The radiogenic isotope compositions of strontium (Sr) and
neodymium (Nd) on the surface of the Earth are powerful tools for tracing
dust sources and sinks on the Earth's surface. To differentiate between the
spatial variabilities in eolian dust sources in key cryospheric regions at
the three poles (the Arctic; Antarctica; and the “third pole”, covering the high mountainous
area in Asia), a dataset of Sr–Nd isotopic
compositions from extremely cold or arid terrestrial environments was
compiled, similar to the method of Blanchet (2019). The database includes Holocene
and Quaternary
snow, ice, sand, soil (loess), sediment, and rock samples from the three poles based on 90 different references
and our own measurement data, with a total of 1989 data points, comprising 206 data
points with different grain sizes and 212 data points with fraction
measurements. There are 485 data points from the third pole, 727 data points
from the Arctic, and 777 data points from Antarctica. The sampling and
measurement methods of these data are introduced. For each pole,
geographical coordinates and other information are provided. The main
scientific purpose of this dataset is to provide a Sr–Nd dataset based on
collective documentation and our own measurements, which will be useful for
determining the sources and transport pathways of dust in snow, ice, rivers,
and oceans at or near the three poles as well as to investigate whether multiple
dust sources are present at each of the poles. This dataset provides
exhaustive detailed documentation of the isotopic signatures at the three
poles during specific time intervals in the Quaternary period, which are
useful for understanding the sources or sinks of eolian dust and sediments
at the three poles. The dataset is available from the National Tibetan
Plateau Data Center (
The role of mineral dust in the Earth system extends well beyond its impact
on the energy balance and involves interactions with the carbon cycle and
glacier melting on global scales (Skiles et al., 2018; Shao et al., 2011).
The transport of dust from the low midlatitudes, which contain major
deserts that are dust sources, to the Arctic region or Antarctic ice sheet
(AIS) is sensitive to amplified high-latitude climatic variability (Bory
et al., 2003a, b; Lupker et al., 2010; Lambert et al.,
2013; Struve et al., 2020). The isotopic compositions of the radiogenic
elements strontium (Sr) and neodymium (Nd) are powerful tools for tracing
dust sources and sinks because their characteristics vary significantly on
the surface of the Earth (including snow, sand, sediment, loess, and eolian
deposits) (Grousset et al., 1992; Chen et al., 2007; Xu et al., 2012;
Robinson et al., 2021). Therefore, the combination of different isotopic
signatures, specifically
The transport of eolian dust from natural desert regions has also been identified in modern snow and ice records at the so-called “third pole” based on Sr–Nd data (Wu et al., 2010; Xu et al., 2012; Du et al., 2015; Dong et al., 2018). Many studies have focused on dust transport from the western Chinese deserts to the Chinese Loess Plateau (CLP), Pacific Ocean, and even the Greenland ice sheet (GrIS) (Biscaye et al., 1997; Chen et al., 2007; Wei et al., 2021). However, it is still a controversial issue; for example, recent results have emphasized that eolian dust from local sources contributes significantly to high-mountain glaciers (Du et al., 2019a; Wei et al., 2021). Additionally, eolian dust from various source regions, including the Saharan Desert in North Africa and the Gobi and Taklimakan deserts in Asia, can be transported to the GrIS, and great uncertainties still exist (Han et al., 2018).
The Sr–Nd data from snow layers in the Berkner Island ice sheet in western Antarctica, for most of the year, support scenarios that involve contributions from proximal sources (Bory et al., 2010). The Sr–Nd data from insoluble dust in snow samples from East Antarctica indicate that long-distance natural dust primarily originates from Australia and that local dust originates from ice-free areas (Du et al., 2018). The Sr–Nd data from the Taylor Glacier zero-age ice samples and snow samples from Roosevelt Island could be explained by a mixture of at least two local sources (Winton et al., 2016; Aarons et al., 2017). The Sr–Nd data from Holocene ice cores from East Antarctica indicate a well-mixed atmospheric background involving a mixture of two or more sources in the Southern Hemisphere (SH) (Aarons et al., 2016, 2017; Delmonte et al., 2019). The amount of isotopic information is currently adequate for Patagonian and non-Patagonian mineral dust exported from southern South America (SSA) and the East Antarctic ice sheet (EAIS) (Grousset et al., 1992; Gaiero et al., 2007; Delmonte et al., 2010a, b, 2019; Delmonte et al., 2013; Blakowski et al., 2016; Aarons et al., 2017). Major efforts have attempted to solve the “puzzle” of the origin of the potential source areas that contribute dust to the Southern Ocean (SO) and the whole AIS (Gili et al., 2022). However, Sr–Nd data are unevenly distributed across the entire AIS. Measuring the Sr–Nd isotopic compositions in ice cores from Antarctica is a major challenge.
