Changes In Snow Cover Research Articles

Spatiotemporal changes in snow cover over China during 1960–2013

Snow cover is highly sensitive to global warming, and it, in return, has huge impacts on global and regional climate change. Because of the limitations of in-situ observations, only a few studies have analyzed the dynamics of snow cover across the whole of China. In this study, we studied the spatio-temporal variabilities of snow cover over China and explored the covary of snow cover with precipitation and temperature in spring and winter based on the surface snow cover observations of 201 meteorological stations in China during 1960–2013. The temporal and spatial distribution characteristics of the mean daily snow depth and the number of snow cover days were analyzed. Results showed snow depth and the number of snow-cover days were high in Northeast China, Eastern China-Inner Mongolia, Northern Xinjiang, Southeastern and South Region of the Tibetan Plateau. The trend of snow depth and the number of snow-cover days varied among different regions. Snow depth recorded at 34.3% of the stations showed an increasing trend during the study period. There were increasing trends of the snow-cover days at 40.8% of stations. Increasing trends of the mean annual daily snow depth and the number of snow-cover days were statistically insignificant (p > 0.05) while an abrupt change of the number of snow cover days was found in 1975. Change points of the mean daily snow depth in autumn and winter occurred in 1987 and 2008, respectively. Change points of the number of snow-cover days in spring and winter occurred in 2009 and 1983, respectively. Snow cover in China covaried with various climate factors, i.e., precipitation or temperature. This study improved our knowledge on snow cover changes and is beneficial for further studies on climate change in China.

Arctic climate: changes in sea ice extent outweigh changes in snow cover

Abstract. Recent declines in Arctic sea ice and snow extent have led to an increase in the absorption of solar energy at the surface, resulting in additional surface heating and a further decline in snow and ice. Using 34 years of satellite data, 1982–2015, we found that the positive trend in solar absorption over the Arctic Ocean is more than double that over Arctic land, and the magnitude of the ice–albedo feedback is four times that of the snow–albedo feedback in summer. The timing of the high-to-low albedo transition has shifted closer to the greater insolation of the summer solstice over ocean, but further away from the summer solstice over land. Therefore, decreasing sea ice cover, not changes in terrestrial snow cover, has been the dominant radiative feedback mechanism over the last few decades.

Snow cover is a neglected driver of Arctic biodiversity loss

Snow has far-reaching effects on ecosystem processes and biodiversity in high-latitude ecosystems, but these have been poorly considered in climate change impact models1,2. Here, to forecast future trends in species occurrences and richness, we fitted species–environment models with temperature data from three climate scenarios and simulated up to a 40% decrease in snow cover duration (SCD)3. We used plot-scale data on 273 vascular plant, moss and lichen species in 1,200 study sites spanning a wide range of environmental conditions typical for mountainous Arctic landscapes (within 165 km2). According to the models, a rise in temperature increased overall species richness and caused only one species to lose all suitable habitat. In contrast, a shorter SCD tempered the effect of increasing temperature on species richness and led to accelerated rates of species’ local extinctions after a tipping point at 20–30% SCD decrease. All three species groups showed similar extinction rates but contrasting species richness responses. Our simulations indicate that future biodiversity patterns in Arctic regions are highly dependent on the evolution of snow conditions. Climate impact models that ignore the effects of snow cover change may provide biased biodiversity projections, with potentially erratic implications for Arctic nature conservation planning. Arctic biodiversity patterns will be highly dependent on the evolution of snow conditions, according to simulation results that integrate observations of vascular plants, mosses and lichens over a range of Arctic landscapes.

How Are Turbulent Sensible Heat Fluxes and Snow Melt Rates Affected by a Changing Snow Cover Fraction?

