Snow cover variability and trend over Hindu Kush Himalayan region using MODIS and SRTM data

Snow cover changes has a direct bearing on the regional and global energy and water cycles, and the change in Earth's climate condition The study of long term altitudinal (spatial and temporal, 2000–2017) in the coverage of snow and glaciers in one of the world’s largest mountainous region, the Hindu Kush Himalayan (HKH) region including Tibet have been studied using remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra (at 5 km grid resolution). Terra provided a unique opportunity to study zonal and hypsographic changes in the intra-annual (growing season and melting season) and inter-annual variations of snow and glacial cover over the HKH region (2000–2017). The zonal and altitudinal (hypsographic) analyses were carried out for melting-season and accumulating-season. The altitude-wise linear trend analysis (Pearson’s) of snow cover, shown as a hypsographic curve, clearly indicate a major decline in snow cover (average of 5 % or more, at 100 m interval aggregates) between 4000–4500 m and 5500–6000 m altitudes, which is consistent with the median trend (Theil-Sen, TS) and the monotonic trend (Mann-Kendall statistics, MK) analysis. The regions and altitudes where major and statistically significant increase (10 to 30 %) or decrease (−10 to −30 %) in snow cover are identified. The extrapolation of the altitude-wise linear trend shows that it may take between ~74 to 7900 year (for 3001–6000 m and 6000–7000 m altitude zones respectively) for mean snow cover to decline approximately 25 % in the HKH region, assuming no-change in other parameters) that affect the snow cover.

Distribution and Attribution of Terrestrial Snow Cover Phenology Changes over the Northern Hemisphere during 2001–2020

Snow cover phenology has exhibited dramatic changes in the past decades. However, the distribution and attribution of the hemispheric scale snow cover phenology anomalies remain unclear. Using satellite-retrieved snow cover products, ground observations, and reanalysis climate variables, this study explored the distribution and attribution of snow onset date, snow end date, and snow duration days over the Northern Hemisphere from 2001 to 2020. The latitudinal and altitudinal distributions of the 20-year averaged snow onset date, snow end date, and snow duration days are well represented by satellite-retrieved snow cover phenology matrixes. The validation results by using 850 ground snow stations demonstrated that satellite-retrieved snow cover phenology matrixes capture the spatial variability of the snow onset date, snow end date, and snow duration days at the 95% significance level during the overlapping period of 2001–2017. Moreover, a delayed snow onset date and an earlier snow end date (1.12 days decade−1, p < 0.05) are detected over the Northern Hemisphere during 2001–2020 based on the satellite-retrieved snow cover phenology matrixes. In addition, the attribution analysis indicated that snow end date dominates snow cover phenology changes and that an increased melting season temperature is the key driving factor of snow end date anomalies over the NH during 2001–2020. These results are helpful in understanding recent snow cover change and can contribute to climate projection studies.

Sea ice and water classification on dual-polarized Sentinel-1 imagery during melting season

We provide a new sea ice and water classification product with high spatial and high temporal coverage using Sentinel-1 Synthetic Aperture Radar (SAR) data. The classification is applied in the Fram Strait region in the Arctic during melting seasons, when the contrast between backscatter intensities of different ice types observed by SAR is reduced due to the melted ice surface and wet snow on sea ice. The wet or melted snow strongly reduces the SAR penetration depth and thus suppresses the volume scattering contribution of sea ice. Furthermore, within the marginal sea ice zone (MIZ)ambiguities between ice and water can result from the effects of winds and ocean currents on the ocean SAR backscatter. On the other hand, under calm conditions the contrast between thin ice and flat open water can be reduced, and thusdecrease the separability of some ice. In summary, the melting season represents the most challenging time of the year forreliable ice-water classification from SAR data. We propose here a new approach to overcome these problems by using amixture statistical distribution based conditional random fields (MSTA-CRF) model. To obtain reliable ice-waterclassification whilst maintaining a fast computation time suitable for operational applications, the MSTA-CRF adopts a superpixel approach in the fully connected CRF model. The MSTA-CRF is a semantic model, which integrates statisticaldistributions (Gamma, Weibull, Alpha-Stable, etc.) to model the backscatters of ice and water and overcome the effects ofspeckle noise and wind-roughened water. Dual-polarization Extended Wide (EW) mode Sentinel-1A/1B SAR data with40 m spatial resolution is available several times per day within the Fram Strait region. Observations from June toSeptember during the six years 2015–2020 are collected and classified into ice and water categories. The classification performance of algorithm is evaluated using ice charts from the Ice Service at the Norwegian Meteorological Institute(MET Norway). The methods of training sample selection, and their application to processing large data volumes andautomatic classification of ice-water are discussed. In the experiment part, we demonstrate that the MSTA-CRF can providea good performance with about 90 % accuracy for ice-water classification, which is better than most of other state-of-theart algorithms. Compared with the 89 GHz microwave radiometer ASI sea ice concentration product, the sea ice extent in Fram Strait derived from MSTA-CRF algorithm is lower during melting seasons from 2015 to 2020, and the monthly Juneto September sea ice area does not change so much in 2015–2017 and 2019–2020, but it has a significant decrease in 2018.

