Increasing atmospheric temperatures over ice cover affect surface processes,
including melt, snowfall, and snow density. Here, we present the Surface Mass
Balance and Snow on Sea Ice Working Group
(SUMup) dataset, a standardized dataset of Arctic and Antarctic observations
of surface mass balance components. The July 2018 SUMup dataset consists of
three subdatasets, snow/firn density
(
Earth's polar regions are warming
at an accelerated rate. As increased air temperatures and associated
feedbacks with radiative heating persist, the ice cover is changing,
particularly at the ice–atmosphere interface (e.g., Vaughan et al., 2003;
Serreze and Francis, 2006; Hall et al., 2013). This change is evident in
declining Arctic sea ice extent (e.g., Richter-Menge et al., 2016) and the
recent acceleration of total mass loss from the Greenland Ice Sheet (GrIS)
and Antarctic ice sheets (AIS) (e.g., Velicogna et al., 2014; IMBIE Team,
2018), which contributed
In 2012, at the Surface Mass Balance and Snow on Sea Ice Working Group (SUMup) meeting, the modeling and remote sensing communities clearly stated to observationalists that the lack of easy-to-access, standardized, in situ measurements hindered scientific achievement. They also emphasized the need for spatially extensive measurements and annual to sub-annual accumulation measurements to coincide with the spatial and temporal scales covered by modeling and remote sensing methods. A public, annual to decadal, standardized time series of measurements was recommended (Koenig et al., 2013). Modeling and remote sensing studies require validation measurements (e.g., Fettweis et al., 2017; Arthern et al., 2006; Burgess et al., 2010; Kuipers Munneke et al., 2015; Koenig et al., 2016), ideally with the model's same spatial (typically tens of kilometers) and temporal (typically sub-annual) resolutions. These observations are needed over large polar regions, which are difficult for an individual researcher to compile. Today, most field measurements for validation are dispersed across multiple data centers/datasets in differing formats. Some previous Arctic and Antarctic studies have compiled large sets of measurements, generally accumulation measurements (e.g., Mock, 1967a, b; Ohmura and Reeh, 1991; Vaughan and Russell, 1997; Favier et al., 2013; US ITASE, Mayewski et al., 2013; Wang et al., 2016; Machguth et al., 2016b; Thomas et al., 2017; Matsuoka et al., 2018), though most cover only a small region of the ice sheet, are not annually resolved, and/or are not publicly available through a data distribution center.
Here, we present the July 2018 SUMup dataset and its three subdatasets: density, accumulation, and snow depth on sea ice. This data paper serves to fully describe the dataset and includes analysis of the data over the GrIS demonstrating how this dataset increases our knowledge of surface mass balance processes by compiling previously dispersed measurements into a standardized dataset. Uses of SUMup include model validation, remote sensing validation and algorithm development, and long-term monitoring efforts. SUMup measurements should not be used to assess individual measurement errors or establish errors on specific retrieval methods. This is because (1) the spatial/temporal variability of snow depth on sea ice is naturally large due atmospheric processes, including accumulation and aeolian processes, further increased by sea ice characteristics such as age, drift, and ridging (e.g., Warren et al., 1999; Sturm et al., 2002), and (2) the spatial/temporal variability of density and accumulation on land ice is also large due to atmospheric processes, including accumulation, temperature, solar radiation and aeolian process, further increased by ice elevation, topography, melt, and water flow processes (e.g., Alley, 1988; Courville et al., 2007; Laepple et al., 2016; Vandecrux et al., 2018). The field measurements in SUMup were not designed to and cannot control this naturally occurring variability.
The SUMup dataset is an expandable, community-based dataset of field
measurements of surface mass balance components that is consistent in format,
properly described through metadata, and publicly available. The July 2018
SUMup dataset contains three subdatasets that consist of measurements of
snow/firn density (
Figure 1 shows the locations of density and accumulation measurements represented by the July 2018 SUMup dataset. Snow depths on sea ice locations are not shown on this map due to the broad spatial sampling. Density and accumulation measurements are often co-located over the ice sheets where ice cores were collected (Fig. 1).
