Spring Snow Cover Research Articles

Detection and Attribution of Observed Changes in Northern Hemisphere Spring Snow Cover

Abstract Significant declines in spring Northern Hemisphere (NH) snow cover extent (SCE) have been observed over the last five decades. As one step toward understanding the causes of this decline, an optimal fingerprinting technique is used to look for consistency in the temporal pattern of spring NH SCE between observations and simulations from 15 global climate models (GCMs) that form part of phase 5 of the Coupled Model Intercomparison Project. The authors examined simulations from 15 GCMs that included both natural and anthropogenic forcing and simulations from 7 GCMs that included only natural forcing. The decline in observed NH SCE could be largely explained by the combined natural and anthropogenic forcing but not by natural forcing alone. However, the 15 GCMs, taken as a whole, underpredicted the combined forcing response by a factor of 2. How much of this underprediction was due to underrepresentation of the sensitivity to external forcing of the GCMs or to their underrepresentation of internal variability has yet to be determined.

Discharge regime and simulation for the upstream of major rivers over Tibetan Plateau

The hydrological regimes for the major river basins in the Tibetan Plateau (TP), including the source regions of the Yellow (UYE), Yangtze (UYA), Mekong (UM), Salween (US), Brahmaputra (UB), and Indus (UI) rivers, were investigated through a land surface model and regression analyses between climate variables and runoff data. A hydrologic modeling framework was established across the TP to link the Variable Infiltration Capacity (VIC) land surface hydrology model with a degree‐day glacier‐melt scheme (VIC‐glacier model) at a 1/12° × 1/12°. The model performance was evaluated over the upper basins of the six rivers. The heterogeneity and scarcity of the meteorological stations are the major limitation for hydrological modeling over the TP. The relative contributions to streamflow from rainfall, snowmelt, and glacier melt for the six basins were quantified via the model framework and simulation. The results suggest that monsoon precipitation has a dominant role in sustaining seasonal streamflow over southeastern regions, contributing 65–78% of annual runoff among the UYE, UYA, UM, US, and UB basins. For the UI, the runoff regime is largely controlled by the glacier melt and snow cover in spring and summer. The contribution of glacier runoff is minor for the UYE and UM (less than 2% of total annual flow), and moderate for the UYA and US basins (5–7% of yearly flow), while glacier melt makes up about 12% and 48% of annual flow for the UB and UI basins, respectively.

Subnivean Arctic and sub-Arctic net ecosystem exchange (NEE)

In the Arctic and sub-Arctic, up to half of annual net ecosystem exchange (NEE) occurs during the snow season. Subnivean soil respiration can persist at a greater rate when the overlying snowpack has a lower thermal conductivity, and the rate of photosynthetic uptake at the start and end of the snow season can be diminished by fractional snow cover. Although recent studies have indicated that uncertainty in model estimates of NEE can be reduced by representing the influence of a modeled snowpack on soil respiration, models of NEE have not represented the influence of snowpack dynamics on processes such as subnivean photosynthesis or CO2 diffusivity, and have not used remote sensing observations to characterize snow season processes. We therefore: (1) review snow season processes and their effects on NEE; (2) assess the suitability of cryospheric remote sensing approaches for models of NEE; and (3) suggest strategies for representing snow season processes in models of NEE. Strategies include: using observations of fractional snow cover in spring and fall to restrict estimates of photosynthetic uptake; combining observations of snow accumulation and soil freeze/thaw with observations of air temperature to generate more realistic estimates of soil temperature and soil respiration; and using observations of depth to estimate the influence of snow accumulation and tree wells on soil respiration. Including remote sensing observations of snow properties in models of NEE could reduce uncertainty in snow season estimates of NEE, resulting in a better understanding of the northern carbon cycle and how it is responding to climate-driven changes in the interconnected biospheric, atmospheric and cryospheric systems.

