Arctic Snowfall from CloudSat Observations and Reanalyses

Abstract

While snowfall makes a major contribution to the hydrological cycle in the Arctic, state-of-the-art climatologies still significantly disagree. We present a satellite-based characterization of snowfall in the Arctic using CloudSat observations, and compare it with various other climatologies. First, we examine the frequency and phase of precipitation as well as the snowfall rates from CloudSat over 2007–10. Frequency of solid precipitation is higher than 70% over the Arctic Ocean and 95% over Greenland, while mixed precipitation occurs mainly over North Atlantic (50%) and liquid precipitation over land south of 70°N (40%). Intense mean snowfall rates are located over Greenland, the Barents Sea, and the Alaska range (>500 mm yr−1), and maxima are located over the southeast coast of Greenland (up to 2000 mm yr−1). Then we compare snowfall rates with the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim, herein ERA-I) and Arctic System Reanalysis (ASR). Similar general geographical patterns are observed in all datasets, such as the high snowfall rates along the North Atlantic storm track. Yet, there are significant mean snowfall rate differences over the Arctic between 58° and 82°N between ERA-I (153 mm yr−1), ASR version 1 (206 mm yr−1), ASR version 2 (174 mm yr−1), and CloudSat (183 mm yr−1). Snowfall rates and differences are larger over Greenland. Phase attribution is likely to be a significant source of snowfall rate differences, especially regarding ERA-I underestimation. In spite of its nadir-viewing limitations, CloudSat is an essential source of information to characterize snowfall in the Arctic.

The Prevalence of Precipitation from Polar Supercooled Clouds

“…When applying the 2C-PC min e Z for "certain" and "possible" precipitation (accounting for radar sensitivity and range resolution), our NSA surface precipitation occurrences are still 5-10 points greater than the higher range of central-Arctic values (~20-40%) estimated by McIlhattan et al (2017) and Edel et al (2020), respectively. These remaining differences are likely attributable to hmin differences (see Section 2) and the spatial distribution of Arctic precipitation (relatively higher over the NSA; cf.…”

“…The precipitation detected with KAZR may be liquid or ice phase. However, since in these datasets Ze usually increases from cloud base to some distance below (not shown), indicating continued ice growth during sedimentation rather than drizzle or rain evaporation, we infer that ice is the dominant precipitation form (e.g., Edel et al, 2020;Rangno & Hobbs, 2001;Shupe, 2011).…”

“…McIlhattan et al (2017) reported that ~10% of shallow supercooled clouds are precipitating to the surface, based on Arctic-wide retrievals from the Cloud Profiling Radar (CPR; Tanelli et al, 2008) onboard CloudSat (Stephens et al, 2002). Edel et al (2020) reported a greater value (~20%) by including a lower likelihood of CPR surface precipitation (see Wood, 2011). Also based on CPR measurements, Zhang et al (2010) found that ~60% of polar midlevel supercooled clouds are precipitating at or below cloud base.…”

Probabilistic Spatial Meteorological Estimates for Alaska and the Yukon

“…It is critical we provide best estimates of these quantities across as much of the Arctic region as possible and include uncertainty for myriad reasons. The Arctic region is important for the global climate and is experiencing the highest rate of warming of any region in the world (Edel et al, 2020; Stocker et al, 2013). Additionally, historical estimates of precipitation and temperature with reliable uncertainty bounds may be used in many impact applications relevant across short‐term and seasonal forecasting, through to climate projections.…”

“…The Arctic is a region of high climate sensitivity with many complex interactions between the atmosphere, ocean, and land including sea ice and snow albedo feedbacks (Stocker et al, 2013; Stroeve et al, 2012). Precipitation plays a critical role in the climate of the Arctic through its impacts on multiple feedback processes such as cloud radiative processes and snow and ice albedo feedbacks (e.g., Edel et al, 2020; Serreze & Barry, 2011). However, complex topography, extremely sparse in situ observations, and a high percentage of frozen precipitation and resultant precipitation undercatch issues limit our ability to produce accurate spatial estimates of meteorological conditions across the Arctic (Serreze et al, 2003).…”