Climate change and global warming strongly impact the cryosphere. The rise
of air temperature and change of precipitation patterns lead to dramatic
responses of snow and ice heat and mass balance. Sustainable field
observations on lake air–snow–ice–water temperature regime have been carried
out in Lake Orajärvi in the vicinity of the Finnish Space Centre, a
Flagship Supersite in Sodankylä in Finnish Lapland since 2009. A
thermistor-string-based snow and ice mass balance buoy called “Snow and ice
mass balance apparatus (SIMBA)” was deployed in the lake at the beginning
of each ice season. In this paper, we describe snow and ice temperature
regimes, snow depth, ice thickness, and ice compositions retrieved from
SIMBA observations as well as meteorological variables based on high-quality
observations at the Finnish Space Centre. Ice thickness in Lake Orajärvi
showed an increasing trend. During the decade of data collection (1) the
November–May mean air temperature had an increasing trend of
0.16 ∘C per year, and the interannual variations were highly
correlated (r= 0.93) with the total seasonal accumulated precipitation;
(2) the maximum granular ice thickness ranged from 15 % to 80 % of the
maximum total ice thickness; and (3) the snow depth on lake ice was not
correlated (r= 0.21) with the total precipitation. The data set can be
applied to investigate the lake ice surface heat balance and the role of
snow in lake ice mass balance and to improve the parameterization of snow
to ice transformation in snow and ice models. The data are archived at
10.5281/zenodo.4559368 (Cheng et al., 2021).
Introduction
The rapid climate warming in the Arctic (Box et al., 2019; Przybylak and
Wyszyński, 2020) has also affected lakes, in particular lake surface
temperatures and lake ice phenology (Woolway et al., 2019). In the Northern
Hemisphere, the lake ice season has become shorter and lake ice has become
thinner, and these trends are projected to continue throughout the 21st
century (Sharma et al., 2019). Lakes are important in the Earth system, as
they can adjust local climate (Brown and Duguay, 2010) and affect the
environment through interactions among physical, hydrological, biological,
and chemical processes (Leppäranta, 2010).
Observations on snow depth and lake ice thickness are needed for (a) monitoring of climate variability and trends (Filazzola et al., 2020); (b) practical applications, such as use of lake ice for winter fishing,
transport, and recreational activities (Leppäranta, 2015); and (c) to
provide initial conditions for operational forecasting (Anderson et al.,
2018). Snow depth and lake ice thickness can be measured manually. For
example, in Finland, lake ice thickness is measured via manual drilling in a
single location in 45 lakes with 10 d intervals throughout the ice
season. However, this requires a lot of manpower and accordingly does not
allow collection of time series with a better spatial and temporal
resolution. During recent decades, the number of manual observations has
strongly declined in many countries (Duguay et al., 2006). Satellite remote
sensing yields information on lake ice cover (Wu et al., 2021) and snow and ice
surface temperature (Cheng et al., 2014) with a sufficiently high spatial
and temporal resolution. Kang et al. (2014) introduced a method to derive
lake ice thickness from coarse-resolution (∼ 10 km) passive
microwave data over large lakes in Canada. However, the transferability of
the method to sub-pixel-scale lakes has not been investigated. Synthetic aperture radar (SAR)
polarimetry has shown some promise in retrieving ice depth over rivers
(Mermoz et al., 2013); as fully polarimetric data are not widely
available from existing SAR sensors to date, extensive testing and application of the
method for lakes are currently lacking.
The SIMBA data set is potentially highly relevant for the development of
land applications for planned and existing passive microwave satellite
sensors, such as the Copernicus Imaging Microwave Radiometer (CIMR), new
Metop multichannel radiometer sensors of EUMETSAT, ESA SMOS, NASA SMAP, and
Chinese sensors. Due to the inherent coarse resolution of these sensors
(tens of kilometres), a key issue is to acquire combined simultaneous data
representing various processes in lakes, in addition to surrounding land
areas. As such, SIMBA forms an integral part of the FMI sensor network
in Sodankylä.