The complex interaction between the atmospheric boundary layer and the heterogeneous land surface is typically not resolved in numerical models approximating the turbulent heat exchange processes. In this study, we consider the effect of the land surface heterogeneity on the spatial variability of near-surface air temperature fields and on snow melt processes. For this purpose we calculated turbulent sensible heat fluxes and daily snow depth depletion rates with the physics-based surface process model Alpine3D. To account for the effect of a heterogeneous land surface (such as patchy snow covers) on the local energy balance over snow, Alpine3D is driven by two-dimensional atmospheric fields of air temperature and wind velocity, generated with the non-hydrostatic atmospheric model ARPS. The atmospheric model is initialized with a set of snow distributions (snow cover fraction and number of snow patches) and atmospheric conditions (wind velocities) for an idealized flat test site. Numerical results show that the feedback of the heterogeneity of the land surface (snow, no snow) on the near-surface variability of the atmospheric fields result in a significant increase in the mean air temperature ∆TA= 1.8 K (3.7 K, 4.9 K) as the snow cover fraction is decreased from a continuous snow cover to 55 % (25 %, 5 %). Surface turbulent sensible heat fluxes and daily snow depth depletion rates are strongly correlated to mean air temperatures, leading to 22-40 % larger daily snow depth depletion rates for patchy snow covers. Mean air temperatures over snow heavily increase with increasing initial wind velocities and weakly increase with an increasing number of snow patches. Numerical results from the idealized test site are compared with a test site in complex terrain. As slope-induced atmospheric processes (such as the development of katabatic flows) modify turbulent sensible heat fluxes, the variation of the surface energy balance is larger in complex terrain than for an idealized flat test site.

Influence of Tibetan Plateau snow cover on East Asian atmospheric circulation at medium-range time scales

The responses of atmospheric variability to Tibetan Plateau (TP) snow cover (TPSC) at seasonal, interannual and decadal time scales have been extensively investigated. However, the atmospheric response to faster subseasonal variability of TPSC has been largely ignored. Here, we show that the subseasonal variability of TPSC, as revealed by daily data, is closely related to the subsequent East Asian atmospheric circulation at medium-range time scales (approximately 3–8 days later) during wintertime. TPSC acts as an elevated cooling source in the middle troposphere during wintertime and rapidly modulates the land surface thermal conditions over the TP. When TPSC is high, the upper-level geopotential height is lower, and the East Asia upper-level westerly jet stream is stronger. This finding improves our understanding of the influence of TPSC at multiple time scales. Furthermore, our work highlights the need to understand how atmospheric variability is rapidly modulated by fast snow cover changes.

Forecasted homogenization of high Arctic vegetation communities under climate change

AbstractAimClimate change results in increasing temperature and modified precipitation regimes in the High Arctic. Models can help anticipate the consequences on future biotic dynamics, e.g. vegetation. In rugged terrain, forecasts should consider fine‐scale spatial variability in environmental conditions that are proximally linked to plant performance. Here, we forecasted Arctic plant community response to future climate change using high‐resolution environmental variables.LocationZackenberg in the High Arctic of Greenland.MethodsUsing a combination of remote‐sensing data and field measurements, we interpolated soil moisture and temperature at 1 m resolution together with spatial data on snow cover and solar radiation. We calibrated stacked species distribution models (S‐SDMs) with data from 200 vegetation plots. To explore the sensitivity of Arctic communities to climate change, we forecasted these models under simulated increases in temperature and changes (positive or negative) in snow cover and soil moisture, corresponding to more winter and/or summer precipitation and higher frequencies of summer droughts.ResultsS‐SDMs associated with high‐resolution environmental variables were able to reproduce the spatial variation in species richness and plant community structure along a mountain slope in Zackenberg. Model forecasts under climate change revealed that most species reacted to a combination of changes in soil moisture and temperature, and changes in these two variables resulted in an extensive restructuring of the distributions of species assemblages. In most scenarios, a gradual homogenization of communities was forecasted due to shrub expansion.Main conclusionsIncreasing temperatures and altered soil moisture were predicted to turn the currently highly heterogeneous tundra landscape at Zackenberg into homogenous dwarf‐shrub tundra. Such homogenization of vegetation communities may have profound ramifications for species, interaction webs, and ecosystem processes via modifications to the surface albedo, energy and water balance, as well as snow accumulation and permafrost.

