This study examines the seasonality of precipitation
amount and δ18O over the monsoon region of China (MRC). We
found that the precipitation amount associated with the East Asian summer
monsoon (EASM) in the spring persistent rain (SPR) region is equivalent to
that of the nonsummer monsoon (NSM). The latter contributes ∼50 % to amount-weighted annual δ18O values, in contrast with
other areas in the MRC, where the δ18O of annual precipitation
is dominated by EASM precipitation. Interannual relationships between the
El Niño–Southern Oscillation (ENSO) index, simulated δ18O data from IsoGSM, and seasonal
precipitation amount in the SPR region were also examined. We found that on
interannual timescales, the seasonality of precipitation amount (EASM / NSM
ratio) was modulated by ENSO and primarily influences the variability of
amount-weighted annual precipitation δ18O values in the SPR
region, although integrated regional convection and moisture source and
transport distance may also play subordinate roles. During El Niño (La
Niña) phases, less (more) EASM and more (less) NSM precipitation leading
to lower (higher) EASM / NSM precipitation amount ratios results in higher
(lower) amount-weighted annual precipitation δ18O values and,
consequently, in higher (lower) speleothem δ18O values.
Characterizing spatial differences in seasonal precipitation is, therefore,
key to correctly interpreting speleothem δ18O records from the
MRC.
Introduction
Summertime rainfall over the MRC is largely associated with the East Asian
summer monsoon (EASM) (Fig. 1a) (Ding, 1992). However, a significant
portion of annual rainfall in southeastern China also occurs during
springtime (i.e., from March to mid-May), known as the spring persistent
rain (SPR). The SPR occurs mostly south of the middle and lower reaches of
the Yangtze river (∼24 to 30∘ N,
110 to 120∘ E) (Fig. 1b) and is a unique synoptic
and climatic phenomenon in East Asia (Tian and Yasunari, 1998; Wan and Wu,
2007, 2009). The SPR is another rainy period before the Meiyu rain period in
early summer, and it covers the region from southeastern China to the south of
Japan. It has long been debated whether the SPR marks the onset of EASM.
Ding (1992) called SPR an “early summer rainy season” and considered it as
a part of the summer monsoon rainfall (Ding et al., 1994). He et al. (2008)
suggested that the SPR marks the establishment of the East Asian subtropical
monsoon, which is considered a component of the EASM. Other studies
suggest that the SPR is unrelated to EASM rainfall, and they consider it as an
extension of winter atmospheric circulation (Tian and Yasunari, 1998; Wan
and Wu, 2009). Wang and Lin (2002) proposed that the SPR over southeastern
China is not a part of the EASM, because the large-scale circulation and
rain-bearing systems differ from those associated with summer monsoon
rainfall. Tian and Yasunari (1998) suggested that the SPR is the effect of
the land–sea thermal contrast, and it is unrelated to topographical effects, as
there is a coherent increase in the spring rain from southeastern China to
southern Japan. Wan et al. (2008a, 2009) proposed that the formation of SPR
is primarily influenced by the mechanical and thermal forcing of the Tibetan
Plateau. Without this topographic element, the SPR rain belt would not
exist. Climatic factors from the mid to high latitudes and the tropics also
influence the interannual variability of the SPR (Feng and Li, 2011; Wu and
Kirtman, 2007; Wu and Mao, 2016).
Overview map showing the spatial distribution of seasonal
precipitation amount in China and locations mentioned in this study. (a) Regional mean EASM (May–September) precipitation amount (mm) in China
from 1951 to 2007. The black squares represent the locations of the Global Network
for Isotopes in Precipitation (GNIP)
stations (TJ is Tianjin, YT is Yantai, SJZ is Shijiazhuang, XA is Xi'an, ZZ is Zhengzhou,
NJ is Nanjing, WH is Wuhan, CS is Changsha, CD is Chengdu, ZY is Zunyi, GY is Guiyang,
GL is Guilin, LZ is Liuzhou, KM is Kunming; details can be found in Table 1). (b) Regional mean SPR (March–April) precipitation amount (mm) in China
from 1951 to 2007. The SPR is obvious in southeastern China from about
24 to 30∘ N and from 110 to
120∘ E. The black circles represent the locations of caves with
published stalagmite records (SH is Shihua cave, Li et al., 2017; HL is Hulu cave,
Wang et al., 2001; SB is Sanbao cave, Cheng et al., 2016; HS is Heshang cave,
Hu et al., 2008; DG is Dongge cave, Yuan et al., 2004; XBL is Xiaobailong cave, Tan
et al., 2017; WY is Wuya cave, Tan et al., 2014; DY is Dayu cave, Tan et
al., 2009; WX is Wanxiang cave, Zhang et al., 2008; HY is Huangye cave, Tan et
al., 2010; EM is E'mei cave, Zhang et al., 2018; and YH is Yuhua cave, Jiang et al.,
2012). Precipitation data source: APHRODITE (Asian
Precipitation – Highly-Resolved Observational Data Integration Towards
Evaluation of Water Resources, APHRO_MA_V1101R2 product, (21)) (Yatagai et al., 2009).
Although considerable emphasis has been placed on understanding the causes
and mechanisms of SPR, little is known about its precipitation δ18O (δ18Op) variability and about the mechanisms
that produce this variability (Tan, 2016; Zhang, 2014). Based on rainfall
monitoring data from eight sites in the EASM region, Tan et al. (2016) found
that, in 2012 CE, the spring rainfall amount was equivalent to the summer
rainfall, but their δ18Op values were different. They
suggested that the seasonal δ18Op variability is affected
by the changes in moisture source but not the precipitation amount
variations. Huang et al. (2017) and Wu et al. (2015) studied the δ18Op variability at the Changsha station located in the SPR
region and its relationship with the El Niño–Southern Oscillation (ENSO)
(Fig. 1a), but they did not focus on the δ18Op variability of
SPR. A better understanding of the δ18Op variability in
the SPR region on seasonal to interannual timescales, however, is crucial
for a robust interpretation of the oxygen isotopic data of Chinese
speleothems from this region (e.g., Cai et al., 2015; Cheng et al., 2009,
2016; Wang et al., 2001, 2008; Yuan et al., 2004; Zhang et al., 2008).
