Regional controls on daily to interannual variations of precipitation isotope ratios in Southeast China: Implications for paleomonsoon reconstruction

Earth and Planetary Science Letters 527 (2019) 115794

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Earth and Planetary Science Letters www.elsevier.com/locate/epsl

Regional controls on daily to interannual variations of precipitation isotope ratios in Southeast China: Implications for paleomonsoon reconstruction Jiaoyang Ruan a , Hongyu Zhang a , Zhongyin Cai b,∗ , Xiaoqiang Yang a,∗ , Jian Yin a a

Guangdong Provincial Key Laboratory of Geodynamics and Geohazards & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Earth Sciences and Engineering, Sun Yat-sen University, Guangzhou 510275, China b Institute of International Rivers and Eco-security, Yunnan University, Kunming 650091, China

a r t i c l e

i n f o

Article history: Received 20 March 2019 Received in revised form 16 August 2019 Accepted 25 August 2019 Available online 9 September 2019 Editor: L. Robinson Keywords: precipitation oxygen isotopes convection moisture source monsoon ENSO

a b s t r a c t Many key paleomonsoon records in East Asia rely on past precipitation isotope ratios (δ 18 Op ) as proxies for hydroclimate, however, the relationship between climate variability and δ 18 Op remains an ongoing debate. Here we investigate dominant climatic drivers and test actively-discussed hypotheses of Southeast China δ 18 Op variability over multiple timescales, using an 8-yr-long daily δ 18 Op record from Guangzhou and updated GNIP dataset from Hong Kong. Our moisture source diagnostic analyses suggest that the primary moisture of Guangzhou precipitation essentially comes from the proximal northern South China Sea (SCS). Convective activities over the primary moisture source regions, measured by cumulative precipitation along back-trajectory, regional precipitation and outgoing longwave radiation, play a key role in regulating δ 18 Op variability across different timescales. These effects can be related to the direct changes of convection intensity over the SCS or the indirect changes associated with the shift of moisture source. In consequence, δ 18 Op records seasonal monsoon dynamics associated with the intertropical convergence zone migration and the El Niño-Southern Oscillation (ENSO). Further tests of different hypotheses and their links to the ENSO via the 2015 strong El Niño event support the above conclusions. Taken together, these results demonstrate that Southeast China δ 18 Op represents a spatial-temporally integrated measure of precipitation and convection tracing from the measured site to moisture sources, shedding light on the interpretation of paleo-isotope data in the monsoon domain. © 2019 Elsevier B.V. All rights reserved.

1. Introduction The Asian Summer Monsoon (ASM) is a highly energetic and archetypal subsystem of the global monsoon (Wang, 2006). Variations in the ASM can provoke abnormal hydroclimatic conditions (e.g., droughts and floods) and lead to catastrophic impacts in vast densely populated regions. Therefore, reconstructing the history of the ASM has long been of scientific interest and provided practical implications for understanding its future change. Among many proxies used to reconstruct ASM history, the oxygen isotope record from natural archives, such as speleothems (Cai et al., 2015; Cheng et al., 2016; Wang et al., 2001; Yuan et al., 2004) and tree rings (Liu et al., 2017; Xu et al., 2016), is the most widely used ones. For instance, the speleothem δ 18 O records from China were able to document the ASM variability back to

*

Corresponding authors. E-mail addresses: [email protected] (Z. Cai), [email protected] (X. Yang).

https://doi.org/10.1016/j.epsl.2019.115794 0012-821X/© 2019 Elsevier B.V. All rights reserved.

640,000 yr before present (Cheng et al., 2016). However, a consensus climatic interpretation of these δ 18 O records has not been reached. It was previously believed that Chinese speleothem δ 18 O records reflect local rainfall variability based on the traditional “amount effect” (the inverse relationship between monthly precipitation δ 18 O and local rainfall amount (Dansgaard, 1964)). Later, some studies have insisted this viewpoint (Tan et al., 2018; Zhang et al., 2008), while many others stated that the speleothem δ 18 O could record the regional precipitation integrated from the cave site to moisture sources (Hu et al., 2008; Yuan et al., 2004), precipitation over the Indian summer monsoon (ISM) domain or the ISM intensity (Cai et al., 2015; Liu et al., 2015), changes in moisture source ratios i.e. between Pacific and Indian oceans (Maher, 2008; Tan, 2014), etc. Despite the uncertainty in different interpretations, there is consensus that paleo-isotope records inherit the isotopic signals of precipitation. Understanding the latter under present day of varying climate is critical to interpreting those records in the past.