Simulation of Past Changes in the Austrian Snow Cover 1948–2009

Abstract A distributed snow model is applied to simulate the spatiotemporal evolution of the Austrian snow cover at 1 km × 1 km spatial and daily temporal resolution for the period 1948–2009. After a comprehensive model validation, changes in snow cover conditions are analyzed for all of Austria as well as for different Austrian subregions and elevation belts focusing on the change in snow cover days (SCDs). The comparison of SCDs for the period 1950–79 to those achieved for 1980–2009 for all of Austria shows a decrease in SCDs with a maximum of >35 SCDs near Villach (Carinthia). The analysis of SCD changes in different subregions of Austria reveals mean changes between −11 and −15 days with highest absolute change in SCDs for southern Austria. Two decrease maxima could be identified in elevations of 500–2000 m MSL (between −13 and −18 SCDs depending on the subregion considered) and above 2500 m MSL (over −20 SCDs in the case of central Austria). The temporal distribution of SCD change in the Austrian subregions is characterized by a reduction of SCDs in midwinter and at the end of winter rather than by fewer SCDs in early winter. With respect to the temporal distribution of SCD change in different elevation belts, changes in elevations below 1000 m MSL are characterized by a distinct reduction of SCDs in January. With increasing elevation the maximum change in SCDs shifts toward the summer season, reaching a maximum decrease in the months of June–August above 2500 m MSL.

Snow Cover Change as a Climate Indicator in Brunswick Peninsula, Patagonia

Snow cover changes are assessed for the Brunswick Peninsula in southern Patagonia (52.9oS to 53.5oS), located on the transition between the wet Pacific Ocean area and the drier leeward side of the Andes. We use the Normalized Difference Snow Index (NDSI) and a new index which we call SnowPower, combining the NDSI and the Melt Area Detection Index (MADI), to reconstruct the snow cover extent and its temporal distribution for the period 2000-2016, based on Moderate Resolution Imaging Spectroradiometer Sensor (MODIS) MOD09GA products. Comparison of these satellite products with available snow duration and snow height data for 2010-2016 at the local Club Andino ski centre of Punta Arenas shows that the NDSI exhibits the best results. A reasonable and significant linear correlation is found between the MODIS NDSI snow cover extent and the mean monthly temperature at Punta Arenas Airport combined with the monthly snow accumulation at Jorge Schythe station at Punta Arenas city for the extended winter period (April to September) from 2000 to 2016. Snow cover changes within this time series are extended to 1972 and 1958 based on historical climate data of Jorge Schythe and Punta Arenas airport, repectively. The results show a significant decreasing trend of snow extent of 19% for Brunswick Peninsula for the 45-year period (1972-2016), which can be attributed to a statistically significant long-term warming of 0.71oC at Punta Arenas during the extended winter (April to September) in the same period. Multiple correlation with different climate variables indicates that solid precipitation has a relevant role on short-term snow cover variability, but is not related to the observed long-term snow cover decrease.

Multi-year particle fluxes in Kongsfjorden, Svalbard

Abstract. High-latitude regions are warming faster than other areas due to reduction of snow cover and sea ice loss and changes in atmospheric and ocean circulation. The combination of these processes, collectively known as polar amplification, provides an extraordinary opportunity to document the ongoing thermal destabilisation of the terrestrial cryosphere and the release of land-derived material into the aquatic environment. This study presents a 6-year time series (2010–2016) of physical parameters and particle fluxes collected by an oceanographic mooring in Kongsfjorden (Spitsbergen, Svalbard). In recent decades, Kongsfjorden has been experiencing rapid loss of sea ice coverage and retreat of local glaciers as a result of the progressive increase in ocean and air temperatures. The overarching goal of this study was to continuously monitor the inner fjord particle sinking and to understand to what extent the temporal evolution of particulate fluxes was linked to the progressive changes in both Atlantic and freshwater input. Our data show high peaks of settling particles during warm seasons, in terms of both organic and inorganic matter. The different sources of suspended particles were described as a mixing of glacier carbonate, glacier siliciclastic and autochthonous marine input. The glacier releasing sediments into the fjord was the predominant source, while the sediment input by rivers was reduced at the mooring site. Our time series showed that the seasonal sunlight exerted first-order control on the particulate fluxes in the inner fjord. The marine fraction peaked when the solar radiation was at a maximum in May–June while the land-derived fluxes exhibited a 1–2-month lag consistent with the maximum air temperature and glacier melting. The inter-annual time-weighted total mass fluxes varied by 2 orders of magnitude over time, with relatively higher values in 2011, 2013, and 2015. Our results suggest that the land-derived input will remarkably increase over time in a warming scenario. Further studies are therefore needed to understand the future response of the Kongsfjorden ecosystem alterations with respect to the enhanced release of glacier-derived material.

