Snow plays an important role in the Arctic climate system,
modulating heat transfer in terrestrial and marine environments and
controlling feedbacks. Changes in snow depth over Arctic sea ice,
particularly in spring, have a strong impact on the surface energy budget,
influencing ocean heat loss, ice growth and surface ponding. Snow conditions
are sensitive to the phase (solid or liquid) of deposited precipitation.
However, variability and potential trends of rain-on-snow events over Arctic
sea ice and their role in sea-ice losses are poorly understood. Time series
of surface observations at Utqiaġvik, Alaska, reveal rapid reduction in
snow depth linked to late-spring rain-on-snow events. Liquid precipitation
is key in preconditioning and triggering snow ablation through reduction in
surface albedo as well as latent heat release determined by rainfall amount,
supported by field observations beginning in 2000 and model results.
Rainfall was found to accelerate warming and ripening of the snowpack, with
even small amounts (such as 0.3 mm recorded on 24 May 2017)
triggering the transition from the warming phase into the ripening phase.
Subsequently, direct heat input drives snowmelt, with water content of the
snowpack increasing until meltwater output occurs, with an associated rapid
decrease in snow depth. Rainfall during the ripening phase can further raise
water content in the snow layer, prompting onset of the meltwater output
phase in the snowpack. First spring rainfall in Utqiaġvik has been
observed to shift to earlier dates since the 1970s, in particular after the
mid-1990s. Early melt season rainfall and its fraction of total annual
precipitation also exhibit an increasing trend. These changes of
precipitation over sea ice may have profound impacts on ice melt through
feedbacks involving earlier onset of surface melt.
Introduction
Arctic sea ice has been experiencing rapid decline in both extent and
thickness in recent decades (Stroeve et al., 2007, 2012; Comiso and Nishio,
2008). The 10 lowest sea ice extent anomalies on record have all occurred
in recent decades. Sea-ice thinning trends (Kwok et al., 2009; Kwok and Untersteiner, 2011) have
been associated with first-year sea ice replacing thicker multi-year sea ice
(Maslanik et al., 2007, 2011; Giles et al., 2008). These changes make Arctic
sea ice more susceptible to variations in thermodynamic forcings, increasing
interannual variability (Kwok et al., 2009; Maslanik et al., 2007, 2011;
Nghiem et al., 2007; Notz, 2009; Laxon et al., 2013). Snow over sea
ice plays an important role in the growth and melt of Arctic sea ice (Maykut
and Untersteiner, 1971; Maykut, 1986; Blazey et al., 2013; Perovich and Polashenski, 2012).
Snow has a lower thermal conductivity and higher albedo (∼0.7–0.9
for wet to dry snow) than sea ice, which limits the absorption of
solar energy by sea ice as well as by the ocean beneath sea ice (Eicken et
al., 2004; Perovich and Polashenski, 2012). Screen and Simmonds (2012) showed that
the fraction of Arctic summer precipitation occurring as snow has declined
in recent decades due to lower-atmosphere warming, and this change of
precipitation has likely contributed to the decrease in sea ice extent by
reducing the area of snow-covered ice and the resulting surface albedo
during summer.
In spring, changes in the amount of snow can either curb or foster sea ice
melt. Thick snow helps maintain high surface albedos during the melt season
(Eicken et al., 2004) and reduces solar heating of ice and upper ocean
(Sturm et al., 2002). In contrast, thin snow melts back earlier in spring
and promotes the formation of melt ponds (Eicken et al., 2004; Petrich et
al., 2012), which absorb approximately 1.7 times more solar radiation than
bare ice and approximately 5 times more than cold, snow-covered sea ice
(Perovich et al., 2002; Perovich and Polashenski, 2012; Webster et al., 2014), accelerating ice decay
and solar heating of the upper ocean in spring.
Spring snow depth on sea ice is very sensitive to the phase of
precipitation. Solid precipitation increases snow depth, protecting sea ice
from melt. Conversely, liquid precipitation heats the snowpack, changes
snow grain morphology and lowers albedo, decreasing snow depth. Data from
Operation IceBridge flights (2009–2013) indicate an average snow depth on
Arctic sea ice of ∼20 cm (22.2 cm in the western Arctic and
14.5 cm in the Beaufort and Chukchi seas) (Webster et al., 2014), which
renders the thin sea-ice snowpack particularly susceptible to earlier
surface ablation and shorter duration as a result of liquid precipitation.
An assessment based on 37 state-of-the-art climate models indicated that in
the future rain is projected to become the dominant form of precipitation
over the Arctic region (Bintanja and Andry, 2017). Rain-on-snow events over
Arctic sea ice are likely to have profound impacts, particularly in late
spring when the snowpack has warmed. However, to date no such investigation
has been completed over Arctic sea ice.
In order to determine how liquid precipitation affects the surface ablation
of sea ice and to assess its quantitative contribution to the reduction in snow
depth over sea ice, here, we investigate the role of liquid precipitation in
initiating snowmelt and the sea ice ablation season based on field
measurements in the coastal Chukchi Sea. An energy balance model was adopted
to help develop a mechanistic interpretation of the observations. The
variability of rain-on-snow events over sea ice and the timing of first
spring rain are analysed using long-term meteorological records available at
Utqiaġvik, Alaska.
Data and methodologyDataMicrometeorological observations at the MB site
A snow and ice mass balance (MB) site has been deployed on undeformed landfast
first-year ice in the Chukchi Sea north of Utqiaġvik since 1990s. At
this location the ice is homogeneous, and it forms primarily through in situ
freezing rather than advection and deformation and provides ice and snow
data representative of level, undeformed ice (Druckenmiller and Haas, 2009).
