Observation and thermodynamic modeling of the influence of snow cover on landfast sea ice thickness in Prydz Bay, East Antarctica

Cold Regions Science and Technology 168 (2019) 102869

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Observation and thermodynamic modeling of the influence of snow cover on landfast sea ice thickness in Prydz Bay, East Antarctica ⁎

T

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Jiechen Zhaoa,b,c, Bin Chengc, , Timo Vihmac, Qinghua Yangd,e, Fengming Huif, , Biao Zhaog,b, Guanghua Haoa, Hui Shena, Lin Zhanga a

National Marine Environmental Forecasting Centre (NMEFC), Beijing 100081, China Laboratory for Regional Oceanography and Numerical Modelling, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China c Finnish Meteorological Institute (FMI), Helsinki 00101, Finland d School of Atmospheric Sciences, and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Zhuhai 519082, China e Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China f School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519082, China g First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Snow depth Ice thickness Sea ice mass balance Thermodynamic modeling Prydz Bay Antarctica

The observed snow depth and ice thickness on landfast sea ice in Prydz Bay, East Antarctica, were used to determine the role of snow in (a) the annual cycle of sea ice thickness at a fixed location (SIP) where snow usually blows away after snowfall and (b) early summer sea ice thickness within the transportation route surveys (TRS) domain farther from coast, where annual snow accumulation is substantial. The annual mean snow depth and maximum ice thickness had a negative relationship (r = −0.58, p < .05) at SIP, indicating a primary insulation effect of snow on ice thickness. However, in the TRS domain, this effect was negligible because snow contributes to ice thickness. A one-dimensional thermodynamic sea ice model, forced by local weather observations, reproduced the annual cycle of ice thickness at SIP well. During the freeze season, the modeled maximum difference of ice thickness using different snowfall scenarios ranged from 0.53–0.61 m. Snow cover delayed ice surface and ice bottom melting by 45 and 24 days, respectively. The modeled snow ice and superimposed ice accounted for 4–23% and 5–8% of the total maximum ice thickness on an annual basis in the case of initial ice thickness ranging from 0.05 to 2 m, respectively.

1. Introduction Snow has several major impacts on sea ice mass balance. Snow causes a strong insulation effect due to its low heat conductivity, which reduces basal ice growth (Maykut and Untersteiner, 1971). Additionally, the snow layer alters the freeboard, which is defined as the distance between the ice surface and the sea water level. A heavy snowpack may generate a negative freeboard, causing surface flooding of ice with snow slush; in freezing conditions, the snow slush can be transformed to snow ice (Eicken et al., 1995; Jeffries et al., 1998; Fichefet and Morales Maqueda, 1999; Nicolaus et al., 2009; Rösel et al., 2018). In early spring, the snowmelt water can refreeze to form superimposed ice at the snow–ice interface (Kawamura et al., 1997). The high albedo of snow ensures that a negative surface radiation budget persists over snow/ice cover because most of the incoming solar radiation is reflected back to the atmosphere. On the other hand, melting ⁎

of snow reduces the surface albedo, which thus enhances further melting (Arndt et al., 2016; Thackeray and Fletcher, 2016). Snow changes the surface energy balance, reducing the transmissivity of incoming shortwave radiation, which affects the light conditions in the snow pack, sea ice, and underlying ocean (Allison et al., 1993; Perovich, 2007; Järvinen and Leppäranta, 2011, 2013). Snow cover is common on Antarctic sea ice. The term “solid precipitation” is the primary source term for snow accumulation, and the depth of snow is highly associated with the precipitation rate in the respective region (Massom et al., 2001). In Lützow-Holm Bay, East Antarctica, the snow depth on landfast sea ice within 20–30 km of the coast is typically 0.2–0.6 m in summer (Uto et al., 2006). In the Weddell Sea, the annual average snow depth is approximately 0.5 m, and the maximum snow depth may reach 1.0 m (Eicken et al., 1994). However, the snow pack is usually thin close to the shore because the strong winds typically following precipitation events blow the snow away

Corresponding authors. E-mail addresses: Bin.Cheng@fmi.fi (B. Cheng), [email protected] (F. Hui).

https://doi.org/10.1016/j.coldregions.2019.102869 Received 19 December 2018; Received in revised form 21 June 2019; Accepted 16 August 2019 Available online 19 August 2019 0165-232X/ © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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Fig. 1. (a) Prydz Bay in East Antarctica; (b) Zhongshan Station and the SIP site; and (c) snow and ice measurement locations along the R/V Xuelong logistic transportation routes. The gray arrow indicates the fetch between Zhongshan Station and the R/V Xuelong, along which the snow depth and ice thickness results are presented.

(Kawamura et al., 1997; Jeffries et al., 2001). In Prydz Bay, first-year landfast sea ice (FYI) forms in winter and breaks up in summer. The observed maximum FYI thickness ranges from 1.4 to 1.8 m by the end of October or early November before melting starts (Lei et al., 2010). Multi-year landfast sea ice (MYI) has been observed, and snow contributes to the MYI thickness of landfast ice via formation of snow ice and superimposed ice (Tang et al., 2006). The MYI thickness observed near Nella Fjord may exceed 3.0 m, with a ~1.0 m thick snow cover (Zhao et al., 2019). Atmospheric conditions such as air temperature, wind speed, and solid precipitation affect the evolution of seasonal landfast ice (Heil et al., 1996). The annual cycle of landfast sea ice in Prydz Bay is affected by the seasonal oceanic heat flux (Heil, 2006, Lei et al., 2010). A sensitivity modeling study revealed that a change of surface albedo from 0.5 to 0.7 during the melting season can alter ice floes from FYI to second-year ice (SYI) or even to MYI (Yang et al., 2015). The surface albedo influences the daily and seasonal cycles of sea ice growth and melting by governing the distribution of solar energy flux (Yang et al., 2016). However, the effect of snow depth on sea ice thickness in Prydz Bay has not been investigated in detail. In this study, we examined the effect of snow depth on landfast ice thickness. We addressed the objectives of the study by different methods: (a) utilization of year-round snow and ice observations at a fixed location, the Zhongshan Station coastal snow and ice observation site (SIP), and thermodynamic sea-ice modeling forced by local weather

