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

Abstract

The Greenland Ice Sheet is now the single largest cryospheric contributor to global sea‐level rise yet uncertainty remains about its future contribution due to complex interactions between increasing snowfall and surface melt. Reducing uncertainty in future snowfall predictions requires sophisticated, physically based climate models evaluated with present‐day observations. The accuracy of modeled snowfall rates, however, has yet to be systematically assessed because observations are sparse. Here, we produce high spatial resolution (15 km) snowfall climatologies (2006–2016) derived from CloudSat's 2C‐SNOW‐PROFILE product to evaluate climate model simulations of snowfall across the Greenland Ice Sheet. In comparison to accumulation datasets acquired from ice cores and airborne accumulation radar, we find that our CloudSat climatologies capture broad spatial patterns of snowfall in both the accumulation and ablation zones. By comparing our CloudSat snowfall climatologies with the Regional Atmospheric Climate Model Version 2.3p2 (RACMO2.3p2), Modèle Atmosphérique Régional 3.9 (MAR3.9), ERA5, and Community Earth System Model version 1 (CESM1), we demonstrate that climate models likely overestimate snowfall rates at the margins of the ice sheet, particularly in South, Southeast, and Northwest Greenland during autumn and winter. Despite this overestimation, there are few areas of the ice sheet where the models and CloudSat substantially disagree about the spatial pattern and seasonality of snowfall rates. We conclude that a combination of CloudSat snowfall observations and the latest generation of climate models has the potential to improve understanding of how snowfall rates respond to increasing air temperatures, thereby constraining one of the largest sources of uncertainty in Greenland's future contribution to global sea levels.

Role of Snowfall Versus Air Temperatures for Greenland Ice Sheet Melt‐Albedo Feedbacks

“…CloudSat is one of the few precipitation radar satellites that samples snowfall poleward of 60°N. However, CloudSat cannot resolve snowfall rates below 1,200 m above the surface due to ground clutter (Palerme et al., 2019; Ryan et al., 2020). Furthermore, its sparse spatiotemporal sampling requires retrievals to be aggregated onto coarse grids that also likely introduces uncertainty (Bennartz et al., 2019; Ryan et al., 2020).…”

“…However, CloudSat cannot resolve snowfall rates below 1,200 m above the surface due to ground clutter (Palerme et al., 2019; Ryan et al., 2020). Furthermore, its sparse spatiotemporal sampling requires retrievals to be aggregated onto coarse grids that also likely introduces uncertainty (Bennartz et al., 2019; Ryan et al., 2020). Discrepancies between modeled and remotely sensed snowfall rates therefore remain mostly unresolved and will continue to be without expansion of the currently sparse network of in situ stations.…”

A Novel Approach to Validate Satellite Snowfall Retrievals by Ground-Based Point Measurements

“…Therefore, the S/P ratio derived from the satellite data is still physically meaningful. There are a few studies in the literature on comparing CloudSat snowfall product with surface station measurements [52][53][54], ground-based radar measurements [55][56][57][58], and model reanalysis [59][60][61]. Most of these studies are conducted over high latitudes.…”

“…Among them, King [53] evaluated CloudSat snowfall product against four station measurements and three gridded snow water equivalent products throughout the Canadian Arctic and found that CloudSat has better performance north of 70 • N, with underestimation compared to measurements at most of the stations and the reanalysis. Ryan et al [61] derived a 15-km resolution snowfall climatology from CloudSat snowfall retrievals over Greenland ice sheet and concluded that CloudSat accumulation climatology has an uncertainty of ± 28% with respect to accumulation rates derived from ice cores. Edel et al [60] compared CloudSat snowfall climatology with several reanalysis datasets and found that similar general geographical patterns are observed in all datasets, although there are significant mean snowfall rate differences over the Arctic between 58 • and 82 • N. Using conventional surface weather station data over Canada, Hiley et al [52] found that CloudSat snowfall retrieval does not correlate well with surface station measurements, except for at some high latitude stations where CloudSat has more frequent sampling and mixed phase precipitation is less of an issue.…”

Applications of a CloudSat-TRMM and CloudSat-GPM Satellite Coincidence Dataset

“…With their frequent coverage over high-latitudes and wide scan swath, high-frequency (e.g., frequencies above~90 GHz) passive MW sensor-based precipitation data have significant potential to provide estimates where cold-season precipitation (i.e., precipitation reaching the surface in its solid phase, and also drizzle and sleet) is an important contribu-tion to total annual precipitation [19][20][21][22][23]. In this section, several recent investigations are highlighted whereby the CloudSat-GPM coincidence dataset is used to validate passive MW-based precipitation estimates, as well as to exploit the three-frequency radar data to address limitations of individual radar retrievals of cold-season precipitation [24].…”