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

sufficiently accurate, during the time period, for the purposes of this study ( Bromwich and Fogt 2004 ; Yu et al. 2010 ). This section describes the primary dataset utilized as well as the methodologies used to explore the data. Of particular interest is the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim, 1979–2013) ( Dee et al. 2011 ), as it has been shown to be the best reanalysis currently available ( Bracegirdle and Marshall 2012 ; Jones and Lister 2015

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

) regions of both positive and negative ΔSD ( Fig. 2 ). Furthermore, all eight phases featured regions of physically coherent variability, which in this study was defined as ΔSD anomalies that were found to be spatially uniform, of the same sign, and of approximately synoptic scale (~1000 km). For example, 7 days after active MJO phase 1 occurrences, negative ΔSD anomalies (as much as −1 cm day −1 ) were located over most of Europe ( Fig. 2a ). As another example, 7 days after active MJO phase 2

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Robert A. Tomas, Clara Deser, and Lantao Sun

in ΔICE_FOM ( Fig. 2e ). Both simulations show negative anomalies in the central Arctic and over most of North America, as well as the eastern North Atlantic extending into southern Europe and northern Africa, with larger magnitudes in ΔICE_SOM_Q20 compared to ΔICE_FOM. Zonally oriented high pressure extends over northern Europe across Siberia in both simulations. A notable difference between ΔICE_FOM and ΔICE_SOM_Q20 is the low pressure center response over the North Pacific in the former but

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

surface climate across northern Europe. During the years when stratosphere sudden warming (SSW) events occur, the Rossby wave associated with El Niño events can propagate upward and disturb the stratosphere in a wave-1 pattern. The abnormal stratosphere signal then propagates down to the troposphere at monthly time scales, producing colder temperatures in northern Europe and warmer temperatures in Canada. This stratosphere pathway appears inconsistent when SSW events are absent (an inactive

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

Precipitation (CMAP) dataset ( Xie and Arkin 1997 ) for precipitation and the interpolated OLR ( Liebmann and Smith 1996 ). All other variables used in this study, including geopotential height and zonal wind, are obtained from the daily-mean European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim, hereafter ERA-I) dataset ( Dee et al. 2011 ). The period of the data is selected to be consistent with our AMIP simulations described above. d. The MJO To investigate the MJO

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N. Fauchereau, B. Pohl, and A. Lorrey

usually extracted using hierarchical or nonhierarchical clustering procedures (see, e.g., Molteni et al. 2006 ; Vautard 1990 ; Michelangeli et al. 1995 ). These studies led to new insights on the potential predictability of extratropical atmospheric patterns in the NH arising from the MJO. For instance, Cassou (2008) showed that the probability of North Atlantic Oscillation (NAO) WRs over the North Atlantic–European sector were significantly modulated by the MJO with an approximate 10-day lag

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Xichen Li, David M. Holland, Edwin P. Gerber, and Changhyun Yoo

Geophysical Fluid Dynamics Laboratory (GFDL). The GFDL dry-dynamical-core serves as a numerical solver of the primitive equations. It is set up with a horizontal resolution of ~3 degrees (T42). The model is initialized with a climatological background flow averaged from the interim European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim; Dee et al. 2011 ) for the JJA austral winter season, from 1979 to 2012. At each time step, an additional forcing is added to the

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

; Turner et al. 2013b ; Schneider et al. 2012 ). The paper is laid out as follows: section 2 provides an overview of the data and methods used, results are given in section 3 , and a discussion and conclusions are offered in section 4 . 2. Data and methods Monthly-mean atmospheric fields are from the European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim; Dee et al. 2011 ) employed at 1.5° × 1.5° resolution for the period 1979–2015. As ERA-Interim is considered

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Hyo-Seok Park, Sukyoung Lee, Seok-Woo Son, Steven B. Feldstein, and Yu Kosaka

Arctic winter, we will show with model calculations that the relatively thin sea ice over the Barents, Kara, and Laptev Seas (see Fig. S1 in the online supplemental material) readily decreases for typical positive downward IR anomalies. In this study, we investigate the increase in wintertime downward IR and the associated sea ice melting (or suppression of sea ice growth). As we will show, Arctic downward IR fluctuates on a weekly time scale. We use the European Centre for Medium-Range Weather

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Ryan L. Fogt and Alex J. Wovrosh

reanalysis data As the ASL resides primarily off the coast of West Antarctica where in situ observations are sparse, this study also implements atmospheric reanalysis data. Several fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim; Dee et al. 2011 ) are examined to validate CAM4. ERA-Interim data were chosen over other contemporary global reanalyses because of its superior performance within the southern high latitudes ( Bracegirdle and Marshall

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