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ARTICLEOPEN

A comprehensive dataset of luminescence chronologies andenvironmental proxy indices of loess-paleosol deposits
across Asia
Guoqiang Li 1 ✉, Zhongfeng Yan1, Yougui Song2 ✉, Kathryn E. Fitzsimmons3 ✉, Shuangwen Yi4 ✉, Shugang Kang2 ✉, Chongyi E5 ✉,Thomas Stevens6,7 ✉, Zhongping Lai8 ✉, Aditi K. Dave 3,9 ✉, Chunzhu Chen10 ✉, Yanqing Deng1 ✉, He Yang11 ✉, Leibin Wang12,
Xiaojian Zhang 4, Caixin Qin1, Qiuyue Zhao13, Jan-Pieter Buylaert14, Tao Lu15, Yixuan Wang15, Xiangjun Liu16, Zhiyong Ling15,
Qiufang Chang17, Haitao Wei1, Xiaoyan Wang1 and Fahu Chen 18 ✉

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Loess-paleosol sequences have been used in Asia to study climate and environmental changes during the Quaternary. The scarcity
of age control datasets and proxy indices analysis data for Asian loess has limited our understanding of loess depositional processes
and the reconstruction of paleoclimatic changes from loess-paleosol records. In this study, we present a dataset that includes 1785
quartz optically stimulated luminescence ages and 1038 K-feldspar post-infrared infrared stimulated luminescence ages from 128
loess-paleosol sequences located in different regions of Asia. We generate 38 high-resolution age-depth models of loess records
based on the provided datasets. We provide data on 12,365 grain size records, 14,964 magnetic susceptibility records, 2204 CaCO3
content records, and 3326 color reﬂection records. This dataset contains the most detailed and accurate chronologies and proxyindex data for loess records in Asia yet published. It provides fundamental data for understanding the spatial-temporal variations inloess depositional processes and climatic changes across the continent during the mid-late Quaternary.
npj Climate and Atmospheric Science (2024)7:7 ; https://doi.org/10.1038/s41612-023-00555-4

INTRODUCTION
Eolian loess is a wind-blown silt-dominated sediment mostly
formed during the Quaternary period, which covers ~10% of theland surface of the planet1,2. Generally, it has a homogenous andporous structure and consists primarily of quartz and feldspar
grains3. Loess in Asia is mainly distributed across arid and semiarid regions of mid-latitude Central and East Asia, with somesporadic distribution in Western Asia4. These regions include thenorthern Iranian loess plateau5, piedmont regions of the highmountains in Central Asia and NW China2, Chinese Central Loess
Plateau6, NE China7, the Tibetan Plateau8 and some coastal
regions of the Shandong Peninsula9, and southwestern andnortheastern Siberia10. Loess-paleosol sequences from the Chinese Central Loess Plateau have long been regarded as globally
important archives for paleoclimatic and environmental changes
due to their relatively continuous and high dust deposition rates
on both orbital and suborbital timescales, thus consequently
acting as sensitive archives of the interplay of the East Asiansummer and winter monsoon (EASM and EAWM,
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respectively)6,11–14. Similarly, loess-paleosol sequences acrossdifferent regions of Asia have great potential for unraveling the
Quaternary climatic and environmental changes in regions of Asia
where the interaction of westerlies, the Asian monsoon systems,
and high altitude air masses inﬂuence the climate over different
timescales15–17. However, loess-paleosol records from different
regions beyond the Chinese Central Loess Plateau are still poorly
understood, primarily due to the limited availability of independent age control and proxy indices for paleoclimaticreconstruction.
Although radiocarbon dating is widely used in terrestrial
settings, this method of is problematic for dating loess deposits
from arid and/or semi-arid regions of mid-latitude Asia because of
the general lack of reliable dating materials (e.g., plant remains
and charcoal)18 and the upper age limits of c. 40 kyr BP which
prevent chronological constraints of the lower parts of long loessrecords. Furthermore, the use of carbonate and bulk organic
matter of loess-paleosol sequence may cause overestimation
related to redeposition of older carbon19,20 or underestimation

