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. (2010) found that a random forest algorithm (a linear SD method that formed predictions from an ensemble of regression trees) outperformed multivariate linear regression when predicting wintertime and summertime surface wind variability at an Australian site. As well, nonlinear regression was found to outperform linear regression in the prediction of temporal variability of daily-averaged wind components in France ( Najac et al. 2009 ; Salameh et al. 2009 ). Taken together, these studies do not
. (2010) found that a random forest algorithm (a linear SD method that formed predictions from an ensemble of regression trees) outperformed multivariate linear regression when predicting wintertime and summertime surface wind variability at an Australian site. As well, nonlinear regression was found to outperform linear regression in the prediction of temporal variability of daily-averaged wind components in France ( Najac et al. 2009 ; Salameh et al. 2009 ). Taken together, these studies do not
uses multiple regression to attribute forcing contributions globally ( Suckling et al. 2017 ), and also supported by other studies that show that subpolar NA variability is largely driven by AMOC changes, with little evidence for a strong AMV–AMOC link ( Marini and Frankignoul 2014 ; Frankignoul et al. 2017 ). In conclusion, combined with the recent downward trend in the new NAVI index, our analysis strongly suggests that the impact of internally generated NA ocean dynamics on Global, NHem, and
uses multiple regression to attribute forcing contributions globally ( Suckling et al. 2017 ), and also supported by other studies that show that subpolar NA variability is largely driven by AMOC changes, with little evidence for a strong AMV–AMOC link ( Marini and Frankignoul 2014 ; Frankignoul et al. 2017 ). In conclusion, combined with the recent downward trend in the new NAVI index, our analysis strongly suggests that the impact of internally generated NA ocean dynamics on Global, NHem, and
referred to as domain 2 ( Fig. 1 ). In addition, domain 3 inside of domain 2 represents the Larsen C region used in the analysis of regional circulation impact on the Larsen C surface melt. Fig . 1. Map of study domains with the outermost boundary representing the area used to perform the EOF/PC analysis (domain 1; same region as in Figs. 2 and 3 ), the dashed box indicating the AP region for the regression analysis (domain 2; same region as in Figs. 5 and 7 ), and the inner box showing the
referred to as domain 2 ( Fig. 1 ). In addition, domain 3 inside of domain 2 represents the Larsen C region used in the analysis of regional circulation impact on the Larsen C surface melt. Fig . 1. Map of study domains with the outermost boundary representing the area used to perform the EOF/PC analysis (domain 1; same region as in Figs. 2 and 3 ), the dashed box indicating the AP region for the regression analysis (domain 2; same region as in Figs. 5 and 7 ), and the inner box showing the
) uncertainty arises from the fact that not all relevant processes are well represented in models. Different scenarios represent uncertainty about changes in radiative forcing due to future emissions. Ideally, several perturbed initial condition runs of each scenario should also be available from each model in order to sample internal variability. These sources of uncertainty can be quantitatively partitioned using simple analysis of variance (ANOVA) frameworks ( Yip et al. 2011 ). The projections presented
) uncertainty arises from the fact that not all relevant processes are well represented in models. Different scenarios represent uncertainty about changes in radiative forcing due to future emissions. Ideally, several perturbed initial condition runs of each scenario should also be available from each model in order to sample internal variability. These sources of uncertainty can be quantitatively partitioned using simple analysis of variance (ANOVA) frameworks ( Yip et al. 2011 ). The projections presented
it is too small compared to the noise to be estimated in the regression model given a single record. All subsequent results do not include estimation of the forced component, that is, are based on Eq. (2) . Using an alternative method, low-frequency component analysis ( Wills et al. 2018 ), to estimate the forced response does not increase the SNR ( supplementary Fig. 5 ). In contrast to the forced component, the across-ensemble spread in estimating the β coefficients for each coupled ocean
it is too small compared to the noise to be estimated in the regression model given a single record. All subsequent results do not include estimation of the forced component, that is, are based on Eq. (2) . Using an alternative method, low-frequency component analysis ( Wills et al. 2018 ), to estimate the forced response does not increase the SNR ( supplementary Fig. 5 ). In contrast to the forced component, the across-ensemble spread in estimating the β coefficients for each coupled ocean
