Variability In Snow Accumulation Research Articles

What recent global atmospheric reanalyses and regional climate models can represent observed snow accumulation on Antarctica?

Recent compiled surface mass balance observations on the Antarctic ice sheet are used to examine the performance of four global reanalysis products and two regional climate models (RCMs), i.e., the European Center for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERAI), the fifth generation ECMWF reanalysis (ERA5), the Climate Forecast System Reanalysis (CFSR), Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA2), Modèle Atmosphérique Régional (MAR), and the Regional Atmospheric Climate Model (RACMO). All these models perform similarly well in reproducing spatial patterns of snow accumulation from the coast to inland plateau. But they differ in their magnitude with respective to in-situ observations with either evident overestimation (MAR) or underestimation (ERAI and CFSR) in the interior of the Antarctica. By comparison, MERRA2 and ERA5 at each elevation bin of 200 m match best with observations. Among the models, ERAI shows the smallest amount of snow accumulation averaged over the entire ice sheet. All models present some skills for reproducing the inter-annual variability in observed snow accumulation. By comparison, ERA5 probably provides the most realistic representation of variability in Antarctic snow accumulation since 1979.

Snow Accumulation Variability at the South Pole From 1983 to 2020, Associated With Central Tropical Pacific Forcing

AbstractDespite a variety of efforts made to measure snow accumulation at the South Pole (SP), snow accumulation changes and their mechanism have not yet been fully explained. Here, SP stake farm measurements, global sea surface temperature observations, and atmospheric circulation data from European Centre for Medium‐Range Weather Forecasts Reanalysis version 5 were used to investigate the annual and seasonal snow accumulation changes at the SP during 1983–2020, and their association with central tropical Pacific Sea surface temperature variations. SP annual snow accumulation decreased significantly for the 1983–2007 period at a rate of −39.7 ± 1.4 mm decade−1, but switched to a dramatically positive trend during 2008–2020 (108.7 ± 2.7 mm decade−1), with the strongest increase in the austral autumn. The switch to a dramatically upward trend can largely be attributed to a cyclonic anomaly over the South Atlantic and an anticyclonic anomaly over the Drake Passage, causing the enhanced advection of warm and wet air into the SP. These circulation patterns were generated by an atmospheric Rossby wave train forced by rapid warming in the central tropical Pacific during 2008–2020.

A method to estimate surface mass-balance in glacier accumulation areas based on digital elevation models and submergence velocities

AbstractMeasuring surface mass-balance in the accumulation areas of glaciers is challenging because of the high spatial variability of snow accumulation and the difficulty of conducting annual field glaciological measurements. Here, we propose a method that can solve both these problems for many locations. Ground-penetrating radar measurements and firn cores extracted from a site in the French Alps were first used to reconstruct the topography of a buried end-of-summer snow horizon from a past year. Using these data and surface elevation observations from LiDAR and Global Navigation Satellite System instruments, we calculated the submergence velocities over the period between the buried horizon and more recent surface elevation observations. The differences between the changes in surface elevation and the submergence velocities were then used to calculate the annual surface mass-balances with an accuracy of ±0.34 m w.e. Assuming that the submergence velocities remain stable over several years, the surface mass-balance can be reconstructed for subsequent years from the differences in surface elevation alone. As opposed to the glaciological method that requires substantial fieldwork year after year to provide only point observations, this method, once submergence velocities have been calculated, requires only remote-sensing data to provide spatially distributed annual mass-balances in accumulation areas.

