Winter Snowfall Research Articles

Variability in observed stable water isotopes in snowpack across a mountainous watershed in Colorado

AbstractIsotopic information from 81 snowpits was collected over a 5‐year period in a large, Colorado watershed. Data spans gradients in elevation, aspect, vegetation, and seasonal climate. They are combined with overlapping campaigns for water isotopes in precipitation and snowmelt, and a land‐surface model for detailed estimates of snowfall and climate at sample locations. Snowfall isotopic inputs, describe the majority of δ18O snowpack variability. Aspect is a secondary control, with slightly more enriched conditions on east and north facing slopes. This is attributed to preservation of seasonally enriched snowfall and vapour loss in the early winter. Sublimation, expressed by decreases in snowpack d‐excess in comparison to snowfall contributions, increases at low elevation and when seasonal temperature and solar radiation are high. At peak snow accumulation, post‐depositional fractionation appears to occur in the top 25 ± 14% of the snowpack due to melt‐freeze redistribution of lighter isotopes deeper into the snowpack and vapour loss to the atmosphere during intermittent periods of low relative humidity and high windspeed. Relative depth of fractionation increases when winter daytime temperatures are high and winter precipitation is low. Once isothermal, snowpack isotopic homogenization and enrichment was observed with initial snowmelt isotopically depleted in comparison to snowpack and enriching over time. The rate of δ18O increase (d‐excess decrease) in snowmelt was 0.02‰ per day per 100‐m elevation loss. Isotopic data suggests elevation dictates snowpack and snowmelt evolution by controlling early snow persistence (or absence), isotopic lapse rates in precipitation and the ratio of energy to snow availability. Hydrologic tracer studies using stable water isotopes in basins of large topographic relief will require adjustment for these elevational controls to properly constrain stream water sourcing from snowmelt.

With warming, spring streamflow peaks are more coupled with vegetation green‐up than snowmelt in the northeastern United States

AbstractIn forested watersheds, climate change may have substantial implications for both ecological and hydrological processes by altering the snow and vegetation seasonal dynamics. However, to date, there have been few studies which systematically analysed their combined effects on seasonal streamflow behaviour at the watershed scale. Using long‐term remote‐sensed vegetation data and snowpack measurements, we characterized the long‐term patterns in the timings of green‐up/senescence and subsequent growing season length, as well as snowpack development/disappearance and subsequent snowpack duration. We further investigated the correlations of phenological and snowpack seasonal variations with the cumulative percentiles of daily streamflow over two transition periods: vernal (between snowmelt and green‐up) and autumnal (between senescence and snowpack development) windows. We found growing season significantly lengthened by ~30 days over the last four decades at the study watershed, driven by both earlier green‐up and later senescence. The long‐term snowfall significantly decreased by ~2.4 cm, and the snowpack duration has been shortened by ~25 days, mostly driven by earlier snow disappearance. While there were no significant changes in the lengths of vernal and autumnal windows, the normalized streamflow distributions over the vernal window have widened and the low‐frequency peak flow over the autumnal window has shifted earlier over the study period. Interestingly, although the snow disappearance timing has advanced by ~18 days, there was no evidence of changes in the timing of spring peak discharge. Our results suggest that with long‐term decreases in winter snowfall and snowpack duration, the impacts of snowpack dynamics on seasonal streamflow regimes over the vernal and autumnal windows have weakened. Meanwhile, the seasonal streamflow dynamics are more closely mediated by vegetation green‐up with time. This study emphasizes the importance of understanding the phenological responses to climate change for prediction of future seasonal flow regimes in forested watersheds.

Inferring watershed‐scale mean snowfall magnitude and distribution using multidecadal snow reanalysis patterns and snow pillow observations

