Assessing changes in the density of snow and firn is vital to
convert volume changes into mass changes on glaciers and ice sheets. Firn
models simulate this process but typically rely upon steady-state
assumptions and geographically and temporally limited sets of field
measurements for validation. Given rapid changes recently observed in
Greenland's surface mass balance, a contemporary dataset measuring firn
compaction in a range of climate zones across the Greenland ice sheet's
accumulation zone is needed. To fill this need, the Firn Compaction
Verification and Reconnaissance (FirnCover) dataset comprises daily
measurements from 48 strainmeters installed in boreholes at eight sites on
the Greenland ice sheet between 2013 and 2019. The dataset also includes
daily records of 2 m air temperature, snow height, and firn
temperature from each station. The majority of the FirnCover stations were
installed in close proximity to automated weather stations that measure a
wider suite of meteorological measurements, allowing the user access to
auxiliary datasets for model validation studies using FirnCover data. The
dataset can be found here: 10.18739/A25X25D7M (MacFerrin et al., 2021).
Introduction
Mass loss from the Greenland ice sheet (GrIS) is currently one of the
largest direct contributors to sea-level rise (IPCC, 2013), and the majority
of that loss since the early 2000s has been due to significant increases in
surface melt and runoff (Velicogna et al., 2014; van den Broeke et al.,
2016; Mottram et al., 2019). In Greenland's accumulation zone, which covers
approximately 80 % of the ice sheet (Box et al., 2006), annual snow
accumulation is buried and densifies until it becomes glacial ice (Bader,
1954; Benson, 1962; Herron and Langway, 1980). Greenland's firn layer can be
up to ∼70 m thick (Schwander et al., 1997). The GrIS's firn
layer has been the subject of recent research for multiple reasons. First,
assessments of Greenland's total mass balance using altimetry products use
satellite-derived measurements of surface height to assess ice sheet volume
but need to resolve the evolution of the firn's porosity before converting
volume change into mass change (i.e., Zwally et al., 2011; Sørensen et al., 2011; Shepherd et al.,
2012; Csatho et al., 2014; McMillan et al., 2016; Smith et al., 2020).
Second, the firn is able to retain part of the meltwater generated at the
surface and buffer sea-level rise (Pfeffer et al., 1991). The firn's
retention capacity depends on (i) the pore volume available for meltwater
storage (Harper et al., 2012), which is decreasing (Vandecrux et al., 2019);
(ii) the firn's cold content, which is the energy required to bring the firn
to the melting temperature (Vandecrux et al., 2020a); and (iii) the
capacity for the meltwater to reach depths where retention is possible,
which is for example reduced in the presence of low-permeability near-surface
ice slabs (Machguth et al., 2016; MacFerrin et al., 2019). Third, the firn
impacts climate records preserved in ice cores. Bubbles of atmospheric
gasses become trapped in closed pores at the firn–ice transition, and
knowledge of the age of the firn at this bubble close-off depth is essential
to accurately establish the chronology of past climate changes (Schwander
and Stauffer, 1984; Schwander et al., 1997). In all these cases, knowledge
of the firn's compaction rate is crucial, yet to date there are relatively
few in situ measurements of firn compaction, and there is no single, widely
accepted model to simulate it. In this paper, we present the Firn Compaction
Verification and Reconnaissance (FirnCover) dataset, which comprises
measurements of firn compaction, depth–density profiles, and temperatures
from eight sites on the GrIS.
Background
Firn densification characterizes a general increase in the firn's bulk density and encompasses
multiple processes. Firn compaction refers specifically to the compression of the firn due
to overburden stress. Firn compaction occurs due to processes operating at
the grain scale such as grain boundary sliding, sintering mechanisms
including dislocation creep and lattice diffusion, and plastic deformation
(Herron and Langway, 1980; Morris and Wingham, 2014). Meltwater refreezing increases the firn
density when surface meltwater or rain refreezes in the firn's pore space
(e.g., Braithwaite et al., 1994; Reeh, 2008). This occurs primarily in the
warmest regions of the ice sheet's accumulation area. The two
above-mentioned phenomena are interconnected because meltwater refreezing
releases latent heat and increases the firn temperature, which accelerates
compaction of surrounding firn (Pfeffer and Humphrey, 1996; Humphrey et al.,
2012). In the highest-elevation zones of the ice sheet, where firn
densification mainly occurs through compaction, the compaction rate in the
near-surface firn varies seasonally due to the fluctuating temperature; the
deeper firn does not experience this seasonal variation in the compaction rate
(e.g., Herron and Langway, 1980; Arthern et al., 2010; Ligtenberg et al.,
2011; Morris and Wingham, 2014). Long-term changes in climate, such as in the air
temperature and accumulation rate, may take many decades before they affect
compaction rates over the full depth of the firn column (Li and Zwally,
2015). In the percolation zone, the seasonal cycle in the near-surface firn
compaction rate is also present. However, the infiltration and refreezing of meltwater can
change the compaction rate on much shorter timescales (days to weeks) as
latent heat rapidly warms the firn, and rapid densification can occur when
the refrozen meltwater fills the pore space. This firn may then compact more
slowly in the future because of its higher density. In this realm, a single
anomalous melt season can significantly affect the depth–density profile
(Brown et al., 2012).