Extreme precipitation records in Antarctica

AbstractMonitoring extreme precipitation records (EPRs), that is, the most extreme precipitation events, is a challenge in Antarctica due to the reduced number of stations available in the continent and the limitations of the instrumentation for measuring solid precipitation. Still, extreme precipitation events may contribute substantially to the variability of ice sheet snow accumulation and even may cause important ecological impacts. This article presents the Antarctic EPRs at different temporal scales, studying the relationship between precipitation amount and temporal duration through a power scaling law, ranging from 1 day to 2 years. This is achieved using precipitation datasets from the ERA5 reanalysis and the RACMO2 regional climate model. Moreover, we present a selection of EPRs case studies examining the synoptic mechanisms that produce such events in Antarctica. Despite ERA5 EPRs are usually lower than those found in RACMO2, they present similar scaling exponents. EPRs are found in Loubet and south Graham Coasts, in the central section of the Antarctic Peninsula, and in the north of Alexander Island, where orographic enhancement increases precipitation amounts. As expected, Antarctic EPRs are much lower than world‐wide EPRs, ranging from 6 to 10% at short temporal scales (from 1 to 10 days) and from 10 to 20% at long temporal scales (from 90 days to 2 years) in ERA5. Regional variability of extreme precipitation scaling exponents show similar spatial patterns than previously calculated precipitation concentration. On the other hand, the lack of summer events in Antarctic EPRs evidences that stronger fluxes in winter play a key role on extreme precipitation during EPR events, which are mainly produced by long‐range transport of moisture by atmospheric rivers impinging on Antarctic mountains.

Temporal variability in snow accumulation and density at Summit Camp, Greenland ice sheet

AbstractA 3-year record of weekly snow water equivalent (SWE) accumulation at Summit Camp, central Greenland ice sheet, obtained by direct sampling, is presented. While the overall SWE accumulation of 24.2 cm w.e. a−1 matches long-term ice core estimates, variability increases at shorter timescales. Half of the annual SWE accumulation occurs during a few large events, with the average accumulation rate decreasing 35% between the first and second halves of the record coinciding with exceptional anticyclonic conditions in the spring and summer of 2019. No seasonality in accumulation is detected. Rather, local accumulation rates appear to be significantly impacted by wind redistribution that obscures temporal patterns in snowfall. Surface snow density is consistent, on average, with previously measured values but does not correlate with near surface temperature or wind speed. Two surface mass balance reanalysis models significantly underestimate accumulation rates at Summit Camp. This is concerning because such models are often used to estimate ice-sheet mass loss.

Long-Term Snow Height Variations in Antarctica from GNSS Interferometric Reflectometry

Acquiring reliable estimates of the Antarctic Ice Sheet surface mass balance is essential for trustworthy predictions of its evolution and future contribution to sea level rise. Snow height variations, i.e., the net change of the surface elevation resulting from a combination of surface processes such as snowfall, ablation, and wind redistribution, can provide a unique tool to constrain the uncertainty on mass budget estimations. In this study, GNSS Interferometric Reflectometry (GNSS-IR) is exploited to assess the long-term variations of snow accumulation and ablation processes. Eight antennas belonging to the Polar Earth Observing Network (POLENET) network are considered, together with the ROB1 antenna, deployed in the east part of Antarctica by the Royal Observatory of Belgium. For ROB1, which is located on an ice rise, we highlight an annual variation of snow accumulation in April–May (~30–50 cm) and ablation during spring/summer period. A snow surface elevation velocity of +0.08 ± 0.01 ma−1 is observed in the 2013–2016 period, statistically rejecting the “no trend” null hypothesis. As the POLENET stations are all located on moving glaciers, their associated downhill motion must be corrected for using an elevation model. This induces an increased uncertainty on the snow surface elevation change determined from GNSS-IR. Among the eight stations analyzed, only three of them show a long-term snow height variation larger than the uncertainties. One is located on the Flask Galcier in the Antarctic Peninsula, with a decrease of more than 4 m between 2012 and 2014, with an uncertainty of 2.5 m. The second one is located on the Lower Thwaites Glacier where we observe, between 2010 and 2020, a snow surface drop of 10 m, with a conservative uncertainty of 1 m. The third station, located on the West Antarctic Ice Sheet (WAIS) divide, shows on the opposite an upward motion from 2005 to 2019, of 1.2 m with an uncertainty of 0.4 m. The snow surface change of the other POLENET stations analyzed is smaller than the uncertainty associated with the glacier slope.