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The dominant drivers forcing the δ 18 Op variation in the ASM region remain in debate, both on the seasonal and interannual timescales. Many existing studies focus on the seasonal scale and apply the anti-correlation of precipitation and δ 18 Op i.e. amount effect. Observations in many parts of the ASM region, however, find that the local amount effect is not significantly pronounced (Cai et al., 2018; Dayem et al., 2010). There is recently a shift in the understanding of the amount effect, which emphases large-scale atmospheric processes in upstream regions (Bowen et al., 2019; Galewsky et al., 2016). More specifically, precipitation and convection activities at the upstream could isotopically deplete lowlevel troposphere water vapor (Kurita, 2013; Lee and Fung, 2008; Risi et al., 2008a) which will feed the precipitation at downstream sites. Thus, δ 18 Op at tropical or monsoonal sites may reflect regionally integrated upstream convections (e.g., Risi et al., 2008b). This property has also been newly proposed in the ASM region (Cai and Tian, 2016a; He et al., 2015; Wei et al., 2018). Besides the amount effect, seasonal scale δ 18 Op variation has been associated with the shift in moisture sources: low δ 18 Op values in the monsoon season is related to strong rainout of oceanic moisture source; high δ 18 Op values during the non-monsoon season is linked to intensified continental moisture recycling (Araguás-Araguás et al., 1998). The ASM δ 18 Op variation on the interannual timescale, especially its linkage with El Niño-Southern Oscillation (ENSO), has increasingly becoming a focus of recent studies (e.g., Cai et al., 2017; Tan, 2014; Yang et al., 2016). The interannual variation of ASM climate is intimately linked with ENSO. In general, El Niño events can cause deficient monsoon rainfall in South Asia, but the decaying phase of El Niño may bring East Asia with floods (e.g., Wang, 2006; Xie et al., 2016). These flood and drought events greatly impact regional agriculture and food security. Several studies have revealed a correlation between ENSO and δ 18 Op in the ASM region, with higher δ 18 Op values during El Niño events but lower in La Nina conditions (Ichiyanagi and Yamanaka, 2005; Ishizaki et al., 2012; Sun et al., 2018). However, different mechanisms have been proposed to explain the linkage between δ 18 Op and ENSO (e.g., Cai et al., 2017; Sun et al., 2018; Tan, 2014). Tan (2014) argued that the high δ 18 Op values during El Niño events were associated with increased proportions of moisture originating from the proximal Pacific Ocean compared to remote Indian Ocean. Cai et al. (2017) suggested that the link between ASM δ 18 Op and ENSO is due to ENSO’s modulation on convection intensity in the moisture source region. Study of δ 18 Op variation at the daily scale is highly important for understanding the dynamic processes of individual precipitation event and the link of its isotopic signal to synoptic weather conditions. He et al. (2015) and Tang et al. (2015) emphasized the role of upstream convection or rainout in controlling daily δ 18 Op at Lhasa and Nanjing, respectively. Except for these few cases, however, previous studies mainly focus on the relationship between daily δ 18 Op and the climate variables in local. For instance, Xie et al. (2011) found that the relationship between Guangzhou daily δ 18 Op and local precipitation amount varies from year to year. Yang et al. (2017) showed that the correlation between Guangzhou daily δ 18 Op and local precipitation amount varies from month to month and that there seemingly has a temperature effect during certain months. Although the relationship between δ 18 Op and local climate variables are spatially variable (Dayem et al., 2010; Johnson and Ingram, 2004), modern observations suggest that δ 18 Op within the ASM region shares similar temporal variability over large spatial scales, especially on the seasonal timescale (Araguás-Araguás et al., 1998; Cai et al., 2018). This similar temporal isotopic variability has also been observed in speleothem records from the ASM region over long-term timescales (Dayem et al., 2010; Liu et al., 2015; Rao et al., 2016).

Fig. 1. Map showing the location of Guangzhou (GZ) and Hong Kong (HK) with regional precipitation (in colors) and 850 hPa wind vector (in arrows) averaged over 1985-2014 from the GPCP and NCEP-NCAR reanalysis data. Dashed lines denote the topography at 3000 m above sea level, delineating the Tibetan Plateau. (For interpretation of the colors in the figure(s), the reader is referred to the web version of this article.)