Several mechanisms including the amount effect, moisture source and transport
distance, integrated regional convection, winter temperature, and
precipitation seasonality have been shown to influence the δ18Op and speleothem δ18O to various degrees
and at different timescales across the MRC (Cai et al., 2018; Caley et al.,
2014; Cheng et al., 2016; Clemens et al., 2010; Dayem et al., 2010; Maher,
2008, 2016; Maher and Thompson, 2012; Pausata et al., 2011; Tan, 2016; Zhang
et al., 2018). The SPR region is located within the area of the EASM, and its
rainy season includes both summertime monsoon rainfall and SPR (Wan and Wu,
2009). Therefore, the factors that influence the δ18Op in
this region are likely complex. The aim of this study is to examine this
climate–δ18O proxy relationship during the instrumental period.
To this end, we compare the seasonal variations of precipitation amount and
δ18Op in the SPR region with other regions of the MRC
and discuss the interannual variations and their relationship with the
large-scale ocean–atmosphere circulation.
Data and methodsMeteorological data
A daily gridded precipitation dataset for 1951–2007 was obtained from
APHRODITE (Asian Precipitation – Highly-Resolved Observational Data
Integration Towards Evaluation of Water Resources, APHRO_MA_V1101R2 product, (21)) (Yatagai et al., 2009). The
regional mean SPR (March–April) and EASM (May–September) precipitation
amounts in China from 1951 to 2007 are shown in Fig. 1, which was exported
based on this dataset using the free software Ferret (https://ferret.pmel.noaa.gov/Ferret, last access: 3 November 2019).
Monthly precipitation datasets of 160 meteorological stations in China for
the period 1951–2014, obtained from the National Climate Center (https://www.ncc-cma.net/, last access: 3 November 2019), were used to characterize the percentage of spring
(March–April) and EASM (May–September) precipitation amount relative to the
annual precipitation amount in China.
Monthly mean δ18Op and precipitation amount data from
meteorological stations across the MRC were obtained from the Global Network
for Isotopes in Precipitation (GNIP) (http://www.iaea.org/, last access: 3 November 2019)
(Table 1 and Fig. 1a). The monthly mean δ18Op data are
used to compare the seasonal to interannual variation of δ18Op in the MRC. The stations near the coast from the
southeastern region of the MRC (Fuzhou, Haikou, Hong Kong, Guangzhou) were
excluded, because their precipitation amount and δ18Op are
significantly influenced by typhoons in summer and autumn. Changsha station
is the only GNIP station in the SPR region.
δ18Op data from IsoGSM simulations
IsoGSM is a water isotope-permitting general circulation model (Yoshimura et
al., 2008). We use the product of IsoGSM nudged toward the NCEP/NCAR
Reanalysis 2 (Kanamitsu et al., 2002) atmosphere and forced with observed
sea-surface temperatures (SST) and sea ice data (Yoshimura et al., 2008). A
detailed description of the model setup can be found in Yoshimura et al. (2008) and Yang et al. (2016). IsoGSM can reproduce reasonably well monthly
variabilities of precipitation and water vapor isotopic compositions
associated with synoptic weather cycles. In order to verify the reliability
of the simulated data from IsoGSM, we first cross-compare the data from
GNIP Changsha station with those from IsoGSM during 1988–1992. The good
replication indicates that both the precipitation amount and the δ18O data from the IsoGSM simulation are consistent (Supplement
Fig. S1).
Ocean–atmosphere circulation index
ENSO plays an important role in governing the climatic variation in the MRC
(e.g., Feng and Hu, 2004; Xue and Liu, 2008; Zhou and Chan, 2007). We used
the Southern Oscillation Index (SOI) and the Multivariate ENSO Index (MEI) to
calculate the correlations between the phases of ENSO, the δ18Op, and the seasonal precipitation amount. The SOI is defined as
the normalized pressure difference between Tahiti and Darwin. Negative and
positive values of SOI represent El Niño and La Niña events,
respectively. The data were obtained from the Australian Government Bureau
of Meteorology (http://www.bom.gov.au/climate/current, last access: 3 November 2019). The MEI
is based on six ocean–atmosphere variables (sea-level pressure, zonal and
meridional components of the surface wind, SST, and total cloudiness fraction
of the sky) over the tropical Pacific, and it is used to examine the role of
ENSO in influencing the rainfall over the MRC. The MEI is defined as the
first principal component of the abovementioned six variable fields. Therefore, it
provides a more complete description of the ENSO phenomenon than a single
variable ENSO index such as the SOI or Niño 3.4 SST (Wolter and Timlin,
2011). Positive and negative values of MEI represent El Niño and La
Niña events, respectively. The data were obtained from the website of
the Earth System Research Laboratory, National Oceanic and Atmospheric
Administration (NOAA) (http://www.cdc.noaa.gov/people/klaus.wolter/MEI, last access: 3 November 2019). Tropical Pacific SST
show a La Niña phase during the period from May 1988 to May 1989 and an
El Niño phase during the period from May 1991 to June 1992. Therefore,
we define 1988–1989 as La Niña years (1988 is the developing year and
1989 is the decaying year of the La Niña event) and 1991–1992 as El
Niño years (1991 is the developing year and 1992 is the decaying year of
the El Niño event) in this paper.
The Arctic Oscillation (AO) can also influence the climate and precipitation
over the MRC (Gong et al., 2001, 2011; He et al., 2017; Li et al., 2014). It
was suggested that a warmer winter in East Asia (a positive winter AO value)
is associated with increased winter rainfall in southern parts of East Asia,
and a positive spring AO is followed by increased rainfall in southern China
but decreased rainfall in the lower valley of the Yangtze river (He et al.,
2017). We also calculated the correlation between the AO index and the
seasonal rainfall amount in our study area. The data were downloaded from
the website of NOAA (https://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/ao.shtml#forecast, last access: 3 November 2019).
