophic turbulence are excited sufficiently strongly and frequently to account for the energy-containing eddies. Optimal perturbations are defined here ...

Abstract: A technique is described for determining the set of patterns in a time-varying field whose corresponding time series remain correlated for the longest times. The basic idea is to obtain patterns that, when projected on a ...

Abstract: Recent studies reveal that randomly forced linear models can produce realistic statistics for inhomogeneous turbulence. The random forcing and linear dissipation in these models parameterize the effect of nonlinear ...

Abstract: This paper presents a framework for quantifying predictability based on the behavior of imperfect forecasts. The critical quantity in this framework is not the forecast distribution, as used in many other predictability ...

Abstract: This paper documents the low-frequency (i.e., decadal) variations of surface temperature for the period 1899?1998 in observations, and in simulations conducted as part of the Intergovernmental Panel on Climate Change (IPCC) ...

Abstract: This paper presents a framework based on Bayesian regression and constrained least squares methods for incorporating prior beliefs in a linear regression problem. Prior beliefs are essential in regression theory when the ...