Kubota et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-019-0283-0(2019) 6:39

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open AccessMillennial-scale variability of East Asiansummer monsoon inferred from sea
surface salinity in the northern East China
Sea (ECS) and its impact on the Japan Sea
during Marine Isotope Stage (MIS) 3
Yoshimi Kubota1*, Katsunori Kimoto2, Ryuji Tada3, Masao Uchida4 and Ken Ikehara5

Abstract
Color alternations in deep-sea sediment in the Japan Sea have been thought to be linked to millennial-scalevariations in the East Asian summer monsoon (EASM), associated with the Dansgaard-Oeschger (D-O) cycles
and Heinrich events in the high-latitude North Atlantic during Marine Isotope Stage 3 (MIS 3). In this study,
we investigate the variability of sea surface salinity (SSS) in the northern East China Sea (ECS) to evaluate the
EASM precipitation in South China and its linkage to the sediment color of the Japan Sea during MIS 3. Hightime resolution (< 100 years) SSS along with sea surface temperature (SST) records were reconstructed using
paired Mg/Ca and the oxygen isotope of planktic foraminifera Globigerionoides ruber sensu stricto from core
KR07-12 PC-01 recovered from the northern ECS. The results indicate that millennial-scale variability of the SSS
is observed with the amplitude of ~ ± 1 during MIS 3. The variations in SSS are well correlated to D-O cycles
and Heinrichs. The EASM precipitation decreases in association with the southward shift of the westerly jetin D-O stadials and Heinrichs, suggesting suppressed moisture convergence along the EASM front associatedwith weakened North Pacific subtropical high in response to the slow-down of the Atlantic Meridional
Overturning Circulation. In a comparison between the SSS in the ECS and the color alternation in the Japan
Sea, closely correlated variations between the two records in the interval 44–34 ka indicate that the SSS in
the ECS plays a crucial role in regulating nutrient and salinity inflow into the Japan Sea. However, the linkage
becomes ambiguous, especially after ~ 30 ka, when the sea level falls toward the level of the last glacial
maximum. This shift is associated with changes in sediment facies, confirming that the underlying mechanism
in regulating the sedimentary change in the Japan Sea depends on the sea level.
Keywords: East Asian summer monsoon, Salinity, East China Sea, MIS 3, Mg/Ca-derived SST, D-O cycles

Background
Since high-resolution, continuous oxygen isotope (δ18O)
records of the Greenland ice cores covering the past >100 ka have demonstrated millennial-scale climatechanges during Marine Isotope Stage (MIS) 3 (59–29ka), the so-called Dansgaard-Oeschger (D-O) cycle. The
D-O cycle attracted considerable attention due to its* Correspondence: yoshimi@kahaku.go.jp
1
Department of Geology and Paleontology, National Museum of Nature andScience, 4-1-1, Amakubo, Tsukuba, Ibaraki 305-0005, Japan
Full list of author information is available at the end of the article

abruptness, large amplitude, high frequency, and potential impact on global climate (e.g., Dansgaard et al.
1993). In the Asian monsoon region, the Quaternary
hemipelagic sediments of the Japan Sea are characterized
by centimeter- to decimeter-scale alternation of dark
and light clay to silty clay, which are bio-siliceous and/or
bio-calcareous to a various degree (Tada et al. 1999;
Tada et al. 2018; Irino et al. 2018). Tada et al. (1999)
demonstrated the occurrence of the basin-widedark-light color alternations in the sediments of theJapan Sea, which are well correlated with D-O cycles.

© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
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(2019) 6:39

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They hypothesized that these sediment color alternations could be attributed to changes in the nutrient andfreshwater influx through the Tsushima Strait due tochanges in the Changjiang (Yangtze) River discharge(i.e., summer precipitation in South China). Namely, increased summer precipitation in the Changjiang Basincaused the expansion of the low-salinity and nutrientenriched coastal waters in the northern part of the East
China Sea (ECS) during the interstadials of the D-O
cycle, which flowed into the Japan Sea and resulted in
the accumulation of organic carbon-rich dark layers(Tada et al. 1999).
Summer precipitation in East Asia is known as Meiyu(China), Baiu (Japan), and Changma (Korea), and is oneof the crucial aspects of the East Asian summer monsoon (EASM) system. A series of high-resolution speleothem δ18O (δ18Osp) records in southeastern and central
China, which had been interpreted as reflecting EASM
intensity/precipitation, demonstrated millennial-scalevariations in association with D-O cycles (Wang et al.
2001; Cheng et al. 2016). However, the interpretation ofthe δ18Osp is controversial as studies using analyses ofδ18O mass balance and climate models suggested thatthe δ18Osp record reflects neither local precipitation nor
EASM circulation intensity, but rather changes in the
moisture source or seasonality of the precipitation(Clemens et al. 2010; Dayem et al. 2010; Pausata et al.
2011; Maher and Thompson 2012). More recently, a
trace element study on one of the Chinese speleothemshas claimed that millennial-scale δ18Osp variations during the last deglaciation were better interpreted aschanges in the EASM circulation, instead of the local
precipitation (Zhang et al. 2018). By contrast, a proxy ofsea surface salinity (SSS) from a marine core provides
reliable information on the changes in regional precipitation, through a freshwater inflow from rivers, which
can further serve to test the hypothesis by Tada et al.
(1999).
The northern ECS is a crucial area, vis-à-vis its
position where the freshwater from the Changjiang(Yangtze) River reaches it and the waters from the
ECS inflow to the Japan Sea (Fig. 1). Ijiri et al. (2005)
reported millennial-scale light δ18O peaks of planktic
foraminifera Globigerinoides ruber sensu stricto(δ18Op) in the northern ECS during Marine Isotope
Stage (MIS) 3 and stated that these peaks might capture larger freshwater discharge events associated with
D-O interstadials. However, their age model did notallow for determination of these timings precisely
enough compared to other records, and the effect oftemperature changes on foraminiferal δ18O was notadequately evaluated because they used an alkenone
temperature proxy, which may have been recording
sea surface temperature (SST) during a different

season and at different depth from those of G. ruber
s.s. (Kim et al. 2015).
Thus, in this study, we reconstructed the local δ18O of
seawater (δ18Ow-local), an indicator of SSS, and SST in
the northern ECS during MIS 3 using paired δ18Op andMg/Ca of G. ruber s.s., and estimated variability of SSS
to test the presence of millennial-scale EASM precipitation oscillations in South China associated with D-O cycles. We used a marine core KR07-12 PC-01 recoveredfrom the northern ECS that covers the time interval
since 44 ka, thus covering the late half of MIS 3.
The ECS is a marginal sea in the northwestern Pacific
Ocean, and the East Asian monsoons control its seasonal ocean current system (e.g., Ichikawa and Beardsley
2002). The SSS in the ECS changes drastically throughout a year, particularly in the northern part, due to theconsiderable influence of river discharge caused by the
EASM precipitation (Ichikawa and Beardsley 2002). The
Changjiang River is the most significant contributor of
freshwater to the ECS, accounting for ~ 90% of thetotal freshwater supplied to the ECS from rivers, andits discharge is characterized by a remarkable seasonal
cycle with its zenith in July and nadir in January(Chen et al. 1994; Isobe et al. 2002). Accordingly, the
SSS in the northern ECS decreases in summer andincreases in winter (Ichikawa and Beardsley 2002).
Changjiang Diluted Water (CDW) is formed by
Changjiang freshwater mixing with the ambient seawater around the estuary, which then flows eastward
into the Japan Sea during the summer (e.g., Ichikawa
and Beardsley 2002; Isobe et al. 2002).
Based on instrumental observations, the maximum
SST near the studied site is 28.4 °C in August, and theminimum is 17.7 °C in February (Japan Oceanographic
Data Center 2004). In contrast, SSS reaches a maximumof 34.7 in February and decreases to a minimum of 33.0
in July, when the maximum discharge from the Changjiang River occurs. The spatial patterns of SST and SSS
during summer are characterized by lower SST and SSS
in the northwest and higher SST and SSS in the southeast part of the northern ECS (Fig. 1). The lower SSS
during the summer suggests that river discharge due tothe EASM dominates the seasonal changes in the Kuroshio (Sun et al. 2005); otherwise, the strengthened Kuroshio results in increased SSS during summer, when its
volume transport is the largest (Ichikawa and Beardsley
1993; Andres et al. 2008). It was demonstrated that the
CDW reaches the easternmost part of the northern ECS
across the shelf break during summer, based on 226Ra
and 228Ra measurements of surface waters (Inoue et al.
2012). Inoue et al. (2012) calculated the relative contribution of the Changjiang freshwater in this area as approximately 2–3% in July and October, based on massbalance calculation of the SSS and 228Ra concentration