(Massom et al., 2001). During spring and summer, at greater distance from the shoreline, snow can be an important contributor to the formation of multi-year ice via snow to ice transformation processes (Arrigo et al., 1997). The wind is a key factor affecting the distribution of snow depth on landfast sea ice. As a result, snow depth close to shore may not be directly related to either the frequency or duration of snowfall. The determination of snow accumulation on Antarctic landfast sea ice remains a challenge. Formation of snow ice is commonly observed on Antarctic sea ice (Eicken, 1998). Because of regional variability of snow depth, the contribution of snow to positive sea ice thickness is sometimes more pronounced than the damping effect of snow on sea ice thickness due to insulation (Powell et al., 2005). Large-scale estimation of snow using passive microwave products and European Centre for Medium-Range Weather Forecasting (ECMWF) reanalysis data shows that snow ice is largely ubiquitous in all regions throughout the growth season and that snow ice production is largely independent of snow depth (Maksym and Markus, 2008). At a summertime drifting ice station in the Weddell Sea, it was found that snow on second-year ice was thicker, colder, denser, and more layered than snow on first-year ice. The ice freeboard was mostly positive, and flooding occurred mostly on first-year ice (Nicolaus et al., 2009). Compared with formation of snow ice, formation of superimposed ice on Antarctic sea ice is less extensive (Massom et al., 2001). However, superimposed ice has been observed on perennial ice in summer and also in many Antarctic coastal regions 2

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2.2. Atmospheric parameters

observations and (b) analyses of snow and ice measurements in a domain where transportation route surveys (TRS) were carried out from November to December. A number of snow and sea-ice model experiments applying different snow and ice conditions at seasonal and annual bases were carried out, and the results were compared with field measurements. The motivation of this work was to understand how snow affects the annual cycle of landfast sea ice, from freezing to melting/breaking, and how snow can actively contribute to snow-ice and superimposed ice formation. Section 2 describes the field observations. The methodology, including the statistical methods and the sea-ice model and parameters, is presented in Section 3. The results are presented in Section 4, and a discussion and conclusions are provided in Section 0.

A weather station (WMO number 89573) is operated annually at Zhongshan Station. Meteorological parameters including atmospheric surface pressure (P), air temperature (Ta), wind speed (Vs), wind direction (Vd), and relative humidity (Rh) are measured every minute. Cloudiness (CN) and visibility are recorded manually every day at UTC 00, 06, 12, and 18 h. The Vs and Vd are measured at the 10 m level. The Ta and Rh are measured at a height of 2 m, respectively. Precipitation (Prec) is not measured at Zhongshan Station, but daily accumulated precipitation is measured at the Russian Progress Station, 1 km east of Zhongshan Station. The measurement is made using a 0.19 m diameter snow gauge surrounded by a plastic shield to reduce the effect of blowing snow on precipitation (Lei et al., 2010).

2. Observations 3. Methodology 2.1. Sea ice and snow 3.1. Statistical methods Zhongshan Station (69°22′ S, 76°22′ E) is located in Prydz Bay, East Antarctica (Fig. 1). It was the second Chinese Antarctic scientific research station established, and it operates year-round. The surrounding coastal area is covered by landfast sea ice during most of the year. During winter, the landfast ice extends 60–100 km north of Zhongshan Station. In December (Antarctic summer), the width of the landfast ice zone from Zhongshan Station to the open sea ranges from 20 to 40 km. In late January, the landfast ice can break up to ~1 km from the shore by the mechanical forces from wind, waves, and tides (Lei et al., 2010). However, some ice floes may survive in narrow fjords or near the shore. Those small ice floes are conjoined by the newly formed sea ice that develops around them in late March and April and are eventually integrated to form level landfast ice in the subsequent winter. Every year, some 10–20 grounded icebergs gather in the sea around Zhongshan Station. The nearest iceberg can be as close as 1 km from the station. Some grounded icebergs remain in this region for up to several years. The grounded icebergs may act as a barrier preventing the dynamic break up of offshore landfast ice in the late summer. Small icebergs often drift away in the summer. Ice rafting sometimes occurs offshore, when the high tide breaks the level ice and piles it up. Precipitation in Prydz Bay is largely in the form of snow (Yu et al., 2018) and often associated with cyclones or storm events. Close to the shoreline, the snow is often blown away by wind, leaving the ice nearly free of snow during most of the ice season. However, at some distance (> 1 km) offshore, the snow depth on sea ice can reach 0.2–0.4 m. Since 2012, snow depth and ice thickness have been measured regularly at the fixed location of SIP (Fig. 1b). The SIP site is 110 m off the coastline, where the water depth is 10 m. The observations usually begin in March, when the ice is strong enough, and end around early December before melting begins. The measurements are made weekly. Ice thickness is measured by drilling 3–5 boreholes and recording the distance from the ice surface to bottom using an ice gauge. Snow depth is measured at three sites, 1–5 m from each ice borehole, using a stainless ruler. The accuracy of both the ice and snow measurements is 0.001 m. The measurements at SIP are part of the Antarctic Fast-Ice Network (AFIN) activity. At the beginning of every December, the Chinese ice breaker R/V Xuelong navigates to the Zhongshan Station to deliver logistical/scientific supplies and carry out personnel replacement. The Xuelong usually anchors at the edge of the landfast ice zone. A logistic transport route survey (TRS) between Zhongshan Station and the R/V Xuelong is explored by a group of 3–4 scientists from Zhongshan Station using snowmobiles. The route surveys between 2011 and 2016 covered roughly 200 km2 of ice field. Snow depth and ice thickness measurements were carried out along routes (Fig. 1c). A total of 511 point measurements were recorded on level ice, deformed ice, ridged ice, and rafted ice. We added 137 measurements made on level ice floe in this study.