Numerous models have been developed to simulate firn compaction and
densification on various timescales (e.g., Herron and Langway, 1980; Zwally
et al., 2011; Arthern et al., 2010; Ligtenberg et al., 2011; Morris and
Wingham, 2014). On yearly and longer timescales, firn depth–density
profiles and compaction rates can be estimated reasonably well using the
mean annual air temperatures and accumulation rates (Herron and Langway,
1980). These firn-model results can be used, e.g., to simulate the long-term
evolution of the firn–ice transition depth for ice-core studies (Goujon et
al., 2003; Rasmussen et al., 2013). On shorter (monthly, daily, or
sub-daily) timescales, firn models can be forced with weather data and/or
outputs from regional climate models (RCMs) to simulate the firn
temperature, density, and thickness change (e.g., Vandecrux et al., 2020a).
Results from these model runs can be used to correct repeat
surface-elevation measurements from altimetry for firn changes (e.g., Smith
et al., 2020). Numerous recent studies have coupled meltwater-percolation
schemes to firn compaction models (e.g., Reeh, 2008; Kuipers-Munneke et al.,
2015; Vionnet et al., 2012; van Pelt et al., 2012; Verjans et al., 2019;
Vandecrux et al., 2020a) to simulate liquid water content, refreezing, and
runoff in the firn.
Most firn densification schemes have been developed using density
profiles observed in firn cores (Herron and Langway, 1980; Sørensen et
al., 2011; Kuipers-Munneke et al., 2015). Some of these models assume that the firn is in a steady state and convert the dated depth–density profile to densification rate. There are several potential issues with this method.
First, it is not necessarily safe to assume that the firn at a given site is
in a steady state. Even if the firn is in a steady state, a compaction rate
derived from the depth–density profile does not provide information about
the firn's response to a transient climate or how its compaction rate varies
on sub-annual timescales. Additionally, density profiles from the
percolation zone cannot disentangle contributions of firn compaction and
meltwater refreezing, which makes it difficult to assess these two processes
in firn models. Some other densification models are tuned to match
firn density observations while forced by RCM-simulated surface forcing. The
biases that may exist in the surface forcing are then compensated for by the
tuning of the densification model, which can then give inappropriate
responses under a different climate forcing.
Among the numerous firn models, none is broadly accepted as a definitive
model. Lundin et al. (2017) showed that these models agree neither in
steady-state nor in transient modes. Further, certain firn models are tuned
specifically for Greenland or Antarctica, despite the fact that the physical
processes driving densification should not vary solely due to geographic
location. Vandecrux et al. (2020b) compared numerous firn-meltwater models
to observations and found that while different models accurately simulated
physical characteristics of different firn zones in Greenland, no single
model accurately represented firn density, temperature, and water content at
all sites.
The uncertainties associated with firn-model development and the
disagreement among the existing models underscore the need for direct
measurements of firn compaction. The direct observation of firn compaction
implies tracking the thickness of a portion of firn (Hamilton et al.,
1998; Arthern et al., 2010), the optical tracking of layers in a borehole
(Hubbard et al., 2020), or the tracking of layers in repeated high-resolution
density profiles (Morris and Wingham, 2014). Most of the firn compaction
measurements have been conducted in Antarctica (Hamilton et al., 1998; Hamilton and Whillans, 2002;
Arthern et al., 2010; Hubbard et al., 2020). Lastly, the only firn compaction
measurements available in Greenland (Morris and Wingham, 2014) derived
average compaction rates over specific periods spanning from 2004 to 2011
and over a single transect in central western Greenland.
To fill this knowledge gap and increase our understanding of firn
densification in Greenland, we present data from the Firn Compaction
Verification and Reconnaissance (FirnCover) project, which monitored firn
compaction between 2013 and 2019 at eight stations on the GrIS. Each station
monitors firn compaction with strainmeters installed over boreholes at
various depth ranges, as well as firn temperature, air temperature, and
surface height. Additionally, we measured depth–density and stratigraphy
profiles of recovered cores and in snow pits during each field visit. In
this paper, we describe the FirnCover stations (Sect. 3) and the dataset
organization (Sect. 4), and then we present a preliminary analysis of the
dataset (Sect. 5).
