Empirical modelling of snow cover duration patterns in complex terrains of Italy

Snow cover duration is a crucial climate change indicator. However, measurements of days with snow cover on the ground (DSG) are limited, especially in complex terrains, and existing measurements are fragmentary and cover only relatively short time periods. Here, we provide observational and modelling evidence that it is possible to produce reliable time-series of DSG for Italy based on instrumental measurements, and historical documentary data derived from various sources, from a limited set of stations and areas in the central-southern Apennines (CSA) of Italy. The adopted modelling approach reveals that DSG estimates in most settings in Italy can be driven by climate factors occurring in the CSA. Taking into account spatial scale-dependence, a parsimonious model was developed by incorporating elevation, winter and spring temperatures, a large-scale circulation index (the Atlantic Multidecadal Variability, AMV) and a snow-severity index, with in situ DSG data, based on a core snow cover dataset covering 97 years (88% coverage in the 1907–2018 period and the rest, discontinuously from 1683 to 1895, from historical data of the Benevento station). The model was validated on the basis of the identification of contemporary snow cover patterns and historical evidence of summer snow cover in high massifs. Beyond the CSA, validation obtained across terrains of varying complexity in both the northern and southern sectors of the peninsula indicate that the model holds potential for applications in a broad range of geographical settings and climatic situations of Italy. This article advances the study of past, present and future DSG changes in the central Mediterranean region.

Twentieth century temperature and snow cover changes in the French Alps

Changes in snow cover associated with the warming of the French Alps greatly influence social-ecological systems through their impact on water resources, mountain ecosystems, economic activities, and glacier mass balance. In this study, we investigated trends in snow cover and temperature over the twentieth century using climate model and reanalysis data. The evolution of temperature, precipitation and snow cover in the European Alps has been simulated with the Modèle Atmospherique Régional (MAR) applied with a 7-km horizontal resolution and driven by ERA-20C (1902-2010) and ERA5 (1981–2018) reanalyses data. Snow cover duration and snow water equivalent (SWE) simulated with MAR are compared to the SAFRAN - SURFEX-ISBA-Crocus - MEPRA meteorological and snow cover reanalysis (S2M) data across the French Alps (1958–2018) and in situ glacier mass balance measurements. MAR outputs provide a realistic distribution of SWE and snow cover duration as a function of elevation in the French Alps. Large disagreements are found between the datasets in terms of absolute warming trends over the second part of the twentieth century. MAR and S2M trends are in relatively good agreement for the decrease in snow cover duration, with higher decreases at low elevation ($\sim $ ∼ 5–10%/decade). Consistent with other studies, the highest warming rates in MAR occur at low elevations (< 1000 m a.s.l) in winter, whereas they are found at high elevations (> 2000 m a.s.l) in summer. In spring, warming trends show a maximum at intermediate elevations (1500 to 1800 m). Our results suggest that higher warming at these elevations is mostly linked to the snow-albedo feedback in spring and summer caused by the disappearance of snow cover at higher elevation during these seasons. This work has evidenced that depending on the season and the period considered, enhanced warming at higher elevations may or may not be found. Additional analysis in a physically comprehensive way and more high-quality dataset, especially at high elevations, are still required to better constrain and quantify climate change impacts in the Alps and its relation to elevation.

Trends in Snow Cover Duration Across River Basins in High Mountain Asia From Daily Gap-Filled MODIS Fractional Snow Covered Area

High Mountain Asia (HMA) has the largest expanse of snow outside of the polar regions and it plays a critical role in climate and hydrology. In situ monitoring is rare due to terrain complexity and inaccessibility, making remote sensing the most practical way to understand snow patterns in HMA despite relatively short periods of record. Here, trends in snow cover duration were assessed using MODIS between 2002 and 2017 across the headwaters of the region’s primary river basins (Amu Darya, Brahmaputra, Ganges, Indus, and Syr Darya). Data limitations, associated with traditional binary mapping and data gaps due to clouds, were addressed with a daily, spatially and temporally complete, snow cover product that maps the fraction of snow in each pixel using spectral mixture analysis. Trends in fractional snow cover duration (fSCD) were calculated at the annual and monthly scale, and across 1,000 m elevation bands, and compared to trends in binary snow cover duration (SCD). Snow cover is present, on average, for 102 days across all basin headwaters, with the longest duration in western basins and shortest in eastern basins. Broadly, snow cover is in decline, which is most pronounced in elevation bands where snow is most likely to be present and most needed to sustain glaciers. Some of the strongest negative trends in fSCD were in the Syr Darya, which has 13 fewer days between 4,000–5,000 m, and Brahmaputra, which has 31 fewer days between 5,000–6,000 m. The only increasing tendency was found in the Indus between 2,000 and 5,000 m. There were differences between fSCD and SCD trends, due to SCD overestimating snow cover area relative to fSCD.