Although considerable Sr–Nd data have been measured, the variations in the characteristics and measurement methods of these data make it necessary to reassess these data on the dust sources in these remote regions. Therefore, the Sr–Nd data obtained from snow, soil, sediment, sand, and other samples should be integrated into a dataset to better serve the environmental and climatic sciences studying the three polar regions in the future. The answers to various questions have been hindered by a paucity of Sr–Nd data, which provide information on potential local and distal dust sources. For these reasons, we measured Sr–Nd data in some samples and collected Sr–Nd data from the literature for the three poles (Fig. 1, Table 1). The objective of this work was to produce a compilation of published and unpublished data from the three poles, and the specific time intervals of Sr–Nd data were limited to the Quaternary period. Furthermore, modern dust (Holocene) in snow or ice and sediment samples from the three poles is further discussed, and the potential dust transport paths in the GrIS and AIS are traced. Similar to the method of Blanchet (2019), here, we compile published and unpublished Sr–Nd data with an integrated filtering system from three remote poles, in which these data were collected in extremely cold and arid environments, and most of the data have not been included in previously published datasets. The dataset will help trace modern natural dust, reconstruct past environments, and extend the database of terrestrial and marine radiogenic Sr and Nd isotope data in the Earth and environmental sciences.
Data distribution locations and sample types for
Sr–Nd data from snow, sand, soil, cryoconite, loess, and sediment samples
from the three poles (namely the high mountainous regions in Asia, the
Arctic, and Antarctica) were collected from our own research and existing
literature (Fig. 1). Sr–Nd data from the third pole cover the area from
40 to 23
Map of the sampling regions in the three poles. The third pole covers
the high mountainous areas in Asia, and data were collected in the area from
40 to 23
Snow, sand, and soil samples associated with third pole glaciers and
potential source areas of dust generation. The headers from left to right represent the following: “Label” – the number
assigned to each glacier; “Subregion” – the isotopic subregion of the third pole; “Glacier name” – the name of the glacier; “Site name” – the name of the
sampling site where the samples were taken; “Longitude” and “Latitude” – the sampling
location; “Mountains” – the mountain/mountain range studied; “Sample type” – snow, sand, or soil; “Elevation” – the elevation in meters above sea level (m a.s.l.); “
In this study, 2 sand samples from Kangerlussuaq in West Greenland,
4 sand samples from on King George Island, and 11 sand samples from
Inexpressible Island in the Ross Sea in West Antarctica were collected and
measured. In general, the upper 2 or 5 cm of surface topsoil
(sand) was collected with a trowel and stored in precleaned plastic bags or
bottles. The sediment samples from shelves and ridges in the Arctic Ocean
(AO), which were mostly retrieved from core archives, were collected through
subsampling the upper surface of the core tops (with rare exceptions)
(Maccali et al., 2018). Different grain sizes (
Snow samples were collected from a snow pit at a vertical resolution of 5–20 cm, following the clean-hands protocol, with sampling personnel wearing
integral Tyvek® bodysuits, non-powdered gloves, and
masks to avoid possible contamination (Xu et al., 2012). In this study, one
1.0 m snow pit with a resolution of 10 cm was dug in the GrIS, and four fresh snow samples (M1, M2, M3, and M4) were sampled
on sea ice in the AO during the Multidisciplinary Drifting Observatory for
the Study of Arctic Climate (MOSAIC) mission in October 2020. Surface (2–10 cm) fresh-snow samples at different resolutions (with different thicknesses,
widths, and lengths) from the Greenland and Antarctica ice sheets were
excavated and placed in 5 L Whirl-Pak bags (Du et al., 2018,
2019a, b). Three horizontal snow layers were collected in Greenland and
Antarctica snow pits (Bory et al., 2003b, 2010). The dust in the
ice cores was extracted using the same method as that for the snow samples.