The FirnCover stations and dataset
The eight FirnCover stations are located in various climate zones of the ice
sheet accumulation area (Fig. 1, Table 1). Two stations, Summit and
EastGRIP, are located in the high-elevation, dry-snow zone of the ice sheet,
where melt rarely occurs and where firn compaction is the dominant
densification process. Six stations are located in the percolation zone of
the ice sheet, where changes in surface meltwater and refreezing are
changing the structure and density profiles of firn (Machguth et al., 2016;
Vandecrux et al., 2018; MacFerrin et al., 2019). The KAN_U,
Dye-2, and EKT stations were installed in spring 2013 and the remainder of
the stations in spring 2015. At every station, additional instruments were
installed in new boreholes upon subsequent visits. The instruments were
generally within 10 m of the tower, and their positions relative to the tower
are given in the table Compaction_Instrument_Metadata (Table A7).
Each station included a suite of instruments, which we detail below, and was
equipped with a tower to hold instrumentation, a data logger (Campbell
CR800), a solar panel, and a battery. Borehole strain rates were recorded
daily, while air temperature, surface height, and firn temperature
measurements were recorded hourly. During most years, summary data from the
instruments were transmitted from each station once per day using an Iridium
short-burst data modem. Full data tables were saved on the data logger and
were read from each station upon visits in the field, which usually occurred
in late April or early May.
FirnCover station locations in Greenland. White lines are 1000 m
(thick) and 250 m (thin) elevation contours from the GIMP digital elevation model (Howat et al., 2014).
FirnCover station locations and 2015–2017 average winter
accumulation (Heilig et al., 2020).
Station nameLatitudeLongitudeElevationWinter accumulation(∘)(∘)(m)(mm w.e.)KAN_U67.00-47.021840249Dye-266.47-46.282119329EKT66.99-44.392361309Saddle66.00-44.502456380NASA-SE66.48-42.502370616Crawford Point69.88-46.991942386Summit72.58-38.503208218EastGRIP75.63-35.942666324The FirnCover strainmeters
The main components of each FirnCover station were borehole strainmeters,
which made daily measurements of borehole lengths. These used the
“coffee-can” method (Hulbe and Whillans, 1994; Hamilton et al., 1998) to
continuously monitor firn compaction, similarly to the method used by Arthern
et al. (2010). Each instrument was composed of a line with a weight attached
to one end and connected to a spring-loaded potentiometer on the other end.
The weight was anchored at the bottom of a borehole, and the potentiometer
was placed at the top of the borehole. As the borehole shortened due to firn
compaction, the potentiometer reeled in the string to maintain tension
(Fig. 2), and a data logger recorded the length of string that had been
reeled in. We here present data from 48 strainmeters installed at eight FirnCover stations. Table 2 lists metadata for each instrument, including
the initial depths of the boreholes.
FirnCover station (left), strainmeter casing (inset), and
strainmeter conceptual design (right).
The potentiometers were high-precision analog HX-PA units from UniMeasure,
Inc. (Bend, Oregon). The end of the potentiometer's steel wire was attached
to a Vectran string that extended to the bottom of the borehole. The string
was anchored using a 0.226 kg lead weight. Each potentiometer was
independently calibrated before installation. Including the potentiometer
accuracy and a minimal elongation of the extended string, measurement
uncertainty in the borehole length is within ±2 cm. Measurement of
the borehole shortening, however, is insensitive to the elongation of the
wire that is under a constant load and can be made with an accuracy of
±2 mm. The potentiometer was enclosed in a weatherproof plastic case
with an opening at the bottom. To stabilize the instrument atop the
borehole, it was installed atop a 0.61 m2 white PVC plastic platform. A
section of PVC pipe (0.1–0.7 m long) was attached to the bottom of the
casing and inserted in the top of the borehole to prevent the collapse of
the top of the borehole and keep the instrument in place. The line lowered
into the borehole was covered with hydrophobic lithium grease to prevent water
from refreezing on it and to keep the line from snagging on the instrument
or freezing to the side of the borehole.