Snow Algae Blooming Are Benefitable for Microinvertebrates Assemblages (Tardigrada and Rotifera) on the Seasonal Snow Patches in Japan

Although studies on snow algae and macroinvertebrates have been frequently conducted on snow patches, no attention have been paid to ubiquitous microinvertebrates which in other cold habitats reach high biomass and play various trophic roles. The aim of this study was to search microinvertebrates in seasonal snow patches in Mt. Gassan, in northern Japan, and identify factors determining their distribution associated with snow algal blooming of various coloration (red, green, and yellow). Microscopic observation revealed presence of two major groups of microinvertebrates Tardigrada and Rotifera. Tardigrades and rotifers were the most abundant and frequent in green snow formed by blooming of Chloromonas sp., but few in red or yellow snow. Body length of tardigrades increased through the melting season and animals laid eggs on colored snow. These results suggest tardigrades successfully grew and reproduced on snow patches. Taking into account the presence of tardigrades and rotifers mostly in green snow (only few found in red and yellow) with high densities, we may assume green snow patches constitute important and unique low-temperature ecosystems for microinvertebrates in a temperate mountainous forest. Area of snow algae blooming worldwide are unrecognized novel habitat for tardigrades and rotifers.

Diurnal Black-Sky Surface Albedo Parameterization of Snow

Surface albedo, the fraction of incoming solar radiation reflected hemispherically by the surface, is an essential climate variable (ECV) directly related to the energy budget of Earth. The presence and properties of snow cover alter surface albedo significantly, with variability in temporal scales reaching from seasonal to diurnal. The diurnal variation of snow albedo is typically parameterized with the solar zenith angle, but it cannot take into account asymmetry with respect to midday. Using the solar azimuth angle instead is suggested, since especially in the melting season the snow albedo varies highly asymmetrically during the day. To derive a general time- and latitude-independent formula, the azimuth angle values are normalized. Baseline Surface Radiation Network data are used to derive an empirical formula for the diurnal variation of snow black-sky surface albedo. The overall accuracy is on the order of 0.02, and the relative accuracy is about 3%.

Retrieval of Melt Pond Fraction over Arctic Sea Ice during 2000–2019 Using an Ensemble-Based Deep Neural Network

The accurate knowledge of variations of melt ponds is important for understanding the Arctic energy budget due to its albedo–transmittance–melt feedback. In this study, we develop and validate a new method for retrieving melt pond fraction (MPF) over Arctic sea ice using all seven spectral bands of MODIS surface reflectance. We construct a robust ensemble-based deep neural network and use in-situ MPF observations collected from multiple sources as the target data to train the network. We examine the potential influence of using sea ice concentration (SIC) from different sources as additional target data (besides MPF) on the MPF retrieval. The results suggest that the inclusion of SIC has a minor impact on MPF retrieval. Based on this, we create a new MPF data from 2000 to 2019 (the longest data in our knowledge). The validation shows that our new MPF data is in good agreement with the observations. We further compare the new MPF dataset with the previously published MPF datasets. It is found that the evolution of the new MPF is similar to previous MPF data throughout the melting season, but the new MPF data is in relatively better agreement with the observations in terms of correlations and root mean squared errors (RMSE), and also has the smallest value in the first half of the melting season.