Multi-scale snowdrift-permitting modelling of mountain snowpack

Abstract. The interaction of mountain terrain with meteorological processes causes substantial temporal and spatial variability in snow accumulation and ablation. Processes impacted by complex terrain include large-scale orographic enhancement of snowfall, small-scale processes such as gravitational and wind-induced transport of snow, and variability in the radiative balance such as through terrain shadowing. In this study, a multi-scale modelling approach is proposed to simulate the temporal and spatial evolution of high-mountain snowpacks. The multi-scale approach combines atmospheric data from a numerical weather prediction system at the kilometre scale with process-based downscaling techniques to drive the Canadian Hydrological Model (CHM) at spatial resolutions allowing for explicit snow redistribution modelling. CHM permits a variable spatial resolution by using the efficient terrain representation by unstructured triangular meshes. The model simulates processes such as radiation shadowing and irradiance to slopes, blowing-snow transport (saltation and suspension) and sublimation, avalanching, forest canopy interception and sublimation, and snowpack melt. Short-term, kilometre-scale atmospheric forecasts from Environment and Climate Change Canada's Global Environmental Multiscale Model through its High Resolution Deterministic Prediction System (HRDPS) drive CHM and are downscaled to the unstructured mesh scale. In particular, a new wind-downscaling strategy uses pre-computed wind fields from a mass-conserving wind model at 50 m resolution to perturb the mesoscale HRDPS wind and to account for the influence of topographic features on wind direction and speed. HRDPS-CHM was applied to simulate snow conditions down to 50 m resolution during winter 2017/2018 in a domain around the Kananaskis Valley (∼1000 km2) in the Canadian Rockies. Simulations were evaluated using high-resolution airborne light detection and ranging (lidar) snow depth data and snow persistence indexes derived from remotely sensed imagery. Results included model falsifications and showed that both wind-induced and gravitational snow redistribution need to be simulated to capture the snowpack variability and the evolution of snow depth and persistence with elevation across the region. Accumulation of windblown snow on leeward slopes and associated snow cover persistence were underestimated in a CHM simulation driven by wind fields that did not capture lee-side flow recirculation and associated wind speed decreases. A terrain-based metric helped to identify these lee-side areas and improved the wind field and the associated snow redistribution. An overestimation of snow redistribution from windward to leeward slopes and subsequent avalanching was still found. The results of this study highlight the need for further improvements of snowdrift-permitting models for large-scale applications, in particular the representation of subgrid topographic effects on snow transport.

Future Antarctic snow accumulation trend is dominated by atmospheric synoptic-scale events

Over the last century, the increase in snow accumulation has partly mitigated the total dynamic Antarctic Ice Sheet mass loss. However, the mechanisms behind this increase are poorly understood. Here we analyze the Antarctic Ice Sheet atmospheric moisture budget based on climate reanalysis and model simulations to reveal that the interannual variability of regional snow accumulation is controlled by both the large-scale atmospheric circulation and short-lived synoptic-scale events (i.e. storm systems). Yet, when considering the entire continent at the multi-decadal scale, only the synoptic-scale events can explain the recent and expected future snow accumulation increase. In a warmer climate induced by climate change, these synoptic-scale events transport air that can contain more humidity due to the increasing temperatures leading to more precipitation on the continent. Our findings highlight that the multi-decadal and interannual snow accumulation variability is governed by different processes, and that we thus cannot rely directly on the mechanisms driving interannual variations to predict long-term changes in snow accumulation in the future.

Temporal and spatial variability in surface roughness and accumulation rate around 88° S from repeat airborne geophysical surveys

