Is There Evidence of Multiple Equilibria in Planetary Wave Amplitude Statistics?

Nitsche, Gregor; Wallace, John M.; Kooperberg, Charles

doi:10.1175/1520-0469(1994)051<0314:iteome>2.0.co;2

Cited by 43 publications

(21 citation statements)

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“…Such a point of view is even strengthened by PDF calculations in the space of the 10 leading PCs where classification was performed: radial PDFs around the individual WR directions are found to be higher by a factor of 20 to 25 than their overall average. Whereas multimodality in planetary wave amplitude distributions (Hansen & Sutera 1986) may be questioned (Nitsche et al 1994), radial PDFs in our PC space display obvious maxima in the WR directions. In Section 1, we distinguished 3 categories of algorithms for the objective classification into WRs.…”

Section: Discussionmentioning

confidence: 66%

Large-scale circulation classification, weather regimes, and local climate over France, the Alps and Western Europe

Plaut

Simonnet²

2001

Clim. Res.

104

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Section: Discussionmentioning

confidence: 66%

Large-scale circulation classification, weather regimes, and local climate over France, the Alps and Western Europe

Plaut

Simonnet²

2001

Clim. Res.

104

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“…Although the statistical significance and robustness of their findings have been subject to criticism (50), direct confrontation of theoretical bimodality with observations has clearly stimulated LFV research during the 1980s.…”

Section: Interpretation Of the Regimes Rossby Wave Propagation And Imentioning

confidence: 99%

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

Ghil

Robertson

2002

Proc. Natl. Acad. Sci. U.S.A.

170

169

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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.

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“…Nevertheless, climate applications have not tended to make use of these methods, but to focus instead on related, but not equivalent, problems like detecting multimodality (e.g. Nitsche et al 1994). Among other things, the marginal distribution in Eq.…”

Section: Mixtures Of Distributionsmentioning

confidence: 99%

Mixtures of stochastic processes: application to statistical downscaling

Katz¹,

Parlange²

1996

Clim. Res.

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Analyses of mixtures of stochastic processes have begun to appear in climate research in recent years. Some general properties of mixtures that are well known within statistics, but not ordinarily utilized in complete generality in climate applications, are reviewed. How these issues apply in certain types of statistical downscaling is described. An important distinction is drawn between 'conditional' models, sometimes utilized in downscaling, and 'unconditional' models, utilized in more traditional approaches. Through a combination of the individual conditional models, a single overall (or 'induced') model is obtained. Among other things, the mixture concept suggests physically plausible mechanisms by which more complex stochastic models could arise in climate applications. As an application, the stochastic modeling of time series of daily precipitation amount conditional on a monthly index of large-(or regional) scale atmospheric circulation patterns is considered. Chain-dependent processes are used both as conditional and unconditional models of precipitation. For illustrative purposes, precipitat~on measurements for a site in Californla, USA, were fitted. How the mixture approach can aid in determining the properties of climate change scenarios produced by downscaling is demonstrated in this example. In particular, changes in the relative frequency of occurrence of the states of the circulation index would be associated not just with changes in mean precipitation, but wlth changes in its variance as well.

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Is There Evidence of Multiple Equilibria in Planetary Wave Amplitude Statistics?

Cited by 43 publications

References 0 publications

Large-scale circulation classification, weather regimes, and local climate over France, the Alps and Western Europe

Large-scale circulation classification, weather regimes, and local climate over France, the Alps and Western Europe

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

Mixtures of stochastic processes: application to statistical downscaling

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