AbstractThe magnitude and spatial heterogeneity of snow are difficult to model in mountainous terrain. We investigated how historic snow patterns from a 32‐year (1985–2016) snow reanalysis, which uses the timing and rate of remotely‐sensed snow disappearance to reconstruct snow water equivalent in past years, could be used to improve current year simulations of snowfall in the Upper Tuolumne, Upper Kings, and Sagehen Creek watersheds in California's Sierra Nevada. Building on past research linking observations of fractional snow‐covered area (fSCA) to spatial patterns of snow water equivalent, fSCA maps were used to identify pairs of dates from historic years in the reanalysis with similar snow accumulation and depletion patterns. Historic snow accumulation patterns were then used to extrapolate snow accumulation observed by snow pillows to 90 m snowfall fields. These 90 m snowfall fields were used as input for snow simulations, the accuracy of which were evaluated versus airborne lidar snow depth observations. Except for water‐year 2015, which had record low snow in the Sierra Nevada and unique snow patterns, normalized snow accumulation and depletion patterns identified from historic dates with spatially correlated fSCA agreed with each other well, with mean absolute differences of approximately 10%. Domain mean winter snowfall inferred from the relationship between historic snow accumulation patterns and current year snow pillow observations had a ±13% interquartile range of biases, relative to the snow reanalysis. Finally, snow depth simulations using snow pillow observed snowfall extrapolated with historic snow accumulation patterns had 70% better coefficients of correlations, and 27% better mean absolute errors, as compared to simulations using more common snowfall forcing. This work demonstrates the benefits of repeatable snowfall patterns and satellite‐era snow reanalyses in mountainous regions with uncertain snowfall magnitude and spatial heterogeneity.

Change in Winter Precipitation Regime across Ontario, Canada

The focus of this study is to investigate the effects of climate change on the hydrologic regimes in Ontario, Canada. The variables include total precipitation, the form of precipitation (snowfall and rainfall), and the temperature during winter. The winter season is hydrologically significant for Canadian conditions. The historical data for 70 years, from 1939 to 2008, on total precipitation, snowfall, rainfall, and temperature over the winter period were analyzed using least-squares regressions, Alexandersson’s Standard Normal Homogeneity Test, and the Mann–Kendall test for 13 stations across Ontario to identify positive and negative trends and their significance. The analysis of the precipitation indices reveals no significant trend in the winter total precipitation, decreasing trends in winter snowfall, and increasing trends in winter rainfall. The snowy day analysis depicts a large scatter across the province, with the number ranging from 40 days to 80 days, which shows that the number of snowy days varies considerably over the years at all stations. The analysis showed that the change in snowy-rainy days is attributed to the significant upward trend of the daily mean winter minimum temperature for almost all the stations. Therefore, the changes in the form of precipitation during winter may affect water management including streamflow, tile drainage flow, soil erosion, sediment and nutrient transport to surface water bodies, and the effectiveness of best management practices being used for managing non-point source pollution.

Synoptic control over winter snowfall variability observed in a remote site of Apennine Mountains (Italy), 1884–2015

Abstract. This work presents a new, very long snowfall time series collected in a remote site of Italian Apennine mountains (Montevergine Observatory, 1280 m above sea level). After a careful check, based on quality control tests and homogenization procedures, the available data (i.e. daily height of new snow) have been aggregated over winter season (December–February) to study the long-term variability for the period 1884–2020. The main evidence emerging from this analysis lies in (i) the strong interannual variability of winter snowfall amounts, (ii) the absence of a relevant trend from the late 19th century to the mid-1970s, (iii) the strong reduction in the snowfall amount and frequency of occurrence from the mid-1970s to the end of the 1990s and (iv) the increase in average snowfall amount and frequency of occurrence in the past 20 years. Moreover, this study shed light on the relationship between the snowfall variability observed in Montevergine and the large-scale atmospheric circulation. Six different synoptic types, describing the meteorological scenarios triggering the snow events in the study area, have been identified by means of a cluster analysis, using two essential atmospheric variables, the 500 hPa geopotential height and the sea level pressure (both retrieved from the third version of the Twentieth Century Reanalysis dataset, which is available for the 1884–2015 period). Such patterns trace out scenarios characterized by the presence of a blocking high-pressure anomaly over Scandinavia or the North Atlantic and by a cold air outbreak involving both maritime and continental cold air masses. A further analysis demonstrates that the identified synoptic types are strongly related with different teleconnection patterns, i.e. the Arctic Oscillation (AO), the Eastern Atlantic Western Russia (EAWR), the Eastern Mediterranean Pattern (EMP), the North Atlantic Oscillation (NAO) and the Scandinavian pattern (SCAND), that govern the European winter atmospheric variability. The relevant decline in snowfall frequency and amounts between the 1970s and 1990s can be mainly ascribed to the strong positive trend of AO and NAO indices, which determined, in turn, a decrease in the incidence of patterns associated with the advection, in central Mediterranean area, of moist and cold arctic maritime air masses. The recent increase in average snowfall amounts can be explained by the reverse trend of the AO index and by the prevalence of a neutral or negative EAWR pattern.

