Cumulative Snowfall Research Articles

Impact of the combined assimilation of GPM/IMGER precipitation and Himawari-8/AHI water vapor radiance on snowfall forecasts using WRF model and 4Dvar system

Impact of the combined assimilation of GPM/IMGER precipitation and Himawari-8/AHI water vapor radiance on snowfall forecasts using WRF model and 4Dvar system

Photovoltaic inverter-based quantification of snow conditions and power loss

Snow is a significant challenge for photovoltaic (PV) systems at northern latitudes, where the pace of deployment is rapid but snow-related power losses can exceed 30% of annual production. Accurate snow-related power loss estimation methods for utility-scale sites can support snow mitigation strategies, inform resource planning and validate predictive snow-loss models. This study builds on our previous work on inverter-based detection of snow, and its implications for utility-scale power production, by validating the accuracy of our snow-loss method across different PV sites and system designs and highlighting its value in bringing greater visibility to PV plant operations in winter. Our estimation method is both novel and scalable, requiring only standard monitoring data to correlate snow-related losses with meteorological data. As demonstrated here, our validation method involved three main steps: 1) estimation of performance losses for multiple systems by comparing measured inverter data to modeled data; 2) application of a detection framework to identify which performance losses are snow-related; and 3) comparison of snow-related losses among three utility-scale sites differing in tilt angle. Results show that utility-scale systems at higher tilt angles consistently shed snow more quickly/completely than their lower-tilt counterparts. Further, monthly and seasonal snow losses are inversely and non-linearly correlated with tilt angle when normalized for cumulative snowfall. These results are consistent with the findings of previous studies and support the broad applicability of this method to fixed-tilt utility-scale PV systems around the world that routinely experience snow-related performance losses.

Identifying snowfall elevation patterns by assimilating satellite-based snow depth retrievals

Precipitation in mountain regions is highly variable and poorly measured, posing important challenges to water resource management. Traditional methods to estimate precipitation include in-situ gauges, Doppler weather radars, satellite radars and radiometers, numerical modeling and reanalysis products. Each of these methods is unable to adequately capture complex orographic precipitation. Here, we propose a novel approach to characterize orographic snowfall over mountain regions. We use a particle batch smoother to leverage satellite information from Sentinel-1 derived snow depth retrievals and to correct various gridded precipitation products. This novel approach is tested using a simple snow model for an alpine basin located in Trentino Alto Adige, Italy. We quantify the precipitation biases across the basin and found that the assimilation method (i) corrects for snowfall biases and uncertainties, (ii) leads to cumulative snowfall elevation patterns that are consistent across precipitation products, and (iii) results in overall improved basin-wide snow variables (snow depth and snow cover area) and basin streamflow estimates.

How Well do Global Snow Products Characterize Snow Storage in High Mountain Asia?

AbstractAccurate characterization of peak snow water storage in High Mountain Asia (HMA) is essential for assessing the water supply to over 1 billion downstream residents. Currently, such characterization still relies on modeling due to measurement scarcity. Here, eight global snow products were examined over HMA using a newly developed High Mountain Asia Snow Reanalysis (HMASR) data set as a reference. The focus of intercomparison was on peak annual snow storage, the first‐order determinant of warm‐season water availability in snow‐dominated basins. Across eight products the climatological peak storage over HMA was found to be 161 ± 102 km3 with an average 33% underestimation relative to HMASR. The inter‐product variability in cumulative snowfall (335 ± 148 km3) explains the majority (>80%) of peak snow storage uncertainty, while significant accumulation‐season snowfall loss to ablation (51% ± 9%) also reveals the critical role of ablation processes on peak snow storage.