The relative humidity was measured with a Campbell CS500 instrument in
2013 and a Campbell HMP155A instrument from 2014 to 2016. Air temperature
was measured 2 m above the ice with a shielded Campbell CS500 sensor in
2013 and with a Campbell HMP155A from 2014 to 2016. The shielded Rotronic
HC2S3 that measured air temperature in 2017 also measured relative humidity.
Wind direction and speed were measured by two R.M. Young and Campbell 5108-L
anemometers, one 2.1 m above the initial snow surface and the other 4.1 m
above the initial snow surface. Data were recorded every 15 min and
transferred via FTP to the University of Alaska Fairbanks, where they were
processed (Druckenmiller and Haas, 2009; Eicken et al., 2012). We used the
data for 2013–2017
(https://arcticdata.io/catalog/\#view/doi:10.18739/A2D08X, last access: 15 March 2018).
Radiation, albedo, surface temperature and snow depth near the MB
site
From April through June 2017, we conducted radiation and surface albedo
measurements near the MB site. The distribution of snow depth at this
location is the same as that at the MB site. Radiation was measured using a CNR4 net
radiometer that records the upwelling and downwelling shortwave and longwave
radiation. The surface albedo was derived from the upward solar radiation
divided by the incident solar radiation. The surface temperature was
measured with a SI-111 infrared radiometer. The sensors were fixed on a
bracket 1.5 m above the snow surface. Data were recorded every 5 min and
collected by LoggerNet 4.0 (CR1000). Snow depth was measured with a
Campbell SR50 sonic ranger fixed to a mast extending through the ice. The
accuracy is about ±1 cm.
Air temperature and precipitation at Utqiaġvik WSO AP
station
The data analysed here comprise daily precipitation and snowfall from
January 1952 to June 2017 for the Utqiaġvik Weather Service Office
airport weather station (WSO AP), located near the coast of the Chukchi Sea
at Utqiaġvik (available from the Alaska Climate Research Center;
http://climate.gi.alaska.edu/acis_data, last access: 2 September 2018). The
snowfall data are given as snow water equivalent (cm w.e.). The snowfall amount
is subtracted from the total precipitation to obtain the rainfall amount
(also in units of cm w.e.).
MethodologyModelling of snow depth, snow density and snow water equivalent (SWE)
The surface energy balance for the snowpack overlying sea ice can be
defined as
Qnet=Q∗+Qs+Ql+C+R,
where Qnet is the net energy flux at the snowpack on sea ice, Q∗
is the net radiative flux, Qs is the turbulent sensible heat flux,
Ql is the turbulent latent heat flux, C is the conductive
heat flux and R is the heat input by rain. Each component of the
surface energy budget is expressed in the unit W m-2. The
net radiative flux is composed of the net shortwave and longwave components,
which are derived from the observed incoming and outgoing radiative fluxes
with a CNR4 net radiometer (see more details in the Data and methodology section). The
sensible heat Qs and latent heat Ql were calculated by
2Qs=-ρacpCHTs-TaVz3Ql=-ρarlCEqs-qaVz,
where Vz is the mean wind speed (m s-1) in 1 h
at a height z and ρa (unit: kg m-3) and cp
(unit: J (kg K)-1) denote the air density and the specific heat
capacity of air. rl is the vaporization enthalpy. Ts-Ta and qs-qa are
the differences in temperature and specific humidity between the snow
surface and atmosphere at the height z. The temperature and humidity are
respectively expressed in the unit K and g m-3.
CH and CE are the bulk transfer coefficients estimated from a simple
non-iterative algorithm (Launiainen and Chengm, 1995) based on the Monin–Obukhov
similarity theory.
The conductive heat flux C was estimated as
4C=-kTs-Ti/Hs5k=0.138-1.01ρs+3.233ρs20.156≤ρs≤0.66k=0.023-1.01ρs+0.234ρs2ρs≤0.156,
where k is the thermal conductivity of snow in W m-1 K, and
Ts is the snow surface temperature in K. The observed ice surface
temperature Ti was applied in this study. Hs is snow depth in m.
k varies with snow density according to the regression in Eqs. ()
and (),
as suggested by Sturm et al. (2002).
For heat input by rain, two settings need to be considered. If rain falls on
snowpack that is at the freezing point,
R=ρwCwrTr-Tm,
where Cw is the heat capacity of water in J (kg K)-1, r is the
rainfall rate (m s-1) and Tr is the rain
temperature (K). Rain is cooled to the freezing point, giving up sensible
heat to warm the snowpack.
If rain falls on a snowpack below the freezing point,
R=ρwCwrTr-Tm+ρwLmr,
where Lm is the latent heat of fusion in J kg-1, and Tm is the
freezing point in K. Rain first cools to the freezing point, giving up
sensible heat. Thereafter the rain will freeze, releasing latent heat, which
can heat the snowpack effectively.
Once the snowpack reaches the warming phase, the positive energy budget is used to melt snow. The amount of snowmelt ΔSWE (snow water
equivalent in m) was estimated as
ΔSWE=-Qnet/ρwLm,
where Qnet is the net energy flux derived from Eq. (), and ρw
denotes water density in kg m-3.
The snow density changes were modelled based on
Δρs=ρsC1SWEexp-C2ρsexp(-0.08(T0-Ta))Δt,
where C1 and C2 are empirical coefficients, which are 7.0 m-1 h-1
and 21.0 cm3 g-1 according to the field measurements
in Yen et al. (1981). T0 is the freezing point temperature. Here,
Δt is equal to 1 h.
Rainfall and variations in air temperature and snow depth recorded
near Utqiaġvik, Alaska. The data were observed at the MB site on Chukchi Sea
landfast ice between January and June in 2013, 2014 and 2015. Amount (mm w.e.)
and timing of rainfall are indicated by blue triangles.