Apparent winterkill of Painted Turtle (<i>Chrysemys picta</i>)

Around the margin of an artificial pond in Ottawa, Ontario, we found 25 Painted Turtles (Chrysemys picta) that appeared to have died over the course of two winters (17 during the first winter and eight during the second). We examined meteorological data to try to determine the cause of the mortality. Summer and fall rains were only slightly below normal in both years, suggesting water levels should have been close to normal. The winter air temperature was warmer than normal and winter snowfall was slightly above normal in both years. Unseasonable weather does not appear to be responsible for the winter mortality and the pond’s maximum depth of 1.7 m should prevent freezing to the bottom. It is possible that the artificial nature of the pond creates suboptimal overwintering habitat, rendering the site an ecological trap; however, there is no direct evidence to support this theory. It is also possible that winter mortality of turtles is widespread at temperate wetlands, but that dead turtles were more detectable at this site because of the bare shoreline around the pond. Winter mass mortality events, if common, may represent an additional threat to turtle populations, which are declining from various anthropogenic threats.

The Role of Weather and Long-Term Prey Dynamics as Drivers of Wolf Population Dynamics in a Multi-Prey System

As climate change accelerates in northern latitudes, there is an increasing need to understand the role of climate in influencing predator-prey systems. We investigated wolf population dynamics and numerical response in Denali National Park and Preserve in Alaska, United States from 1986 to 2016 under a long-term range of varying climatic conditions and in the context of prey vulnerability, abundance, and population structure using an integrated population modeling approach. We found that wolf natality, or the number of wolves added to packs, increased with higher caribou population size, calf:cow ratio, and hare numbers, responding to a 1-year lag. Apparent survival increased in years with higher calf:cow ratios and cumulative snowfall in the prior winter, indicators of a vulnerable prey base. Thus, indices of prey abundance and vulnerability led to responses in wolf demographics, but we did not find that the wolf population responded numerically. During recent caribou and moose population increases wolf natality increased yet wolf population size declined. The decline in wolf population size is attributed to fewer packs in recent years with a few very large packs as opposed to several packs of comparable size. Our results suggest that territoriality can play a vital role in our study area on regulating population growth. These results provide a baseline comparison of wolf responses to climatic and prey variability in an area with relatively low levels of human disturbance, a rare feature in wolf habitat worldwide.

Historical Trends and Projections of Snow Cover over the High Arctic: A Review

Snow is the dominant form of precipitation and the main cryospheric feature of the High Arctic (HA) covering its land, sea, lake and river ice surfaces for a large part of the year. The snow cover in the HA is involved in climate feedbacks that influence the global climate system, and greatly impacts the hydrology and the ecosystems of the coldest biomes of the Northern Hemisphere. The ongoing global warming trend and its polar amplification is threatening the long-term stability of the snow cover in the HA. This study presents an extensive review of the literature on observed and projected snow cover conditions in the High Arctic region. Several key snow cover metrics were reviewed, including snowfall, snow cover duration (SCD), snow cover extent (SCE), snow depth (SD), and snow water equivalent (SWE) since 1930 based on in situ, remote sensing and simulations results. Changes in snow metrics were reviewed and outlined from the continental to the local scale. The reviewed snow metrics displayed different sensitivities to past and projected changes in precipitation and air temperature. Despite the overall increase in snowfall, both observed from historical data and projected into the future, some snow cover metrics displayed consistent decreasing trends, with SCE and SCD showing the most widespread and steady decreases over the last century in the HA, particularly in the spring and summer seasons. However, snow depth and, in some regions SWE, have mostly increased; nevertheless, both SD and SWE are projected to decrease by 2030. By the end of the century, the extent of Arctic spring snow cover will be considerably less than today (10–35%). Model simulations project higher winter snowfall, higher or lower maximum snow depth depending on regions, and a shortened snow season by the end of the century. The spatial pattern of snow metrics trends for both historical and projected climates exhibit noticeable asymmetry among the different HA sectors, with the largest observed and anticipated changes occurring over the Canadian HA.