The percentage of spring (a, March to April) and EASM (b,
May to September) precipitation amount relative to the annual precipitation
amount in the study area. Panels (c) and (d) are similar to (a) and (b), except that spring
precipitation is shown from March to May in (c) and EASM precipitation between
June and September in (d). The Jiangxi and Hunan provinces (JX_HN) are highlighted in jade color. The monthly precipitation data
(1951–2014) from 11 meteorological stations (Jiujiang, Guixi, Nanchang,
Guangchang, Ji'an, Ganzhou, Changsha, Yueyang, Hengyang, Chenzhou, Xinning)
in Jiangxi Province and the eastern Hunan Province were used to examine
the relationship between ocean-atmospheric circulation, precipitation amount,
and δ18O in the SPR region. The red, pink, and green polygons in
panel (a) indicate southeastern, northern, and southwestern regions of the MRC,
respectively.
Back-trajectory and moisture source contribution calculations
The HYbrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT)
(Stein et al., 2015) was used to perform air mass back-trajectory
calculations for the GNIP Changsha station during the period 1988–1992. In
order to qualitatively assess the moisture source regions and transport
paths for rainy season precipitation, only air mass back trajectories for
precipitation-producing days were used. Trajectories were initiated four
times daily (at 00:00, 00:60, 12:00, and 18:00 UTC) during precipitating
days (> 1 mm precipitation per day) and their air parcel was released
at 1500 m above ground level and moved backward by winds for 120 h (5 d). To identify the moisture uptake locations along the back trajectories
during 1988–1992, we followed the method described in Sodemann et al. (2008)
and Krklec et al. (2018). Two criteria (i.e., a more conservative threshold
of positive gradient in specific humidity (0.2 g kg-1 within 6 h) and initial
relative humidity of more than 80 %) were used to identify moisture uptake
locations along the back trajectories. Following the methodology of Krklec
et al. (2018), we calculated the contributions of moisture uptake locations
en route to the precipitation in GNIP Changsha station and provided a map
showing the percentage of moisture uptake contributing to Changsha
precipitation during La Niña (1988–1989) and El Niño (1991–1992)
years. A grid of 0.5∘×0.5∘ was used for the computation of
the moisture uptake locations.
ResultsProportions of SPR, EASM, and NSM precipitation over MRC
We calculated the mean ratios of spring (March–April) to annual
precipitation (denoted as spring / annual) and EASM (May-to-September) to annual
precipitation (denoted as EASM / annual) ratios for the period 1951–2014. Figure 2a shows
that the mean percentage of spring / annual in southeastern China (about
20 to 33∘ N and 107 to 122∘ E,
red rectangle in Fig. 2a), which range from 10 % to 25 % and from 0 % to 10 % in
northern (about 33 to 53∘ N and 100 to
134∘ E, pink polygon in Fig. 2a) and southwestern regions of the
MRC (about 20 to 33∘ N, 9 to
107∘ E, green polygon in Fig. 2a), respectively. The Jiangxi and
the eastern Hunan provinces, the core regions of the SPR, show the highest
mean percentage of spring / annual within the MRC (20 %–25 %), which is
consistent with the results from the previous studies (Tian and Yasunari,
1998; Wan and Wu, 2009). Fig. 2b shows that the mean percentage of
EASM / annual is 40 %–70 % in southeastern China and 70 %–95 % in other
regions of the MRC. Conversely, the mean percentage of nonsummer monsoon
precipitation to annual precipitation (NSM / annual) is 30 %–60 % in
southeastern China and 5 %–30 % in other regions of the MRC, and it reaches the
maximum in the SPR region (45 %–60 %). This indicates that the proportion of
EASM precipitation (40 %–55 %) is nearly equivalent to the proportion of NSM
precipitation (45 %–60 %) in the SPR region.
Monthly mean δ18Op(a) and
precipitation amount (b) data from GNIP stations in northern region of the
MRC (black lines), southwestern region of the MRC (green lines),
southeastern China (red lines), and the SPR region (blue lines, Changsha
station) as grouped in Table 1. (The spatial distribution of the GNIP
stations are shown in Fig. 1a.) (c) Monthly mean precipitation data from the
meteorological stations closest to the GNIP stations in northern region of
the MRC (black lines), southwestern region of the MRC (jade lines),
southeastern China (red lines), and the SPR region (blue lines).
Usually, the SPR period lasts from March to mid-May (Wan and Wu, 2009) and
the EASM period lasts from mid-May to September (Wang and Lin, 2002);
however, the onset and/or retreat time of SPR and EASM and their intensities vary
in different years (Zhou and Chan, 2007). The EASM starts late (late May to
early June) and tends to be weaker during El Niño years (Huang et
al., 2012), and EASM precipitation amount over southeastern China is reduced
when the SPR starts later and lasts longer (until late May) (Wan et
al., 2008a). Therefore, if we define the March-to-May precipitation as SPR
and the June-to-September precipitation as EASM in El Niño years, the
mean percentage of SPR / annual in the SPR region is 35 %–45 % (Fig. 2c),
the mean percentage of EASM / annual is only 30 %–40 % (Fig. 2d), and the
mean percentage of NSM / annual is 60 %–70 %. However, in other regions of the
MRC, the mean percentage of EASM / annual (65 %–90 %) is still much higher
than the mean percentage of NSM / annual (10 %–35 %) (Fig. 2c and d).
Conversely, during La Niña years, the March–April and May-to-September
precipitation should be defined as SPR and EASM precipitation, respectively
(Fig. 2a and b). Therefore, the distribution of EASM vs. NSM
precipitation amount in the SPR region is distinctly different from that in
other regions of the MRC, and the ratio of EASM / NSM precipitation amount in
the SPR region might be influenced by ENSO. We discuss this in detail in the
Sect. 4.2.
