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    Diagram of blowing snow processes, which include sublimation of blowing snow particles, saltation, and suspension. The figure is adapted from Déry and Taylor (1996).

  • View in gallery

    Diagram of the coupled blowing snow–snow–ice system, which includes the PIEKTUK-D blowing snow model (Déry et al. 1998), the SNTHERM snow model (Jordan 1991), and the sea ice model (NEW-RPN-BS system).

  • View in gallery

    Relationship between (top) observed snow depth (cm, two sets of daily average values represented by crosses with dashed line and by circles with solid line) and (bottom) wind speed (m s−1, hourly values, solid line). Arrows illustrate strong wind events corresponding to dips in snow depth.

  • View in gallery

    (a) Sublimation rates and (b) accumulated sublimation of blowing snow simulated by PIEKTUK-D from November 1997 to September 1998. The temporal resolution of estimates is hourly.

  • View in gallery

    Simulated (a) snow depth and (b) ice thickness for blowing snow. Measurements are shown in crosses and circles. The simulated variables are hourly values while the measurements are daily averages.

  • View in gallery

    Evaluation of (a) conductive heat fluxes (W m−2), (b) temperature at the snow–ice interface (K) for NEW-RPN and NEW-RPN-BS and measurements of temperature, and (c) mean average error and STDE in temperature for NEW-RPN and NEW-RPN-BS. The verification period is from October 1997 to October 1998. Measurements and estimates are hourly values.

  • View in gallery

    Evolution of snow density (kg m−3) in the snowpack simulated by (a) NEW-RPN and (b) NEW-RPN-BS from October 1997 to October 1998. Dark blue color represents air. The values of density vary between 0 (blue) and 1000 kg m−3 (red). (c) The histogram of snow density simulated by the two models. The results are simulated hourly.

  • View in gallery

    As in Fig. 7 but for grain size (mm).

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Blowing Snow on Arctic Sea Ice: Results from an Improved Sea Ice–Snow–Blowing Snow Coupled System

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  • 1 Meteorological Research Division, Environment Canada, Dorval, Quebec, Canada
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Abstract

A one-dimensional (1D) version of a blowing snow model, called PIEKTUK-D, has been incorporated into a snow–sea ice coupled system. Blowing snow results in sublimation of approximately 12 mm of snow water equivalent (SWE), which is equal to approximately 6% of the annual precipitation over 324 days from 1997 to 1998. This effect leads to an average decrease of 9 cm in snow depth for an 11-month simulation of the Surface Heat Budget of the Arctic Ocean (SHEBA) dataset (from 31 October 1997 to 1 October 1998). Inclusion of blowing snow has a significant impact on snow evolution between February and June, during which it is responsible for a decrease in snow depth error by about 30%. Between November and January, however, other factors such as regional surface topography or horizontal wind transport may have had a greater influence on the evolution of the snowpack and sea ice. During these few months the new system does not perform as well, with a snow depth percentage error of 39%—much larger than the 12% error found between February and June. The results also indicate a slight increase of 4 cm on average for ice thickness, and a decrease of 0.4 K for the temperature at the snow–ice interface. One of the main effects of blowing snow is to shorten the duration of snow cover above sea ice by approximately 4 days and to lead to earlier ice melt by approximately 6 days. Blowing snow also has a very small impact on internal characteristics of the snowpack, such as grain size and density, leading to a weaker snowpack.

Current affiliation: University of Manitoba, Winnipeg, Manitoba, Canada.

Corresponding author address: Yi-Ching Chung, Centre for Earth Observation Science, University of Manitoba, 125 Dysart Road, Winnipeg, MB R3T 2N2, Canada. E-mail: chung@cc.umanitoba.ca

Abstract

A one-dimensional (1D) version of a blowing snow model, called PIEKTUK-D, has been incorporated into a snow–sea ice coupled system. Blowing snow results in sublimation of approximately 12 mm of snow water equivalent (SWE), which is equal to approximately 6% of the annual precipitation over 324 days from 1997 to 1998. This effect leads to an average decrease of 9 cm in snow depth for an 11-month simulation of the Surface Heat Budget of the Arctic Ocean (SHEBA) dataset (from 31 October 1997 to 1 October 1998). Inclusion of blowing snow has a significant impact on snow evolution between February and June, during which it is responsible for a decrease in snow depth error by about 30%. Between November and January, however, other factors such as regional surface topography or horizontal wind transport may have had a greater influence on the evolution of the snowpack and sea ice. During these few months the new system does not perform as well, with a snow depth percentage error of 39%—much larger than the 12% error found between February and June. The results also indicate a slight increase of 4 cm on average for ice thickness, and a decrease of 0.4 K for the temperature at the snow–ice interface. One of the main effects of blowing snow is to shorten the duration of snow cover above sea ice by approximately 4 days and to lead to earlier ice melt by approximately 6 days. Blowing snow also has a very small impact on internal characteristics of the snowpack, such as grain size and density, leading to a weaker snowpack.