GIS and RS Based Approach for Monitoring the Snow Cover Change in Gilgit Baltistan

GIS and RS Based Approach for Monitoring the Snow Cover Change in Gilgit Baltistan

Spatial-temporal variations analysis of snow cover in China from 1992−2010

Snow cover is an important component of land cover on the Earth’s surface and is also a good indicator of climate change. Hence, monitoring the spatial distribution and temporal variation of snow cover is of great significance to the study of the global water cycle and climate change. Traditional snow cover monitoring has been primarily based on in situ observations; however, the uneven and low-density distribution of meteorological stations made it difficult to reflect the overall picture of snow cover in some regions. To solve this problem, we used the Presence and Background Learning (PBL) algorithm to estimate the snow cover in China and obtained the 5 days (5 d) snow cover maps of the Special Sensor Microwave/Imager (SSM/I). The PBL model is a type of one-class classifier that needs no negative training sample in the training set. The cornerstone of this method is to combine SSM/I Brightness Temperature data and in situ observations to estimate the probability of the existence of snow cover based on the Artificial Neural Network (ANN). The estimation result indicates that the average annual overall accuracy of the PBL model in China is 0.88, which shows good agreement with the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover products. Compared with other snow cover products, such as MODIS/Terra Snow Cover 8-Day L3 Global 500 m Grid (MOD10A2) and AMSR-E/Aqua 5-Day L3 Global Snow Water Equivalent EASE-Grids(AE_5DSno), the performance of the PBL model is better at predicting the snow cover distribution in China. After obtaining the SSM/I 5d snow cover maps of China, we analysed the temporal and spatial variations of snow cover in China during 1992−2010 using the Mann-Kendall test, which included the variation of annual snow cover days, seasonal snow cover distribution and change characteristics, and the variation of the stable snow cover area. The results suggest that China’s snow cover is primarily distributed in the Tibetan Plateau, Xinjiang, northeast China, and Inner Mongolia, which are all high altitude or high latitude regions. From 1992−2010, the following occurred. (1) The number of snow cover days decreased significantly in the three major snow cover regions of China due to rising temperatures, while there was an observably upward trend in the northwestern Tibetan Plateau with the increase of precipitation. (2) The snow cover area of Xinjiang and Northeast-Inner Mongolia reached its maximum in winter and was relatively small in the spring and autumn. The Tibetan Plateau’s snow cover area is generally the largest in the spring, and it is also very large in the autumn and winter. Thus, the seasonal variation characteristics of snow cover there is not as obvious as that of the other two major snow regions. (3) In the four years of 1996, 2003, 2004 and 2006, the snow cover area in the spring in Xinjiang was much lower than that in the autumn, which indicates a “warmer winter” event caused by warmer than usual temperatures in the winter. (4) Although no significant change in the snow cover area was found in all regions during the study period, the change in snow cover on the Tibetan Plateau is particularly noteworthy due to its area having increased greatly since 2005, which might be the beginning of the increase of snow cover area in this region. (5) The stable snow cover area of China is 3.39 million km2. In the three major snow cover regions, the stable snow cover area of the Tibetan Plateau is the largest (1.68 million km2), the Northeast-Inner Mongolia region is the second largest (1.05 million km2), and the Xinjiang region is the smallest (0.63 million km2). The stable snow cover area had no significant variation trend in China during 1992−2010—only the snow cover days of the Tibetan Plateau had a larger interannual fluctuation.