Abstract. We use repeat high-resolution airborne geophysical data consisting of laser altimetry, snow, and Ku-band radar and optical imagery acquired in 2014, 2016, and 2017 to analyze the spatial and temporal variability in surface roughness, slope, wind deposition, and snow accumulation at 88∘ S, an elevation bias validation site for ICESat-2 and potential validation site for CryoSat-2. We find significant small-scale variability (<10 km) in snow accumulation based on the snow radar subsurface stratigraphy, indicating areas of strong wind redistribution are prevalent at 88∘ S. In general, highs in snow accumulation rate correspond with topographic lows, resulting in a negative correlation coefficient of r2=-0.32 between accumulation rate and MSWD (mean slope in the mean wind direction). This relationship is strongest in areas where the dominant wind direction is parallel to the survey profile, which is expected as the geophysical surveys only capture a two-dimensional cross section of snow redistribution. Variability in snow accumulation appears to correlate with variability in MSWD. The correlation coefficient between the standard deviations of accumulation rate and MSWD is r2=0.48, indicating a stronger link between the standard deviations than the actual parameters. Our analysis shows that there is no simple relationship between surface slope, wind direction, and snow accumulation rates for the overall survey area. We find high variability in surface roughness derived from laser altimetry measurements on length scales smaller than 10 km, sometimes with very distinct and sharp transitions. Some areas also show significant temporal variability over the course of the 3 survey years. Ultimately, there is no statistically significant slope-independent relationship between surface roughness and accumulation rates within our survey area. The observed correspondence between the small-scale temporal and spatial variability in surface roughness and backscatter, as evidenced by Ku-band radar signal strength retrievals, will make it difficult to develop elevation bias corrections for radar altimeter retrieval algorithms.

Impact of fire on montane snowpack energy balance in Snow Gum forest stands

Forest stands fundamentally alter hydrology of a region through impacting area micrometeorology and corresponding variability in snow accumulation and melt. Bushfires significantly change these interactions through removal of forest canopy, darkening of tree stems, and post-fire stem decay. This study quantified the impact of pre- and post-bushfire E. pauciflora (Snow Gum) forest stands on snowpack energy balance in the Snowy Mountains of Southeast Australia. Forest canopy cover in undisturbed forest stands moderated snowpack energy exchange through reductions to incoming shortwave radiation and turbulent fluxes. Energy flux to the snowpack was at a maximum in the fire-disturbed forest due to lower snowpack albedo resulting in greater net shortwave radiation and an increased sensible heat emission from decaying tree stems. The fire-disturbed forest also experienced the largest evaporation rate with 8.1% of snowpack SWE being lost to the atmosphere. Dominant energy fluxes to the snowpack were shortwave radiation in the unforested area and fire-disturbed forest stand, and sensible heat in the undisturbed forest stand.

How Do We Define Climate Change? Considering the Temporal Resolution of Niveo-Meteorological Data

Historically, snowpack trends have been assessed using one fixed date to represent peak snow accumulation prior to the onset of melt. Subsequent trend analyses have considered the peak snow water equivalent (SWE), but the date of peak SWE can vary by several months due to inter-annual variability in snow accumulation and melt patterns. A 2018 assessment evaluated monthly SWE trends. However, since the month is a societal construct, this current work examines daily trends in SWE, cumulative precipitation, and temperature. The method was applied to 13 snow telemetry stations in Northern Colorado, USA for the period from 1981 to 2018. Temperature trends were consistent among all the stations; warming trends occurred 63% of the time from 1 October through 24 May, with the trends oscillating from warming to cooling over about a 10-day period. From 25 May to 30 September, a similar oscillation was observed, but warming trends occurred 86% of the time. SWE and precipitation trends illustrate temporal patterns that are scaled based on location. Specifically, lower elevations stations are tending to record more snowfall while higher elevation stations are recording less. The largest SWE, cumulative precipitation, and temperature trends were +30 to −70 mm/decade, +30 to −30 mm/decade, and +4 to −2.8 °C/decade, respectively. Trends were statistically significance an average of 25.8, 4.5, and 29.4% of the days for SWE, cumulative precipitation, and temperature, respectively. The trend in precipitation as snow ranged from +/−2%/decade, but was not significant at any station.