Kubota et al. Progress in Earth and Planetary ScienceKubota et al. Progress in Earth and Planetary Science(2019) 6:39

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a

b

c

d

Fig. 1 a Locality map showing site of KR07-12 PC-01 and reference sites. b Site location in the ECS during MIS 3 and the shore area (shadedarea) at − 80 m sea level during MIS 3. c SST (Locarnini et al. 2010) and d SSS in July (Antonov et al. 2010) in the ECS

of surface water samples collected between 2008 and2010. Instrumental SSS data around the core site showsa negative correlation with the Changjiang River discharge during the wet season for the past 50 years(Kubota et al. 2015), confirming that the studied site isan appropriate location to reconstruct SSS.

Methods/Experimental
The piston core KR07-12 PC-01 (31° 40.63′ N, 129°01.98′ E, 736 m water depth) was composed ofolive-black or olive-gray, homogenous, well-bioturbatedclay. Scattered shell fragments and mottled texturescaused by burrows were occasionally identified (Fig. 2).
Ash layers were identified at depth intervals of 98.0–
118.0 cm and 947.5–990.3 cm, which correspond to the
K-Ah and Aira (AT) tephras, respectively, based on themorphology and refractive index of glass shards in eachtephra, and heavy mineral assemblages (Table 1). An ash
pocket was found at 170.0–172.0 cm, identified as Sz-S
(Table 1). The refractive index of each volcanic glass
shard was measured using the RIMS-2002 analyzer(Danhara et al. 1992) at Kyoto Fission Track Co. Ltd.
The other ashes found at 75.0–84.0 cm and 188.0–190.0
cm are unknown.
Approximately 10 cm3 of bulk sediment samples was
subsampled at 2.5 cm intervals and washed using a
63 μm mesh to concentrate foraminiferal tests for 14C
dating, δ18Op, and Mg/Ca measurements. For 14C
dating, approximately 3–5 mg of the planktic foraminifera Neogloboquadrina dutertrei, from the size fraction> 250 μm, was collected from 15 horizons (Table 2). Allof the 14C data were acquired at the NIES-TERRA AMS
facility, National Institute for Environmental Studies(Tanaka et al. 2000; Yoneda et al. 2004; Uchida et al.

Kubota et al. Progress in Earth and Planetary Science(2019) 6:39

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Fig. 2 Age-depth relationship of core KR07-12 PC-01 together with 2σ age uncertainties and columnar section. The black circles and lines represent
14
C-based datum and its age model. The red line indicates the fine-tuned age model. The blue crosses represent the tie points in the finetuned age model

2004). We analyzed 244 samples in total for paired
δ18Op and Mg/Ca. The time resolution of the analyses
was ~ 80 years for MIS 3, ~ 130 years for early MIS 2
and Last Glacial Maximum (LGM), and > 300 years forthe last deglaciation. For δ18Op and trace element analyses, approximately 30–40 individual G. ruber s.s. waspicked from 250 to 355 μm size fractions and crushed tohomogenize. After being cleaned by milli-Q water andmethanol, approximately 50–70 μg of foraminiferal tests
was separated and used for the oxygen isotope analysis.
The δ18Op was measured by two Finnigan MAT 252
Stable Isotope Ratio Mass Spectrometers with a Keil III
carbonate device installed at the Mutsu Institute for
Oceanography in the Japan Agency for Marine-Earth
Science and Technology and at the University of Tokyo.
The reproducibility of the measurement was better than
± 0.05‰ (1σ) for δ18Op, as determined by replicate

measurements of international standards NBS-19(RM8544 Limestone) and JCp-1 (Coral Porites sp.), provided by the Geological Survey of Japan (GSJ/AIST).
Additional cleaning steps were performed for traceelement analyses. The samples were cleaned using the reductive method, including reductive and oxidative cleaning steps published by Boyle and Keigwin (1985), with aslight modification (Kubota et al. 2010). Mg/Ca analysis
was performed using a Thermo Finnigan ELEMENT 2
High-Resolution Multi-Sector Inductively Coupled Plasma
Mass Spectrometer (HR-ICP-MS) at the Mutsu Institutefor Oceanography in the Japan Agency for Marine-Earth
Science and Technology. Isotopes of four elements (24Mg,
44
Ca, 48Ca, and 55Mn) were analyzed using Sc as an internal standard (Uchida et al. 2008). Four working standards were prepared by successive dilutions of the stock
standard solutions to match the concentrations of CaKubota et al. Progress in Earth and Planetary Science(2019) 6:39

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Table 1 Results of tephra analyses
Sample ID

Sub-bottom
depth (cm)Glass type

KRP10650

98–118bw > > pm

KRP10711

170–172

KRP11507

947.5–990.3

Heavy minerals

Glassmorphology

Refractive Index of glass shard
Maximum

Minimum

Mean

Mode

Correlation

Opx, Opq, Cpx

H > T, C

1.5102

1.51981.5123

1.511

pm

Opx, Cpx, Opq (Ap, GHb)

T, C, H

1.50671.5102

1.509

1.509

S-Sz

bw > > pm

Opx, GHb, Ap, Cpx, Bt

H > T, C

1.4982

1.50111.5001

1.5

AT

K-Ah

Glass type: bw bubble-wall type, pm pumice type. Heavy minerals: Opx orthopyroxene, Cpx clinopyroxene, GHb green hornblende, Opq opaque minerals, Btbiotite, At apatite. Glass morphology (after Yoshikawa 1976): H large broken bubble-wall type, T fibrous and pumice type, C intermediate type(approximately 100 ppb, 500 ppb, 2 ppm, and 5 ppm) andMg (0.05 ppb, 0.2 ppb, 1.0 ppb, and 5 ppb), respectively,
which covered the Ca and Mg concentration ranges of all
samples (Kubota et al. 2010, 2015). The precision of thereplicate analysis of the working standard for Mg/Ca wasbetter than ± 0.09 mmol/mol, corresponding to ± 0.3 °C in
temperature scale. Mn/Ca was analyzed to monitor
Mn-Fe oxide contamination. Mn/Ca was under 200 μmol/
mol in most of the samples (Additional file 1: Figure S1),
but exceeded 400 μmol/mol in 5 samples. However, thesevalues are not excluded because there is no correlation between Mg/Ca and Mn/Ca (Additional file 1: Figure S1). In
order to examine the homogeneity of the sample, G. ruber
s.s. were repicked from 66 randomly selected horizons ofcore KR07-12 PC-01 and run for Mg/Ca analyses with thesame cleaning protocol. The reanalyzed Mg/Ca value at1392.1–1389.6 cm was 5.0 °C lower than the first one andwas discarded. An average of the differences in Mg/Ca
values of the replicate analyses for the rest of 65 horizons
was 0.16 mmol/mol (~ 0.7 °C). Compared with the Mg/Ca
values of U1429 for the same period in the previous study(Clemens et al. 2018), an average of KR07-12 PC-01 wasslightly lower by 0.157 mmol/mol than those of U1429.
Table 2 Results of AMS radiocarbon and calendar ages
Depth (cm)

