Satellite Observations of Snowfall Regimes over the Greenland Ice Sheet

Abstract

Abstract. The mass of the Greenland Ice Sheet (GIS) is decreasing due to surface melt and ice dynamics. Snowfall both adds mass to the GIS and has the capacity to reduce surface melt by increasing surface brightness, reflecting additional solar radiation back to space. This study leverages the synergy between two satellite instruments, CloudSat's Cloud Profiling Radar (CPR) and CALIPSO's Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), to identify snowfall cases over the GIS and partition them into two regimes: those associated with exclusively ice-phase cloud processes (IC) and those involving mixed-phase processes indicated by the presence of super-cooled liquid water (CLW). Overall, most CPR observations of snowfall over the GIS come from IC events (70 %), however, during the summer months, close to half of the snow observed is produced in CLW events (45 %). IC snowfall plays a dominant role in building the GIS, producing ~80 % of the total estimated 399 Gt yr−1 accumulation. Beyond the cloud phase that defines the snowfall regimes, the macrophysical cloud characteristics are distinct as well; the mean IC geometric cloud depth (~4 km) is consistently deeper than the CLW geometric cloud depth (~2 km), consistent with previous studies based on surface observations. Two-dimensional histograms of the vertical distribution of CPR reflectivities show that IC events demonstrate consistent growth toward the surface while CLW events do not. Analysis of ERA5 reanalyses shows that IC events are associated with cyclone activity and CLW events occur under large scale anomalous high pressure. Ground-based data is used to estimate the sensitivity of CloudSat's CPR to the two snowfall regimes, finding that the space-based radar is sensitive enough to detect ~95 % of IC snowfall cases and ~75 % of CLW snowfall cases seen at the surface.

Strong Summer Atmospheric Rivers Trigger Greenland Ice Sheet Melt through Spatially Varying Surface Energy Balance and Cloud Regimes

“…The spatial patterns of LWP and IWP in the hybrid RACMO-satellite data are reproduced well by ERA5 and MERRA-2, with higher amounts of LWP and IWP across the western GrIS during AR 90+ events compared to "no AR" conditions. This west-to-east gradient in LWP aligns with other studies showing that snowfall from clouds containing liquid water is more frequent over western than eastern Greenland during summer, and that snow-producing clouds containing liquid water at Summit Station tend to be produced by air masses that first pass over southwest Greenland (Pettersen et al 2018;McIlhattan et al 2019). LWP appears to still be underestimated by ERA5 and MERRA-2 on AR 90+ days, with LWP > 40 g m −2 extending to higher elevations of the western GrIS accumulation zone in the hybrid RACMO-satellite product compared with ERA5 and MERRA-2.…”

“…Off the southeast coast of Greenland, the seasonally weak Icelandic Low appears as a broad closed MSLP contour on basin 6 "no AR" days, but is replaced by an anomalous anticyclone on AR 90+ days. The combination of low pressure to the west of Greenland and high pressure to the east generates southerly advection of anomalously warm, moist air over western Greenland on basin 6 AR 90+ days, a pattern that has also been shown to enhance snowfall from liquid-containing clouds in the western Greenland accumulation zone and at Summit Station (Pettersen et al 2018;McIlhattan et al 2019). In the middle troposphere (Fig.…”

Evaluation of CloudSat's Cloud‐Profiling Radar for Mapping Snowfall Rates Across the Greenland Ice Sheet

“…We hypothesize that CloudSat does not observe these snowfall events because they originate below 1,200 m above the surface (the height at which CloudSat observes snowfall) from shallow, mixed-phased clouds. Such snowfall regimes have been observed by CloudSat's CPR at Summit (McIlhattan et al, 2019;Pettersen et al, 2018). Therefore even though CloudSat appears to accurately observe the intensity of most snowfall events, it may systematically underestimate mean snowfall rates in regions where shallow precipitation events contribute a significant proportion of the total snowfall.…”