To install each instrument, a borehole was drilled into the snow and firn
using a Kovacs (diameter 9 cm) coring drill. The weighted Vectran string was
then lowered into the borehole, and the potentiometer platform was placed
atop of the borehole (Fig. 2). The length of the string was set so that
the potentiometer's steel cable was near its full extension, maximizing the
distance over which the borehole shortening could be observed. Some
instruments were installed on the surface and thus measured both the
compaction of snow and underlying firn. Other instruments were
installed at the bottom of snow pits, beneath the annual layer of snow, to
measure the compaction of the underlying firn only. In dry-snow regions
(Summit, EastGRIP) all instruments were installed directly on the surface,
while instruments in the percolation zone were mixed between surface and
snow pit installations (see non-zero initial depth of borehole top in Table 2). The depth of each borehole was measured both along the core (by
reassembling core segments on the surface) and by using the Vectran line to
directly measure the borehole. Instrument nos. 1–10, installed in 2013, use
the approximate core length (as borehole length was not measured); the
remaining instruments use the measured borehole length. Borehole and
core-length measurements typically agreed to within 0–8 cm.
FirnCover instrument metadata.
SiteInstrumentRecording startRecording endInitial depth ofInitial depth ofIDdatedateborehole top (m)borehole bottom (m)Crawford Point2227 May 201510 Oct 20181.0317.332326 May 201510 Oct 201802.12427 May 201510 Oct 20181.099.382527 May 201510 Oct 20181.135.174217 May 201610 Oct 2018018.094823 May 201710 Oct 2018022.3Dye-249 May 20134 Sep 20190259 May 20134 Sep 20191.3511.3569 May 20134 Sep 20191.3517.352121 May 20154 Sep 20190.8518.853910 May 20164 Sep 2019017.34711 May 20174 Sep 2019022.85EKT719 May 20134 Sep 201902819 May 20134 Sep 20191.256.25919 May 20134 Sep 20191.2511.251019 May 20134 Sep 20191.2517.25128 May 20154 Sep 20190.914.9363 May 201631 Jul 2019017.95445 May 20174 Sep 2019022.24EastGRIP2628 May 201510 Oct 2018015.832728 May 201510 Oct 201804.122829 May 201510 Oct 201808.052929 May 201510 Oct 2018015.534016 May 201610 Oct 2018016.284918 May 201710 Oct 2018020.38KAN_U130 Apr 20139 May 20191.26.2230 Apr 20139 May 201902330 Apr 20139 May 20191.220.5115 May 20159 May 20190.6414.143529 Apr 20169 May 2019016.514328 Apr 20179 May 20190.7822.86NASA SE1312 May 201528 May 2018016.41412 May 201528 May 201802.051512 May 201520 May 2018081612 May 201528 May 2018016.2456 May 201728 May 2018022.17Saddle1716 May 201531 Aug 2017016.11816 May 201531 Aug 201702.031916 May 201531 Aug 201708.172016 May 201531 Aug 2017016.3386 May 201631 Aug 2017018.53468 May 201731 Aug 2017022.34Summit3029 May 20157 Oct 2018015.733129 May 20157 Oct 201804.233229 May 20157 Oct 201807.773330 May 20157 Oct 2018015.794117 May 20167 Oct 2018016.085021 May 20177 Oct 2018021.99
Firn density profiles at the first visit of each site. These and
other density profiles from FirnCover are available from Koenig and
Montgomery (2019).
Air temperature, surface height, and firn temperature observations
Each FirnCover station was equipped with a Campbell L109 air-temperature
thermistor with a six-plate radiation shield, which measured air temperature
hourly at an approximately 2 m height. Snow-surface height was measured
from 2015 onward with an SR50 sonic ranging sensor mounted on the tower
crossbeam. A string of twenty-four 1 kΩ resistance-temperature diodes
(RTDs, from Omega, Class A, IEC 60751 standard) measured firn
temperatures from 0 to approximately 14 m depth (every 0.5 m from 0–10 m
depth, every 1 m thereafter). The manufacturer-stated precision of the RTDs
is ±0.2∘C. Some RTD-string boreholes were less than 14 m due to
accumulated drill shavings at the bottom of the boreholes. RTD measurements
are corrected for wire resistance (by measuring across a 25th bare wire
without an RTD), and measured resistances are converted to temperature using
formulae provided by the RTD manufacturer. The RTD strings were installed in
separate boreholes that were backfilled with snow. The initial installation
depths of each RTD string are noted in the Station_Metadata table (Table A5). The daily depth of
each thermistor is calculated by adding the original installation depth to
the snow depth measured by the sonic ranging sensor. Unlike air temperature,
surface height and firn temperature are available as daily averages.
Firn core and snow pit observations
Firn cores were retrieved from each of the FirnCover strainmeter boreholes.
To understand the structure of the firn at each FirnCover instrument, the
cores were visually inspected for stratigraphic layers (snow, firn, ice lenses, etc.) at a
∼1 cm vertical resolution and cut into segments to measure
density at a ∼10 cm resolution (Fig. 3). Density profiles
from all cores logged by FirnCover field campaigns are included in NASA's
SUMup dataset (Montgomery et al., 2018; Koenig and Montgomery, 2019).