Guangzhou and Hong Kong from Southeast (SE) China are located in the front area of the ASM when it blows across mainland China. Thus, the δ 18 Op analyses from these two sites could provide a step forward for the understanding of δ 18 Op systematics in inland SE China which is at the downstream of Guangzhou and Hong Kong. In this study, we explore the regional influence of SE China δ 18 Op by addressing the climatic drivers of δ 18 Op variability in a newly published 8-yr-long daily record from Guangzhou and up to date Global Network for Isotopes in Precipitation (GNIP) timeseries from Hong Kong. Firstly, we quantified the moisture sources of Guangzhou precipitation by identifying moisture uptake events along the air mass back trajectories calculated for a thousand days of precipitation. Then, we investigated the relationship of SE China δ 18 Op with both local and regional climate conditions across a range of timescales by correlating the δ 18 Op record to local as well as large-scale climatic variables in particular in the identified moisture sources. Lastly, we tested the ENSO-δ 18 Op correlation and the relevant mechanisms across the 2015 super El Niño event. Our results highlight the key role of large-scale precipitation and convective activities in the South China Sea (SCS) primary moisture sources in determining SE China δ 18 Op records at various timescales. 2. Data and methods 2.1. Precipitation isotopes and meteorological data Our analyses adopted a newly released δ 18 Op record from Guangzhou meteorological station, SE China (113.32◦ E, 23.13◦ N, 7 m a.s.l.) (Fig. 1). This record includes 1000 daily event-based measurements of δ 18 Op and precipitation amount from 2007 to 2014 (Fig. 2a) (Yang et al., 2017). Monthly and annual average δ 18 Op values were calculated from the data weighted by  event-based  precipitation amount: δ 18 Op = δi Pi / Pi , where the δi and Pi refer to δ 18 Op and corresponding precipitation amount, respectively. To strengthen annual-scale analyses with higher confidence level, monthly Hong Kong δ 18 Op data over 1979-2016 was retrieved from the GNIP (http://www-naweb.iaea.org/napc/ih/IHS_resources_ gnip.html) and their amount-weighted annual mean values were calculated (Fig. 2b-c). Guangzhou and Hong Kong, ca. 100 km apart, both are located in the low-lying Pearl River Delta and under very similar metrological conditions (Fig. 1). Monthly Guangzhou

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mathematic description of this method is given in Sodemann et al. (2008) and we followed the same procedure described in Cai et al. (2018). As the Lagrangian moisture source diagnostic method has been documented in detail in previous studies (Sodemann et al., 2008; Stohl and James, 2004), we only describe the method briefly. The specific humidity along the back trajectory was calculated in the HYSPLIT model. The changes in specific humidity of air parcels were assumed to reflect the addition of evaporating source or the loss of moisture by precipitation. Therefore, after accounting the loss of moisture along the trajectory, the contribution of evaporation sources to precipitation at the study site can be quantitatively estimated by projecting the precipitation at the study site back to previous source locations. Fig. 2. The δ 18 Op variations of Guangzhou and Hong Kong on daily (a), monthly (b), and annual (c) timescales. Note that the upper x-axis is related to (a) and (b) panels while the lower x-axis is related to (c) panel.