The linear correlation coefficient between snow and ice thickness, which was defined as the ratio of the covariance between snow and ice thickness and the product of the standard deviations of snow and ice thickness, was calculated in this study. The calculated correlation coefficient was evaluated by the Sample Correlation Coefficient Table. A hypothesis test of the significance of the correlation coefficient was performed to determine whether the linear relationship was strong enough. The value of the correlation coefficient r and the observation number n needed to be examined together. A numeric value p was used in this study to indicate the significance of the correlation coefficient. 3.2. Snow and ice model (HIGHTSI) The snow and ice model HIGHTSI is a one-dimensional model. HIGHTSI was initially designed for a brackish water (e.g., Baltic Sea and Bohai Sea) sea ice study (Launiainen and Cheng, 1998; Cheng et al., 2003, 2006). However, the model has been further developed and applied for snow and sea ice studies in the Arctic (Cheng et al., 2008; Yang et al., 2012; Wang et al., 2015; Merkouriadi et al., 2017) and Antarctic (Vihma et al., 2002; Yang et al., 2015; Zhao et al., 2017). HIGHTSI solves the partial differential heat conduction equation for snow and ice layers:

(ρc )s, i

∂qs, i (z , t ) ∂Ts, i (z , t ) ∂ ⎛ ∂Ts, i (z , t ) ⎞ ks, i = − , ∂z ∂t ∂z ⎝ ∂z ⎠ ⎜

⎟

(1)

where T is temperature, ρ is density, c is specific heat, and k is thermal conductivity. The subscripts s and i denote snow and ice, respectively. q (z, t) is the amount of solar radiation penetrating below the surface. The vertical coordinate z is taken as positive downward, and t denotes time. The ice–snow interface temperature is calculated via a flux continuity equation: ks∂Ts/∂z|snow = ki∂Ti/∂z|ice. Modeling of the penetration of solar radiation within snow and ice was adapted from Grenfell and Maykut (1977) using a two-layer scheme (Launiainen and Cheng, 1998), which ensures the quantitative calculation of sub-surface snow and ice melting (Cheng et al., 2003, 2006, 2008). A surface layer is defined, and a snow and ice surface heat balance equation is used to calculate surface temperature and melt:

(1 − αi, s ) Qs − I0 + εQd − Qb (Tsfc ) + Qh (Tsfc ) + Qle (Tsfc ) + Fc (Tsfc ) − Fm = 0,

(2)

where α is surface albedo, Qs is the incoming shortwave radiative flux at the surface, and I0 is the solar radiation penetrating below the surface layer. Qd is the incoming atmospheric longwave radiation, Qb is the longwave radiation emitted by the surface, and ɛ is the surface emissivity (0.97). Qh and Qle are the turbulent fluxes of sensible heat and latent heat, respectively. The turbulent surface fluxes of sensible heat 3

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and latent heat are calculated, taking the atmospheric stratification into account (Launiainen, 1995), on the basis of the observed Vs, Ta, and Rh as well as the modeled surface temperature (Tsfc). Fc is the surface conductive heat flux, and Fm is the heat flux due to surface melting: ρs,i Lf dhs,i/dt = Fm. Lf is the latent heat of fusion, and h is the thickness of ice or snow. The freezing and melting at the ice bottom are determined by the conductive heat flux at the lower part of the ice floe and the upward oceanic heat flux below the ice bottom:

− (ki ∂Ti / ∂z )bot + Fw = −ρi (Lf ) dH / dt ,

(3)

where ρi is the sea ice density at the basal layer, and dH/dt is the ice growth rate at the bottom. Tbot = Tf is the sea water freezing temperature, and Fw is the oceanic heat flux. The thermal properties (ρ, c, and k) of sea ice are parameterized according to Yen (1981) and Pringle et al. (2007). The snow insulation effect is considered by taking into account a time-dependent snow density parameterization (Anderson, 1976) and snow heat conductivity (Sturm et al., 1997). The refreezing of slush formed by ice surface flooding (snow ice, Hsi) and the refreezing of surface and subsurface melted snow (superimposed ice, Hsui) at the snow–ice interface are considered in the model. The ice surface flooding is a result of isostatic imbalance of snow load on top of the ice floe. For ocean conditions, snow ice is different from superimposed ice because snow slush is a mixture of salty sea water, snow and ice crystal, and air bubbles. Its density can be as high as 960 kg/m3 (Adolphs, 1998). For the brackish Baltic Sea, the measured snow ice density was 875 kg/m3 (Palosuo, 1965). We assumed that the snow ice density is the average of these two values. Superimposed ice is formed from melting snow slush, and its density is smaller than that of salty snow ice and sea ice. The HIGHTSI parameters applied in this study are given in Table 1. They are largely based on the literature and in situ measurements. The basic external forcing for HIGHTSI consists of wind speed (Vs), air temperature (Ta), relative humidity (Rh), cloudiness (CN), and precipitation (Prec). Qs and Qd can also be input parameters if measurements are available. In this study, we use parameterized values according to Shine (1984) and Prata (1996), respectively. The surface albedo was parameterized according to Briegleb et al. (2004).

Fig. 2. (a) Snow depth and (b) ice thickness at SIP and (c) daily mean air temperature at the Zhongshan Station. A 30-day running mean air temperature at the Zhongshan Station was applied for the air temperature plot.