Current affiliation: University of Manitoba, Winnipeg, Manitoba, Canada.

Corresponding author address: Yi-Ching Chung, Centre for Earth Observation Science, University of Manitoba, 125 Dysart Road, Winnipeg, MB R3T 2N2, Canada. E-mail: chung@cc.umanitoba.ca

1. Introduction

Blowing snow is important for snow and ice evolution in the Arctic because strong winds can occur as frequently as once every four days over the Arctic Ocean (Xiao et al. 2000). These frequent wind events can produce blowing snow sublimation and snow transport, which results in accumulation or erosion, and thus accelerates the depletion of the snowpack in spring (Déry and Taylor 1996; Déry and Yau 2001b). When the wind speed is greater than a certain threshold value, snow particles start to bounce and saltate, become suspended in the atmosphere, and so cause snow sublimation (Yang and Yau 2008). Blowing snow sublimation can act as a significant source of water vapor and sink of sensible heat in the air (Déry and Taylor 1996). Snow transport in prairie fields may remove as much as 75% of the annual snowfall, and about half of this sublimates into the atmospheric boundary layer (Pomeroy and Gray 1994). As much as 28% of the winter snowfall has been reported to sublimate in a small northern basin of the Arctic (Pomeroy et al. 1997). Benson (1982) found that on the Arctic coast of Alaska, 32% of snowfall sublimated because of blowing snow, while 11% of snowfall was redistributed horizontally. As much as 10%–50% of the snow cover may sublimate or be redistributed to the atmosphere by blowing snow (Liston and Sturm 1998; Essery et al. 1999; Pomeroy and Essery 1999; Hasholt et al. 2003). The combination of surface and blowing snow sublimation could lead to a reduction of snow water equivalent (SWE) between 10 and 85 mm according to Essery et al. (1999) and Déry and Yau (2001b).

The possibility of coupling blowing snow models with other models to investigate the meteorological and hydrological impacts of blowing snow is discussed in several studies. For example, Déry and Yau (2001a) coupled the blowing snow model PIEKTUK-D (named after the Inuktituk word for blowing snow) with the Mesoscale Compressible Community (MC2) model in order to improve understanding of interactions between snow drift and the atmospheric boundary layer (ABL). Their findings suggest that an inclusion of blowing snow could help explain part of the erroneous estimates in the forecasts of near-surface meteorological fields at high latitudes. Rutter et al. (2008) evaluated mass and energy exchanges between the atmosphere and snow in Colorado between December 2002 and June 2003 using the National Operational Hydrologic Remote Sensing Center Snow Model (NSM), which consists of a modified version of the Snow Thermal Model (SNTHERM) (Jordan 1991) and the Prairie Blowing Snow Model (PBSM; Pomeroy et al. 1993). Nishimura et al. (2005) used the SNOWPACK model developed at the Swiss Federal Institute for Snow and Avalanche Research coupled with a snow drift model called SnowTran3D (also developed for the prediction of avalanches) on the mountain terrain of Niseko, Japan, in February and March 2003. Liston and Hiemstra (2008) incorporated blowing snow sublimation and redistribution into the snow model (SnowModel) with a data assimilation scheme (SnowAssim) to more accurately simulate SWE and its spatial distribution. An intercomparison among blowing snow models, including PIEKTUK-D (Déry et al. 1998), WINDBLAST (Mann 1998), and SNOWSTORM (Bintanja 2000), indicated that model results are generally similar for the thermodynamic effects of blowing snow sublimation on the atmospheric boundary layer (Xiao et al. 2000). Besides model simulations, the only experimental study was conducted by Wever et al. (2009). They developed experiments in a cold wind tunnel and compared the measurements to a 1D model of drift and drifting snow sublimation. They found that the measured drifting snow sublimation appeared to be consistent, though slightly larger than theoretical values found in the model study.