Snowfall and snow accumulation during the MOSAiC winter and spring seasons

Abstract. Data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition allowed us to investigate the temporal dynamics of snowfall, snow accumulation and erosion in great detail for almost the whole accumulation season (November 2019 to May 2020). We computed cumulative snow water equivalent (SWE) over the sea ice based on snow depth and density retrievals from a SnowMicroPen and approximately weekly measured snow depths along fixed transect paths. We used the derived SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were also compared with ERA5 reanalysis snowfall rates for the drift track. We found an accumulated snow mass of 38 mm SWE between the end of October 2019 and end of April 2020. The initial SWE over first-year ice relative to second-year ice increased from 50 % to 90 % by end of the investigation period. Further, we found that the Vaisala Present Weather Detector 22, an optical precipitation sensor, and installed on a railing on the top deck of research vessel Polarstern, was least affected by blowing snow and showed good agreements with SWE retrievals along the transect. On the contrary, the OTT Pluvio2 pluviometer and the OTT Parsivel2 laser disdrometer were largely affected by wind and blowing snow, leading to too high measured precipitation rates. These are largely reduced when eliminating drifting snow periods in the comparison. ERA5 reveals good timing of the snowfall events and good agreement with ground measurements with an overestimation tendency. Retrieved snowfall from the ship-based Ka-band ARM zenith radar shows good agreements with SWE of the snow cover and differences comparable to those of ERA5. Based on the results, we suggest the Ka-band radar-derived snowfall as an upper limit and the present weather detector on RV Polarstern as a lower limit of a cumulative snowfall range. Based on these findings, we suggest a cumulative snowfall of 72 to 107 mm and a precipitation mass loss of the snow cover due to erosion and sublimation as between 47 % and 68 %, for the time period between 31 October 2019 and 26 April 2020. Extending this period beyond available snow cover measurements, we suggest a cumulative snowfall of 98–114 mm.

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.

Observational study of a severe snowfall avalanche over a state in North India in November 2019 using GIS

Heavy snowfall in the first week of November 2019 caused snow avalanche that resulted in huge loss in the form of lives and properties over Kashmir Valley in North India. About 17 people were killed including 11 soldiers by the snow avalanche resulting from heavy snowfall. Data from seven stations of India Meteorological Department (IMD) have been used to study the snowfall events. These stations include Kupwara, Gulmarg, Sher-e-Kashmir University of Agricultural Sciences & Technology (SKUAST), Pahalgam, Kokernag, Qazigund and Konibal. Kokernag recorded the highest snowfall of about 120 mm on November 8. SKUAST recorded snowfall of about 80 mm on November 8. Heavy snowfall started on November 6 and lasted up to November 8, 2019. Gulmarg, Kokernag and Kupwara reported cumulative snowfall of more than 100 mm during November 6–8, 2019. These stations are located in hilly region. Heavy snowfall combined with steep slope resulted in heavy snow avalanche causing heavy damage. An army camp situated in northern Gulmarg was swept away due to avalanche resulting in four causalities. Heavy snowfall also affected agriculture, horticulture, transportations and economy of the valley.

Snow Depth Estimation on Slopes Using GPS-Interferometric Reflectometry.

Snow is one of the most critical sources of freshwater, which influences the global water cycle and climate change. However, it is difficult to monitor global snow variations with high spatial–temporal resolution using traditional techniques due to their costly and labor-intensive nature. Nowadays, the Global Positioning System Interferometric Reflectometry (GPS-IR) technique can measure the average snow depth around a GPS antenna using its signal-to-noise ratio (SNR) data. Previous studies focused on the use of GPS data at sites located in flat areas or on very gentle slopes. In this contribution, we propose a strategy called the Tilted Surface Strategy (TSS), which uses the SNR data reflected only from the flat quadrants to estimate the snow depth instead of the conventional strategy, which employs all the SNR data reflected from the whole area around a GPS antenna. Three geodetic GPS sites from the Plate Boundary Observatory (PBO) project were chosen in this experimental study, of which GPS sites p683 and p101 were located on slopes with their gradients up to 18% and the site p025 was located on a flat area. Comparing the snow depths derived with the GPS-IR TSS method with the snow depth results provided with the GPS-PBO, i.e., GPS-IR with the conventional strategy, the Snowpack Telemetry (SNOTEL) network measurements and gridded Snow Data Assimilation System (SNODAS) estimates, it was found that the snow depths derived with the four methods had a good agreement, but the snow depth time series with the GPS-IR TSS method were closer to the SNOTEL measurements and the SNODAS estimates than those with GPS-PBO method. Similar observations were also obtained from the cumulative snowfall time series. Results generally indicated that for those GPS sites located on slopes, the TSS strategy works better.