Thermistor-string-based snow and ice mass balance apparatus (SIMBAs) have been
applied for more than a decade to measure snow depth, ice thickness, and
temperature profile from air through snow and ice to water (Jackson et al.,
2013). Most SIMBAs have so far been deployed in polar sea ice (Lei et al.,
2018), but lake ice has also been studied (Cheng et al., 2014; Wei et al.,
2016). In this paper we describe SIMBA observations from an ongoing programme
that started in Lake Orajärvi in northern Finland in 2009. Supporting
meteorological observations from the Finnish Meteorological Institute Arctic
Research Centre (FMI-ARC) are also presented. The objectives of the SIMBA
programme were
to evaluate the cost effectiveness of SIMBA buoys in a remote lake
environment;
to monitor climate variability and change as reflected in snow depth as well
as lake ice thickness and composition;
to investigate (a) atmospheric forcing on lake ice growth and melt, (b) the
role of snow in lake ice mass balance via formation of superimposed ice due
to refreezing of meltwater and rain and formation of snow ice due to
flooding under a heavy snow load, and (c) the role of granular ice in lake
ice phenology;
to develop better parameterizations of snow-to-ice transformation in
numerical snow and ice models.
ObservationSodankylä supersite
The SIMBA programme at Lake Orajärvi is a component of the FMI
Sodankylä supersite. The Finnish Meteorological Institute's Arctic Space
Centre (FMI-ARC) in Sodankylä (67.367∘ N, 26.629∘ E), Finland, is a super-observation site where various Earth observations
(upper-air chemistry and physics, atmospheric column measurements, snow and
soil hydrology, biosphere–atmosphere interaction) and ground truth
measurements for satellite calibration–validation are carried out
continuously (Fig. 1). The site is equipped with comprehensive in situ and remote
sensing instrumentation placed in the forests, wetlands, and freshwater
bodies, which are the main land cover types in the area. In this paper we
focus on the cryospheric in situ observations of snow cover and lake ice as well as
meteorological parameters.
Schematic diagram of the FMI-ARC supersite observational systems
at Sodankylä. The original diagram is at https://litdb.fmi.fi/ (last access: 10 August 2021). The frames in red and text with yellow background
describe the measurements addressed in this paper.
The sub-Arctic climate and the geographic location between continental and
marine climate zones result in a high inter-annual, seasonal, and
synoptic-scale variation in local weather conditions, enabling development
of very different kinds of snowpack structures on land (Tikkanen, 2005) and
snow and ice composition on lakes (Cheng et al., 2014). Lake Orajärvi is a
boreal medium-sized lake located in Sodankylä municipality in
eastern Lapland. The lake has a surface area of about 11 km2 with an
average depth of 4.4 m and a maximum depth of 11 m close to the southern
shore of the lake (Fig. 2a). The estimated water volume in the lake is
0.0485 km3, and the shore length is 28 km. The lake surface elevation
is 182 m above sea level. The ice season typically starts in November and
lasts until May. The first snowfall typically occurs in late October, but
the snow may melt during warmer autumn days. The seasonally permanent winter
snow accumulation usually starts between mid-November and early December.
Snow is present on the lake ice surface every winter season.
(a) The location of Lake Orajärvi in Finnish Lapland and a map
of lake Orajärvi and the local catchment, where the open black square marks
the SIMBA site, and the white circle is the Finnish Space Centre. (b) Snapshots of
SIMBA deployment in Lake Orajärvi and a weather station at FMI-ARC main
camp. A raft was anchored in the lake in October 2019 aiming to extend the
lake observations beyond the ice season.
SIMBASIMBA programme
SIMBA buoys have been deployed in Lake Orajärvi since 2009. The 2009
deployment was probably the internationally first SIMBA application for a
lake study. In each winter when ice was formed in Lake Orajärvi, one
SIMBA was deployed around mid-December at the same site,
67.35∘ N, 26.83∘ E, some 500 m from the
shoreline. At the time of deployment, the snow depth, lake ice thickness, and
ice freeboard were measured. A supporting frame made of fibreglass was
constructed on lake ice, and the SIMBA main control Peli case was placed on
top of it (Fig. 2b). A separate wooden pole with scale was standing
vertically to hold the thermistor string. An ice borehole was drilled
through the ice layer, and the thermistor string was placed in it. The scene
was left as is, and then the thermistor string was frozen with
surrounding water in the borehole. The SIMBA operated in the lake over the
winter and most of the spring melting season. The recovery of SIMBA usually took
place in late April but in some years as late as mid-May. Snow and
ice conditions around the deployment site were documented and measured
before dismantling the SIMBA camp. The documentation on SIMBA deployment and
recovery is provided along with the SIMBA data as online files (see data
availability). Table 1 summarizes the SIMBA deployment and recovery status.