Observed air temperature, snow depth and liquid precipitation over
coastal Chukchi Sea ice in 2000, 2001, 2002, 2006, 2009, 2016.
The snow depth Hs was modelled by
Hs=SWE+ΔSWEρwρs+SWEnewρw/ρsnew,
where SWE is snow water equivalent in m, ρs is snow density
in kg m-3, and SWEnew is new deposited snow in snow water equivalent (m).
The density of new fallen snow ρsnew is 102 kg m-3 on average
derived from the field measurement in Chukchi Sea 2017.
The energy required to reach the isothermal state was calculated according
to [ciρw SWE (Tave-Tm)] by Dingman (2015), where
CI is the heat capacity of snow or ice (2.1 kJ kg-1∘C-1),
Tave is the average temperature of the snowpack,
Tm is the freezing point of snow, ρw is the density of water,
and SWE is snow water equivalent in m. The temperature profile of the snowpack
used to track the timing of the isothermal state was measured with a
CRREL-designed thermistor string employing Beaded Stream thermistors. The
thermistors were spaced 2 cm apart and measure temperature with
0.1 ∘C accuracy.
Energy balance of snow over sea ice during the early stage of melt
season. Observed net solar radiation, albedo, net longwave radiation and
timing of rainfall from 22 May through 1 June 2017 (local
time). Calculated sensible heat, latent heat, heat conductive flux and
energy budget during the same period are also shown. Rain heat includes the
heat that rain directly brings into the snowpack and the latent heat release
when the rain freezes within the snowpack. Grey shading shows the timing of
rainfall.
Observed snow morphology at different depths of the snowpack over
ice in the Chukchi Sea north of Utqiaġvik from 23 May through 27 May 2017. The reference ruler is 0.5 mm long.
Observed and modelled snow density, thickness and snow water
equivalent (SWE) at different stages of snowmelt. A sensitivity experiment
without rain was conducted for the same time period, and the corresponding
SWE is also shown. Rainfall, snow density and depth were observed at the
surface of Chukchi Sea landfast ice from 22 May through 1 June 2017. A
detailed description of the ablation process is provided
in the “Observations and model simulations of key processes” section.
Model experiments
Two experiments were conducted to quantitatively estimate the contribution
of rain to snow ablation. In the control run, we applied the meteorological
observations to drive the model and simulate the snow depth, snow density
and SWE. These observations include wind, air temperature, relative
humidity, snow surface temperature, upward and downward longwave radiation,
incoming solar radiation, surface albedo, rainfall, snowfall, snow
temperature and snow–ice interface temperature. The measured snow depth and
snow density were used to validate the model results. In the sensitivity
experiment, we excluded the impacts of rain by lowering albedo and
eliminating the latent heat and sensible heat terms contributed by rainfall.
As observed in this study and previous studies, such as Perovich et
al. (2002, 2017) and Perovich and Polashenski (2012), rain can decrease the surface albedo by
∼0.1 within a few hours. This impact on albedo is quite different from that
of a gradual warming or melting process. The latter needs ∼10
days to reduce the albedo by the same amount (Perovich et al., 2002, 2017).
In the sensitivity experiment, we derived an evolutionary sequence of albedo
without rain based on a simplifying assumption, in which albedos are
linearly extrapolated based on the observations of the previous 3 days using
the method of Perovich et al. (2017) for the period 24 May–3 June
(also see their Figs. 2 and 3). In contrast,
the control experiment includes all impacts of rainfall on surface
properties and fluxes and therefore draws on the observed albedo time
series from the same period. The observed downwelling solar irradiance was
applied to calculate the absorbed solar radiation with and without rain.
Water content of snowpack at different depths observed over ice in the Chukchi
Sea north of Utqiaġvik during 23 May through 27 May 2017.
Units: cm3 water 100 cm-3 snow.
Date (local time) 23 May24 May 25 May 26 May27 MaySnow depth (cm)14:4514:5016:5515:3017:1015:3015:002.51.75.85.34.86.02.312.31.85.85.55.06.02.412.35.56.44.75.11.912.35.04.42.410.45.03.23.52.75.81.811.12.43.52.85.61.410.53.63.43.45.81.75.53.75.72.06.77.52.12.75.04.73.811.31.75.45.14.010.92.15.44.45.75.610.05.94.48.72.35.37.64.74.6Significance testing
We calculated the significance value of a linear trend for first rainfall
date, rainfall in May, total precipitation and rainfall : total precipitation
ratio in May using Student's t test. The trend is significant when p≤0.05 with 95 % confidence.
Observed evidence of rapid reduction in snow depth associated with liquid
precipitation
Field measurements at a mass balance site (MB site) on landfast sea ice near
Utqiaġvik, Alaska, in April–June from 2013 to 2015 revealed rapid
declines in snow depth once non-freezing rain fell on the snow. Figure 1
shows the variations of snow depth and surface air temperature observed in
2013–2015. It appears that snow depth on sea ice started to decrease when
air temperature rose above the freezing point (0∘). Snow depth then
decreased sharply and persistently during subsequent days (6, 3 and 6 days
for 2013, 2014 and 2015, respectively). The change in surface air
temperature itself cannot explain such rapid reduction in snow depth since
surface air temperature fluctuates above and below the freezing point at
this time. Rather, the first non-freezing rain events of the year that were
immediately followed by the rapid decrease in snow depth might be
responsible for such a phenomenon. Our available observations from prior years
at Utqiaġvik and in the ice pack of the Chukchi Sea corroborate these
findings (Fig. 2) as do studies suggesting that the transition into the
Arctic surface melt season is linked to pronounced synoptic events, rather
than through gradual heating processes (Alt, 1987; Persson et al.,
1997; Stone et al., 2002; Wang et al., 2005; Sharp and Wang, 2009; Persson and Ola, 2012).
