The power spectrum of temperatures in central England

Bain, W.C.

doi:10.1002/qj.49710243219

Cited by 13 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is apparent that very few cycles are real effects. Notable examples are the early work by Alter (1933), who examined correlograms of English rainfall data from I 7z7 to 1929 finding no evidence of long-term cycles, by Julain (1970), who used cross-spectral analysis in a study that led him to reject the concept of a zonal index cycle and by Bain (1976), who reexamined the power spectra of Manley's revised temperature data for central England (Manley, 1974). By contrast, some cycles, notably one of slightly over two years (the quasibiennial oscillation found in the analysis of many climatological series), do seem to be real eflects ( Lansberg et al, 1963;Van Loon and Jenne, 1970).…”

Section: Markov Processesmentioning

confidence: 97%

Statistical methods in physical geography

Unwin

1977

Progress in Physical Geography: Earth and Environment

View full text Add to dashboard Cite

This essay presents a state of the art review of statistical applications in physical geography. Initially, methods drawn from traditional statistics based upon classical probability theory are described and evaluated. It is argued that explicitly, or, more usually, implicitly such methods assume independence of the observations in both space and time making it incumbent upon statistical physical geography to reject what one authority has called its former 'timebound preoccupation' (Chorley, 1962, 6) in favour of time-less 'equilibrium' morphological systems analysis. Next, I go on to examine the development and potential of models based upon the theory of stochastic processes as it relates to the evolution of systems in space and time in which observations are not assumed to be independent of each other. It is suggested that many problems are better cast in this, or a mixed stochastic/deterministic framework which will allow the time dimension to be reincorporated into physical geography..In a review of this kind, traditional subdivisions of physical geography do not form an adequate organizational framework and are becoming increasingly irrelevant to what physical geographers actually do. Instead, statistical concepts and techniques will be discussed according to the method of systems analysis to which they are most suited. According to Chorley and Kennedy ( t g7 i ), four levels of systems analysis are possible. Morphological systems deal with networks of structural relationships between parts of systems; these have been analysed by classical statistical methods. Cascading systems are defined by the paths followed by throughputs of energy and mass, and like process-response systems in which these cascade flows are linked to morphological transformations, can be analysed using stochastic process theory. Finally, physical geographers are also interested in control systems resulting from the interaction of human decision-making and physical process-response for which statistical decision theory provides a useful analytic framework but which will not be considered in the present work. Although this classification was designed for geomorphological work, a similar scheme for climatological studies has been proposed and exemplified by Terjung (1976). I The analysis of morphological systems At this level of analysis, statistical methods are used to investigate the

show abstract

Section: Markov Processesmentioning

confidence: 97%

Statistical methods in physical geography

Unwin

1977

Progress in Physical Geography: Earth and Environment

View full text Add to dashboard Cite

show abstract

“…The doubled sunspot cycle may well have greater physical significance than the 1 1-year cycle (Bain, 1976). This possible physical significance should be treated with caution because of the possible presence of red noise in the spectra.…”

Section: Figure 3 Duero Basin Regions Defintd According To Spectra Omentioning

confidence: 99%

“…The maximum entropy method (MEM) is an autoregressive method of spectral estimates developed by Burg (1967Burg ( , 1968Burg ( , 1975) and has become increasingly used for spectral analysis of a large variety of data records in meteorology (Fichtl and McVehil, 1970;Bain, 1976;Dyer, 1976; Leite and Peixoto, 1992;Peixoto and Oort, 1992), hydrology (Shahane et af, 1977), solid earth geophysics (Courtillot and Le Mouel, 1976;Rubi, 1980;Egozcue and Canet, 1984) and astronomy (Jensen and Ulrych, 1973). Algorithms have been developed by Andersen (1978), Barrodale and Erickson (1980), Marple (1980), and Lim and Malik (1981).…”

Section: Introductionmentioning

confidence: 99%

Maximum entropy spectral analysis of the Duero Basin

Leite

Peixóto

1995

Intl Journal of Climatology

View full text Add to dashboard Cite

Maximum entropy spectral analysis for the estimation of power spectra is used to study monthly and annual spectra of air temperature and precipitation at 63 weather stations in Duero Hydrographic Basin. Annual data are first submitted to the maximum entropy method (MEM) and the resulting power found are analysed. These spectra were then stratified according to the spectral peaks iind on this basis several climatic regions were defined for the Duero Basin. The MEM spectral estimates of monthly values clearly show the annual and semi-annual cycle.

show abstract

“…However, the large majority of the surveys on self-similarity in climate data have only been devoted to the analysis of memory effects [12][13][14][15][16] in the form of linear dependencies, i.e, dependencies detected when the Pearson correlation function is computed. In that case, the existence of memory in the data is quantified by the power spectrum exponent β, which is related to a single value of α and that coin- which is the longest instrumental daily data set available on this subject.…”

Section: Introductionmentioning

confidence: 99%

Components of multifractality in the central England temperature anomaly series

Souza

Queirós

Grimm

2013

Chaos: An Interdisciplinary Journal of Nonlinear Science

View full text Add to dashboard Cite

We study the multifractal nature of the Central England Temperature (CET) anomaly, a time series that spans more than 200 years. The data are analyzed in two ways: as a single set and by using a sliding window of 11 years. In both cases, we quantify the width of the multifractal spectrum as well as its components, which are defined by the deviations from the Gaussian distribution and the dependence between measurements. The results of the first approach show that the key contribution to the multifractal structure comes from the dynamical dependencies, mainly weak ones, followed by a residual contribution of the deviations from the Gaussian. The sliding window approach indicates that the peaks in the evolution of the non-Gaussian contribution occur almost at the same dates associated with climate changes that were determined in previous works using component analysis methods. Moreover, the strong non-Gaussian contribution from the 1960s onwards is in agreement with global results recently presented.

show abstract

The power spectrum of temperatures in central England

Cited by 13 publications

References 10 publications

Statistical methods in physical geography

Statistical methods in physical geography

Maximum entropy spectral analysis of the Duero Basin

Components of multifractality in the central England temperature anomaly series

Contact Info

Product

Resources

About