-versus-observed temperature trend ratios for the k th observational dataset (see Table 5 in the supplemental material). These results suggest that the values shown in the main text are robust to different plausible choices of a 24 . Finally, we note that model and observational temperature data were processed in exactly the same way; that is, model-versus-observed differences in TMT cr trends are not attributable to differences in the applied regression coefficients. APPENDIX B Statistical Analysis a. Terminology
-versus-observed temperature trend ratios for the k th observational dataset (see Table 5 in the supplemental material). These results suggest that the values shown in the main text are robust to different plausible choices of a 24 . Finally, we note that model and observational temperature data were processed in exactly the same way; that is, model-versus-observed differences in TMT cr trends are not attributable to differences in the applied regression coefficients. APPENDIX B Statistical Analysis a. Terminology
averaged to obtain annual data, giving equal weight to each month. We used all available scenario runs listed in Table 1 . If there were multiple runs per scenario from a single AOGCM, then we used up to five ensemble members for which temperature and precipitation data were available in our analysis. Table 1. Short description of the forcing scenarios considered in this study. Regional averages were calculated as area-weighted means over all AOGCM grid points falling into the polygons specifying our
averaged to obtain annual data, giving equal weight to each month. We used all available scenario runs listed in Table 1 . If there were multiple runs per scenario from a single AOGCM, then we used up to five ensemble members for which temperature and precipitation data were available in our analysis. Table 1. Short description of the forcing scenarios considered in this study. Regional averages were calculated as area-weighted means over all AOGCM grid points falling into the polygons specifying our
, is a transition zone from the low variance in the tropics to high variance in the high latitudes, reflecting tropical processes and extratropical weather systems. A correlation matrix in effect scales variance everywhere to unity so that the identified mode is not skewed toward the high-latitude process. The extended regional patterns of EOFs are subsequently obtained by a regression analysis using the associated EOF time series. For display purposes, we scale the regression patterns by the one
, is a transition zone from the low variance in the tropics to high variance in the high latitudes, reflecting tropical processes and extratropical weather systems. A correlation matrix in effect scales variance everywhere to unity so that the identified mode is not skewed toward the high-latitude process. The extended regional patterns of EOFs are subsequently obtained by a regression analysis using the associated EOF time series. For display purposes, we scale the regression patterns by the one
been present in each time series were removed prior to the ensuing analysis by applying an ordinary least squares regression and keeping the detrended residuals as anomaly time series. In doing so we avoided the need to simulate a series of synthetic time series, whose variance components may not have been fully representative of that naturally present in coastal waters. These detrended anomaly time series (henceforth simply called time series) represent a range of time scales from 72 to 519 months
been present in each time series were removed prior to the ensuing analysis by applying an ordinary least squares regression and keeping the detrended residuals as anomaly time series. In doing so we avoided the need to simulate a series of synthetic time series, whose variance components may not have been fully representative of that naturally present in coastal waters. These detrended anomaly time series (henceforth simply called time series) represent a range of time scales from 72 to 519 months
(cold) conditions in the EP, consistent with Gray (1988) and Goulet and Duvel (2000) . Therefore, they concluded that the phase speed of the MJO is significantly higher during El Niño years. In an observational analysis combined with a theoretical validation, Liu et al. (2016) demonstrated that the boreal summer intraseasonal oscillation over the western North Pacific is dominated by a relatively high-frequency (low-frequency) oscillation during El Niño (La Niña) summers. More recently
(cold) conditions in the EP, consistent with Gray (1988) and Goulet and Duvel (2000) . Therefore, they concluded that the phase speed of the MJO is significantly higher during El Niño years. In an observational analysis combined with a theoretical validation, Liu et al. (2016) demonstrated that the boreal summer intraseasonal oscillation over the western North Pacific is dominated by a relatively high-frequency (low-frequency) oscillation during El Niño (La Niña) summers. More recently