δ 18 Op is strongly correlated with the GNIP Hong Kong δ 18 Op during their overlapping period 2007-14 (R = 0.83, P < 0.001) implying that they share similar temporal variability (Fig. 2b). The apparent higher annual mean δ 18 Op at Guangzhou in 2012 is mainly because of its anomalous lower precipitation in the isotopically depleted July 2012 (Fig. 2c). Daily metrological data was collected from official Guangzhou meteorological database. Global precipitation field with a spatial resolution of 2.5◦ × 2.5◦ was obtained from the Global Precipitation Climatology Project (GPCP) Version 2.3 (https://www.esrl. noaa.gov/psd/data/gridded/data.gpcp.html). Convection activity was inferred using NOAA Interpolated Outgoing Longwave Radiation (OLR) data with a spatial resolution of 2.5◦ × 2.5◦ (https://www. Humidity esrl.noaa.gov/psd/data/gridded/data.interp_OLR.html). and air temperature were derived from the NCEP/NCAR reanalysis dataset (https://www.esrl.noaa.gov/psd/data/gridded/data.ncep. reanalysis.derived.pressure.html). Ocean temperature data was obtained from NOAA Extended Reconstructed Sea Surface Temperature (SST) Version 5 (https://www.esrl.noaa.gov/psd/data/gridded/ data.noaa.ersst.v5.html). The El Niño-Southern Oscillation (ENSO) condition was assessed using the Niño 3.4 SST Index (https:// origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ detrend.nino34.ascii.txt). 2.2. HYSPLIT moisture source diagnostic To assess probable moisture origins and transport paths, air mass back trajectory was calculated for each monitored precipitation day using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model version 4.0 (Stein et al., 2015) and based on the NOAA-NCEP/NCAR reanalysis global meteorological field. Back trajectory was computed for a time period of 240 hr and started at 1500 m above ground level, a level expected for major condensation and strong horizontal moisture transport in this region (Sun et al., 2019). The selection of 240 hr as back trajectory calculation length covers most of the lifetime of water vapor in the atmosphere as the mean residence time of atmospheric vapor is 8-9 days (van der Ent and Tuinenburg, 2017). We briefly classified the moisture source and origin from Asian continent, Indian Ocean, or pan Pacific Ocean as the end point of back trajectory located in the northwest, southwest, or east of Guangzhou city (23.1◦ N, 113.3◦ E). Then we estimated the relative precipitation contribution of these three sources by weighting the trajectory from each region by respective precipitation amount. By projecting the precipitation at Guangzhou back to previous moisture uptake events, we were able to quantify the location and contribution of moisture sources of Guangzhou precipitation. The

3. Results and discussion 3.1. Moisture sources Air mass back trajectory analyses for the one thousand precipitation days show that the precipitation-producing air parcels arrive at Guangzhou mainly from its south, originating either from the Indian Ocean or the pan Western Pacific area (Fig. 3a). During 2007-14, trajectories from the Indian Ocean and the Pacific Ocean account for more than 90% of the precipitation at Guangzhou, while trajectories from the Asian continent merely account for less than 10%. The greater precipitation contribution of parcels from ocean sources is supported by the higher moisture content of these parcels when they arrive at Guangzhou and a tendency for larger moisture loss from these parcels shortly prior to their arrivals (Fig. 3a). Importantly, back-trajectory-based Lagrangian moisture source diagnostic results for all the precipitation events during 2007-14 suggest that the moisture contributing to Guangzhou precipitation essentially comes from its adjacent seas, i.e. the northern SCS and the East China Sea (Fig. 3b). This pattern is consistent with the previous studies in South China (Cai et al., 2018; Huang et al., 2018) showing a major moisture source of nearby seas. The result highlights the importance of diagnostic analysis in constraining the probable moisture source location and asks for additional studies on how the identified moisture source itself and meteorological conditions in moisture source regions influence δ 18 Op variability. The linkage between moisture sources and δ 18 Op variability at seasonal and interannual scales thus will be further analyzed using this technique (sections 3.3 and 3.4; e.g., the seasonal pattern of moisture sources showing in Fig. 3c-d). 3.2. The δ 18 Op -climate link on the daily scale In general, there is a negative correlation between daily δ 18 Op values and local precipitation amount at Guangzhou (Xie et al., 2011; Yang et al., 2017). The Pearson linear correlation coefficient for the whole analysis period (2007-14) is −0.40 (Table 1) (Fig. S1). We further calculated the cumulative precipitation along air mass back trajectories to reflect the distillation process during vapor transport, and analyzed the correlations between Guangzhou δ 18 Op and the precipitation accumulated from 1 day prior to arrival to 7 days prior to arrival. The correlation peaked on 5 days prior to arrival (R = −0.58, P < 0.001) (Fig. 4), indicating that higher upstream total rainfall amount is generally associated with lower isotope ratios. Compared with precipitation on site, the cumulative precipitation shows higher correlation with δ 18 Op , which leads us to conclude that the daily δ 18 Op variability reflects a regionally- and temporally-integrated meteorological condition better than just local transient condition.