SUMup measurements were collected, formatted, and compiled primarily through
two methods: (1) searching data archives that traditionally host cryospheric
data, which included Pangaea (
New and unique data sources are included in the SUMup dataset. Notably, the snow density subdataset includes snow pit data from Carl Benson's Greenland traverses in the early 1950s and data from 1955 that previously had not been digitally scanned (Benson, 2013, 2017). The 1955 notebooks are only archived in the National Snow and Ice Data Center paper archives. The SUMup dataset also includes snow accumulation measurements from Summit Station, Greenland's stake network called the Bamboo Forest (Dibb and Fahnestock, 2004), and corresponding density measurements at monthly temporal resolution (Dibb et al., 2007). Additionally, more widely used data sources are included, such as US International Trans-Antarctic Scientific Expedition (US ITASE, Mayewski et al., 2013) ice cores, the Program for Arctic Regional Climate Assessment (PARCA, Mosley-Thompson et al., 2001) ice cores, and the Greenland Inland Traverse (GrIT, Hawley et al., 2014) snow pits and ice cores. Section 2.4 provides more details on the specific sources for each of the three subdatasets, including the complete list of all citations.
The parameters for each snow density measurement in the SUMup dataset with a brief description and the unit of measurement.
The SUMup dataset will continue to expand on an annual basis as new measurements are taken and/or old measurements are discovered. Beyond expanding the current subdatasets, we expect to add additional subdatasets on surface mass balance processes which may include, but are not limited to, snow/ice albedo, snow temperature, and short-wave/long-wave radiation measurements. The community is encouraged to contribute data or suggest missing data sources/types to add to SUMup by contacting the authors directly.
Each measurement contains common variables, including the date taken,
latitude, longitude, surface elevation if on land, the measurement itself,
error associated with the measurement, the method by which the measurement
was taken, and a citation to which the measurement can be sourced back. By
convention, negative latitudes represent south and negative longitudes
represent west. For measurements that did not specify a specific month and
day for the measurement, but provided only the year (“yyyy”), the date was
entered as “yyyy0000”. A fill value of
If any of the original measurements/metadata were unclear or non-existent,
the original author of the data was contacted to clarify inconsistencies or
questions. Snow density measurements that exceeded a physically plausible
range from
The snow/firn density subdataset of SUMup is the largest, containing over
2 100 000 unique measurements of density at different depths (Fig. 1).
Table 1 describes the parameters for each density measurement. The
measurement methods include density cutters of different sizes (generally
from 100 to 1000 cm
The parameters for each snow accumulation measurement on land ice in the SUMup dataset with a brief description and the unit of measurement.
The snow accumulation on land ice subdataset of SUMup contains over 230 000 unique measurements (Fig. 1). Table 2 describes the parameters for each accumulation measurement. The measurement methods include ice cores and/or boreholes, snow pits, radar isochrones, and stake measurements. Arctic measurements are predominantly from ice cores and stake measurements and include one radar near-annual transect in southeastern Greenland (Bolzan and Strobel, 1999a–g, 2001a, b; Mosley-Thompson et al., 2001; Dibb and Fahnestock, 2004; Miège et al., 2013). The Antarctic measurements are predominantly from ice cores and include two radar transects, one in West Antarctica and one in East Antarctica (Wagenbach et al., 1994a; Graf et al., 1999a–m; Schlosser and Oerter, 2002a, b; Spikes et al., 2005; Graf and Oerter, 2006a–y; Anschütz and Oerter, 2007a–f; Banta et al., 2008; Oerter, 2008a–o; Fernandoy et al., 2010a–c; Ferris et al., 2011; Verfaillie et al., 2012; Burgener et al., 2013; Mayewski et al., 2013; Medley et al., 2013; Philippe et al., 2016). In most instances accumulation (in water equivalent, w.e.) was provided in the original measurement; however, the Summit Station, Greenland, Bamboo Forest measurements consist of weekly surface height change at 100 stakes along with snow density (Dibb and Fahnestock, 2004). We multiplied the height change by the coincident snow density and averaged across all stakes to get accumulation measurements for SUMup. Similarly, the Bolzan and Strobel data (1999a–g, 2001a, b) provided a snow pit depth, year, and density that were converted to accumulation. Most of the accumulation measurements are annually resolved, with the major exceptions being the radar measurements, which are approximately decadal, and Bamboo Forest data, which are approximately monthly.
The parameters for each snow depth on sea ice measurement in the SUMup dataset with a brief description and the unit of measurement.
