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Atmospheric Controls on Seasonal and Interannual Variations in the Precipitation Isotope in the East Asian Monsoon Region

ZHONGYIN CAI Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China

LIDE TIAN Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, and CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, China

(Manuscript received 21 May 2015, in ﬁnal form 30 November 2015)

ABSTRACT

Understanding variations in isotopic composition of precipitation from monsoon regions is crucial for its utilization in paleoclimate studies. This study explores the relationship between precipitation d18O data for the East Asian monsoon (EAM) region archived in Global Network for Isotopes in Precipitation (GNIP) and the cloud data archived in ISCCP and their linkage with large-scale atmospheric circulation patterns. Results show that precipitation d18O are significantly and positively correlated with cloud-top pressure (CTP) on both local and regional scales. Mechanically speaking, the stronger the monsoon convection precipitation, the higher the cloud and the lower the condensation temperature and thus the lower the precipitation d18O. This result implies that the sharp drop in precipitation d18O in the early summer in monsoonal Asia is related to the atmospheric circulation pattern rather than the different moisture sources, as was previously assumed. This result helps explain the processes leading to the observed ‘‘amount effect.’’ A comparison of atmospheric circulation patterns with precipitation d18O on an interannual scale shows that the positive CTP anomalies in the central Indo-Pacific within the weak Walker circulation (El Niño) can be associated with positive d18O anomalies, while negative CTP anomalies in the central Indo-Pacific within the strong Walker circulation (La Niña) can be linked to negative d18O anomalies. This result further confirms the aforementioned conclusion. This is important for understanding paleoclimatic change in monsoonal Asia, as interannual variations in stable isotopes in that region have received less attention in the past.

1. Introduction precipitation provides meaningful references for inter- preting isotope records. In many tropical as well as Oxygen and hydrogen stable isotope records premonsoon regions, an inverse relationship between pre- served in natural archives, such as ice cores (e.g., Pang cipitation d18O and precipitation amount on a monthly et al. 2014; Thompson et al. 2000), speleothems (e.g., scale, called the ‘‘amount effect,’’ has long been observed Z. Liu et al. 2014; Wang et al. 2001), and tree-ring cel- (Araguás-Araguás et al. 1998; Dansgaard 1964; Rozanski lulose (e.g., McCarroll and Loader 2004; Shi et al. 2011), et al. 1993). However, this inverse relationship is not uni- constitute valuable archives for delineating past mon- versal for all monsoon regions (e.g., Breitenbach et al. soonal climates and hydrological cycles. A better un- 2010). Besides, the relationship between precipitation iso- derstanding of the isotopic composition of modern tope values and meteorological parameters are changeable between seasons (e.g., Yang et al. 2011; Yao et al. 2013; Yu et al. 2008), regions or locations (e.g., Gao et al. 2011; Corresponding author address: Lide Tian, Key Laboratory of Peng et al. 2010; Yang et al. 2011), and time periods Tibetan Environment Changes and Land Surface Processes, In- (Rozanski et al. 1993; Tan 2014; Xie et al. 2011)in stitute of Tibetan Plateau Research, Chinese Academy of Sciences, Building 3, Courtyard 16, Lin Cui Road, Chaoyang District, Beijing monsoon regions. These complex relationships make 100101, China. the explanation of variations in isotopic composition E-mail: [email protected] of precipitation and any interpretation of isotope