between early May and late November shows large interannual variations. The seasonal mean snow depth remained thin during 2012–2014 and 2016 (Table 2), but the 2015 season was exceptional, with a mean snow depth of 0.17 m. The observed initial ice thickness showed large variation from early March to mid-May. The FYI usually starts to form in February/March in open water near the coast. The large ice thicknesses (bold numbers in Table 2) observed in March and April are most likely SYI because the average monthly air temperatures in March and April are −9.8 °C and −13.9 °C, respectively. The ice surface was almost snow free (Fig. 2a). Applying the Stefan Law under such a temperature condition, the calculated thermodynamic growth of bare ice cannot yield > 0.43 m and 0.68 m by the end of March and April, respectively. From May onward, the measured ice thickness showed a similar increasing trend for all seasons. The annual maximum ice thickness was reached in November (Fig. 2b) in most seasons, and varied between 1.4 m and 1.8 m. By early December, the ice thickness was quite consistent, with an interannual mean of about 1.5 m. The mean air temperature for the years 2012–2016 was −10.4 °C, with a standard deviation of ± 0.64 °C (Fig. 2c). It was 0.6 °C colder than the climatological mean between 1989 and 2014. During March–April and October–December, the temperature changes are similar among each of these seasons. The variability becomes large between May and September, with some extreme warming events occurring during mid-winter. The warming events are attributed to frequent storm intrusions. The frequency and strength of storms have varied each year, resulting in a high variability in the surface air temperature records. The coldest air temperatures are often recorded during the polar night, but these can also occur during late May (e.g., 2015) and late September (2016). The annual mean snow depth at SIP has a significant negative correlation (−0.58, p < .05) with the annual maximum ice thickness (Fig. 3a), indicating a profound insulation effect of snow cover on total ice mass balance at this location. The snow depth versus ice thickness relationship measured during TRS is shown in Fig. 3b. The observed ice thickness values exceed 1 m. As measurements were made on level ice floe, the ice thickness is driven

4. Results 4.1. Observed snow depth and ice thickness distribution Time series of snow depth, ice thickness, and air temperature at SIP are plotted in Fig. 2, and the observed snow depth and ice thickness are summarized in Table 2. Snow remains thin in March and April, and the snow pack is typically thicker during the polar night. This is associated with the frequent winter storms, which transport abundant atmospheric moisture to the study region, resulting in heavy snowfall. Snow depth Table 1 Parameters applied in the HIGHTSI model. Variable Sea ice density (ρi) Sea water density (ρw) Average bulk snow density (ρs) Slush density (ρslush) Superimposed ice density (ρsui) Snow ice density (ρsi) Latent heat of fusion (Lf) Sea water freezing temperature (Tf) Surface emissivity (ε) Thermal conductivity of pure ice (k) Thermal conductivity of snow (ks) Specific heat of sea ice (ci) Specific heat of snow (cs)

Value

Source 3

910 kg/m 1020 kg/m3 320 kg/m3 920 kg/m3 850 kg/m3 918 kg/m3 0.33 × 106 J/kg 1.9 °C 0.97 2.03 W/m K 0.31 2093 J/kg K 2093 J/kg K

Lei et al. (2010) Literature Huwald et al. (2005) Saloranta (2000) Wang et al. (2015) Assumed Literature Lei et al. (2010) Vihma et al. (2009) Yen (1981) Pringle et al. (2007) Literature Literature

4

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Table 2 Summary of snow and ice observations from seasons of 2012 to 2016 at SIP. Year

2012 2012 2013 2013 2013 2013 2013 2014 2015 2016

Measurement

Ice thickness (m)

Snow depth (m)

Measurement number

First

Last

First

Last

Mean

First

Last

Mean

Mar 11 Mar 10 Apr 12 Apr 4 Apr 6 May 10 May 14 Apr 25 Apr 15 Apr 16

Dec 11 Dec 5 Dec 1 Dec 1 Dec 1 Dec 1 Dec 1 Nov 29 Dec 2 Nov 22

0.22 1.16 0.69 0.35 0.88 0.64 0.76 0.47 0.30 0.70

1.4 1.38 1.64 1.45 1.60 1.53 1.46 1.57 1.44 1.57

0.86 1.04 1.27 1.01 1.25 1.18 1.11 1.21 0.92 1.26

0 0 0.08 0.05 0.06 0.08 0.03 0.03 0.01 0.1

0 0 0 0.03 0.04 0 0 0 0.26 0.2

0.007 0.01 0.05 0.07 0.1 0.06 0.05 0.02 0.17 0.13

99 121 34 39 37 30 17 30 34 32

The “First” and “Last” columns refer to the first and last measurement dates and values, respectively. The last column represents the total sampling numbers. The large ice thicknesses highlighted in bold font are most likely the SYI in March and April.

Fig. 4. The average snow depth and ice thickness measurements along the meridional fetch (gray arrow in Fig. 1) on undeformed ice floes from TRS between 2011 and 2016. The numbers in the parentheses represent the number of observations used for the statistics calculations.

sites 9 km farther north, the snow pack was much thicker (0.52 ± 0.11 m), and the ice had a thickness of 1.35 ± 0.13 m. Assuming average snow and sea ice densities of 330 kg/m3 and 910 kg/ m3, respectively, an average 0.52 m of snow on top of 1.35 m of sea ice could yield a negative freeboard of 0.06 m, according to Archimedes' Law. The flooded snow slush is likely to form snow ice in the cold condition. Snowmelt at the surface could contribute to superimposed ice in late spring and early summer.

Fig. 3. The relationship between (a) annual maximum ice thickness and mean snow depth at SIP and (b) snow and ice thickness measured on undeformed level ice floe during TRS. Circles represent the observations, and gray lines represent the trends. The bar subplot shows the distribution of measured snow depth.

mostly by thermodynamic growth and probably also snow–ice interaction (snow ice or superimposed ice formation). The snow depth varied between 0.06 m and 1.0 m, whereas the undeformed ice thickness ranged between 1.0 m and 1.9 m. Snow depth was negatively correlated with ice thickness, but the correlation was very weak (−0.2, p < .02). This occurred because the measurements were made in a relatively short period in November and December, and the measurements were scattered to cover a large area. The spatial variability largely dominates the snow/ice thickness relationship. If the measurements had covered the seasonal scale with a high spatial sampling resolution on level ice, a strong negative correlation between snow depth and ice thickness would probably have been observed. The data on snow depth and ice thickness collected from TRS were averaged along the meridional direction (Fig. 4). In a distance of about 6.75 km from the coast, the snow depth and ice thickness revealed a negative correlation, which is in agreement with the SIP site measurements during October to December, in which the mean snow depth was 0.05 ± 0.07 m and the mean ice thickness was 1.53 ± 0.15 m. At