The snow depth on sea ice subdataset is the sparsest within SUMup, with
The goal of the SUMup dataset is that it can be broadly used by the
scientific community for a variety of research studies. Tables 4 and 5
provide the basic descriptive statistics for each subdataset for the Arctic
and Antarctic, respectively. These tables provide a coarse overview of the
data; however, when using the SUMup datasets, subsetting by location, time,
depth, etc. will likely be required for specific applications. The minimum
value for accumulation in the Arctic is
The descriptive statistics for all of the Arctic measurements in
SUMup, including the minimum (min), maximum (max), mean, median, standard
deviation (SD), and number of measurements (
The descriptive statistics for all of the Antarctic measurements in
SUMup, including the minimum (min), maximum (max), mean, median, standard
deviation (SD), and number of measurements (
Field data collected over the vast polar regions have spatial and temporal sampling bias, as the time, cost, and logistics to systematically sample these regions is unreasonable. We describe the SUMup dataset here to elucidate possible bias. All the measurements in SUMup, with the exception of one location, were collected during the spring/summer season for that polar region, roughly April through August for the Arctic and October through February for the Antarctic. Summit Station, Greenland, the only GrIS station with year-round operations, is the one exception in the dataset where temporally consistent, year-round measurements are taken. Below, we summarize the spatial and temporal distributions of the SUMup dataset by subdatasets. For the two largest subdatasets, snow density and accumulation, we present analysis over the GrIS (Sect. 3.4). This analysis is meant to be an introduction to the dataset and is not exhaustive. We encourage the community to continue to use and more fully exploit this dataset. Figure 2 provides a bar graph showing the measurement methods that make up each subdataset showing that measurement techniques with high spatial (e.g., radar isochrones) and high depth (e.g., neutron probes) resolution dominate the number of measurements in a subdataset; however, they often have limited spatial coverage with respect to the entire region.
Bar charts showing the measurement methods in the
Histograms showing the date taken and associated fraction of the
density dataset for
Measurements were compiled of snow/firn density that cover
The density subdataset is dominated (98 % of data) by high vertical depth
resolution measurements (millimeter scale for
Figure 3 provides histograms showing the fraction of density measurements
taken by year for Antarctica, Greenland excluding Summit Station, and Summit
Station. (Summit Station was defined as a bounding box of 72 to
73
Histograms showing the fraction of the density dataset by mid-point
sampling depths for
Histograms showing the date taken and associated fraction of the
accumulation dataset for each area examined:
Figure 4 provides an overview of the distributions of depths sampled by the
density subdataset. Overall, the number of measurements decreases with depth.
The Antarctic measurements decrease less uniformly with depth, which is
related to the larger number of deeper ice cores. The majority of Greenland
measurements are above 5 m and there are very few measurements below 20 m,
demonstrating the large number of shallow cores collected across Greenland.
At Summit Station, the majority of the measurements are taken above 1 m as a
result of systematic tasking to dig
Measurements of accumulation over land ice were taken at
In total, 62 % of the
accumulation measurements are from the Arctic, all within Greenland, with
Figure 5 provides histograms showing the fraction of accumulation measurements taken by year for Antarctica, Greenland excluding Summit Station, and Summit Station. Year, in this case, is defined as the year in which the ice core, snow pit, etc. were collected/dug. The histograms for the Antarctic and Greenland show sporadic spikes through time corresponding to major collection campaigns, similar to yet more exaggerated than in the density subdataset. Antarctic measurements peak in the early 2000s when US ITASE ice cores were collected in West Antarctica (Mayewski et al., 2013). Greenland accumulation measurements peak in the late 1980s with ice cores preparing for the GISP2 core and in the late 1990s when the PARCA ice cores (Mosley-Thompson et al., 2001) were collected. Summit Station has a constant monthly collection of accumulation measurements from August 2003 to August 2016 from the Bamboo Forest measurements (Dibb and Fahnestock, 2004) and represents the only year-round collection of accumulation measurements in the SUMup dataset.
Histograms showing the fraction of accumulation measurements by year
for
Histogram showing the fraction of snow depth on sea ice measurements by year.
While understanding that the date when accumulation measurements are taken is important, it is also important to understand the year represented by a sample, corresponding to the depth. Figure 6 provides the distribution of years when annual accumulation was measured from 1950 to present. Antarctica has a relatively even distribution of accumulation measurements until 2000 when the number of samples decreases. This decrease is due to the fact that many of the cores collected by US ITASE from 2006 to 2008 in East Antarctica could not be dated to determine accumulation and also shows that most of the firn cores collected date back to 1950 or later. The Greenland accumulation measurements peak between 1980 and 2000. The mostly shallow ice cores in Greenland, and relatively higher accumulation rates compared to Antarctica, result in less data from 1950 to 1980 in the ice cores. The sharp decline in the 2000s is due to a lack of coring efforts that occurred during that decade in Greenland. Summit Station has a consistent year-round sampling of accumulation from 2003 to 2016. These systematic measurements significantly outnumber the single measurements per year collected from ice cores at Summit Station that sample the decades before 2000.