Dataset structure and handling
The FirnCover dataset is organized in a single .hdf5 file, which comprises
four data tables and three metadata tables. Table 3 gives a summary of the
data tables, and Tables A1 to A7 detail the variables contained in each
table.
Overview of the FirnCover data tables.
Table nameContentFurther details inCompaction_Dailysite name, daily timestamp, instrument ID, compaction ratio, potentiometer wire correction ratio, potentiometer cable length, compaction borehole length, top and bottom depthTable A1Air_Temp_HourlySite, hourly timestamp, air temperatureTable A2Meteorological_Dailysite name, daily timestamp, battery minimum and maximum voltage, panel mean temperature, air hourly minimum, median and maximum temperature, sonic ranger quality raw and corrected distance, raw and interpolated snow depthTable A3Firn_Temp_Dailysite name, daily timestamp, thermistor average and maximum resistance value, uncorrected and corrected temperature value, average resistance of the cable used for correction, depth of the sensorsTable A4Station_Metadatasite name, Iridium URL, latitude, longitude, installation date, comments, thermistor string number, thermistor installation date, number of thermistors usable, depths at installation, direction and distance from towerTable A5Station_Visit_Notessite name, date of visit, notes from each visitTable A6Compaction_Instrument_Metadata.instrument ID, site name, installation date, borehole top and bottom depth from surface, initial length, direction and distance from tower, borehole ID in SUMup firn density datasetTable A7
At most strainmeters, the first weeks to months of record show relatively
high compaction rates. This initial period of increased compaction is more
pronounced for instruments installed at the surface than the ones installed
at the bottom of a snow pit (Table 2). We consider these high initial
compaction rates to be the result of the instrument settling over the snow
and firn. This period of initial settling needs to be discarded to study the
firn after it adapted to the presence of the instrument. At
KAN_U, where the deeper firn is rich in ice (Fig. 3),
settling of the instrument was mainly due to the surface snow and took about
a month. At Summit, where the firn has no ice layers, settling took about
2 months. For the preliminary analysis presented below, we discard the first 60 d of
recordings for each instrument, but a site-specific analysis of instrument
settling may allow the recovery of more observations within that period.
Some compaction data were read directly from the station's data logger in
32-bit floating-point format. For measurements where data tables were unable
to be directly read due to lack of revisiting, data summaries from Iridium
transmissions were used with 16-bit floating-point values. Due to the
limited data resolution, borehole lengths recorded from Iridium
transmissions exhibit a 2 mm stepwise discretization rather than smooth
continuous measurements. This can influence compaction rates when computed
as derivatives of borehole lengths over time. In the present analysis, we
use a 2-month-long running mean to smooth the borehole length. This
filtering removes most of the noise, but it may also smooth part of the seasonal
changes in compaction rates. The dataset includes the unfiltered data, and
we recommend that users apply their own filtering strategy specific to their
needs.
Four of the stations had periods when the entire station was not recording
data. These were Summit, from 21 May to 23 August 2017; EKT, from
11 May 2017 to 18 May 2018; KAN_U from 7 November 2017 to
30 April 2018 and from 13 January to 20 February 2019; and Saddle from
30 May 2015 to 5 May 2016. For a number of the instruments, there are
periods of data that we consider suspicious because of abrupt jumps in the
compaction rates. We hypothesize that this could be due to ice buildup on
the cable that prevented the instrument from working, and once the cable
became free the instrument began to work again. The suspicious measurements
are listed in Table 4. We exclude these suspicious data from our analysis in
Sect. 5. For transparency, they are still included in the released
dataset, but we advise caution when using them.
Periods with suspicious recordings removed from the analysis.
Instrument IDFailure start dateFailure end date1320 February 2018–1029 July 2019–42–14 November 201748–18 November 20174827 May 201819 July 20181–1 December 201335–1 September 20164316 July 2018–
Borehole length changes. The legend indicates the ID of each
instrument as reported in Table 2 and the initial length of each borehole.
June–July–August are highlighted in orange.
It is possible that our compaction measurements could be affected by
horizontal divergence (Horlings et al., 2021). However, for the present
analyses, we consider these effects to be negligible, which is consistent
with firn-densification modeling efforts in Greenland (Kuipers-Munneke et
al., 2015). A more thorough analysis could use ice velocity measurements
(e.g., Joughin et al., 2016) to explicitly account for the effects of ice
flow.