Winter storms accelerate the demise of sea ice in the Atlantic sector of the Arctic Ocean

A large retreat of sea-ice in the ‘stormy’ Atlantic Sector of the Arctic Ocean has become evident through a series of record minima for the winter maximum sea-ice extent since 2015. Results from the Norwegian young sea ICE (N-ICE2015) expedition, a five-month-long (Jan-Jun) drifting ice station in first and second year pack-ice north of Svalbard, showcase how sea-ice in this region is frequently affected by passing winter storms. Here we synthesise the interdisciplinary N-ICE2015 dataset, including independent observations of the atmosphere, snow, sea-ice, ocean, and ecosystem. We build upon recent results and illustrate the different mechanisms through which winter storms impact the coupled Arctic sea-ice system. These short-lived and episodic synoptic-scale events transport pulses of heat and moisture into the Arctic, which temporarily reduce radiative cooling and henceforth ice growth. Cumulative snowfall from each sequential storm deepens the snow pack and insulates the sea-ice, further inhibiting ice growth throughout the remaining winter season. Strong winds fracture the ice cover, enhance ocean-ice-atmosphere heat fluxes, and make the ice more susceptible to lateral melt. In conclusion, the legacy of Arctic winter storms for sea-ice and the ice-associated ecosystem in the Atlantic Sector lasts far beyond their short lifespan.

Meteorological triggering scenarios of tree-ring-based snow avalanche occurrence on scree slopes in a maritime climate, Eastern Canada

ABSTRACTStatistical relationships between weather conditions and the release of snow avalanches in the low-elevation coastal valleys of the northern Gaspé Peninsula are still poorly validated. As such, we explored climate–avalanche relationships through classification tree algorithms applied to tree-ring reconstructions of avalanche events. In order to assess the contribution of local factors on avalanche activity, avalanche regimes on east- and west-facing slopes were analyzed and compared. The results showed that avalanches on east-facing slopes appear to be primarily related to large cumulative snowfall in January, February, and March. On west-facing slopes, avalanches are mainly due to episodic snowfall and warming temperatures. However, both sides of the valleys showed the potential for the release of large avalanches in November and December, which is earlier than expected by the literature. Indeed, the weather variability at that time of the year (temperature oscillation around 0ºC) appears to favor the formation of an early, unstable snowpack and subsequent triggering of avalanches, such as the wet slab avalanche recorded by a time-lapse camera in November 2014. This camera provided a useful insight on the capacities of classification-tree models to link the yearly resolution of tree-ring data with weather triggers at different timescales.

Sensitivity of Seasonal Snowfall Attribution to Atmospheric Rivers and Their Reanalysis‐Based Detection

AbstractWe characterize the sensitivity of atmospheric river (AR)‐derived seasonal snowfall estimates to their atmospheric reanalysis‐based detection over Sierra Nevada, USA. We use an independent snow data set and the ARs identified with a single detection method applied to multiple atmospheric reanalyses of varying horizontal resolutions, to evaluate orographic relationships and contributions of individual ARs to the seasonal cumulative snowfall (CS). Spatial resolution differences have relatively minor effects on the number of ARs diagnosed, with higher‐resolution data sets identifying four more AR days per year, on average, during the 1985–2015 winters. However, this can lead to ~10% difference in AR attribution to the mean domain‐wide seasonal CS and differences up to 47% snowfall attribution at the seasonal scale. We show that identifying snow‐bearing ARs provides more information about the seasonal CS than simply knowing how many ARs occurred. Overall, we find that higher‐resolution atmospheric reanalyses imply greater attribution of seasonal CS to ARs.