Components of the surface energy budget, required for the snowpack to
reach the isothermal state (warming phase).
DateModelledModelledModelledObserve netAbsorbedEnergyHeat broughtAbsorbedObserved netlatentsensibleheatlongwavesolarrequiredinto thesolarsolarheatheatconductiveradiationradiationto reachsnow byradiationradiation(KJ m-2)(KJ m-2)flux(KJ m-2)withoutthe isothermalrain (KJ m-2)due to(KJ m-2)(KJ m-2)rainstate(direct heatreduced albedo(KJ m-2)(KJ m-2)input +by rainlatent heat)(KJ m-2)24 May 2017 10:000.32.0-3.7-34.698.311.522.6120.824 May 2017 11:005.37.0-4.3-121.8100.339.185.7186.024 May 2017 12:00-1.4-1.3-4.4-222.3206.50.0167.2373.7Total4.17.7-12.4-378.7405.0-75.450.5275.5680.525 May 2017 04:00-11.5-9.6-2.8-22.07.533.06.213.725 May 2017 05:00-5.8-3.3-2.4-15.914.933.09.524.325 May 2017 06:000.11.1-2.6-1.029.90.016.346.225 May 2017 07:00-8.7-11.3-2.9-21.925.4131.821.446.925 May 2017 08:00-10.4-14.0-3.4-22.450.30.043.894.1Total-36.3-37.1-14.0-83.2128.0-109.7197.797.1225.127 May 2017 05:008.622.25.2-136.938.129.326.464.527 May 2017 06:001.22.95.8-163.933.7150.533.567.227 May 2017 07:006.715.45.8-153.599.50.055.8155.327 May 2017 08:007.126.02.4-151.7156.30.0118.4274.727 May 2017 09:007.524.11.4-188.6150.9138.7156.2307.1Total31.190.620.5-794.5478.5-449.5318.5390.4868.9Observations and model simulations of key processes
A primary mechanism for the acceleration of surface melt and ablation is the
rain-induced rapid lowering of surface albedo. In order to evaluate this
impact, we conducted field measurements of surface albedo in conjunction
with characterization of the state of the snow and ice cover on Chukchi Sea
ice in April and June 2017. Observations showed that surface albedo
decreased sharply on 24, 25 and 27 May by 0.12, 0.10
and 0.13, respectively, coinciding with the occurrence of rain-on-snow
events (Fig. 3). The observed snow morphology (Fig. 4) and water content
(Table 1) indicated significant melt of the snowpack after rainfall for
these 3 days, corresponding to a decrease in snow depth (Fig. 5).
Comparison with the earlier two warming events (15 and 18 May),
with temperatures at or above the freezing point, demonstrates
that the warming events alone did not result in such a rapid decrease in
albedo but that liquid precipitation plays a key role. Consistently, two
earlier studies also supported that a sharp drop in surface albedo by over
0.05 within a single day was associated with a rain-on-snow event, different
from the gradual decline in surface albedo associated with seasonal surface
warming and individual warming events (Perovich et al., 2002, 2017). Such
rapid decrease in surface albedo may result in a significant increase in the
absorbed shortwave flux. In addition, the rain can directly bring heat into
the snow layer and heat the snowpack interior through the release of
latent heat during refreezing of rainwater in the early stages of snow warming.
Comparison of air temperature observed at WSO AP (Utqiaġvik Weather Service Office
airport weather station) and the MB site during
January–June 2013 (a), 2014 (b) and 2015 (c).
In order to quantitatively estimate the contribution of liquid precipitation
towards rapid decreases in snow depth, we consider three basic snowmelt
phases: warming, ripening and meltwater output phase (Dingman, 2015). For
the warming phase, the absorbed energy raises the average snowpack
temperature to the freezing point and the snowpack becomes isothermal. Only
in an isothermal snowpack is the absorbed energy transformed effectively
into snowmelt, initiating the snowpack ripening phase, which in turn leads
into the meltwater output phase.
Based on our latest and most comprehensive field measurements, the surface
energy budget and contribution from each component were estimated, to
identify the dominant factors governing the warming phase of snowmelt
during three key periods with rainfall occurrence. The first rainfall in
2017 was recorded as starting at 10:00 on 24 May (the average snow
cover temperature was -0.7∘C). The observed snow temperature
showed that the upper layers of the snowpack (16 cm) became isothermal after
2 h. The observed snow particle size and water content data indicate
that the upper 10 cm began to melt immediately (Fig. 4 and Table 1).
Interestingly, the modelled shortwave radiation absorbed by the snow layer
without rainfall (405 KJ m-2) could offset the heat loss (-391 KJ m-2)
from longwave radiative loss and heat conduction, but the
residual – which includes latent and sensible heat – was too small to
increase the temperature of the snow layer to the freezing point. During
this period, rain changed the energy balance, initiating the warming phase
of snowmelt in two ways: (1) increasing the absorption of solar radiation
(275.47 KJ m-2) by lowering surface albedo and (2) transferring heat into
the snowpack. At the same time, such rain events may exceed the storage
capacity of water in the snowpack since the snow temperature was still low
at this time. As a result, water drains downward, forming ice layers in the
lower part of the snowpack and releasing latent heat (contributing in total
50.5 KJ m-2). Therefore, rainfall is believed to be the main factor in
rapidly warming the snow layer to an isothermal state in this case.
Timing of air temperature exceeding 0∘ for the first time
and the timing of first rainfall in spring at the WSO AP site, 1952–2015. The dashed
red line (a) corresponds to the first instance of air temperature
above 0 ∘C. The solid red line (b) indicates the first warming
event continuing for at least 4 days. p denotes the significance value of the
linear trend.