Data overview and preliminary analysis
Figure 4 shows the change in borehole length measured by each potentiometer
at the eight sites. NASA-SE shows the steepest borehole shortening with
instrument no. 36 installed in 2017 and also the largest change in borehole
length, -1.25 m, for the 16.2 m long borehole no. 16 installed in 2015. This
rapid shortening of the borehole largely stems from the climatic conditions
at NASA-SE because (1) high accumulation rates create a thick, low-density
layer near the surface (Fig. 3), which compacts faster than high-density
snow, and (2) the fast buildup of new snow atop the borehole increases
overburden pressure quickly, which speeds up the densification rate. At Dye-2
and KAN_U, the borehole shortening is the least pronounced.
This is likely due to higher air temperatures, higher melt, and lower
snowfall at these sites; together they lead to higher firn density and ice
content, which decreases the compaction rate (Fig. 3). At the other sites,
total borehole shortening ranges from a few centimeters to about a meter at
EKT depending on the climatic conditions and the length of the observation
period. Most sites, especially Summit and EastGRIP, show a seasonality
in the borehole shortening rate: boreholes shorten faster (steeper curve in
Fig. 4) during and after summer months (orange-shaded areas) and more slowly
(flattening of curves in Fig. 4) in the winter and spring months.
Smoothed daily compaction rates. The legend indicates the ID of
each instrument as reported in Table 2 and the initial length of each
borehole. June–July–August are highlighted in orange. Note the different
y axes.
Daily air temperature (red line, left axis) and surface height
(blue line, right axis). June–July–August are highlighted in orange. Note
the different y axes.
Firn temperatures (interpolated) and surface height (solid blue
line) observed at the FirnCover stations.
Average 10 m firn temperatures for the 2015–2019 period along with
average air temperature at each site and difference between the two. Only
years that have more than 90 % available temperature readings are used for
the average.
Site2015–2019 average 10 mAverage airYears used for the averageDifferencefirn temperature (∘C)temperature (∘C)air-temperature calculation(∘C)Summit-28.8-26.22016-2.6KAN_U-9.5-16.62014–20167.1Crawford Point-13.9-16.220162.3EKT-17.9-20.22014–20172.3Saddle-17.9-18.120160.2Dye-2-13.3-19.22016–20185.9
The difference between sites can be further investigated by looking at the
compaction rates, which are calculated by taking the time derivative of the
borehole length data (Fig. 5). As discussed above, NASA-SE shows the
largest daily changes and KAN_U the lowest. At each site,
instruments installed in deeper boreholes show a larger magnitude of daily
compaction compared to shorter instruments. The faster compaction after the
installation of the instrument appears as large initial firn compaction
rates in Fig. 5. Faster compaction during the first summer following the
installation of the instruments also stems from the conduction of warmer
surface temperatures down to the instrument. These warmer firn temperatures
during summer increase the firn compaction rates. As mentioned previously,
daily compaction rates at KAN_U are lower than at other sites
due to the presence of a ∼5 m thick ice slab at that site.
The seasonality of the daily compaction rates is clearly visible at the dry-snow sites, Summit and EastGRIP, but also at sites in the percolation areas:
NASA-SE, Crawford Point, EKT, and Saddle. Daily compaction rates peak in the
autumn and reach a minimum at the end of the winter (Fig. 5). The delay
between the highest surface temperatures in summer and the highest
compaction rate is due to the time the surface temperatures need to diffuse
down to the depth of the firn that the instrument is measuring.
The FirnCover dataset also includes measurements of air temperature, surface
height, and (at all sites except EastGRIP and NASA-SE) firn temperature. These data enable us to relate the compaction rates (Figs. 4 and 5) to the
surface and subsurface conditions (Figs. 6 and 7).
The firn temperature measurements, in particular, allow analyses using the
actual firn conditions rather than using average air temperature as a proxy
for firn temperature, which is commonly done. For each site, we interpolate
the 10 m firn temperature and average it over the 2015–2019 period covered
by the measurements (Table 4). We compare this firn temperature to the
average air temperature calculated for the years where more than 90 % of
the temperature measurements are available (Table 4). The average air
temperature and interpolated 10 m firn temperatures are rarely equivalent
(Table 4). At Summit, the 10 m firn temperatures are 2.6 ∘C lower than
the average air temperature. This is due to strong near-surface atmospheric
inversion and radiative cooling of the surface (e.g., Miller et al., 2017).
At all the other sites, the 2 m firn temperature is higher than the average
air temperature. We attribute this to meltwater percolation and latent heat
release at depth (e.g., Pfeffer and Humphrey, 1996; Humphrey et al., 2012).