Observations of late winter Canadian tundra snow cover properties

AbstractTundra snow cover is important to monitor as it influences local, regional, and global‐scale surface water balance, energy fluxes, as well as ecosystem and permafrost dynamics. Observations are already showing a decrease in spring snow cover duration at high latitudes, but the impact of changing winter season temperature and precipitation on variables such as snow water equivalent (SWE) is less clear. A multi‐year project was initiated in 2004 with the objective to quantify tundra snow cover properties over multiple years at a scale appropriate for comparison with satellite passive microwave remote sensing data and regional climate and hydrological models. Data collected over seven late winter field campaigns (2004 to 2010) show the patterns of snow depth and SWE are strongly influenced by terrain characteristics. Despite the spatial heterogeneity of snow cover, several inter‐annual consistencies were identified. A regional average density of 0.293 g/cm3 was derived and shown to have little difference with individual site densities when deriving SWE from snow depth measurements. The inter‐annual patterns of SWE show that despite variability in meteorological forcing, there were many consistent ratios between the SWE on flat tundra and the SWE on lakes, plateaus, and slopes. A summary of representative inter‐annual snow stratigraphy from different terrain categories is also presented. © 2013 Her Majesty the Queen in Right of Canada. Hydrological Processes. © 2013 John Wiley & Sons, Ltd.

Study of aerosol effect on accelerated snow melting over the Tibetan Plateau during boreal spring

In the present study, a coupled atmosphere–ocean global climate model (CSIRO-Mk3.6) is used to investigate the role of aerosol forcing agents as drivers of snow melting trends in the Tibetan Plateau (TP) region. Anthropogenic aerosol-induced snow cover changes in a warming climate are calculated from the difference between historical run (HIST) and all forcing except anthropogenic aerosol (NoAA). Absorbing aerosols can influence snow cover by warming the atmosphere, reducing snow reflectance after deposition. The warming the rate of snow melt, exposing darker surfaces below to short-wave radiation sooner, and allowing them to heat up even faster in the Himalayas and TP.The results show a strong spring snow cover decrease over TP when absorbing anthropogenic aerosol forcing is considered, whereas snow cover fraction (SCF) trends in NoAA are weakly negative (but insignificant) during 1951–2005. The enhanced spring snow cover trends in HIST are due to overall effects of different forcing agents: When aerosol forcing (AERO) is considered, a significant reduction of SCF than average can be found over the western TP and Himalayas. The large decreasing trends in SCF over the TP, with the maximum reduction of SCF around 12–15% over the western TP and Himalayas slope. Also accelerated snow melting during spring is due to effects of aerosol on snow albedo, where aerosol deposition cause decreases snow albedo. However, the SCF change in the “NoAA” simulations was observed to be less.

Recent changes in pan‐Arctic melt onset from satellite passive microwave measurements

A new satellite passive microwave (PMW) melt onset retrieval algorithm based on temporal variations in the differences of the brightness temperature between 19 and 37 GHz is shown to be as effective as radar (e.g., QuikScat) measurements. The PMW technique shows improved melt estimates that are more closely linked to observed snow‐off dates than previous studies. An integrated pan‐Arctic (north of 50°N) melt onset date (MOD) dataset is produced by combining estimates on land and sea ice for the entire satellite PMW record. During the 1979–2011 period, significant trends of 2~3 days (decade)−1 to earlier MOD are mainly concentrated over the Eurasian land sector of the Arctic, consistent with changes in spring snow cover extent observed with visible satellite data. The variability and change in melt onset are largely driven by spring surface air temperature, with insignificant influence from low‐frequency modes of atmospheric circulation.

A retrospective analysis of pan Arctic permafrost using the JULES land surface model

There is mounting evidence that permafrost degradation has occurred over the past century. However, the amount of permafrost lost is uncertain because permafrost is not readily observable over long time periods and large scales. This paper uses JULES, the land surface component of the Hadley Centre global climate model, driven by different realisations of twentieth century meteorology to estimate the pan-arctic changes in near-surface permafrost. Model simulations of permafrost are strongly dependent on the amount of snow both in the driving meteorology and the way it is treated once it reaches the ground. The multi-layer snow scheme recently adopted by JULES significantly improves its estimates of soil temperatures and permafrost extent. Therefore JULES, despite still having a small cold bias in soil temperatures, can now simulate a near-surface permafrost extent which is comparable to that observed. Changes in snow cover have been shown to contribute to changes in permafrost and JULES simulates a significant decrease in late twentieth century pan-Arctic spring snow cover extent. In addition, large-scale modelled changes in the active layer are comparable with those observed over northern Russia. Simulations over the period 1967–2000 show a significant loss of near-surface permafrost—between 0.55 and 0.81 million km2 per decade with this spread caused by differences in the driving meteorology. These runs also show that, for the grid cells where the active layer has increased significantly, the mean increase is ~10 cm per decade. The permafrost degradation discussed here is mainly caused by an increase in the active layer thickness driven by changes in the large scale atmospheric forcing. However, other processes such as thermokarst development and river and coastal erosion may also occur enhancing permafrost loss.