14
C age
(year BP)14

2σ calibrated calendar
ages (year BP)

20–25

2283

33

1815–2215

C age error

78–80.55790

426125–6479

180.5–183

11,76948

13,159–13,489

280.5–285.5

14,37355

16,764–17,414

381–386

15,56659

18,307–18,733

481.5–486.5

17,32375

20,210–20,808

581.3–586.3

18,581

78

21,865–22,389

682.3–687.3

20,11488

23,525–24,134

785–787.5

22,039103

25,746–26,202

885–887.5

24,17598

27,637–28,1251083.3–1085.8

29,033132

32,265–33,404

1184.1–1186.6

31,295

152

34,538–35,259

1283.6–1286.1

33,168166

36,311–37,596

1384.6–1389.6

35,930204

39,677–40,813

1484.1–1486.6

39,650408

42,479–43,851

Since the Mg/Ca values of U1429 were confirmed by aninternational CaCO3 reference standard, coral Porites standard material JCp-1 (4.199 ± 0.065 mmol/mol, Hathorne et al.
2013), we added 0.157 mmol/mol (~ 0.6 °C) to the original
Mg/Ca values of KR07-12 PC-01 to adjust to the value ofthe U1429.
Although Mg/Ca of foraminiferal calcite is primarily controlled by temperature, a secondary effect of the salinity has
been pointed out; Mg/Ca increases when salinity increases(e.g., Lea et al. 1999). In this study, in order to examine
the salinity effect on the results to better estimate the SSS
variability in the northern ECS, we tested three calibration
methods to obtain SST and δ18Ow-local as follows: (1) aconventional Mg/Ca calibration without salinity correction (referred to “no salinity correction”), (2) an Mg/Ca
calibration with salinity correction (“salinity correction”),(3) an Mg/Ca calibration with salinity correction in
addition to incorporating the effect of changes in endmember δ18Ow (“endmember correction”). The SSS is reconstructed in the methods of “salinity correction” and“endmember correction.” Here, we refer to “endmember”
as physical values such as SSS and δ18Ow in the source regions. The purpose of the “endmember correction” is to
examine whether or not changes in the δ18Ow in thesource region account for changes in the δ18Ow in thenorthern ECS. The results are compared in Fig. 3.
First, for the “no salinity correction” calculation, we used
an Mg/Ca calibration (Eq. 1) by Dekens et al. (2002), without correction for sediment water depth, and Bemis et al.’s(1998) δ18O–temperature relationship (low light) (Eq. 2).
The δ18Ow-local was obtained after removing the global sea
level effect from total δ18Ow using Waelbroeck et al.’s(2002) curve (Eq. 3; Thirumalai et al. 2016).
Mg=Ca ¼ exp ð0:09  T Þ=0:38
T ¼ 16:5−4:8  δ18 Op −δ18 Ow þ 0:27
δ18 Ow ¼ δ18 Ow−local −0:008  sea level

ð1Þ


ð2Þ
ð3Þ

Here, T, δ18Op, and δ18Ow denote temperature, δ18Oof planktic foraminiferal calcite, and δ18O of water.
δ18Ow in Eq. 3 includes local and global (sea level) components. Second, for the “salinity correction” calculation,
we used an Mg/Ca calibration with salinity correction

(2019) 6:39

Page 6 of 16(Eq. 4; Tierney et al. 2015) instead of Eq. 1. The SST andδ18Ow-local were derived using a MATLAB script,
Paleo-Seawater Uncertainty Solver (PSU Solver) (Thirumalai et al. 2016) with the methodology of Clemens etal. (2018) as explained below. As no regional Mg/Ca
calibration exists for the ECS, we employed the Mg/Ca
calibration of Tierney et al. (2015) that utilized all available culture data in a multivariate calibration that accounts for both salinity and temperature. We utilized
the δ18O-temperature relationship of Eq. 2 and the ECS
seawater δ18Ow-local-salinity relationship of Horikawa etal. (2015) (Eq. 5), as well as the global sea-level curve of
Waelbroeck et al. (2002) (Eq. 3).

this section. Before the calculation, each data set was
linearly interpolated (100-year step).

Kubota et al. Progress in Earth and Planetary Science

Mg=Ca ¼ exp ð0:084  T þ 0:051  S−2:54Þ

ð4Þ

δ18 Ow−local ¼ 0:23  S−7:74

ð5Þ

Here, S represents salinity.
Third, for the “endmember correction” calculation, we
replaced Eq. 5 to 6 (LeGrande and Schmidt 2011). Thismodel (Eq. 6) allows slope (a) and intercept (b) to varythrough time so that the effect of temporal changes inendmembers is captured. The following two steps wereconducted to obtain “a” and “b” in Eq. 6: step 1—estimate of the freshwater δ18O to obtain intercept “b,” step
2—estimate of the Kuroshio water δ18O and salinity toobtain slope “a.” Eventually, errors are given based on
the Monte Carlo simulation, as will be explained later inδ18 Ow ¼ a  S þ b

ð6Þ

In step 1, we regard the freshwater δ18O of the Changjiang River as intercept “b” (Kubota et al. 2015). Following Kubota et al. (2015), we utilized a composite recordof Cheng et al.’s (2016) δ18Osp to estimate the freshwater
δ18Ow (Kubota et al. 2015), on the assumption that thetemporal variations in δ18Osp reflect the drip water δ18O
in the caves, hence, the river water δ18O. We used thetotal δ18Ow, which includes local and global components, in Eq. 6 instead of δ18Ow-local to avoid correcting
a global component for δ18Osp. Here, we define that thevariability in intercept “b” is equal to that in δ18Osp(Eq. 7). Both sides of Eq. 7 represent subtracting a modern value from values at a given time in the past. We
employed − 7.74‰, which is derived from Eq. 5 (Horikawa et al. 2015), as the modern intercept (left side of
Eq. 7). For the modern δ18Osp value (right side of Eq. 7),
an average for the last 1 ka of the Chinese speleothem
record (Cheng et al. 2016) was employed. The timeseries of the endmember δ18Ow is shown in Additional file 2: Figure S2. The adequacy of these parameters will be discussed in the “Results and Discussion”
section.

d
a

b
e
c

Fig. 3 Comparison among the results with three different calibrations for KR07-12 PC-01: “No salinity correction” (gray), “salinity correction” (red),and “endmember correction” (blue). a δ18O of planktic foraminifer (δ18Op). b SST. c δ18Ow-local. d SST vs. δ18Ow-local derived from “no salinity
correction” calibration method. e SST vs. δ18Ow-local derived from “salinity correction” calibration method

