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Adèle Révelard, Claude Frankignoul, Nathalie Sennéchael, Young-Oh Kwon, and Bo Qiu

atmospheric response seems to be primarily driven by the decadal variability of the KE. Indeed, repeating the analysis, but regressing onto a high-pass- and low-pass-filtered KE index with a cutoff at 6 years gave very similar results when using the low-pass filtered KE index, but different and more noisy ones when using the high-pass filtered one (not shown). b. SST anomalies and heat flux feedback The KE variability influences the atmosphere through SST changes that generate air–sea heat flux anomalies

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Yi-Hui Wang and W. Timothy Liu

associated with the Niño-3.4 index are removed. After separating the large-scale and frontal-scale signals and removing the tropical variability, we perform regression analysis to the KE index and the individual climate variables point by point. The KE index is normalized by its standard deviation. The regression coefficient at each point represents the changes in one specific variable as the normalized KE index increases by one unit. The light green contours in the figures enclose areas where the

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Xiaohui Ma, Ping Chang, R. Saravanan, Dexing Wu, Xiaopei Lin, Lixin Wu, and Xiuquan Wan

related variables is computed when carrying out SVD analysis. Additionally, we carried out lag-regression analyses to examine relationships between SST and extreme flux events. These analyses are based on seasonal mean anomalies and the results will be discussed in section 4 . 3. Event-day and non-event-day fluxes and the associated storms a. Characteristics of extreme flux events Figure 2a shows the NDJFM daily climatology of THF and the associated standard deviation for the KER (top graph) and GSR

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Bunmei Taguchi, Niklas Schneider, Masami Nonaka, and Hideharu Sasaki

latter. The westward intensification and concentration of the signals with initially broad meridional scale is consistent with jet-trapped Rossby waves proposed by Sasaki et al. (2013) . Fig . 6. (a) Lagged correlation (color shading) and regression (contours, every 0.1 K) coefficients of annual mean based on the Ishii analysis associated with the standardized reference time series of averaged over the KE region (33°–38°N, 145°–170°E; cyan boxes in the panels for lag 0 yr). Positive (negative

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Hyodae Seo, Young-Oh Kwon, Terrence M. Joyce, and Caroline C. Ummenhofer

Joyce 2013 ). In (b) and (c), the mean position of the GS is shown as thick black lines; and in (b) the 6°, 8°, and 10°C isotherms by thin black contours. Tropical influence is removed based on the linear regression on the leading principal components of the tropical Indo-Pacific SST and tropical Atlantic SST. Focusing on interannual to longer time scales, Kwon and Joyce (2013) used lead–lag regression analysis to find a significant relationship between the GSI and the North Atlantic SST when the

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R. Justin Small, Frank O. Bryan, Stuart P. Bishop, and Robert A. Tomas

://doi.org/10.1175/JCLI-D-12-00062.1 . 10.1175/JCLI-D-12-00062.1 Clement , A. , K. Bellomo , L. N. Murphy , M. A. Cane , T. Mauritsen , G. Rädel , and B. Stevens , 2015 : The Atlantic multidecadal oscillation without a role for ocean circulation . Science , 350 , 320 – 324 , https://doi.org/10.1126/science.aab3980 . 10.1126/science.aab3980 Cohen , J. , and P. Cohen , 1983 : Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences . Lawrence Erlbam

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Masayo Ogi, Bunmei Taguchi, Meiji Honda, David G. Barber, and Søren Rysgaard

these marginal seas. Figure 2 shows September sea-ice concentration regressed on the maximum Okhotsk sea-ice coverage in the following winter. The regression and correlation patterns yield a large loading and high correlation over the East Siberian Sea. Figure 3 show the time series of the September sea-ice concentration averaged over the East Siberian Sea (the box area in Fig. 2 ) and the maximum Okhotsk sea ice in the following winter. These time series are hereafter referred to as “the

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Young-Oh Kwon and Terrence M. Joyce

variability of the winter meridional heat fluxes by transient eddies is correlated with the north–south fluctuations of the three ocean fronts, with more detailed analysis in the case of the synoptic transient eddy heat flux and GS. The forcing and response causality is implied from the lag regressions as already explained in section 2d . The regression pattern with the atmosphere leading the ocean index is interpreted as the atmospheric pattern responsible for (i.e., forcing) the changes in the ocean

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R. Justin Small, Frank O. Bryan, Stuart P. Bishop, Sarah Larson, and Robert A. Tomas

VDIFF below. Further, terms ii and iii will be combined and referred to as OHFC. The heat budget was initially computed for individual grid cells, and then the budget terms are linearly regressed onto the tendency term following Doney et al. (2007) . Monthly anomalies are defined for all variables as departures from the average seasonal cycle of the simulation. For the spatial-scale analysis of section 4 , the budget terms on the 0.1° grid are smoothed with boxcar averages. (The total budget term

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Yuta Ando, Masayo Ogi, and Yoshihiro Tachibana

observed SAT and SST anomalies during P1 and P2, respectively, in 2012. Over the Japanese islands, the observed SATs during P1 were positive almost everywhere ( Fig. 3c ), in contrast to the expectation based on the regression analysis ( Fig. 3a ). Only in a few narrow areas in southern Japan were weak negative SATs observed ( Fig. 3c ). This result suggests that other factors overwhelmed the cooling influence of the AO and WP during P1. By contrast, during P2 the estimated SAT anomalies ( Fig. 3b

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