This difference is largest at KAN_U where the firn is
7 ∘C warmer than the average air temperature. At Saddle, the firn
temperature is within 1 ∘C of the average air temperature. We interpret
this as the neutralization of the two processes mentioned above: heat loss
through radiative cooling at the surface and latent heat release during
meltwater refreezing. This site-specific difference between 10 m firn
temperature and average air temperature shows the limitation of firn
compaction parameterizations that use air temperature as a proxy for firn
temperature.
Code availability
All the scripts used to load, process, and plot the FirnCover dataset are
available here: 10.5281/zenodo.5854253
(Vandecrux et al., 2022).
Data availability
The FirnCover dataset is available at 10.18739/A25X25D7M (MacFerrin et al., 2021). The firn
density profiles at the firn cover sites are available here: 10.18739/A26D5PB2S (Koenig and Montgomery, 2019).
Summary remarks
We present data from 48 strainmeters installed at eight sites located in
different climatic zones of the Greenland ice sheet and covering the
2013–2019 period. Additional surface and firn measurements available at each
of the FirnCover sites are firn density, air temperature, surface height, and
firn temperatures. These data will allow future work to investigate the
interannual and seasonal response of firn compaction to surface and
subsurface conditions. We also note that several other measurements are
available at some of the FirnCover sites: at KAN_U the
PROMICE automatic weather station has been operating since 2009 (Fausto et
al., 2021); at Crawford Point, Saddle, NASA-SE, Summit, and Dye-2, GC-Net
weather stations document the history of these sites back to the 1990s and
are still operating (Steffen et al., 1996). At Summit, extensive
instrumentation is measuring the atmospheric conditions and the surface
energy budget (e.g., Miller et al., 2017). At Dye-2, upward-looking ground-penetrating radar (Heilig et al., 2018) and time-domain resistivity probes
(Samimi et al., 2020) are also available for the 2016 melt season to detail
meltwater percolation. These measurements, combined with the FirnCover
compaction data, potentially allow investigations of how meltwater affects
firn compaction. The FirnCover dataset will help to evaluate and calibrate
firn models and help reduce uncertainty when using these models to interpret
satellite altimetry measurements or calculating the past, current, and future
mass balance of polar ice sheets. The dataset can be found here: 10.18739/A25X25D7M.
Compaction_Daily table, storing daily compaction
records for each FirnCover instrument.
Field nameCommentssitenameName of the FirnCover sitedaynumber_YYYYMMDDYear, month, and day of the measurementCompaction_Instrument_IDLinked to Compaction_Instrument_Metadata tableCompaction_Ratio_MedThe ratio of the compaction line measurement (fraction of total instrument cable length), values 0 to 1, inclusive; uses a median value of six daily measurementsCompaction_Wire_Correction_Ratio_MedThe ratio of the wire resistance as a fraction of the total line resistance, values 0 to 1, inclusive (typically below 0.001)Compaction_Cable_Distance_mDistance the instrument wire is extended, typically 0–2 m (up to 5 m for extended-cable instruments)Compaction_Borehole_Length_mLength of the borehole at that time step, combines the updated cable length with the initial borehole lengthBorehole_Depth_Top_mDepth from the surface to the top of the borehole at that time step, combining the initial borehole depth (0 for the surface) and the sonic ranger snow depth measurementBorehole_Depth_Bottom_mDepth from the surface to the bottom of the borehole, computed as Borehole_Depth_Top_m + Compaction_ Borehole_ Length_m
Air_Temp_Hourly table.
Field nameCommentssitenameName of the FirnCover sitedaynumber_YYYYMMDDYear, month, and day of the measurementhournumber_HHHour of the day (0 through 23)AirTemp_C∼2 m air temperature at that hour, measured by the shielded L109 thermistor, in degrees Celsius; actual height of the temperature measurement can be derived by adding 28 cm to the sonic ranger height in the Meteorological_Daily table
Meteorological_Daily table.
Field nameCommentssitenameName of the FirnCover sitedaynumber_YYYYMMDDYear, month, and day of the measurementBattV_min_VMinimum station battery voltage for the dayBattV_max_VMaximum station battery voltage for the dayPanelTemp_mean_CMean daily temperature (∘C) measured on the data logger inside the logger boxAirTemp_min_CMinimum daily air temperature (∘C) measured hourlyAirTemp_max_CMaximum daily air temperature (∘C) measured hourlySonicRangeQualityThe quality score value of the sonic ranging sensor, chosen as the highest quality of 24 daily measurements, ranges from 162 to 600 with good quality scores below 210SonicRangeQualityCode0 is good; 1 is questionable; 2 is poor; 3 is no measurementSonicRangeDist_Raw_mThe raw distance measured by the sonic ranger, before temperature correctionSonicRangeAirTemp_CThe air temperature at the time of the sonic ranger measurementSonicRangeDist_Corrected_mThe corrected distance measured by the sonic rangerAccum_Snow_Depth_mThe accumulated snow depth since the instruments' installation, corrected for tower raises upon revisits
Firn_Temp_Daily table.
