“Waves” vs. “particles” in the atmosphere's phase space: A pathway to long-range forecasting?

Abstract

Thirty years ago, E. N. Lorenz provided some approximate limits to atmospheric predictability. The details-in space and time-of atmospheric flow fields are lost after about 10 days. Certain gross flow features recur, however, after times of the order of 10 -50 days, giving hope for their prediction. Over the last two decades, numerous attempts have been made to predict these recurrent features. The attempts have involved, on the one hand, systematic improvements in numerical weather prediction by increasing the spatial resolution and physical faithfulness in the detailed models used for this prediction. On the other hand, theoretical attempts motivated by the same goal have involved the study of the large-scale atmospheric motions' phase space and the inhomogeneities therein. These ''coarse-graining'' studies have addressed observed as well as simulated atmospheric data sets. Two distinct approaches have been used in these studies: the episodic or intermittent and the oscillatory or periodic. The intermittency approach describes multiple-flow (or weather) regimes, their persistence and recurrence, and the Markov chain of transitions among them. The periodicity approach studies intraseasonal oscillations, with periods of 15-70 days, and their predictability. We review these two approaches, ''particles'' vs. ''waves,'' in the quantum physics analogy alluded to in the title of this article, discuss their complementarity, and outline unsolved problems.

Weather Regime Prediction Using Statistical Learning

“…This variability is characterized by the existence of large-scale persistent and recurrent flow patterns called weather regimes (Ghil and Robertson 2002;Molteni 2002). Several regimes have been identified in a consistent way by using diverse statistical and combined stochastic-dynamical methods.…”

“…Such studies have been carried out using observed atmospheric data, as well as output from numerical models. The results do vary to a certain extent, as summarized by Ghil and Robertson (2002), according to the nature and length of the dataset, as well as to its preparation. For instance, when using monthly mean data, Stephenson et al (2004) find that the existence of separate climate regimes is elusive.…”

“…A review of classification methods and results is included, for example, in Ghil and Robertson (2002) and Molteni (2002).…”

Dynamical prediction of Arctic sea ice modes of variability

“…Markov chain models of discrete states have been applied to determine the evolution of a number of weather and climate phenomena (e.g. Fraedrich and Klauss 1983;Ghil and Robertson 2002;Jones 2009). For continuous variables this process is referred to as a first-order autoregressive (AR1) model or red noise process.…”

“…Nonlinear geophysical fluid dynamics that govern the motion of climate system components can lead to the emergence of quasi-stationary flow regimes in the form of persistent or recurrent large-scale modes or patterns. Weather regimes are examples of such flow regimes that are manifested as particular atmospheric conditions on a regional scale with time scales roughly on the range of 10-100 days (Reinhold and Pierrehumbert 1982;Barnston and Livezey 1987;Vautard and Legras 1988;Ghil and Robertson 2002). The application of the concept of weather regimes in the analysis of mid-and high-latitude 1 3 synoptic systems has provided us with a deeper understanding of intrinsic climate variability (Molteni et al 1990;Michelangeli et al 1995;Cassou et al 2004;Guemas et al 2009), with potential benefits to weather and climate prediction capability (Mo and Ghil 1988;Brankovic and Molteni 1997;Cassou 2008;Riddle et al 2013) and possibly to longterm climate change (Corti et al 1999).…”