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Fig. 3. (a): The cluster mean of air mass back trajectories computed for one thousand precipitation-producing days during 2007-14. The numbers in percentage are precipitation-weighted proportions of the trajectories originated southwest, east, and northwest from Indian Ocean (IO), pan Pacific Ocean (PO) and Asian Continent (AC), respectively. (b)-(d): Spatial distribution of fractional moisture contribution to Guangzhou precipitation on the annual scale (b), for the May-September season (c), and for the October-April season (d). The black dots donate the location of Guangzhou. The primary moisture source of the northern SCS is highlighted by dashed rectangles. Table 1 The correlation (R) of δ 18 Op to precipitation and OLR both locally and, in the identified northern SCS primary moisture source (10-22.5◦ N, 107.5-120◦ E). Guangzhou daily data was used for daily and monthly calculations while GNIP Hong Kong 1979-2016 was used for annual calculations. The asterisk indicates the significant level <0.001.

Daily Monthly Annual

Precipitation on site

5-d precipitation along trajectory

−0.40* −0.53* −0.09

−0.58* −0.58* n.a.

Precipitation in SCS source

OLR in SCS source

n.a.

n.a. 0.69* 0.67*

−0.66* −0.57*

Fig. 5. The box-whisker plot of Guangzhou monthly precipitation and δ 18 Op during 2007-14.

Fig. 4. Guangzhou daily δ 18 Op versus the 120-hr cumulative precipitation along trajectory. Inserted plot showing the variation of δ 18 Op -precipitation correlation (R) with the cumulative time (hours), and note that the peak correlation appears at 120 hrs (5 days).

3.3. The δ 18 Op -climate link on the seasonal scale Deciphering seasonal variation mode is vital for further interannual studies. Similar with the daily data, the monthly δ 18 Op is significantly correlated with the precipitation amount, and show

a higher correlation coefficient with the cumulative precipitation than the local one (Table 1). Comparison between the long-term mean monthly local precipitation and δ 18 Op values shows two phases among a single year (Fig. 5). As shown in Fig. 5, the δ 18 Op has lower values from May to October while the precipitation has higher values from April to September. It is worth noting that the timing of precipitation peak is not in phase with the timing of the most negative δ 18 Op (Fig. 5). More specifically, the precipitation peaks during May and June, while the lowest δ 18 Op occurs during July, August and September. This lag further suggests that local precipitation may only play a secondary role in controlling δ 18 Op variability. Based on these seasonal patterns in δ 18 Op and precipitation, we divided a year into two seasons of May to September (May-September) and October to April (October-April), roughly representing the rainy and dry seasons, respectively. Further, we analyzed the moisture sources in the two seasons to investigate the relationship between moisture source and δ 18 Op . Overall, the major moisture source during May-September and October-April share a similar region over northern SCS (Fig. 3c-d).

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Oceanic moisture source is dominant and has similar contributions during the two seasons: 83.8% during May-September (Fig. 3c) and 84% during October-April (Fig. 3d). Baker et al. (2015) analyzed the moisture sources for central and eastern China and also found that continental recycling does not show clear seasonality. Thus, these results do not support the idea that seasonal δ 18 Op variation reflects shifting moisture sources between ocean and land (e.g., Araguás-Araguás et al., 1998; Xie et al., 2011). During May-September, there is a southwest-ward stretch of moisture source regions relative to annual average condition and this is associated with the southwest monsoonal flow during these months (Fig. 3c). Baker et al. (2015) also showed that there are increased Indian Ocean moisture contributions during the summer monsoon season for central and eastern China. During OctoberApril, a large portion of moisture comes from the East China Sea, which is associated with northeast winds during these months (Fig. 3d). This portion of moisture source is located in higher latitudes and in general, the moisture evaporated from these areas is more depleted in heavy isotopes than from the northern SCS (Cai et al., 2018). However, the δ 18 Op values are higher during these months than in monsoon seasons (Fig. 5). On the other hand, one may argue that some portion of moisture from the southwest during May-September shows an apparent longer transport distance, which may be associated with prolonged rainout in-route and give rise to the lower δ 18 Op values in those months. We note that, however, precipitation at Guangzhou during October-April also receives a significant amount of moisture from the seas to the far northeast (Fig. 3d). Taken together, these results suggest that changes in the original moisture source signal (the δ 18 O of source vapor or specific source location) alone cannot explain the observed seasonal variation in δ 18 Op , but rather indicate that processes occurring in the moisture source region and along transport pathways may have played a more important role. Upstream convection and rainout processes were proposed as a control on the ASM precipitation isotope compositions (e.g., Cai and Tian, 2016a; He et al., 2015; Ishizaki et al., 2012). When evaporated vapor passes through the oceanic source regions, deep convection, vertical air motions and microphysical processes govern rain formation and associated fractionation could alter the isotopic signatures of the vapor and precipitation in downstream areas (Bowen et al., 2019; Galewsky et al., 2016). Therefore, we analyzed the relationship between δ 18 Op and atmospheric convection field as measured by outgoing longwave radiation (OLR) at different time scales. The correlations between monthly Guangzhou δ 18 Op and regional precipitation or OLR exhibits similar spatial patterns (Fig. 6). The highly negative correlation areas between δ 18 Op and precipitation amount are mainly located in the northern Indian Ocean, SCS and western-equatorial Pacific Ocean, so do the highly positive correlation areas between δ 18 Op and OLR. It is worth noting that the high correlation areas between δ 18 Op and OLR is larger than that between δ 18 Op and precipitation. The positive correlation between δ 18 Op and OLR suggests that lower δ 18 Op is associated with stronger convection. These high correlation areas cover the major moisture source region of the northern SCS and expand to a broader region. This relationship between Guangzhou δ 18 Op and convection activity at large spatial scales mainly reflects: (1) the influence of convection activity on vapor isotope compositions in the moisture source region (northern SCS) and the propagation of these effects to the δ 18 Op at Guangzhou; and (2) the spatial autocorrelation of convection activity in the ASM region (Cai and Tian, 2016a). The influence of convection activity on precipitation and vapor isotopes has been extensively discussed in recent years (Cai et al., 2018; Galewsky et al., 2016; Moore et al., 2014; Risi et al., 2008a). In short, the depleting effect of deep convection on va-