An analysis of present and future seasonal Northern Hemisphere land snow cover simulated by CMIP5 coupled climate models

Abstract. The 20th century seasonal Northern Hemisphere (NH) land snow cover as simulated by available CMIP5 model output is compared to observations. On average, the models reproduce the observed snow cover extent very well, but the significant trend towards a reduced spring snow cover extent over the 1979–2005 period is underestimated (observed: (−3.4 ± 1.1)% per decade; simulated: (−1.0 ± 0.3)% per decade). We show that this is linked to the simulated Northern Hemisphere extratropical spring land warming trend over the same period, which is also underestimated, although the models, on average, correctly capture the observed global warming trend. There is a good linear correlation between the extent of hemispheric seasonal spring snow cover and boreal large-scale spring surface air temperature in the models, supported by available observations. This relationship also persists in the future and is independent of the particular anthropogenic climate forcing scenario. Similarly, the simulated linear relationship between the hemispheric seasonal spring snow cover extent and global mean annual mean surface air temperature is stable in time. However, the slope of this relationship is underestimated at present (observed: (−11.8 ± 2.7)% °C−1; simulated: (−5.1 ± 3.0)% °C−1) because the trend towards lower snow cover extent is underestimated, while the recent global warming trend is correctly represented.

Spring snow cover loss exceeds model predictions

Spring snow cover extent in the Northern Hemisphere has declined to record lows in recent years, Derksen and Brown report. They analyzed spring (April–June) snow cover extent from the National Oceanic and Atmospheric Administration snow chart climate data record. They found that recent years show statistically significant reductions in snow cover compared to the historic record. In Eurasia, new records for the lowest spring snow cover extent have been set every year since 2008, and in North America, record lows have been set in 3 of the past 5 years.

Spring snow cover extent reductions in the 2008–2012 period exceeding climate model projections

Analysis of Northern Hemisphere spring terrestrial snow cover extent (SCE) from the NOAA snow chart Climate Data Record (CDR) for the April to June period (when snow cover is mainly located over the Arctic) has revealed statistically significant reductions in May and June SCE. Successive records for the lowest June SCE have been set each year for Eurasia since 2008, and in 3 of the past 5 years for North America. The rate of loss of June snow cover extent between 1979 and 2011 (−17.8% decade−1) is greater than the loss of September sea ice extent (−10.6% decade−1) over the same period. Analysis of Coupled Model Intercomparison Project Phase 5 (CMIP5) model output shows the marked reductions in June SCE observed since 2005 fall below the zone of model consensus defined by +/−1 standard deviation from the multi‐model ensemble mean.

A longer climate memory carried by soil freeze–thaw processes in Siberia

The climate memory of a land surface generally persists for only a few months, but analysis of surface meteorological data revealed a longer-term climate memory carried by soil freeze–thaw processes in Siberia. Surface temperature variability during the snowmelt season corresponds reasonably well with that in the summer of the following year, when most stations show a secondary autocorrelation peak. The surface temperature memory is thought to be stored as variations in the amount of snowmelt water held in the soil, and through soil freezing, which emerges as latent heat variations in the near-surface atmosphere during soil thawing approximately one year later. The ground conditions are dry in the longer-term climate memory regions, such as eastern Siberia, where less snow cover (higher surface air temperature) in spring results in less snowmelt water or lower soil moisture in the summer. Consequently, through soil freezing, it will require less latent heat to thaw in the summer of the following year, resulting in higher surface air temperature. In addition to soil moisture and snow cover, soil freeze–thaw processes can also act as agents of climate memory in the near-surface atmosphere.