Most of these investigations examined surface roughness, friction velocity, snow particle sizes, drift particle distribution, transport, turbulent heat fluxes, and rate due to blowing snow in the boundary layer, but very few have distinguished the effect of blowing snow on the seasonal evolution of snow and sea ice. Meanwhile, the significance of blowing snow sublimation has been argued in some studies (e.g., Steffen and DeMaria 1996; Papakyriakou 1999; Pomeroy and Essery 1999; Pomeroy and Li 2000; Persson et al. 2002; Savelyev et al. 2006).

The main objective of this study is to investigate the effect of blowing snow on the simulation of snow and sea ice in the Arctic Ocean. Chung et al. (2010) coupled a multilayer snow model (SNTHERM) with the sea ice model used in the numerical weather prediction system currently operational at the Meteorological Service of Canada (MSC). They evaluated this coupled system in a 1D mode using meteorological observations from SHEBA’s Pittsburgh site in the Arctic Ocean collected during 1997/98 (Perovich et al. 1999). Based on the results and on the literature (Sturm et al. 2002; Déry and Tremblay 2004; Chung et al. 2010), it was argued that part of the error for the new Recherche Prévision Numérique (NEW-RPN) snow depth during SHEBA’s frozen period could be explained by blowing snow effects. This suggestion was further justified with a sensitivity analysis that indicated a close link between wind speed and snow depth (Chung et al. 2010). To examine the impact of blowing snow sublimation on snow and sea ice characteristics, a 1D version of PIEKTUK-D has been added to their coupled snow–sea ice system.

This paper is organized as follows. A brief description of the coupled modeling system and of the experimental dataset is given in section 2. The experimental setting, model comparison, and simulation results are presented in section 3. A summary with conclusions and discussion of further model adjustments are found in the final section.

2. Modeling system and datasets

a. Blowing snow model

The PIEKTUK model (also spelled “PIQTUQ,” an Inuktituk word for blowing snow) has been developed to simulate blowing snow transport and sublimation with different versions, including a spectral version PIEKTUK-S (Déry et al. 1998), a bulk version PIEKTUK-B (Déry and Yau 1999), a bulk version based on double-moment microphysics PIEKTUK-D (Déry and Yau 2001b), and a further evolution applied over heterogeneous surfaces called PIEKTUK-TUVAQ (Déry and Tremblay 2004). PIEKTUK has been used for temporal evolution of blowing snow mixing ratio, particle distributions, and sublimation rates by considering diffusion, settling, and sublimation of blowing snow. Among these versions, PIEKTUK-D has been chosen for incorporating with the snow–ice system because double-moment schemes have been successfully used for modeling microphysical processes (e.g., Harrington et al. 1995; Reisner et al. 1998) at a reasonable computing cost compared with the spectral version PIEKTUK-S, but with more accuracy and details than the single-moment bulk version PIEKTUK-B (Déry and Yau 2001b).

The PIEKTUK-D blowing snow model is based on the assumption of a pure snow surface [i.e., without protruding vegetation (Déry et al. 1998)] to estimate both the zeroth moment (N) and the third moment (qb) of the particle size distribution function F(r) using Eqs. (3)(6) presented below. For the conservation equation, PIEKTUK-D represents turbulent diffusion with net changes of snow particle number density. For the wind profile, PIEKTUK-D uses a logarithmic vertical profile and considers that the turbulent mixing length increases with height and reaches a maximum at upper levels (Taylor 1969). The eddy diffusion coefficient is considered as constant in time but increasing with height until it reaches a maximum value.