Key Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China.
State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an, China. 3Department of Geosciences, University of
Tübingen, Schnarrenbergstrasse 94‐96, 72076 Tübingen, Germany. 4School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China. 5Qinghai Provincial Key
Laboratory of Physical Geography and Environmental Processes, School of Geographical Science, Qinghai Normal University, Xining 810008, China. 6Department of Earth
Sciences, Uppsala University, Villavagen 16, Uppsala 75236, Sweden. 7Department of Geosciences and Geography, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland.
8
Institute of Marine Sciences, Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Protection, Shantou University, Shantou, Guangdong 515063, China.
9Interdisciplinary Research Institute on Bionanosciences, Babes Bolyai University, Cluj-Napoca, Romania. 10School of Geographic Science, Nantong University, Nantong 226007,
China. 11School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610000, China. 12Centre for Climate and Environmental Changes, School of
Geographical Sciences, Guangzhou University, Guangzhou, Guangdong 510006, China. 13Key Laboratory of Tourism and Resources Environment in Universities of Shandong,
Taishan University, 271000 Taian, China. 14Department of Physics, Technical University of Denmark, DTU Risø Campus, Roskilde, Denmark. 15Key Laboratory of Salt Lake Geology
and Environment of Qinghai Province, Qinghai Institute of Salt Lakes, Chinese Academy of Sciences, Xining 810008, China. 16School of Geography and Tourism, Jiaying
University, Meizhou, China. 17School of Geography and Tourism, Zhengzhou Normal University, Zhengzhou, China. 18Key Laboratory of Alpine Ecology (LAE), CAS Center for
Excellence in Tibetan Plateau Earth Sciences, Institute of Tibetan Plateau Research, Chinese Academy of Sciences (CAS), Beijing 100101, China. ✉email: gqli@lzu.edu.cn;
ygsong@loess.llqg.ac.cn; kathryn.ﬁtzsimmons@uni-tuebingen.de; ysw7563@nju.edu.cn; kshg@ieecas.cn; echongyi@163.com; thomas.stevens@geo.uu.se;zhongping_lai@stu.edu.cn; aditikrishna.dave@gmail.com; pollenchencz@gmail.com; dengyq19@lzu.edu.cn; yanghe@cuit.edu.cn; fhchen@itpcas.ac.cn
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related to younger materials incorporation through bioturbation,
penetration, and subsequent decomposition of rootlets ortranslocation of soluble organic matter21,22.
Luminescence dating techniques, which include the widelyused optically stimulated luminescence (OSL) and infraredstimulated luminescence (IRSL), determine the ages of eolian
deposits by directly measuring last exposure of quartz or feldspar
minerals to light, and thereby the depositional process23. Quartz
OSL dating is now widely applied to the dating of late Quaternary
sediments24, and has been successfully used to establish thechronology of loess records from different regions ofAsia2,5,8–10,16,17,25–28. Owing to the luminescence signal saturation
characteristics of the quartz mineral, it has been observed thatquartz OSL dating can be used to reliably date loess samples up to
50–60 ka from the Chinese Central Loess Plateau and up to
40–70 ka for samples from Tibetan Plateau and Central
Asia16,17,29–33. The more recently developed approach of
K-feldspar post-InfraRed InfraRed (pIRIR) dating, using a post-IR
IRSL signal, can be used to date loess samples up to
250–300 ka34–36 and has been successfully applied to loesspaleosol sequences in different regions of Asia7,16,17,25,30,37,38.
High-resolution luminescence dating of loess-paleosol sequencesfrom the Tianshan Mountain piedmont, Tibetan Plateau, northern
Iran loess plateau and Chinese Central Loess Plateau shows that
depositional hiatuses spanning a few ka to as much as 60 ka
occurred in loess-paleosol sequences in these regions5,16,25,30,32,39,
suggesting that high-resolution absolute chronological information is necessary for the reconstruction and interpretation of
paleoclimatic changes from loess records. Furthermore, proxyindex analyses of loess records from different regions of arid midlatitude Central Asia are still limited, especially the data spanningthe past glacial-interglacial cycle. As yet, there is no publicly
available dataset covering systematic quartz and K-feldspar
luminescence chronologies and proxy index analyses from loesspaleosol sequences from different regions of Asia. Indeed, data
scarcity has signiﬁcantly hindered our understanding of loessdepositional processes and reconstruction of paleoclimaticchanges from loess-paleosol records in different regions of Asia.
In this study, we compile a comprehensive dataset of quartz
OSL and K-feldspar pIRIR ages, and construct high-resolution agedepth models from loess records in Asia spanning the past
250–300 ka, based on these ages. Furthermore, we also collate
proxy index datasets of grain size, magnetic susceptibility, CaCO3
content and color reﬂection for loess records across different
regions of Asia (Supplementary Fig. 1). This dataset comprises
1785 quartz and 1038 K-feldspar pIRIR ages from 128 loesspaleosol sequences and 38 high-resolution age-depth models of
loess records, and a total of 12,365 granulometric records
including mean grain size, clay fraction (e.g., <2 µm, %), silt
fraction (e.g., 2–63 µm, %) and sand fraction (>63 µm, %), 14,964
magnetic susceptibility records, 2204 CaCO3 content records and3326 color reﬂections records from loess-paleosol sequences from
Asia. This dataset can provide the fundamental basis for analyzing
the characteristics of loess and paleosols in different geographicalregions of Asia, and clarifying spatio-temporal changes in loessdepositional processes and loess mass accumulation rate acrossAsia during mid to -late Quaternary. This dataset can also be used
for late Quaternary climatic reconstruction for mid-latitude Asia, as
well as for comparison with climatic reconstructions fromneighboring European loess records40. In addition, thedataset also facilitates evaluation and understanding of how dust
emission and loess depositional processes respond to theinﬂuence of and/or the interaction with different climatic systems
of westerlies, EASM and EAWM over varying timescales.
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RESULTS
Luminescence chronologies and proxy indices of Asian loess
A total of 128 loess-paleosol sections across different regions ofAsia were investigated and sampled (Supplementary Fig. 2),
including 5 sections with depths of 1.24–50.00 m from northern
Iranian loess Plateau, 32 sections with depths of 1.4–44.0 m fromCentral Asia, 52 sections with depths of 0.48–36.20 m from the
Tibetan Plateau, 20 sections with depths of 2.85–36.00 m from
Chinese Central Loess Plateau, 13 sections from the Shandong
Peninsula of eastern China, 4 sections from NE China, and2 sections from western Siberia, respectively. Detailed information
and references of these sections can be found in the Supplementary Table 1. The data corresponding to 2823 luminescence ages
including 1785 quartz OSL and 1038 K-feldspar pIRIR ages werecollected from published records of the aforementioned 128
loess-paleosol sequences across different regions of Asia. The agedepth modeling of 38 loess-paleosol sections, spanning the past
250–300 ka, was reconstructed based on close-spaced quartz OSLages and/ or K-feldspar pIRIR ages from each section. As far as weknow, this dataset includes the most detailed and accurate
previously published record of quartz OSL and K-feldspar pIRIR
ages, as well as a number of age-depth models based on
luminescence ages for eolian records across Asia.
A total of 12,365 grain size, 14,964 magnetic susceptibility, 2204
CaCO3 content and 3326 color reﬂection data was collected forsamples from these loess-paleosol sequences. It must be notedthat 51 of the 128 sections compiled this paper does not have any
data on proxy indices, and hence, this should be kept in mind
while reconstructing large-scale palaeoclimatic variations over
Asia. The detailed location and depth information of each section
is listed in Supplementary Table 1, and illustrated here in Fig. 1.
These loess-paleosol sections compiled here, not only cover alarge geographical extent but also different geomorphologicaland elevation settings, and spans different climatic systems ofwesterlies, EASM, EAWM, and ISM2,6,11,26. Thus, the ~250 ka datasetof absolute chronology and proxy indices of these loess-paleosol
sequences, makes this the most comprehensive and representative dataset to date for mid to late Quaternary loess depositionand paleoclimatic reconstruction across mid-latitude Asia. The
dataset presented in this article is freely available through the
National Tibetan Plateau/Third Pole Environment Data Center,
https://doi.org/10.11888/Paleoenv.tpdc.30049641.
DISCUSSION
The concentration of U in loess samples from Asia generally varies
between 1 and 4 ppm, except a few samples that either have very
low values of 0.5–1.0 ppm or very high values of 4–5 ppm. The
average U concentration of loess samples from all over Asia is
2.53 ± 0.01 ppm (n = 2246), while the regional average U concentration of loess samples from Western Asia, Central Asia, Tibetan Plateau,
Chinese Central Loess plateau, NE China, Shandong Peninsula andWestern Siberia is 2.44 ± 0.04 ppm (n = 104), 2.93 ± 0.03 ppm(n = 528), 2.41 ± 0.03 ppm (n = 537), 2.57 ± 0.02 ppm (n = 716),
1.93 ± 0.04 ppm(n = 66),
1.98 ± 0.03 ppm(n = 237)
and2.73 ± 0.12 ppm (n = 58), respectively (Fig. 2a). The concentration of
Th in loess samples from Asia varies between 6 and 16 ppm, whereina few samples either have comparatively slightly higher or lower
values of 16–37 and 2–6 ppm, respectively. The average Th
concentration of loess samples across mid-latitude Asia is11.21 ± 0.05 ppm (n = 2398), while the regional average Th concentration of loess samples from Western Asia, Central Asia, Tibetan
Plateau, Chinese Central Loess Plateau, NE China, Shandong Peninsula
and Western Siberia is 9.44 ± 0.15 ppm (n = 104), 11.54 ± 0.09 ppm(n = 528), 11.45 ± 0.13 ppm (n = 537), 11.32 ± 0.06 ppm (n = 868),
8.49 ± 0.20 ppm(n = 66),
11.25 ± 0.13 ppm(n = 237)
and10.42 ± 0.17 ppm (n = 58), respectively (Fig. 2b). The K content of
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Fig. 1 The map of loess distribution and locations of loess-paleosol sections in the different regions of Asia. The software 91 satellite map(https://www.91weitu.com/) was used to download the satellite imagery from Google Earth. The map generation was performed in software
ArcGIS 10.7.