SIMBA deployment and recovery days and simultaneous in situ observed snow
depth (hs), total ice thickness (Hi), and ice freeboard (Hfb,
defined as negative if the lake water level was above the snow–ice
interface) The seasonal mean values were derived from SIMBA-ET and SIMBA-HT
observations. Hgi is the granular ice and Hci is the congelation ice
thickness. NA – not available. The SD is the standard
deviation.
The seasonal mean values of Hi, hs, Hgi, and Hci were
calculated by the SIMBA algorithm (Cheng et al., 2020). The seasonal mean
value of ice freeboard (Hsfb) was calculated based on time series of
snow depth (hs), granular ice thickness (Hgi), and columnar ice
thickness (Hi) according to Archimedes' principle:
Hsfb=Hi+Hgi-(hsρs+Hgiρgi+Hiρi)/ρw, where ρs, ρgi, ρi, and ρw are seasonal mean densities of snow, granular ice and columnar ice,
and lake water, assumed to be 320, 890 and 910,
and 1000 kg/m3, respectively.
SIMBA buoy
SIMBA is a thermistor-string-based snow and ice mass balance apparatus. It
has been developed by the Scottish Association for Marine Science (SAMS)
Research Services Ltd (SRSL) in UK. SIMBA consists of a simple, robust
thermistor string with 240 temperature sensors distributed evenly (2 cm
intervals) along a 4.8 m long heat-shrink PVC plastic sleeve coated flat
white wire. The white heat-shrink sleeve is used to minimize the possibility of
solar heating of the sensors. The accuracy of the SIMBA thermistor sensor is
±0.1 ∘C, which is comparable with other types of
thermistor-string-based IMBs (Richter-Menge et al., 2006). Each sensor
measures the environment temperature (SIMBA-ET). The resolution of the
thermistor sensor is 0.0625 ∘C; i.e. smaller changes cannot be
detected even if the absolute accuracy of the sensor would allow it. In
addition, the thermistor chain is equipped with heaters, i.e. resistor
components mounted next to the temperature-sensing elements. A weak voltage
(8 V) supply is connected to provide gentle identical heating of each sensor
on the chain. The SIMBA heating cycle is usually long enough, often 60 or 90 s, for the temperature rise at the sensor to reach a steady state. Thermal
conductivity determines how the heat is conducted away from the heated sensors
placed in air, snow, ice, and lake water. As a result, the SIMBA-HT profiles
can greatly enhance the detection of the interfaces between air, snow, ice,
and water. The heating cycle is applied once per day. The SIMBA-HT is
controlled to not disturb the SIMBA-ET measurements, which are typically carried out
four times per day (Jackson et al., 2013). SIMBA also includes a
built-in GPS to record SIMBA drift positions (for sea ice applications), a
magnetometer for tilt and floe rotation, a barometer for surface air
pressure, and an external sensor to measure near-surface ambient air
temperature. An iridium modem is applied for data transmission. SIMBA has
been used in various field campaigns targeting snow and ice mass balance in
seasonal ice covers in lakes (Cheng et al., 2014) and polar oceans
(Hoppmann et al., 2015; Provost et al., 2017; Lei et al., 2018, 2021). Table 1
presents a summary of SIMBA observations in Lake Orajärvi.
Weather station
Meteorological data were collected at FMI-ARC station (67.3666∘ N,
26.6290∘ E, WMO code 02836) 11 km from Lake Orajärvi. The data
sets include wind speed (Va), air temperature (Ta), relative humidity (RH),
cloudiness (cn), longwave (Ql) and shortwave (Qs) radiation, snow depth on land
(Hs), and precipitation (Prec) (Table 2). The radiative fluxes were measured on a
10 m high tower above treetops using Kipp & Zonen CM11 pyranometers
(305–2800 nm) and Kipp & Zonen CG4 pyrgeometers (4500–42 000 nm). Snow
depth (Campbell Scientific SR50) and precipitation (OTT Pluvio2) at ground
level were also measured. All measurements were taken once a minute and
aggregated to 1 h time intervals.
Data descriptionSIMBA data
The main output of a SIMBA buoy is the time series of environment (SIMBA-ET)
and heating (SIMBA-HT) temperature measured at different depths from the
lake water through ice and snow to air.
