Distortion Effects of the Anomaly Method of Removing Seasonal or Diurnal Variations from Climatological Time Series

Fuenzalida, Humberto; Rosenblüth, Benjamín

doi:10.1175/1520-0450(1986)025<0728:deotam>2.0.co;2

Cited by 3 publications

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“…This in practice produces a distortion effect (see Fuenzalida and Rosenblüth 1986), which is evident in the power transfer function. This in practice produces a distortion effect (see Fuenzalida and Rosenblüth 1986), which is evident in the power transfer function.…”

Section: Spectral Filtering Properties Of X-11 and Traditional Promentioning

confidence: 99%

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The Variability of Seasonality

2005

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Seasons are the complex nonlinear response of the physical climate system to regular annual solar forcing. There is no a priori reason why they should remain fixed/invariant from year to year, as is often assumed in climate studies when extracting the seasonal component. The widely used econometric variant of Census Method II Seasonal Adjustment Program (X-11), which allows for year-to-year variations in seasonal shape, is shown here to have some advantages for diagnosing climate variability. The X-11 procedure is applied to the monthly mean Niño-3.4 sea surface temperature (SST) index and global gridded NCEP-NCAR reanalyses of 2-m surface air temperature. The resulting seasonal component shows statistically significant interannual variations over many parts of the globe. By taking these variations in seasonality into account, it is shown that one can define less ambiguous ENSO indices. Furthermore, using the X-11 seasonal adjustment approach, it is shown that the three cold ENSO episodes after 1998 are due to an increase in amplitude of seasonality rather than being three distinct La Niña events. Globally, variations in the seasonal component represent a substantial fraction of the year-to-year variability in monthly mean temperatures. In addition, strong teleconnections can be discerned between the magnitude of seasonal variations across the globe. It might be possible to exploit such relationships to improve the skill of seasonal climate forecasts.

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Section: Spectral Filtering Properties Of X-11 and Traditional Promentioning

confidence: 99%

“…In practice, this causes distortion effects on short climatological time series (see Fuenzalida and Rosenblüth 1986). …”

Section: Trend and Seasonal Filtersmentioning

confidence: 99%

The Variability of Seasonality

2005

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“…Let X represent a low‐resolution time series of SSTAs, such as 3 month averages, and x a more finely resolved SSTA time series, such as weekly averages. Here “anomalies” are created by subtracting the average of each point in the annual cycle from the full series (the “anomaly method” [ Fuenzalida and Rosenblüth , ], e.g., subtracting the December through February (DJF)‐average SST from each DJF SST); both X and x are therefore centered about 0. We use weekly averaged values for the finely resolved data because their SST values are as close as possible to “instantaneous” while providing good geographical coverage and reasonable instrumentation error.…”

Section: Determining the Linear Dynamics Stochastic Forcing In The Trmentioning

confidence: 99%

Stochastic forcing of north tropical Atlantic sea surface temperatures by the North Atlantic Oscillation

Penland

Hartten

2014

Geophys. Res. Lett.

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The North Atlantic Oscillation (NAO) is a rapidly decorrelating process that strongly affects the climate over the Atlantic and the surrounding continents. Although the NAO itself is basically unpredictable on seasonal timescales using statistical methods, NAO forcing is here shown to significantly affect sea surface temperatures (SSTs) evolving on those timescales. Results using linear inverse modeling (LIM) imply that the NAO index and its convolution with deterministic SST dynamics account for nearly half the unpredictable component of north tropical Atlantic SST at lead times greater than 9 months; adding this component to hindcasts at a lead of 48 weeks increases correlation with north tropical Atlantic SST from about 0.4 to about 0.6. Rapid fluctuations during boreal winter and spring, when the NAO is strongest, affect SST predictability throughout the entire year.

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Adaptive filtering and maximum entropy spectra with application to changes in atmospheric angular momentum

Penland¹,

Ghil²,

Weickmann³

1991

J. Geophys. Res.

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The spectral resolution and statistical significance of a harmonic analysis obtained by low-order maximum entropy methods (MEM) can be improved by subjecting the data to an adaptive filter. This adaptive filter consists of projecting the data onto the leading temporal empirical orthogonal functions obtained from singular spectrum analysis (SSA). The combined SSA-MEM method is applied both to a synthetic time series and a time series of atmospheric angular momentum (AAM) data. The procedure is very effective when the background noise is white and less so when the background noise is red. The latter case obtains in the AAM data. Nevertheless, we detect reliable evidence for intraseasonal and interannual oscillations in AAM. The interannual periods include a quasi-biennial one and a low-frequency one, of 5 years, both related to the E1 Nifio/Southern Oscillation. In the intraseasonal band, separate oscillations of about 48.5 and 51 days are ascertained. , b]. MEM has the advantage of being able to find sharp spectral peaks which the fast Fourier transform (FFT) [cf. Bloomfield, 1976] or lag-autocorrelation methods [Blackman and Tukey, 1959] fail to isolate. However, use of MEM with a number of lags large enough to resolve closely lying peaks in the frequency spectrum usually results in spurious spectral features as well. Getting rid of the spurious peaks often reduces the valuable resolution too.The resolution of spectral peaks in a short, noisy time series will obviously be improved if some of the noise in the series can be removed. The trick is to do this without losing a significant portion of the signal. In this paper we show how the resolution of low-order MEM can be significantly improved by adaptive filtering.The adaptive filter is provided by singular spectrum analysis (SSA) [see Broomhead and King, 1986;Fraedrich, 1986;Vautard and Ghil, 1989]. Although the word "spectrum" is often used to denote a Fourier spectrum, the spectral decomposition of a time series in terms of orthonormal bases other than sines and cosines is obviously possible. The particular interest of SSA is that the basis functions in terms of which the data are decomposed are determined from the time series itself. The geophysical significance of these basis functions, called temporal empirical orthogonal functions (T-EOFs), and of their time-dependent coefficients, the temporal principal components (T-PCs), has attracted substantial interest recently [Vautard and Ghil, 1989; Ghil and Mo, 1991a, b; Rasmusson et al., 1990].Although the T-EOFs are interesting in their own right, we propose here to use them in order to refine a more traditional form of Fourier analysis for two reasons: first, scientists are accustomed to visualizing sinusoidal oscillations; second, additional information can be extracted from the T-PCs by applying MEM to them. In particular, use is made of the fact that the power spectral density of a stationary process is equal to the sum of the power spectra of its T-PCs [Vautard and . SSA also provides the statistical dimens...

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Distortion Effects of the Anomaly Method of Removing Seasonal or Diurnal Variations from Climatological Time Series

Cited by 3 publications

References 0 publications

The Variability of Seasonality

The Variability of Seasonality

Stochastic forcing of north tropical Atlantic sea surface temperatures by the North Atlantic Oscillation

Adaptive filtering and maximum entropy spectra with application to changes in atmospheric angular momentum

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