Winter Ecosystem Respiration and Sources of CO2 From the High Arctic Tundra of Svalbard: Response to a Deeper Snow Experiment

AbstractCurrently, there is a lack of understanding on how the magnitude and sources of carbon (C) emissions from High Arctic tundra are impacted by changing snow cover duration and depth during winter. Here we investigated this issue in a graminoid tundra snow fence experiment on shale‐derived gelisols in Svalbard from the end of the growing season and throughout the winter. To characterize emissions, we measured ecosystem respiration (Reco) along with its radiocarbon (14C) content. We assessed the composition of soil organic matter (SOM) by measuring its bulk‐C and nitrogen (N), 14C content, and n‐alkane composition. Our findings reveal that greater snow depth increased soil temperatures and winter Reco (25 mg C m−2 d−1 under deeper snow compared to 13 mg C m−2 d−1 in ambient conditions). At the end of the growing season, Reco was dominated by plant respiration and microbial decomposition of C fixed within the past 60 years (Δ14C = 62 ± 8‰). During winter, emissions were significantly older (Δ14C = −64 ± 14‰), and likely sourced from microorganisms decomposing aged SOM formed during the Holocene mixed with biotic or abiotic mineralization of the carbonaceous, fossil parent material. Our findings imply that snow cover duration and depth is a key control on soil temperatures and thus the magnitude of Reco in winter. We also show that in shallow Arctic soils, mineralization of carbonaceous parent materials can contribute significant proportions of fossil C to Reco. Therefore, permafrost‐C inventories informing C emission projections must carefully distinguish between more vulnerable SOM from recently fixed biomass and more recalcitrant ancient sedimentary C sources.

Effects of changes in seasonal snow-cover on litter decomposition and soil nitrogen dynamics in forests.

Changes in snow-cover patterns induced by global climate change profoundly influence ecological processes in terrestrial ecosystems, including litter decomposition and soil nutrient cycling. Forest, a major terrestrial ecosystem, plays a crucial role in global biogeochemical cycling. Here, we reviewed the effects of changes in seasonal snow-cover on litter decomposition and soil nitrogen (N) cycling in forests. Global climate change would result in increasing or decreasing seasonal snow-cover depending on local conditions, with direct and indirect effects on forest litter decomposition. The changes in seasonal snow-cover would directly affect decomposition process by changing environmental temperature and moisture, litter quality, and decomposer dynamics, and would indirectly influence decomposition via altering community structure, vegetation phenology, and soil nutrients. Meanwhile, the changes in seasonal snow-cover would modify forest soil N dynamics through changing N enrichment, soil temperature and moisture, freeze-thaw cycle, forest community, subnivean fauna and microorganisms. Further studies in this field should focus on: 1) employing experiments with divergent protocols to simulate diverse changing patterns of seasonal snow-cover under the global climate change scenarios; 2) the effects of the seasonal snowmelt leaching on forest litter decomposition and soil N dynamics; 3) elucidating mechanisms underlying forest litter decomposition and soil N dynamics driven by changes in seasonal snow-cover patterns in different ecosystems and climate zones; and 4) quantifying the instantaneous and prolonged effects of changes in seasonal snow-cover on forest litter decomposition and soil N dynamics in the snow-covered and snow-free seasons, respectively. These studies will provide theoretical basis and solid data support for the understanding and model-prediction of the responses of the biogeochemical cycle in terrestrial ecosystems to global climate change.

Developing a composite daily snow cover extent record over the Tibetan Plateau from 1981 to 2016 using multisource data