Suppression of Arctic Sea Ice Growth in the Eurasian–Pacific Seas by Winter Clouds and Snowfall

Abstract The ongoing Arctic warming has been pronounced in winter and has been associated with an increase in downward longwave radiation. While previous studies have demonstrated that poleward moisture flux into the Arctic strengthens downward longwave radiation, less attention has been given to the impact of the accompanying increase in snowfall. Here, utilizing state-of-the-art sea ice models, we show that typical winter snowfall (snow water equivalent) anomalies of around 1.0 cm, accompanied by positive downward longwave radiation anomalies of ∼5 W m−2, can cause basinwide sea ice thinning by around 5 cm in the following spring over the Arctic seas in the Eurasian–Pacific seas. In extreme cases, this is followed by a shrinking of summer ice extent. In the winter of 2016/17, anomalously strong warm, moist air transport combined with ∼2.5-cm increase in snowfall (snow water equivalent) decreased spring ice thickness by ∼10 cm and decreased the following summer sea ice extent by 5%–30%. This study suggests that small changes in the pattern and volume of winter snowfall can strongly impact the sea ice thickness and extent in the following seasons.

Characteristic Analysis of Dual-Polarization Weather Radar Echoes of Convective Precipitation and Snowfall in the Mount Everest Region

This paper introduces the X-band weather radar dual-polarization parameters of isolated convective cell precipitation and meso/microscale snowfall on Mount Everest and presents the first precipitation observations based on dual-polarization weather radar in this area. Compared with the Chengdu Plain, Mount Everest experienced convective precipitation on smaller horizontal and vertical scales with a narrower Zdr probability density spectrum (uniformly distributed around approximately 0). The Zh profile on Mount Everest displayed two peaks, unlike that over the plains, and the precipitation at the strong convective core was denser. Furthermore, during winter snowfall on the northern slope of Mount Everest, when the boundary layer exhibited sufficient water vapor and dynamic uplift, due to the low boundary layer temperature (<0 °C), water vapor produced stratiform clouds in the middle and lower layers (approximately 1.5 km above ground level (AGL)). Water vapor condensation at 1.5–2.5 km AGL led to latent heat release, which increased the temperature of regional stratiform clouds with increasing height. Consequently, the temperature at the stratiform cloud top height (2.5 km AGL) unexpectedly exceeded 0 °C. Additionally, the −20 °C isotherm was at approximately 4 km AGL, indicating that the middle- and upper-layer atmospheric temperatures remained low. Therefore, thermal instability occurred between the stratiform cloud top height and the middle/upper atmosphere, forming convective motion. These findings confirm the occurrence of elevated winter snowfall convection above Mount Everest and may have certain reference value for retrieving raindrop size distributions, quantitatively estimating precipitation, and parameterizing cloud microphysical processes in numerical prediction models for the Qinghai-Tibetan Plateau.

Effect of Aging and Moisture Damage on Fatigue Cracking Properties in Asphalt Mixtures

Recently, damage to asphalt pavements in South Korea has increased because of direct and indirect factors caused by abnormal climatic changes, such as torrential rains, prolonged heatwaves in summer, and heavy snowfall in winter. Additionally, the use of medium and heavy vehicles is also a contributing factor. Therefore, an experimental procedure to study the moisture damage and fracture properties of asphalt pavements considering the recent changes in precipitation properties, deterioration of road pavement, and traffic conditions in South Korea was developed in this study. Additionally, changes in material properties according to the indoor aging process and fatigue crack resistance were evaluated for hot mix and warm mix asphalt mixtures of the same grade and different initial production and construction temperatures. To evaluate the effect of aging and moisture damage on fatigue cracking, the experimental coefficient values of the predictive model were calculated for each condition. It was observed that the material properties of hot mix asphalt mixtures changed with an increase in aging and moisture damage. The service life of the pavement was reduced by approximately 40–80% owing to moisture damage, whereas aging had a greater effect on fatigue life as the service life increased.

Deepened snow cover mitigates soil carbon loss from intensive land‐use in a semi‐arid temperate grassland