loess samples from Asia varies between 0.8% and 2.8%, except one
sample (TC-2) that has a high value of 3.5%. The average K% of loesssamples from Asia is 1.890 ± 0.005% (n = 2398), and the regional
average K content of loess and paleosol samples from Western Asia,
Central Asia, Tibetan Plateau, Chinese Central Loess Plateau, NE China,
Shandong Peninsula and Western Siberia is 1.84 ± 0.03 (%) (n = 104),
2.02 ± 0.01 (%) (n = 528), 1.83 ± 0.01 (%) (n = 537), 1.86 ± 0.01 (%)(n = 868), 2.18 ± 0.02 (%) (n = 66), 1.81 ± 0.02 (%) (n = 238) and1.74 ± 0.03 (%) (n = 58), respectively (Fig. 2c). The concentration of Rb
in loess samples from Central Asia and the Tibetan Plateau varies
between 65 and 140 ppm, with an average of 97.34 ± 0.74 ppm(n = 327) (Fig. 2d). There is no substantial trend of U, Th, K and Rb
concentration changes for loess samples from western Asia to central
and eastern Asia, as well as for loess samples from different latitude
from western Siberia to Tibetan Plateau.
The observed water content of loess samples from Western Asia
to Central and Eastern Asia varies from 0 to 25%, except a few
samples that have a slightly higher water content of 25–40%. The
average water content of all loess samples from across midlatitude Asia is 10.63 ± 4.00% (n = 700) (Fig. 3). The life-averagewater content used in dose rate calculation basically varies
between 5 and 25%, with an average value of 11.66 ± 4.66%(n = 2328), which is fundamentally consistent with the averageobserved water content of 10.63 ± 4.33% (n = 700). Thus, indicating the suitability of life-average water content used in dose rate
calculation. The calculated dose rates of different grain size quartz
and -feldspar by using the radioactive element concentration of U,
Th, K and Rb and water content is shown in Fig. 4. The calculated
dose rate of most 63–90 μm size quartz samples varies between
2.2 and 4.3 Gy ka−1 with an average of 3.06 ± 0.01 Gy ka−1(n = 631), the calculated dose rate of most 38–63 μm size quartz
samples varies between 2.5 and 5.5 Gy ka−1 with an average of
3.49 ± 0.04 Gy ka−1 (n = 248), and the calculated dose rate of most
4–11 μm size quartz samples varies between 2.5 and 4.6 Gy ka−1
with an average of 3.69 ± 0.02 Gy ka−1 (n = 386). While thecalculated dose rate for 63–90 μm size K-feldspar samples varies
between 2.5 and 4.7 Gy ka−1 with an average value of
3.53 ± 0.01 Gy ka−1 (n = 610), the calculated dose rate for
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38–63 μm size K-feldspar samples varies between 3.4 and 4.2 Gyka−1 with an average value of 3.68 ± 0.06 Gy ka−1 (n = 14), and thecalculated dose rate for 4–11 μm size K-feldspar samples varies
between 3.1 and 5.7 Gy ka−1 with an average value of
4.18 ± 0.06 Gy ka−1 (n = 99); these values are slightly higher
compared to quartz samples due to internal dose rate contribution from K and Rb in K-feldspar grains.
The 1785 quartz OSL ages on loess samples from 121 loesssections in Asia range from 0.1 to 115.0 ka, where most ages areless than 80 ka (Fig. 5). The error on most loess quartz OSL ages
vary between 3 and 10%, and has an average error of 7.18 ± 0.08(%) (n = 1785). A high error of 10–44% has been observed foryoung samples with ages less than 2 ka, which is likely due to theweak luminescence signal of young samples and the systematic
errors related to quartz OSL dating.
A total of 921 pIRIR ages (including pIR50IR290 and pIR200IR290ages) were compiled on loess samples from 12 sections andranges from 2 to 300 ka. Here, only 2 samples have an age of lessthan 2 ka, while the ages of the 866 samples are larger than 10 ka(Fig. 6). All ages larger than 250 ka are considered as minimum
ages due to the saturation and/or near saturation of the pIRIR
signals, which can be seen in the pIRIR ages older than 250 ka, thathave not shown an obvious increase with depth in QS16 and ZD17
from Central Asia and JingbianA–E sections from Chinese Central
Loess Plateau. The error on most pIR200IR290 ages of K-feldspar
samples generally varies between 1 and 10%, except a few highvalues of 10–45% for ages less than 10 ka (Fig. 6b). The average
error on pIR200IR290 ages for all samples are 5.45 ± 0.13% (n = 642).
The pIR50IR290 ages of loess samples from Western Asia, Central
Asia, NE China, Shandong Peninsula and Western Siberia mostly
range from 3 to 141 ka (Fig. 6a). The error on most K-feldspar
pIR50IR290 ages varies between 2 and 21%, and has an average
error of 6.66 ± 0.13% (n = 279). The 5 youngest samples in thepIRIR age dataset have a large error of 10–20%, compared to theother 266 samples, that have an error of less than 10%.
As illustrated in Fig. 6c, the 117 pIR50IR170 ages from sections
LJW10 and NLT17 from the Tianshan Mountain region, sections
HS18, Hebei and LD from NE Tibetan Plateau, and section XZP
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Fig. 2 The summary of concentrations of radioactive elements data for loess samples from different regions of Asia. a–d is U, Th, K, andRb concentrations, respectively.