Kubota et al. Progress in Earth and Planetary Scienceb−ð−7:74Þ ¼ δ18 Osp −ð−9:0Þ

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ð7Þ

(Reimer et al. 2013). For the local reservoir correction(ΔR), − 93 ± 69 years, a weighted mean of the 10 nearest
locations to the core site (available at http://calib.org/
marine/) was applied. Constant linear sedimentation
rates were assumed between two adjacent 14C-based
age-controlling points (listed in Table 2) when constructing the age model (Fig. 2). In addition to the 14C-based
datums, published calendar ages of the K-Ah (7.165–
7.303 ka) and AT (30.009 ± 0.189 ka) tephras, dated on
Lake Suigetsu sediments in SG06 core (Smith et al.
2013), were used as the age datums. In this age model,
instantaneous sedimentation was assumed between the
top and bottom of the two thick ash layers (K-Ah 98.0–
118.0 cm, AT 947.5–990.3 cm). A published 14C age of
S-Sz is 11.295 ± 0.30 ka derived from soil materials below
the tephra layer (Okuno et al. 1997), which was calibrated to a calendar age of 12.631–13.768 ka (2σ) in thisstudy using CALIB 7.1 (Stuiver et al. 2016) with
IntCal13 (Reimer et al. 2013). We did not apply therecalibrated age of S-Sz in our model since its uncertainty is large. The S-Sz age with our age model, which
is 12.4 ka, is younger than the 2σ probability rangeof the soil samples, probably because the soil materials were sampled below the tephra layer (Okuno etal. 1997).
The age model was further fine-tuned to a composite
record of the Chinese δ18Osp (Additional file 3: Figure
S3 and Additional file 4: Table S1), assuming an
in-phase relationship between the climate in the northern ECS and hydroclimate variability over China as mentioned in Clemens et al. (2018). This assumption isjustified in the following discussion by correlating the
AT tephra layer in our core and its age with others. AT
tephra is most precisely dated using the Suigetsu Lake
sediment, whose microfossil 14C ages are generally consistent with the IntCal13 curve for a 31–29 ka interval(Reimer et al. 2013). Although the speleothem 14C datafrom Hulu Cave, China, was available back to 27 ka in
2013, a recently published 14C data set from the same
cave confirmed the consistency with IntCal13 between
10 and 33 ka within the uncertainty range of less than ±0.5 ka (Cheng et al. 2018 and included figures). Correlating the AT tephra with the speleothem record is possible
through IntCal13 without consideration of the marine
reservoir, since the ages of our core around 30 ka rely on
the age determined by the Suigetsu Lake sediment. We
expect that the age uncertainty range of this correlation
is probably less than 0.5 ka. Compared with the timeseries of δ18Osp, a positive peak in δ18Op in KR07-12
PC-01 at 30 ka is highly likely correlated to a positive
peak in δ18Osp. This agreement allows us to conclude
that this assumption is plausible and further infer thatin-phase correlation is highly likely for other climatic
events during MIS 3. An out-of-phase relationship is

In step 2, we obtained another set of δ18Ow and S as
input data in Eq. 6 to determine slope “a” using ahigh-time resolution paired Mg/Ca and δ18Op data of G.
ruber from core MD06-3067 (6° 31′ N, 126° 30′ E, 1575
m water depth) in the western tropical Pacific (Additional file 2: Figure S2; Bolliet et al. 2011). We regard
this site as a reference location for the endmember ofthe Kuroshio water, since the site of core MD06-3067 isaffected by the Mindanao Current that shares the same
origin of waters, North Equatorial Current, with the
Kuroshio. A strong upwelling affects intermediate andsubsurface waters in this region, but the upwelled water
does not reach the surface upper 75 m (Udarbe-Walker
and Villanoy 2001) where G. ruber dwells. Therefore, it
is inferred that surface water in this region still holds theorigin’s water properties. In fact, the SSS at nearby site
MD06-3067 in the western tropical Pacific is 34.0–34.3
during July to September (World Ocean Atlas 2009;
Antonov et al. 2010), which is similar to the SSS in the
Kuroshio region in the ECS. Although low-resolution
Mg/Ca and δ18O data exists in the north of the bifurcation latitude (~ 14° N) of the Kuroshio at Benham Rise
(MD06-3047B; Jia et al. 2018), the site MD06-3067 is abetter site in terms of the time resolution, as a major
process of this “endmember correction” is to evaluate
the influence of the millennial-scale variability in thesource region. The δ18Ow and SSS of core MD06–3067
are derived from PSU Solver with Eqs. 2–4 andδ18Ow-salinity equation for Palau (Conroy et al. 2017).
In the final step, Eqs. 2–4, 6, and 7 were solved with
the input data derived from steps 1 and 2. We performed the Monte Carlo simulation to constrain errors
associated with the age uncertainty among the records
and temperature, salinity, and δ18Ow reconstructions
propagated from analytical errors for measurements of
Mg/Ca and δ18Op. In the Monte Carlo simulation,
temperature, salinity, and δ18Ow at a given time “t” were
calculated with randomly selected input data (Mg/Caand δ18Op of KR07-12 PC-01, δ18Osp, and δ18Ow andsalinity of MD06-3067) in the range of t – 0.5 ka< t < t + 0.5 ka. The analytical error was prescribed to the
Mg/Ca and δ18Op data, incorporated as a normal distribution (Thirumalai et al. 2016). We set 0.16 mmol/mol and0.05‰ for the Mg/Ca and δ18O uncertainties, respectively.
Median value and standard deviation (1σ) are yielded ateach time step after 500 repetitions of the simulation (Fig. 3).
All of the calculations were performed on MATLAB.

Results and Discussion
Age model

Conventional 14C ages were calibrated to calendar ages
using CALIB 7.1 (Stuiver et al. 2016) with Marine13

(2019) 6:39

Page 8 of 16

especially unlikely, as the time offset between negativeand positive peaks is beyond the age uncertainty.
The age-depth curve of the fine-tuned age model wascompared with the age model based only on 14C datums
in Additional file 3: Figure S3. The fine-tuned age model
did not contradict the 14C-based ages as the obtained
ages with the fine-tuned age model at the horizons of
the 14C datums were within the 2σ uncertainties (Fig. 2
and Additional file 3: Figure S3). The average linear sedimentation rates were approximately 40 cm/ka during
MIS 3 and 15 cm/ka during the Holocene. The highest
one (~ 50 cm/ka) was found at LGM to deglaciation.
Our sampling interval is frequent enough to satisfy therequired Nyquist frequency (1/500 year−1 in this case),
which is 1/2 of the expected frequency to obtain accurate peaks in a time series data. The time resolution of
the 14C-based datums is 3–4 ka as listed in Table 2. We
assume that the age uncertainty of the sampling horizons between the datums is similar to those at the datums based on the linear age-depth relationship in ourage model (Fig. 2).

