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Kian Abbasnezhadi, Alain N. Rousseau, Étienne Foulon, and Stéphane Savary

and complementary identifiability analysis . Hydrol. Processes , 28 , 3947 – 3961 , . 10.1002/hyp.9882 Brabets , T. P. , and M. A. Walvoord , 2009 : Trends in streamflow in the Yukon River Basin from 1944 to 2005 and the influence of the pacific decadal oscillation . J. Hydrol. , 371 , 108 – 119 , . 10.1016/j.jhydrol.2009.03.018 Brasnett , B. , 1999 : A global analysis of snow depth for numerical

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Yanbo Nie and Jianqi Sun

al. 2011 ), and atmospheric teleconnections, such as the Arctic Oscillation (AO) ( Jiang and Li 2011 ; Huang et al. 2012 ; Yang et al. 2012 ; Zhang et al. 2014 ), North Atlantic Oscillation (NAO) ( Xu et al. 2012 ; Feng et al. 2014 ; Song et al. 2014 ; Zhang et al. 2014 ), and Silk Road pattern ( Dong et al. 2018 , 2019 ). Generally, rain gauges provide the most reliable measurements to observe precipitation, but this may not be the case in SWC because the spatial representativeness of

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Natalie Teale and David A. Robinson

water vapor fluxes to precipitation to quantify each flux’s contribution to the annual regional precipitation. Additionally, time series analyses of these fluxes are being conducted to determine if and, if so, how these flux patterns have changed over the past century. Also to be examined are possible changes in the seasonality and phase of precipitation produced by each of the fluxes, as well as relationships between the North Atlantic Oscillation, the Pacific North American pattern, and other

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Steven M. Martinaitis, Stephen B. Cocks, Micheal J. Simpson, Andrew P. Osborne, Sebastian S. Harkema, Heather M. Grams, Jian Zhang, and Kenneth W. Howard

passed with nonzero values (QC Flag = 1). Some seasonal oscillations were noted in the data. Increased passing nonzero values and decreased passing zero values were observed during the DJF periods due to large-scale synoptic systems moving across the CONUS. Seasonal variations were more remarkable within the observations labeled with QC Flag = −10 ( Fig. 15 ), the predominant conditionally passed flag for zero gauge observations that accounted for an average of 7.30% of all observations ( Table 4

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Scott Lee Sellars, Xiaogang Gao, and Soroosh Sorooshian

are well-known climate states [e.g., the Arctic Oscillation (AO), Madden–Julian oscillation (MJO), and El Niño–Southern Oscillation (ENSO)] that often impact precipitation and temperature over the western United States ( Redmond and Koch 1991 ; Dracup and Kahya 1994 ; Cayan et al. 1999 ; Barlow et al. 2001 ; Cook et al. 2004 , 2007 ). Research has shown similarities in both the physical precipitation features (e.g., area averages) and the particular climate phenomena that influence the

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David Barriopedro, Célia M. Gouveia, Ricardo M. Trigo, and Lin Wang

), although such relationship is nonstationary ( Kumar et al. 1999 ; R. Wu and B. Wang 2002 ). Other reported factors affecting seasonal precipitation in China are the Arctic Oscillation/North Atlantic Oscillation (AO/NAO; Gong and Wang 2003 ; Sung et al. 2006 ), the stationary planetary waves ( Chen et al. 2005 ), the Antarctic Oscillation (AAO; Nan and Li 2003 ), dynamic and thermal effects of the Tibetan Plateau ( Wu and Zhang 1998 ; Hsu and Liu 2003 ), and the Eurasian snow cover ( Zhang et al

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Michael J. DeFlorio, Duane E. Waliser, Bin Guan, David A. Lavers, F. Martin Ralph, and Frédéric Vitart

or not additional forecast skill and utility can be gained during certain phases of climate mode variability. The relationship between AR forecast utility and climate variations is examined here using the El Niño–Southern Oscillation (ENSO), the Arctic Oscillation (AO), and the Pacific–North America (PNA) teleconnection pattern. These climate variations are a focus because AR frequency is sensitive to the phase of each of these modes over particular regions around the globe ( GW2015 ). In

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Edwin Sumargo and Daniel R. Cayan

cloud variability and climate patterns are also examined using a set of low-frequency weather anomalies metrics commonly known as teleconnection indices ( Wallace and Gutzler 1981 ; Franzke et al. 2001 ). These metrics include the monthly versions of Pacific–North American (PNA), the Arctic Oscillation (AO), and the Niño-3.4 indices from the NOAA Climate Prediction Center database ( ). 3. Methods a. Determining the clear-sky albedo and the cloud albedo GOES albedo α

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J. E. Cherry, L-B. Tremblay, M. Stieglitz, G. Gong, and S. J. Déry

. 16657 – 16672 . 10.1029/1999JD900158 Déry, S. J. , and Yau M. K. , 2002 : Large-scale mass balance effects of blowing snow and surface sublimation. J. Geophys. Res. , 107 . 4679, doi:10.1029/2001JD001251 . Déry, S. J. , and Wood E. F. , 2004 : Teleconnection between the Arctic Oscillation and Hudson Bay river discharge. Geophys. Res. Lett. , 31 . L18205, doi:10.1029/2004GL020729 . Déry, S. J. , Crow W. T. , Stieglitz M. , and Wood E. F. , 2004 : Modeling snowcover

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A. Rinke, C. Melsheimer, K. Dethloff, and G. Heygster

thermodynamic influences. The spatial distribution of TWV is strongly coupled with temperature via the thermodynamic constraint of the Clausius–Clapeyron equation (temperature dependence of the saturation water vapor pressure). However, the spatial variability of Arctic TWV is dominated by changes in the large-scale dynamics, accompanied by changes in cyclone paths and/or intensity. Previous studies found a clear dependence of the Arctic moisture budget on the North Atlantic Oscillation–Arctic Oscillation

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