Development of Global Snow Cover—Trends from 23 Years of Global SnowPack

Abstract

Globally, the seasonal snow cover is the areal largest, the most short-lived and the most variable part of the cryosphere. Remote sensing proved to be a reliable tool to investigate their short-term variations worldwide. The medium-resolution sensor MODIS sensor has been delivering daily snow products since the year 2000. Remaining data gaps due to cloud coverage or polar night are interpolated using the DLR’s Global SnowPack (GSP) processor which produces daily global cloud-free snow cover. With the conclusion of the hydrological year 2022 in the northern hemisphere, the snow cover dynamics of the last 23 hydrological years can now be examined. Trends in snow cover development over different time periods (months, seasons, snow seasons) were examined using the Mann–Kendall test and the Theil–Sen slope. This took place as both pixel based and being averaged over selected hydrological catchment areas. The 23-year time series proved to be sufficient to identify significant developments for large areas. Globally, an average decrease in snow cover duration of −0.44 days/year was recorded for the full hydrological year, even if slight increases in individual months such as November were also found. Likewise, a large proportion of significant trends could also be determined globally at the catchment area level for individual periods. Most drastic developments occurred in March, with an average decrease in snow cover duration by −0.16 days/year. In the catchment area of the river Neman, which drains into the Baltic Sea, there is even a decrease of −0.82 days/year.

Time series analysis of remotely sensed snow cover data: revealing permafrost thermal state and vegetation dynamics

“…The information provided by ESA CCI Permafrost is averaged over calendar years, but snow cover duration is based on hydrological years (meteorological autumn to summer). Recent studies show that changes in snow cover usually occur at the end of autumn/beginning of winter (Roessler and Dietz 2022). This annual update of land cover classes, for example, was also the reason why the ESA product was used and not specific circumarctic products such as the CAVM (Walker et al 2005;Raynolds 2022).…”

Global and Regional Snow Cover Decline: 2000–2022

“…Data were processed into seasonal and annual averages for December-January-February (12-01-02), March-April-May (03-04-05), June-July-August (06-07-08), September-October-November (09-10-11), and annually based on the snow-year or hydrologic-year (Northern Hemisphere: September to August of the following year, Southern Hemisphere: March to February of the following year) [20].…”

“…Even though SCE globally has decreased in the last four decades, there has been considerable inter-annual variability [18]. Anomalously cold periods and large snowfalls in recent winters have been experienced in North America, Asia, and Europe [19], leading to increasing SCE for some areas [20]. Indications are that the quick warming of the Arctic is associated with changes in atmospheric circulation [21,22] and may be responsible for these anomalous events and areas of increasing SCE.…”

“…The MODIS snow products have been validated by many studies [25][26][27][28][29], such as Hall and Riggs (2007), who found an overall absolute accuracy of the base 500 m resolution data to be about 93% [24]. This study uses MODIS data because it now has a greater than 23-year time span of consistent global-scale snow data, which have been used in numerous snow cover studies [8,10,16,20,25,30]. The MOD10C2 data set was used because it is a consistent data set, which minimizes cloud cover contamination, has over 23 years of reliable data, has been used in many other SCE studies [8,[31][32][33] and has been validated, such as by Lei et al (2011) who evaluated the snow identification accuracies of the MOD10C2 data to station data in northeast China and found the accuracy to be greater than 88%, with cloud cover being the main problem [34].…”

A European Time Series of Daily Snow Cover From Avhrr Data Over 36 Years: Results of the Timeline Project

“…On the one hand, we considered the SCD of the entire hydrological year (from the beginning of meteorological autumn to the end of meteorological summer, 365 days) and, on the other hand, we divided it into an early snow season (until mid-winter, 136 days) and a late snow season (after mid-winter, 229 days). A pixel-based trend analysis of the three mentioned snow cover seasons was carried out as in [8]. In order to analyze the statistical significance of the trends of SCD, the Mann-Kendall (MK) test was performed, the magnitude of the trend was determined by the Theil-Sen slope.…”