SIMBA-ET
For each season, we have up to 241 time series of temperature (SIMBA-ET) at
different depths. For those sensors located in the air, the temperature
differences between the sensors are small, as the air in the lowermost 1.5 m
layer mixes effectively and the sensors are close to each other. The
temperatures inside snow reveal much larger vertical gradients because snow
has a small thermal conductivity. The temperature profile in ice has a smaller
vertical gradient compared to that in snow, since the thermal conductivity
of ice is larger than that of snow. At the ice bottom, temperature is at the
freezing point and gradually increases towards the lake bottom. Figure 3
shows an example of seasonal SIMBA-ET. One can estimate the heat fluxes
within snow and ice and those at the air–snow, snow–ice, and ice–water
interfaces.
Illustrations of SIMBA-ET data: (a) time series of SIMBA-ET during
observation period; (b) one snapshot (19 January 2014 08:00 UTC) of vertical
SIMBA-ET profile through air–snow–lake ice–water; (c) SIMBA-ET field observed
by 240 sensors. Sensor 1 was placed in air and sensor 240 in water.
SIMBA-HT
SIMBA-HT shows the temperature increase in the medium when each sensor was
contacted during a short heating period of 60 and 90 s. The temperature
changes are largely dependent on the thermal diffusivity of the surrounding
medium. Low heating power ensures that the increasing temperature will not
be too high to melt snow and ice in contact with the sensor and guarantee a
fast restoration of environment temperature around the sensor before the next
SIMBA-ET observation and above all to minimize SIMBA power consumption. One
example of SIMBA-HT is given in Fig. 4.
Illustrations of SIMBA-HT: (a) a snapshot (25 January 2015, 18:00
UTC) of vertical profile of observed temperature increase after 60 s. (b) SIMBA-HT (60 s) field observed by 240 sensors. (c) Same as (a) but after
heating for 90 s, and (d) SIMBA-HT (90 s) field observed by 240 sensors.
SIMBA snow depth and ice thickness
Snow depth and ice thickness are derived from SIMBA-ET and SIMBA-HT data. A
common procedure is to look at SIMBA-ET temperature profiles manually and
identify sudden changes of vertical temperature gradient to locate the
air–snow, snow–ice, and ice–water interfaces. The snow depth is then
calculated as the distance between the air–snow and snow–ice interfaces, and
the ice thickness is the distance between the snow–ice and ice–water
interfaces. However, a manual procedure is a heavy task, especially if SIMBA
operation covers a long period or one would need real-time SIMBA results.
Several studies have been carried out aimed at development of an algorithm to
obtain snow depth and ice thickness automatically (Liao et al., 2019; Zuo et al., 2018; Cheng et al., 2020).
Below we present an example of the application of the Cheng et al. (2020)
algorithm to retrieve snow depth and ice thickness from SIMBA data observed
in Lake Orajärvi. When SIMBA was deployed, the initial sensor position
at the snow–ice interface is known and we defined it as Zgi0, i.e. zero
reference position for granular ice. During the observation period, if the
initial snow–ice interface moves upward from Zgi0, which is a
common phenomenon in Arctic lakes, the distance between Zgi0 and the moving
snow–ice interface is the new granular ice thickness formed by snow-to-ice
transformation. The depth difference between total ice thickness and
granular ice thickness is the congelation ice formed at the ice bottom.
Figure 5 shows the air–snow, snow–ice, and ice–water interfaces with SIMBA-ET
(a) and SIMBA-HT (b) as the background. For better clarity, the 5 d running
average can be produced as the final products.
Time series of sensor position for the air–snow (white), snow–ice
(green), and ice–water (yellow) interfaces, identified applying the SIMBA
algorithm. The SIMBA-ET observation is illustrated as background in (a) and
SIMBA-HT ratio (HT60/HT90) in (b). The black dashed line shows the sensor
number (120) at the initial ice surface (Zgi0). For clarity, we only
illustrate sensors 50–150.
Using the snow–ice interface as the zero-reference level, time series can be
calculated for the snow depth, snow–ice thickness, total ice thicknesses,
and ice freeboard. Figure 6 is an example of the 2019/2020 time series,
indicating that the lake ice was mainly granular ice, which was related to
heavy snowfall during the ice season. The snow depth observed at the FMI-ARC
weather station on land was the highest in a decade. A few in situ observations
(symbols in Fig. 6) were made during the ice season. Point comparison between
SIMBA algorithm detected and in situ observed values ranged from 2 up
to 12 cm. The mean biases are 5, -1, 3, and 2 cm for snow depth,
freeboard, and granular and total ice thickness, respectively. Small values were
largely due to the compensation effect. To validate the algorithm, a lot more
in situ observations are needed. Such analyses can be found in Cheng et al. (2020).