DOI: 10.1175/JCLI-D-15-0363.1

Ó 2016 American Meteorological Society Unauthenticated | Downloaded 09/30/21 04:57 AM UTC 1340 JOURNAL OF CLIMATE VOLUME 29 records from monsoon regions difficult. For example, the convection system (water vapor recycling) (Kurita the ice core d18O record from Dasuopu, in monsoonal et al. 2011; Lawrence and Gedzelman 1996; Risi et al. central Himalaya, has aroused great debate (e.g., 2008a, 2010). Thus, the more intense the convection, the Thompson et al. 2000; Tian et al. 2003; Vuille et al. more efficient the water vapor recycling and the lower 2005). The climatic interpretation of speleothem d18O the precipitation d18O. The degree of organization of records from southeastern China has also remained convection also contributes to the amount effect since controversial (e.g., Z. Liu et al. 2014; Maher and d18O is lower in precipitation from mesoscale convec- Thompson 2012; Pausata et al. 2011). tion than that from a disorganized convection system In the Asian monsoon region, changes in moisture (Kurita 2013). sources and/or their relative contributions have long Most of these studies focused on synoptic and mi- been recognized as the main factors responsible for the crophysical processes. Lee et al. (2007) suggested that seasonal variability in the isotopic composition of pre- the balance between precipitation and evaporation is cipitation, with low (high) values in summer (winter and the major factor controlling the isotopic composition of early spring) and an abrupt drop in the isotope ratio precipitation and vapor. Moore et al. (2014) further during onset of the summer monsoon (e.g., Araguás- studied the moisture budget controls on the isotopic Araguás et al. 1998; Breitenbach et al. 2010; Xie et al. composition of precipitation and concluded that con- 2011). It has been argued that the shift between oceanic vergence of water vapor is the primary determination of and continental moisture sources causes a correspond- the amount effect in steady state, which represents the ing shift in isotopic composition. amount effect on monthly and seasonal scales. Many recent studies have emphasized the role of Cloud-top height (the altitude that cloud top reaches) precipitation formation and postcondensation processes is a good indicator of both convection and convergence in explaining the amount effect and the variations in strength. Norris (2005) demonstrated that more upper- isotopic composition of precipitation from tropical and level cloud cover is accompanied by surface conver- monsoon regions (e.g., He et al. 2015; Kurita 2013; Lee gence, while less upper-level cloud is accompanied by and Fung 2008; Risi et al. 2008a,b). Since the amount surface divergence. Some studies hypothesized that effect was termed, three hypotheses have been used to isotopic depletion in intense monsoon precipitation may explain it (Dansgaard 1964). First, the gradual removal be related to a higher (lower) water vapor condensation of heavy isotopes by rainout results in isotopic depletion altitude (temperature) but without direct proofs in the remaining vapor and subsequent condensate. (Deshpande et al. 2010; Tian et al. 2001a). Scholl et al. Second, evaporation of raindrops below the cloud base (2009) used radar echo tops to prove this hypothesis on a will lead to an increase in d18O. As the increases of seasonal scale in Puerto Rico. Thus, we hypothesized precipitation rate, raindrop size, and relative humidity in that the amount effect in the East Asian monsoon intense convection systems will reduce this effect, the (EAM; one major system of the Asian monsoon system) heavy rain in intense convection systems will have lower region is caused by convective intensity and that the isotope values (e.g., Lee and Fung 2008; Risi et al. isotopic composition of precipitation from this region is 2008a). But a recent study suggested that raindrop related to cloud-top height. evaporation does not have a great contribution to the Additionally, cloud-top height is affected by atmo- amount effect as was assumed (Moore et al. 2014). spheric circulation. Cloud-top height ascends and cloud Third, isotopic equilibration between raindrops and thickness increases after the onset of the summer mon- ambient water vapor will make raindrops ‘‘forget’’ their soon in Asia (Wang and Wang 2016). Cloud-top height isotopic composition at cloud base and become enriched over the EAM region is significantly higher in the in heavy isotopes. As bigger raindrops will have longer summer monsoon season than in other periods (Chae equilibration time but shorter residence time in the and Sherwood 2010; Luo et al. 2013). Over a longer time boundary layer, heavy rain is less equilibrated with scale, variations in cloud-top height over Indo-Pacific surrounding vapor than light rain (e.g., Field et al. 2010; regions exhibit a clear correlation with the signal from Lee and Fung 2008). This makes raindrops in heavy rain El Niño–Southern Oscillation (ENSO) (Davies and less isotopically enriched, resulting in the amount effect. Molloy 2012; Eastman et al. 2011; Lelli et al. 2014; Su In addition, the continuous rain–vapor isotopic ex- and Jiang 2013). Therefore, the variations in isotopic change will lower both precipitation and vapor isotope composition of precipitation from the EAM region may values (Rozanski et al. 1993). The injection of this iso- be linked with atmospheric circulation. topically depleted vapor and subsidence of more de- In this study, we examined the relationship between pleted vapor from the environment make the isotope precipitation isotope data from the Global Network for values of vapor in the boundary layer lower, which feeds Isotopes in Precipitation (GNIP) and cloud-top height

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three stations were used to verify whether the results obtained from Hong Kong were universal to the EAM region. These four stations represent three different climatic observational conditions: two coastal stations [Hong Kong and Guangzhou (near coast), China], an inland continental station (Kunming, China), and an oceanic island station (Diliman, Philippines). In addition, water vapor isotope retrievals (version-4 level-3 monthly data from 2005 to 2009 with a 48 longitude 3 28 latitude resolution) from TES onboard NASA’s Aura (Worden et al. 2006, 2011) were also used in this study to explore the variation in isotopic composition of water vapor and its linkage with cloud-top height (available online at http://tesweb.jpl.nasa.gov/data/). TES data mainly reflect trace concentration in the midtropo- sphere between 850 and 400 hPa (Worden et al. 2006). Since TES cannot retrieve d18O, dD is used, but varia- d d18 d FIG. 1. Long-term (1984–2009) averaged CTP (hPa) showing tions in D and O are paralleled (variations in D are 18 cloud-top height and (a) strong convective activity as low CTP in approximately 8 times larger than that in d O). the boreal summer (June–August) and (b) weak convective activity Besides isotope data, satellite products were also used as high CTP in the boreal winter (December–February) in the to reconstruct cloud-top height and convective activity. Asian monsoon region. Black dots depict locations of d18O Three-hourly (D1 dataset) and monthly (D2 dataset) monitoring stations: 1) Hong Kong (HK), 2) Guangzhou (GZ), 3) Kunming (KM), and 4) Diliman (DI). cloud data from the International Satellite Cloud Cli- matology Project (ISCCP) are available for 26 complete 3 reconstructed from satellite retrievals. Taking advan- years from 1984 to 2009 at spatial resolutions of 280 km 83 8 tage of water vapor isotope retrievals from the tropo- 280 km (equal area map) and 2.5 2.5 (equal angle spheric emission spectrometer (TES), we attempted to map) (Rossow and Schiffer 1999). Global Precipitation use processes inherent in vertical vapor transport and Climatology Project (GPCP) long-term (1981–2010) 83 8 precipitation formation to explain the potential re- mean monthly precipitation data with a 2.5 2.5 res- lationships and the amount effect. Then we investigated olution (Adler et al. 2003) were used to depict a regional the likely correlation between large-scale atmospheric seasonal precipitation cycle. Long-term (1981–2010) av- circulation patterns and precipitation d18O in addition to eraged daily outgoing longwave radiation (OLR) data 83 8 cloud-top height on seasonal and interannual scales in with a 2.5 2.5 resolution (Liebmann 1996)werealso order to isolate atmospheric controls on the pre- used to illustrate the seasonal north–south migration of cipitation d18O in the EAM region. the tropical convective center as it corresponds to the intertropical convergence zone (ITCZ). The ISCCP D1 and D2 data are available online (http://isccp.giss.nasa. 2. Data and methods gov). Both the GPCP and OLR data are also available Monthly precipitation isotope data for 1984–2009 online (http://www.esrl.noaa.gov/psd/). were retrieved from GNIP [jointly organized by the To determine the interannual variability of d18Oand International Atomic Energy Agency (IAEA) and the cloud-top pressure (CTP) and their long-term relationship, World Meteorological Organization (WMO)]. Event- we first removed seasonality by calculating the monthly based observation data for Guangzhou precipitation for anomalies (i.e., we calculated the monthly anomalies rel- 2007–09 were provided by Xie et al. (2011). All the ative to the long-term mean monthly value of each month precipitation isotope data and the corresponding land to remove the seasonal cycle). Then the monthly anoma- surface air temperatures (LSATs) as well as pre- lies were smoothed by applying a 12-month running mean cipitation amount data used in this study are available (12MRM). To investigate further d18O interannual vari- online (https://nucleus.iaea.org/wiser). We used pre- ability, we used the Southern Oscillation index (SOI), cipitation isotope data from four stations: Hong Kong which is defined as the pressure difference between Tahiti (26 years of available records), Kunming (15 years of and Darwin, Australia (Ropelewski and Jones 1987)andis available records), Diliman (10 years of available re- calculated by the Australian Bureau of Meteorology. (The cords), and Guangzhou (Fig. 1). Primary results were calculating method and data are described at http://www. from the Hong Kong station. Results from the other bom.gov.au/climate/current/soihtm1.shtml.)

