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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

( PAGES2k Consortium 2017 ) and other data collections ( Anderson et al. 2019 ). The last millennium simulation CCSM4, a coupled atmosphere–ocean–sea ice simulation covering the period from 850 to 1850, is used as the base model in LMR2. The CCSM4 simulations use the Community Atmosphere Model version 4 (CAM4) and external forcing that includes volcanic eruptions, total solar irradiance, and the principal long-lived greenhouse gases (e.g., CO 2 , CH 4 , and N 2 O). More details on the CCSM4 and how it

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Robert A. Tomas, Clara Deser, and Lantao Sun

, outweighing the thermodynamically induced warming from Arctic sea ice loss. While most of the climate impacts from Arctic sea ice loss are expected to occur at middle and high latitudes, recent work has shown that ocean–atmosphere coupling may extend the reach of these impacts into the tropics and Southern Hemisphere ( D15 ). The dynamical ocean response, in particular, plays a key role in communicating the effects of Arctic sea ice loss to the entire globe via a weakening of the northward oceanic heat

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Xiaojun Yuan, Michael R. Kaplan, and Mark A. Cane

1. Introduction Improving our understanding of the dynamics of the atmosphere–ocean–sea ice system and the connecting mechanisms between the high and low latitudes has become increasingly important to climate science in the face of a rapidly warming world. The polar regions and the cryosphere in both hemispheres are active components in global climate. For example, changes within the polar regions dictate the strength of the thermal gradient between the tropics and the poles. Climate changes

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

and Wallace 2000 ) and the Pacific–South American (PSA; Mo and Ghil 1987 ) pattern. The SAM is characterized by out-of-phase sea level pressure (SLP) variations between midlatitudes (~40°S) and high latitudes (~65°S). Previous studies have suggested that the SAM is generated primarily by the internal dynamics of the SH atmosphere, which involves eddy–mean flow interactions (e.g., Yu and Hartmann 1993 ; Feldstein and Lee 1998 ; Hartmann and Lo 1998 ; Limpasuvan and Hartmann 2000 ; Lorenz and

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

, doi: 10.1175/JCLI-D-11-00685.1 . Bromwich , D. H. , and R. L. Fogt , 2004 : Strong trends in the skill of the ERA-40 and NCEP–NCAR reanalysis in the high and middle latitudes of the Southern Hemisphere, 1958–2001 . J. Climate , 17 , 4603 – 4619 , doi: 10.1175/3241.1 . Ciasto , L. M. , and D. W. J. Thompson , 2008 : Observations of large-scale ocean atmosphere interaction in the Southern Hemisphere . J. Climate , 21 , 1244 – 1259 , doi: 10.1175/2007JCLI1809.1 . Clem , K. R. , and

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Changhyun Yoo, Sungsu Park, Daehyun Kim, Jin-Ho Yoon, and Hye-Mi Kim

function of wind), and the background wind plays a critical role in wave excitation and propagation ( Sardeshmukh and Hoskins 1988 ; Jin and Hoskins 1995 ). Park (2014a) presents a unified convection scheme (UNICON) that can replace both deep and shallow convection schemes. With the UNICON, the MJO simulation in the Community Atmosphere Model version 5 (CAM5; Park et al. 2014 ) is significantly improved ( Park 2014b ). Specifically, CAM5 with the UNICON can capture the MJO variability centered

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David P. Schneider, Clara Deser, and Tingting Fan

, nonzonal trends in the atmospheric circulation at middle-to-high latitudes, contributing to Antarctic sea ice anomalies ( Holland and Kwok 2012 ; Li et al. 2014 ) and to warming trends on the Antarctic Peninsula ( Ding and Steig 2013 ) and in West Antarctica ( Ding et al. 2011 ; Schneider et al. 2012a ). The most cited cause of the increase in the SAM since the early 1960s is polar stratospheric ozone depletion (e.g., Thompson et al. 2011 ; Previdi and Polvani 2014 ). In observations over

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Michael Goss, Steven B. Feldstein, and Sukyoung Lee

falls outside of the middle 95% of subsample mean differences for that grid point is considered statistically significant. Finally, for the sea ice concentration composite, statistical significance is evaluated only for those locations in which the absolute difference between the composite sea ice concentration values in the constructive and destructive cases is greater than 0.1%, in order to neglect fluctuations in regions with near-100% ice cover. Finally, in order to test the sensitivity to

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Bradford S. Barrett, Gina R. Henderson, and Joshua S. Werling

et al. 2015 ). Given the role spring snow plays in a wide range of current and time-lagged atmospheric and hydrologic processes, it is critical to understand its variability on the intraseasonal time scale. Three publicly available datasets are used to establish these connections between the MJO and snow depth. They are each described below. a. Atmospheric data To explore the state of the atmosphere under different lagged phases of the MJO, both at the surface and midtropospheric levels

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Kyle R. Clem and James A. Renwick

. 2011 ) at 1.5° latitude–longitude resolution are employed starting in 1979. The SON atmospheric trends across the South Pacific/West Antarctic region since 1979 are similar among contemporary reanalyses ( Clem and Fogt 2015 ), and since a number of studies have shown ERA-Interim best reproduces the high-latitude Southern Hemisphere atmosphere ( Bromwich et al. 2011 ; Bracegirdle and Marshall 2012 ), only results using ERA-Interim are presented. Results using reanalysis data are briefly validated

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