The
Recent warming over the GrIS, including a melt event in 2012 that covered
nearly the entire surface (Nghiem et al., 2012), has increased both snow
density and snow accumulation in recent decades (e.g., Morris and
Wingham, 2014; Machguth et al., 2016a; Overly et al., 2016). Improved
measurements, or models, of density and its evolution with time are needed to
reduce uncertainties when converting altimetry measurements into total ice
sheet mass balance using altimetry (e.g., Zwally and Jun, 2002; Shepherd et
al., 2012) and for converting radar isochrones into measurements of
accumulation (e.g., Koenig et al., 2016). Many models use mean annual
temperature and accumulation to model the spatial and temporal evolution of
density (e.g., Herron and Langway, 1980; Reeh et al., 2005; Kuipers Munneke
et al., 2015). Some studies, however, show that density models generally
underestimate surface (
Figure 8 shows the distribution of density measurements with elevation and
latitude compared to the total distribution of elevations and latitudes for
the entire GrIS. The fraction of the elevation at 250 m bins (red line of
Fig. 8) for the Greenland Ice Sheet is derived from the CryoSat-2 Greenland
digital elevation model (DEM; Helm et al., 2014a, bv). Figure 8 uses similar
graphing techniques to those of Fausto et al. (2018) to clearly show sampling
bias in the observation dataset. If there were no sampling bias, the fraction
of measurements would be similar to the fraction of values from the DEM. This
is not the case. For elevation (Fig. 8a) we see that elevations below 3000 m
are undersampled, with the exception of the 1750–2000 m bin, and elevations
above 3000 m are largely oversampled. The measurements are therefore biased
to higher, inland elevations which, if averaged, would likely cause a low
bias in sampled densities. Figure 8b shows that our dataset is sampled best
over central Greenland. More measurements are required from lower elevations
and southern (
Histogram showing the fraction of the density subdataset by modeled 3 m annual air temperature. Red line shows 1990–2015 annual average MAR3.5 model 3 m air temperature distribution for each grid cell across the ice sheet.
Scatterplot showing the MAR 3.5 modeled mean annual 3 m air
temperature in the year the density was measured compared to the mean density
in the top
Because mean annual air temperature is a parameter often used to model
density (e.g., Herron and Langway, 1980; Reeh et al., 2005), Fig. 9 shows the
distribution of density measurements in Greenland in relation to 3 m mean
annual air temperature estimated by the Modèle Atmosphérique
Régional (MAR) model version 3.5 (Fettweis et al., 2013) with a
horizontal resolution of 25 km. We used the National Centers for
Environmental Prediction–National Center for Atmospheric Research Reanalysis
version 1 (NCEP-NCARv1) forced MAR 3.5 simulation (run from 1948 to 2015) to
find the mean annual 3 m air temperature for the year corresponding to when
the density measurement was taken. The NCEP-NCAR forcing was chosen because
it is more reliable than ERA forcings (Fettweis et al., 2017). The red line
in Fig. 9 shows the distribution of annual average temperatures (derived from
1990 to 2015) for the entire GrIS. Figure 9 clearly shows a preferential
sampling of GrIS regions with lower temperatures. Cold temperatures
(
Figure 10 plots all sites in Greenland with density measurements coincidently
sampled to depths of 10, 25, 50, and 100 cm compared to the mean annual
temperature. No clear relationship (Pearson correlation coefficient,
Plot of mean density (circle) and
Snowfall over the GrIS can also be parameterized by elevation and latitude.