From the night of 24 May to the morning of 25 May, the snow
temperature fell below the freezing point (-1.0∘C), and then
rainfall occurred at 04:00 on 25 May. The snow temperature
observations demonstrated that the snow layer reached an isothermal state
after 5 h. During this period, the solar radiation absorbed by the
snow cover would have been 128 KJ m-2 if there were no rainfall, which
is not enough to make up for the total energy loss (171 KJ m-2) mainly
from the longwave loss (-83 KJ m-2) and from sensible (-36 KJ m-2)
and latent heat transfer (-37 KJ m-2) and heat conduction
(-14 KJ m-2). Due to the occurrence of rainfall and the resulting
albedo reduction, the snowpack absorbed an additional 97 KJ m-2 of
solar radiation, and the rain also brought 198 KJ m-2 into the snowpack, mainly through latent heat release and direct heat input. Most of the
rain-induced energy transfer was used to warm the snowpack; once the snowpack reached the warming phase, the remaining energy was used to melt the
snow further, pushing the snowpack into ripening phase.
The variation trend of rainfall, total precipitation and R:P
ratio at Utqiaġvik for May, 1952–2015. p denotes the significance value of the
linear trend.
The variation trends of rainfall, snowfall and precipitation for
each month at Utqiaġvik from January 1952 to December 2015. The trend is
characterized by the slope of the linear regression equation of the time
series (- indicates 0.05 significance level or better; = indicates 0.02 significance level or
better).
A heavy snowfall occurred during the evening of 25 May through the
morning of 26 May. A constant SWE and reduced snow thickness
indicated that there was significant snow densification during the daytime
on 26 May, which was confirmed by the increase in snow density (Fig. 5).
From the evening of 26 May to the morning of 27 May, the snow
temperature decreased to -2.9∘C. Subsequently, rainfall occurred
at 05:00 on 27 May, and the entire snowpack reached an isothermal
state within 5 h after the rainfall, as observed in the snow temperature
record. During this period, the heat loss from longwave radiation was
larger than other components of the heat budget (-795 KJ m-2). The
absorbed solar radiation (479 KJ m-2), latent heat (31 KJ m-2)
and sensible heat (91 KJ m-2) were far from enough to offset this part
of the energy budget in the absence of rainfall. The rainfall contributed
390 KJ m-2 to the energy balance by reducing the surface albedo and
contributed 318 KJ m-2 by bringing heat directly into the snowpack and
releasing latent heat (the latter accounted for the main contribution).
The model results shown above demonstrate that liquid precipitation can lead
to completion of the warming phase within several hours, subsequently
initiating the melt season (Fig. 5 and Table 2). Once the warming phase is
reached, the remaining energy is used to further melt the snow, producing
significant meltwater flow and contributing to snowpack ripening, together
with the subsequent absorption of solar radiation (some of which was also
contributed by rain-on-snow). According to Table 2, the remaining energy
was 377 KJ m-2 on 24 May. For this period, 534 KJ m-2 was
needed to push the snowpack into the ripening phase. The remaining energy
contributed substantially to attainment of the ripening phase, which lasted
only briefly on 24 May due to rapid warming; on 25 May, the
remaining energy was 143 KJ m-2, and on 27 May,
it was
86 KJ m-2. Subsequently, the absorbed energy drove further snowmelt, with
water content of the snowpack increasing until meltwater output occurred,
with an associated rapid decrease in snow depth. If rainfall occurs during
the ripening phase, it increases water content in the snow layer, pushing
the snowpack into the meltwater output phase. This is confirmed by the
model simulations showing that SWE decreased significantly within a few
hours after each rainfall. In the absence of rainfall, warming is mostly
sluggish, and the snow depth reduction is much more gradual as snowmelt
proceeds, as was the case in 2002 (Fig. 2). A comparison of modelled SWE for
the cases with and without rainfall demonstrates that in the case in which the
longwave radiant flux is kept consistent with the observations, in the
absence of rain, the snowpack does not undergo such rapid ablation (Fig. 5).
In addition to the contribution of surface ablation in reducing snow depth,
the physical properties of the snow itself will affect the decrease in snow
depth to a certain extent when ablation begins. For example, brine may
collect at the surface of the sea ice cover as a result of expulsion through
surface cracks (Tucker III et al., 1992) and will wick into the bottom layers
of the snowpack through capillary action. Consequently, the base of the
snowpack can consist of such brine-wetted snow (Martin, 1979), with liquid
water present even at low temperatures due to the high salinity of the brine
(Geldsetzer et al., 2009). Previous work in the Arctic, including at the
location studied here, has established that for Arctic snowpack (in contrast
with the Antarctic) typically only the lowermost centimetres of the snowpack
exhibit higher salt content (Domine et al., 2004; Douglas et al., 2012).
Therefore the presence of brine-wetted snow may accelerate the transition of
the lowermost snow layers into the ripening phase during ablation but does
not impact the onset of melt in the surface layers of the snowpack.
In addition, in our field work, we found depth hoar to be commonly present
at the bottom of the snowpack. Depth hoar is a typical stratigraphic element
of the basal layers of the Utqiaġvik snowpack during the spring season, widely
confirmed in previous studies (e.g. Hall et al., 1986; Domine et al.,
2012). Depth hoar is conducive to discharge of meltwater and subsidence of
the snow cover surface, thereby promoting rapid reduction in snow depth. In
theory, the presence of both depth hoar and brine-wetted snow supports the
rapid reduction of snow depth through the process outlined in this study,
though further observations are required to establish the relative
importance of this process.