Fig. 3The observed water content and water content used in age calculation for loess samples from different regions of Asia.

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Fig. 4 The summary of dose rate data of samples from loess-paleosol sequences in different regions of Asia. a–c are the dose rate data of
63–90, 38–63 and 4–11 μm quartz samples, respectively. d–f are the dose rata data of 63–90, 38–63 and 4–11 μm K-feldspar samples,
respectively.

Fig. 5 The quartz OSL ages (solid symbol) and relative error of ages (hollow symbol) of samples from loess-paleosol sections in different
regions of Asia.

from Shandong Peninsula ranges from 0.4 to 76.0 ka, where mostof ages are less than 50 ka. The pIR50IR170 ages larger than 50 ka
can be used as minimum ages due to the underestimation of
pIR50IR170 signal for samples larger than 50 ka33,39. The error on
most pIR50IR170 ages of K-feldspar samples generally vary between
4 and 8%, and has an average error of 6.29 ± 0.49% (n = 117). The
error on 7 very young samples of ages less than 4 ka, varies

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between 8 and 50%, and shows an obvious increase compared torelatively older samples.
A total of 38 age-depth model were generated for loesspaleosol sequences from Asia, including 22 from Central Asia
(sections ZSP, Zhaosu, SG18, Xiaoerbulake, Talede, XEBLK, KETB,
XY17, NLK, ZD17A, ZD17B, ZD17C, ZD17D, KS15, NLT17, LJW10,
QS16, BYH10, CJB21-1, CJB21-2, BG21-1 and XG21-1), 3 from
Tibetan Plateau (sections XY18, RYS17 and HS18), 10 from Chinese
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Fig. 6 The summary of K-feldspar pIRIR ages for samples from loess-paleosol sections in different regions of Asia. a is the K-feldspar
pIR50IR290 ages (solid symbol) and relative error of ages (hollow symbol). b is the K-feldspar pIR200IR290 ages (solid symbol) and relative error ofages (hollow symbol). c is the K-feldspar pIR50IR170 ages (solid symbol) and relative error of ages (hollow symbol).

Central Loess Plateau (sections GL, Longgugou, JingbianA,
JingbianB, JingbianC, JingbianD, JingbianE, WN2, WN and GB),and 2 from Shandong Peninsula (Sections FoCun2 and ShaoZhuang), and 1 from Western Siberia (section Solonovka). Agedepth models were generated at 1–2 cm intervals and span thepast 250 ka. All age-depth models for loess sequences are
generated considering the discontinuity in deposition due tohiatus in ages with depth, which becomes apparent with highresolution
luminescencedatingofloess-paleosol
sequences2,9,10,16,17,25,27,30,41. The error on age-depth modelingof loess-paleosol sequences varies between 1 and 7% for
Pleistocene records, and yields an average error of 3.52 ± 0.23%(n = 36). By contrast, the error on age-depth modeling of loesspaleosol sequences varies 2–18% for Holocene records, with anaverage error of 8.84 ± 0.90% (n = 25).
The comparison of quartz OSL age and K-feldspar pIRIR ages,
including pIR50IR290 and pIR200IR290 ages, for loess samples fromdifferent regions of Asia show that they are generally consistent
with each other for samples less than ~50 ka, consequently reafﬁrming the reliability of quartz OSL and K-feldspar pIRIR ages forsamples less than ~50 ka. However, for samples older than 50 ka,
quartz OSL ages show an obvious underestimation compared toK-feldspar pIR50IR290 and pIR200IR290 ages; most likely caused by
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dose region. The underestimation of quartz OSL ages for samples
older than 50 ka is commonly observed for loess samples from
Asia, which is likely caused by the offset of the natural signal
generated dose response curve compared to the laboratory dose
generated dose response curve in high dose regions(>200 Gy)29,42. The K-feldspar pIR50IR170 ages are generally
consistent with quartz OSL ages, but obvious underestimation
can be seen compared to K-feldspar pIR200IR290 ages for samples
older than 40–50 ka, most likely caused by the instability of the
pIR50IR170 signal for old samples. As such, quartz OSL andK-feldspar pIR50IR170 ages less than 40 ka can be used for loesspaleosol chronology determination, while the quartz OSL andK-feldspar pIR50IR170 ages older than 50 ka are used as minimum
ages in chronology determination (Fig. 7).
The mean grain size of loess-paleosol sequences from midlatitude Asia varies between 15 and 60 µm, with an average mean
grain size of 31.41 ± 0.12 µm (n = 13,651), which shows that loessin Asia is dominated by silt. Notable exceptions to this are loesssamples from XY18 and Sagaxi sections in the Tibetan Plateau andthe SBH section in NE China that has a mean grain size of 60–150
and 60–100 µm respectively. In general, the sand component(>63 µm, %) shows consistent variation with mean grain size (Fig.
8). The sand fraction of loess in Asia generally varies between 0
and 40%, with an average of 15.92 ± 0.14 µm (%) (n = 8458), while
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Fig. 7 The comparison of K-feldspar pIRIR ages and quartz OSLages for loess samples from different regions of Asia. a is thecomparison of pIR50IR170 ages and quartz OSL ages. b isthe comparison of pIR50IR290 ages and quartz OSL ages. c is thecomparison of pIR200IR290 ages and quartz OSL ages.