Figure 11 shows the distributions of the accumulation measurements over the
GrIS by elevation and latitude. As with the density measurements the
accumulation measurements all come from high elevations on the GrIS (
Summit Station is the only site in the dataset, and on the GrIS, that has been systematically sampled for density and accumulation on a nearly monthly basis. Hence, it is the only location on the GrIS to watch the long-term, decadal, seasonal evolution of snow surface density. Figure 12 shows the monthly mean surface density to depths of 10, 25, 50, and 100 cm. A seasonal cycle is evident in the 10 and 25 cm depth mean densities with a decrease (trough) in density in late summer (August/September) and an increase (peak) in April. The decrease in summer density is likely due to surface hoar, a low-density snow crystal formation that is well known to form at Summit Station in the summer when wind speeds are low and humidity relatively high (e.g., Alley et al., 1990; Albert and Schultz, 2002; Dibb and Fahnestock, 2004). As wind speeds increase and water vapor decreases in the winter the surface snow increases in density. The seasonal signal in density is damped out by 1 m depth at Summit Station. Figure 12 also shows larger natural variability in average density measurements in the top 50 cm compared to the top 100 cm. This is expected as the deeper snow is more insulated from atmospheric and radiative processes in this dry-snow-zone location.
Plot of mean accumulation (circle) and
Figure 13 shows the monthly mean accumulation at Summit Station. Accumulation is highly variable, with slightly lower values in the early summer months (May/June/July). Dibb and Fahnestock (2004) also showed a similar trend in stake measurements and Summit Station from just 2 years of data and explained that the summer season may not actually be seeing a decrease in accumulation, but that thinning layers and densification may be causing the stake measurements to not rise as much in the summertime compared to the wintertime when a snowfall event occurs. Determining whether there is a true decrease in summer accumulation or increase in snow/firn compaction rate at Summit Station requires additional research.
The SUMup dataset is currently available through the Arctic Data Center. It hosts our three subdatasets in both csv and netcdf formats along with metadata files to further explain the methods and citations. The dataset will be updated annually.
We present and describe the SUMup
dataset, an expandable, community-based dataset of field measurements of
surface mass balance components that is consistent in format, has clearly
defined metadata, and is publicly available. The subdatasets include compiled
measurements of snow/firn density (
As seen in SUMup, the measurements over the GrIS and AIS are sporadic in time
and space, peaking during specific field campaigns and lapsing in between,
which makes monitoring change with and understanding processes from field
measurements difficult. This is especially prevalent for parameters like
density and accumulation that change with both seasonal and climatic
atmospheric conditions. Overall, there are gaps in density and accumulation
data from
Density and accumulation measurements of the GrIS oversample cooler, inland regions and undersample coastal, warmer regions. Oversampling these regions may lead to an underestimation of the total average surface density, especially in the summer season, when the measurements are undersampling regions with significant melt processes that increase density. No clear relationship between mean annual temperature and density is seen in the data until a depth of 1 m where a relationship between higher temperatures and increased density is observed. This suggests that additional parameters, such as wind speed and radiative balance, should be considered when modeling density for the GrIS at SUMup density locations and depths above 1 m. Summit Station, Greenland, is the only location with year-round density and accumulation measurements in the dataset, and on the GrIS, and seasonal cycles are evident in accumulation rate and density for depths above 50 cm.
Our analysis of the SUMup dataset shows gaps in ice sheet measurements in the recent decades and in low-elevation regions on the periphery of the ice sheets. These are the exact regions where climate change will have and has had the largest effects on the Greenland and Antarctic ice sheets (e.g., Shepherd et al., 2012; IMBIE Team, 2018; Enderlin et al., 2014) and where additional future measurements are warranted.
We encourage the cryospheric community to contribute additional field data to the SUMup dataset. We also encourage the cryospheric community, including modelers and scientists working in the field of remote sensing, to use this dataset for model validation for surface mass balance and satellite- or airborne-sensor algorithm development. SUMup is a dynamic, living dataset and is expected to be expanded and released annually.
LM compiled the SUMup dataset into the July 2017 and July 2018 datasets, developed the metadata, and reformatted the dataset. She made all figures for this paper and co-wrote the paper. LK co-wrote this paper and developed the first SUMup dataset in 2013. PA helped with the development of the SUMup dataset, performed the initial comparison of the SUMup data to the MAR model, and contributed to the writing of this paper.
The authors declare that they have no conflict of interest.
Lynn Montgomery and Lora Koenig acknowledge National Science Foundation grant PLR 1603407 for funding this work. We thank our two anonymous reviewers and our editor, Reinhard Drews, for providing thorough insight and commentary that helped to greatly improve the quality of the manuscript. Publication of this article was funded by the University of Colorado Boulder Libraries Open Access Fund. Edited by: Reinhard Drews Reviewed by: two anonymous referees