4.2. Modeled snow depth and ice thickness distribution 4.2.1. Model forcing and experiment configuration The model experiments focused on the 2015/2016 season. Two modeling periods were defined: a) 15 April–2 December 2015, when the observed snow depth and ice thickness were available and b) the annual cycle from 15 April 2015–14 April 2016. The annual time series and distributions of meteorological parameters are presented in Fig. 5. The annual mean wind speed was 6.4 m/s, which is comparable with the climatological mean (7.0 ± 0.5 ms−1) between 1989 and 2014. The majority of the wind speed observations ranged between 1 ms−1and 10 ms−1. The highest wind speed (27.6 ms−1), which was associated with a cyclone, was recorded on 2 September (Fig. 5a). The annual mean air temperature was −11.2 °C, which is 1.4 °C lower than the climatological mean (−9.8 ± 0.9 °C). The lowest air temperature, 5

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Fig. 5. Observed time series and value distributions of meteorological parameters at Zhongshan weather station during the 2015/2016 annual cycle for wind speed (a, b), air temperature (c, d), relative humidity (e, f), and weekly mean cloud fraction (g, h). Illustration (i) is the time series of observed snow depth at SIP (blue line) and observed precipitation rate (red bar) at the Russian Progress Station. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

which occurred on 8 July, was −39.9 °C. The annual mean relative humidity was 60%, which exceeded the climatological mean of 58 ± 7%. The sky was covered by clouds during most of the year. At SIP, snow and ice measurements were terminated on 2 December 2015 because melting started and the ice became unstable. The weekly measured snow depth was linearly interpolated at 1 h time intervals. This observed snow depth was used as the input for HIGHTSI reference run. The oceanic heat flux (Fw) has an annual cycle in Prydz Bay (Lei et al., 2010; Yang et al., 2015). The maximum Fw is often observed in the early winter when the freezing season begins, and the minimum value appears by the end of the calendar year when ice reaches its maximum thickness, i.e., Fw decreases with increasing ice thickness. Based on previous investigations of Fw in Prydz Bay (Heil et al., 1996; Lei et al., 2010; Yang et al., 2015; Zhao et al., 2019), we constructed a time series of Fw ranging from maximum 35W/m2 in April to a minimum 5W/m2 in December (Fig. 5j) for this study.

end of May, snow depth decreased gradually in June and July before a large snow accumulation (~0.5 m) in August. Snow depth had a decreasing trend from September to early October, before it increased again until early December. The observed maximum snow depth was 0.49 m, which is much higher than the climatological mean of 0.06 m near the cost. The modeled ice thickness agreed reasonable well with the observations (Fig. 6). The initial ice growth in middle April followed the observation. When snow started to accumulate in early May, the modeled ice growth rate became slower, but the ice thickness still increased gradually toward the end of June, before speeding up in July. This result is coherent with the thinner snow in July. From August to December, the ice growth was consistent with the observations. The

4.2.2. Reference run To assess the performance of HIGHTSI, a reference model experiment was carried out by applying the prescribed snow depth as input. The initial ice thickness according to the measurements was 0.3 m. Snow started to accumulate by the end of April, and a rapid increase of snow depth was observed in May, resulting in a snow accumulation of about 0.3 m. A decrease of snow depth in the middle of May was associated with snow drifting. After an increase of snow depth toward the

Fig. 6. Comparison between observed (dots) and modeled (solid line) ice thickness for the reference run. 6

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difference of initial snow density of 300 kg/m3 resulted in a difference in the modeled maximum and average ice thickness of as much as 0.28 and 0.19 m, respectively. 4.2.5. Annual model experiments Three model experiments were carried out to investigate the effect of snow on the annual sea ice thickness. The model experiments were named A1: bare ice without snow, A2: prescribed snow depth based on in situ measurements, and A3: time-dependent snow accumulation based on observed precipitation. In A2, the prescribed snow depth was the same as that used for the reference run until 2 December 2015. During summer months the snow depth remained at zero. Therefore, we extrapolated the last snow measurement of 0.26 m linearly down to 0 m until 11 December using a 30 mm/day melting rate, which has been discovered in the Prydz Bay area (Lei et al., 2010). During the remaining period (12 December–14 April), precipitation may occur (Fig. 5i); however, due to strong wind, snow accumulation is often very limited (Fig. 2a). Hence, snow depth was assumed to remain at zero. In A3, the observed precipitation from 15 April to 2 December was used as an external forcing for snow accumulation. Precipitation during the remaining period was assumed to drift away without contributing to snow accumulation. A simple temperature criterion was applied to discriminate snow accumulation from precipitation, i.e., if temperature is below 0.5 °C, the precipitation is considered as snowfall (Yang et al., 2012). A 147 mm snow water equivalent (SWE) was accumulated during this period, and it converts roughly to 0.46 m of snow, which is close to the maximum observed value of 0.49 m at SIP (Fig. 2a). The entire annual precipitation was 203 mm SWE, which converts to 0.6 m of snow accumulation. This indicates that a lot of snow precipitation drifted away. The annual precipitation was 37.5 mm higher than the climatological mean value of 165.5 mm measured at the Russian Progress Station during 2003–2016 (Yu et al., 2018), but it was 28 mm lower than that measured at the Australian Davis Station (Heil, 2006). The modeled snow and ice temperature fields as well as the snow depth and ice thickness are illustrated in Fig. 9. Between 15 April and 1 October, the average air temperature was −18.2 °C (cf., Fig. 4b), and the ice was under a consistent growth phase for all model experiments. The freezing rates in response to the different snow conditions were 8.3 mm/day (A1), 5.2 mm/day (A2), and 4.7 mm/day (A3). The ice could grow to 1.41 m (A1), 0.88 m (A2), and 0.8 m (A3) during this period. The different snow scenarios cause differences of ice growth of up to 0.53 m (A2) and 0.61 m (A3), respectively. Under the same atmospheric conditions during this period, the average in-ice temperature for A1, A2, and A3 was −7.7 °C, −5.4 °C, and −5.1 °C, respectively. The corresponding in-ice average conductive heat fluxes were 33 W/m2 (A1), 28 W/m2 (A2), and 27 W/m2 (A3). The temperature difference between A2 and A3 was about 0.3 °C. The difference is not very large because the average snow depths during this period were close to each other: 0.16 m (A2) and 0.19 m (A3). From early October to early December, the air temperature was still negative, but it started to increase gradually. The increase of in-ice temperature was much faster for bare ice (0.1 °C/day) than for snowcovered ice (0.06 °C/day for A2 and 0.03 °C/day for A3). Ice surface started to melt on 2 November (A1), 11 December (A2), and 17 December (A3). The onset of ice bottom melting occurred on 12 November (A1) and 6 December (A2, A3). Accordingly, the snow cover delayed the onset of surface melting by up to 45 days and bottom melting by 24 days. The minimum ice thickness after the summer melt season was 0.44 m (A1), 0.50 m (A2), and 0.56 m (A3). Without snow, the ice started to melt early, but the maximum ice thickness was larger. For snow-covered sea ice, the onset of ice melting was late, but the maximum ice thickness was small. The early onset of melting of thick ice floe and late onset of thin ice resulted in comparable values of minimum annual ice thickness for the snow-free and snow-covered ice, respectively. Without taking snow into account, the annual maximum ice