Yearly Variations in GCM Concentrations in Female Mountain Hares (Lepus timidus) and the Effect of Pregnancy

The measurement of glucocorticoid metabolites (GCMs) in faeces has become a widely used and effective tool for evaluating the amount of stress experienced by animals. However, the potential sampling bias resulting from an oversampling of individuals in different states of pregnancy has rarely been investigated. In this study, we validate a noninvasive method for measuring gestagen metabolites in female mountain hares (Lepus timidus) under controlled conditions. We also measured the concentration of gestagen metabolites of females in a free-ranging population during the early breeding and post-breeding periods from 2014 to 2019. We found significant yearly variations in gestagen metabolites, which were related to the condition of the females due to the snow cover duration before and at the start of the reproduction period. GCMs were significantly influenced by the gestagen metabolite levels. These results are important for improving the interpretation of GCM concentrations in free-ranging populations during the breeding and reproductive periods.

COVID-19 lockdowns show reduced pollution on snow and ice in the Indus River Basin

Melting snow and ice supply water for nearly 2 billion people [J. S. Mankin, D. Viviroli, D. Singh, A. Y. Hoekstra, N. S. Diffenbaugh, Environ. Res. Lett. 10, 114016 (2015)]. The Indus River in South Asia alone supplies water for over 300 million people [S. I. Khan, T. E. Adams, “Introduction of Indus River Basin: Water security and sustainability” in Indus River Basin, pp. 3−16 (2019)]. When light-absorbing particles (LAP) darken the snow/ice surfaces, melt is accelerated, affecting the timing of runoff. In the Indus, dust and black carbon degrade the snow/ice albedos [S. M. Skiles, M. Flanner, J. M. Cook, M. Dumont, T. H. Painter, Nat. Clim. Chang. 8, 964−971 (2018)]. During the COVID-19 lockdowns of 2020, air quality visibly improved across cities worldwide, for example, Delhi, India, potentially reducing deposition of dark aerosols on snow and ice. Mean values from two remotely sensed approaches show 2020 as having one of the cleanest snow/ice surfaces on record in the past two decades. A 30% LAP reduction in the spring and summer of 2020 affected the timing of 6.6 km3 of melt water. It remains to be seen whether there will be significant reductions in pollution post−COVID-19, but these results offer a glimpse of the link between pollution and the timing of water supply for billions of people. By causing more solar radiation to be reflected, cleaner snow/ice could mitigate climate change effects by delaying melt onset and extending snow cover duration.

Observed snow depth trends in the European Alps: 1971 to 2019

The European Alps stretch over a range of climate zones which affect the spatial distribution of snow. Previous analyses of station observations of snow were confined to regional analyses. Here, we present an Alpine-wide analysis of snow depth from six Alpine countries – Austria, France, Germany, Italy, Slovenia, and Switzerland – including altogether more than 2000 stations of which more than 800 were used for the trend assessment. Using a principal component analysis and k-means clustering, we identified five main modes of variability and five regions which match the climatic forcing zones: north and high Alpine, north-east, north-west, south-east, and south and high Alpine. Linear trends of monthly mean snow depth between 1971 and 2019 showed decreases in snow depth for most stations from November to May. The average trend among all stations for seasonal (November to May) mean snow depth was −8.4 % per decade, for seasonal maximum snow depth −5.6 % per decade, and for seasonal snow cover duration −5.6 % per decade. Stronger and more significant trends were observed for periods and elevations where the transition from snow to snow-free occurs, which is consistent with an enhanced albedo feedback. Additionally, regional trends differed substantially at the same elevation, which challenges the notion of generalizing results from one region to another or to the whole Alps. This study presents an analysis of station snow depth series with the most comprehensive spatial coverage in the European Alps to date.