The PIEKTUK-D model represents the sublimation of blowing snow by considering the change in mass due to sublimation using the original Thorpe and Mason formulation (Schmidt 1982)
e1
where m is the mass of a single ice sphere (kg), σ is the water vapor deficit with respect to ice (eei)/ei, e is the vapor pressure of water in air (Pa), ei is the saturation vapor pressure over ice (Pa), Ta is the ambient air temperature (K), Ls is the latent heat of sublimation (2.838 × 106 J kg−1), D is the molecular diffusivity of water vapor in air (2.25 × 10−5 m2 s−1), NNu and NSh are the Nusselt and Sherwood numbers [the calculation of these two variables can be found in Xiao et al. (2000)], K is the thermal conductivity of air (2.4 × 10−2 W m−1 K−1), Rυ is the gas constant for water vapor (461.5 J kg−1 K−1), Qr is the net radiation transferred to the particle (W), and r is the radius of a blowing snow particle (m).
The transferred net radiation can be prescribed as (Schmidt 1991)
e2
where αp denotes shortwave albedo and Q* represents incoming radiation (W m−2).
The number density—the zeroth moment of the particle size distribution function—is introduced with an explicit equation:
e3
where z denotes the vertical coordinate (m), t denotes the time (s), N is the number density (m−3), KN represents the eddy diffusivity (m2 s−1), υN is the terminal velocity for the total number of blowing snow particles (m s−1), and SN is the rate of change of particle numbers due to sublimation (m−3 s−1).
The mixing ratio of blowing snow—the third moment of the particle size distribution function—is introduced with an explicit equation, while the water vapor mixing ratio and air temperature in a column of air can be expressed as follows:
e4
e5
e6
in which qb represents the mixing ratio of blowing snow (kg kg−1); qυ represents the mixing ratio for water vapor (kg kg−1); Kb, Kh, and Kυ are the eddy diffusivities for blowing snow, heat, and moisture, respectively (m2 s−1); υb is the settling velocity (m s−1); and Sb is the bulk sublimation rate (kg kg−1 s−1).

Schematically represented in Fig. 1, the 1D version of PIEKTUK-D describes the effects of blowing snow processes due to sublimation, saltation, and suspension. It should also be mentioned that, because of the one-dimensional aspect of the modeling system, sources or sinks associated with horizontal transport of snow cannot be quantified (or even represented) and, consequently, it will not be possible to present the effect of horizontal transport of blowing snow on the evolution of the snowpack. Without the vertical component of wind, the local erosion cannot be estimated in one column. Only the blowing snow sublimation is evaluated in this study.

Fig. 1.
Fig. 1.

Diagram of blowing snow processes, which include sublimation of blowing snow particles, saltation, and suspension. The figure is adapted from Déry and Taylor (1996).

Citation: Journal of Hydrometeorology 12, 4; 10.1175/2011JHM1293.1

b. Coupled snow–ice system

As shown in Fig. 2, the blowing snow component has been linked to the snow–sea ice system developed and used in Chung et al. (2010). The coupled model system used in the present study is the same as in Chung et al. (2010) except that a column of snow mass loss due to blowing snow sublimation (estimated from PIEKTUK-D) is applied as an external input in the SNTHERM snow model. Because of the high variability in new snow density (Roebber et al. 2003), this snow mass is transformed into snow layer thickness using the time-varying snow density simulated by SNTHERM rather than using a constant value for the entire column.

Fig. 2.
Fig. 2.

Diagram of the coupled blowing snow–snow–ice system, which includes the PIEKTUK-D blowing snow model (Déry et al. 1998), the SNTHERM snow model (Jordan 1991), and the sea ice model (NEW-RPN-BS system).

Citation: Journal of Hydrometeorology 12, 4; 10.1175/2011JHM1293.1

c. SHEBA dataset

As in Chung et al. (2010), the coupled system was run for one site of Ice Station SHEBA, called the “Pittsburgh” site, on snow-covered multiyear ice that drifted more than 1400 km between 74° and 81°N (Persson et al. 2002) from 31 October 1997 to 1 October 1998. The details of the SHEBA datasets for the evaluation and forcing of the coupled system are described in Chung et al. (2010). Inputs for PIEKTUK-D include hourly values of meteorological data, such as wind speed at two heights (2 and 10 m), air temperature at 10 m, relative humidity at 10 m, and atmospheric pressure. Table 1 provides the mean values of these measurements during the SHEBA year, including air temperature, wind speed, relative humidity (with respect to ice), incoming shortwave radiation, incoming longwave radiation, precipitation rate, albedo, and atmospheric pressure. The monthly averaged air temperature ranges from approximately −30° to 0°C during the SHEBA year, and relative humidity with respect to ice is greater than 93% through all SHEBA months, which implies that sublimation rates should be small because of high relative humidity during the Arctic winter conditions (Andreas et al. 2002).

Table 1.

Mean monthly climatological data during SHEBA periods.

Table 1.

The control experiment (NEW-RPN) and the experiment with blowing snow (NEW-RPN-BS) were run using the same hourly meteorological data over the same period and under the same initialization of snow and ice.

d. Initialization

As described in Chung et al. (2010), the sea ice model includes two layers of sea ice (166 cm in total). The sea ice temperature at the snow–ice interface was set to 260.0 K and the temperature at the base of the ice pack to 271.2 K according to the measurements. The temperature inside the ice is then obtained by linear interpolation between the top and base of the ice pack.