Variability of rain-on-snow events
Having demonstrated the profound impact of rainfall on snow depth and
ablation, we explore variability in the timing of first rain-on-snow events
since the start of the available record. Due to the lack of long-term
continuous observations over sea ice, we employ observations from
Utqiaġvik WSO AP station, which is close to the MB site. Precipitation
and surface air temperature have been measured at WSO AP since 1902 with
large data gaps prior to 1952. Here we use air temperature and precipitation
data from 1952 to 2017. A comparison of surface air temperature between WSO
AP and the MB site shows close correspondence (Fig. 6). The amount of liquid
precipitation was not recorded at the MB site, but we did record the timing
of rainfall on sea ice or at the laboratory near the MB site from April
through June in the field expedition of 2015 and 2017. This timing is in
good agreement with the records from WSO AP. Hence, meteorological
conditions at WSO AP are representative of the MB site.
As shown in Fig. 7, the first rain-on-snow events of the year have been
shifted to earlier dates over the past 60 years (2.8 days decade-1, P<0.01). This trend towards earlier spring rainfall has been more
pronounced since the early 2000s (26.9 days decade-1 during 2000–2015,
P<0.01), which is consistent with the accelerated decline of Arctic
sea ice since the early 2000s. Meanwhile, the timing of surface air
temperature rising above the freezing point has also been occurring earlier for the past
60 years (3.0 days decade-1; P<0.01; Fig. 7). There is a clear
relationship between the timing of first rainfall and the timing of air
temperature rising above the freezing point (r=0.66). After removing the
linear trend, the correlation is 0.57 (p<0.01). On average, the
timing of air temperature rising above the freezing point is earlier than
the first rainfall event by 9.1 days, suggesting that air temperature
exceeding the freezing point is not in and of itself a driver of rain-on-snow
events. Further analysis indicates that in some years (32 %), after the
warming events continued for 1–2 days, air temperature dropped again without
occurrence of recorded rainfall. Similarly, warming events of 3 days'
duration without rainfall account for 21 % of all cases. Hence, we
recalculate the timing of warming events that persisted for at least 4 days.
Results show that these two measures of spring warming are highly
positively correlated (r=0.96). After removing the linear trend, the
correlation is still strong (r=0.95, p<0.01), suggesting that the
year-to-year variability of the timing of first spring rainfall is closely
tied to the timing of persistent warming events (Fig. 7), which might be
associated with large-scale weather events.
Prior to the mid-1990s there was almost no rainfall in May (Fig. 8). Since
then, the amount of rainfall has increased, especially in the past 10 years.
Rainfall amount in May has been increasing significantly over the past 60 years
(Fig. 9), with a linear trend of 0.43 mm decade-1 during 1952–2015
(p<0.01) and 1.4 mm decade-1 since the mid-1990s (p<0.01).
By contrast, the total precipitation did not change significantly
before and after the mid-1990s but has increased substantially over the
past few years (Fig. 8). The trend towards higher ratios of rain : total
precipitation (R:P ratio) in May has been significant over the past 60 years (0.04 per decade;
p<0.01; Fig. 8), especially after the mid-1990s (0.09 per decade; p<0.01).
Discussion and conclusions
Snow on sea ice strongly impacts the surface energy budget, driving ocean
heat loss, ice growth and surface ponding. While the role of snow depth and
snowfall variations is well understood, this study demonstrated that rain-on-snow events are a critical factor in initiating the onset of surface melt
over Arctic sea ice, primarily through reduction in surface albedo as well
as latent heat release. By pushing the snowpack into the isothermal,
ripening and meltwater output phase, liquid precipitation can sharply reduce
snow depth and initiate the onset of rapid surface ablation. The increases
in downwelling longwave fluxes through cloud warming associated with
rainfall events contribute to warming of the snowpack to the melting point
but are not sufficient to drive the temperature of the entire snow layer
into an isothermal state on short timescales. In contrast, the occurrence
of liquid precipitation can induce a quick transition of the snow
temperature from diurnally varying to an isothermal state. The observations
at Utqiaġvik and in the offshore Chukchi Sea ice pack suggest that at
least in some years rain-on-snow events act as an effective, mostly
irreversible trigger for the transition into the surface ablation season. In
cases in which melt onset occurred in the absence of rain, increases in
downward longwave fluxes largely offset the longwave radiation heat loss of
snow and are thus key to melt initiation (Mortin et al., 2016). However, as
shown from our observations and model results, snowmelt triggered by
increases in net longwave radiation is much slower than that driven by
liquid precipitation. This study deepens the understanding of the trigger
mechanism of sea ice ablation, which is helpful in improving the modelling
and seasonal prediction of Arctic sea ice extent.
This study assembles process studies and long-term
observations at an important coastal site in North America for the first time, showing that
onset of spring rainfall over sea ice has shifted to earlier dates since the
1970s, in particular since the mid-1990s. Early melt season rainfall and its
fraction of total annual precipitation also exhibit an increasing trend.
Based on the observational evidence and model results, we speculate that
earlier and increasing liquid precipitation leads to earlier and more rapid
melt of snowpack over sea ice, allowing for earlier formation of melt ponds.
This strengthens the ice–albedo feedback, leading to greater ice mass loss
in summer (Perovich et al., 1997; Stroeve et al., 2014), with the resulting
thinner ice in turn reducing the ice pack in September (Notz, 2009). This
inference needs further confirmation in future studies.
Data availability
The micrometeorological observations (air temperature, relative humidity)
at the MB site data can be accessed
at 10.18739/A2D08X (Eicken, 2016). Air temperature and precipitation at
Utqiaġvik WSO AP station are available from the Alaska Climate Research
Center at http://climate.gi.alaska.edu/acis_data (last access: 2 September 2018).