a linear salinity-δ18Ow relationship is confirmed (e.g.,
Horikawa et al. 2015), reflecting mixing between the
freshwater from the Changjiang River and seawater(Kuroshio Water) (e.g., Horikawa et al. 2015; Kubota etal. 2015). Namely, the salinity and δ18Ow at the studied
site can be interpreted in the context of a simplified two
endmember mixing model of the freshwater from the
Changjiang River and Kuroshio Water (Kubota et al.
2015). By contrast, the following factors are involved incontrolling the ECS δ18Ow in the past: (1) changes inδ18Ow and salinity in the source region and (2) changes
in mixing ratio between the Changjiang River freshwaterand Kuroshio Water (≒salinity at the studied site). An
effect of the global ice volume is also involved in thechanges in endmember δ18Ow and salinity. As the calibration method of “endmember correction” described in
the “Methods/Experimental” section incorporates thechanges in endmember δ18Ow and salinity in the source
region through time, the result derived from these calculations gives a better estimate, especially for the salinity.
As a result, neither SST nor δ18Ow with “endmember
correction” has a large difference from those with “salinity correction” (Fig. 3), indicating that the “salinity correction” is a reasonably close approximation of thosevariabilities.
We utilized the δ18Osp data as the input parameter todetermine the freshwater δ18O (Eq. 7) in the “endmember correction” method. In this method, we assume
that the variability of the freshwater δ18O is the same asthat of the δ18Osp. In general, δ18Osp can be regarded asa function of drip water δ18O and cave temperature, unless precipitation of the speleothems is at equilibrium(Hendy 1971). When drip water δ18O increases and/or
cave temperature decreases, the δ18Osp increases. The
absence of any identification of the kinetic effect on the
published δ18Osp from the Chinese caves enables us tointerpret the δ18Osp as reflecting drip water δ18O andcave temperature (Wang et al. 2008). The assumption
we apply to Eq. 7 does not incorporate the temperaturecomponent in estimating the endmember freshwater
δ18O. However, the cave temperature variability, if it isdeduced from the SST in the ECS, would be correlatedwith the temporal variation in δ18Osp (Additional file 3:
Figure S3). Thus, incorporating the temperature component is supposed to suppress the variability of the endmember freshwater δ18O and the calculation without thetemperature component results in giving a maximum
range of the variability of the freshwater δ18Ow. Moreover, high variability in the freshwater δ18O is expected
to decrease the salinity variability or freshwater contribution in the northern ECS (Eqs. 6 and 7). Nevertheless,the SSS reconstruction with “endmember correction”
still shows high variability in KR07-12 PC-01 (ca. ± 1)
during MIS 3 (Fig. 3), indicating that the changes in

Kubota et al. Progress in Earth and Planetary Science

Salinity effect on Mg/Ca calibration

This section discusses the results derived from “no salinity correction” and “salinity correction.” The temporal
patterns of the variations in SST and δ18Ow-local with thetwo calibrations agree well. However, the result with salinity correction for KR07-12 PC-01 shows less amplitude
than that without salinity correction for both SST andδ18Ow-local (Fig. 3), indicating that salinity effect on Mg/
Ca amplifies the variability of SST. This is indicative in
Fig. 3, showing that the positive correlation between
SST and δ18Ow-local is stronger in “no salinity correction”
than in one with salinity correction for MIS 3. Furthermore, these relationships depend on the sea level for thecase without salinity correction, and the positive correlation becomes stronger as the sea level lowers (Fig. 3d,e). Given that the river mouth of the Changjiang Riverapproaches as the sea level lowers, the sea-level dependence of the SST-δ18Ow-local relationship reflects a change
in salinity effect on the studied site in association withsea-level lowering. Alternatively, one would argue thatthe low sea-level stand prevents water inflow from the
ECS to the Japan Sea, resulting in reduced freshwatertransport into the Japan Sea and increased salinity variability in the northern ECS. Therefore, we conclude that
SST and δ18Ow-local reconstructions with salinity correction are a better estimate than the calibration without
salinity correction.
Calibrating the local salinity variations

We interpret the δ18Ow-local as a proxy for SSS and willrefer to it in the discussion as such. In the modern ECS,

(2019) 6:39

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endmember δ18Ow have little impact in the northern
ECS. Even though the freshwater δ18O is highly variablewith the amplitude of ± 1‰, its effect is diminished at
the studied site due to a smaller contribution of the
freshwater to the northern ECS compared with the seawater. Furthermore, neither the variability in δ18Ow nor
the salinity in the western Pacific warm pool has a significant impact on the SSS reconstruction in the ECS.

Regional and global factors affecting the Japan Sea records

Kubota et al. Progress in Earth and Planetary Science

Changes in SST, local seawater δ18O, and SSS

The output data from “endmember correction” will be
used for discussion hereafter. As planktic foraminifer G.
ruber s.s. is abundant in warm seasons in the northern
ECS (Yamasaki et al. 2010), we interpret that our results
reflect the signals of the warm months (Kubota et al.
2015). The δ18O, SST, and δ18Ow-local of KR07-12 PC-01
replicate those of U1429 (Additional file 3: Figure S3),
but have higher time resolution. The SST of core
KR07-12 PC-01 ranges from 20.2 °C to 24.4 °C, with anaverage of 22.3 ± 0.7 °C (1σ) during MIS 3 (Figs. 3 and4), characterized by 1–3 °C amplitude variations at themillennial scale. The SST increases at D-O interstadials
10 and 9 in Greenland and decreases in association with
Heinrich event 4 (H4). Four high SST events from 38 to
32 ka can be correlated to D-O interstadials 8–5. Heinrich event 3 (H3) is recognizable, but the magnitude ofthe decrease in SST is similar to other SST minima.
While D-O interstadial 3 is evident in the SST records,
D-O interstadial 4 is less distinct. Heinrich event 2 (H2)
is characterized by a pronounced decrease in SST by ~3 °C. The average SST in LGM is 22.3 °C, which is thesame as that in MIS 3. Thus, the LGM is not the coldest
period in the last 44 ka. This is a regional phenomenon
as a similar trend is found in the middle Okinawa
Trough (Chen et al. 2010) and western Pacific warm
pool (Stott et al. 2002) based on Mg/Ca-based SST fromG. ruber. However, alkenone-based SST, which is interpreted as reflecting an annual mean SST, shows the lowest values during LGM in the northern ECS (Ijiri et al.
2005; Clemens et al. 2018). The proxy-dependent results
suggest seasonality in the SST evolution since the lastglacial period.
The SSS in the northern ECS is highly variable, ranging from 31.5–34.0 during MIS 3. Millennial-scale variation is recognized in SSS with major negative peaks inassociation with D-O interstadials 10–5 and 3 (Fig. 4). A
predominant positive shift is found at H4, while H3 isless distinct. SST and SSS tend to covary on the millennial scale (H4, D-O 8, D-O 5, and H3). Contrary to theother high SSS events in association with H4 and H3,
H2 is characterized by a negative shift, which suggests
an alternative underlying mechanism of the EASM
precipitation.