Fig. 7. Modeled maximum ice thickness versus prescribed snow depth in each experiment (dot) and the nonlinear regression (solid curve). The circles present the observed maximum ice thickness and snow depth at SIP.

observed mean ice thickness was 0.92 m, with a standard deviation of ± 0.34 m. The modeled value was 0.94 m, with a standard deviation of ± 0.33 m. The correlation coefficient between observed and simulated ice thickness was very high (0.99). The bias and root mean square error (RMSE) between the observed and modeled results were 0.003 m and 0.06 m, respectively. This experiment revealed that the HIGHTSI parameters as well as both the upper and lower boundaries were configured reasonably and that the modeled ice thickness reacted realistically to the prescribed snow depth and oceanic heat flux. 4.2.3. Effect of snow depth on sea ice thickness The time series of snow depth on sea ice off of the Zhongshan Station usually showed a high-frequency variation after each snowfall episode. The drastic reduction of snow depth is caused by wind-driven dynamics (Lei et al., 2010). To investigate the thermodynamic impact of snow on sea ice, a group of model experiments was carried out with a constant snow depth varying from 0 to 0.15 m. The weather forcing conditions were the same as those in the reference run. The maximum ice thickness versus snow depth is shown in Fig. 7. The maximum ice thickness varies from 1.78 m to 1.32 m in response to a snow depth variation from 0 to 0.15 m. The relation between the maximum ice thickness and snow depth is nonlinear. However, if a linear regression is applied, a significant negative relationship (−0.83, p < .01) is obtained. 4.2.4. Sensitivity of snow density on sea ice thickness Snow density changes with time (Anderson, 1976). In the Antarctic, the large-scale mean snow density ranges from 290 to 390 kg/m3 (Massom et al., 2001). Snow density affects the thermal heat conductivity (Sturm et al., 1997) and eventually affects the ice mass balance. Four model experiments were carried out using an initial snow density of 150 kg/m3 (fresh fallen snow), 250 kg/m3 (new snow), 350 kg/m3 (average snow), and 450 kg/m3 (old snow), respectively, with a constant snow depth of 0.1 m. All of the simulations were started on 15 April and ended on 2 December. In each model run, snow density was parameterized according to Anderson (1976) and heat conductivity of snow was calculated based on the parameterization of Sturm et al. (1997). The results of the experiments are shown in Fig. 8. For a very fresh snow (ds = 150 kg/m3), the snow density increased gradually and reached 350 kg/m3 by the end of the simulation. For old snow (ds = 450 kg/m3), the densification of snow was quite limited. For the different densities (150, 250, 350, and 450 kg/m3), the modeled maximum ice thicknesses were 1.24, 1.29, 1.38, and 1.52 m, and the modeled mean thicknesses were 0.79, 0.82, 0.89, and 0.98 m, respectively. Lower snow density led to lower heat conductivity of snow, resulting in less ice growth. During the simulation period of 230 days, the 7

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Fig. 8. The evolution of snow density and ice thickness in the four experiments.

landfast ice near the coast in early summer may be subject to mechanical break up due to the impact of high wind or ocean currents and tides when the thickness is still 0.5 m–1.0 m (Lei et al., 2010; Yang et al., 2015). Our modeled ice thickness was around the threshold between FYI and SYI or MYI. 4.2.6. Sensitivity experiments on snow ice and superimposed ice formation Farther north from Zhongshan station, the effect of wind on snow drifting was reduced and snow depth increased by > 0.5 m in summer season (Fig. 4) before the onset snowmelt. It is interesting to consider how snow ice and superimposed ice would form in terms of different initial ice thickness. Therefore, we carried out a number of model experiments at the annual cycle applying the A3 weather forcing but with the entire annual precipitation for snow accumulation. To discern the impact of initial ice thickness on the formation of snow ice and superimposed ice, we started the model runs on 14 February, when the freezing season begins. The initial ice thickness ranged from 0.05 m to 2.0 m. The modeled snow depth and total ice thickness are given in Fig. 10, and the snow and ice mass balance components are given in Table 3. For the thin ice (< 0.2 m), the total ice freezing can reach around 1.1 m before melting begins. The modeled snow ice and superimposed ice values are 0.24–0.25 m and 0.08 m, respectively. Together, they

Fig. 9. Modeled snow and ice temperature field for three experiments: (a) annual in-ice temperature field without snow, (b) annual snow and ice temperature fields when the snow depth was prescribed, and (c) annual snow and ice temperature fields when the snow depth was modeled using observed precipitation as a model input.

thickness reached 1.75 m in early November, which agrees well with previous observations (Lei et al., 2010). With snow considered, the modeled annual maximum ice thickness decreased to 1.45 m (A2) and 1.38 m (A3). These values are almost within the range of the observed maximum ice thickness values of between 1.44 m and 1.82 m during the years 2012–2016 at SIP. The modeled ice mass balance components (A3) are freezing at the ice bottom of 0.84 m until early summer and 0.98 m for the entire annual cycle. The total ice melting was 0.82 m, in which 0.24 m was bottom melting and 0.58 m was ice surface and internal melting. The snow ice and superimposed ice formation amounts were 0.1 m and 0.14 m, contributing 7% and 10% of the total maximum ice thickness, respectively. Compared with seasonal modeling without snow (Yang et al., 2015), the minimum annual ice thickness ranged from 0.5 to 0.6 m. The