Projected decline in spring snow depth on Arctic sea ice caused by progressively later autumn open ocean freeze‐up this century

We present the first analysis of snow depth on Arctic sea ice in the Coupled Model Intercomparison Project 5 (CMIP5) because of its importance for sea ice thermodynamics and ringed seal (Phoca hispida) habitat. Snow depths in April on Arctic sea ice decrease over the 21st century in RCP2.6, RCP4.5, and RCP8.5 scenarios. The chief cause is loss of sea ice area in autumn and, to a lesser extent, winter. By the end of the 21st century in the RCP8.5 scenario, snowfall accumulation is delayed by about three months compared to the late 20th century in the multi‐model mean. Mean April snow depth north of 70°N declines from about 28 cm to 16 cm. Precipitation increases as expected in a warmer climate, but much of this increase in the Arctic occurs as rainfall. The seasonality of snowfall rate grows, with increasing rates in winter and decreasing rates in summer and autumn, but the cumulative snowfall from September to April does not change. Ringed seals depend on spring snow cover on Arctic sea ice to create subnivean birth lairs. The area with snow depths above 20 cm — a threshold needed for ringed seals to build snow caves — declines by 70%.

High Arctic plant community responses to a decade of ambient warming

The Arctic biome is subject to large climatic changes. Pivotal to our understanding of the ecological consequences of these changes is the understanding of how the vegetation responds to the altered abiotic conditions. To examine this, we conducted detailed vegetation composition analyses in permanent, replicated plots in eight widespread, distinct plant communities within the Zackenberg Valley in high Arctic Greenland in 1997 and 2008. This period was characterised by a considerable increase in summer temperature and declining spring snow cover. Across plant communities, we found substantial changes in cover of several functional groups and species, biodiversity and community structure. The fen area in particular had markedly changed, most likely in response to a decreasing supply of melt water. Other vegetation types, however, seemed resistant towards the changed environmental conditions. Hence, while some general patterns emerged, the response differed markedly between plant communities, and often with contrasting responses. Our results stress that although some high Arctic plant communities exhibit a relatively high degree of resistance towards environmental changes, the derived ecological effects of climate change in other plant communities may be substantial even on a decadal scale, and, hence, occur at a fast pace.

A remote sensing approach to biodiversity assessment and regionalization of the Canadian boreal forest

Successful conservation planning for the Canadian boreal forest requires biodiversity data that are both accessible and reliable. Spatially exhaustive data is required to inform on conditions, trends and context, with context enabling consideration of conservation opportunities and related trade-offs. However, conventional methods for measuring biodiversity, while useful, are spatially constrained, making it difficult to apply over wide geographic regions. Increasingly, remotely sensed imagery and methods are seen as a viable approach for acquiring explicit, repeatable and multi-scale biodiversity data over large areas. To identify relevant remotely derived environmental indicators specific to biodiversity within the Canadian boreal forest, we assessed indicators of the physical environment such as seasonal snow cover, topography and vegetation production. Specifically, we determined if the indicators provided distinct information and whether they were useful predictors of species richness (tree, mammal, bird and butterfly species). Using cluster analysis, we also assessed the applicability of these indicators for broad ecosystem classification of the Canadian boreal forest and the subsequent attribution of these stratified regions (i.e. clusters). Our results reveal that the indicators used in the cluster creation provided unique information and explained much of the variance in tree (92.6%), bird (84.07%), butterfly (61.4%) and mammal (22.6%) species richness. Spring snow cover explained the most variance in species richness. Results further show that the 15 clusters produced using cluster analysis were principally stratified along a latitudinal gradient and, while varied in size, captured a range of different environmental conditions across the Canadian boreal forest. The most important indicators for discriminating between the different cluster groups were seasonal greenness, a multipart measure of climate, topography and land use, and wetland cover, a measure of the percentage of wetland within a 1 km2 cell.

