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Xiaofang Feng, Qinghua Ding, Liguang Wu, Charles Jones, Ian Baxter, Robert Tardif, Samantha Stevenson, Julien Emile-Geay, Jonathan Mitchell, Leila M. V. Carvalho, Huijun Wang, and Eric J. Steig

–2017. In (a)–(f), the total variance explained by each EOF is indicated in the parentheses. In (h)–(j), stippling indicates statistically significant correlations at the 95% confidence level. To detect whether the IPO-BT exists over the past 400 years, we employ EOF analysis on annual mean Z500 in EKF400 after reducing the effects of external forcing by regressing out the global mean Z500. The IPO-BT mode can be captured as EOF1 in EKF400 ( Fig. 6a ), similar to that in all reanalyses ( Figs. 3b and

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M. Nuncio and Xiaojun Yuan

). Fig . 2. Regressions of SST anomalies with the SST EC of the (a) first, (b) second, and (c) third SVD mode from the SVD analysis, respectively. Regressions that are significant at 90% confidence level or greater are shown in color shades. The second mode displays a pattern characterized by warm (cold) SST anomalies in the western/central (eastern) tropical Pacific, cold anomalies in the eastern equatorial Indian Ocean, and warm anomalies in the subtropical gyre of the Indian Ocean ( Figs. 1c and

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Lee J. Welhouse, Matthew A. Lazzara, Linda M. Keller, Gregory J. Tripoli, and Matthew H. Hitchman

linear interactions during the austral summer. Composite analysis finds, as in prior literature ( Schneider et al. 2012 ), a striking similarity between the La Niña T 2m and the SAM T 2m signals. In the interest of exploring the signal without the SAM, the regression of the SAM has been removed from the T 2m during austral summers of both El Niño and La Niña events. The December–February (DJF) period was chosen as this is the period that shows a strong trend toward positive SAM, while other

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Kyle R. Clem, James A. Renwick, and James McGregor

Southern Hemisphere: summer is December–February (DJF), autumn is March–May (MAM), winter is June–August (JJA), and spring is September–November (SON). Summer refers to the December year (i.e., data span March 1979–February 2016). Relationships with ENSO, SAM, and the atmospheric circulation/sea ice are investigated using linear regression and correlation analysis. The principal component time series are first standardized and therefore regression coefficients are with respect to changes of one

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Jin-Yi Yu, Houk Paek, Eric S. Saltzman, and Tong Lee

orthogonal function (EOF) analysis to the covariance matrix of the area-weighted monthly Z500 anomalies over 0°–90°S following Mo (2000) . The two leading EOF modes, which account for 22% and 12% of the total variance, are the SAM and PSA, respectively. The corresponding standardized principal components (PCs) are referred to as the SAM and PSA indices, and their austral spring (SON) values averaged from the monthly PCs are shown in Figs. 1c and 1d . Similar EOFs and PCs were obtained by repeating the

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Graham R. Simpkins, Yannick Peings, and Gudrun Magnusdottir

of standardized SLP anomalies between the east Pacific (5°N/S, 80°–130°W) and Indonesian (5°N/S, 90°–140°E) regions. It is highly correlated with the traditional Southern Oscillation index (SOI; 0.76 based on all months) but is used to capture more of the large-scale WC variability. Prior to analysis, all indices are standardized such that subsequent regressions relate to a one standard deviation positive event. c. Data processing and statistical methods Monthly anomalies for all datasets are

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Bradley P. Goodwin, Ellen Mosley-Thompson, Aaron B. Wilson, Stacy E. Porter, and M. Roxana Sierra-Hernandez

atmospheric component (SOI) is removed. Analysis of the PDO residuals (SOI linearly removed) regressed onto gridded SLP and OLR fields demonstrates its influence on the Southern Ocean and Antarctic continent from 1979 to 2012 when reanalysis data are most reliable in the SH. This time period was divided into two subsets to represent the dominant warm phase of the PDO (1979–98) and the recent shift toward a cold phase (1999–2012). The influence of a warm phase PDO on the SLP and OLR is apparent in the NH

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David P. Schneider, Clara Deser, and Tingting Fan

of the 5-member ensembles. Linear least squares analysis is used to compute trends and regression coefficients. Throughout this paper, the calculation of statistical significance is based on the two-sided Student’s t test methodology and adjustment for autocorrelation outlined by Santer et al. (2000) . Both the sample size and the degrees of freedom for indexing the critical t value are adjusted according to the lag-1 autocorrelation of the residuals. Note that this significance test is

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Ariaan Purich, Matthew H. England, Wenju Cai, Yoshimitsu Chikamoto, Axel Timmermann, John C. Fyfe, Leela Frankcombe, Gerald A. Meehl, and Julie M. Arblaster

50°–15°S, 150°E–160°W; Fig. 1b ). Notably, we do not filter the IPO index. This is because we are interested in both the long-term trend in the IPO, which is in itself a low-pass filter analysis, as well as its interannual variations (e.g., for calculating the interannual regression with SIC). Without filtering, the TPI strongly resembles the Niño-3.4 index (e.g., Newman et al. 2016 ); however, the TPI also takes into account variability in midlatitude regions (black boxes marked with the

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Kyle R. Clem and James A. Renwick

, Colorado, from their website ( ; Liebmann and Smith 1996 ). Trends in sea ice concentration are also briefly assessed. Sea ice concentration data are from the Hadley Centre Sea Ice and Sea Surface Temperature dataset ( Rayner et al. 2003 ) employed at 1° latitude–longitude resolution starting in 1979, accessed freely online ( ). b. Methods Data analysis methods include linear regression, linear congruency

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