Field nameCommentssitenameName of the FirnCover sitedaynumber_YYYYMMDDThe day of the readingRTD_Ohms_AvgAverage RTD resistance readingRTD_Ohms_MaxMaximum RTD resistance readingRTD_Temp_Avg_Uncorrected_CAverage RTD temperature reading (∘C)RTD_Temp_Max_Uncorrected_CMaximum RTD temperature reading (∘C)RTD_Line_Correction_Ohms_AvgThe line correctionRTD_Temp_Avg_Corrected_CAverage RTD temperature reading (∘C), with adjustment for wire resistanceRTD_Temp_Max_Corrected_CMaximum RTD temperature reading (∘C), with adjustment for wire resistance
Station_Metadata table.
Field nameCommentssitenameName of the FirnCover siteiridium_URLThe online URL where transmissions are collectedlatitudeThe WGS84 latitude of the station upon installationlongitudeThe WGS84 longitude of the station upon installationinstallation_daynumber_YYYYMMDDThe day the station was installedcommentsGeneral comments about the station upon its installationRTD_stringnumberThe string serial number of the RTD string installed at the stationRTD_installation_daynumber_YYYYMMDDThe day the RTD was installed at the stationRTD_top_usable_RTD_numNumber (from the top) of the first usable RTD sensor; non-usable sensors could not be inserted in the snow and were left lying on the surfaceRTD_depths_at_installation_mThe 24 length depths of each RTD at installationRTD_direction_from_tower_degreesThe compass direction (non-corrected for declination) from the station tower to the RTD stringRTD_distance_from_tower_mThe distance from the station tower to the RTD string
Station_Visit_Notes table.
Field nameCommentssitenameName of the FirnCover sitedaynumber_YYYYMMDDDay of the visitvisit_notesNotes about the site visit or revisit
Compaction_Instrument_Metadata
table. Installation depths and positions of each FirnCover compaction
instrument.
Field nameCommentsinstrument_IDUnique identification number of the instrumentsitenameName of the FirnCover siteinstallation_daynumber_YYYYMMDDDate that the instrument was installedborehole_top_from_surface_mThe top of the borehole from the surface upon installation, in meters (0 for surface, negative numbers for beneath the surface)borehole_bottom_from_surface_mThe depth from the surface to the bottom of the borehole, in metersborehole_initial_length_mThe length of the borehole, in metersinstrument_has_wire_correctionWhether the instrument installed has a wire-resistance correction sensor installed or not.direction_from_tower_degreesThe compass direction (not corrected for declination) from the tower to the instrument.distance_from_tower_mThe distance (in m) from the tower to the instrument.borehole_IDThe identifying name of the core taken from the borehole, where stratigraphy and density were measured (names consistent with cores in the NASA SUMup dataset)borehole_ID_is_directA “direct” (true) core density profile came straight from that borehole; if “indirect” (false), that core was not profiled for density directly, and this links to a nearby adjacent core measured at the same time, typically within a 10–20 m distanceAuthor contributions
MJM conducted the conceptualization, field investigation, and data curation; acquired funding; and developed the methodology. CMS participated in the
conceptualization, funding acquisition, field investigation, formal analysis,
and visualization. BV participated in the field investigation, formal
analysis, and visualization. EDW and WA participated in the funding
acquisition and supervision. All authors contributed to the manuscript
preparation.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
All authors acknowledge the
work and efforts of multiple field logistic partners and team members for
their essential help in maintaining the datasets and instruments: Karen Alley, Charalampos Charalampidis, William Colgan, Federico Covi,
Alex Crawford, Mark Eijkelboom, Shane Grigsby, Achim Heilig, Darren Hill,
Horst Machguth, Shawn Marshall, Asa Rennermalm, Samira Samimi, Tasha Snow,
Aleah Sommers, Dirk van As, the Summit Station scientific team,
the EastGRIP team, and Polar Field Services. Lastly, we thank Ian McDowell,
Joel Harper, and Megan Thompson-Munson for constructive comments on the
manuscript.
Financial support
This research has been supported by NASA Headquarters (grant nos. NNX15AC62G and NNX10AR76G).
Review statement
This paper was edited by Ge Peng and reviewed by Joel Harper, Ian McDowell, and Megan Thompson-Munson.
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