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Fig. 6. The correlation of Guangzhou monthly δ 18 Op with regional precipitation (a) and OLR (b). Merely the correlation coefficients exceeding 99% confidence level are shown. Black dots donate the location of Guangzhou, while dashed rectangles indicate the northern SCS primary moisture source.

por isotopes is mainly through following processes: (1) enhanced re-evaporation in downdrafts; (2) enhanced vertical mixing; and (3) stronger and more top-heavy vapor convergence. They consequently lead to lower δ 18 Op values as the 18 O-depleted vapor joins the subsequent convection system. Southeast China δ 18 Op may thus record the isotopic effects of convection along precipitationproducing moisture trajectories. Seasonal variations of monsoon trough and associated northsouth migrations of intertropical convergence zone (ITCZ) are the major factor influencing the convection intensity over the moisture source region of SE China (the northern SCS) (Wang, 2006). ITCZ varies from day to day and especially carries seasonal signals. Since the key moisture source regions are located in the north edge of ITCZ, the seasonal migration of ITCZ can significantly influence the convective strength there. Our results show that stronger convection in the moisture source region gives rise to lower δ 18 Op values during the monsoon season, which is consistent with the idea that the ITCZ moves north and leads to lower isotope ratios. According to Fig. 6, besides the high correlation areas north of the equator, there is correspondingly high correlation areas with opposite signs south of the equator which manifests nearly the ITCZ southern limit. This counterpart phenomenon further verifies that precipitation isotopes respond to the seasonal migration of ITCZ. Combined with the previous results in Section 3.2, we come to the conclusion that at the seasonal scale, δ 18 Op shows high response to monsoon dynamics associated with ITCZ migrations which is achieved by changing convection intensity over moisture source region. 3.4. The δ 18 Op -climate link on the annual scale We further performed spatial correlation analyses using the annual δ 18 Op , precipitation and OLR data. As shown in Fig. 7, the high correlation areas in Fig. 6 shrink to the area over the SCS and western equatorial Pacific Ocean. With the seasonal effects eliminated, the apparent zonal counterpart phenomenon is gone. The seasonal ITCZ migration and resultant monsoon dynamics are

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Fig. 7. The correlation of Hong Kong annual δ 18 Op with regional precipitation (a) and OLR (b). Merely the correlation coefficients exceeding 90% confidence level are shown. Black dots donate the location of Hong Kong, while dashed rectangles indicate the northern SCS primary moisture source.