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3. Results TABLE 1. Correlation coefficients between monthly precipitation d18O, precipitation amount (P), and LSAT in Hong 18 a. Relationship between precipitation d O and CTP Kong and CTP and CTT in the Hong Kong grid box, during 1984– 2009. Boldface values indicate a correlation coefficient exceeding d18 For Hong Kong, monthly precipitation O corre- the 0.01 significance level. lates negatively with precipitation amount and LSAT d18 between 1984 and 2009 (Figs. 2a,b and Table 1). How- O P LSAT CTP CTT ever, the annual weighted mean precipitation d18O d18O 1.00 correlates neither with annual mean LSAT (r 5 0.04, P 20.57 1.00 . LAST 20.68 0.58 1.00 and p 0.1) nor with annual precipitation amount 2 2 52 . CTP 0.71 0.72 0.81 1.00 (r 0.05, and p 0.1). There must therefore be some CTT 0.68 20.72 20.72 0.98 1.00 unknown factors controlling the variability of d18Oin the long term (see sections 3c and 4). We analyzed the relationship between monthly d18O shows a robust positive correlation with CTT, with r 5 values in Hong Kong precipitation and CTP and cloud- 0.68. Monthly precipitation amount also shows a strong top temperature (CTT) over the 2.5832.58 Hong Kong correlation with CTP and CTT (Table 1). On a daily grid box (hereafter termed the Hong Kong grid, ibid. scale, the variation in d18O in Guangzhou precipitation other stations). Results are presented in Figs. 2c,d and shows less dependence on CTP and CTT over the Table 1. Monthly d18O and CTP values exhibit strong Guangzhou grid [the correlation coefficient is r 5 0.41 positive correlations, with r 5 0.71. Although the pre- (p , 0.01) between daily d18O and CTP and r 5 0.33 cipitation d18O is negatively correlated with LSAT, it (p , 0.01) between daily d18O and CTT].

18 FIG. 2. Relationship between monthly d O in Hong Kong precipitation and (a) local precipitation amount, (b) local LSAT, (c) CTP in the 2.5832.58 Hong Kong grid box, and (d) CTT in the 2.5832.58 Hong Kong grid box. Black lines indicate linear regression lines.

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scale is not dependent on the climatic conditions of the stations (e.g., inland continental or oceanic island). For example, at the Hong Kong station, the positive correlation between precipitation d18O and CTP is most prominent for the large convective zone spanning from the Bay of Bengal (BOB) in the west to the central North Pacific in the east, with a pronounced negative correlation in the corresponding Southern Hemisphere area (southern Indian Ocean, from Asian–Australian land bridge to central Pacific). For the inland station at Kunming, although the correlation between d18O and CTP over the local grid is weak (r 5 0.23, and p , 0.01), the large-scale d18O–CTP correlation is similar to that for Hong Kong. For the oceanic island station at Diliman, d18O correlates significantly with CTP on the local grid (r 5 0.58, and p , 0.01); the large-scale correlation is similar to, if slightly weaker than, the other two stations, albeit for a restricted area. This consistency in large-scale, calculated positive correlations for the Indian Ocean and western Pacific (north of the equator) and significantly negative correlations for the tropical Indian and Pacific Oceans (south of the equator) implies that large-scale atmospheric circulation patterns may play an important role in determining the spatial distribution of any correlation between d18O and CTP. The differences between