Relating regional and point measurements of accumulation in southwest Greenland

Abstract. In recent decades, the Greenland ice sheet (GrIS) has frequently experienced record melt events, which have significantly affected surface mass balance (SMB) and estimates thereof. SMB data are derived from remote sensing, regional climate models (RCMs), firn cores and automatic weather stations (AWSs). While remote sensing and RCMs cover regional scales with extents ranging from 1 to 10 km, AWS data and firn cores are point observations. To link regional scales with point measurements, we investigate the spatial variability of snow accumulation (bs) within areas of approximately 1–4 km2 and its temporal changes within 2 years of measurements. At three different sites on the southwestern GrIS (Swiss Camp, KAN-U, DYE-2), we performed extensive ground-penetrating radar (GPR) transects and recorded multiple snow pits. If the density is known and the snowpack dry, radar-measured two-way travel time can be converted to snow depth and bs. We spatially filtered GPR transect data to remove small-scale noise related to surface characteristics. The combined uncertainty of bs from density variations and spatial filtering of radar transects is at 7 %–8 % per regional scale of 1–4 km2. Snow accumulation from a randomly selected snow pit is very likely representative of the regional scale of 1–4 km2 (with probability p=0.8 for a value within 10 % of the regional mean for KAN-U, and p>0.95 for Swiss Camp and DYE-2). However, to achieve such high representativeness of snow pits, it is required to determine the average snow depth within the vicinity of the pits. At DYE-2, the spatial pattern of snow accumulation was very similar for 2 consecutive years. Using target reflectors placed at respective end-of-summer-melt horizons, we additionally investigated the occurrences of lateral redistribution within one melt season. We found no evidence of lateral flow of meltwater in the current climate at DYE-2. Such studies of spatial representativeness and temporal changes in accumulation are necessary to assess uncertainties of the linkages of point measurements and regional-scale data, which are used for validation and calibration of remote-sensing data and RCM outputs.

The role of cyclone activity in snow accumulation on Arctic sea ice

Identifying the mechanisms controlling the timing and magnitude of snow accumulation on sea ice is crucial for understanding snow’s net effect on the surface energy budget and sea-ice mass balance. Here, we analyze the role of cyclone activity on the seasonal buildup of snow on Arctic sea ice using model, satellite, and in situ data over 1979–2016. On average, 44% of the variability in monthly snow accumulation was controlled by cyclone snowfall and 29% by sea-ice freeze-up. However, there were strong spatio-temporal differences. Cyclone snowfall comprised ~50% of total snowfall in the Pacific compared to 83% in the Atlantic. While cyclones are stronger in the Atlantic, Pacific snow accumulation is more sensitive to cyclone strength. These findings highlight the heterogeneity in atmosphere-snow-ice interactions across the Arctic, and emphasize the need to scrutinize mechanisms governing cyclone activity to better understand their effects on the Arctic snow-ice system with anthropogenic warming.

Major factors affecting the snow simulations by the JULES in New Zealand

AbstractSnow frequently occurs over New Zealand. The seasonal snow accumulation can be about 3 m deep in early spring at some locations. Thus, for numerical weather prediction and climate modelling over New Zealand, a reliable, sophisticated snow model that can properly describe the physical processes associated with snow cover is required. In the past, owing to a lack of snow observations, modelling and verification of snow processes was limited over New Zealand. In the present study, the multilayer snow scheme of the Joint UK Land Environment Simulator (JULES)—the land surface model used in New Zealand regional weather and climate models—was used to simulate the snow water equivalent (SWE) and snow density at two sites in the South Island of New Zealand. The model captured seasonal and interannual variability in snow accumulation and melt. However, large errors in the simulated SWE (up to 300 mm) were found during snowmelt periods and in relatively warm winters when a significant amount of liquid water was present in snow. Sensitivity tests showed that errors in the simulated long wave radiation (a negative bias of about 6 W/m2), snow albedo (0.05–0.10) and air temperature for snow occurrence ( T c, about 0.5 K) were the major factors causing large errors in the simulated SWE. In addition, the simulated snow density was lower than observed. The relatively warm and humid maritime climate of New Zealand appears to make snow simulation in New Zealand more sensitive to long wave radiation, snow albedo and Tc than for continents.