Author contributions
TD, CX, JL and HE jointly
conceived the study and wrote the paper with additional input from WH,
ZD, AM and JJ. AM, HE and TD conceived field measurements and generated in situ data and associated products
used in this study. TD performed the analyses. All of the authors
discussed the results and contributed to interpretations.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
This study is funded by the National Key Research
and Development Program of China (2018YFC1406103, 2018YFA0605901), the National Nature
Science Foundation of China (NSFC 41425003 and 41401079), the key project of
CAMS “Research on the key processes of cryospheric rapid changes”
(KJZD-EW-G03-04) and the Climate Program Office, NOAA, U.S. Department of
Commerce (NA15OAR4310163 and NA14OAR4310216). MB site data at Utqiaġvik
were collected through support of the U.S. National Science Foundation, grant
PLR- 0856867. We thank Chris Polashenski and Nicholas Wright for providing the
snow temperature profile data at the MB site in 2017, funded under NSF grant
ARC-1603361, the “Snow, Wind and Time” project.
Review statement
This paper was edited by John Yackel and reviewed by three anonymous referees.
References
Alt, B. T.: Developing synoptic analogs for extreme mass balance conditions
on Queen Elizabeth Island ice caps, J. Clim. Appl. Meteor., 26,
1605–1623, 1987.
Bintanja, R. and Andry, O.: Towards a rain-dominated Arctic, Nat. Clim.
Change., 7, 263–267, 2017.Blazey, B. A., Holland, M. M., and Hunke, E. C.: Arctic Ocean sea ice snow
depth evaluation and bias sensitivity in CCSM, The Cryosphere, 7, 1887–1900,
10.5194/tc-7-1887-2013, 2013.Comiso, J. C. and Nishio, F.: Trends in the sea ice cover using enhanced
and compatible AMSR-E, SSM/I, and SMMR data, J. Geophys. Res., 113, C02S07,
10.1029/2007JC004257, 2008.
Dingman, S. L.: Physical hydrology, 3rd edition, Waveland Press, Inc.
223–226, 2015.Domine, F., Sparapani, R., Ianniello, A., and Beine, H. J.: The origin of sea
salt in snow on Arctic sea ice and in coastal regions, Atmos. Chem. Phys., 4,
2259–2271, 10.5194/acp-4-2259-2004, 2004.Domine, F., Gallet, J. C., Bock, J., and Morin, S.: Structure, specific
surface area and thermal conductivity of the snowpack around Barrow, Alaska,
J. Geophys. Res., 117, D00R14, 10.1029/2011JD016647, 2012.Douglas, T. A., Domine, F., Barret, M., Anastasio, C., Beine, H. J.,
Bottenheim, J., Grannas, A., Houdier, S., Netcheva, S., Rowland, G.,
Staebler, R., and Steffen, A.: Frost flowers growing in the Arctic
ocean-atmosphere-sea ice-snow interface: 1. Chemical composition, J.
Geophys. Res., 117, D00R09, 10.1029/2011JD016460, 2012.
Druckenmiller, M. L. and Haas, C.: Integrated Ice Observation Programs, Book
Chapter, Sea-Ice Handbook, edited by: Eicken, H., University of Alaska Press, 2009.Eicken, H.:
Automated ice mass balance site (SIZONET), Arctic Data Center,
10.18739/A2D08X, 2016.Eicken, H., Grenfell, T. C., Perovich, D. K., Richter-Menge, J. A., and Frey, K.:
Hydraulic controls of summer Arctic pack ice albedo, J. Geophys. Res., 109,
C08007, 10.1029/2003JC001989, 2004.Eicken, H., Gradinger, R., Heinrichs, T., Johnson, M. A., Lovecraft, A. L.,
and Kaufman, M.: Automated ice mass balance site (SIZONET), UCAR/NCAR – CISL –
ACADIS, Dataset, 10.5065/D6MW2F2H, 2012.
Geldsetzer, T., Langlois, A., and Yackel, J. J.: Dielectric properties of
brine-wetted snow on first-year sea ice, Cold Reg. Sci.
Technol., 58, 47–56, 2009.Giles, K. A., Laxon, S. W., and Ridout, A. L.: Circumpolar thinning of
Arctic sea ice following the 2007 record ice extent minimum, Geophys. Res.
Lett., 35, L22502, 10.1029/2008GL035710, 2008.
Hall, D. K., Chang, A. T. C., and Foster, J. L.: Detection of the depth-hoar
layer in the snow-pack of the Arctic coastal plain of Alaska, U.S.A, using
satellite data, J. Glaciol., 32, 87–94, 1986.
Kwok, R. and Untersteiner, N.: The thinning of Arctic sea ice, Phys.
Today, 64, 36–41, 2011.Kwok, R., Cunningham, G. F., Wensnahan, M., Rigor, I., Zwally, H. J., and
Yi, D.: Thinning and volume loss of the Arctic Ocean sea ice cover:
2003–2008, J. Geophys. Res., 114, C07005, 10.1029/2009JC005312, 2009.
Launiainen, J. and Chengm B. A.: simple non-iterative algorithm for
calculating turbulent bulk fluxes in diabatic conditions over water,
snow/ice and ground surface, Rep. Ser. Geophys., 33, p. 16, 1995.Laxon, S. W., Giles, K. A., Ridout, A. L., Wingham, D. J., Willatt, R.,
Cullen, R., Kwok, R., Schweiger, A., Zhang, J., Haas, C., Hendricks, S.,
Krishfield, R., Kurtz, N., Farrell, S., and Davidson, M.: CryoSat-2
estimates of Arctic sea ice thickness and volume, Geophys. Res. Lett., 40,
732–737, 10.1002/grl.50193, 2013.