Snowfall and snow cover evolution in the Eastern Pre-Pyrenees (NE Iberian Peninsula)

Snow cover has significant impacts on geoecological dynamics as well as on socio-economical systems. An accurate quantification of snow precipitation patterns in mountain regions is needed to better understand the spatio-temporal implications of snow cover. The objective of this work is to characterize the patterns of solid precipitation and snow cover in two high Mediterranean massifs. To this purpose, we analyse instrumental data series of snowfall and snow depth of Port del Comte (2316 m a.s.l.) and Cadí-Nord (2134 m). Both stations are situated in the eastern Pre-Pyrenees and include 14 consecutive snow seasons from November to May, allowing to (i) explore the dependence of the main drivers of snowpack: temperature and snowfall; (ii) find out the most frequent circulation weather types associated with high intensity snowfall events, and finally (iii) investigate the role of the North Atlantic Oceanic (NAO) teleconnection pattern explaining snow cover evolution during the winter season. Data show that snowfall is controlled by similar weather types in both stations that resulted in similar snowfall averages: 205 cm and 258 cm at Port del Comte and Cadí-Nord, respectively. Nevertheless, local factors interfere with the amount of snow depth recorded, which is moderately different between stations. Whereas Cadí-Nord records a seasonal mean of 66 cm, Port del Comte records a smaller quantity of 25 cm with a high interannual and seasonal variability. In fact, snowfall recurrence, snow amount or duration in the ground is considerably variable among years (CV20). In these stations, snow cover duration is determined by the precipitation in the form of snow falling during the previous months. Snowfalls in moderate to severe episodes (15 cm in 24 h) are mainly driven by Atlantic flows, mostly from NW. In addition, NAO pattern is negatively correlated with snowfall in November and December months (R-0.50), showing a weaker and not statistically significant correlation during the rest of the winter season.

Observed snow depth trends in the European Alps 1971 to 2019

&lt;p&gt;The European Alps stretch over a range of climate zones, which affect the spatial distribution of snow. Previous analyses of station observations of snow were confined to regional analyses, which complicates comparisons between regions and makes Alpine wide conclusions questionable. Here, we present an Alpine wide analysis of snow depth from six Alpine countries: Austria, France, Germany, Italy, Slovenia, and Switzerland; including altogether more than 2000 stations, of which more than 800 were used for the trend assessment. Using a principal component analysis and k-means clustering, we identified five main modes of variability and five regions, which match the climatic forcing zones: north &amp; high Alpine, northeast, northwest, southeast, and south &amp; high Alpine. Linear trends of monthly mean snow depth between 1971 and 2019 showed decreases in snow depth for most stations for November to May. The average trend among all stations for seasonal (November to May) mean snow depth was -8.4 % per decade, for seasonal maximum snow depth -5.6 % per decade, and for seasonal snow cover duration -5.6 % per decade. However, regional trends differed substantially after accounting for elevation, which challenges the notion of generalizing results from one region to another or to the whole Alps. This study presents an analysis of station snow depth series with the most comprehensive spatial coverage in the European Alps to date.&lt;/p&gt;

Snow cover duration trends observed at sites and predicted by multiple models

The 30-year simulations of seasonal snow cover in 22 physically based models driven with bias-corrected meteorological reanalyses are examined at four sites with long records of snow observations. Annual snow cover durations differ widely between models, but interannual variations are strongly correlated because of the common driving data. No significant trends are observed in starting dates for seasonal snow cover, but there are significant trends towards snow cover ending earlier at two of the sites in observations and most of the models. A simplified model with just two parameters controlling solar radiation and sensible heat contributions to snowmelt spans the ranges of snow cover durations and trends. This model predicts that sites where snow persists beyond annual peaks in solar radiation and air temperature will experience rapid decreases in snow cover duration with warming as snow begins to melt earlier and at times of year with more energy available for melting.

Lack of phenological shift leads to increased camouflage mismatch in mountain hares

Understanding whether organisms will be able to adapt to human-induced stressors currently endangering their existence is an urgent priority. Globally, multiple species moult from a dark summer to white winter coat to maintain camouflage against snowy landscapes. Decreasing snow cover duration owing to climate change is increasing mismatch in seasonal camouflage. To directly test for adaptive responses to recent changes in snow cover, we repeated historical (1950s) field studies of moult phenology in mountain hares ( Lepus timidus ) in Scotland. We found little evidence that population moult phenology has shifted to align seasonal coat colour with shorter snow seasons, or that phenotypic plasticity prevented increases in camouflage mismatch. The lack of responses resulted in 35 additional days of mismatch between 1950 and 2016. We emphasize the potential role of weak directional selection pressure and low genetic variability in shaping the scope for adaptive responses to anthropogenic stressors.