Intercomparison of Snow Melt Onset Date Estimates From Optical and Microwave Satellite Instruments Over the Northern Hemisphere for the Period 1982–2015

Abstract

Robust melt season timing and length estimates are important for hydrological and climatological applications; due to the large area and sparse in situ measurements, snow melt monitoring at the continental scale is only possible from satellites. We intercompared melt onset date (MOD) estimates obtained from optical and microwave satellite sensors over the Northern Hemisphere between 1982 and 2015 and subsequently analyzed the causes of the similarities and dissimilarities found. The optical satellite data are based on the mean surface albedo from the Satellite Application Facility for Climate Monitoring (CM SAF) CLouds, Albedo and RAdiation second release Surface ALbedo (CLARA‐A2 SAL) data set. The microwave satellite data are based on temporal variations in the differences of the brightness temperature from satellite passive microwave radiometers. The analysis shows that the microwave‐based method detects melt onset on average 10 days later than the albedo‐based method, which results from the different melt detection methods; the albedo‐based method observes the point when the spring snow metamorphism begins to have a detectable effect on snow albedo, whereas the microwave‐based method detects the appearance of meltwater in snowpack. The difference in MOD decreases in forests, because canopy protects snow from sunlight delaying snow metamorphism. Additionally, we analyzed the MOD estimates for trends across the Northern Hemisphere and separately for Eurasia and North America. A statistically significant negative trend toward earlier melt onset exists in all cases, which is consistent with previous studies.

Surface melting over the Greenland ice sheet from enhanced resolution passive microwave brightness temperatures (1979–2019)

“…Because of the large difference between dry and wet snow emissivity, even relatively small amounts of liquid water have a dramatic effect on the Tb values (e.g., Tedesco, 2009), making PMW data extremely suitable for mapping the extent and duration of melting at large spatial scales and high temporal resolution (in view of their insensitivity to atmospheric conditions at the low frequencies of the microwave spectrum). Consequently, PMW data have been widely adopted in melt detection studies and different remote sensing techniques have been proposed in the literature (e.g., Steffen et al, 1993;Joshi et al, 2001;Liu et al, 2005;Aschraft and Long, 2006;Macelloni et al, 2007;Tedesco et al, 2007;Kouki et al, 2019;Tedesco and Fettweis, 2020).…”

An Ensemble Learning Model Reveals Accelerated Reductions in Snow Depth Over Arctic Sea Ice Under High‐Emission Scenarios

“…Compared with the on‐site data, the albedo used had a relative accuracy of 3%–15% in ice‐covered regions (Karlsson et al., 2017b). Currently, there are few satellite‐derived albedo products available, and many albedo‐related studies have adopted CM SAF data sets (Kouki et al., 2019; Riihelä et al., 2019; Shao & Ke, 2015). Therefore, CM SAF albedo data were selected to assess the CMIP6 models.…”

“…Compared with the on‐site data, the albedo used had a relative accuracy of 3%–15% in ice‐covered regions (Karlsson et al., 2017b). Currently, there are few satellite‐derived albedo products available, and many albedo‐related studies have adopted CM SAF data sets (Kouki et al., 2019; Riihelä et al., 2019; Shao & Ke, 2015). Therefore, CM SAF albedo data were selected to assess the CMIP6 models. Optimal interpolation of sea surface temperature (OISST): The daily OISST data (Banzon et al., 2016; Huang et al., 2021) came from the National Oceanic and Atmospheric Administration Physical Science Laboratory (PSL) (PSL, 2020) and were obtained based on ship, buoy and AVHRR observations via the optimal interpolation method.…”

Evaluation of E3SM land model snow simulations over the western United States

“…Duration was calculated as the number of days between Accumulation_onset date and End_date. Depletion_onset_date and Midpoint_date were determined by fitting the f sno time series during the snowmelt season using the sigmoid function (Anttila et al, 2018;Böttcher et al, 2014;Kouki et al, 2019) as follows:…”