Fig. 10. Modeled snow depth and ice thickness for an annual cycle beginning on 14 February with application of different initial ice thickness values (Table 3). The entire observed precipitation was used to convert to snow accumulation (Fig. 5i). The figure legend shows the initial ice thickness (m) for each model experiment. 8

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Table 3 Modeled annual snow depth (Hs) and ice thickness (Hi) components (m) using different initial ice thickness. Hi (initial)

Hs (mean)

Hs (max)

Hsi

0.05 0.10 0.20 0.60 1.0 1.6 2.0

0.20 0.21 0.22 0.24 0.26 0.28 0.31

0.35 0.35 0.36 0.38 0.41 0.48 0.52

0.25 0.25 0.24 0.22 0.19 0.13 0.08

(23%) (23%) (23%) (18%) (13%) (7%) (4%)

Hi (mean)

Hi (max)

Hsui

0.72 0.73 0.75 0.92 1.15 1.57 1.89

1.07 1.07 1.08 1.23 1.43 1.79 2.06

0.08 0.08 0.08 0.09 0.09 0.12 0.12

(8%) (8%) (7%) (7%) (6%) (7%) (5%)

Hsi and Hsui represent the thickness of snow ice and superimposed ice, respectively. The numbers in the parentheses represent the percentage of the maximum ice thickness.

this conclusion. On the other hand, in situ measurements reported by previous studies have confirmed the simultaneous existence of FYI and SYI near the coast of Prydz Bay (He et al., 1998; Zhao et al., 2017). At SIP, snow insulation had a major effect on the annual maximum ice thickness. A negative correlation (r = −0.58, p < .05) was observed between mean snow depth and maximum ice thickness. The modeling experiments using prescribed snow depth suggested an even a stronger negative causal relationship between snow depth and maximum ice thickness (r = −0.83, p < .01). However, for the TRS domain farther off the coast, the correlation between snow depth and ice thickness was much weaker because of (a) spatial and temporal variations in wind-driven snow redistribution in the region (Zhao et al., 2019) and (b) deeper snow pack. Hence, the insulating effect there limited sea ice growth; however, according to the modeling results, this effect was partly compensated by ice growth via the formation of snow ice and superimposed ice. The modeled seasonal maximum ice thickness ranged from 1.78 to 1.32 m for a prescribed snow depth ranging from 0 to 0.15 m, respectively. This is consistent with the observed maximum ice thickness of between 1.82 m and 1.44 m in Prydz Bay. In the annual model experiments, in cases with snow cover, the sea ice freezing rates were reduced by 40% compared to the snow-free case. The in-ice temperature increased more slowly in the snow-covered cases than in the snowfree case. The onsets of ice surface and ice bottom melting were delayed by 45 and 24 days, respectively. In theory, fresh new snow (150 kg/m3) in early April may become 2.3 times more dense (350 kg/m3) before the onset of melting in early December. The model experiments showed that such snow metamorphism may result in 23% additional ice growth on a seasonal scale because of the higher heat conductivity of dense snow. The mean regional proportions of snow ice calculated from ice cores varied from 8% to 38%. In the Bellingshausen Sea and Amundsen Sea, superimposed ice made up approximately 5% of the ice (Massom et al., 2001). In the sensitivity experiments in our study, snow ice showed a 4–23% contribution and superimposed ice contributed 5–8% of the total maximum ice thickness, which is consistent with the observations. The results obtained for Prydz Bay characterize the conditions over most of the Antarctic land-fast sea ice zone, at least in the following aspects. Due to downslope winds, the snow pack is thinner near the coast than farther out over the sea ice, and summer snowmelt contributes to ice thickness via the formation of superimposed ice (Massom et al., 2001). The latter effect compensates the insulation effect of snow of reducing sea ice growth. However, the snow and sea ice conditions in Prydz Bay are also affected by certain characteristics specific to the region. First, the wind speeds over Prydz Bay are stronger than those over most sea regions along the Antarctic coastline (Zhang et al., 2015), which tends to reduce nearshore snow depth and make snow depth more variable spatially. Second, precipitation in Prydz Bay is characterized by no or very little precipitation on the majority of days but extreme precipitation on rare days; the latter contributes the majority of the annual precipitation (Yu et al., 2018). This favors large temporal variations in snow depth. The spatiotemporal variations in snow depth,

account for 30% of the maximum ice thickness. For an initial ice thickness of 0.6 m and 1 m, the modeled maximum ice thickness reached 1.23 m and 1.43 m, respectively. The snow ice and superimposed ice accounted for 20–25% of the total maximum ice thickness. For thicker ice of 1.6 m and 2.0 m, melting occurred at ice bottom due to large oceanic heat flux after summer. The modeled maximum ice thickness was 1.79 m and 2.06 m, respectively. Snow ice and superimposed ice accounted for 10–14% of the maximum ice thickness. The modeled snow depth increases with increasing initial ice thickness because for thicker ice floe, the buoyancy is large and snowfall tends to accumulate on ice rather than forming of snow slush due to a smaller buoyancy for a thinner ice floe. Therefore, snow contributed more to the maximum ice thickness of thinner ice floe than of thicker ice floe. In early December, the modeled snow depth was between 0.35 m and 0.5 m, close to the TRS measurements farther away from the coast. The annual maximum ice thickness appeared by the end of December, and the snow remained at an average thickness of about 0.15 m before the next freezing season started. For the model runs, the snow ice and superimposed ice contributed 4–23% and 5–8% of the total maximum ice thickness, respectively. 5. Discussion and conclusions The interannual variations of snow depth at SIP were large. The seasonal mean snow depth ranged from 0.01 m to 0.17 m. The observations revealed that the average snow depth in the early ice season (March-April) was of the order of 0.002 m, which is consistent with previous measurements (Lei et al., 2010). The temporal variation of snow depth from April to December is highly associated with precipitation patterns. In some seasons, snow depth near the shore may be substantial, resulting in large annual and interannual variations during winter month. Farther from the coast, the snow pack was thick in November to December, with a mean value of 0.45 m in the TRS domain. Similar snow distribution patterns have been found in coastal regions in other parts of the Antarctic. For example, a transect survey at LützowHolm Bay, East Antarctica, found that low snow accumulations occurred at sites nearest the coast but that snow depth increased significantly with increasing distance from shore (Kawamura et al., 1995). Based on observations from multiple Russian expeditions in the East Antarctica, Fedotov et al. (1998) found that the snow depth on the fast ice zone 6–15 km offshore was up to 0.6–1.0 m, which is similar to the TRS observation in this study. The ice thickness at SIP showed much less interannual variability. The seasonal mean ice thickness ranged from 0.86 m to 1.27 m. The maximum ice thickness, which varied interannually between 1.44 m and 1.82 m, was reached by the end of November, and the difference of thickness was attributable to the SIP snow onset scenario. During the freezing season under the average climatology in Prydz Bay, the FYI formation estimated by Stefan's Law can reach 0.43 m in one month (March) and 0.68 m in two months (March and April). Hence, the ice thicknesses > 0.68 m observed in March or April were most likely SYI floes. If ice core samples were to be collected, it would help us confirm 9