FIG. 3. (a) Spatial distribution of correlation coefficients between the areas north and south of the equator can be largely monthly d18O in Hong Kong precipitation and CTP; (b),(c) as in attributed to the inverse seasonal variation of CTP in (a), but for Kunming and Diliman, respectively. Correlation co- these two regions (see Fig. 1). Because of the cross- efficient contours between monthly CTPs in the Hong Kong grid equatorial movement of the ITCZ, when there is intense and other grid boxes are drawn in (a). Positive (negative) contours convective activity in the Northern Hemisphere, there are in black (red); the thickened contours represent values equal to 0.3 or 20.3, and the contour interval is 0.1. Black dots indicate is a conversely weak convective zone in the Southern locations for each station. The black rectangles show the R1 zone Hemisphere. delineated in section 3b. Large-scale atmospheric circulation can cause homo- geneous changes in CTP in these regions (Fig. 3a). Luo Given similar daily precipitation d18O trends over the et al. (2013) demonstrated that cloud-top heights for Nagqu basin in the central Tibetan Plateau (TP) (Tian deep convective cores in southern China and the et al. 2001b), similar seasonal d18O patterns over Yangtze and Huai River basins were very similar. Ad- southern TP (Yao et al. 2013), and the significant spatial ditionally, rainout history during moisture transport and consistency between atmospheric circulation patterns, upwind convection (Gao et al. 2013; Ishizaki et al. 2012; especially the movement of the ITCZ, we explored the Risi et al. 2008b; Vuille et al. 2005) also accounts for the relationship between precipitation d18O and CTP on a significant correlation between d18O and large-scale regional scale. Figure 3 shows the spatial distribution of CTP. In contrast, when one goes farther inland, the correlation coefficients between monthly CTP and d18O converged vapor is more influenced by the upwind for the three stations with relatively longer records convection, which will reduce the correlation between [Hong Kong (Fig. 3a), Kunming (Fig. 3b), and Diliman d18O and local CTP (the case of Kunming). (Fig. 3c)] as well as correlation coefficients between CTP To address quantitatively the relationship between over the Hong Kong grid and CTP over other grid boxes precipitation d18O and large-scale CTP, we defined R1 (Fig. 3a). The strong correlations for all three stations asacorezone(108–308N, 1008–1508E; note that it is suggest that monsoon precipitation d18O indeed cap- 108–308N, 1008–1528E for the water vapor analysis—this tures large-scale convective variability. The consistency small difference is due to different spatial resolutions in the spatial pattern of the correlation coefficients be- between the datasets). This area is roughly in agreement tween d18O and CTP at these three stations indicates with the low-latitude EAM region (Ding and Chan 2005; that the relationship between d18O and CTP on a large Tao and Chen 1987).

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altitudes, intense uplift of water vapor leads to enhanced Rayleigh fractionation at colder CTTs and brings more water vapor to the end of the Rayleigh distillation process (at high altitudes but with low isotope values).

Under such circumstances, the decreases in TES dDtv and precipitation d18O are associated with the decrease in CTP. Based on these results, we can propose an explanation for the mechanism controlling the isotopic composition of EAM precipitation. In a strong convective system, intense upward air currents and water vapor convergence form very high, thick clouds with extremely low cloud-top temperatures, finally forming large amounts of precipitation with low isotope values because of the ‘‘temperature effect.’’ During periods of weak convective activity, although LSAT may be FIG. 4. Vertical profiles of mean q, AT, and dDy (TES dDy) over lower, water vapor convergence is relatively weak and R1 from 2005 to 2009 (note that the data for April–June 2005 are clouds form at low altitudes with a higher CTT and d d missing). The Rayleigh distillation Dy (Mod Dy) curve is also hence produce less precipitation but with higher iso- shown in the figure. tope values. In addition, other processes, like water vapor recy- As is shown in Fig. 4, isotopic composition of water cling, may work on the short-term scale (e.g., Risi et al. vapor (dDy) (5-yr mean from 2005–09) over R1 gradu- 2008a,b). But the main focus of this study is to explain ally decreases with altitude and depicts a high de- the climatological and large-scale controls on isotopic pendence on temperature (the correlation coefficient composition of precipitation from the EAM region. between dDy and air temperature is r 5 1.00, and p , Besides, we may expect a positive correlation between 0.01), which implies that condensate that forms at higher deuterium excess [d excess; equal to dD 2 8d18O, de- altitude with lower temperature will be depleted in fined by Dansgaard (1964)] and convection (i.e., during heavy isotopes. The vertical dDy profile is generally in- the strong convection, the d excess should be higher) terpreted by the Rayleigh distillation model (e.g., based on the water vapor recycling hypothesis (Risi et al. Ehhalt et al. 2005) and the deviation of dDy from the 2008b). While based on observations from the EAM Rayleigh distillation curve can provide useful in- region (e.g., Araguás-Araguás et al. 1998), the seasonal formation to diagnose other processes controlling the variations in d excess may not concur with this hypoth- dDy (Lee et al. 2007; Samuels-Crow et al. 2014). Despite esis as the d excess is generally lower but the convection considerable deviations at the upper troposphere, the is much stronger in summer than in winter. observed dDy does not deviate much from the Rayleigh b. Seasonal monsoon circulation as a driver of distillation curve [calculated by using the observed seasonal patterns values at 825 hPa as the initial condition and the specific humidity q and air temperature (AT) profiles as con- Figure 6 illustrates the long-term averaged seasonal straints] (Fig. 4). d18O cycle for all four stations (Fig. 6a), the long-term Figure 5 shows the time series of R1 total column averaged seasonal cycle of associated parameters over water vapor isotope values (dDtv) in comparison with R1 (CTT, CTP, and precipitation rate; Fig. 6b), and the R1 CTP, R1 CTT, and precipitation d18O at three sites long-term zonal-averaged (1008–1508E) latitude–time (Hong Kong, Diliman, and Guangzhou; no available map for OLR (Fig. 6c). Except for the exceptionally low data for Kunming station during this period) from 2005 d18O values in Kunming precipitation for the July– to 2009. It is seen that during months with lower CTP, December period (because of its inland location and 18 18 TES dDtv, and precipitation d O are relatively lower high altitude of 1892 m MSL), the d O seasonal cycles than that in other months. TES dDtv is linked with CTP for all four stations are highly similar, with summer (the correlation coefficient is r 5 0.44, and p , 0.01), monsoon minima relating to intense convective activity CTT (the correlation coefficient is r 5 0.42, and p , (low OLR), low CTP and CTT, and enhanced pre- 0.01), and precipitation d18O. Lower CTP means stron- cipitation (Fig. 6). ger uplift of water vapor. The covariation of these var- We compared the seasonal changes in these parame- iables implies that when the cloud tops reach higher ters (Table 2 shows the correlation coefficients between