The initialization for snow includes a snowpack composed of three layers (5 cm in total), along with a 1-cm slush layer with density profiles ranging from 200 to 330 kg m−3, based on the measurements (Sturm et al. 2002). Initial snow temperatures range from 252.5 K at the top of the snowpack to 260.0 K at the base of the snowpack (based on measurements), and initial grain size ranges from 0.5 to 2.0 mm (Chung et al. 2010). The time steps for the snow model are automatically determined to meet convergence criteria (the minimum is 1 s).

The initialization for blowing snow includes 50 layers for the suspension layer, which extends from 0.1 m to 1 km above the surface. The model time step is set to 1 s in the integrations, while the meteorology input and output are both on an hourly basis.

3. Results

a. Temporal evolution

1) Relationship between blowing snow and wind speed

Strong wind transport can induce sublimation (e.g., Serreze et al. 1997), leading to a decrease in snow depth—a fact that could be reflected in the dips in snow depth corresponding with strong winds, as pointed out by the first arrow in the time series of snow depth and low-level wind speed shown in Fig. 3. From the third to fifth arrow, snow depth appears to decrease before the strong wind events, but this is likely related to the poor temporal sampling of snow depth measurements compared with that of wind measurements. A significant snow depth decrease indicated by the fourth arrow in March may have been caused by a combination of blowing snow sublimation and of wind transport related to thin, light snow layers deposited by spring flurries (Sturm et al. 2002), which can be easily moved by wind transport or sublimated.

Fig. 3.
Fig. 3.

Relationship between (top) observed snow depth (cm, two sets of daily average values represented by crosses with dashed line and by circles with solid line) and (bottom) wind speed (m s−1, hourly values, solid line). Arrows illustrate strong wind events corresponding to dips in snow depth.

Citation: Journal of Hydrometeorology 12, 4; 10.1175/2011JHM1293.1

The decrease in snow depth at the Pittsburgh site (and the corresponding increase in the main line average) indicated by the second arrow in December may have been caused by blowing snow sublimation as well as wind transport (Jordan et al. 1999). Snow erosion during high-wind events may explain more of these abrupt decreases in snow depth observed in December, which have also been observed in alpine terrain (e.g., Déry et al. 2010). Sturm et al. (2002) indicated that two deposition events, which occurred at the Seattle site on 1 December 1997 and between 29 January and 7 February 1998, could account for 39% of the total SWE and for 7 cm of snow depth. In spring, deposition is less than 15% of the total SWE—not as much as during winter. For this reason, almost no wind transport of snow occurred between 1 April and 15 May (Sturm et al. 2002). Since the two-dimensional effects of snow transport cannot be represented using a 1D coupled system, we will restrict our analysis to the effects of sublimation only, assuming that there are no net sources or sinks due to horizontal snow transport.

Li and Pomeroy (1997) found that snow transport occurred with winds between 7.7 (dry snow) and 9.9 m s−1 (wet snow) from an analysis of hourly observations from 16 stations on the western Canadian prairies over six winters. Savelyev et al. (2006) concluded that snow drifting began once wind exceeds 7 m s−1 and that visibility was seriously affected (<4 km) once winds exceeded 9 m s−1 based on the Canadian Arctic Shelf Exchange Study (CASES) conducted in 2004. Li and Pomeroy (1997) also found that threshold wind is also affected by snow condition—for example, snowpack temperature, snow particle bonding, cohesion, and kinetic friction. They suggested that the threshold velocity of drifting or blowing snow can be obtained as
e7

All these suggested values (mentioned above) for the threshold wind speed as well as those provided by Eq. (7) have been tested and yielded very similar results (based on a sensitivity study of snow depth, ice thickness, and snow–ice interface temperature, not shown). Equation (7) has been used in this coupled system for the wind speed threshold.

2) Sublimation

Results shown in Fig. 4a indicate that sublimation rates can reach a maximum value of 0.08 mm h−1 SWE. It can be seen that loss due to sublimation occurs more frequently in March, August, September, and December. In Fig. 4b, the accumulated blowing snow sublimation by PIEKTUK-D (excluding surface sublimation) reaches 12 mm SWE over 324 days from 1997 to 1998. This is equal to about 6% of total precipitation eroded over SHEBA during blowing snow (179 mm SWE for total snowfall), which is reasonable compared to other studies. For example, Déry and Yau (2001b) estimate about 3 mm SWE for blowing snow sublimation and 7 mm SWE for surface sublimation with PIEKTUK-D using measurements from a Canadian Arctic tundra site over 210 days from 1996 to 1997, and they also found that a combination of surface and blowing snow sublimation may erode approximately 20% of the annual snowfall (Déry and Yau 2002).