the samples of sandy loess at sections XY18 from the Tibetan
Plateau, TJD from Shandong Peninsula and SBH from NE China
have a high value of 40–80%. The silt component (2–63 µm, %) ofloess in Asia generally varies between 60 and 90%, with anaverage of 74.96 ± 0.13 µm (%) (n = 7897) of silt in Asian loesssamples. The clay component (<2 µm, %) of loess in Asia is
generally less than 25%, with an average of 8.70 ± 0.05 µm (%)(n = 7,899) of clay in Asian loess samples, with the exception of
the NLT17 section in Arid Central Asia that has a comparatively
higher clay content of 25–40%17. There is no obvious trend for thevariation of mean grain size, sand, silt and clay components ofloess samples from western to central and eastern Asia, as well asfrom western Siberia to Central Asia and Tibetan Plateau (Fig. 8).
The magnetic susceptibility of loess deposits reﬂects the type,concentration, and grain size of magnetic minerals comprising thesediment1. In high precipitation regions (with precipitation of
more than 400 mm y−1), magnetic enhancement is mainly related
to the formation of ferri and ferromagnetic minerals during postdepositional pedogenesis, therefore, the χlf variation was used as
an indicator of pedogenesis controlled by the availability ofeffective moisture43. In drier areas of Central Asia (with precipitation less than 400 mm y−1), χlf of loess deposits is interpreted aslargely relating to the grain-size distribution of magnetic minerals,
which has a close relationship to wind intensity and presence
input of magnetic minerals, rather than the pedogenic
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processes44. However, the parameter χfd is sensitive to the content
of super-paramagnetic (SP) grains <0.03 μm that are controlled bypedogenic intensity45, making χfd a reliable proxy index for
pedogenesis and effective moisture in both relatively dry
and moist areas of Asia16. The χlf is generally less than 160(10−8 m3 kg−1) for most loess samples from Western and Central
Asia2, but shows an obvious increase to 200–300 (10−8 m3 kg−1)
for loess samples from the Chinese Central Loess Plateau andeastern China14. The average χlf values of loess samples from
Western and Central Asia are 63.60 ± 0.28 (10−8 m3 kg−1)(n = 8054), which can be attributed to a mostly arid environment
in these regions. The average χlf values of loess samples from
Chinese Central Loess Plateau and Shandong Peninsula of eastern
China are 76.38 ± 0.82 (10−8 m3 kg−1) (n = 5601), which is slightly
higher compared to Western and Central Asia; which most likely
occurs due to a relatively moist environment in these regions. The
χfd values of samples from loess-paleosols of mid-latitude Asia
varies between 0 and 12% with an average value of 1.47 ± 0.02 (%)(n = 8262), and has relatively higher values for paleosols and lower
values for loess units, thus indicating generally weak pedogenesis
and low effective moisture in mid-latitude Asia. The relatively highχfd values of 4–10% are mostly observed in loess samples fromintermountain basins and high mountain regions of Western andCentral Asia, and monsoonal NE China and Shandong Peninsula(Fig. 9).
Carbonates in loess sequences consist of primary carbonate(e.g., calcite and dolomite) and secondary (or pedogenic)
carbonates (mainly calcite) that is formed after deposition.
Increased precipitation induces leaching and reprecipitation
processes of carbonate during pedogenesis46, thus carbonate
content can be used as an indicator of precipitation in theregion47. Increased precipitation would cause strong leaching andthus high carbonate content in loess below the paleosol unit,
while low precipitation would result in weak leaching andtherefore low carbonate content in the loess unit. The CaCO3
content of loess samples from mid-latitude Asia varies between 5
and 24%, except for loess samples from the NLT17 section fromCentral Asia and WN section from Chinese Central Loess Plateau
that yield values of <5%. The average CaCO3 content is
12.35 ± 0.09% (n = 2204) for all loess samples from Asia. There isno obvious trend in CaCO3 content for loess samples from Central
Asia to Tibetan Plateau and Chinese Central Loess Plateau (Fig. 9).
Redness (a*) of loess and paleosol sediments is controlled by
the content of hematite and other observed iron hydroxides48–50.
Thus, relative redness can be used to reﬂect the relative degree ofchemical weathering of sediments12. Under warm and humid
conditions, Fe2+ is oxidized to Fe3+ and eventually incorporated
into stable hematite or goethite, resulting in reddish paleosols anda higher a* value51. High redness of loess-paleosol sequences frommid-latitude Asia is related to strong chemical weathering andpedogenesis under a warm and moist environment52. The
yellowness (b*) is affected by the mass fraction of goethite, as
well as iron minerals such as pyrite, marsh iron ore and limonite,that can reﬂect the relative change in precipitation in theregion49,50. The lightness (L) is related to the content of TOC
and CaCO3, such that an increase of CaCO3 and decrease of TOC
would cause a high L value, and can therefore, be used as anindicator of vegetation and precipitation changes in theregion52,53. The variation in Lightness (L) parameter of loesspaleosol sequences decreases slightly from Western and Central
Asia to Shandong Peninsula of eastern China. In contrast thevariation of a* and b* in loess-paleosol sequences shows an
obvious increase from Central Asia and Qinghai-Tibetan Plateau to
Shandong Peninsula, as a result of increasing precipitation fromarid inland Asia to monsoonal East Asia (Fig. 10). The a* values ofloess samples in Asia varies between 1 and 9%, with an average of
4.00 ± 0.02% (n = 3321), while the b* values of loess samples
ranged from 1 to 30%, with an average of 16.65 ± 0.08 (%)
npj Climate and Atmospheric Science (2024) 7

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G. Li et al.