The wolverine's niche: linking reproductive chronology, caching, competition, and climate

Wolverines are demographically vulnerable and susceptible to impacts from climate change. Their distribution is correlated with persistent spring snow cover, but food-based explanations for this relationship have not been explored. We synthesize information on the timing of both wolverine reproductive events and food availability to improve our understanding of the behaviors, habitat features, and foods that influence reproductive success. Wolverine births are constrained to a brief period of the year and occur at an earlier date than other nonhibernating, northern carnivores. Our examination suggests that this timing is adaptive because it allows wolverines to take advantage of a cold, low-productivity niche by appending the scarce resources available during winter to the brief period of summer abundance. The wolverine's bet-hedging reproductive strategy appears to require success in 2 stages. First, they must fuel lactation (February-April) with caches amassed over winter or acquisition of a sudden food bonanza (e.g., winter-killed ungulates); otherwise, early litter loss occurs. Next, they must fuel the majority of postweaning growth during the brief but relatively reliable summer period of resource abundance. The 1st stage is likely dependent on scavenged ungulate resources over most of the wolverine's range, whereas the 2nd stage varies by region. In some regions the 2nd stage may continue to be focused on scavenging ungulate remains that have been provided by larger predators. In other regions the 2nd stage may be focused on predation by wolverines on small prey or neonatal ungulates. During all seasons and regions, caching in cold, structured microsites to inhibit competition with insects, bacteria, and other scavengers is likely a critical behavioral adaptation because total food resources are relatively limited within the wolverine's niche. Habitat features that facilitate caching, e.g., boulders and low ambient temperatures, are likely important and could be related to the limits of distribution. This “refrigeration-zone” hypothesis represents a food-based explanation for the correlation between wolverine distribution and persistent spring snow cover. Understanding regional differences in foods that fuel reproduction and underlying causes to the limits of distribution could be important for maintaining wolverine populations in the future.

Satellite Snowcover Monitoring and Snowmelt Runoff Simulation of Manas River in Tianshan Region

The updrafted Snowmelt Runoff Model(SRM) was applied to the daily snowmelt runoff simulation of Manas River based on the spring snowcover monitoring of Tianshan Region by using the NOAA/AVHRR satellite data.The results showed that the coefficient of determination of the simulation could reach 0.89,and the deviation of the runoff volumes was within 5.1.If the parameters can be adjusted according to the method described in this paper,the model will be easily used to the similar basins.

Climate change predicted to shift wolverine distributions, connectivity, and dispersal corridors

Boreal species sensitive to the timing and duration of snow cover are particularly vulnerable to global climate change. Recent work has shown a link between wolverine (Gulo gulo) habitat and persistent spring snow cover through 15 May, the approximate end of the wolverine's reproductive denning period. We modeled the distribution of snow cover within the Columbia, Upper Missouri, and Upper Colorado River Basins using a downscaled ensemble climate model. The ensemble model was based on the arithmetic mean of 10 global climate models (GCMs) that best fit historical climate trends and patterns within these three basins. Snow cover was estimated from resulting downscaled temperature and precipitation patterns using a hydrologic model. We bracketed our ensemble model predictions by analyzing warm (miroc 3.2) and cool (pcm1) downscaled GCMs. Because Moderate-Resolution Imaging Spectroradiometer (MODIS)-based snow cover relationships were analyzed at much finer grain than downscaled GCM output, we conducted a second analysis based on MODIS-based snow cover that persisted through 29 May, simulating the onset of spring two weeks earlier in the year. Based on the downscaled ensemble model, 67% of predicted spring snow cover will persist within the study area through 2030–2059, and 37% through 2070–2099. Estimated snow cover for the ensemble model during the period 2070–2099 was similar to persistent MODIS snow cover through 29 May. Losses in snow cover were greatest at the southern periphery of the study area (Oregon, Utah, and New Mexico, USA) and least in British Columbia, Canada. Contiguous areas of spring snow cover become smaller and more isolated over time, but large (>1000 km2) contiguous areas of wolverine habitat are predicted to persist within the study area throughout the 21st century for all projections. Areas that retain snow cover throughout the 21st century are British Columbia, north-central Washington, northwestern Montana, and the Greater Yellowstone Area. By the late 21st century, dispersal modeling indicates that habitat isolation at or above levels associated with genetic isolation of wolverine populations becomes widespread. Overall, we expect wolverine habitat to persist throughout the species range at least for the first half of the 21st century, but populations will likely become smaller and more isolated.