Products derived based on SIMBA data: snow depth (blue), ice
freeboard (cyan), granular ice thickness (magenta), and total ice thickness
(green). The symbols represent in situ observations of snow depth (red cross), ice
freeboard (blue dot), granular ice thickness (magenta dot), total ice thickness (black cross), and the
initial freezing-up day (red circle). The black solid line denotes the snow depth on
land.
Weather data
The observed daily mean values of meteorological parameters for all seasons
are presented in Fig. 7. The inter-annual mean, maximum, and minimum air
temperatures are -2.5, -16.5, and
-5.5 ∘C, respectively. The air temperature reveals a
constant decreasing pattern from November to January. The coldest months are
January and February. From March onward, the air temperature increased
gradually due to increasing solar radiation (Fig. 7c). The inter-annual
average, maximum, and minimum downward longwave radiative fluxes are 259, 309, and 201 W/m2, respectively. The corresponding
values for downward shortwave radiative fluxes are 64, 97, and 26 W/m2.
The observed (dots) daily mean air temperature (a), snow depth
(b), downward shortwave radiative flux (c), and longwave radiative flux (d) for each ice
season between 1 November and 31 May. The solid lines represent decadal
daily maximum (red), minimum (green), and average (black) values. The shadow
area represents the standard deviation (SD). For snow depth, daily mean
values are given as thin colour lines.
Summary of various meteorological and physical observations between
1 November and 31 May. For meteorological parameters (Va, Ta, RH, cn, Qs, Ql) the values are
seasonal mean ± standard deviation.
Tprec: total accumulated precipitation in water equivalent (mm); Hsmax: the
maximum observed snow depth on land.
AFDD: the accumulated freezing degree day, the sum of daily mean air
temperature below freezing point; ATDD: the accumulated thawing degree day,
the sum of daily mean air temperature above freezing point.
Figure 7b clearly indicates that snow depth for the 2019/2020 season
represented an extreme condition in a decade. There is an increasing trend
of total precipitation during the ice season (Fig. 8). The total seasonal
accumulated total precipitation is highly correlated (correlation
coefficient r= 0.93) with the seasonal mean air temperature. The
correlations between seasonal mean–maximum snow depth and corresponding air
temperature are much lower: r= 0.40 and r= 0.38, respectively. The
correlation between total accumulated precipitation and maximum snow depth
was 0.55. The difference is contributed by the snow drift and changes of
snow metamorphism.
The accumulated total precipitation and mean air temperature
between 1 November and 31 May.
DiscussionsInter-annual variation in SIMBA snow and ice products
Applying the SIMBA algorithm (Cheng et al., 2020), we obtained lake snow and
ice products for all seasons (see data availability). Figure 9 shows the
observed seasonal maximum values for the snow depth, maximum total ice
thickness, and maximum granular ice thickness. During the observation
period, both snow depth and ice thickness showed increasing trends. The
increase in granular ice thickness is the fastest among all the snow and ice
components. It reached the maximum 80 % of the total ice thickness in
2019/2020. In Lake Orajärvi, snow mass has contributed to the ice
thickness during every winter season. The maximum granular ice thickness was
on average about 40 % of the maximum total ice thickness during the data
period. For all seasons, the correlation coefficient between the maximum
granular ice thickness and the maximum ice thickness was 0.64. The
occurrence of maximum lake snow is, on average, about 1 month prior to
the maximum granular ice formation (Fig. 10). Because of snow-to-ice
transformation, the time series of snow depth in the lake is not correlated
with the snow depth on land. The snow depth on lake ice ranged from 25 % to
43 % of that on land. On average the ratio was 0.33, some 11 % less
than observed for a lake in southern Finland (Kärkäs, 2000). In
several seasons, when SIMBA was recovered in late April or early May, the
entire snow layer on lake ice was transferred to granular ice. Granular ice
reached its maximum value when the ice surface was free of snow.
SIMBA observed seasonal maximum snow depth (red), maximum total
ice thickness (blue), maximum granular ice thickness (green), and the ratio
between granular ice and total ice thickness (black) during observation
seasons.
Seasonal maximum snow depth, granular ice thicknesses,
congelation ice thicknesses, and the date when those values were observed.
