气候变化领域集成服务门户(CCPortal): Spatial and temporal variability of snowfall over Greenland from CloudSat observations

CCPortal

DOI	10.5194/acp-19-8101-2019
	Spatial and temporal variability of snowfall over Greenland from CloudSat observations
	Bennartz R.; Fell F.; Pettersen C.; Shupe M.D.; Schuettemeyer D.
发表日期	2019
ISSN	16807316
起始页码	8101
结束页码	8121
卷号	19 期号:12
英文摘要	We use the CloudSat 2006-2016 data record to estimate snowfall over the Greenland Ice Sheet (GrIS). We first evaluate CloudSat snowfall retrievals with respect to remaining ground-clutter issues. Comparing CloudSat observations to the GrIS topography (obtained from airborne altimetry measurements during IceBridge) we find that at the edges of the GrIS spurious high-snowfall retrievals caused by ground clutter occasionally affect the operational snowfall product. After correcting for this effect, the height of the lowest valid CloudSat observation is about 1200m above the local topography as defined by IceBridge. We then use ground-based millimeter wavelength cloud radar (MMCR) observations obtained from the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit, Greenland (ICECAPS) experiment to devise a simple, empirical correction to account for precipitation processes occurring between the height of the observed Cloud-Sat reflectivities and the snowfall near the surface. Using the height-corrected, clutter-cleared CloudSat reflectivities we next evaluate various Z-S relationships in terms of snowfall accumulation at Summit through comparison with weekly stake field observations of snow accumulation available since 2007. Using a set of three Z-S relationships that best agree with the observed accumulation at Summit, we then calculate the annual cycle snowfall over the entire GrIS as well as over different drainage areas and compare the derived mean values and annual cycles of snowfall to ERA-Interim reanalysis. We find the annual mean snowfall over the GrIS inferred from CloudSat to be 34±7.5 cm yr-1 liquid equivalent (where the uncertainty is determined by the range in values between the three different Z-S relationships used). In comparison, the ERA-Interim reanalysis product only yields 30 cm yr-1 liquid equivalent snowfall, where the majority of the underestimation in the reanalysis appears to occur in the summer months over the higher GrIS and appears to be related to shallow precipitation events. Comparing all available estimates of snowfall accumulation at Summit Station, we find the annually averaged liquid equivalent snowfall from the stake field to be between 20 and 24 cm yr-1, depending on the assumed snowpack density and from CloudSat 23±4.5 cm yr-1. The annual cycle at Summit is generally similar between all data sources, with the exception of ERAInterim reanalysis, which shows the aforementioned underestimation during summer months. © Author(s) 2019.
语种	英语
scopus关键词	annual cycle; CloudSat; GIS; satellite data; snow; spatial variation; temporal variation; topography; Arctic; Greenland; Greenland Ice Sheet
来源期刊	Atmospheric Chemistry and Physics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/144325
作者单位	Earth and Environmental Sciences Department, Vanderbilt University, Nashville, TN, United States; Space Science and Engineering Center, University of Wisconsin - Madison, Madison, WI, United States; Informus GmbH, Berlin, Germany; Physical Sciences Division, Cooperative Institute for Research in Environmental Science, NOAA Earth System Research Laboratory, Boulder, CO, United States; ESA ESTEC, Noordwijk, Netherlands
推荐引用方式 GB/T 7714	Bennartz R.,Fell F.,Pettersen C.,et al. Spatial and temporal variability of snowfall over Greenland from CloudSat observations[J],2019,19(12).
APA	Bennartz R.,Fell F.,Pettersen C.,Shupe M.D.,&Schuettemeyer D..(2019).Spatial and temporal variability of snowfall over Greenland from CloudSat observations.Atmospheric Chemistry and Physics,19(12).
MLA	Bennartz R.,et al."Spatial and temporal variability of snowfall over Greenland from CloudSat observations".Atmospheric Chemistry and Physics 19.12(2019).