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18 FIG. 5. (a) Temporal variations of Hong Kong, Diliman, and Guangzhou precipitation d O

from 2005 to 2009; (b) as in (a), but for R1 dDtv, R1 CTT, and R1 CTP. We added 50& to dDtv to improve readability. these parameters) and found that both d18O and premonsoon region associated with low OLR (Fig. 6c) cipitation rate show consistent change with CTP and and CTP (Fig. 1a) in the Northern Hemisphere, with CTT (Fig. 6 and Table 2). In the summer monsoon consistently low d18Ovalues(Fig. 6a). After mid- season, intense upward air currents and water vapor September, the western North Pacific summer mon- convergence form extremely high cloud, with corre- soon (WNPSM) and Indian (or South Asian) summer spondingly low temperatures leading to heavy rainfall monsoon (ISM) start to retreat (Wang and LinHo but with depleted 18O. During winter, clouds form at low 2002) and the convective center moves rapidly altitudes and less vapor condenses to form precipitation, southward. In response, precipitation d18O observed but any condensate contains higher isotope values be- at the three tropical stations shows a synchronized and cause of higher CTTs than in summer. rapid increase from September to October. However, Figure 6 shows how seasonal precipitation d18O vari- Kunming is located in the ISM and WNPSM transi- ability in the EAM region corresponds with north–south tional zone, so d18O starts its rapid increase from migration of the ITCZ (reflected by variations in the October to November, lagging slightly behind the OLR). During the spring, the ITCZ starts moving other three stations. Actually, different parts of the northward and crosses the equator in mid-May, as ob- Asian monsoon region also have different summer served by the seasonal pattern in OLR distribution in monsoon onset dates, which make the abrupt drop in low-latitude regions (Fig. 6c). This shift corresponds to d18O also occur on different dates (e.g., Yang et al. the onset of the summer monsoon in the South China 2012). The variations in d18O for the four stations are Sea (SCS; Ding and Chan 2005; B. Liu et al. 2014). This similar during the summer monsoon onset using the onset process is characterized by abrupt changes in cir- monthly data. After the withdrawal of the summer culation and rainfall patterns. Around mid-May, the monsoon, the ITCZ retreats to the Southern Hemi- near-equatorial convection center suddenly moves sphere, while in the EAM region, OLR increases; northward, and OLR values in the 58–258N, 1008–1508E lower cloud tops correspond to the observed higher sector significantly decrease. The seasonal d18O transi- d18O values in precipitation. tion is consistent with the cross-equatorial shift of the c. ENSO and interannual variability ITCZ earlier in the summer. After the onset of the summer monsoon in the SCS, The interannual variability of d18O in Hong Kong it advances stepwise, exhibiting three standing stages precipitation is difficult to explain using the amount ef- and two abrupt northward shifts during June–August fect (refer to the poor correlation in section 3a), but it (Ding and Chan 2005; Wang and LinHo 2002). During can be explained by cloud-top height [the correlation this period, the ITCZ remains in the tropical summer coefficient of weighted annual mean d18O with annual

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TABLE 2. Correlation coefficients between long-term mean monthly precipitation d18O for the four stations with CTP, CTT, and precipitation rate (Prec) over R1. Boldface values indicate a correlation coefficient exceeding the 0.01 significance level.