Investigating the Variability of High-Elevation Seasonal Orographic Snowfall Enhancement and Its Drivers across Sierra Nevada, California

Abstract While orographically driven snowfall is known to be important in mountainous regions, a complete understanding of orographic enhancement from the basin to the mountain range scale is often inhibited by limited distributed data and spatial and/or temporal resolutions. A novel, 90-m spatially distributed snow water equivalent (SWE) reanalysis was used to overcome these limitations. Leveraging this SWE information, the interannual variability of orographic gradients in cumulative snowfall (CS) was investigated over 14 windward (western) basins in the Sierra Nevada in California from water years 1985 to 2015. Previous studies have not provided a detailed multidecadal climatology of orographic CS gradients or compared wet-year and dry-year orographic CS patterns, distributions, and gradients across an entire mountain range. The magnitude of seasonal CS gradients range from over 15 cm SWE per 100-m elevation to under 1 cm per 100 m with a 31-yr average of 6.1 cm per 100 m below ~2500 m in the western basins. The 31-yr average CS gradients generally decrease in higher elevation zones across the western basins and become negative at the highest elevations. On average, integrated vapor transport and zonal winds at 700 hPa are larger during wet years, leading to higher orographically driven CS gradients across the Sierra Nevada than in dry years. Below ~2500 m, wet years yield greater enhancement (relative to dry years) by factors of approximately 2 and 3 in the northwestern and southwestern basins, respectively. Overall, the western Sierra Nevada experiences about twice as much orographic enhancement during wet years as in dry years below the elevation corresponding to the 31-yr average maximum CS.

A study of heavy snowfall in Kashmir, India in January 2017

Kashmir valley witnessed multiple heavy snowfall spells in January 2017 that triggered an avalanche, killing 24 people, including 20 Indian Army Personnel, whose camp was struck by the avalanche. This study aims to present an analysis of the heavy snowfall events that occurred the majority of districts in Kashmir during January 2017 using station data from the India Meteorological Department (IMD). Results show that there were three episodes of intense snowfall over different parts of Kashmir. Maximum daily accumulated snowfall measurements of about 11.5 and 9.2cm were recorded at Pahalgam and Banihall stations, respectively, on 26 January 2017. Gulmarg also recorded a maximum daily accumulated snowfall of 8.8cm on 25 January 2017. The snowfall anomaly data show that Gulmarg, Srinagar, Kupwara, Banihall, Quazigund and Pahalgam witnessed the highest snowfall of recent decades. Gulmarg and Pahalgam recorded cumulative snowfall measurements of about 38.4 and 30cm, respectively, during January. Data from the region reveal that the accumulated snow from three episodes of intense snowfall during 6–8, 15–17 and 25–27 January 2017 resulted in an avalanche over Kashmir.

Implications of Detection Methods on Characterizing Atmospheric River Contribution to Seasonal Snowfall Across Sierra Nevada, USA

AbstractThis study investigates the extent to which the diagnosed contribution of atmospheric rivers (ARs) to the seasonal cumulative snowfall (CS) is related to the AR detection approach utilized. Using both satellite integrated water vapor (IWV)‐based and reanalysis integrated vapor transport (IVT)‐based methodologies, the corresponding AR‐derived CS distributions were characterized over the Sierra Nevada (USA) from 1998 to 2015. AR detection methods indicated that ARs yield greater orographic enhancement of the seasonal CS than non‐AR storms above ~2,100–2,300 m for the IWV‐based approach and over all elevations for the IVT‐based detection approach across the western (i.e., windward) Sierra Nevada. Due to differences in the methodologies, the IWV‐based approach diagnosed 2.1 times fewer ARs than the IVT‐based approach. As a result, the ARs diagnosed using the IWV‐based detection method yielded an average 33% of the total range‐wide CS annually as opposed to 56% from the IVT‐based detection method.

Climatology of seasonal snowfall accumulation across the Sierra Nevada (USA): Accumulation rates, distributions, and variability

