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Eric Brun, Vincent Vionnet, Aaron Boone, Bertrand Decharme, Yannick Peings, Rémi Valette, Fatima Karbou, and Samuel Morin

; Peings et al. 2010 ). They highlight the importance of initialization in terms of snow cover extent for accurately reproducing the springtime temperature anomalies over Europe and North America. In addition to its local impact, snow is expected to influence remote large-scale atmospheric modes of variability. In particular, several studies suggest that Siberian snow cover extent in autumn is linked to the subsequent Arctic Oscillation phase in winter. This teleconnection is supported both by

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Siraj ul Islam, Stephen J. Déry, and Arelia T. Werner

period. Additional studies examined the teleconnections of El Niño–Southern Oscillation (ENSO) and Pacific decadal oscillation (PDO) to western Canada’s hydrology and snow ( Moore 1991 ; Hsieh and Tang 2001 ; Whitfield et al. 2010 ). Hsieh and Tang (2001) investigated the influence of teleconnections on 1 April snowpack accumulations in the upper Columbia River basin, with La Niña and a low Pacific–North American pattern yielding large positive SWE anomalies. Rodenhuis et al. (2009) reported

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Paul A. Dirmeyer and Mei Zhao

1. Introduction Seasonal climate prediction has made great strides during the last two decades, primarily based on the confirmation that variations in tropical SST exert a strong influence on global weather and climate ( Trenberth et al. 1998 ). In particular, many studies have pinpointed the role of El Niño–Southern Oscillation (ENSO) phenomenon in the tropical Pacific in influencing climate variability. Much of the realizable skill in seasonal climate prediction today derives from the ENSO

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Do Hyuk Kang, Xiaogang Shi, Huilin Gao, and Stephen J. Déry

Columbia River basin, with cool phases of the El Niño–Southern Oscillation and the Pacific–North American pattern yielding large positive SWE anomalies. Danard and Murty (1994) report a 2.4 mm yr −1 decline in SWE across the FRB between 1966 and 1989 concurrent with streamflow decreases in October. Déry et al. (2012) compile a comprehensive runoff dataset for 139 sites across the watershed spanning a century (1911–2010) and concluded that there is increasing interannual variability in runoff

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Gregory R. Markowski and Gerald R. North

example, Ropelewski and Halpert (1996) describe regional precipitation distribution changes worldwide where they previously found influence. While earlier studies used mainly an atmospheric variable for prediction, often the Southern Oscillation index, SST was soon identified as the likely major source of seasonal timescale effects ( Philander 1990 ). Detailed, quantitative, statistical studies of SST influence on continental climates became practical with the release of adequate long-term data

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Rajesh R. Shrestha, Markus A. Schnorbus, and Alex J. Cannon

approach could be to condition the historical traces based on climate states at the time of forecast, for example, the phase of El Niño–Southern Oscillation (ENSO; Wang et al. 2011 ; Yuan et al. 2013 ). Other possibilities of improving the forecast could be to assimilate data for initial hydrologic conditions, for example, soil moisture and snow water equivalent (SWE; e.g., Mahanama et al. 2008 ; Li et al. 2009 ; Shukla and Lettenmaier 2011 ), and postprocessing of the ESP outputs (e.g., Shi et

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Sebastian A. Krogh, John W. Pomeroy, and James McPhee

impacts on river flow. In spite of these concerns, very few studies of the hydrology and climatology of the region have been conducted. Aravena (2007) developed a 400-yr precipitation reconstruction using tree ring data and glacier fluctuations in the Austral Chilean Andes, finding important decadal variations for the northwest and central Patagonia and also a strong biannual oscillation for the southernmost region. Garreaud et al. (2009) described the mean annual and decadal patterns of

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Viviane B. S. Silva and Ernesto H. Berbery

1997 ; Liebmann et al. 2004 ). This dipole structure is very robust and has been found in several studies at intraseasonal to interannual time scales. Each phase of the mode has distinct moisture fluxes ( Doyle and Barros 2002 ; Diaz and Aceituno 2003 ) that affect the atmospheric water budget ( Herdies et al. 2002 ). Casarin and Kousky (1986) suggested that the increased precipitation might be related to the preferred phasing of synoptic waves due to variations of the Madden–Julian oscillation

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Wang Fu and Scott Steinschneider

seasons ( Carter and Steinschneider 2018 ). This motivates the present study, which seeks to determine the large-scale climate patterns that drive winter precipitation over the Great Lakes, and the potential for seasonal prediction to inform flood preparedness and water level management of the lake system. There is a rich literature on the associations between large-scale modes of atmospheric and oceanic variability [e.g., Pacific–North American (PNA), North Atlantic Oscillation (NAO), El Niño

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Charlotte M. Emery, Cédric H. David, Konstantinos M. Andreadis, Michael J. Turmon, John T. Reager, Jonathan M. Hobbs, Ming Pan, James S. Famiglietti, Edward Beighley, and Matthew Rodell

oscillations, especially at validation gauges ( Fig. S8 ). A close examination of these oscillations reveals an apparent 2-day period around the gauge observations that appears to be a series of successive over and undercompensation of the corrections. These oscillations are the source of degraded NSE noted for one validation gauge and explain the relatively lower NSE values over validation gauges compared to assimilation gauges ( Table 1 ). The oscillations are likely a source for the instabilities noted

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