Snow cover condition across the Tibetan Plateau (TP) is not only a significant indicator of climate change but also a vital variable in water availability because of its water storage function in high-mountain regions of Southwest China and the surrounding Asian countries. Limited by low spatial resolution, incomplete spatial coverage, and short time span of the current snow cover products, the long-term snow cover change across the TP under the climate change background remains unclear. To resolve this issue, a composite long-term gap-filled TP daily 5-km snow cover extent (SCE) record (TPSCE) is generated by integrating SCE from the Advanced Very High-Resolution Radiometer (AVHRR) surface reflectance climate data record (CDR) and several existing snow cover data sets, with the help of a decision tree snow cover mapping algorithm, for the period 1981–2016. A snow discrimination process was used to classify the land surface into snow (pre-TPSCE) and non-snow using AVHRR surface reflectance CDR. To fill gaps caused by invalid observations and cloud contamination in pre-TPSCE, several existing daily SCE products, including MOD10C1, MYD10C1, IMS, JASMES, and a passive microwave snow depth data set are employed in the composition process. The daily snow discrimination accuracy, tested by ground snow-depth observations during 2000–2014, shows that the TPSCE captures the distribution of snow duration days (R2 = 0.80, bias = 3.93 days) effectively. The comparison between the TPSCE and fine-resolution snow cover maps (MCD10A1-TP) indicates high comparability between the TPSCE and MCD10A1-TP. In addition, cross-comparisons with changes in temperature, precipitation, and land surface albedo indicate that the TPSCE is reliable in climate change studies. In summary, the TPSCE is spatially complete and covers the longest period among all current snow cover products from satellite observations. The TPSCE seamlessly records changes in snow cover across the TP over the past 36 years, thereby providing valuable snow information for climate change and hydrological studies.

Socio-hydrology of \u201cartificial glaciers\u201d in Ladakh, India: assessing adaptive strategies in a changing cryosphere

The consequences of even small glacier decrease and changes of seasonal snow cover are critical for the functioning of meltwater-dependent mountain agriculture. In order to deal with recurrent water scarcity, different types of ice reservoirs, commonly called “artificial glaciers,” have been introduced in Ladakh and promoted as appropriate adaptive strategies to cope with changes in the cryosphere. The resulting seasonal ice reservoirs increase meltwater availability during the critical period of water scarcity in spring. We examine the efficacy of 14 ice reservoirs through a long-term analysis of their functioning within the environmental and socioeconomic context of Ladakh. Using multi-temporal satellite data (1969–2017), close range photogrammetry, and repeat field measurements (2014 and 2015), we provide an inventory and typology of these ice reservoirs and estimate storage volume of one selected structure, which ranges from 1010 to 3220 m3 of water. We extrapolate this volume to all ice reservoirs and estimate potential irrigation cycles of cropped areas, which vary between less than 0.1 in unfavorable cases and almost 3 in optimal cases and years. Based on interviews and field surveys (2007–2017), we discuss the benefits perceived by local smallholders, such as the reduction of seasonal water scarcity and resulting crop failure risks together with the possibility of growing cash crops. We argue that “artificial glaciers” are remarkably suited to the physical environment. However, their usefulness as a climate change adaptation strategy is questionable because climatic variability, natural hazards, and an incomplete integration into the local socioeconomic setting significantly reduce their efficacy.

Bacterial and fungal communities in boreal forest soil are insensitive to changes in snow cover conditions.

The northern regions are experiencing considerable changes in winter climate leading to more frequent warm periods, rain-on-snow events and reduced snow pack diminishing the insulation properties of snow cover and increasing soil frost and freeze-thaw cycles. In this study, we investigated how the lack of snow cover, formation of ice encasement and snow compaction affect the size, structure and activities of soil bacterial and fungal communities. Contrary to our hypotheses, snow manipulation treatments over one winter had limited influence on microbial community structure, bacterial or fungal copy numbers or enzyme activities. However, microbial community structure and activities shifted seasonally among soils sampled before snow melt, in early and late growing season and seemed driven by substrate availability. Bacterial and fungal communities were dominated by stress-resistant taxa such as the orders Acidobacteriales, Chaetothyriales and Helotiales that are likely adapted to adverse winter conditions. This study indicated that microbial communities in acidic northern boreal forest soil may be insensitive to direct effects of changing snow cover. However, in long term, the detrimental effects of increased ice and frost to plant roots may alter plant derived carbon and nutrient pools to the soil likely leading to stronger microbial responses.