d18O HK DI GZ KM CTP CTP 0.95 0.92 0.87 0.82 — CTT 0.96 0.92 0.88 0.83 1.00 Prec 20.96 20.94 20.90 20.90 20.97

To make a comparison between d18O and the ENSO signal we converted the d18O and CTP and CTT values into monthly anomalies and then compared the varia- 18 18 tions in 12MRM d O anomalies (d Oa) and 12MRM CTP anomalies (CTPa) with 12MRM SOI. Results show that the 12MRM R1 CTPa and SOI are strongly correlated (r 5 0.67, and p , 0.01). Table 3 presents the calculated correlation coefficients between 12MRM 18 d Oa and local precipitation amount anomalies (Pa), LSAT anomalies (LSATa), R1 CTPa, and SOI for the three stations (Hong Kong, Kunming, and Diliman) that have relatively longer records. The regression results 18 show that d Oa, R1 CTPa, and SOI are strongly co- 18 correlated, whereas the correlations between d Oa and FIG. 6. (a) Seasonal patterns in long-term averaged monthly Pa or LSATa are weak and/or insignificant for the three precipitation d18O for the four stations: Hong Kong, Diliman, stations. Figure 7 shows the temporal changes in Guangzhou (weighted monthly mean), and Kunming; (b) seasonal 18 12MRM SOI, R1 CTPa, and d Oa for Hong Kong patterns of long-term mean monthly CTT, CTP, and precipitation ñ rate (Prec) over R1; (c) and latitude–time map of long-term mean precipitation. Generally, the El Ni o warm phases ap- 18 (1981–2010) OLR averaged over 1008–1508E, showing the seasonal pear parallel with positive CTP and d O anomalies, displacement of the ITCZ. while the La Niña cold phases appear parallel with negative CTP and d18O anomalies (Fig. 7). These fea- mean R1 CTP (CTT) is r 5 0.46, and p , 0.05 (r 5 0.40, tures may be caused by changes in the Walker circula- and p , 0.05)]. These relationships suggest that the in- tion during the ENSO cycle. terannual variability of d18O is related to atmospheric A standard Walker circulation model presupposes circulation patterns rather than local meteorological that air ascends over the western Pacific and surround- parameters. The ENSO signal is the clearest indicator of ing regions through enhanced convection and then de- tropical interannual variability. Previous studies have scends over the eastern Pacific (Krishnamurti et al. found that variabilities in the EAM and cloud-top 2013). On a long-term mean scale, the CTP over the heights over Indo-Pacific regions are linked to this central Indo-Pacific (the eastern Indian Ocean, the ocean–atmosphere coupled phenomenon (e.g., Davies western Pacific Ocean, and the connecting regions) is and Molloy 2012; Ding et al. 2014; Lelli et al. 2014; Li et al. 2007; Rasmusson and Wallace 1983; Webster and d18 Yang 1992). Further, d18O in the Malan ice core, on the TABLE 3. Correlation coefficients between 12MRM Oa for three stations and 12MRM Pa, LSATa, CTPa, and SOI. Boldface northern margins of the ISM region, was found to be and italic values indicate a correlation coefficient exceeding the influenced by the North Atlantic Oscillation (NAO) and 0.01 and 0.05 significance levels, respectively. ENSO, which modulate the strength of the westerlies d18 and the ISM (Wang et al. 2003). A recent study also Oa found a connection between ENSO and southern TP ice HK KM DI d18 core O records (Gao et al. 2016). We therefore in- Pa 20.22 20.18 20.34 18 2 vestigated whether monsoon precipitation d O is linked LSATa 0.14 0.13 0.13 to the ENSO signal and, if so, what the possible un- CTPa 0.49 0.69 0.90 2 2 2 derlying processes could be. SOI 0.72 0.64 0.69

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FIG. 8. (a) Composite of differences between CTP values (hPa) during El Niño periods (SOI ,28) and La Niña periods (SOI . 8) 18 (El Niño minus La Niña) from 1984 to 2009; (b) spatial distribution FIG. 7. The 12MRM time series for SOI, R1 CTP , and d O in a a ñ Hong Kong precipitation from 1984 to 2009 (seasonal cycle of CTPa during an El Ni o phase (1997); and (c) spatial distribution ñ removed). of CTPa during a La Ni a phase (1999 and 2000). lower than in the eastern Pacific region both in summer the western Pacific and tropical Indian Ocean but posi- and winter (Fig. 1). During El Niño warm phases, the tive in the central Pacific (Fig. 8c). This rapid change more intense upward air masses move to the warm wa- from El NiñotoLaNiña leaves a clear imprint in pre- ters of the central Pacific. Furthermore, convection is cipitation d18O records (Fig. 7). In 1997, d18O was higher suppressed in the western Pacific and surrounding re- than its long-term average, while since 1999, it has ex- gions and intensified in the central and eastern Pacific hibited negative anomalies lasting for a few years. The relative to normal modes; this corresponds to positive changes in the Walker circulation thus drive the in- 18 18 R1 CTPa and EAM precipitation d Oa (Fig. 7). During terannual variations in EAM precipitation d O. Such converse La Niña cold phases, upward air masses move variations in d18O in EAM precipitation therefore bear farther west. Convection is enhanced in the western the ENSO signal. Pacific and surrounding regions but suppressed in the Both changes in precipitation seasonality and changes 18 central Pacific; this corresponds to negative R1 CTPa in monthly d O can modify the precipitation-weighted 18 18 and EAM precipitation d Oa (Fig. 7). Figure 8a shows annual mean d O. To separate these two parts, we used the composite of differences in CTP values during El the decomposition method mentioned in Liu and Niño periods (SOI ,28) and La Niña periods (SOI . Battisti (2015) to evaluate the role of changes in pre- 18 8) (El Niño minus La Niña) from 1984 to 2009. As EAM cipitation seasonality (d Ops; assuming that the precipitation d18O is controlled by CTP on a large scale, monthly precipitation d18O in 2000 is the same as that in the contrast between weak and strong Walker circula- 1997 and then calculating the difference between tions results in the CTP over the central Indo-Pacific and weighted annual mean d18O in 1997 and 2000—1997 EAM precipitation d18O being higher during El Niño minus 2000) and the role of changes in precipitation 18 18 periods but lower during La Niña periods (Figs. 7 d O(d Oiso; method is similar to that for calculating 18 and 8a). d Ops but assuming that the monthly precipitation For example, in 1997, distinct and positive CTPa amount is the same). The results from Hong Kong are values occur from the central-western Pacific to the that the difference in weighted annual mean d18O be- 18 central-eastern Indian Ocean, whereas negative CTPa tween 1997 and 2000 (1997 minus 2000) is 1.4&, d Ops 18 values occur in the central-eastern tropical Pacific is 20.6&, and d Oiso is 1.9&. These results imply that 18 (Fig. 8b). In 1999–2000, CTPa values become negative in the difference in weighted annual mean d O between