Martin, S.: A field study of brine drainage and oil entrapment in first-year
sea ice, J. Glaciol., 22, 473–502, 1979.Maslanik, J., Drobot, S., Fowler, C., Emery, W., and Barry, R.: On the
Arctic climate paradox and the continuing role of atmospheric circulation in
affecting sea ice conditions, Geophys. Res. Lett., 34, L03711,
10.1029/2006GL028269, 2007.Maslanik, J., Stroeve, J., Fowler, C., and Emery, W.: Distribution and
trends in Arctic sea ice age through spring 2011, Geophys. Res. Lett., 38,
L13502, 10.1029/2011GL047735, 2011.
Maykut, G. A.: The surface heat and mass balance, in: The geophysics of sea ice,
edited by: Untersteiner, N., New York, Plenum Press, 395–463, 1986.
Maykut, G. A. and Untersteiner, N.: Some results from a time dependent
thermodynamic model of sea ice, J. Geophys. Res., 76, 1550–1575, 1971.
Mortin, J., Svensson, G., Graversen, R. G., Kapsch, M. L., Stroeve, J. C.,
and Boisvert, L. N.: Melt onset over Arctic sea ice controlled by
atmospheric moisture transport, Geophys. Res. Lett., 43, 6636–6642, 2016.Nghiem, S. V., Rigor, I. G., Perovich, D. K., Clemente-Colón, P.,
Weatherly, J. W., and Neumann, G.: Rapid reduction of Arctic perennial sea
ice, Geophys. Res. Lett., 34, L19504, 10.1029/2007GL031138, 2007.
Notz, D.: The future of ice sheets and sea ice: Between reversible retreat
and unstoppable loss, P. Natl. Acad. Sci. USA, 106, 20590–20595, 2009.Perovich, D. K. and Polashenski, C.: Albedo evolution of seasonal Arctic
sea ice, Geophys. Res. Lett., 39, L08501, 10.1029/2012GL051432, 2012.
Perovich, D. K., Bruce, C. E., and Richter-Menge, J. A.: Observations of the
annual cycle of sea ice 824 temperature, Geophys. Res. Lett., 24,
555–558, 1997.Perovich, D. K., Grenfell, T. C., Light, B., and Hobbs, P. V.: Seasonal
evolution of the albedo of multiyear Arctic sea ice, J. Geophys. Res.,
107, 8044, 10.1029/2000JC000438, 2002.Perovich, D., Polashenski, C., Arntsen, A., and Stwertka, C.: Anatomy of
a late spring snowfall on sea ice, Geophys. Res. Lett., 44, 2802–2809,
10.1002/2016GL071470, 2017.
Persson, P. and Ola, G.: Onset and end of the summer melt season over sea ice:
thermal structure and surface energy perspective from SHEBA, Clim. Dynam., 39,
1349–1371, 2012.
Persson, P., Ola, G., Ruffieux, D., and Fairall, C. W.: Recalculations of pack ice
and lead surface energy budgets during LEADEX 92, J. Geophys. Res., 102,
25085–25089, 1997.Petrich, C., Eicken, H., Polashenski, C. M., Sturm, M., Harbeck, J. P.,
Perovich, D. K., and Finnegan, D. C.: Snow dunes: A controlling factor of
melt pond distribution on Arctic sea ice, J. Geophys. Res., 117, C09029,
10.1029/2012JC008192, 2012.
Screen, J. A. and Simmonds, I.: Declining summer snowfall in the Arctic:
Causes, impacts and feedbacks, Clim. Dynam., 38, 2243–2256, 2012.
Sharp, M. and Wang, L.: A five-year record of summer melt on Eurasian
Arctic ice caps, J. Climate, 22, 133–145, 2009.Stone, R. S., Dutton, E. G., Harris, J. M., and Longenecker, D.: Earlier spring
snowmelt in northern Alaska as an indicator of climate change, J. Geophys.
Res., 107, 4089, 10.1029/2000JD000286, 2002.Stroeve, J., Holland, M. M., Meier, W., Scambos, T., and Serreze, M.: Arctic
sea ice decline: Faster than forecast, Geophys. Res. Lett., 34, L09501,
10.1029/2007GL029703, 2007.
Stroeve, J. C., Holland, M. M., Kay, J. E., Malanik, J., and Barrett, A. P.:
The Arctic's rapidly shrinking sea ice cover: A research synthesis, Clim.
Change, 110, 1005–1027, 2012.
Stroeve, J. C., Markus, T., Boisvert, L., Miller, J., and Barrett, A.:
Changes in Arctic melt season and implications for sea ice loss, Geophys.
Res. Lett., 41, 1216–1225, 2014.Sturm, M., Perovich, D. K., and Holmgren, J.: Thermal conductivity and heat
transfer through the snow on the ice of the Beaufort Sea, J. Geophys. Res.,
107, 8043, 10.1029/2000JC000409, 2002.
Tucker III, W. B., Perovich, D. K., Gow, A. J., Weeks, W. F., and Drinkwater,
M. R.: Physical properties of sea ice relevant to remote sensing, in: Microwave Remote Sensing of Sea Ice,
edited by: Carsey, F., Geophysical Monograph, American Geophysical Union, 9–28, Chapter 2, 1992.Wang, L., Sharp, M. J., Rivard, B., Marshall, S., and Burgess, D.: Melt
season duration on Canadian Arctic ice caps, 2000–2004, Geophys. Res.
Lett., 32, L19502, 10.1029/2005GL023962, 2005.
Webster, M. A., Rigor, I., Nghiem, S. V., Kurtz, N. T., Farrell, S. L.,
Perovich, D. K., and Sturm, M.: Interdecadal changes in snow depth on Arctic
sea ice, J. Geophys. Res., 119, 5395–5406, 10.1002/2014JC009985, 2014.
Yen, Y.: Review of thermal properties of snow, ice and sea ice, US Army Cold
Regions Research and Engineering Laboratory, Report 81–10, Hanover, NH,
USA, 1981.