Seasonal precipitation δ18Op and amount over the MRC
We compared the seasonal variations of precipitation amount and δ18Op in the SPR region with those in other regions of the MRC
by using data from the GNIP stations. According to the spatial distribution
of EASM precipitation as discussed in Sect. 3.1, we assigned Zhengzhou,
Xi'an, Yantai, Shijiazhuang, and Tianjin GNIP stations to northern region of
the MRC; Kunming, Guiyang, Zunyi, and Chengdu GNIP stations to southwestern
region of the MRC; and Changsha, Guilin, Liuzhou, Nanjing, and Wuhan GNIP
stations to southeastern China. Only the Changsha GNIP station is located in
the SPR region (Table 1 and Fig. 1a).
GNIP stations used for the comparison of the seasonal
precipitation amount and δ18Op in the MRC.
CategorySitesNorthern region of the MRCZhengzhou (34∘43′12′′ N, 113∘39′00′′ E)Xi'an (34∘18′00′′ N, 108∘55′48′′ E)Yantai (37∘31′48′′ N, 121∘24′00′′ E)Shijiazhuang (38∘1′60′′ N, 114∘25′01′′ E)Tianjin (39∘6′00′′ N, 117∘10′01′′ E)Southwestern region of the MRCKunming (25∘1′00′′ N, 102∘40′59′′ E)Guiyang (26∘34′60′′ N, 106∘43′01′′ E)Zunyi (27∘41′60′′ N, 106∘52′48′′ E)Chengdu (30∘40′12′′ N, 104∘1′12′′ E)Southeastern region of the MRCChangsha (28∘11′60′′ N, 113∘4′01′′ E)Guilin (25∘4′12′′ N, 110∘4′48′′ E)Liuzhou (24∘21′00′′ N, 109∘24′00′′ E)Nanjing (32∘10′48′′ N, 118∘10′48′′ E)Wuhan (30∘37′12′′ N, 114∘7′48′′ E)
The seasonal variation of δ18Op in the MRC is
consistently related to the onset, advancement, and retreat of the EASM. The
δ18Op values decrease in May as the summer monsoon starts
(Fig. 3). The δ18Op values are relatively low during the
monsoon season (June–August) (Fig. 3) because of the long-distance
transport of water vapor from the distal Indian Ocean to the MRC. Along this
pathway, progressive rainout associated with regional convection leads to
more negative δ18Op values via Rayleigh distillation
(Baker et al., 2015; He et al., 2018; Liu et al., 2010; Moerman et al.,
2013; Tan, 2014). The δ18Op values become progressively
higher as the EASM withdraws in September (Fig. 3). From October to next
April, the δ18Op values are rather high (Fig. 3),
resulting from the short-distance transport of water vapor from the western
Pacific Ocean or local moisture recycling and local convection (He et al.,
2018; Moerman et al., 2013; Tan et al., 2016; Wu et al., 2015). The low
δ18Op values in winter in northern region of the MRC are
caused by the temperature effect, but it is less important because of its
small contribution to the amount-weighted mean annual precipitation δ18O (δ18Ow) (Cheng et al., 2012). Therefore, the
seasonal δ18Op values over the MRC show a broadly
consistent pattern reaching a maximum in March–April and a minimum in
July–August in the MRC with the exception of low winter δ18Op values in northern region of the MRC.
Given that there are only a few years of data from those GNIP stations, we
obtained the mean monthly precipitation amount from the nearest
meteorological station to each GNIP station in the MRC for the period
1951–2014 (Fig. 3c). Both datasets show that the seasonal variation of
precipitation amount in southeastern China, especially in the SPR region, is
different from that in other regions of the MRC (Fig. 3b and c). The
precipitation amount in March and April before the onset of EASM is high
over southeastern China. It is even higher than the summer monsoon
precipitation amount in June, July, and August in other regions of the MRC.
In the SPR region, the summer monsoon precipitation amount in July–August is
much smaller than the precipitation in March–April. However, in other
regions of the MRC, the summer monsoon precipitation in July–August is the
highest of the whole year.
Moisture source contribution to precipitation in Changsha station
We identified the moisture uptake locations along the back trajectories and
calculated their contributions to the precipitation at the GNIP Changsha
station during EASM and NSM seasons in a La Niña phase (1988–1989) with
low δ18Op anomalies and in an El Niño phase
(1991–1992) with high δ18Op anomalies (Fig. 4). The
results show that the moisture uptake locations and contributions during the
EASM season are similar between El Niño and La Niña phases as well
as those during the NSM season. During the EASM season, the moisture sources
are mainly from South China Sea–South China, the Bay of Bengal–Indochinese Peninsula, and the Indian Ocean, while the remaining ones are from North
China–western Pacific (Fig. 4a and c). In previous studies researchers
mainly focused on the variations in moisture source during the EASM season
(Baker et al., 2015; Cai et al., 2017; Tan, 2014). In this study, however,
we also analyzed the back trajectories during the NSM season because NSM
precipitation contributes ∼50 % to the annual precipitation
in the SPR region. It shows that the NSM moisture sources originate from
the South China Sea and southern China; the remaining ones are driven from local
evaporation. Compared to the moisture sources during the EASM season, very
few moisture sources are indicated for the Bay of Bengal–Indochinese Peninsula
and the Indian Ocean during the NSM season.
Seasonal distribution of moisture uptake contributing to
Changsha precipitation in El Niño and La Niña years. Panels (a) and (b) show the moisture source uptake locations and their contribution to
precipitation during EASM and NSM seasons in a La Niña phase
(1988–1989), respectively; panels (c) and (d) are the same as (a) and (b) but for
an El Niño phase (1991–1992). The black star indicates the Changsha GNIP
station.
