Snowfall and snow accumulation processes during MOSAiC

Author(s):  
David N. Wagner ◽  
Matthew D. Shupe ◽  
Ola G. Persson ◽  
Taneil Uttal ◽  
Markus Frey ◽  
...  

<p>Due to logistical challenges, snowfall in the high Arctic has rarely been measured, which is particularly valid for longer time-spans and the polar night. When estimating snowfall with precipitation gauges, a snowfall reference and detailed knowledge of how the precipitated snow accumulated or eroded is required.</p><p>To overcome snowfall uncertainties and to improve accumulated and eroded snow estimates, we used data from precipitation gauges, snow particle counters (SPCs) and a K<sub>a</sub>-Band ARM Zenith Radar (KAZR) installed on and around research vessel (RV) Polarstern during the snow accumulation season of MOSAiC (October 2019 - May 2020). In addition to this, direct snow water equivalent (SWE) measurements were conducted and SWE estimates were retrieved from SnowMicroPen (SMP) measurements distributed all over the floe. The evolution of accumulated snow mass was finally computed by applying a simple fitted z-SWE function to snow depths that were measured approximately weekly along a fixed transect path with a Magnaprobe. The transects paths were along two distinct ice types: predominantly level remnant ice that at the start of the winter had large refrozen melt ponds, and predominantly deformed thick second year ice (SYI).</p><p>We could show that at least 34 mm of snow has accumulated and approximately 9 kg m<sup>-2</sup> of snow mass was eroded between 31 October 2019 and 26 April 2020. In the beginning of the winter, the total estimated SWE on level remnant ice was only 42 % of SWE on deformed SYI. By end of April 2020 the values almost equalized as the snow mass on remnant ice reached almost 90 % of the snow mass over deformed SYI.</p><p>Based on the SWE evolution of the snowpack, we validated precipitation sensors and the reanalysis ERA5 for their capability to estimate snowfall. Eroded snow mass, among other processes, led to a discrepancy of precipitation- sensor estimated snowfall and computed SWE of the snow cover from 20 February 2020 on. However, for the time period before the first net erosion could be observed we found best agreements of cumulated snowfall and SWE for the Vaisala Present Weather Detector (PWD22) installed on the vessel (RMSE = 2 mm) and for snowfall retrievals from the KAZR (RMSE = 4 mm). Other sensors largely overestimated snowfall (corrected OTT Pluvio<sup>2</sup>: 14 mm; Vaisala PWD22 on the ice: 26 mm, OTT Parsivel<sup>2</sup> on RV Polarstern: 51 mm). ERA5 overestimates snowfall too, with 13 mm and an increasing positive bias from March 2020 on. With horizontal snow mass fluxes derived from SPCs we could show that the Vaisala PWD22 on RV Polarstern was effectively protected against blowing snow. This, however, greatly affected snowfall measurements of instruments collocated on the ice. Further, we investigated a high-wind event in February 2020 resulting in high blowing snow mass fluxes and an average eroded snow mass of 5.5 kg m<sup>-2</sup>. The lifted blowing snow particles from the surface led to strong overestimation of snowfall from instruments installed on the ice which cannot be corrected with conventional correction methods.</p>

2006 ◽  
Vol 7 (6) ◽  
pp. 1259-1276 ◽  
Author(s):  
Glen E. Liston ◽  
Kelly Elder

Abstract SnowModel is a spatially distributed snow-evolution modeling system designed for application in landscapes, climates, and conditions where snow occurs. It is an aggregation of four submodels: MicroMet defines meteorological forcing conditions, EnBal calculates surface energy exchanges, SnowPack simulates snow depth and water-equivalent evolution, and SnowTran-3D accounts for snow redistribution by wind. Since each of these submodels was originally developed and tested for nonforested conditions, details describing modifications made to the submodels for forested areas are provided. SnowModel was created to run on grid increments of 1 to 200 m and temporal increments of 10 min to 1 day. It can also be applied using much larger grid increments, if the inherent loss in high-resolution (subgrid) information is acceptable. Simulated processes include snow accumulation; blowing-snow redistribution and sublimation; forest canopy interception, unloading, and sublimation; snow-density evolution; and snowpack melt. Conceptually, SnowModel includes the first-order physics required to simulate snow evolution within each of the global snow classes (i.e., ice, tundra, taiga, alpine/mountain, prairie, maritime, and ephemeral). The required model inputs are 1) temporally varying fields of precipitation, wind speed and direction, air temperature, and relative humidity obtained from meteorological stations and/or an atmospheric model located within or near the simulation domain; and 2) spatially distributed fields of topography and vegetation type. SnowModel’s ability to simulate seasonal snow evolution was compared against observations in both forested and nonforested landscapes. The model closely reproduced observed snow-water-equivalent distribution, time evolution, and interannual variability patterns.