no longer reflected in precipitation isotopes on the interannual scale. Similar with the spatial correlation at the seasonal timescale, high correlation areas overlap with the precipitation-producing vapor source region, despite that the high correlation at the interannual scale is not that strong. Weaker correlations at the interannual scale may reflect the very strong seasonal response and its overprints on the annual-scale climate change. Besides the high correlation in monthly data, annual precipitation and OLR averaged over the northern SCS also shows high correlation with annual δ 18 Op (Table 1), which illustrate that the δ 18 Op in SE China responds to the regional meteorological condition in its moisture source region. 3.4.1. Response of δ 18 Op to the ENSO The high correlation between Hong Kong δ 18 Op and eastcentral equatorial Pacific sea surface temperature (particularly NINO 3.4) (Fig. S2) supports the mounting argument for the ENSO footprints in the isotope records in EAM regions. However, the relevant processes and/or mechanisms linking the ASM δ 18 Op and ENSO remain an open discussion (i.e. circulation effect vs regional convection) (e.g., Cai et al., 2017; Tan, 2014). A plausible way to reconcile the two mechanisms could be that the circulation effect (Tan, 2014) may stem from the varied convective strengths over the Indian and Pacific oceans (Cai et al., 2017) rather than the absolute transport distance of the vapor from two oceans. To further explore this issue, we focus on the latest super El Niño event in the following parts. The NINO3.4 SST index shows very high anomalies indicating a strong El Niño event in 2015, and correspondingly, the δ 18 Op shows high positive anomalies (Fig. S3). Tan (2014) proposed that ENSO modifies ASM isotope records by altering the ratio of the Pacific-versus remote Indian Oceansourced moisture (i.e. circulation effect). If ENSO dominated the δ 18 Op variability through the change of moisture source, one should notice a significant difference of the moisture source between El Niño events and other years. However, the precipitationproducing moisture source shows small change during the 2015

Fig. 8. The comparison between the cluster mean of air mass back trajectories in 2015 and 2007-14 (a), and the difference between fractional moisture contribution to Guangzhou precipitation in 2015 and 2007-14 (b). Black dot donates the location of Guangzhou, while dashed rectangle indicates the northern SCS primary moisture source.

strong El Niño: the precipitation weighed moisture percentage with a Pacific Ocean origin mildly decreased from 43% in 2007-14 to 36% in 2015 while those with Indian Ocean and Asian continental origins slightly increased (Fig. 8a). This change pattern contradicts with the one predicted from the circulation effect where increased Indian Ocean moisture origins give rise to lower δ 18 Op values. Furthermore, comparison of the moisture source pattern between 2015 and 2007-14 shows small change (<0.7% in fraction) within SCS and the East China Sea (Fig. 8b). Such change would only have small effects on the δ 18 Op and might explain parts of the observed δ 18 Op anomaly in 2015. These observations suggest that δ 18 O records in SE China may not be primarily driven by the variation of moisture source. Another mechanism explaining the isotope-ENSO linkage invokes ENSO’s influence on regional climate, especially the convection and precipitation in moisture source regions (Cai et al., 2017; Ishizaki et al., 2012). As can be seen from Fig. 9, the climate in SCS region bears large change in 2015. Precipitation is lower and OLR is much higher than the 2007-14 average, indicating that the convective strength over the SCS is weakened. Less precipitation and suppressed convection in this region reduce the depleting effect of deep convection on water vapor (Cai and Tian, 2016b), and thus lead to higher δ 18 Op values in downstream areas, which is consistent with the results shown in Fig. 6. These observations further verify the above conclusions and strengthen the authenticity of SE China δ 18 Op -SCS meteorological condition relationship. Concluding from our findings, the convective intensity and precipitation in the SCS (which is the major precipitation-producing moisture source region) will be recorded in SE China δ 18 Op when the SCS vapor passes the continent and feeds local rains and plays a primary role in transferring ENSO signal to the δ 18 Op in SE China.

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across downstream regions. Modern observations and paleo-data studies suggested that the δ 18 O records from vast ASM regions shares similar temporal variability on seasonal to orbital scales (Cai et al., 2018; Dayem et al., 2010; Rao et al., 2016), therefore, the results of this study could provide a broader implication to downstream sites and shed light on our understanding of the underlying forcing of the synchronized δ 18 O patterns across large geographical areas. When interpreting paleo-δ 18 O records from a specific site, however, local context should always be kept in mind before solid conclusions are drawn. 4. Conclusions

Fig. 9. Regional precipitation (a) and OLR (b) anomalies during 2015 compared with 2007-14. Black dots donate the location of Guangzhou, while dashed rectangles indicate the northern SCS primary moisture source.