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Fig. 8 The summary of grain size data of samples from loess-paleosol sections in different regions of Asia. a–d are the mean grain size, silt,clay, and sand component changes, respectively.

(n = 3237). The lightness (L) of loess samples from Asia varies
between 20 and 78%, with an average value of 60.61 ± 0.15 (%)(n = 3326). The L parameter does not show any obvious trend
across different geographical regions of mid-latitude Asia, which ismost likely caused by poor vegetation and low precipitation
changes in the mostly arid and semi-arid loess depositional
regions of mid-latitude Asia.
We compiled a comprehensive dataset of previously publishedenvironmental proxy indices of grain size, magnetic susceptibility,
CaCO3, Chroma reﬂection and luminescence ages as well asreconstructed age-depth models for loess-paleosol sequencesfrom different regions of Asia. The dataset comprises 1785 quartz
OSL ages and 1038 K-feldspar pIRIR ages from 128 loess-paleosol
sequences spanning the past 300 ka and 12,365 grain size, 14,964
magnetic susceptibility, 2204 CaCO3, and 3326 chroma data from
51 loess-paleosol sequences. This dataset provides the mostdetailed overview of luminescence ages and environmental proxy
indices for loess-paleosol sequences across Asia. The loesspaleosol sequences compiled here, covers a large geographic
extent, with different elevations, diverse geomorphic settings andclimatic systems of mid-latitude westerlies and low-latitude EASM
and EAWM, which makes the dataset qualiﬁed for large-scalestudies across the Asia. The age data from the loess-paleosol
sequences can be used to calculate the dust depositional rate ofloess across different regions of Asia, thus providing fundamental
npj Climate and Atmospheric Science (2024) 7

input related to dust contribution as boundary conditions forpaleoclimatic modeling over different timescales over the past
300 ka. The proxy indices dataset consisting of grain size,
magnetic susceptibility, CaCO3, and chroma reﬂection can beused to analyze the characteristics of loess and paleosols acrossdifferent regions of Asia over different timescales. This systematic
dataset can provide important information on loess formation anddepositional processes as well as paleoclimatic reconstructions indifferent climatic and geomorphological settings, that can help us
understand the how the interplay of the EASM and westerlies
inﬂuence climatic changes over mid-latitude Asia over glacialinterglacial cycles. Considering its broad spatial and temporal
coverage, this dataset can serve as an important data source forunderstanding the processes and mechanism of desertiﬁcation ofinland Asia, and for forecasting the impact of future climaticchanges (e.g., warming) on desertiﬁcation at regional and global
scales.
METHODS
Laboratory measurements of luminescence dating of
Asian loess
All luminescence ages compiled in this paper, were pretreatedand measured under subdued red or/and yellow light conditions
Published in partnership with CECCR at King Abdulaziz University

G. Li et al.

9Fig. 9 The summary of magnetic susceptibility and CaCO3 content of samples from loess-paleosol sections in different regions of Asia.
a–c is the χlf, χfd, and CaCO3 content changes, respectively.

in various Luminescence dating laboratories across the world. In
general, luminescence samples were collected in opaque tubes.
The bulk material from the light-exposed ends of the tubes wasused to determine radioelement content for dose-rate analysis.
The remaining sample was treated with 10% HCl and 10–20%
H2O2 to remove carbonate and organic materials respectively, andthen wet-sieved to retrieve the different grain size fractions of <38
or <63 µm, 38–63 µm, 63–90 and 90–125 µm. Following which
standard heavy liquid separation was performed for the different
grain size fractions using densities of 2.75, 2.62 and 2.58 g cm−3 toseparate heavy minerals, quartz and K-feldspar fractions, respectively. The quartz fraction was further treated with 40% HF for atleast 40 min to remove the outer layer impacted by alpharadiation along with any remaining feldspar grains. In some cases,
K-feldspar grains were also treated by digestion in 10% HF for
10–40 min to remove the outer layer affected by alpha radiation.
Both quartz and K-feldspar fractions were ﬁnally treated with
1 mol L−1 HCl for 10–15 min to remove ﬂuorides created during
the HF etching. The ﬁne grain (4–11 µm) quartz samples wereprepared by settling the <63 or <38 µm sieved fraction using
stokes law. The resulting ﬁne grain polymineral fraction was
further etched with hexaﬂuorosilicic acid for 7–14 days to obtainﬁne grain quartz.
Luminescence measurements were conducted on Risø-TL/OSLDA-20 readers equipped with blue LEDs (470 nm, ~80 mW cm−2)
Published in partnership with CECCR at King Abdulaziz University

and IR LEDs (870 nm, ~135 mW cm−2) or/and Daybreak 2200 OSL
reader equipped with blue (470 ± 5 nm) LED units and IR LEDs
(880 ± 60 nm). Laboratory irradiation was carried out using 90Sr/90Y
sources mounted on the reader, with a calibrated dose rate of~0.08–0.13 Gy ka−1. The quartz OSL signal was detected using a
7.5 mm Schott U-340 glass ﬁlter, while the K-feldspar IRSL signals
were detected through a combination of BG-39 and Corning-759
ﬁlters.
For quartz OSL dating sample, the purity of each sample waschecked by using IR-depletion tests and/or IR/OSL signal ratio54.
The quartz OSL equivalent dose (De) values were measured byusing single-aliquot regeneration (SAR) protocols55, or a combination of SAR and SGC (Standardized Growth Curve) measurements56. The ﬁne-grained quartz sensitivity-corrected multiplealiquot regenerative-dose (SMAR) OSL dating protocol was usedfor De determination of samples from the Weinan (WN) section,
China57. The double-single-aliquot regeneration protocol58 (DSAR),
which includes an IR irradiation prior to OSL measurement in eachcycle of the SAR protocol, was used to evaluate De values from ﬁne
grains from Remizovka (REM), Panﬁlov (PAN) and Ashubulak (ASH)from Central Asia32. Dose recovery tests were conducted on
representative samples of each loess-paleosol section to selectsuitable preheat temperature for De measurement and check thereliability of SAR protocol to loess samples from different regions.
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G. Li et al.