Snow or ice core samples represent either bulk samples or have different
grain sizes (
Sr–Nd isotope datasets from snow, ice cores, sand, sediment, soil, and loess samples from the third pole, the Arctic, and Antarctica were compiled. Data were collected from 90 different references with 2847 data points. In total, 485 data points were collected from the third pole, 727 data points were collected from the Arctic, and 777 data points were collected from Antarctica. In addition, 259 data points were collected from the pan-third pole (including the Tibetan Plateau, the Pamirs, Hindu Kush, Tienshan, the Iranian Plateau, Caucasus, the Carpathians, and the surrounding deserts), and 181 data points were included from potential source areas (PSAs) in the SH. Details on the geographical coordinates and original information can be found in this dataset, and the locations of these samples are shown on maps (Figs. 2, 4, 5, and 7). To keep the naming scheme uniform, the dataset assembled the names of each sample based on the work by Blanchet (2019). This dataset was built by incorporating data from the literature and our own database; in particular, units, source or sink, and geographical coordinates are marked in the dataset. Note that whether the Sr–Nd data represent source or sink information needs to be further determined by the detailed depositional environment or sampling locations. For example, the loess samples from the CLP represent a sink, but they also represent a dust source for the Pacific Ocean. Therefore, these samples were marked as a mixture. The sediment samples from the coasts of the SO or AO (rivers and dune sand) were also marked as a mixture. An overview of the input data is shown in Table 1. This study focuses on large amounts of different data, including data on snow, ice, sand, soil, loess, and sediment. These data are based on our own measurements, author contributions (data published), and literature searches.
All procedures were performed in clean lab facilities. The sand, loess,
sediment, cryoconite, and dust samples extracted from snow and ice cores were
generally digested with ultrapure acid (HNO
The grain-size effect in different samples resulted in variations in the
The
Therefore, the provenance, grain size, lithogenic nature, and measurement methods of the Sr–Nd data from different media in this dataset must be considered when interpreting Sr–Nd isotope compositions in order to obtain good results.
Table 2 and Fig. 2 provide an overview of the information on samples from the third pole (the serial number of glaciers, subregions, glacier name, name of the sampling site where the samples were taken, sample type, sample age, sample elevation, and sampling longitude and latitude). The dust in snow and ice in the third pole originates from PSAs; therefore, the Sr–Nd data from these samples represent the characteristics of sinks. The Sr–Nd data from local or arid desert sand and soil represent the characteristics of PSAs. As an example, the isotopic signatures of the insoluble dust in snow and ice (sinks) at the third pole can be used to identify PSAs based on the data and geographic characteristics of sand and soil samples from the local exposed bedrock and distal arid deserts, from which dust is transported over long distances. Standard Sr–Nd measurement methods were applied to the snow samples (Xu et al., 2012; Du et al., 2015, 2019a; Dong et al., 2018; Wei et al., 2019, 2021), and standard measurement methods were applied to the sand or surface dust samples (Chen et al., 2007; Nagatsuka et al., 2010). The data results seem to remain fully consistent with these references.
On the basis of sorting criteria for determining PSAs based on the distributions of mountains and glaciers, geographic features, and isotopic values (snow or ice from the third pole glaciers, sand and soil from local and distal arid deserts), the third pole was divided into the following six isotopic subregions (Fig. 2):
There is an increasing
The glacier and desert distributions in western China. The different colored oval and rectangular shapes represent the six subregions in the third pole: PSAs and glaciers, the Tienshan Mountains, the Kunlun Mountains, the Qilian Mountains, the Himalaya Mountains, and the Hengduan Mountains. Numbers and white squares represent 22 glaciers (snow samples were collected from these glaciers), the names of which are shown in Table 2. The numbered circles represent the 10 desert or sandy areas in China: 1. Gurbantünggüt Desert, 2. Onqin Daga sandy land, 3. Horqin sandy land, 4. Hunlun Buir sandy land, 5. Taklimakan Desert, 6. Qaidam Desert, 7. Badain Jaran Desert, 8. Tengger Desert, 9. Hobq Desert, and 10. Mu Us Desert. The green solid circles represent sand and soil samples. (This figure was created with ArcGIS.)
Box plot of the Sr–Nd isotope signatures of third pole PSAs and
snow samples. Samples are located in each PSA based on the data from
Table 2 (the number of samples for each subregion are presented,
Considerable Sr–Nd data have been obtained from modern snow and ice samples from the Arctic and surface (including sea-ice-transported sediments) sediment in the AO. These data cover the entire Arctic and represent the characteristics of a sink (Fig. 4). The data points are presented in Table 3. Sr–Nd data from arid deserts (East Asian and Saharan deserts) have been compiled in previous datasets (Blanchet, 2019; Robinson et al., 2021), and these data are useful for tracing terrigenous material transport in the Arctic. For user-friendly selection of the Sr–Nd data according to the modern environmental characteristics and the geographical location, Sr–Nd data from deep ice cores are not included in Fig. 5. We compared the Sr–Nd data from the surface snow (sink) and marine sediment (sink or source) samples in the Arctic (Figs. 5, 6). Based on the isotopic signals of these samples, geologic units, adjacent seas, and drainage basins of the main river systems in the Arctic, the Sr–Nd patterns can be divided into 12 subregions according to Maccali et al. (2018).