Two decades of altered snow cover does not affect soil microbial ability to catabolize carbon compounds in an oceanic alpine heath

Snow strongly affects ecosystem functioning in alpine environments with potential carry-over effects outside of snow periods. However, it is unclear whether changes in snow cover affect microbial community functioning in summer. In a field experiment, we tested whether manipulation of snow cover affected the functional capabilities of the microbial community either directly, or indirectly through concomitant changes in the vegetation. While 23 years of differential snow depth and persistence fundamentally changed the vegetation composition, the microbial community's ability to catabolize a range of carbon compounds was not altered. Instead, soil moisture content was the key driver of carbon catabolism by the microbial community.

VARIABILITY OF THE ICELANDIC LOW AND THE AZORES HIGH IN JANUARY AND THEIR INFLUENCE ON CLIMATIC CONDITIONS IN POLAND

The study analysed long-term changes and the variability of the Icelandic Low and the Azores High in January and their influence on thermal, precipitation and nival conditions in Poland in the period 1901–2000. There were no statistically significant (0.05) trends of analysed centres of action in January in the 20th century. However, in the second half of the century the center of the Icelandic Low in January moved northwards and the sea-level pressure in the center of the Azores High increased significantly. The latter change was the reason for the significant increasing difference between the Azores High and the Icelandic Low. This change caused the intensification of western advection over Europe. Changes in sea-level pressure in the Icelandic Low and the longitude of the Azores High in January explain about 40% of temperature and snow cover variability in Poland. The variability of some features of the centres of action describes the changes of precipitation and snow cover in Poland better than the NAO index.

Canadian snow and sea ice: historical trends and projections

Abstract. The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state of the art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. Here, we present an assessment from the CanSISE Network on trends in the historical record of snow cover (fraction, water equivalent) and sea ice (area, concentration, type, and thickness) across Canada. We also assess projected changes in snow cover and sea ice likely to occur by mid-century, as simulated by the Coupled Model Intercomparison Project Phase 5 (CMIP5) suite of Earth system models. The historical datasets show that the fraction of Canadian land and marine areas covered by snow and ice is decreasing over time, with seasonal and regional variability in the trends consistent with regional differences in surface temperature trends. In particular, summer sea ice cover has decreased significantly across nearly all Canadian marine regions, and the rate of multi-year ice loss in the Beaufort Sea and Canadian Arctic Archipelago has nearly doubled over the last 8 years. The multi-model consensus over the 2020–2050 period shows reductions in fall and spring snow cover fraction and sea ice concentration of 5–10 % per decade (or 15–30 % in total), with similar reductions in winter sea ice concentration in both Hudson Bay and eastern Canadian waters. Peak pre-melt terrestrial snow water equivalent reductions of up to 10 % per decade (30 % in total) are projected across southern Canada.

Changes in Andes snow cover from MODIS data, 2000–2016

Abstract. The Andes span a length of 7000 km and are important for sustaining regional water supplies. Snow variability across this region has not been studied in detail due to sparse and unevenly distributed instrumental climate data. We calculated snow persistence (SP) as the fraction of time with snow cover for each year between 2000 and 2016 from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors (500 m, 8-day maximum snow cover extent). This analysis is conducted between 8 and 36∘ S due to high frequency of cloud (> 30 % of the time) south and north of this range. We ran Mann–Kendall and Theil–Sens analyses to identify areas with significant changes in SP and snowline (the line at lower elevation where SP = 20 %). We evaluated how these trends relate to temperature and precipitation from Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA2) and University of Delaware datasets and climate indices as El Niño–Southern Oscillation (ENSO), Southern Annular Mode (SAM), and Pacific Decadal Oscillation (PDO). Areas north of 29∘ S have limited snow cover, and few trends in snow persistence were detected. A large area (34 370 km2) with persistent snow cover between 29 and 36∘ S experienced a significant loss of snow cover (2–5 fewer days of snow year−1). Snow loss was more pronounced (62 % of the area with significant trends) on the east side of the Andes. We also found a significant increase in the elevation of the snowline at 10–30 m year−1 south of 29–30∘ S. Decreasing SP correlates with decreasing precipitation and increasing temperature, and the magnitudes of these correlations vary with latitude and elevation. ENSO climate indices better predicted SP conditions north of 31∘ S, whereas the SAM better predicted SP south of 31∘ S.