2013 ◽  
Vol 7 (1) ◽  
pp. 709-741 ◽  
Author(s):  
T. Sauter ◽  
M. Möller ◽  
R. Finkelnburg ◽  
M. Grabiec ◽  
D. Scherer ◽  
...  

Abstract. The redistribution of snow by drifting and blowing snow frequently leads to an inhomogeneous snow mass distribution on larger ice caps. Together with the thermodynamic impact of drifting snow sublimation on the lower atmospheric boundary layer, these processes affect the glacier surface mass balance. This study provides a first quantification of snowdrift and sublimation of blowing and drifting snow on Vestfonna ice cap (Svalbard) by using the specifically designed "snow2blow" snowdrift model. The model is forced by atmospheric fields from the Weather Research and Forecasting model and resolves processes on a spatial resolution of 250 m. Comparison with radio-echo soudings and snow-pit measurements show that important local scale processes are resolved by the model and the overall snow accumulation pattern is reproduced. The findings indicate that there is a significant redistribution of snow mass from the interior of the ice cap to the surrounding areas and ice slopes. Drifting snow sublimation of suspended snow is found to be stronger during winter. It is concluded that both processes are strong enough to have a significant impact on glacier mass balance.


2020 ◽  
Author(s):  
Fabiola Pinto Escobar ◽  
Pablo A. Mendoza ◽  
Thomas E. Shaw ◽  
Jesús Revuelto ◽  
Keith Musselman ◽  
...  

<p>Snow water equivalent is highly heterogeneous due to the spatial distribution of precipitation, local topographic characteristics, effects of vegetation, and wind. In particular, the latter has important effects on such distribution, controlling the preferential deposition of snowfall, transport (either by saltation or suspension) on the ground, and sublimation of blowing snow. In this work, we analyze the effects of incorporating redistribution by wind transport when modeling the seasonal water balance in two experimental catchments: (i) the Izas catchment (0.33 km²), located in the Spanish Pyrenees, with an elevation range of 2000-2300 m a.s.l., and (ii) Las Bayas catchment (2.45 km²), located in the extratropical Andes Cordillera (Chile) and elevation between 3400 and 3900 m a.s.l. After assessing model simulations using time series of snow depth and terrestrial lidar scans, we examine the water balance at the annual and seasonal scales, quantifying the different fluxes that govern snow accumulation and melting with a spatially distributed model that considers the physics of transport and the sublimation of blowing snow. Moreover, we characterize the sensitivity of dominant processes to changes in precipitation and temperature. The results of this investigation have important implications on current and future research, allowing to contrast wind effects in the spatio-temporal patterns of accumulation and melting in alpine and subalpine areas, identifying those processes that will be most affected under projected climatic conditions.</p>


2013 ◽  
Vol 7 (4) ◽  
pp. 1287-1301 ◽  
Author(s):  
T. Sauter ◽  
M. Möller ◽  
R. Finkelnburg ◽  
M. Grabiec ◽  
D. Scherer ◽  
...  

Abstract. The redistribution of snow by drifting and blowing snow frequently leads to an inhomogeneous snow mass distribution on larger ice caps. Together with the thermodynamic impact of drifting snow sublimation on the lower atmospheric boundary layer, these processes affect the glacier surface mass balance. This study provides a first quantification of snowdrift and sublimation of blowing and drifting snow on the Vestfonna ice cap (Svalbard) by using the specifically designed snow2blow snowdrift model. The model is forced by atmospheric fields from the Polar Weather Research and Forecasting model and resolves processes on a spatial resolution of 250 m. The model is applied to the Vestfonna ice cap for the accumulation period 2008/2009. Comparison with radio-echo soundings and snow-pit measurements show that important local-scale processes are resolved by the model and the overall snow accumulation pattern is reproduced. The findings indicate that there is a significant redistribution of snow mass from the interior of the ice cap to the surrounding areas and ice slopes. Drifting snow sublimation of suspended snow is found to be stronger during spring. It is concluded that the redistribution process is strong enough to have a significant impact on glacier mass balance.