Temporal variation in lightness (L*) of the hemipelagic
sediments in the Japan Sea shows a millennial-scale temporal change similar to δ18O of the Greenland ice cores(Tada et al. 1995; Wang and Oba 1998; Tada et al. 1999).
The L* of the sediment principally reflects organic carbon content (Corg), which is controlled by primary production and burial rate, except for during the lowest
sea-level stand, such as LGM, when L* is affected by pyrite content (Tada et al. 1999). Tada et al. (1999) suggested that changes in freshwater discharge from the
Changjiang and Yellow Rivers, and consequent changes
in the spatial extent of the nutrient-rich and low-salinity
ECS coastal water, were the main cause of changes in anutrient influx into the Japan Sea, and primary production there during MIS 3. The increased influence of the
ECS coastal water not only enhanced primary productivity, but also reduced deep-water ventilation, leading to
the development of the anoxic bottom waters and deposition of the dark layers (Tada et al. 1999; Irino et al. 2018).
This hypothesis can be tested by comparing the temporalvariations in the SSS in the northern ECS with L* of thehemipelagic sediments in the Japan Sea (Watanabe et al.
2007). While the nutrient and salinity flux into the Japan
Sea is regulated by the EASM, the strength of the East
Asian winter monsoon (EAWM) is a crucial factor to forma high-oxygenated deep-water mass called the Japan Sea
Proper Water (JSPW) (Suda 1932; Nitani 1972; Ikehara andFujine 2012; Gamo et al. 2014). The cold winds of the winter monsoon cool the surface water along the far eastern
coast of Russia and promote the formation of sea ice, which
increases the sea surface density to form the JSPW (Suda
1932; Nitani 1972). In addition to the factors describedabove, sea-level change controls the influx of the ECS waters into the Japan Sea as the narrow and shallow Tsushima
Strait prevents the inflow of the ECS waters at a low
sea-level stand. As the Japan Sea is connected to otherocean basins only through shallow (< 130 m in sill depth)
and narrow (< 90 km in width) straits in addition to the
Tsushima Strait, the sea-level change and resulting restriction of the seawater inflow from other basins are crucial tocontrol the SSS in the Japan Sea. Thus, the following three
climatic/oceanic factors that potentially control the sediment color of the Japan Sea are discussed in this section:(1) SSS in the northern ECS, (2) EAWM strength, and (3)global sea-level change. Factors 1 and 3 regulate the nutrient influx into the Japan Sea, while all three factors are involved in the deep-water formation and decomposition of
the organic material in the sediment-water interface on thesea floor.
In this study, we used a high time resolution (1 cm interval) L* record of core MD01-2407 in the Japan Sea (Bassinot and Blatzer 2002), which was recalibrated to calendar
ages using Marine13 (Additional file 5, Additional file 6:

Kubota et al. Progress in Earth and Planetary Science(2019) 6:39

Page 10 of 16

a

b

c

d

e

Fig. 4 Comparison of the ECS records and other regions. a δ18O from NGRIP with age model GICC05 (Svensson et al. 2008). b SST (black) and c
SSS (black) of KR07–12 PC-01 with 1σ uncertainties superimposed on δ18Osp (orange) from China (Cheng et al. 2016). d L* (purple; Bassinot andBlatzer 2002) and ESR (green; Nagashima et al. 2011) of MD01-2407 in the Japan Sea, recalibrated to IntCal13. e Gulang mean grain size, Loess
Plateau, China (Sun et al. 2012). Heinrich events 2 (H2), 3 (H3), and 4 (H4) are highlighted in blue. D-O cycle numbers are shown in the NGRIP
δ18O. The high SSS events in the ECS are highlighted in yellow. The 14C-based datums (circles) and tie points (crosses) are shown in b for KR07-12
PC-01 and in d for MD01-2407. Red triangles (AT) represent AT tephra

Figure S4, and Additional file 7: Table S2), for comparison
among the records (Fig. 4). The temporal resolution of the
1 cm interval data is approximately 120 years. In the intervals 44–34 ka (D-O11–7) and 30–28 ka (H3–D-O 4), theJapan Sea L* profile varies in phase with the northern ECS
SSS. The dark (light) layers are correlated to the low (high)
SSS in the northern ECS. Since AT tephra is found in bothcores, the timing of each environmental change in bothseas can be constrained (Fig. 2). In the Japan Sea, AT
tephra is found at the base of the light later (H3), which iscorrelated with one of the high SSS events in the ECS.
Based on the comparison above, the changes in the SSS in
the northern ECS and consequent changes in nutrient andsalinity input into the Japan Sea had been a dominant factor altering the sediment color in the Japan Sea in theseintervals. The variations in SSS in the northern ECS andassociating surface water density in the Japan Sea had
been amplifying the variability of the sediment color
through the process of production of deep water (Tada etal. 1999). The variations in the EAWM probably had alsoenhanced the variability of the sediment color during thistime period. In fact, a high-resolution quartz grain sizerecord of loess from Gulang section in the Chinese Loess
Plateau, which is an indicator of EAWM, shows a goodcorrelation with the L* during the interval 44–34 ka. By
contrast, the millennial-scale linkage between the northern ECS and Japan Sea became less clear after 34 ka,
which is probably linked to the sea-level lowering.
Lambeck et al. (2014) have reported a rapid sea-level fall(by ~ 40 m in less than 2000 years) at ~ 30 ka, following
the gradual fall starting from ~ 35 ka. The rapid and substantial fall in sea level from ~ − 80 to ~ − 120 m at 30 ka

(2019) 6:39

Page 11 of 16

would have reduced the water exchange between thenorthern ECS and Japan Sea from 20% of today to almost
zero (Additional file 8: Figure S5). Previous studies haverevealed that the SSS in the Japan Sea had been lowering
toward LGM, which was inferred from a shift to low
values in planktic foraminiferal δ18O in the Japan Sea after
30 ka (Oba et al. 1991; Kido et al. 2007; Sagawa et al.
2018). The sediment facies of the dark layers also shiftedfrom bioturbated layers interbedded by fine laminations tofinely laminated layers in association with the decrease in
the SSS in the Japan Sea around 28 ka (Watanabe et al.
2007). This facies change is accompanied by an increasein S/Corg, suggesting the euxinic condition in the bottom
water (Watanabe et al. 2007), likely caused by the poor
ventilation of the bottom water and not by the increase in
the primary productivity, as the nutrient input from the
ECS was reduced significantly (Watanabe et al. 2007). The
comparison of the SSS in the ECS and L* in this study indicates that they are not correlated well in this interval,
suggesting the millennial-scale variations in the ECS SSS
had almost no impact on the Japan Sea at the lower sea
level. Thus, there seems to be a threshold around the sea
level of − 80 to − 120 m from a mode where the Japan Sea
responds well to the ECS variation to the other mode where
it does not. Meanwhile, one still can find interbedded lightlayers, such as H2, after 28 ka (Watanabe et al. 2007). Theselight layers might be caused by a different mechanism fromthose during MIS 3. We speculate that deposition of thelight layers might be involved in a millennial-scale sea-level
rise that increases the ECS water influx to increase thedensity of the Japan Sea. In this case, the deep water ventilation plays a more significant role in controlling the L*. Although the timing of the rapid sea-level rise is an issue(Yokoyama and Esat 2011), based on studies on an oxygenisotope record from the Red Sea and isotopes in corals
from Papua New Guinea, it is now accepted that 15–20 m,
and possibly up to 30 m, of sea level increases is associatedwith Heinrich events (Broecker 1994; Siddall et al. 2003;
Yokoyama and Esat 2011), or with the warm period after
the termination of a Heinrich event (Arz et al. 2007). Achange in sea level of 15–20 m corresponds to over 10%
change in the cross section in the Tsushima Strait, potentially leading a massive change in the water volume exchange between the two ocean basins.