Abstract Carbon (C) loss due to soil erosion is a major issue in semi‐arid grasslands. The extent of soil erosion is determined by soil properties and vegetation structure, especially during the non‐growing season. In many Inner Mongolian grasslands, intensive land‐use, such as overgrazing and mowing, has severely reduced plant cover and damaged soil structure, which has exacerbated soil C loss by erosion. At the same time, increasing winter snowfall due to climate change is stimulating plant growth and altering plant composition. However, we do not know how changes in winter snow cover interact with land‐use practices to regulate soil C loss due to erosion. Here, we conducted a 6‐year snow manipulation experiment under different land‐use practices (control; moderately mowed, MM; heavily mowed, HM) to measure net changes in soil depth, soil C, plant biomass and vegetation structure. After 6 years, soil C loss under ambient snow was three times greater in the MM and four times greater in the HM treatment compared with controls during non‐growing season. However, deepened winter snow alleviated erosion‐induced soil C loss by 14%, 47%, 16% in the controls, MM and HM treatments respectively. The severity of soil C loss declined with increasing above‐ground biomass (AGB), surface root biomass and vegetation structure. Vegetation structure and AGB explained more of the variation in soil C loss than surface root biomass, possibly because a complex canopy and plant cover increases overall surface roughness, thereby reducing soil C loss. Intensified land‐use reduced AGB, surface root biomass and vegetation structure, but deepened snow increased overall surface roughness by promoting AGB. Hence, our study demonstrates that deepened snow can alleviate soil C loss due to land‐use practices by promoting AGB. A free Plain Language Summary can be found within the Supporting Information of this article.

Inter-annual Variability of Snowfall in the Lower Peninsula of Michigan

Winter snowfall, particularly lake-contributed snowfall, has a significant impact on the society and environment in the Great Lakes regions including transportation, tourism, agriculture, and ecosystem. Understanding the inter-annual variability of snowfall will provide sound basis for local community safety management and reduce its environmental impacts on agriculture and ecosystems. This study attempts to understand the trend and inter-annual variability in snowfall in the Lower Peninsula of Michigan (LPM) using statistical analysis based on snowfall measurements from eight weather stations. Our study demonstrates that snowfall has significantly increased from 1932 to 2015. Correlation analysis suggests that regional average air temperatures have a strong negative relationship with snowfall in the LPM. On average, approximately 27% of inter-annual variability in snowfall can be explained by regional average air temperatures. ENSO events are also negatively related to snowfall in the LPM and can explain ~8% of inter-annual variability. The North Atlantic Oscillation (NAO) does not have strong influence on snowfall. Composite analysis demonstrates that on an annual basis, more snowfall occurs during the years with higher maximum ice cover (MIC) than during the years with lower MIC in Lake Michigan. Higher MIC is often associated with lower air temperatures which are negatively related to snowfall. This study could provide insight on future snow related climate model improvement and weather forecasting.

Different Couplings Between Precipitation and Deformation at the Same Site: A Case Study at Central Pyrenees (Spain)

AbstractImpact of hydrology‐associated deformation spans from the study of the rock hydrological properties and aquifer dynamics to the identification of geological disaster precursors. We analyze a comprehensive set of hydrology‐associated deformation signals, recorded by two underground 70 m long strainmeters (GAL16 and LAB780) operating under the Central Pyrenees (Spain) from the end of 2011 to the end of 2018. The two strainmeters measure extensional strain at the picostrain (10−12) level along two nearly orthogonal horizontal directions. Sign (compression or extension along any of the two directions), size, and time evolution of hydrology‐associated deformation vary considerably from season to season. We identify four classes of signals, related to summer rainfalls (producing compression along both GAL16 and LAB780), late‐spring and autumn rainfalls (producing extension along GAL16 and compression along LAB780), snow melting (producing opposite diurnal cycles along GAL16 and LAB780), and winter snowfalls (producing extension along both GAL16 and LAB780). Similar precipitations of the same class can give rise to differently sized signals; differences may be due to infiltration and/or surface load inhomogeneities as well as to complexities of the underground hydrogeological set up, which can lead to nonlinear, intrinsically time‐dependent, responses. Our modeling sheds light on the diverse effects associated with different environmental conditions. The variety of hydrology‐associated deformation we report is warning for the interpretation of all kinds of deformation data, as for their difficult modeling, the causative physical mechanisms, and the extreme care that is needed when removing hydrologically induced effects. Our insights are particularly useful in mountain areas.