2017 ◽  
Vol 18 (6) ◽  
pp. 1707-1713 ◽  
Author(s):  
Yixin Wen ◽  
Pierre Kirstetter ◽  
J. J. Gourley ◽  
Yang Hong ◽  
Ali Behrangi ◽  
...  

Abstract Snow is important to water resources and is of critical importance to society. Ground-weather-radar-based snowfall observations have been highly desirable for large-scale weather monitoring and water resources applications. This study conducts an evaluation of the Multi-Radar Multi-Sensor (MRMS) quantitative estimates of snow rate using the Snowpack Telemetry (SNOTEL) daily snow water equivalent (SWE) datasets. A detectability evaluation shows that MRMS is limited in detecting very light snow (daily snow accumulation <5 mm) because of the quality control module in MRMS filtering out weak signals (<5 dBZ). For daily snow accumulation greater than 10 mm, MRMS has good detectability. The quantitative comparisons reveal a bias of −77.37% between MRMS and SNOTEL. A majority of the underestimation bias occurs in relatively warm conditions with surface temperatures ranging from −10° to 0°C. A constant reflectivity–SWE intensity relationship does not capture the snow mass flux increase associated with denser snow particles at these relatively warm temperatures. There is no clear dependence of the bias on radar beam height. The findings in this study indicate that further improvement in radar snowfall products might occur by deriving appropriate reflectivity–SWE relationships considering the degree of riming and snowflake size.


2021 ◽  
Author(s):  
David N. Wagner ◽  
Matthew D. Shupe ◽  
Ola G. Persson ◽  
Taneil Uttal ◽  
Markus M. Frey ◽  
...  

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 (HS) and density retrievals from a SnowMicroPen (SMP) and approximately weekly-measured snow depths along fixed transect paths. Hence, the computed SWE considers surface heterogeneities over an average path length of 1469 m. We used the SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were compared with ERA5 reanalysis snowfall rates for the drift track. Our study shows that the simple fitted HS-SWE function can well be used to compute SWE along a transect path based on SMP SWE retrievals and snow-depth measurements. We found an accumulated snow mass of 34 mm SWE until 26 April 2020. Further, we found that the Vaisala Present Weather Detector 22 (PWD22), 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, however, it also systematically underestimated snowfall. The OTT Pluvio2 and the OTT Parsivel2 were largely affected by wind and blowing snow, leading to higher measured precipitation rates, but when eliminating drifting snow periods, especially the OTT Pluvio2 shows good agreements with ground measurements. A comparison with ERA5 snowfall data reveals a good timing of the snowfall events and good agreement with ground measurements but also a tendency towards overestimation. Retrieved snowfall from the ship-based Ka-band ARM Zenith Radar (KAZR) shows good agreements with SWE of the snow cover and comparable differences as ERA5. Assuming the KAZR derived snowfall as an upper limit and PWD22 as a lower limit of a cumulative snowfall range, we estimate 72 to 107 mm measured between 31 October 2019 and 26 April 2020. For the same period, we estimate the precipitation mass loss along the transect due to erosion and sublimation as between 53 and 68 %. Until 7 May 2020, we suggest a cumulative snowfall of 98–114 mm.