DiscussionAmount-weighted mean annual precipitation δ18O
In principle, the amount-weighted mean annual precipitation δ18O (δ18Ow) can be calculated from the sum of
monthly weighted isotopic values divided by the total amount of
precipitation as
δ18Ow=(PJan×δ18OJan+PFeb×δ18OFeb+…PDec×δ18ODec)/(PJan+PFeb+…PDec).
Based on the characteristics of the precipitation amount and δ18O during EASM and NSM seasons in the MRC, Eq. (1) can be
written in the following mode:
δ18Ow≈(PEASM-mean×δ18OEASM-mean+PNSM-mean×δ18ONSM-mean)/(PEASM-mean+PNSM-mean)=EASM%×δ18OEASM-mean+NSM%×δ18ONSM-mean,
where PEASM-mean and PNSM-mean are the mean precipitation
amounts of EASM and NSM, respectively; δ18OEASM-mean and δ18ONSM-mean are the mean values of EASM and NSM precipitation, respectively;
and EASM % and NSM % are the mean percentages of the EASM and NSM
precipitation amounts, respectively.
Therefore, we can consider that the δ18Ow is controlled by
both precipitation amount and δ18Op during the EASM and
NSM seasons in the MRC. Given the relationship between monthly precipitation
amount and δ18Op in the MRC (Fig. 3), we find that (1) in northern and southwestern regions of the MRC δ18Ow values are
mainly controlled by the amount and δ18O of EASM
precipitation, because the precipitation amount of the EASM with rather low
δ18Op values accounts for 70 % of the annual
precipitation and the NSM precipitation is only a small contribution to the
δ18Ow (less than 30 %). (2) In southeastern China,
especially in the SPR region, the precipitation amount of the NSM with
rather high δ18Op values even exceeds that of the EASM
with rather low δ18Op values, and it also has an important
effect on δ18Ow. Hence, δ18Ow in the SPR
region is affected by both EASM and NSM precipitation. In addition, except
for the effect of the seasonal distribution of precipitation amount, the
seasonal δ18O itself also attributes to the δ18Ow, which is related, among others, to the variations in
integrated regional convection and moisture source and transport distance
(Cai et al., 2018; Baker et al., 2015; Huang et al., 2017; Tan et al.,
2016).
In order to separate the influences of precipitation seasonality and monthly
δ18Op, we used the decomposition method used by Liu and
Battisti (2015) and Cai and Tian (2016) to evaluate the role of changes in
precipitation seasonality (δ18Ops; assuming that the
monthly precipitation δ18Op in El Niño years 1988–1989
is the same as that in La Niña years 1991–1992). We then calculated the
difference between precipitation δ18Ow in El Niño
years and La Niña years and the change in precipitation δ18O (δ18Oiso; method is similar to that for
calculating δ18Ops but assuming that the monthly
precipitation amount is the same). The results for the Changsha station
indicate that the difference in precipitation δ18Ow
between El Niño years (1988–1989) and La Niña years (1991–1992)
(i.e., El Niño minus La Niña) is 2.7 ‰, δ18Ops is 1.3 ‰, and δ18Oiso
is 1.3 ‰. These results imply that the difference in
δ18Ow between El Niño and La Niña conditions
reflects the differences of both the δ18Op and the
precipitation seasonality.
Tan (2014) suggested that positive (negative) δ18Ow
anomalies during El Niño (La Niña) phases reflect more (less) water
vapor originating from the nearby South China Sea and the western Pacific
Ocean (characterized by rather high δ18Op values) relative
to the remote Indian Ocean (showing comparable low δ18Op values). By using the HYSPLIT model, however, Cai et al. (2017)
demonstrated that the moisture sources vary little between years with
relatively high and low δ18O values (corresponding to El
Niño and La Niña years) in the EASM region; hence, EASM precipitation
is primarily derived from the Indian Ocean, while the Pacific Ocean moisture
is a minor contributor. This is consistent with our results (Fig. 4). In
addition, by using a Lagrangian precipitation moisture source diagnostic,
Baker et al. (2015) suggested that the moisture uptake area in the Pacific
Ocean does not differ significantly between summer and winter and is thus a
minor contribution to monsoonal precipitation; changes in moisture
transport, however, may impact the δ18O variation of EASM
precipitation. Dayem et al. (2010) also proposed that several processes
(e.g., source regions, transport distance and types of precipitation)
contribute to the δ18Op variation. We found that the
moisture sources in the Bay of Bengal–Indochinese Peninsula and the Indian
Ocean were less important during the NSM season compared to the EASM season
(Fig. 4). The moisture uptake area in the EASM season does not differ
significantly between El Niño and La Niña years nor in the NSM
season. Their contributions to the whole precipitation in El Niño and La
Niña years, however, are different (Fig. 4). The variation in moisture
source during the EASM period, to some extent, might contribute to changes
in δ18Ow, but it is not the main factor. We emphasize
the effect of NSM precipitation amount on δ18Ow in the SPR
region, and we made an attempt to analyze the relationship between the
seasonal precipitation amount and δ18Ow with ENSO phase on
interannual timescale in the next section.
Interannual variation of precipitation amount and δ18Ow over the SPR region influenced by ENSO
The ENSO is a coupled ocean–atmosphere phenomenon controlling the
interannual variation in precipitation amount and δ18O over southeastern China (e.g., Feng and Hu, 2004; He et al., 2018; Huang
et al., 2017; Moerman et al., 2013; Tan et al., 2014; Xue and Liu, 2008;
Yang et al., 2016). Our analysis of the 1988–1992 data from the Changsha
GNIP station suggests that the mean value of δ18Ow
(-6.73 ‰) in La Niña years (1988–1989) is
significantly more negative than during El Niño years (1991–1992;
-4.11 ‰). However, there is no significant variation in
the annual precipitation amount between La Niña and El Niño years
(Fig. 5a). The difference of δ18Ow between La Niña
and El Niño phases cannot be explained by variations in annual
precipitation amount. This is consistent with the analyses based on
instrumental meteorological data (Huang et al., 2017; Tan, 2014) and climate
simulations (Yang et al., 2016). Previous studies showed that during El
Niño years, the EASM is generally weak and the integrated regional
convection decreases in the EASM region, thereby leading to higher δ18Op values, while the effect of La Niña is opposite (Cai et
al., 2018; Gao et al., 2013; Zwart et al., 2016). Continental moisture
recycling or local convection during the NSM season has limited impact on
δ18Op relative to the integrated regional convective
activities during the EASM season. As we discussed in Sect. 4.1, however,
the difference in δ18Ow between El Niño and La
Niña years is influenced by both the δ18Op and the
precipitation seasonality. Indeed, there is more summer monsoon
precipitation in June to September during La Niña years (1988–1989) but
more SPR in March–April during El Niño years (1991–1992), though the
annual precipitation amounts are similar (Fig. 5b). We find that the
δ18Ow variability is broadly consistent with the variation
in the ratio of EASM / NSM precipitation amount during 1988–1992 (Fig. 5).