millennial-scale (e.g., Zhang et al. 2018). In the earlier
stage of the studies on δ18Osp, the δ18Osp were interpreted as the amount of summer precipitation at a given
cave location (Wang et al. 2001). Subsequently, other interpretations were proposed: the fraction of water vapor
removed from air masses along the moisture trajectory
between the tropical Indo-Pacific and the cave sites (Huet al. 2008) or isotopic fluctuation in the moisture source
region (Pausata et al. 2011). By contrast, the most recent
study argued that the δ18Osp is better understood as ameasure of the large-scale monsoon circulation, notreflecting the local precipitation (Liu et al. 2014). This
interpretation is also put forward in a study on trace elements of speleothems, which suggests a wetter conditionin H1 and Younger Dryas during the last deglaciation(Zhang et al. 2018). In their discussion, when the δ18Osp
becomes heavier, which is traditionally interpreted as“weak monsoon,” the local summer rainfall increases in
the Changjiang River catchment basin (Zhang et al.
2018). Our new SSS result indicates that most D-O stadials and H4 and H3 intervals correspond to less EASM
precipitation and D-O interstadials correspond to more
EASM precipitation. Our result contradicts what wasclaimed in Zhang et al. (2018), suggesting that themillennial-scale summer precipitation response might
depend on a global climate setting, such as sea level. In
fact, H2 in the ECS, characterized by the decreased SSS,
is different from other stadials and Heinrichs. A complex
response in the summer precipitation was also suggested
based on pollen record from Lake Suigetsu, central
Japan, which indicates a wetter condition during H1compared to the following Bølling-Allerød (Nakagawa et
al. 2006).
The SSS variation in the ECS and its relationship to
the westerly jet will give us a new insight into the variability of summer precipitation and its relationship tomonsoon circulation itself. Today, the EASM front migrates northward from South China from May andreaches the North China-Inner Mongolia regions in August. From the meteorological aspect, the northward migration of the EASM front follows the northward shift of
the westerly jet over East Asia on a seasonal scale(Sampe and Xie 2010). Therefore, a proxy of the positionof the westerly jet is regarded as an indicator of the
EASM circulation. On an interannual time scale, thenegative correlation between EASM rainfall in the
Changjiang River basin and the summer southerly windintensity in the northernmost region of the EASM wasobserved (Jiang et al. 2008); the EASM circulation wasstronger, and the Changjiang River basin had less precipitation. A similar relationship was observed on amillennial-scale during the Holocene, based on a proxyof the westerly jet and summer precipitation records
from inland China and ECS (Nagashima et al. 2013).

Kubota et al. Progress in Earth and Planetary Science

Summer precipitation variability and westerly jet in
East Asia

The most prominent feature in our new records is that
the SSS in the ECS varies in association with the D-O
cycles, which can provide fundamental information on
summer precipitation changes under the EASM system.
Although the Chinese δ18Osp is one of the mostwell-known proxies from the EASM region, the interpretation of the Chinese δ18Osp is still controversial on a(2019) 6:39

Page 12 of 16

According to Nagashima et al. (2011), the position of thewesterly jet has varied in harmony with D-O cycles in
MIS 3, based on the electron spin resonance (ESR) signal intensity of quartz in the fine silt fraction of theJapan Sea sediments, which is interpreted as a proxy fordust provenance, sourced either from the Taklimakan
Desert or the Mongolian Gobi (Fig. 4). The higher(lower) ESR signal intensity during stadials (interstadials)
suggests the southern (northern) position of the westerly
jet and weaker (stronger) EASM circulation (Nagashima
et al. 2011). In the interval 44–34 ka, the increased
EASM precipitation in the Changjiang River basin
followed the northward shift of the westerly jet position(EASM front), which was opposite to what was observed
on a millennial-scale during the Holocene (Nagashima
et al. 2013) and on an interannual to decadal scale today(e.g., Jiang et al. 2008).

in Heinrichs is caused by the reduction of the moisturesupply from the surrounding oceans, probably linked to theweakening of the subtropical high in the North Pacific (Sunet al. 2012). We infer that, under this mechanism, the migration pattern of the westerly jet and associated position of
the EASM front do not regulate the special summer precipitation distribution in the same manner as today, butmoisture budget in the atmosphere plays a dominant role.
However, an alternative explanation is required for the opposing precipitation trend, such as H2 and H1, when thesummer precipitation pattern is opposite to what is observed for MIS 3. A recent study points out that the internal climate variability is important in the rainfall
variation in East Asia’s mid-latitude on an interannual anddecadal scale today (Ueda et al. 2015). Internal variability is
the natural variability of the climate system that occurs in
the absence of external forcing, and includes processes intrinsic to the atmosphere, the ocean, and the coupled
ocean-atmosphere system (Deser et al. 2010; Deser et al.
2012). Interannual and decadal scale variabilities arise fromthe intrinsic variability of the system due to a random stochastic process and results from dynamic and thermodynamic interactions of the coupled ocean-atmospheresystem (Deser et al. 2012). Ueda et al. (2015) argue that today’s interannual and interdecadal precipitation patternover mid-latitude East Asia is caused by a combination of
the atmospheric internal variability and SST in the western
Pacific and Indian Ocean. On a millennial time scale, the
EAWM variability after 30 ka, decoupled to the North Atlantic climate (Sun et al. 2012), appears to lack the forcing
outside of the East Asian climate system, perhaps resulting
from the increased intrinsic variability of the EAWM. One
would argue that the increased summer precipitation insouthern China in H2 and H1 is interpreted as reflecting
the increased convection along the slope of the Tibetan
Plateau, induced by the longer placement of the EASM
front in the southern position (Zhang et al. 2018). Alternatively, however, the summer precipitation response in H2
and H1 may be explained in the context of the random stochastic processes in the EASM system as well as theEAWM. In the latter case, the overall monsoon system in
East Asia would randomly vary, rather than responding to
the North Atlantic forcing during MIS 2. Either way, theopposite millennial-scale EASM precipitation pattern during MIS 2 leads to a conclusion that the regional precipitation is not a dominant factor controlling themillennial-scale Chinese δ18Osp in this period.

Kubota et al. Progress in Earth and Planetary Science

Global context of East Asian monsoon variations

A comparison of the Chinese δ18Osp record and loess grain
size indicates that the EASM and EAWM are coupled on amillennial time scale during the interval 60–34 ka (Sun etal. 2012). Their variations are well correlated to the North
Atlantic climate variability as well (Sun et al. 2012). In the
study of Sun et al. (2012), North Atlantic water-hosing experiments, using the Community Climate System Model
Version 3 to mimic the Heinrich or stadial events, suggest
a dynamical response of the EASM and EAWM systems to
the suppressed Atlantic Meridional Overturning Circulation (AMOC) and cooling in the North Atlantic. The freshwater forcing increases the latitudinal temperature gradientand induces stronger EAWM during winter (Sun et al.
2012). During summer, cooling in North Atlantic results ina southward shift of the Intertropical Convergence Zone,
followed by a stronger Walker circulation and weaker
EASM (Zhang and Delworth 2005). The moisture convergence along the EASM front and precipitation in the
Changjiang River basin is suppressed as the North Pacificsubtropical high weakens in response to the freshwater forcing during summer (Sun et al. 2012). However, an opposite summer precipitation response is suggested in anumerical simulation using the Community Earth System
Model (Zhang et al. 2018). In this simulation, a stronger
meridionaltemperature
gradient,
andresultantsouthward-shifted and strengthened westerly jet, leads toenhanced convection along the slope of the Tibetan Plateau, increased precipitation in southern China, and decreased precipitation in northern China. Thus, theprecipitation response in the East Asian monsoon region
depends on the climate models, being not constrained by
the numerical models as depicted in Kageyama et al.
(2013). Conversely, our records give a constraint on themechanism of the EASM response to AMOC variability
during MIS 3, suggesting that the decreased precipitation

