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 HOME > Monitoring Weather & Climate > Teleconnections > Teleconnection Pattern Calculation Procedures
 
1.  Arctic / Antarctic Oscillation (AO/AAO)
2.  North Atlantic Oscillation / Pacific - North American pattern (NAO/PNA)

 
1. Arctic / Antarctic Oscillation (AO/AAO)

To identify the leading teleconnection patterns in the atmospheric circulation, Emperical Orthogonal Function (EOF) was applied to the monthly mean 1000-hPa (700-hPa) height anomalies poleward of 20° latitude for the Northern (Southern) Hemisphere. The leading EOF modes capture the maximum amount of explained variance. The NCEP/NCAR reanalysis dataset was employed at a horizontal resolution of (lat,lon)=(2.5°X2.5°) for the period 1979 to 2000. The seasonal cycle has been removed from the monthly mean height field. The covariance matrix is used for the EOF analysis. To ensure equal area weighting for the covariance matrix, the gridded data is weighted by the square root of the cosine of latitude.

The loading pattern of AO (AAO) is defined as the first leading mode from the EOF analysis of monthly mean height anomalies at 1000-hPa (NH) or 700-hPa (SH). Note that year-round monthly mean anomaly data has been used to obtain the loading patterns. Since the AO and AAO have the largest variability during the cold sesaon (variance of AO/AAO), the loading patterns primarily capture characteristics of the cold season patterns.

Daily and monthly AO (AAO) indices are constructed by projecting the daily and monthly mean 1000-hPa (700-hPa) height anomalies onto the leading EOF mode. Both time series are normalized by the standard deviation of the monthly index (1979-2000 base period). Since the loading pattern of AO (AAO) is obtained using the monthly mean height anomaly dataset, the index corresponding to each loading pattern becomes one when it is normalized by the standard deviation of the monthly index.

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2. North Atlantic Oscillation / Pacific - North American pattern (NAO/PNA)

Calculating the Daily PNA and NAO teleconnection indices

The calculation procedure and base period have changed for calculating the daily NAO and PNA teleconnection indices. These changes have been made to eliminate inconsistencies in the way that the monthly and daily indices are calculated.

The procedure used to calculate the daily PNA and NAO teleconnection indices is based on the Rotated Principal Component Analysis (RPCA) used by Barnston and Livezey (1987, Mon. Wea. Rev., 115, 1083-1126). This procedure isolates the primary teleconnection patterns for all months and allows time series of the patterns to be constructed. To obtain the teleconnection patterns, the RPCA technique is applied to monthly standardized 500-mb height anomalies obtained from the CDAS in the analysis region 20°N-90°N between January 1950 and December 2000. Click here for more information on the teleconnection pattern calculation procedures.

The monthly teleconnection patterns are now linearly interpolated to the day in question, and therefore account for the seasonality inherent in the NAO and PNA patterns. Previously, the annual mean PNA and NAO patterns were used, which were based on monthly non-standardized anomalies. The standardized anomalies are now calculated based on the 1950-2000 climatological daily mean and standard deviation, whereas the anomalies were previously calculated from the 1971-2000 base period daily means.

The daily teleconnection indices are now calculated using the Least Squares regression approach identical to that used for the monthly indices. Therefore, all of the teleconnection patterns valid for the day in question are now recognized when calculating the PNA and NAO indices. The daily indices now represent the combination of teleconnection patterns that accounts for the most spatial variance of the observed anomaly map on any given day. Previously, the indices represented the spatial correlation between the annual mean loading pattern of the NAO or PNA and the daily height anomalies, and did not account for the spatial overlap that exists amongst the various teleconnection patterns.

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* Variance of monthly mean indices

variance of normalized monthly indices

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