Unfortunately, the data series of the Changsha GNIP station is too short (5 years) to evaluate the relationship between the EASM / NSM ratio and δ18Op in the SPR region. Therefore, we used the average
precipitation data from 11 meteorological stations (1951–2014) in
Jiangxi Province and the eastern Hunan Province (Fig. 2, i.e., from the core
area of the SPR) as well as the δ18O data obtained from the IsoGSM
simulation (1979–2009) to examine the relationship between ENSO, AO, δ18Ow, and precipitation amount in the SPR region on interannual
timescales.
Comparison between ENSO events, precipitation amount,
and δ18Ow at the Changsha GNIP station for the
period 1988–1992. (a) Comparison between annual precipitation amount,
δ18Ow, and the EASM / NSM ratio. (b) Comparison of mean
monthly precipitation amount between La Niña (1988–1989) and El Niño
(1991–1992) years. In this calculation, the temporal coverage of the annual
precipitation and the precipitation δ18Ow is from January
to December, the EASM precipitation is from May to September, and the NSM
precipitation is from January to April and from October to December.
We calculated correlation coefficients between the simulated δ18Ow; the SOI; the MEI; the EASM / NSM ratio; and the annual, EASM,
and NSM precipitation amounts for 1979–2009 (Table 2 and Fig. 6). The
results show that the time series of the simulated δ18Ow
data significantly correlates with the SOI (r=-0.52, p < 0.01) and the MEI
(r=0.51, p < 0.01), consistent with the positive relationship between the ENSO
index and δ18Ow observed in modern precipitation (Huang et
al., 2017; Tan, 2014; Yang et al., 2016) as well as in the δ18O
records of speleothems and tree-ring cellulose (Tan, 2016; Xu et al., 2013,
2016a, b; Zhang et al., 2018). Furthermore, the same relationship holds
for the Changsha GNIP station (Fig. 5a). This indicates that the
precipitation δ18Ow is higher (lower) during the El
Niño (La Niña) phase in the SPR region. There is, however, no
significant correlation between the δ18Ow with the annual,
EASM, or NSM precipitation amounts. This indicates that on interannual
timescales, the δ18Ow is not controlled by the annual or
EASM precipitation amount in southeastern China, consistent with the result
based on instrumental data from the Changsha station (Fig. 5a) and other
studies (Tan et al., 2014; Yang et al., 2016). The time series of the
simulated δ18Ow data correlates with the EASM / NSM ratio
(r=-0.36, p < 0.05) (Fig. 6 and Table 2), suggesting that the precipitation
δ18Ow may be influenced by the precipitation seasonality
(i.e., EASM / NSM ratio) modulated by ENSO on interannual timescales.
Applying a 2-year smoothing, the time series of the simulated δ18Ow data significantly correlates with the annual precipitation
(r=-0.89, p < 0.01), the EASM precipitation (r=-0.91, p < 0.01), and the EASM / NSM ratio
(r=-0.81, p < 0.01) (Table 2 and Fig. 6). This indicates that on interannual to
decadal timescales the precipitation δ18Ow might reflect
changes in EASM precipitation amount and also the annual precipitation
amount and the EASM / NSM ratio, because the EASM / NSM ratio and annual
precipitation amount are significantly dominated by the EASM precipitation
amount (Table 2).
Correlation between the time series of the simulated δ18Ow (a, black line, from May to next April); MEI (b, pink line,
from October to next June); annual (c, purple line, from May to next April),
EASM (d, red line, from May to September), and NSM (e, blue line, from
October to next April) precipitation amounts; and the EASM / NSM ratio (f,
green line) in the SPR region for 1979–2009. The correlation coefficient
between δ18Ow with MEI and EASM / NSM ratio is 0.55
(p < 0.01) and -0.36 (p < 0.05), respectively. Applying a
2-year smoothing to δ18Ow is significantly correlated with
annual precipitation (r=-0.89, p < 0.01), EASM precipitation
(r=-0.92, p < 0.01), and the EASM / NSM ratio (-0.81, p < 0.01).
Correlation coefficients between the time series of
precipitation δ18Ow; MEI; the EASM / NSM ratio; and the
annual, EASM, and NSM precipitation amounts in the SPR region for 1979–2009.
The temporal
coverage of the annual precipitation and the precipitation δ18Ow is from May to next April, the EASM precipitation is from May to September, and the NSM precipitation is from October to next April. The temporal coverage of the MEI and the SOI is from October to April.
* Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).
