Robustness of Low-Frequency Circulation Patterns Derived from EOF and Rotated EOF Analyses

Cheng, Xinhua; Nitsche, Gregor; Wallace, John M.

doi:10.1175/1520-0442(1995)008<1709:rolfcp>2.0.co;2

Cited by 86 publications

(44 citation statements)

References 11 publications

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“…The 4 leading SLP EOFs and the 2 leading precipitation EOFs have been retained in the PCA pre-filtering in order to optimize the stability of the CCA patterns. The stability has been assessed by a Monte Carlo test on the congruence coefficient (Richman 1986, Cheng et al 1995 of patterns obtained from 200 random subsamples with 90 temporal records each (out of a total of 186 in the calibration period). This PCA pre-filter selection (4, 2) shows non-overlapping North et al (1982) sampling error bars and, consequently, no degenerate multiplet is likely to be broken in this truncation.…”

Section: Linear Cca Dmmentioning

confidence: 99%

Improved field reconstruction with the analog method: searching the CCA space

Fernández¹,

Saenz²

2003

Clim. Res.

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The analog based downscaling method is revisited in an application to precipitation data for the northern coast of the Iberian Peninsula. Analog situations of a large-scale predictor are searched in the historical record and the regional-scale predictand is reconstructed by using the analog records found. The usual approach is insensitive to the predictand variable, and we present a new approach using Canonical Correlation Analysis (CCA), which consists in projecting the predictor field onto the spatial patterns obtained in a CCA between the predictor and predictand variables and searching for analogies in this dimension-reduced predictor. This approach is tested against the usual analog search, based on the projection onto the patterns derived from Principal Component Analysis (PCA), and the more commonly used linear CCA downscaling technique. In a projection space of the same dimension, the new approach performs better in reconstructing the precipitation (based on correlation and variance skill scores) than the PCA approach. The CCA linear method yields a similar correlation skill by comparison to our new approach, but reconstructs a much lower fraction of the variance. The non-normality of the probability density function inherent to the precipitation data is partly lost by the linear method, whereas it is preserved by the analog methods. A sensitivity analysis on several parameters of the analog search was also conducted. The improvement of the CCA approach over analogs seems to be related to the identification in the predictor field of the areas most closely connected to the predictand.

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Section: Linear Cca Dmmentioning

confidence: 99%

Improved field reconstruction with the analog method: searching the CCA space

Fernández¹,

Saenz²

2003

Clim. Res.

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“…The use of the correlation matrix rather than the covariance matrix implies a standardization of the original timeseries to zero means and unit standard deviations prior to the extraction of the PCs. To enhance the interpretability of the factor loadings, the principal components were subjected to orthogonal (varimax) rotation (Richman, 1986;Cheng et al, 1995). This regionalization of climatic variability helps separate ENSO from other modes.…”

Section: Methodsmentioning

confidence: 99%

Joint modes of climate variability across the inter‐Americas

Jury

Malmgren

2011

Intl Journal of Climatology

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Surface temperature (Ts), sea-level pressure (SLP), and zonal wind (U) fields from NCEP/NCAR reanalysis spanning the period 1949-2006 have been jointly analysed by means of principal component analysis to assess the dominant modes of climate variability in the inter-American region, including the eastern Pacific and Atlantic Oceans. Two aspects of these time-series were analysed: the annual cycle and its residual. Seasonal modes 1 and 2 take the form of north-south dipoles of Ts over oceanic and terrestrial environments. Seasonal mode 3 derives from SLP changes over the Amazon with a Caribbean wind response. Temporal fluctuations in the residual (ANOM) fields are resolved by six modes. ANOM1-3 is dominated by SLP and Ts changes across the equator in the Atlantic and east Pacific. The ANOM4 pattern is an isolated wind mode in the NE Atlantic. ANOM5 and 6 are dominated by sea temperature patterns in the South Atlantic and in the path of African easterly waves crossing the tropical Atlantic. The residual time scores are marked by significant warming (Ts) trends and co-located lower pressure. Cycles in the anomaly time scores tend to occur at 2-to 5-year periods. Modes dominated by atmospheric variables exhibit greater high-frequency 'noise' than oceanic modes. Predictability is assessed through development of multivariate regression models trained on Caribbean rainfall and related target time-series. The seasonal dipole between North and South America is found to modulate Pacific El Niño-Southern Oscillation and tropical Atlantic influence on Caribbean rainfall.

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“…Thus we proceeded to apply a "factor rotation" procedure to the PCA results. Such an additional step has been shown to yield much more stable patterns in relatively short data samples (Cheng et al, 1995). Here we applied a VARI-MAX rotation procedure (Cheng et al, 1995) to the leading five PCs of each model and of the observations.…”

Section: Eof Analysismentioning

confidence: 99%

“…Such an additional step has been shown to yield much more stable patterns in relatively short data samples (Cheng et al, 1995). Here we applied a VARI-MAX rotation procedure (Cheng et al, 1995) to the leading five PCs of each model and of the observations. The choice of the number of PCs was based on Rule N, a test for EOF variance separation, based on the normalized EOF variance and a standard error thereof, derived from parametric considerations (North et al, 1982;Preisendorfer, 1988).…”

Section: Eof Analysismentioning

confidence: 99%

Coupled climate model simulations of Mediterranean winter cyclones and large-scale flow patterns

Ziv

Kushnir

Nakamura

et al. 2013

Nat. Hazards Earth Syst. Sci.

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The study aims to evaluate the ability of global, coupled climate models to reproduce the synoptic regime of the Mediterranean Basin. The output of simulations of the 9 models included in the IPCC CMIP3 effort is compared to the NCEP-NCAR reanalyzed data for the period 1961–1990. The study examined the spatial distribution of cyclone occurrence, the mean Mediterranean upper- and lower-level troughs, the inter-annual variation and trend in the occurrence of the Mediterranean cyclones, and the main large-scale circulation patterns, represented by rotated EOFs of 500 hPa and sea level pressure.

The models reproduce successfully the two maxima in cyclone density in the Mediterranean and their locations, the location of the average upper- and lower-level troughs, the relative inter-annual variation in cyclone occurrences and the structure of the four leading large scale EOFs. The main discrepancy is the models' underestimation of the cyclone density in the Mediterranean, especially in its western part. The models' skill in reproducing the cyclone distribution is found correlated with their spatial resolution, especially in the vertical. The current improvement in model spatial resolution suggests that their ability to reproduce the Mediterranean cyclones would be improved as well

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Robustness of Low-Frequency Circulation Patterns Derived from EOF and Rotated EOF Analyses

Cited by 86 publications

References 11 publications

Improved field reconstruction with the analog method: searching the CCA space

Improved field reconstruction with the analog method: searching the CCA space

Joint modes of climate variability across the inter‐Americas

Coupled climate model simulations of Mediterranean winter cyclones and large-scale flow patterns

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