Snow cover over the Northern Hemisphere plays a crucial role in the Earth's hydrology and surface energy balance, and modulates feedbacks that control variations of global climate. While many of these variations are associated with exchanges of energy and mass between the land surface and the atmosphere, other expected changes are likely to propagate downstream and affect oceanic processes in coastal zones. For example, a large component of the freshwater flux into the Arctic Ocean comes from snow melt. The timing and magnitude of this flux affects biological and thermodynamic processes in the Arctic Ocean, and potentially across the globe through their impact on North Atlantic Deep Water formation.Several recent global remotely sensed products provide information at unprecedented temporal, spatial, and spectral resolutions. In this article we review the theoretical underpinnings and characteristics of three key products. We also demonstrate the seasonal and spatial patterns of agreement and disagreement amongst them, and discuss current and future directions in their application and development. Though there is general agreement amongst these products, there can be disagreement over certain geographic regions and under conditions of ephemeral, patchy and melting snow.

[1] A variety of methods are available to estimate values of meteorological variables at future times and at spatial scales that are appropriate for local climate change impact assessment. One commonly used method is Change Factor Methodology (CFM), sometimes referred to as delta change factor methodology. Although more sophisticated methods exist, CFM is still widely applicable and used in impact analysis studies. While there are a number of different ways by which change factors (CFs) can be calculated and used to estimate future climate scenarios, there are no clear guidelines available in the literature to decide which methodologies are most suitable for different applications. In this study several categories of CFM (additive versus multiplicative and single versus multiple) for a number of climate variables are compared and contrasted. The study employs several theoretical case studies, as well as a real example from Cannonsville watershed, which supplies water to New York City, USA. Results show that in cases when the frequency distribution of Global Climate Model (GCM) baseline climate is close to the frequency distribution of observed climate, or when the frequency distribution of GCM future climate is close to the frequency distribution of GCM baseline climate, additive and multiplicative single CFMs provide comparable results. Two options to guide the choice of CFM are suggested. The first option is a detailed methodological analysis for choosing the most appropriate CFM. The second option is a default method for use under circumstances in which a detailed methodological analysis is too cumbersome.

In this paper we use a satellite-derived data set to explore spatial and temporal variations of snow extent across Northern Hemisphere continents during the last three decades. These weekly visible-wavelength satellite maps of Northern Hemisphere snow extent produced by the National Oceanic and Atmospheric Administration constitute the longest consistently-derived satellite record of any environmental variable. We document the considerable intra-annual variability of snow extent, and show that during each month, fluctuations over relatively small areas are responsible for the majority of the year-to-year variability. Regions that cover less than 6% of Northern Hemisphere lands north of 20 Њ N explain 62%-92% of the interannual variance across the continents. On average, snow was more extensive across both Eurasia and North America from the 1970s to middle 1980s than during the late 1980s to late 1990s. During late winter, spring and summer, snow extent has decreased since the middle 1980s, while during fall to middle winter, snow extent has remained relatively constant. Accurate information on continental snow extent is critical for weather and hydrologic forecasting; for understanding hemispheric-scale atmospheric circulation, thermal variations, and regional snow extent; and for using snow as a credible indicator of climate variability and change.