To explore the relationship between ocean-atmospheric circulation (e.g.,
ENSO, AO) and the seasonal precipitation amount, we calculated correlation
coefficients between the SOI; MEI; AO index; the EASM / NSM ratio; and the
annual, EASM, and NSM precipitation amounts for 1951–2010 (Table 3). The mean
value of October to next June SOI correlates with the EASM precipitation amount
(r=0.26, p < 0.05), NSM precipitation amount (r=-0.51, p < 0.01), and the EASM / NSM ratio
(r=0.52, p < 0.01) (Table 3). The mean value of October to next June MEI correlates
with the EASM precipitation amount (r=-0.29, p < 0.05), NSM precipitation amount
(r=0.54, p < 0.01), and the EASM / NSM ratio (r=-0.55, p < 0.01) (Table 3). This indicates,
on interannual timescales, decreased EASM precipitation during the
developing stage of El Niño and increased NSM precipitation during the
mature stage of El Niño, resulting in lower EASM / NSM ratios during El
Niño phases and vice versa. There is, however, no significant
correlation between the SOI, MEI, and the annual precipitation amount. In
addition, the EASM / NSM ratio significantly correlates with the EASM
(r=0.64, p < 0.01) and the NSM (r=-0.70, p < 0.01) (Table 3).
Previous studies found that decreased summer rainfall in the south of the
Yangtze river occurs during the developing stage of El Niño, resulting
from a southward shift of the subtropical high associated with colder SST in
the western tropical Pacific and weak convective activities in the South
China Sea and the Philippines (Huang and Wu, 1989; Zhang et al., 1999). Kong
and Tu (2003) found that there is less EASM rainfall in May–September in the
lower reaches of the Yangtze river valley during 14 El Niño events since
the 1950s. The same relationship is observed in the May–October rainfall
reconstruction based on tree-ring cellulose δ18O (Xu et al.,
2016a) during El Niño phases. Cooler summer SST in the western Pacific
led to a weakened western Pacific subtropical high resulting in less
rainfall during May–October in the middle-to-lower reaches of the Yangtze river (Liu and Li, 2011; Xu et al., 2016a) and vice versa. Increased
rainfall in autumn, winter, and spring (i.e., NSM) occurred in southern China
during the mature stage of El Niño (Wan et al., 2008b; Wang et al., 2000;
Zhang et al., 1999, 2015; Zhou, 2011; Zhou and Wu, 2010).
During these phases, lower-level southwesterly anomalies over the South
China Sea transport more moisture into southeastern China, leading to increased
NSM precipitation (Wang et al., 2000; Zhang et al., 1999; Zhou, 2011; Zhou
and Wu, 2010). These conclusions are consistent with our findings in the SPR
region. It is notable that there is no significant variation in EASM
precipitation amount in our study area during the decaying stage of El
Niño, although increased summer rainfall was observed in southern China
(Huang and Wu, 1989).
We also find that the May AO index significantly and negatively correlates
with the annual (r=-0.42, p < 0.01) and EASM (r=-0.39, p < 0.01) precipitation amounts in
the SPR region (Table 3). This is consistent with previous observations that
the positive May AO index is followed by decreased summer precipitation
amount in the lower Yangtze river valley (Gong and Ho, 2002; He et al.,
2017). It was suggested that a stronger May AO is associated with a
northwards movement of the summer jet stream, leading to drier conditions in
the lower Yangtze river. The positive spring AO gives rise to warmer
equatorial SSTs between 150–180∘ E and weakens summer
subtropical high in the western North Pacific. Consequently, decreased
summer precipitation occurs in the lower Yangtze river (Gong et al., 2011).
There is, however, no significant correlation between the AO index and the
NSM precipitation amount and the EASM / NSM ratio in the SPR region (Table 3).
This might be because the influence of AO on the winter climate varied
spatially and temporally, resulting from the unstable relationship between
the AO index and the East Asian winter monsoon (He et al., 2017; Li et al.,
2014). This indicates that the AO mainly influences the changes in EASM and
annual precipitation amount but not the precipitation seasonality (i.e.,
EASM / NSM ratio) in the SPR region. The February AO index positively
correlates with precipitation amount in February (r=0.28, p < 0.05).
Given the relationship between δ18Ow, SOI, MEI, and
seasonal precipitation amount, we find that less EASM during the developing
stages of El Niño and more NSM precipitation during the mature stages of
El Niño lead to lower EASM / NSM ratios, resulting in higher δ18Ow values in the SPR region during El Niño phases and vice
versa. We therefore suggest that over the SPR region the precipitation
seasonality (i.e., the EASM / NSM ratio) modulated by ENSO primarily
influences the interannual variability of δ18Ow. The AO
mainly influences changes in EASM and annual precipitation amount but not
the precipitation seasonality (i.e., EASM / NSM ratio) in the SPR region.
Correlation coefficients between the time series of the
MEI; the EASM / NSM ratio; and the annual, EASM, and NSM precipitation amounts
in the SPR region for 1951–2010. The temporal coverage of the annual precipitation and the
δ18Ow is from May to next April, the EASM precipitation is
from May to September, and the NSM precipitation is from October to next
April. The temporal coverage of the SOI and MEI is from October to next
June.
Annual precipitationEASM precipitationNSM precipitationEASM / NSM ratioSOI (Oct to next Jun)-0.150.26*-0.51**0.52**MEI (Oct to next Jun)0.15-0.29*0.54**-0.55**AO (May)-0.42**-0.39**-0.170.18Annual precipitation0.67**0.69**-0.05EASM precipitation-0.070.64**NSM precipitation-0.70**
* Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).
Implication for paleoclimatic reconstructions
Although speleothem δ18O records have massively improved our
understanding of the EASM variability on different timescales, the
significance and quantification of these proxy records is still a subject of
debate, because speleothem δ18O is influenced by several
competing factors. We emphasize that the spatial differences in seasonal
precipitation over the MRC are key to understanding the speleothem δ18O–climate relationship. Fig. 1 illustrates that (1) Wanxiang
(Zhang et al., 2008), Dayu (Tan et al., 2009), Huangye (Tan et al., 2010),
Wuya (Tan et al., 2014), Shihua (Li et al., 2017), and Xiaobailong (Tan et
al., 2017) caves are located in the northern and southwestern part of the
MRC, where δ18Ow is primarily controlled by the EASM and
annual precipitation amount. Therefore, these records show a significant
correlation with the instrumental precipitation and the regional
drought/flood (D/F) index obtained from historical documents (e.g., Li et
al., 2017; Liu et al., 2008; Tan et al., 2009, 2010, 2014, 2017; Zhang et
al., 2008). (2) Dongge (Yuan et al., 2004), Heshang (Hu et al., 2008), Hulu
(Wang et al., 2001), Yuhua (Jiang et al., 2012), and E'mei (Zhang et al.,
2018) caves are located in southeastern China, where δ18Ow is
not only affected by EASM precipitation but also by NSM precipitation.