Inter-annual variation in temperature conditions
According to weather observations in Sodankylä, the air temperature
increased by about 0.16 ∘C per year during the last decade. For
the period from 1980–2020, the air temperature has an increasing trend of
about 0.06 ∘C per year. On average, the increase in air
temperature in the last decade is about 3 times faster than in the past 40 years in
agreement with the findings of Przybylak and Wyszyński (2020) for the
high Arctic. The accumulated precipitation correlated better to the maximum
snow depth on land (r= 0.55) than the mean snow depth (r= 0.45). It
is, however, not correlated (r= 0.21) with snow depth on the lake ice.
The seasonal AFDD and ATDD for each winter season are shown in Fig. 11. A
negative decrease in AFDD was seen in response to the increase in air
temperature. AFDD is directly linked with thermodynamic ice formation.
During a given period, a decrease in AFDD is expected to result in less
formation of columnar ice. However, during our observation period, the total
ice thickness revealed an increasing trend. The increase in ice thickness is
due to snow–ice formation. The trend of ATDD is very insignificant,
suggesting that the melting of lake ice due to temperature increase has not
increased much during the observation decade.
The seasonal accumulated freezing degree day (AFDD) and thaw
degree day (ATDD) during the observation period (2009/2010–2019/2020).
Challenges of the SIMBA programme
SIMBA observations in Lake Orajärvi represent a small but sustainable
programme, operating for a decade so far. A few times we have encountered
malfunction of SIMBA, especially in the early phases of the SIMBA programme.
In recent years, SIMBA has become more robust without the need for heavy-duty
maintenance during field measurements, and the system has been remarkably
improved with respect to the quality of HT measurements. Several snow and
ice products can be derived from SIMBA's two types of temperature (SIMBA-ET
and SIMBA-HT) measurements. The SIMBA programme has largely benefited from the
Sodankylä supersite infrastructure, where the comprehensive and high-standard meteorological observations are available.
Challenges remain in further improvement of the SIMBA programme. Due to safety
issues, SIMBA must be deployed and recovered when ice is strong enough.
Hence, the early freeze-up and late break-up cannot be monitored. In autumn
2019, a wooden floating raft was deployed and anchored in Lake Orajärvi.
SIMBA was, for the first time, deployed during the ice-free season on 1 October.
This kind of deployment will also be carried out in the future, allowing
year-round SIMBA measurements.
Part of the thermistor chain exposed in the air above the snow surface may
suffer from frost in winter or from solar heating in spring, and also the
sensors in the upper layers of snow and ice may suffer from solar heating,
resulting in large uncertainties in SIMBA-ET and SIMBA-HT readings. To
compensate for the effect of temperature errors on snow depth detection, one
solution is to deploy acoustic rangefinder sounders (ARSs) to measure the
evolution of snow surface. In fact, an ARS has been deployed in the past two
winter seasons. These data sets can also be used to understand the effect of
wind on snow drift and quantify snow surface sublimation in winter.
During the melting season, both SIMBA-ET and SIMBA-HT strongly increase in the
upper part of the ice, resulting in an isothermal status of the entire ice
column. In this condition, SIMBA snow depth and ice thickness values are
liable to large errors. A combination of SIMBA observations and numerical
model experiments may yield more reliable results in such conditions.
SIMBA measurements have been taken automatically, but it is still important
to carry out in situ observations, such as collecting ice core and snow samples, as
such observations cannot be made by automatic instruments.
Data availability
The data are archived at
10.5281/zenodo.4559368 (Cheng et al., 2021). The four
zip files should be unzipped in different file folders, preferably using
zip-file names as the folder names. A readme file exists in each folder. The
in situ snow depth and ice thickness observations for 2009/2010–2012/2013 as well
as a description file of SIMBA deployment and recovery for each ice season
(SIMBA_D&R_all_Years.docx)
are provided.