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1997 (an El Niño condition) and 2000 (a La Niña con- significant contribution to the high winter and early dition) mainly comes from the differences of d18O itself spring d18O values, while an oceanic moisture rainout rather than the seasonality of precipitation. Neverthe- effect leads to low summer d18O values. This hypothesis less, the results here are just from a case study. Further is widely used as an explanation of the seasonality of evaluation is needed in future study. EAM precipitation d18O (e.g., Peng et al. 2010; Wu et al. 2015; Xie et al. 2011). Although this interpretation can explain the low summer d18O values and high winter and 4. Discussion early spring d18O values, it fails to address quantitatively There is always a debate whether LSAT or pre- the abrupt fall in d18O values during the onset of the cipitation amount controls monsoon region pre- summer monsoon, a phenomenon found across an ex- cipitation d18O (e.g., Pang et al. 2014; Thompson et al. tensive monsoon precipitation area from the EAM re- 2000; Yang et al. 2011). And the climatic controls on gion to the ISM region. The apparent increase in precipitation d18O are crucial for rebuilding paleo- precipitation in early summer is not in phase with the climates using various isotope records. In Hong Kong, sharp fall in precipitation d18O as oceanic moisture in- precipitation d18O clearly correlates with local pre- creases, where the latter lags behind the former (e.g., cipitation amount and LSAT on a seasonal scale, but the Breitenbach et al. 2010; Yang et al. 2012). Recycled interannual-scale correlation is weak. By comparing moisture from the continent cannot form significant precipitation d18O, precipitation amount, CTP, and precipitation because of limited vapor in the air. CTT, we gained a clearer understanding of the useful- Meanwhile, every May, rainfall increases significantly in ness of EAM precipitation isotopes. We found that both monsoon regions but with high d18O in precipitation. d18O and precipitation amount are strongly correlated This makes the explanation of moisture sources linking with CTP and CTT and were able to explain the corre- with d18O difficult in the premonsoon season. lation we observed. During a period of intense mon- We have established a robust seasonal relationship soonal convection, when the clouds ascend to higher between precipitation d18O and CTP and concluded that altitudes, the lower temperatures at such heights will seasonal changes in precipitation d18O are linked to produce an enhanced Rayleigh fractionation and thus vapor condensation height. This does not concur with cause low d18O in precipitation. This is supported by the the theory of changes in moisture sources. Intense con- observed positive correlation between precipitation vection and water vapor convergence result in conden- d18O and CTT, even though precipitation d18O corre- sation at higher altitudes and consequently colder lates negatively with LSAT. In this sense, the tempera- temperatures, finally leading to the observed drop in ture effect still holds vis-à-vis monsoon precipitation monsoon-onset precipitation d18O. Only with the onset d18O but not the observed LSAT. of the monsoon with its strong convective precipitation, The failure of the amount effect on an interannual does d18O drop significantly. Therefore, the earlier ex- scale suggests that the traditional interpretation of d18O planation of a sharp shift of precipitation d18O in earlier as an indicator of local precipitation amount may not be monsoon may not reasonable. Our work implies that feasible in some monsoon areas in Asia. However, a early monsoon precipitation probably has an oceanic positive correlation between d18O and CTP and CTT moisture source rather than a westerly moisture supply. remains constant on both seasonal and interannual A recent study using satellite observations of dDy in scales. Because variations in CTP and CTT are related water vapor demonstrated that the main parameter to atmospheric circulation, we conclude that the sea- controlling summer (June–September) dDy at Lhasa, in sonal and interannual patterns observed in EAM the southern TP, was not the moisture source but rather precipitation isotopes are driven by atmospheric transportation paths through convective regions (He circulation. et al. 2015). The influence of moisture sources on year- Quite a few studies focusing on EAM region pre- long precipitation isotopes in monsoonal Asia may thus cipitation isotopes have linked seasonal changes in d18O be further addressed by measuring the isotopic compo- to changes in moisture sources (e.g., Araguás-Araguás sition of water vapor. et al. 1998; Peng et al. 2010; Wu et al. 2015; Xie et al. Few studies have succeeded in establishing a clear 2011). The sharp fall in summer monsoon precipitation annual-scale relationship between precipitation d18O d18O in circa June–July is usually explained by the shift and climatic factors in monsoon regions. Here, we of a westerly continental moisture supply to ocean present a robust relationship between precipitation d18O monsoon sources. Araguás-Araguás et al. (1998) sug- and CTP on an interannual scale. This result reveals that gested that, in monsoon areas, recycled moisture origi- mean annual precipitation d18O in the EAM region nating from the continent and adjacent seas makes a cannot simply be explained by LSAT or precipitation