AbstractA detailed picture of how snowfall varies across high‐elevation mountain ranges in both space and time remains a knowledge gap in understanding the montane hydrologic cycle. Previous studies generally used point‐scale snow measurements in an attempt to represent the spatial variability of snowfall across a range; however, these traditional approaches provide incomplete insight into the cumulative snowfall (CS) distribution from the basin‐scale to the mountain range‐scale. In this study, a high‐resolution, spatially distributed snow reanalysis was utilized to characterize 31 winters (water years 1985–2015) of snowfall distributions, snowfall accumulation rates, and snowstorms across the Sierra Nevada (USA). The CS data set (quantified in units of snow water equivalent) was verified against over 2600 station years of in situ observations. The seasonal CS was found to have mean and root‐mean‐squared differences of −4 and 12 cm, respectively, and a correlation of 0.96 with snow pillow observations. Using this novel CS information, results indicated that the CS accumulates rapidly across all 20 basins examined with, on average, at least 50% of the integrated CS accumulating in less than or equal to 6 days or three snowstorms over each basin. The largest (or leading) snowstorms each season yield ∼27% of the CS, on average, and most frequently last 4 days. Across the range, over 40% of the leading snowstorms occur in February. This study showed that the hydroclimatology of the Sierra Nevada is driven by hydrological extremes as manifested in the high interannual variability of its seasonally integrated CS, 4.4–41.3 km3, during the 31 years.

Formation and strengthening of layers of dry faceted crystals above artificial melt–freeze crusts from overburden stress in a controlled environment

Layers of faceted crystals were grown from naturally fallen snow above wet snow layers in a temperature-controlled laboratory. Static loads were then applied to the weak layers to represent overburden snow. Eleven experiments with constant loads were analyzed, equivalent to 260 to 1500 Pa of overburden stress. The density of the slab above the weak layer increased with time, following a power law relationship with an average exponent of 0.10. Shear strength of the weak layer increased with time for all experiments, also following a power law relationship. Early-time rates of strength gain averaged 250 Pa·day−1 for the constant-load experiments over the first 3–5 days, decreasing to an average of <1 Pa·day−1 after approximately 30 days. Exponents for the power law relationships ranged between 0.09 and 0.35 with an average of 0.26 ± 0.08. Sintering was likely the dominant process for strength gain, although densification probably contributed as well. Three experiments were conducted in which the overburden was increased in stages; these exhibited an average strength gain rate of 270 Pa·day−1 over 4–8 days with approximately linear relationships, highlighting the importance of cumulative snowfall for layer strength gain.

Spatiotemporal variability in snow cover from 1987 to 2011 in northern China

The spatial and temporal distributions of snow cover were analyzed using daily snow depths derived from special sensor microwave imager and advanced microwave scanning radiometer- Earth observing system data between 1987 and 2011 in northern China. To monitor the annual and interannual snow cover variability, snow cover days (SCD), monthly mean snow depth (MMSD), and cumulative snowfall (CS) were derived, and their variation slopes and relative standard deviations were calculated. The results showed that the snow cover reached its maximum values of depth and area in January and February. The MMSD, SCD, and CS interannual variation presented spatial heterogeneity. The average snow depth exhibited insignificant changes in most areas. For the annual SCD change trend, an insignificant decrease, significant decrease, insignificant increase, and significant increase in coverage characterized approximately 37.6%, 19.3%, 41.0%, and 1.1% of the total area, respectively. For the annual CS variation trend, an insignificant decrease, significant decrease, insignificant increase, and significant increase characterized approximately 30.2%, 2.4%, 57.1%, and 9.2% of the area, respectively. According to the snow depth variation in different climate zones, the snow depth decreased in the early and late periods in the snow season in all climate regions, but the fluctuations and change trends in these climate zones were different. The decreasing trend was more obvious in plateau climate zone than in other zones. The fluctuation amplitude was greatest in the temperate monsoon climate zone. (c) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)

Managing Risk of Ski Resorts with Snow Options

The main problem for the tourism businesses which operate in ski resorts is that their revenue are strictly connected to the cumulative snowfall level. The uncertainty about the snowfall due the climate changing has made this type of business highly risky, for this reason few or no investments are made every year to improve the resorts, the slopes or the lifts; continuing in this way, the resort in few years will face to a loss in competitiveness, thus less tourists, less revenue and less business. The present work purposes to show a possible hedging strategy against the financial problems due to the scarcity of snow: the weather derivatives. Due to the fact that in Italy there are many ski resorts situated at an altitude from 1300m to 2000m, we focus our analysis on Andalo, in Italian Alps and we obtain the estimated prices of a put snow option by applying three different methods proposed in literature. We suggest, finally, the ideal prices for Paganella lift operator in Andalo, showing how the use of weather derivatives may be very helpful in the risk management for ski resort related to snow scarcity events.