Population vulnerability to climate change linked to timing of breeding in boreal ducks

AbstractIdentifying and understanding why traits make species vulnerable to changing climatic conditions remain central problems in evolutionary and applied ecology. We used spring snow cover duration as a proxy for phenological timing of wetland ecosystems, and examined how snow cover duration during spring and during the entire snow season affected population dynamics of duck species breeding in the western boreal forest of North America, 1973–2007. We predicted that population level responses would differ among duck species, such that late‐nesting species with reduced flexibility in their timing of breeding, i.e. scaup (Aythya spp.) and scoter (Melanitta spp.), would be more strongly affected by changing snow cover conditions relative to species better able to adjust timing of breeding to seasonal phenology, i.e. mallard (Anas platyrhynchos) and American wigeon (Anas americana). Population growth rates of scaup and scoter were positively linked to spring snow cover duration; after accounting for effects of density dependence, larger populations resulted after springs with long snow cover duration than after springs with short snow cover duration. In contrast, population growth rates of mallard and wigeon were either negatively or only weakly associated with snow cover duration. Duck population models were then incorporated with snow cover duration derived from climate model simulations under the A2 emission scenario, and these predictions suggested that late‐nesting duck species will experience the most severe population declines. Results are consistent with a hypothesis that the gradual climatic warming observed in the western boreal forest of North America has contributed to and may continue to exacerbate population declines of scaup and scoter.

Contribution of late spring Eurasian snow cover extent to Canadian winter temperatures

AbstractThis study examines intercontinental linkages between late spring and early summer Eurasian snow cover extent (SCEss) anomalies and the following winter temperature anomalies over Canada for the 1972–2006 period. The structure of the second interannual mode of Canadian winter temperatures variability captures the SCEss related modulation. The North Atlantic winter atmospheric circulation changes associated with the SCEss, resembling the negative phase of the North Atlantic Oscillation (NAO), suggest a possible pathway for the SCEss influences on the Canadian winter temperatures.Regression and composite analyses show that the SCEss relate robustly to the Canadian winter climate. Larger‐than‐normal SCEss is associated with below normal winter temperatures in south‐central Canada and above normal temperatures over northeastern Canada. Predictive skill of Canadian winter temperatures based on a cross‐validated regression model shows that the SCEss offers the predictive potential over regions of Canada where El Niño‐Southern Oscillation (ENSO) related skill is weak or nonexistent. Analysis of winter extreme minimum temperatures, by a non‐stationary generalized extreme value model, with the SCEss as a covariate, exhibits statistically significant changes over Canada resembling a pattern similar to that of winter mean temperatures. Wavelet analysis shows significant coherence between the SCEss and the second mode of winter temperature variability in the 8–12‐year band. Copyright © 2011 Crown in the right of Canada. Published by John Wiley & Sons, Ltd

Northern Hemisphere spring snow cover variability and change over 1922–2010 including an assessment of uncertainty

Abstract. An update is provided of Northern Hemisphere (NH) spring (March, April) snow cover extent (SCE) over the 1922–2010 period incorporating the new climate data record (CDR) version of the NOAA weekly SCE dataset, with annual 95% confidence intervals estimated from regression analysis and intercomparison of multiple datasets. The uncertainty analysis indicates a 95% confidence interval in NH spring SCE of ±5–10% over the pre-satellite period and ±3–5% over the satellite era. The multi-dataset analysis shows larger uncertainties monitoring spring SCE over Eurasia (EUR) than North America (NA) due to the more complex regional character of the snow cover variability and larger between-dataset variability over northern Europe and north-central Russia. Trend analysis of the updated SCE series provides evidence that NH spring snow cover extent has undergone significant reductions over the past ~90 yr and that the rate of decrease has accelerated over the past 40 yr. The rate of decrease in March and April NH SCE over the 1970–2010 period is ~0.8 million km2 per decade corresponding to a 7% and 11% decrease in NH March and April SCE respectively from pre-1970 values. In March, most of the change is being driven by Eurasia (NA trends are not significant) but both continents exhibit significant SCE reductions in April. The observed trends in SCE are being mainly driven by warmer air temperatures, with NH mid-latitude air temperatures explaining ~50% of the variance in NH spring snow cover over the 89-yr period analyzed. However, there is also evidence that changes in atmospheric circulation around 1980 involving the North Atlantic Oscillation and Scandinavian pattern have contributed to reductions in March SCE over Eurasia.