Fig. 4.
Fig. 4.

(a) Sublimation rates and (b) accumulated sublimation of blowing snow simulated by PIEKTUK-D from November 1997 to September 1998. The temporal resolution of estimates is hourly.

Citation: Journal of Hydrometeorology 12, 4; 10.1175/2011JHM1293.1

3) Snow depth

The simulated snow depth and ice thickness for NEW-RPN and NEW-RPN-BS are given in Fig. 5. The results reveal that blowing snow sublimation causes a significant reduction (difference of 9 cm in average) in snow depth. After January, NEW-RPN-BS improves snow depth with a smaller root-mean-square error (RMSE) (8.3 cm) compared to NEW-RPN RMSE (9.7 cm).

Fig. 5.
Fig. 5.

Simulated (a) snow depth and (b) ice thickness for blowing snow. Measurements are shown in crosses and circles. The simulated variables are hourly values while the measurements are daily averages.

Citation: Journal of Hydrometeorology 12, 4; 10.1175/2011JHM1293.1

To quantify the effect of blowing snow, the percentage error (PE) can be calculated as
e8
where Xe is the estimated quantity and Xa is the measured quantity. The estimated snow depth is greatly improved during between February and June, suggesting that blowing snow has interesting potential for improving model performance in late winter and early spring (with a snow depth PE reduced from 42% to 12%). This could also be explained by the fact that snow is observed to be less affected by horizontal wind transport from February to June (Sturm et al. 2002). During that period, a snow depth PE of 12% could be partially attributed to horizontal wind transport. As mentioned earlier, wind transport has been estimated from observations to be less than 15% of the total SWE in spring—not as large as during winter (Sturm et al. 2002).

On the other hand, a gradual increase in snow depth at the Pittsburgh site from December 1997 to February 1998, opposite to the observed trend, implies that other factors may have influenced the snow evolution, such as horizontal wind transport. During this period, NEW-RPN-BS underestimates snow depth (RMSE = 12.4 cm) compared to NEW-RPN (RMSE = 8.1 cm). Both models still fail to catch the observed trend. Blowing snow sublimation is responsible for only 7% of PE changes in the estimates, whereas horizontal wind transport might be responsible for part of the remaining error (39%). The two models exhibit similar behaviors in summer and fall. Results shown in Fig. 5a also indicate that blowing snow shortens the snow cover duration by approximately four days.

4) Ice thickness

The NEW-RPN and NEW-RPN-BS simulations for ice thickness are compared against measurements in Fig. 5b. It can be noted that NEW-RPN-BS is able to better represent ice thickness, implying that the inclusion of blowing snow may improve the model performance for ice thickness. In this case, the RMSE is 3.8 cm for NEW-RPN-BS—much smaller than that of NEW-RPN (10.6 cm). Blowing snow is responsible for a slight ice thickness increase, especially in the coldest portion of the test period with a difference of about 4 cm in average. Blowing snow is also found to affect the onset of ice melt after the snow ablation, with differences of six days.

5) Temperature at the snow–ice interface

The thermal conductive fluxes at the snow–ice interface simulated with the NEW-RPN and NEW-RPN-BS configurations are compared in Fig. 6a for SHEBA’s Pittsburgh site. The negative values from the simulation indicate that fluxes flow from the ice toward the snowpack. The results show that, in general, heat is lost from ice to snow before snow depletion. Moreover, the fluxes display great temporal variability during winter and progressively decrease in magnitude during spring. The differences between the two experiments are relatively small (mean of 0.88 W m−2), except for a few days in June that correspond to the different timing of complete snowmelt.

Fig. 6.
Fig. 6.

Evaluation of (a) conductive heat fluxes (W m−2), (b) temperature at the snow–ice interface (K) for NEW-RPN and NEW-RPN-BS and measurements of temperature, and (c) mean average error and STDE in temperature for NEW-RPN and NEW-RPN-BS. The verification period is from October 1997 to October 1998. Measurements and estimates are hourly values.