Unauthenticated | Downloaded 09/30/21 04:57 AM UTC 15 FEBRUARY 2016 C A I A N D T I A N 1349 change but rather by cloud-top height and monsoonal Shi et al. 2011; Tian et al. 2003). Our findings may intensity. EAM variability is linked to ENSO variability therefore also aid the interpretation of d18O records through an ocean–atmosphere coupled system, while from other natural archives in monsoon regions. ENSO itself is also caused by the interactions between Although the data presented in this paper are from the ocean and atmosphere, with hemispheric and global EAM region, the results probably hold true for other impacts (Li et al. 2007; Rasmusson and Wallace 1983; monsoon regions such as the TP. Precipitation in Webster and Yang 1992). ENSO triggers shifts in trop- Kunming is more influenced by the ISM than the EAM. ical deep convection, thus influencing EAM pre- But the similarity of the large-scale d18O–CTP correla- cipitation d18O. During the warm phases of ENSO and tion for Kunming and other stations implies that the El Niño events, convective activities in the central Indo- finding from this study may apply to the ISM region. Our Pacific are suppressed; this reduces vapor condensation results may also help explain the ice core isotope record, height and causes an increase in EAM precipitation since ice cores from the southern TP were significantly d18O. Conversely, during La Niña events, the opposite influenced by the ISM. TP ice core d18O records have conditions prevail and lead to a relatively lower EAM been found to have a teleconnection with large-scale precipitation d18O. This mechanism can result in a atmospheric circulation variability—for example, the negative correlation between EAM precipitation d18O ENSO, Pacific decadal oscillation (PDO), and/or NAO and SOI. As the ENSO signal reflects changes in the (Gao et al. 2016; Wang et al. 2003; Yang et al. 2000). global Walker circulation, we may expect the relation- Isotope records from ice cores in regions with monsoon ship between precipitation d18O and the ENSO signal to vapor sources could be combined with our results in any exist more widely. In fact, significant relationships be- interpretation of the effects of large-scale atmospheric tween d18O and the ENSO signal have been discerned circulation. However, our results are based on data from using ice cores from several globally disparate regions— recent decades only, and whether this relationship holds for example, the TP (Brown et al. 2006; Gao et al. 2016; for a longer time scale, such as one backdated to the last Wang et al. 2003; Yang et al. 2000) and the tropical glacial period, remains to be investigated. Andes (Bradley et al. 2003; Hoffmann et al. 2003; Thompson et al. 2013; Vuille et al. 2003). 5. Conclusions Our findings may help interpret isotope records obtained from natural archives in monsoon regions. Spe- A range of studies reveal a seasonal precipitation leothem oxygen isotope records in monsoon regions ‘‘amount effect’’ in precipitation isotopes in the Asian have been widely used in paleomonsoon studies. How- monsoon region. It is difficult to establish a relationship ever, the climatic interpretation of speleothem d18O on an interannual scale because of the complexity of the records remains problematic, particularly in the EAM influencing and/or controlling factors. This makes any region (e.g., Z. Liu et al. 2014; Maher and Thompson explanation of paleoisotope archives in the Asian 2012; Pausata et al. 2011). All these studies suggested monsoon region difficult and sometimes equivocal. In that the speleothem d18O signal is mainly inherited from this new work, we established a robust relationship be- precipitation d18O. Therefore, a better understanding of tween EAM precipitation d18O and CTP on both sea- the response of precipitation isotopic variability to sonal and interannual scales. changes in climatic and/or environmental factors is im- A significant positive correlation exists between EAM portant for the interpretation of speleothem d18O precipitation d18O and CTP and CTT on both local and records. large scales. Intensifying convection and water vapor Since atmospheric circulation drives the seasonal and convergence induces an increase in cloud-top height interannual patterns of EAM precipitation d18O, d18O and a decrease in CTT, thus causing a fall in d18O, and could be used as a proxy for atmospheric circulation. vice versa. Cloud-top height as a control on d18O is ap- d18O appears to be an integrative recorder of large-scale parent over both seasonal and interannual time scales. convection variability and atmospheric circulation his- Our results confirm that seasonal monsoon circulation tory. This is important for any new interpretation of and changes in the Walker circulation drive d18O vari- speleothem d18O records in the EAM region because ability. They also help explain the relationship between any interpretation must rely on an understanding of the the sharp fall in early summer precipitation d18O and principal controls on long-term variability in pre- increasing precipitation amounts in monsoon regions. cipitation d18O. Besides speleothems, isotope records The change in precipitation d18O is linked with the from other natural archives (e.g., ice cores and tree-ring change of precipitation pattern, especially with the cellulose) are also strongly linked to the isotopic com- beginning of convective precipitation accompanying position of precipitation during their development (e.g., the monsoon, and it can significantly decrease the

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