中部山岳地域における気象観測の現状とその意義

The Japanese Alps collectively refer the Hida Mountains, Kiso Mountains, and Akaishi Mountains, ranging approximately 200 km north to south and 100 km east to west. In terms of span, the Japanese Alps region includes the Ryohaku Mountains to the west and Mt. Fuji, Mt. Yatsugatake, and Mikuni Mountains to the east, making the geographical extent of the region more than 200 km in the east-west direction. The maximum altitude of the Japanese Alps is around 3000 m; however, climatic conditions vary widely because the region lies at the center of Honshu, between the Japan Sea coast and the Pacific coast, and the northern area is known to experience one of the heaviest snowfalls in the world. It is thought that mountainous areas are particularly sensitive to global-scale environmental changes, such as warming. When attempting to evaluate the effect of a global-scale warming event on regional environmental change in the high-altitude Japanese Alps, we note a lack of high-altitude meteorological observation data. This presents difficulties when evaluating the effects of warming on ecological systems and water resources in mountainous regions. In this study, we discuss long-term variations of winter temperature, as well as the amount of snowfall and depth of snow cover at twelve observational stations in the Japan Alps region. At eleven sites other than Mt. Fuji, a trend of increasing annual minimum temperature is recognized, which is statistically significant at the 1% significance level. At locations such as Mt. Fuji, which are located at extremely high altitudes, the annual minimum temperature over the last several decades has not been seen to increase or decrease. The trend of decreasing annual cumulative snowfall is statistically significant at the 10% level at Takada, Toyama and Kanazawa stations. At the other eight sites, it is shown that the recent annual cumulative snowfall does not show either an increasing or decreasing trend. The increasing trend of the annual maximum snow depth is statistically significant at the 5% level at Mt. Fuji, whereas a decreasing trend of the annual maximum snow depth is statistically significant at the 10% level at Takada and Kanazawa. At the other nine sites, annual maximum snow depth has not shown a statistically significant change in recent decades. Recent studies report that the amount of snowfall in Japan will decrease as a result of global warming; however, these studies use data collected at low altitudes. It is, therefore, important to question whether the same theory can be applied to high-altitude mountainous areas. In high-altitude areas of the Japanese Alps region, it is reasonable to expect that a cold atmosphere will be maintained even with global warming, and an increase in evaporated moisture vapor in the air caused by warming on a global scale will increase the amount of snowfall in the region.

A minimal, statistical model for the surface albedo of Vestfonna ice cap, Svalbard

Abstract. The ice cap Vestfonna is located in northeastern Svalbard and forms one of the largest ice bodies of the Eurasian Arctic. Its surface albedo plays a key role in the understanding and modelling of its energy and mass balance. The principle governing factors for albedo evolution, i.e. precipitation and air temperature and therewith snow depth and melt duration, were found to vary almost exclusively with terrain elevation throughout the ice cap. Hence, surface albedo can be expected to develop a comparable pattern. A new statistical model is presented that estimates this mean altitudinal albedo profile of the ice cap on the basis of a minimal set of meteorological variables on a monthly resolution. Model calculations are based on a sigmoid function of the artificial quantity rain-snow ratio and a linear function of cumulative snowfall and cumulative positive degree days. Surface albedo fields of the MODIS snow product MOD10A1 from the period March to October in the years 2001–2008 serve as a basis for both calibration and cross-validation of the model. The meteorological model input covers the period September 2000 until October 2008 and is based on ERA-Interim data of a grid point located close to the ice cap. The albedo model shows a good performance. The root mean square error between observed and modelled albedo values along the altitudinal profile is 0.057±0.028 (mean ± one standard deviation). The area weighted mean even reduces to a value of 0.054. Distinctly higher deviations (0.07–0.09) are only present throughout the very lowest and uppermost parts of the ice cap that are either small in area or hardly affected by surface melt. Thus, the new, minimal, statistical albedo model presented in this study is found to reproduce the albedo evolution on Vestfonna ice cap on a high level of accuracy and is thus suggested to be fully suitable for further application in broader energy or mass-balance studies of the ice cap.