The timing of snowmelt and amount of winter precipitation have limited influence on flowering phenology in a tallgrass prairie

A growing body of work indicates that the timing of flowering of temperate angiosperms has been affected by shifts in climate since the 1970s. Sensitivity in flowering phenology to changing temperatures has been particularly well-documented, but widespread phenological sensitivity to changing precipitation patterns in temperate communities has only been shown in a few studies. The exception is relationships between snowpack and early flowering in alpine environments, whereby the timing of flowering herbs had strong associations with winter precipitation and the snowmelt timing. Based on these results, we hypothesized that populations in temperate latitudes characterized by strong seasonality and winter snowfall would demonstrate associations between timing of snowmelt and flowering phenology. We combined a historical dataset of first flowering dates in a Minnesota tallgrass prairie with climatic data to construct a structural equation model, testing hypotheses on the relationships between winter precipitation, temperature, and flowering phenology. While temperature had a strong effect on flowering phenology, winter precipitation had a significant relationship with only a few species. The species affected by snow were later flowering species, which is inconsistent with our prediction that winter precipitation affects early flowering phenology. These results suggest future changes in precipitation will have differing consequences depending on region.

Impact of Winter Snowfall on Vegetation Greenness in Central Asia

Snowfall is an important climatic variable with remarkable impacts on vegetation greenness in the alpine and extra-tropical regions. Central Asia (CA) is located in the middle latitude with high mountains, and the ecosystem is vulnerable to climate change in CA. In this region, the vegetation response to winter snowfall and its interactions with growing season climatic factors on vegetation greenness is still unclear. Thus, this study attempted to examine the impact of winter snowfall on vegetation greenness with remote-sensing vegetation index and reanalysis climatic data, and to investigate the interactions between winter snowfall and growing season climatic factors and their influence on vegetation growth via path analysis. The results showed that there is a generally positive correlation between winter snowfall and vegetation greenness from May to September and during the whole growing season (April to September). This positive correlation was significant in 8% of the study area for the whole growing season. However, the increase in winter snowfall was not beneficial to the regional vegetation growth in April. The vegetation response to winter snowfall also relates to land-cover type and elevation. The vegetation greenness in grassland was depicted to be more sensitive to winter snowfall than that in forestland. The response turned from positive to negative when the elevation increased from below 3000 m to above 3000 m. Moreover, the impact of winter snowfall on vegetation greenness was not regulated by temperature and precipitation in the growing season in more than 70% of CA. The impact relates to the interaction with April temperature in Central Kazakhstan, and is regulated by growing season precipitation in North Kazakhstan where annual precipitation mainly occurs in the growing season. The impact of winter snowfall on vegetation greenness is more important than growing season precipitation and temperature in some areas, since annual precipitation does not concentrate in the growing season or the relative increase of winter snowfall is great in these places. The results of the present study improve the understanding of vegetation response to climate change, and provide a scientific reference for environmental protection in CA and similar regions.

Interrogating glacier mass balance response to climatic change since the Little Ice Age: reconstructions for the Jotunheimen region, southern Norway

Developing a long‐term understanding of the cryosphere is important in the study of past climatic change. Here we used a nested approach combining diverse instrumental (monthly meteorological data from four weather stations, as well as gridded data) and proxy data (based on blue intensity measurements from local tree ring records) to create a reconstruction of past summer temperature for the central Jotunheimen area in southern Norway. This record was then used to reconstruct annual glacier mass balance from 1962, the start of the yearly measurements, back to 1722, immediately prior to the regional Little Ice Age maximum. Our reconstruction of the ‘average’ Jotunheimen cumulative glacier mass balance is based on three representative glaciers (Storbreen, Hellstugubreen and Gråsubreen) that were synthesized into one composite record which we term ‘Gjennomsnittsbreen’ (‘mean glacier’ in Norwegian) to filter out localized controls on the behaviour of individual glaciers. While not ignoring the role of precipitation on glacier mass balance, our reconstruction demonstrates that glaciers in this region exhibit a strong summer temperature control and appear to have been declining more or less continuously since the mid‐18th century. However, it also shows that this long‐term trend of overall retreat in Jotunheimen is punctuated by relatively short‐lived periods of neutral or occasionally positive glacier mass balance, signifying periods of stillstand or small‐scale glacier advance. These periods or ‘events’ in our reconstruction were compared with an independent record of 12 moraine‐building events developed using lichenometry. A minimum of 10 of the moraine‐building events identifiable in our reconstruction were also identifiable in the lichenometric data which affords confidence in the performance of our interrogative model. A critical implication of this successful glacier mass balance reconstruction based on just summer temperature is that for Jotunheimen – in contrast to Norwegian maritime glaciers further to the west – there is no need (as was proposed in some previous studies) to invoke large, prolonged increases in winter snowfall to explain glacier advances, not even for events such as the Little Ice Age.

