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Gokhan Danabasoglu, Steve G. Yeager, Young-Oh Kwon, Joseph J. Tribbia, Adam S. Phillips, and James W. Hurrell

which the AMOC maximum transport shows a roughly monotonic weakening (see Fig. 1a ). We use annual-mean fields in the present study except for the boundary layer depth where March-mean data are utilized. The time-mean distributions for CCSM4 represent 600-yr means for years 700–1299. Standard correlation, regression, spectral analysis, and empirical orthogonal function (EOF) methods are employed. All the time series are detrended using a linear least squares fit prior to analysis. There are a few

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A. Gettelman, J. E. Kay, and J. T. Fasullo

−2 . Thus, the fast response is small. Since we are mostly concerned with the differences between simulations with the same CO 2 perturbation, using the total response should not affect the feedback analysis. b. Correlation/regression analysis The analysis will first relate climate sensitivity to feedbacks. We use linear correlation analysis to relate global mean feedback values to climate sensitivity. Similar analysis methods are used by Soden and Vecchi (2011) and Zelinka et al. (2012a

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Clara Deser, Adam S. Phillips, Robert A. Tomas, Yuko M. Okumura, Michael A. Alexander, Antonietta Capotondi, James D. Scott, Young-Oh Kwon, and Masamichi Ohba

L y from the ERA-40 reanalysis and ENSO period from the SODA ocean analysis, which is forced by ERA-40. Characteristics of the GFDL CM2.1 ENSO simulation have been reported in many studies including Wittenberg (2009) . In all products, the period was estimated from the frequency corresponding to the maximum spectral power as in Capotondi et al. (2006) . The meridional scale was estimated for each model using the distance between the zero crossing points of the zonal average of the regression

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Shih-Yu Wang, Michelle L'Heureux, and Jin-Ho Yoon

of tropical wind stress anomalies is often interpreted as the stochastic forcing of ENSO ( Alexander et al. 2008 ). Two subtropical–tropical patterns are strongly related to NPO variability and are both significantly correlated to ENSO up to 6–12-month lead time. Both are identified using a maximum covariance analysis (MCA) of low-latitude low-level winds and SSTA: the Pacific meridional mode (PMM), which is based on the eastern half of the North Pacific ( Chiang and Vimont 2004 ; Chang et al

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Keith Oleson

strongly negatively correlated with changes in clouds and rural soil moisture and positively correlated with leaf plus stem area index (lsai) (as indicated by the simple correlation coefficients in Table 5 ). The corresponding simple correlations for the nocturnal UHI are of the same sign but somewhat weaker for clouds and stronger for lsai. When the effects of the other variables are held constant in the multiple regression analysis (the standardized partial regression coefficients in Table 5 ), the

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Laura Landrum, Marika M. Holland, David P. Schneider, and Elizabeth Hunke

drives the wintertime SIC anomalies. Positive but much smaller correlations of dynamically driven ice area anomalies are also present in April suggesting that enhanced ice transport into the region reinforces the thermodynamic tendencies. These positive correlations are consistent with an approximately 40-day-earlier ice advance into the region (not shown) as diagnosed from regression analysis. The positive ice area tendency term correlations continue and reinforce the anomalous ice concentration

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Matthew C. Long, Keith Lindsay, Synte Peacock, J. Keith Moore, and Scott C. Doney

available observational data. Two configurations of CESM1 are considered: 1) the fully coupled Earth system model, including ocean, sea ice, land, and atmosphere models; and 2) the ocean-ice component models forced by atmospheric reanalysis data. Our analysis is aimed at identifying model biases and examining the model's twentieth-century mean state, seasonal cycle, interannual variability, and transient response. Furthermore, we explicitly test the degree to which the fully coupled model is able to

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Ernesto Muñoz, Wilbert Weijer, Semyon A. Grodsky, Susan C. Bates, and Ilana Wainer

3°N and 10°S) that is not observed in the same analysis from observations ( Deser et al. 2006 ). In addition to a tropical Atlantic lagged response to ENSO, there is increasing evidence (based on observations and modeling studies) that the equatorial Atlantic SST anomalies are anticorrelated with tropical Pacific SSTs as an intrinsic phenomenon. This tropical Pacific–Atlantic interbasin anticorrelation is also evident (and more widespread) in the sea level pressure (SLP) anomalies. Some studies

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Susan C. Bates, Baylor Fox-Kemper, Steven R. Jayne, William G. Large, Samantha Stevenson, and Stephen G. Yeager

of the 25-yr changes than the Δ 25 values derived formally from linear regressions. We restrict this analysis to the latitudinal band 40°S–40°N in order to exclude any influence of changes in sea ice coverage, especially in light of the large decrease in Arctic sea ice cover in the late twentieth century in both the model and observations, and because the PBL flux has its largest response to SST in this band ( Large and Yeager 2012 ). The “typical” values and resulting 25-yr changes for both

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Esther C. Brady, Bette L. Otto-Bliesner, Jennifer E. Kay, and Nan Rosenbloom

30°S. The simulated LGM tropical terrestrial surface air temperature cooling of 2.9°C is just more than half of the 5.4(±0.3)°C cooling estimated by the meta-analysis of pollen, snow-line, and noble gas proxies of Ballantyne et al. (2005) , although some of the proxies used in Ballantyne et al. (2005) , such as snow lines, are also influenced by changes in precipitation. The model LGM cooling estimates yield a simulated tropical land MAT/SST cooling ratio of 1.4 using the full grid averages, or

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