Citation: Journal of Hydrometeorology 12, 4; 10.1175/2011JHM1293.1

The temperatures at the snow–ice interface for both models match relatively well with the measurements at the Pittsburgh site until February (Fig. 6b). It seems the model is not able to capture the intense and prolonged cooling that occurs in February (a cooling of approximately 7 K in two weeks). As a consequence, the coupled system overestimates the temperature, but the experiment with blowing snow exhibits smaller errors during that period (Figs. 6b,c). Compared with meteorological inputs to the models, the overestimation in February may be caused by erroneous estimates of snow depth due to a combination of significant wind transport, ending of polar night, and dramatic variations of atmospheric pressure and of relative humidity in late winter amplified by frequent melt and freeze cycles in the snowpack in early spring. Blowing snow can decrease the insulation and growth of snow depth, thus leading to a temperature decrease at the snow–ice interface, forcing ice growth and enhancing sensible heat fluxes from the ocean (Huwald et al. 2005).

Figure 6c shows the model errors [with values for bias and for the standard deviation of the errors (STDE)] for NEW-RPN and NEW-RPN-BS. The bias is 1.5 K for the NEW-RPN-BS simulations—lower than the mean value of 2.0 K for NEW-RPN. The inclusion of blowing snow also leads to a slight decrease of STDE for the snow–ice interface temperature—from a value of 1.3 K for NEW-RPN to a value of 1.2 K for NEW-RPN-BS. The error variance is thus smaller in the NEW-RPN-BS simulations (mostly during snow-covered periods), which implies that blowing snow may not only affect snow but may also affect the thermal processes and improve temperature estimates at the snow–ice interface. A PE for temperature at the interface is less than 1%.

b. Vertical structure

The temporal evolution from October 1997 to October 1998 of the snow density and grain size produced by NEW-RPN and NEW-RPN-BS are illustrated in the upper panels of Figs. 7 and 8. Two histograms using information from all the snow layers collected throughout the study period are shown in the lower panels (Figs. 7c and 8c). Snow density and grain size have a significant effect on snow metamorphism.

Fig. 7.
Fig. 7.

Evolution of snow density (kg m−3) in the snowpack simulated by (a) NEW-RPN and (b) NEW-RPN-BS from October 1997 to October 1998. Dark blue color represents air. The values of density vary between 0 (blue) and 1000 kg m−3 (red). (c) The histogram of snow density simulated by the two models. The results are simulated hourly.

Citation: Journal of Hydrometeorology 12, 4; 10.1175/2011JHM1293.1

Fig. 8.
Fig. 8.

As in Fig. 7 but for grain size (mm).

Citation: Journal of Hydrometeorology 12, 4; 10.1175/2011JHM1293.1

Figures 7a,b indicate that the evolution of snow density from NEW-RPN-BS is similar to that from NEW-RPN. More quantitatively, Fig. 7c reveals that the snow density tends to decrease when blowing snow is included, with peak values of 200–300 kg m−3 (40% for NEW-RPN and 46% for NEW-RPN-BS). In NEW-RPN, 12% of the simulated snowpack has a density smaller than 200 kg m−3, whereas in NEW-RPN-BS, 15% of the simulated snowpack has the same feature. The results also indicate that NEW-RPN-BS produces less wind slab layers (48% for NEW-RPN and 38% for NEW-RPN-BS for density greater than 300 kg m−3 in Fig. 7c), which is likely related to the increased deposition due to snow drift and an extra impulse normal to the surface from eddies (Clifton et al. 2006), especially near the surface of the snowpack. The snow stratigraphy in the lower snowpack also differs because blowing snow weakens the upper snowpack. Typically, high-density snow layers may form in the middle or bottom of the snowpack through the winter to spring, but the occurrence of these slabs is less when blowing snow is included. The ice formations with densities as large as 900 kg m−3 at the bottom of the snowpack still do not form until spring, with the same results (1%) found by the two models in Fig. 7c.