Ecological plasticity of denning chronology by American black bears and brown bears

Ecological plasticity may index the capacity of individuals, populations, and species to respond to stochasticity. Few studies have demonstrated mechanisms influencing expression of plasticity and less have addressed proximate and causal mechanisms at multiple scales. We used linear models and meta-analyses to investigate factors influencing among and within population plasticity in den chronology for American black bears (Ursus americanus) and brown bears (U. arctos) in North America. We found later mean den entrance date and longer mean duration of denning among populations for both species in response to apparent increasing food availability during the growing season and denning period, along with later onset and shorter duration of mean snow accumulations ≥10 cm. Greater variability in den chronology appeared to be in response to greater variability in snow accumulation and duration precluding access to forage. Observed patterns of plasticity in den chronology varied among age-reproductive groups with male bears denning later and for shorter duration than sub-adults, non-parturient females, and parturient females. This pattern supports expected allometric energetic demands and energetic expenditures required for reproduction. Furthermore, our results align with previously published works indicating that plasticity in den chronology in response to relaxed selective pressure of food availability is energetically advantageous under the cost-benefit hypothesis of energy conservation. Understanding how species respond to variation in selective pressures is central to predicting these effects and our understanding of evolutionary and ecological processes.

Snowpack influences spatial and temporal soil nitrogen dynamics in a western U.S. montane forested watershed

AbstractDeclines in winter snowpack have increased the severity of summer droughts in western U.S. forests, with the potential to also impact soil available nitrogen (N). To understand how snowpack controls spatiotemporal N availability, we examined seasonal N dynamics across elevation, aspect, and topographic position (hollow vs. slope) in a forested watershed in the northern Rocky Mountains. As expected, peak snow‐water equivalent (SWE) was generally greater at higher elevations and on north‐facing aspects. However, the effects of topographic position and snowdrift led to variability in snow accumulation at smaller spatial scales. Spatial patterns of the snowpack, in turn, influenced soil moisture and temperature, with greater SWE leading to generally higher soil moisture levels during the summer and smaller temperature fluctuations throughout the year. Wetter conditions in spring or fall generally supported greater inorganic N pools, but at the driest locations (low‐elevation slope), pulses of N mineralization in summer may have played important roles in overall N dynamics. More importantly, soil moisture during the summer appeared to be more influenced by antecedent snowpack from the previous year than by current‐year summer rain. Subsequently, N mineralization under snowpack may be strongly influenced by soil moisture and temperature conditions from the previous fall, before snowpack accumulation. Together, our results indicate that snowpack strongly influences N dynamics beyond the current growing season in western coniferous forests through mediation of soil moisture and temperature, and suggest that further decline in winter snowpack may affect these forests through constraints in both water and N availability.

A low-cost autonomous rover for polar science

Abstract. We present the developmental considerations, design, and deployment of an autonomous modular terrestrial rover for ice-sheet exploration that is inexpensive, easy to construct, and allows for instrumentation customization. The total construction cost for this rover is less than USD 3000, approximately one-tenth the cost of existing platforms, and it can be built using facilities frequently available at academic institutions (machine shop, 3-D printer, open-source hardware and software). Instrumentation deployed on this rover can be customized; the rover presented in this study was equipped with a dual-frequency GPS receiver and a digital SLR camera for constructing digital elevation models using structure-from-motion (SfM) photogrammetry. We deployed this prototype rover on the Northeast Greenland Ice Stream to map local variations in snow accumulation and surface topography. The rover conducted four autonomous missions based out of the East Greenland Ice-Core Project (EastGRIP) camp during July 2017, measuring surface elevation transects across the hazardous ice-stream shear margins. During these missions, the rover proved capable of driving over 20 km on a single charge with a drawbar pull of 250 N, sufficient to tow instrumentation of up to 100 kg. The rover also acquired photographs that were subsequently used to construct digital elevation models of a site monitored for spatiotemporal variability in snow accumulation, demonstrating adequate stability for high-resolution imaging applications. Due to its low cost, low-power requirements, and simple modular design, mass deployments of this rover design are practicable. Operation of the rover in hazardous areas circumvents the substantial expense and risk to personnel associated with conventional, crewed deployments. Thus, this rover is an investigatory platform that enables direct exploration of polar environments considered too hazardous for conventional field expeditions.