Conclusions
A thermistor-string-based snow and ice mass balance apparatus (SIMBA) has
been deployed in an Arctic lake since 2009. The measurements covered most of the ice season from mid-December to late April–early May. SIMBA-ET
and SIMBA-HT temperature observations are described in this paper. The daily
snow depth and ice thickness were derived from the SIMBA temperature field
applying a validated automatic algorithm (Cheng et al., 2020). The
meteorological parameters for winter seasons (1 November–31 May) are also
collected and discussed. During the investigation decade, the air
temperature in the ice season has had an increasing trend of 0.16 ∘C per year. The warming rate is comparable to the result found
for the high Arctic by Przybylak and Wyszyński (2020). The increase in
air temperature in the winter season is highly correlated (0.93) with total seasonal
accumulated precipitation. This is because warm winters in the study
region are also wet and characterized by a high cyclone activity. Transient
cyclones are vital for the transport of warm, moist air masses to northern
Europe (Wickström et al., 2020). The precipitation in the 2019/2020 season
represented an extreme episode during the study decade. Despite the air
temperature increase, the total maximum ice thickness in the lake has an
increasing trend. The increase in maximum ice thickness is due to the
increase in granular ice. The interannual variability of maximum granular
ice thickness is large, ranging from 15 % to 80 % of the total maximum ice
thickness. The time series of the SIMBA ET and HT allow identification of
moving air–snow, snow–ice, and ice–water interfaces. Because of the air
temperature increase, the seasonal AFDD reduces. This results in a
decreasing impact of below-zero air temperatures on lake ice growth during
the freezing season, as the growth of columnar ice is reduced.
Simultaneously, the role of precipitation in total ice formation is enhanced
because snow–ice and superimposed ice contribute to an increasing fraction
of the total ice thickness. The trend in ATDD was negligible, suggesting
that the effect of air temperature on ice melting has remained unchanged.
To our knowledge, this is the first decadal-scale SIMBA data set ever
collected from an Arctic lake. The data provide information on snow and ice
mass balance and the controlling atmospheric factors. The measurements will
continue in the future.
The weather observations, e.g. decadal time series of daily maximum and
minimum weather parameters, can be used to estimate snow and ice conditions
in the lake applying a snow and ice model (e.g. Cheng et al., 2014). The SIMBA
data are not only suitable for snow and ice surface heat and mass balance
studies. The temperatures at the ice bottom and in the water below are
valuable to understand the lake thermal structure and water–ice heat
transfer (Huang et al., 2019b).
The SIMBA programme, with Lake Orajärvi as a test bed, offers excellent
opportunities for dissemination of cryospheric knowledge and related
outreach, providing rich possibilities for community collaborations both
nationally and internationally. The observed changes in snow depth and
composition of lake ice contribute to better understanding of cryospheric
aspects of climate change. For example, parameterizations of the discovered
snow and ice processes can be improved in climate models.
Snow and ice measurements similar to those in Lake Orajärvi have been
recently initiated in Wulaingsuhai lake in an arid climate zone in
Inner Mongolia of China. The observations focused on lake ice mass balance
(Lu et al., 2020) and energy budget, in particular the solar radiation (Cao
et al., 2020). In the long run, the corresponding lake snow and ice
measurements at both sites and possible similar observations in a
thermokarst lake (e.g. Huang et al., 2019a, b) at Qinghai–Tibet Plateau,
often referred to as the “Third Pole of the Earth”, can be used together to
carry out coordinated research.
Author contributions
BC and TV initiated the SIMBA programme and drafted the manuscript; YC, BC, FZ, and
YQ developed the SIMBA algorithm, processed the SIMBA data, and analysed the
results; JP, AK, and JL are responsible for supersite and various
meteorological in situ observations and data collections. All authors contributed
to writing the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
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Special issue statement
This article is part of the special issue “Extreme environment datasets for the three poles”. It is not associated with a conference.
Acknowledgements
We are grateful to Pekka Kosloff for carrying out fieldwork in Lake Orajärvi for all the winter seasons. The logistical assistance provided by Jyrki Mattanen in FMI-ARC, Sodankylä, is acknowledged. We are grateful for comments from Keith Jackson, the one anonymous reviewer, and the topical editor Xin Li that helped to improve the manuscript.
Financial support
This research has been supported by the Horizon 2020 (INTAROS (grant no. 727890)), the Academy of Finland (grant no. 317999), and the Ministry of Science and Technology of the People's Republic of China (grant no. 2017YFE0111700 – MARIS). Yubing Cheng and Fei Zheng were supported by the National Natural Science
Foundation of China (grant 441 no. 41876012; 41861144015) and the Key Research
Program of Frontier Sciences, CAS (grant no. ZDBS-LY-DQC010) and Light of
West China, CAS Program (E129030101, Y929641001).
Review statement
This paper was edited by Xin Li and reviewed by Keith Jackson and one anonymous referee.
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