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due to the strong winds and extreme precipitation events, further affect the ice thickness. Considering the conditions within Prydz Bay, our results are in agreement with those of Heil (2006), who observed at the Australian Davis Station, some 100 km away from Zhongshan Station, that increase of cumulative solid precipitation is associated with thinner annual maximum landfast sea ice. Manual snow depth and ice thickness observations have been carried out mostly at SIP. Autonomous measurements covering a large area off the Zhongshan Station, e.g., in the TRS domain, are necessary to obtain a better understanding of the spatial and temporal variation of snow depth and ice thickness. Ice core samples can be useful for identifying the portions of snow ice and superimposed ice in the total ice thickness, and it would be helpful if these were collected in future field measurements. The logistical support for Zhongshan Station requires improved snow and ice services for the land-fast ice region in Prydz Bay. An ongoing work is the simulation of snow depth and ice thickness at a regional scale. Such a model experiment could contribute to the Year of Polar Prediction (YOPP) Project and have practical applications for snow and ice services in the region.

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Acknowledgements We are grateful to Rongbin Li, Qizhen Sun, Zhongxiang Tian, and Xiaopeng Han of the Zhongshan Station wintering team, who worked on in situ data collections at SIP and in the TRS domain during 2011–2016. Comments from three anonymous reviewers and editor-inchief Prof. Jukka Tuhkuri helped greatly to improve this paper. This study was supported financially by the National Natural Science Foundation of China under Contract 41876212 and 41676176, the Academy of Finland under Contract 304345, and the Opening Fund of Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, CAS under Contract LPCC2018001 and LPCC2018005. References Adolphs, U., 1998. Ice thickness variability, isostatic balance and potential for snow ice formation on ice floes in the south polar Pacific Ocean. J. Geophys. Res. 103 (C11), 24675–24691. https://doi.org/10.1029/98JC02414. Allison, I., Brandt, R.E., Warren, S.G., 1993. East Antarctic Sea ice: albedo, thickness distribution and snow cover. J. Geophys. Res. 98, 12417–12429. https://doi.org/10. 1029/93JC00648. Anderson, E., 1976. A Point Energy and Mass Balance Model for a Snow Cover. NOAA Technical Report. NWS 19, USA, pp. 150. Arndt, S., Willmes, S., Dierking, W., Nicolaus, M., 2016. Timing and regional patterns of snowmelt on Antarctic Sea ice from passive microwave satellite observations. J. Geophys. Res. Oceans. 121 (8), 5916–5930. https://doi.org/10.1002/ 2015JC011504. Arrigo, K.R., Worthen, D.L., Lizotte, M.P., Dixon, P., Dieckmann, G., 1997. Primary production in Antarctic Sea ice. Science. 276, 394–397. https://doi.org/10.1126/ science.276.5311.394. Briegleb, B.P., Bitz, C.M., Hunke, E.C., Lipscomb, W.H., Holland, M.M., Schramm, J.L., Moritz, R.E., 2004. Scientific Description of the Sea Ice Component in the Community Climate System Model, version 3. (NCAR/TN-463+STR). Cheng, B., Vihma, T., Launiainen, J., 2003. Modelling of the superimposed ice formation and sub-surface melting in the Baltic Sea. Geophysica. 39 (1–2), 31–50. https://doi. org/10.3189/172756406781811277. Cheng, B., Vihma, T., Pirazzini, R., Granskog, M., 2006. Modeling of superimposed ice formation during spring snowmelt period in the Baltic Sea. Ann. Glaciol. 44 (1), 139–146. https://doi.org/10.3189/172756406781811277. Cheng, B., Zhang, Z.H., Vihma, T., Johansson, M., Bian, L.G., Li, Z.J., Wu, H.D., 2008. Model experiments on snow and ice thermodynamics in the Arctic Ocean with CHINAREN 2003 data. J. Geophys. Res. 113, C09020. https://doi.org/10.1029/ 2007JC004654. Eicken, H., 1998. Factors determining microstructure, salinity and stable-isotope composition of Antarctic Sea ice: Deriving modes and rates of ice growth in the Weddell Sea. In: Jeffries, M.O. (Ed.), Antarctic Sea Ice Physical Processes, Interactions and Variability. AGU Antarctic Research Series 74. pp. 89–122. Eicken, H., Lange, M.A., Hubberten, H.W., Wadhams, P., 1994. Characteristics and distribution patterns of snow and meteoric ice in the Weddell Sea and their contribution to the mass balance of sea ice. Ann. Geophys. 12 (1), 80–93. https://doi.org/10. 1007/s00585-994-0080-x. Eicken, H., Fischer, H., Lemke, P., 1995. Effects of the snow cover on Antarctic Sea ice and

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