Hence, according to Wang et al. (2001), speleothem δ18O from
Hulu cave reflects the ratio of summer-to-winter precipitation amount.
Factors related to the NSM (e.g., moisture source, integrated regional
convection, precipitation seasonality, winter temperature) have also been
taken into consideration in the interpretation of speleothem δ18O in southeastern China (e.g., Baker et al., 2015; Cai et al.,
2018; Cheng et al., 2016; Clemens et al., 2010; Dayem et al., 2010; Zhang et
al., 2018). On the other hand, the high percentage of NSM precipitation with
relatively high δ18Op values in southeastern China should be
an important reason why δ18Ow and speleothem δ18O are much lower and their variability is much larger in
southwestern China than in southeastern China (Li et al., 2016; Liu et al.,
2010; Zhang et al., 2018), except for the influence of integrated regional
convection and moisture source and transport distances during the EASM
season.
We find that the precipitation seasonality modulated by ENSO mainly controls
the δ18Ow values in the SPR region, with lower (higher)
EASM / NSM ratios associated with El Niño (La Niña) phases resulting
in higher (lower) δ18Ow values. Therefore, we suggest that
the interannual variability of speleothem δ18O in the SPR
region is primarily controlled by precipitation seasonality (i.e., the
EASM / NSM ratio) modulated by ENSO. In addition, the ENSO index in the SPR
region also significantly correlates with the EASM precipitation amount on
interannual timescales and the precipitation δ18Ow
negatively correlates with the EASM precipitation amount on interannual-to-decadal timescales, implying that additional studies are needed to
disentangle the main driving factor(s) (e.g., EASM precipitation amount vs.
EASM / NSM ratio) operating on different timescales. Few speleothem δ18O records have been published for the SPR region so far (Jiang et
al., 2012; Zhang et al., 2018). Such long-term records, however, are
critically needed to examine the climate–proxy relationship both on
interannual and on decadal to millennial timescales.
Conclusions
We find that the distribution of seasonal precipitation amount in
southeastern China, especially in the SPR region, is different from other
regions of the MRC for the time interval of this study (1951–2014 CE). In
the SPR region, the mean precipitation amount of the EASM is equivalent to
that of the NSM. However, in northern and southwestern regions of the MRC,
the mean percentage of EASM to the annual precipitation amount exceeds
70 %. The seasonal δ18Op in the MRC shows broadly
consistent variations with relatively low and high values for EASM and NSM
precipitation, respectively. The low δ18Op values
associated with winter precipitation in northern region of the MRC, however,
represent only a minor contribution to δ18Ow. Thus, the
NSM precipitation in the SPR region also has an important effect on δ18Ow, but the δ18Ow in northern and
southwestern regions is primarily influenced by EASM precipitation.
Based on a statistical analysis of the ENSO index, simulated δ18O data, and seasonal precipitation amount in the SPR region, we find
that less (more) EASM and more (less) NSM precipitation leads to a lower
(higher) EASM / NSM ratio resulting in higher (lower) δ18Ow
in the SPR region during El Niño (La Niña) phases. The AO mainly
influences the changes in EASM and annual precipitation amount but not the
precipitation seasonality (e.g., EASM / NSM ratio) in the SPR region.
Recognizing this spatial difference in seasonal precipitation is essential
for a robust interpretation of speleothem δ18O in the MRC. On
interannual timescales, speleothem δ18O variability in northern
and southwestern regions of the MRC is primarily influenced by the EASM or
the annual precipitation amount. In the SPR region, however, precipitation
seasonality (i.e., the EASM / NSM ratio) modulated by ENSO plays a key role in
governing speleothem δ18O variability, although integrated
regional convection and moisture source and transport distance may also have
subordinate impacts.
Data availability
The National Climate Center (https://www.ncc-cma.net/, last access: 3 November 2019) provided the monthly precipitation data, the IAEA (http://www.iaea.org/, last access: 3 November 2019) provided the GNIP precipitation isotope data, the Australian Government Bureau of Meteorology (http://www.bom.gov.au/climate/current, last access: 3 November 2019) provided the SOI data, and the NOAA (http://www.cdc.noaa.gov/, last access: 3 November 2019) provided the MEI and AO data. The related computations were provided in the “Data and methods” section. Correspondence and requests for materials should be addressed to Haiwei Zhang (zhanghaiwei@xjtu.edu.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/cp-16-211-2020-supplement.
Author contributions
HZ designed the research and wrote the first draft of the article.
HC, YC, CS, and AS helped to revise the article. GK and HL
helped to get the IsoGSM simulation data. All authors discussed the results
and provided input on the article.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank the editor Helen McGregor and the reviewer Norbert Frank as well as three anonymous referees for their comments and suggestions. Thanks to Ming Tan for reviewing the article and giving many constructive
suggestions. Thanks to Kristina Krklec, Hui Tang, and Zhongyin Cai for
helping with the back-trajectory analyses and moisture source calculations.
Financial support
This research has been supported by the NSFC (grant no. 41502166), the China Postdoctoral Science Foundation (grant no. 2015M580832), the State Key Laboratory of Loess and Quaternary Geology (grant no. SKLLQG1046), and the Key Laboratory of Karst Dynamics, Ministry of Land and Resources of the People's Republic of China (MLR) and Guangxi Zhuang Autonomous Region (GZAR) (grant no. KDL201502).
Review statement
This paper was edited by Helen McGregor and reviewed by Norbert Frank and three anonymous referees.
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