A Reassessment of North American River Basin Cool‐Season Precipitation: Developments From a New Mountain Climatology Data Set

AbstractCharacterizing hydrological processes on large scales is challenging due to limitations of observational networks, remoting sensing platforms, and modeling techniques. Water balances have larger uncertainties in mountain regions, where orographic processes produce high spatial variability in precipitation patterns and snow accumulation. Recent work suggests current water budgets underestimate mountain snow water storage, perhaps indicating biases in modeled precipitation. We assess whether global hydroclimate data sets underestimate precipitation for six North American watersheds, ranging from 3–70% mountainous. By selecting a single representative year for each watershed, we compare relatively high‐resolution precipitation estimates from the Weather Research and Forecasting (WRF) regional climate model with four global products: Modern‐Era Retrospective Analysis for Research and Applications, version 2, the Global Land Data Assimilation System, the Global Precipitation Climatology Project, and the Climate Research Unit's climate data set. Comparisons to WRF precipitation suggest that observation‐based gridded data products do not produce reasonable estimates of watershed‐scale cool‐season precipitation, underestimating by 1–69%. The Global Precipitation Climatology Project and the Climate Research Unit data set have average biases of −26% and −38%, respectively. The Modern‐Era Retrospective Analysis for Research and Applications version 2 and the Global Land Data Assimilation System show smaller underestimates relative to WRF (−17% and −21%, respectively), with nearly all mean bias from the mountains (underestimated by 27% and 39%) rather than the topographically simpler lowlands (underestimated by 5% and 2%). We suggest global products fail to capture orographic enhancement of precipitation, resulting in large underestimates of precipitation, snowfall, and snow water storage in mountains of selected North American watersheds, which highlights the need for more accurate precipitation estimates to accurately assess spatiotemporal variations in the water cycle.

The Seasonal Snow Cover Dynamics: Review on Wind-Driven Coupling Processes

The temporal evolution of seasonal snow cover and its spatial variability in environments such as mountains, prairies or polar regions is strongly influenced by the interactions between the atmospheric boundary layer and the snow cover. Wind-driven coupling processes affect both mass and energy fluxes at the snow surface with consequences on snow hydrology, avalanche hazard and ecosystem development. This paper proposes a review on these processes and combines the more recent findings obtained from observations and modelling. The spatial variability of snow accumulation across multiple scales can be associated to wind-driven processes ranging from orographic precipitation at large scale to preferential-deposition of snowfall and wind-induced transport of snow on the ground at smaller scales. An overview of models of varying complexity developed to simulate these processes is proposed in this paper. Snow ablation is also affected by wind-driven processes. Over continuous snow covers, turbulent fluxes of latent and sensible heat, as well as blowing snow sublimation, modify the mass and energy balance of the snowpack and their representation in numerical models is associated with many uncertainties. As soon as the snow cover becomes patchy in spring local heat advection induces the developement of stable internal boundary layers changing heat exchange towards the snow. Overall, wind-driven processes play a key role in all the different stages of the evolution of seasonal snow. Improvements in process understanding particularly at the mountain-ridge and the slope scale, and processes representations in models at scales up to the mountain range scale, will be the basis for improved short-term forecast and climate projections in snow-covered regions.

Evaluation of Seasonal and Synoptic Changes in Snow Accumulation in Antarctica between Five Reanalyses Products and In Situ Observations

The performance of recent reanalysis products (i.e., ERA-Interim, NCEP2, MERRA, CFSR, and JRA-55) was evaluated based on in situ observations from nine automatic weather stations and one stake network to investigate the monthly and seasonal variability of the surface mass balance in Antarctica. Synoptic precipitation simulations were also evaluated by an investigation of high precipitation events. The seasonal variations showed large fluctuations and were inconsistent at each station, probably owing to the large interannual variability of snow accumulation based on the short temporal coverage of the data. The ERA-Interim and JRA-55 datasets revealed better simulated precision, with the other three models presenting similar simulations at monthly and seasonal timescales. The JRA-55 dataset captured a greater number of synoptic high precipitation events at four of the nine stations. Such events at the other five stations were mainly captured by ERA and CFSR. The NCEP2 dataset was more weakly correlated with each station on all timescales. These results indicate that significant monthly or seasonal correlations between in situ observations and the models had little effect on the capability of the reanalyses to capture high precipitation events. The precision of the five reanalysis datasets widely fluctuated in specific regions or at specific stations at different timescales. Great caution is needed when using a single reanalysis dataset to assess the surface mass balance over all of Antarctica.