3.5. Implications for δ 18 O-based paleoclimate reconstruction Although southern Chinese speleothem δ 18 O records have provided some of the best evidence for past hydroclimate change in the EAM region (e.g., Cheng et al., 2016), interpretations of these proxy records remain highly controversial and often involve multiple atmospheric processes and parameters. While some interpretations applied local rainfall amount (Tan et al., 2018; Zhang et al., 2008), other studies of model and proxy proposed the regional influence at the upstream of monsoon winds (Hu et al., 2008; Liu et al., 2014; Pausata et al., 2011; Yuan et al., 2004). This study provides further evidence that local precipitation amount only plays a secondary role in controlling δ 18 Op , and thus the interpretation of δ 18 O records as precipitation amount at the site where archives were retrieved should be treated with cautious. In contrast, the result highlights the importance of large-scale atmospheric processes occurring in the monsoon upstream and moisture source regions. More specifically, our multi-timescale attribution analyses have demonstrated that the atmospheric convection and precipitation processes that take place over the latest 5-day before the airmass’ arrival best explain the δ 18 Op variability at Guangzhou and Hong Kong. Moreover, the moisture source diagnostics suggest that these processes happen mostly over the nearby northern SCS rather than remote Indian Ocean or Western Pacific as often assumed in paleoclimate studies. Taken together, these results help improve the interpretation of the δ 18 O record from this area as proxies for source region (i.e., SCS) climate variability and based on which, one may forecast a close coupling between the regional terrestrial δ 18 O record and the SCS oceanographic evolution history inferred by marine sediments. Guangzhou and Hong Kong are located in the coast area of SE China and features the first oceanic moisture source signal when the monsoon blows towards inland China. When remaining vapor is transported further inland, δ 18 Op therein could record similar variability and thus give rise to the synchronicity in δ 18 O trends

Regression analyses show that the local amount effect at Guangzhou exists on daily-to-monthly timescales, but disappears over longer timescales. Instead, synoptic precipitation along air mass trajectory plays a more important role. Small seasonal shifts in moisture source regions and evidence for the primary moisture source from nearby northern SCS suggest that moisture source itself has limited impacts on the seasonal δ 18 Op variability at Guangzhou. The depleting effect due to intensified convection or elongated transport distance on source vapor and downstream propagation of this effect play a key role in transferring the seasonal monsoon and ITCZ migration signals into precipitation isotopes. This mechanism also works on the interannual scale linking high δ 18 Op values with El Niño and low values with La Nina. The dynamics of the ENSO influence is exemplified via the latest super El Niño event showing that the δ 18 Op anomaly of ∼1h in 2015 is attributed to the significant precipitation and convection reduction in moisture source regions. In accordance with recent work (Cai et al., 2017; Ishizaki et al., 2012), the result highlights the importance of upstream convection in translating the ENSO signal to the isotope records in SE China. Taken together, this study lends support to the interpretation of paleo-δ 18 O records in SE China as proxies for large-scale rainfall variability tracing from the measured site towards moisture sources. Acknowledgements We thank X. Yang and T. Yao at Institute of Tibetan Plateau Research, CAS who made their Guangzhou daily δ 18 Op data publicly available. The IAEA (GNIP database) provided the long-term precipitation isotope dataset from Hong Kong. The NOAA Air Resources Laboratory (ARL) provided the HYSPLIT transport and dispersion model. The NASA Goddard Space Flight Centre provided GPCP data. The NOAA/OAR/ESRL PSD provided OLR data. The NOAA CPC provided the Niño 3.4 SST index. Daily meteorological data was provided by Guangzhou meteorological office. We thank X.-S. Wang at McGill University for his valuable comments on this manuscript. This research was supported by the National Natural Science Foundation of China (Grant No. 41807415), the Fundamental Research Funds for the Central Universities (Grant No. 32110-31610332), and the China Postdoctoral Science Foundation (Grant No. 2019M653505). X.Q.Y. acknowledges support from the NSFC grants 41672162 and 41872217 and Guangdong Province Introduced Innovative R&D Team of Geological Processes and Natural Disasters around the South China Sea 2106ZT06N331. Appendix A. Supplementary material Supplementary material related to this article can be found online at https://doi.org/10.1016/j.epsl.2019.115794. References Araguás-Araguás, L., Fröehlich, K., Rozanski, K., 1998. Stable isotope composition of precipitation over southeast Asia. J. Geophys. Res. 103, 28721–28742.

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