Effect of snow cover on water content, carbon and nutrient availability, and microbial biomass in complexes of biological soil crusts and subcrust soil in the desert

Winter snowfall is an important water resource for organisms in desert ecosystems. Changing snow depth is expected to exert a potential influence on growth and ecological functions of biological soil crusts (BSCs), which are known as a dominant functional vegetation unit of desert ecosystems. However, limited attention has been paid to this potential effect on BSCs. A field simulation experiment was conducted to measure the water content, nutrient concentrations (organic carbon; total nitrogen; available nitrogen; available phosphorus; available potassium; ammonium nitrate; and nitrate nitrogen), and microbial biomass (carbon, nitrogen, and phosphorus) in samples (cyanobacterial and lichen crusts and subcrust soil) under snow cover of different depths after a four year experiment (from October 2013 to April 2017) in the Gurbantunggut Desert. The results showed that water content increased with increasing snow depth (P < 0.05). Snow removal and snow cover reduction decreased nutrient and microbial biomass concentrations in both biocrust types (P < 0.05). The concentrations of most measured nutrients and microbial biomass decreased significantly when snow depth was increased to twice ambient conditions (P < 0.05). Moreover, the concentrations of nutrients and microbial biomass were significantly influenced by snow depth (P < 0.05), crust type (P < 0.001), and their interaction (P < 0.05). The results demonstrated that variation in snow depth adversely affect the carbon and nutrient availability and microbial biomass in BSCs. Such changes might lead to a cascading effect in species composition and function of BSC communities in arid regions.

Trends and projection of heavy snowfall in Hokkaido, Japan as an application of self-organizing map

AbstractThis paper showed the frequency of local-scale heavy winter snowfall in Hokkaido, Japan, its historical change, and its response to global warming using self-organizing map (SOM) of synoptic-scale sea-level pressure anomaly. Heavy snowfall days were here defined as days when the snowfall exceeded 10 mm in water equivalent. It was shown that the SOMs can be grouped into three categories for heavy snowfall days: 1) a passage of extratropical cyclones to the south of Hokkaido, 2) a pressure pattern between the Siberian high and the Aleutian low, and 3) a low-pressure anomaly just to the east of Hokkaido. Groups 1 and 2 were associated with heavy snowfall in Hiroo (located in southeastern Hokkaido) and in Iwamizawa (western Hokkaido), respectively, and heavy snowfall in Sapporo (western Hokkaido) was related to Group 3. The large-ensemble historical simulation reproduced the observed increasing trend in Group 2 and future projection revealed that Group 2 was related to a negative phase of the Western Pacific pattern and the frequency of this group would increase in the future. Heavy snowfall days associated with SOM Group 2 would also increase due to the increase in water vapor and preferable weather patterns in global warming climate, in contrast to the decrease of heavy snowfall days in other sites associated with SOM Group 1.

Remote Effects of IOD and ENSO on Motivating the Atmospheric Pattern Favorable for Snowfall Over the Tibetan Plateau in Early Winter

The interannual variation of snowfall over the Tibetan Plateau (TP) in early winter (November–December) and its related atmospheric attribution are clarified. Meanwhile, the influence of tropical sea surface temperatures (SSTs) on TP snowfall is investigated by diagnostic analyses and Community Atmosphere Model (CAM5) simulations. The leading mode of TP snowfall in early winter features a spatially uniform pattern with remarkable interannual variability. It is found that the Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO) are main external forcing factors for TP snowfall. Positive IOD with positive ENSO and positive IOD with neutral ENSO cases both have remote impact on motivating Southern Eurasia (SEA) pattern, which can induce an anomalous cyclone around the TP. The corresponding anomalous ascending motion and cold air in the mid-upper troposphere provide the dynamical and thermal conditions for heavy snowfall. The low-level southwesterly winds are enhanced over the Arabian Sea and Bay of Bengal, bringing abundant water vapor into the TP for excessive snowfall. Furthermore, CAM5 simulation experiments forced by IOD- and ENSO-related SST anomalies are performed to verify their combined and independent effects on TP snowfall in early winter. It is confirmed that either positive IOD or El Niño has certain impacts on motivating circulation anomalies favorable for snowfall over the TP. However, IOD plays a leading role in producing the excessive snowfall-related atmospheric conditions, and there is an asymmetric influence of ENSO and IOD on the TP snowfall.