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Ross D. Brown and Philip W. Mote

1. Introduction Snow cover represents a spatially and temporally integrated response to snowfall events, and the sequence of snowfall and melt events determines not just the quantity of water stored as snow but also snowpack condition (e.g., grain size and compaction), which in turn determines avalanche risk, energy required for melting, albedo of snow, and much more. Snowpack takes on special significance in mountain regions where snow stores enormous quantities of water, altering the ecologic

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Robert J. Allen and Charles S. Zender

). Changing concentrations of atmospheric aerosols may also have an impact on AO trends ( Chung and Ramanathan 2003 ; Allen and Sherwood 2011 ), but this effect remains highly uncertain. Significant observational evidence relates Eurasian snow cover to the AO, influencing its phase, strength, and interannual variability ( Cohen and Entekhabi 1999 ; Saito and Cohen 2003 ; Cohen and Barlow 2005 ). Observations also support a snow–AO mechanism, whereby anomalously high Eurasian snow cover in autumn

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Yves Lejeune, Ludovic Bouilloud, Pierre Etchevers, Patrick Wagnon, Pierre Chevallier, Jean-Emmanuel Sicart, Eric Martin, and Florence Habets

al. 2004 ; Sicart et al. 2005 ). However, although the impact of snow cover on the water supply is nonnegligible, the evolution and variability of snow cover on nonglacierized areas have not, to our knowledge, been studied on a local scale in this region. This present study focuses on the melting of the snow cover on a moraine site located at 4795 m MSL in the Charquini area (Bolivia, 16°17′S, 68°32′W) of the tropical Andes. Snowmelt is calculated by applying the energy balance to a control

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Pavel Ya Groisman, Richard W. Knight, Vyacheslav N. Razuvaev, Olga N. Bulygina, and Thomas R. Karl

1. Introduction In the twentieth century, northern Eurasia was the region with the largest and steadiest increase of surface air temperature, which became the most pronounced during the past 50 years ( Fig. 1 ). This warming should manifest itself in changes of environmental characteristics affecting both terrestrial ecosystem dynamics and human activity. The length of time when the soil is unfrozen, frozen, and/or when it is covered by snow is among the most important of these characteristics

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Otto Hyvärinen, Kalle Eerola, Niilo Siljamo, and Jarkko Koskinen

1. Introduction Snow cover and snow depth are important parameters for numerical weather prediction (NWP) and hydrological models, especially in springtime during the melting period. Essential characteristics include snow water equivalent (SWE), snow depth, and snow covered area. For hydrology, the maximum SWE prior to the onset of spring snowmelt is typically the most important snow characteristic for operational runoff and river discharge forecasts. In NWP models, the snow cover affects the

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Stefan Sobolowski, Gavin Gong, and Mingfang Ting

1. Introduction Anomalous continental-scale snow cover can influence both local and downstream climate because of its radiative and thermal properties, which act to modify the overlying atmosphere (e.g., Barnett et al. 1989 ; Cohen and Entekhabi 2001 ; Cohen and Rind 1991 ; Leathers and Robinson 1993 ). These influences may occur from regional to hemispheric spatial scales and immediate to seasonal time scales. Snow cover is a seasonally varying land surface state that covers much of the

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Daniel B. Walton, Alex Hall, Neil Berg, Marla Schwartz, and Fengpeng Sun

1. Introduction California’s Sierra Nevada is a high-elevation mountain range with complex topography and significant seasonal snow cover. Anthropogenic warming in the region is expected to cause large snowpack reductions by the end of the twenty-first century ( Pierce and Cayan 2013 ). Locations with baseline temperatures near freezing are vulnerable to snow cover loss because of less snowfall as a fraction of precipitation ( S / P ) and earlier snowmelt. Areas experiencing snow cover loss are

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Sujay V. Kumar, Christa D. Peters-Lidard, Kristi R. Arsenault, Augusto Getirana, David Mocko, and Yuqiong Liu

spatially and temporally consistent estimates of snow conditions. Primarily, there are two types of spaceborne remotely sensed measurements of snow processes: 1) snow cover area (SCA) is typically measured using visible or infrared satellite sensors, exploiting the high reflectance of snow-covered areas compared to areas with no snow cover; and 2) passive microwave (PM)-based measurements of snow depth and snow water equivalent (SWE). Measurements made in the visible spectrum provide observations at

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Nicholas P. Klingaman, Brian Hanson, and Daniel J. Leathers

1. Introduction a. A case for snow cover Surface boundary forcing exerts a strong influence on monthly and seasonal climate variability ( Shukla 1984 ). Previous studies have emphasized sea surface temperatures (SSTs; e.g., Ropelewski and Halpert 1986 ; Bhatt et al. 1998 ) and soil moisture (e.g., Karl 1986 ; Wolfson et al. 1987 ; Wang and Kumar 1998 ) as boundary conditions; snow cover has only recently become a focus in investigations of seasonal atmospheric variability. Namias (1962

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Yeon-Woo Choi and Joong-Bae Ahn

with autumn snow cover variation ( Cohen and Entekhabi 1999 , 2001 ; Cohen and Fletcher 2007 ; Cohen et al. 2002 , 2007 , 2014 ; Smith et al. 2011 ). These previous studies suggested that the autumn snow cover anomaly over the Eurasia continent may enhance the vertical propagation of planetary waves through the expansion of the high pressure system over the Siberian region, which results in a weakened stratospheric polar vortex. The associated stratospheric circulation plays an important role

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