10

Fig. 10 The summary of color reﬂection changes of samples from loess-paleosol sections in different regions of Asia. a–c is the lightness,redness, and yellowness changes, respectively.

Six to thirty aliquots of each sample were analyzed for
determination of De, and consequently the age of the sample.
The total dose rate of each quartz sample was calculated frommeasurements of radioactive element concentrations in thesample, with a small contribution from cosmic rays. The U, Th, K
and Rb concentrations of each sample was determined using
either Neutron Activation Analysis (NAA), Inductively-coupledplasma mass spectrometry (ICP-MS), inductively-coupled plasma
optical emission spectroscopy (ICP-OES) or high-resolution gamma
spectrometry. The dose rate contribution from Rb concentrations
is very small (e.g., <1%) (and has been ignored in dose rate
calculations for samples from most sections, except sections SG18,
XY17, ZD17, KS15, NLT17, LJW10, BYH10 and QS16 from Tianshan
Mountain regions and sections CJB21-1, CJB21-2, BG21-1 and
XG21-1 from Hexi corridor of Central Asia, and sections XY18,
RYS17, Sagaxi, and HS18 from NE Tibetan Plateau, and Zouyu
section from Shandong Peninsula. Conversion factors were used
to convert measured radioactive element concentrations to beta
and gamma dose rates59. The cosmic dose rate of each sample iscalculated using the sample burial depth and the altitude andelevation of the loess section according to Prescott and Hutton(1994)60. The life-average water content of loess samples fromeach section were estimated based on the observed watercontent, and the uncertainty of water content changes after burial,
taking in account the slight loss of sample moisture that occurs
naturally during sampling and transport. The OSL age of eachquartz sample was calculated by using the ratio of measured
quartz De value to the annual dose rate23.
npj Climate and Atmospheric Science (2024) 7

The K-feldspar pIRIR dating method, utilizing the pIR50IR290
signal36, pIR200IR290 signal61, and pIR50IR170 signal62 was used tocoarse-grained (e.g., 63–90 µm) and ﬁne-grained (e.g., 4–11 µm)
K-feldspar samples from loess sections. The pIR50IR290 signal and
pIR200IR290 signal dating can be used to date samples with ages
ranges from few ka to 130–150 ka, and few ka to 250–300 ka,
respectively, while the pIR50IR170 signal dating can be used to dateyoung samples from few hundred years to tens of thousands ofyears17,62. In the pIR50IR290 and pIR200IR290 dating protocols, apreheat of 320 °C for 60 s was followed by an IRSL stimulation at
50 °C or 200 °C, which was followed by a high temperature IR
stimulation at 290 °C. In the pIR50IR170 dating protocol, a preheatof 200 °C for 60 s was followed by IRSL stimulation at 50 °C. Thiswas followed by a pIRIR measurement at a high stimulation
temperature of 170 °C. In some cases, prior to all IRSL measurements, the aliquots were held at the measurement temperature
for a few seconds without IR stimulation in order to reduce theintensity of the residual isothermal luminescence after preheating.
In general, test doses between 30 and 50% of the De values of thesample were used in all pIRIR measurement protocols7,63. The
suitability of pIRIR dating protocols using pIR50IR290, pIR200IR290
and pIR50IR170 signals for samples from each loess-paleosol
sequence was checked using dose recovery tests on samples
bleached in sunlight and/or modern analog samples64. Residual
dose corrections on these K-feldspar ages were performed byconducting bleaching tests, wherein the residual pIRIR signals
measured from samples bleached under sunlight or by a solar
simulator are subtracted from the De value of each sample36.
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G. Li et al.

11
Total dose rate calculations for K-feldspar sample consisted ofexternal dose rate of radioactive elements (U, Th, K and Rb) in thesample, internal dose rate of K-feldspar grains, and dose rate
contribution from cosmic rays. Published conversion factors wereused to convert measured radioactive element concentrations tobeta and gamma dose rates59. Internal dose rates for K-feldspar
samples were calculated using a K content of 12.5 ± 0.5%65,66 anda Rb content of 400 ± 100 ppm67. The cosmic dose rate of eachsample is calculated according to Prescott and Hutton60 taking
into account sample burial depth, and the altitude and elevationof the loess section. The life-average water content of loesssamples from each section was estimated based on the observed
water content, and the uncertainty of water content changes after
burial, which is similar that performed for quartz samples. The
pIRIR age of each sample was calculated as a ratio of the measured
De value to the annual dose rate.
Age-depth modeling of loess-paleosol sequences in Asia
Both quartz and K-feldspar pIRIR dating generated individual ages
at distinct depth intervals in the individual loess sequences.
Construction of a continuous paleoclimatic record requires thedevelopment of an age-depth model that can be used tointerpolate between these discrete ages. The age-depth modelof loess sections was established using Bacon age-depth
modeling68, which uses Bayesian statistics to reconstruct accumulation histories for deposits, and can be used to reduce the sizeof age-depth model error estimates by considering the stratigraphic positions of the ages. The age-depth modeling of 22
loess-paleosol sections from central Asia, 10 sections from ChineseCentral Loess plateau, 1 section from western Siberia and3 sections from Tibetan Plateau, and 2 sections from Shandong
Peninsula of eastern China, spanning the past 250–300 ka, wasreconstructed based on close-spaced quartz OSL ages and/ or
K-feldspar pIRIR ages from each section.
Grain size measurement
A total of 12,365 grain size samples collected from 46 loesspaleosol sections (sections Remizovka (REM), Panﬁlov (PAN),
Ashubulak (ASH), ZSP, SGX, Zhaosu, SG18, Xiaoerbulake, Talede,XEBLK, KETB, XY17, NLK, ZD17, KS15, NLT17, LJW10, QS16, BYH10,
CJB21-1, CJB21-2, BG21-1 and XG21-1 from Central Asia, sections
Sagaxi, SNP, LXP, YJP1, XY18, RYS17 and HS18 from the Tibetan
Plateau, sections GL, JY12, BGY Holocene site, Xifeng, XF06,
Longgugou, JingbianA–E, WN2, WN and GB sections from the
Chinese Central Loess Plateau, sections SBH and NYZG from
Northeastern China, and sections FoCun2, ShaoZhuang, LGZ and
TJD from Shandong Peninsula) from Central and East Asia wereanalyzed.
For grain size analyses, in most cases sediment samples were
heated with 10 ml of 30% H2O2 and 10 ml of 10% HCl to remove
organic matter and carbonate material. The sample was thensuspended in deionized water for 24 h to remove acidic ions.
Following this, the samples were deﬂocculated using 10 ml of
0.1 mol L−1 (NaPO3)6 in an ultrasonicator for 10 min69. Preparedsamples were then measured using a Malvern Master Sizer 2000
with a range of 2–2000 µm, or using a CILAS 1190 LD laser particle
analyzer with a range of 0.04–2500 μm.
Magnetic susceptibility measurement
A total of 14,964 magnetic susceptibility samples collected from46 loess-paleosol sections (Section YE from Western Asia, sections
Remizovka (REM), Panﬁlov (PAN), Ashubulak (ASH), ZSP, Zhaosu,
SG18, Xiaoerbulake, Talede, XEBLK, KETB, XY17, NLK, ZD17, KS15,
NLT17, LJW10, QS16, BYH10, CJB21-1, CJB21-2, BG21-1 and XG21-1
from Central Asia; sections Sagaxi, YJP1, XY18, RYS17 and HS18
from Tibetan Plateau; sections GL, JY12, BGY Main site, BGY
Published in partnership with CECCR at King Abdulaziz University