Distribution of sampling sites in the Arctic. The types of samples are denoted using different shapes and colors (see legend; Table 3). The abbreviations used in the figure are as follows: AO – Arctic Ocean, BCS – Bering–Chukchi Sea, BS – Barents Sea, CAA – Canadian Arctic Archipelago, ESS – East Siberian Sea, KS – Kara Sea, LS – Laptev Sea, and SV – Svalbard. (This figure was created with ArcGIS.)
Snow, cryoconite, sand, soil, and sediment samples located in the Arctic. The headers from left to right represent the following: “Label” – the number assigned to each sample; “Subregion” – the name of a mountain range, ocean or ice sheet; “Location” – the name of the sampling site where the samples were taken; “Sample type” – snow, cryoconite, sand, or soil; Reference – reference publications. The reader is referred to the caption of Fig. 6 for subregion definitions. NA stands for not available.
Sr–Nd data from the East Greenland Ice Core Project (EGRIP) and the North
GRIP (NGRIP) were obtained from snow pits. Sr–Nd data were also measured in
the Greenland Ice Core Project (GRIP), the Greenland Ice Sheet Project 2 (GISP2), and the North Greenland Eemian Ice Drilling (NEEM) ice cores, and in the Renland, Site A, Hans Tausen, and Dye 3
shallow ice cores. The Sr–Nd data exhibit large differences in these samples
(Fig. 5). The Sr–Nd data from NGRIP snow indicated that the dust sources
were variable and showed complicated dust sources at the same location (Bory
et al., 2002, 2003b). Much more Sr–Nd data have been recently
measured from sand, soil, cryoconite, moraine, and englacial dust samples on
the periphery of the GrIS (Nagatsuka et al., 2016). In these samples,
the
The mainstream view of the provenance of dust in inland Greenland deep ice
cores (GISP2 and GRIP) is that the dust is from the eastern Asian deserts
(the Gobi and Taklimakan deserts) based on the best Sr–Nd data matches
during the last glacial period (Biscaye et al., 1997; Svensson et al.,
2000; Újvári et al., 2015). High-resolution Sr isotope data from the
Greenland NEEM ice core suggest that there was a significant Saharan dust
influence in Greenland during the last glacial period (Han et al., 2018).
The Sr–Nd data (
Surface eolian dust from mid- or high-latitude continental weathering and
arid deserts may be the most important dust contributor to snow and ice
cores. The
Box plot for the Sr–Nd isotopic signatures of the Arctic, including
the 12 subregion samples of snow, sand, soil, and sediment from sea-ice and
sediment cores in the dataset (the number of samples for each subregion is
presented,
Terrigenous material from the Arctic marginal seas, including the
Bering–Chukchi Sea (BCS), the Barents Sea (BS), the Canadian Arctic Archipelago
(CAA), the East Siberian Sea (ESS), the Kara Sea (KS), and the Laptev Sea (LS), is
transported to and deposited in the AO and may be the primary material
source for marine sediment. The Sr–Nd data from Arctic surface sediments
were based on the literature (Fig. 6), and most samples were sieved at
By integrating the literature and adding data with new evidence, the dust
provenances of low-elevation areas on the periphery of the AIS in the
Holocene (including modern) are discussed. The dataset provides a
comprehensive overview of the state of knowledge of dust sources and sinks
in different sectors of the AIS and PSAs in the SH. The location of Sr–Nd
datasets from different sectors of Antarctica and the AIS are presented in Fig. 7. The Sr–Nd data from Antarctica are not evenly distributed, and more data
have been measured in western Antarctica and the Ross Sea. The Sr–Nd data
from PSAs in the SH (Australia, SSA, and SA) clearly reflect the
characteristics of these regions and provide insight for tracing dust
source–sink paths. For example, the
The locations of the samples for Sr–Nd isotope ratios from Antarctica and PSAs in this database. The dust transport paths are marked with yellow arrows, based on previous studies (Gaiero et al., 2007; Shao et al., 2011; Gili et al., 2022). (This figure was created with ArcGIS.)
The Sr–Nd data from the marine sediment (near-core-top samples) from the
circum-Antarctica region and terrigenous materials (eolian dust, glacial
drift, and dust in ice core) from the AIS are presented in Fig. 8. The ages
of these samples were limited to the Holocene. We compared these data with
PSA samples from the SH. At some sites, with
Sr and Nd isotopic compositions of Holocene samples (black circles) from the AIS and ice-free areas on its periphery as well as eolian dust samples (surface samples with no accurate ages) from PSAs in Australia, southern Africa, and South America. The colors were determined by inverse distance-weighted interpolation using ArcGIS.