Figures 8a,b show that the evolution of snow grain sizes from NEW-RPN-BS is very similar to that from NEW-RPN. This is confirmed by the histogram in Fig. 8c, which shows that the grain size increases slightly with peak values of 1–1.5 mm in response to blowing snow (30% for NEW-RPN and 34% for NEW-RPN-BS). This increase is still insufficient, however, to significantly improve this aspect of the snowpack simulation; that is, simulated grain size profiles still exhibit much smaller values (42% by NEW-RPN and 46% by NEW-RPN-BS for grain size between 1 and 2 mm in Fig. 8c) than the observed faceted crystals of 10 mm observed by Sturm et al. (2002). As in Chung et al. (2010), the snow model is still not able to produce depth hoar because SNTHERM simply does not include the mechanism of natural convection of air in snow, which is related to depth hoar and affects the grain growth. The feedback between saturated flow and grain growth in spring needs to be considered for future analysis of depth hoar. And it seems that, for this particular case, blowing snow does not significantly affect vapor diffusion and snow metamorphism related to grain growth and depth hoar. Even though blowing snow increases snow grain size and decreases snow density—leading to a weaker snowpack—its impact on the internal snowpack is small.

4. Summary and conclusions

The effect of blowing snow sublimation on the simulation of snow and sea ice in the Arctic Ocean is examined in this study. Numerical simulations were run with and without a 1D version of the PIEKTUK-D blowing snow model in the coupled snow–sea ice system for the Pittsburgh site of Ice Station SHEBA on snow-covered multiyear sea ice between 74° and 81°N from 31 October 1997 to 1 October 1998.

Even though an evaluation of the performance of the blowing snow model is difficult in this study because of a lack of the observational evidence that would be required for this, it is still possible to examine its impact (positive or negative) on both the snow and ice components. Modeling results with PIEKTUK-D show that sublimation rate reaches a maximum value of 0.08 mm h−1 SWE during strong wind events. This leads to a total accumulated blowing snow sublimation of approximately 12 mm SWE—about 6% of annual precipitation lost over SHEBA.

Based on this effect, blowing snow is responsible for decrease (9 cm in average) in snow depth over the SHEBA year and for a shortening of the snow-covered period by approximately four days. Blowing snow reduces errors on modeled snow depth by approximately 30%. The remaining error (12%) could be at least partially related to horizontal wind transport (not represented). This system may not be suitable in early winter because the 39% error for the period between November and January could still not be explained after an inclusion of blowing snow sublimation. Those erroneous estimates could be related to horizontal wind transport or topographic influences (Iacozza and Barber 2010). However, the results presented in this study suggest that blowing snow has a greater potential for improving model performance in late winter and spring (between February and June, at least for this particular dataset).

The modeling results further indicate that the impact of blowing snow on the snow–ice interface and sea ice is limited but cannot be ignored. For example, the simulated thermal conductive fluxes at the snow–ice interface, directed from ice to snow, are increased when blowing snow is included because a thinner snowpack increases heat loss from the ocean through ice and snow to the atmosphere. In a consistent manner, a decrease of 0.4 K is found for the temperature at the snow–ice interface due to blowing snow (i.e., related to the snow depth decrease). Furthermore, a slight increase of about 4 cm on average for ice thickness has been found for the new experiment. Overall, most aspects of the numerical simulation are still improved by the inclusion of the blowing snow process, including significant improvement for the prediction of the onset of ice melt (by approximately six days), which is crucial to the thermohaline circulation of the circumpolar ocean and the highly ice-dependent polar ecosystem (Moritz and Perovich 1996).

It is important to realize that the present study is limited by the one-dimensional nature of the modeling system and by the unavailability of data. It is understood that ignoring horizontal redistribution of the snowpack due to wind effects could be a poor assumption in certain situations, as stated by several studies that have shown such processes to be more important than sublimation (e.g., Mott and Lehning 2010). It should be emphasized further that the importance of drifting and blowing snow sublimation cannot be determined from the present study because it does not consider three-dimensional boundary layer effects.

Finally, more recent datasets such as the Canadian Arctic Shelf Exchange Study (CASES), with more detailed blowing snow observations for 16 weeks from January to May 2004, could be used for further model validation (Savelyev et al. 2006), especially for the wind transport aspect if represented by the modeling system. Other improvements could be included such as the use of the more advanced triple-moment version of the blowing snow model (PIEKTUK-T; Yang and Yau 2008), which could be coupled with the snow–ice system. The present conclusions could be reexamined in the context of fully three-dimensional atmospheric models, and the same concept of snow model coupling with underlying soil or ice could be used for other applications, such as urban snow evolution, avalanche hazard, or river reservoir inflows for hydroelectric generation.

Acknowledgments

This work was supported by the Government of Canada Program for the International Polar Year (IPY) under the Thorpex Arctic Weather and Environmental Prediction Initiative (TAWEPI Project 2006-SR1-CC-088). We thank Agnieszka Barszcz for providing the 1D version of PIEKTUK-D.

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