Conclusion
We reconstructed SST, δ18Ow -local, and SSS using thepaired Mg/Ca and δ18Op in the northern ECS. To better
estimate the SSS variability, we tested the three calibration methods to reconstruct these records. The SST record derived from the Mg/Ca calibration without salinity

Kubota et al. Progress in Earth and Planetary Science(2019) 6:39

correction shows higher variability than the one withsalinity correction, and its variability depends on the sea
level. The SSS reconstruction is not significantly affectedby the endmember δ18Ow and salinity variability in thesource regions, indicating that the modern local
δ18Ow-salinity calibration is applicable to the MIS 3 case.
The most prominent feature of our SSS record is themillennial-scale variations in association with the D-O
cycle in Greenland and the Chinese δ18Osp, where high
SSS events coincide with D-O stadials and Heinrichs,
while low SSS events coincide with D-O interstadials.
The comparison of the SSS in the northern ECS, theproxy of the EAWM, and the sediment color in theJapan Sea reveal that the changes in nutrient and salinity
flux in the Japan Sea induced by the Changjiang River
discharge in addition to the strength of the EAWM are
likely the primary factors determining the surface productivity changes manifesting as color changes in the sediments of the Japan Sea when the sea level was higher
than ca. − 80 m. The glacioeustatic sea-level changes reduced the influx of the ECS coastal water entering into
the Japan Sea, especially after ~ 30 ka, at which the linkage between the SSS in the northern ECS and the sediment color of the Japan Sea became less clear.
The SSS variation in the ECS and its relationship to
the westerly jet indicates that the decreased EASM precipitation in the Changjiang River basin followed thesouthward shift of the westerly jet position, which is opposite to what was observed on a millennial-scale during
the Holocene and interannual time scale today. We inferthat the moisture convergence along the EASM front
and overall moisture budget in the atmosphere, which
would be altered in association with the D-O cycles andHeinrichs, plays a dominant role in controlling the
EASM precipitation in southern China during MIS 3.
The precipitation response during MIS 2 is opposite to
MIS 3, suggesting the alternative mechanism involved at
different global climate backgrounds.

Additional files
Additional file 1: Figure S1. (a) Mg/Ca vs. Mn/Ca of KR07-12 PC-01 (allrange) and (b) Mg/Ca vs. Mn/Ca (0–200 μmol/mol). (PDF 201 kb)
Additional file 2: Figure S2. Comparison among δ18Ow of KR07-12 PC01 (red) and δ18Ow of endmembers. The freshwater endmember isderived from Chinese speleothem δ18O (blue; Cheng et al. 2016), andKuroshio Water endmember is derived from MD06–3067 (purple; Bolliet
et al. 2011). The vertical axis on the left is for the freshwater endmember
and on the right is for KR07-12 PC-01 and MD06-3067. (PDF 165 kb)
Additional file 3: Figure S3. Comparison of the results of KR07-12 PC01, U1429 (Clemens et al. 2018), and Chinese speleothem δ18O (δ18Osp)
with (a) 14C-based age and (b) fine-tuned age. The top panels show comparisons between the planktic foraminiferal δ18O (δ18Op) of KR07-12 PC-01
and U1429 and δ18Osp. The middle and bottom panels show SST and local
δ18Ow (δ18Ow-local), respectively. Open circles on the top of the figure
represent the 14C-based datums and 2σ uncertainties. Crosses and triangles

Page 13 of 16

in the right panel indicate the tie points and the ages of the horizons of 14C
datums with the fine-tuned age model, respectively. (PDF 276 kb)
Additional file 4: Table S1. The tie points of the fine-tuned age modelof KR07-12 PC-01. (XLS 27 kb)
Additional file 5: Age model for MD01-2407. AMS 14C ages for core
MD01-2407 published in Yokoyama et al. (2007) were recalibrated tocalendar age with Marine13 (ΔR = 45) (Reimer et al. 2013). All of thecalendar age and depths are listed in Table S1. Choosing a reasonablesedimentation rate, seven datums were not employed in our age model(Table S1). Before 50 ka, we refer to datums in Kido et al. (2007). (DOCX 15 kb)
Additional file 6: Figure S4. Depth vs. calendar age for core MD012407. Data in blue are not used for the age model. (PDF 75 kb)
Additional file 7: Table S2. The calendar ages and depths for core
MD01-2407 and references. (XLSX 10 kb)
Additional file 8: Figure S5. Depth in the Tsushima Strait vs. crosssection area in the Tsushima Strait (Oba 1988). (PDF 70 kb)
Abbreviations
EASM: East Asian summer monsoon; EAWM: East Asian winter monsoon;
ECS: East China Sea; JSPW: Japan Sea Proper Water; LGM: Last Glacial Maximum;
MIS: Marine Isotope Stage; δ18O: Oxygen isotope; δ18Op: Oxygen isotope ofplanktic foraminifera; δ18Osp: Oxygen isotope of speleothems; δ18Ow: Oxygenisotope of water; δ18Ow-local: Oxygen isotope of local seawater
Acknowledgments
We thank the onboard scientists and cruise staffs of KR07-12. We also
thank T. Omura, H. Yamamoto, M. Takada, M. Sato, Y. Nakamura, N. Kisen,
N. Nakamura, and A. Kobayashi for their assistance in our experiments.
We are grateful to K. Nagashima, T. Sagawa, T. Irino, and T. Nakagawa
for their helpful comment and suggestion.
Funding
JSPS KAKENHI Grant supported this work number 23221022 awarded to RT,
and grants from the National Museum of Nature and Science and Japan
Agency for Marine-Earth Science and Technology (JAMSTEC) from the
Ministry of Education, Culture, Sports, Science, and Technology (MEXT,
Japan). A grant-in-aid funded YK for JSPS Fellows, grant number 10914, andProgram for Advancing Strategic International Networks to Accelerate theCirculation of Talented Researchers, grant number R2901.
Availability of data and materials
The datasets used and/or analysed during the current study are available
from the corresponding author on reasonable request.
Declarations
The data sets supporting the conclusions of this article are included in thisarticle and its additional files.
Authors’ contributions
RT proposed the topic for YK’s doctoral research. YK carried out theexperimental study under the guidance of RT and KK. MU and KI analyzed
the 14C and tephra, respectively, and helped in their interpretation. YK wrote
a draft of the manuscript, and RT collaborated with the corresponding
author in the construction of the manuscript. All authors read and approved
the final manuscript.
Authors’ information
RT was a co-chief scientist of KR07-12, and YK and KI were on-board scientists of KR07-12. RT and KK supervised YK’s doctoral research.
Competing interests
The authors declare that they have no competing interests.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in publishedmaps and institutional affiliations.

(2019) 6:39

Page 14 of 16

Author details
1
Department of Geology and Paleontology, National Museum of Nature andScience, 4-1-1, Amakubo, Tsukuba, Ibaraki 305-0005, Japan. 2ResearchInstitute for Global Change, Japan Agency for Marine-Earth Science andTechnology, 2-15 Natsushima-Cho, Yokosuka, Kanagawa 237-0061, Japan.
3
Department of Earth and Planetary Science, Graduate School of Science,,
The University of Tokyo, 7-3-1, Hongo, Bunkyo-Ku, Tokyo 113-0033, Japan.
4
National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki
305-8506, Japan. 5Geological Survey of Japan, National Institute of Advanced
Industrial Science and Technology, 1-1-1 Higashi, Tsukuba 305-8567, Japan.

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