New Sr–Nd data from coastal and low-elevation sites were measured in
ice-free areas near the Filchner–Ronne Ice Shelf, Ross Ice Shelf, and Amery
Ice Shelf (Fig. 7). The Sr–Nd isotope compositions of four sand samples from
southern King George Island (South Shetland Islands) in West Antarctica were
characterized by less-radiogenic
However, because the Sr–Nd data differ significantly among some of the
regions and are similar among others, care must be taken when
directly comparing these data to precisely explain the observed isotopic
compositions in ice core records. For example, there is overlap in the Sr
and Nd isotopic compositions of King George Island, SSA (Patagonia), and the
McMurdo dry valleys. The Sr–Nd data from Inexpressible Island also overlap
with the other end-members (SA, New South Wales, and Prydz Bay). Therefore,
dust from low-latitude regions (New South Wales and SA) cannot be excluded
from East Antarctica (Du et al., 2018; Gili et al., 2022). Another example
is the characteristics of snow layers of the Berkner Island ice sheet in
western Antarctica. These data can be partly explained by the surface
sediment samples from the Weddell Sea sector, with
Information on Sr–Nd data in Antarctic ice cores during the Holocene and
glacial–interglacial times is presented by integrating the literature (Du, 2022). To obtain enough dust particles, samples with different age
intervals were merged. For example, each sample represents approximately
40–160 years for the Vostok ice core, which may cover about a few thousand years to obtain a single large-volume sample (Delmonte et al., 2008). Alternatively,
several ice core sections from different depths were integrated to obtain a
few large samples for the Sr and Nd isotope analyses of the Talos Dome ice
core (Delmonte et al., 2010b). A relatively high resolution (spanning
between
However, samples from glacial stages (stage 4 at
All datasets and the associated metadata table presented in this study are
available from the Big Earth Data Platform for Three Poles. The dataset
can be downloaded from
An integrated Sr–Nd dataset for the remote three poles is presented, and
these data were not easily collected because of the extremely cold and
high-elevation environments. The dataset is complicated and includes snow,
sand, soil, loess, deposits, sediment, and other sample types. We present
case studies of snow, ice cores, and sediment samples to demonstrate the
Sr–Nd characteristics of the third pole glaciers and Arctic and Antarctic
ice sheets. These integrated data can provide a new perspective on present
and paleodust sources and sinks at the three poles and, more importantly,
clearly emphasize the following points for potential users of the datasets
provided with this paper:
This Sr–Nd dataset enables us to map sampling locations in the remote three
poles, while the use of sorting criteria related to the sampling location,
type, or resolution permits us to trace the dust sources or sinks based on
isotopic signatures. For the third pole, the Sr–Nd isotopic data were divided into subregions,
and the integration of these data from sand/soil and snow samples in six
subregions allowed us to clearly understand the Sr–Nd data characteristics
of the third pole. These data will be useful for users seeking to trace the
local or long-distance-transported dust from the source to the sink. The Sr–Nd characteristics in snow/ice and sediment samples show that there
are significant differences among the different subregions in the Arctic,
which will be useful for tracing dust sources and sinks The new data from Arctic and Antarctic samples emphasize that the ice-free
regions on the periphery of the ice sheets may be important local dust
sources. However, there is Sr–Nd data overlap in the low-latitude regions in
Antarctica, and the paucity of data in Antarctica is severe; thus, future
studies should concentrate on this aspect.
CX, ZD, and AS designed the study. ZD, JY, CX, and AS wrote the manuscript. ZD, LW, NW, SW, and YL collected the samples in the field and produced the data. ZD, NW, LW, SW, YL, ZZ, JX, and XM performed the analysis. All authors contributed to the final form of the paper.
The contact author has declared that none of the authors has any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the special issue “Extreme environment datasets for the three poles”. It is not associated with a conference.
We thank everyone involved in the snow
sample collection from the central Arctic Ocean, within the framework of the
RV
This research has been supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XAD19070103), the National Natural Science Foundation of China (grant nos. 42071086 and 41971088), the State Key Laboratory of Cryospheric Science (grant no. SKLCS-ZZ-2022), and the Youth Innovation Promotion Association of the Chinese Academy of Sciences (grant no. 2020419).
This paper was edited by Xin Li and reviewed by Cecile Blanchet and two anonymous referees.