2019 ◽  
Author(s):  
Benjamin Walter ◽  
Hendrik Huwald ◽  
Josué Gehring ◽  
Yves Bühler ◽  
Michael Lehning

Abstract. Modelling and forecasting wind-driven redistribution of snow in mountainous regions with its implications on avalanche danger, mountain hydrology or flood hazard is still a challenging task often lacking in essential details. Measurements of drifting and blowing snow for improving process understanding and model validation are typically limited to point measurements at meteorological stations, providing no information on the spatial variability of horizontal mass fluxes or even the vertically integrated mass flux. We present a promising application of a compact and low-cost radar system for measuring and characterizing larger scale (hundreds of meters) snow redistribution processes, specifically blowing snow off a mountain ridge. These measurements provide valuable information of blowing snow velocities, frequency of occurrence, travel distances and turbulence characteristics. Blowing snow velocities measured with the radar are validated by comparison against wind velocities measured with a 3D ultrasonic anemometer. A minimal blowing snow travel distance of 60–120 m is reached in 10–20 % of the time during a snow storm, depending on the strength of the storm event. The relative frequency of transport distances decreases exponentially above the minimal travel distance, with a maximum measured distance of 280 m. The travel distance is linearly correlated with the wind velocity, revealing a threshold for snow particle entrainment and transport of 6.75 m s−1. Turbulence statistics did not allow to draw a conclusion on whether low-level low-turbulence jets or highly turbulent gusts are more effective in transporting blowing snow over longer distances. Drone-based photogrammetry measurements of the spatial snow height distribution revealed increased snow accumulation in the lee of the ridge being the result of the measured local blowing snow conditions.


2010 ◽  
Vol 14 (7) ◽  
pp. 1401-1415 ◽  
Author(s):  
M. K. MacDonald ◽  
J. W. Pomeroy ◽  
A. Pietroniro

Abstract. A modelling study was undertaken to evaluate the contribution of sublimation to an alpine snow mass balance in the Canadian Rocky Mountains. Snow redistribution and sublimation by wind, snowpack sublimation and snowmelt were simulated for two winters over an alpine ridge transect located in the Canada Rocky Mountains. The resulting snowcover regimes were compared to those from manual snow surveys. Simulations were performed using physically based blowing snow (PBSM) and snowpack ablation (SNOBAL) models. A hydrological response unit (HRU)-based spatial discretization was used rather than a more computationally expensive fully-distributed one. The HRUs were set up to follow an aerodynamic sequence, whereby eroded snow was transported from windswept, upwind HRUs to drift accumulating, downwind HRUs. That snow redistribution by wind can be adequately simulated in computationally efficient HRUs over this ridge has important implications for representing snow transport in large-scale hydrology models and land surface schemes. Alpine snow sublimation losses, in particular blowing snow sublimation losses, were significant. Snow mass losses to sublimation as a percentage of cumulative snowfall were estimated to be 20–32% with the blowing snow sublimation loss amounting to 17–19% of cumulative snowfall. This estimate is considered to be a conservative estimate of the blowing snow sublimation loss in the Canadian Rocky Mountains because the study transect is located in the low alpine zone where the topography is more moderate than the high alpine zone and windflow separation was not observed. An examination of the suitability of PBSM's sublimation estimates in this environment and of the importance of estimating blowing snow sublimation on the simulated snow accumulation regime was conducted by omitting sublimation calculations. Snow accumulation in HRUs was overestimated by 30% when neglecting blowing snow sublimation calculations.


Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 363
Author(s):  
George Duffy ◽  
Fraser King ◽  
Ralf Bennartz ◽  
Christopher G. Fletcher

CloudSat is often the only measurement of snowfall rate available at high latitudes, making it a valuable tool for understanding snow climatology. The capability of CloudSat to provide information on seasonal and subseasonal time scales, however, has yet to be explored. In this study, we use subsampled reanalysis estimates to predict the uncertainties of CloudSat snow water equivalent (SWE) accumulation measurements at various space and time resolutions. An idealized/simulated subsampling model predicts that CloudSat may provide seasonal SWE estimates with median percent errors below 50% at spatial scales as small as 2° × 2°. By converting these predictions to percent differences, we can evaluate CloudSat snowfall accumulations against a blend of gridded SWE measurements during frozen time periods. Our predictions are in good agreement with results. The 25th, 50th, and 75th percentiles of the percent differences between the two measurements all match predicted values within eight percentage points. We interpret these results to suggest that CloudSat snowfall estimates are in sufficient agreement with other, thoroughly vetted, gridded SWE products. This implies that CloudSat may provide useful estimates of snow accumulation over remote regions within seasonal time scales.


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