Holocene site, Xifeng, XF06, Longgugou, Xunyi, JingbianA-E, WN2,WN and GB from Chinese Central Loess Plateau; sections SBH and
NYZG from Northeastern China, and sections Yizheng, Maba, LGZ
and TJD from Shandong Peninsula) from Asia were analyzed.
Samples were air dried and packed into 10 cc plastic pots. Low
frequency (470 Hz) magnetic susceptibility (χlf) and high frequency(4700 Hz) magnetic susceptibility (χhf) measurements were carriedout using a Bartington Instrument MS2B Meter or using a AGICO
Kappabridge MFK2. The percentage of mass-speciﬁc frequencydependent susceptibility (χfd) was calculated by using the function(1)70.
χfd ¼

100  ðχIf  χhf Þ
χIf

(1)

CaCO3 content measurement
A total of 2204 CaCO3 content samples from 5 sections (sections
SG18, XY17, ZD17, NLT17 and QS16) from Central Asia, 1 section
from Tibetan Plateau (section RYS17) and 1 section from ChineseCentral Loess Plateau (section WN) were analyzed. The total
carbonate content of each air-dried sample was measured by thegasometric method after samples were air-dried. A solution of
10% HCl was added to weighed samples and the induced volumeof CO2 was measured by a U-tube manometer. The carbonate
content (CaCO3) was then calculated according to the CO2volume, temperature and atmospheric pressure when the sample
was measured71.
Chroma reﬂectance measurement
A total of 3326 samples were compiled for color reﬂectance from
17 loess-paleosol sections from Asia (section YE from Western
Asia, sections ZSP, SGX, Zhaosu, SG18, Xiaoerbulake, Talede, XY17,
ZD17, NLT17, QS16 from Central Asia, sections Sagaxi, YJP1, XY18
and RYS17 from Tibetan Plateau, and sections FoCun2 andShaoZhuang from Shandong Peninsula of eastern China). In thelaboratory, the samples were air-dried and ~0.5 g of each sample
was extracted. The dry samples were grounded using a mortar
and pestle. Sediment color was measured on each air-driedsample after samples were disaggregated. Measurements wereconducted on a Konica- MinoltaCM700d color meter. Colorreﬂectance, deﬁned by CIE L*a*b*, where the measured color
indices L* indicates lightness, that deﬁnes black at 0 and white at100, while a* and b* refer to redness and yellowness in
Red–yellow chromaticity49,50.
DATA AVAILABILITY
The compiled dataset from previously published studies consisting of U, Th, K and Rb
concentration, water content, dose rate, quartz and K-feldspar ages, age-depth
models of loess-paleosol sequences, as well as the environmental proxy indices ofmean grain size, clay, silt and sand components, magnetic susceptibility (χlf, χfd),
CaCO3, and Chroma reﬂection of loess-paleosol sequences from Asia are accessible atthe National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/
10.11888/Paleoenv.tpdc.300496)41.

Received: 11 October 2023; Accepted: 19 December 2023;

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ACKNOWLEDGEMENTS
This research is supported by the Fundamental Research Funds for the Central
Universities (lzujbky-2021-ey22), the Gansu Province Outstanding Youth Fund
(23JRRA1016), the National Natural Science Foundation of China (Grant No.
42071101), and Youth Innovation Promotion Association Chinese Academy of
Sciences (Y2022102).

AUTHOR CONTRIBUTIONS
G.L. conceptualized this study. G.L. and Z.Y. established the dataset and written theﬁrst drift of the manuscript. G.L., Y.S., K.E.F., S.Y., S.K., C.E., T.S., Z. Lai, A.K.D., H.Y., Q.Z.,
J.-P.B., Y.W., X.L., Z. Ling, Q.C., H.W., L.W. and F.C. contributed and collated the data,and performed quality control. Z.Y., T.L., X.W. and C.Q. prepared data ﬁles and ﬁgures.
All authors participated in the creation of the dataset, and revised the manuscript
and online supporting information.

Published in partnership with CECCR at King Abdulaziz University

COMPETING INTERESTS
The authors declare no competing interests.

ADDITIONAL INFORMATION
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41612-023-00555-4.
Correspondence and requests for materials should be addressed to Guoqiang Li,
Yougui Song, Kathryn E. Fitzsimmons, Shuangwen Yi, Shugang Kang, Chongyi E,
Thomas Stevens, Zhongping Lai, Aditi K. Dave, Chunzhu Chen, Yanqing Deng, He
Yang or Fahu Chen.
Reprints and permission information is available at http://www.nature.com/
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