Extreme precipitation in eastern China (EC) is closely related to the diversity of the decaying phases of El Niño (warm-pool El Niño, i.e., WP El Niño and cold-tongue El Niño, i.e., CT El Niño), but little attention is paid to how the El Niño event variability influences precipitation sources for EC from an isotopic perspective. Stable isotopes are ideal physical tracers that can distinguish different sources of precipitation and quantify their relative contributions to precipitation. Accordingly, this study investigates spatiotemporal variations of water vapor flux and oceanic fraction to precipitation during different ENSO events by an isotopic mixing model. The results show that spatiotemporal patterns of moisture divergence for the decaying phase of WP El Niño are different from that of CT El Niño. The oceanic fraction anomalies present similar spatiotemporal trends with advection fraction anomalies. The spatiotemporal variations of precipitation source anomalies for different El Niño events are closely related to atmospheric circulations, i.e., the intensity and location of the western Pacific subtropical high (WPSH). These findings provide isotopic insights into the precipitation sources by El Niño events in EC. Future studies may further focus on the mechanisms producing extreme precipitation between the two kinds of El Niño.

  • The impacts of different El Niño events on the regional water cycle are estimated from an isotopic perspective.

  • The anomalies of oceanic moisture in summer precipitation during CT El Niño are more obvious than those in WP El Niño.

  • These findings point out that CT El Niño should be paid immediate attention to for summer precipitation in eastern China.

El Niño–Southern Oscillation (ENSO) exhibits a considerable degree of diversity in the sea surface temperature anomaly (SSTA) variations and corresponding hydro-meteorological impact. According to the location of SSTA in the Pacific Ocean, ENSO can be classified into eastern Pacific (EP) and central Pacific (CP) events (Li et al. 2014; Veldkamp et al. 2015). For CP events, the SSTA occurs in the EP, which is different from CT El Niño with the largest SSTA in the tropical CP. During most ENSO episodes, the SSTA develops in boreal summer, peaks in winter, and subsequently decays. Thus, summer in the preceding year is regarded as the developing phase, and winter is the mature phase of ENSO events. After the mature winter comes spring and summer, both of which belong to the decaying phase (Kug et al. 2009; Räsänen & Kummu 2013; Wen et al. 2019). A number of studies have revealed the correlations between ENSO events and extreme precipitation. Gershunov & Barnett (1998) estimated the influence of ENSO on intraseasonal extreme precipitation in the Contiguous United States based on observations and model results. The results indicated that El Niño and La Niña play a significant modulating role in extreme precipitation in the United States. Alexander et al. (2009) applied the self-organizing maps (SOMs) to investigate the response of extreme precipitation to ENSO events and showed that very strong precipitation extremes were associated with the first pattern (strong La Niña) and the last pattern (strong El Niño). Costa et al. (2021) used the principal component analysis and cluster analysis techniques to identify the relationships between precipitation extremes and El Niño. They found that extreme events are closely related to the ENSO phases (El Niño, La Niña, and Neutral). Cao et al. (2024) investigated how the diversity of ENSO events impacts the occurrence of extreme precipitation over EP and concluded that during CP El Niño, there is a higher (lower) possibility of extreme precipitation over the Yangtze River (Mei-Yu rainband in China, Baiu in Japan, and Changma in South Korea).

Eastern China (EC) usually experiences extreme precipitation in summer (Chen et al. 2005; Wang & Zhou 2005; Li et al. 2019). As presented in Figure 1, EC summer precipitation mainly comes from three sources: advection moisture from the Pacific Ocean or upwind regions, transpiration moisture from plants, and evaporation moisture from soil or surface water. The pattern of extreme precipitation in EC is mainly dominated by oceanic moisture from the Pacific Ocean (Mei et al. 2015; Peng et al. 2020b). ENSO, as an energetic interannual variation in SSTA over the Pacific Ocean, is an important factor influencing the spatiotemporal variations of oceanic moisture transport for EC (Zhou & Wu 2010; Ouyang et al. 2014; Cao et al. 2017; Guo et al. 2019). The SSTA could change Hadley circulation and associated atmospheric transport, thus influencing the oceanic moisture moving to EC for precipitation (Bjerknes 1966, 1969; Bin et al. 2000; Pasquini & Depetris 2010). A quantitative estimation of how the ENSO impacts the oceanic fraction of precipitation for EC is essential for improving our understanding and prediction of EC extreme precipitation.
Figure 1

Schema diagram of the three-component mixing model applied for precipitation formation in EC. Precipitation is composed of advection vapor from the upwind sub-region, local evaporation vapor, and transpiration vapor.

Figure 1

Schema diagram of the three-component mixing model applied for precipitation formation in EC. Precipitation is composed of advection vapor from the upwind sub-region, local evaporation vapor, and transpiration vapor.

Close modal

The diversity of ENSO would trigger different atmospheric circulations over EC in summer. Compared with developing summer, the western Pacific subtropical high (WPSH) in the decaying phases is more significant and extends westward (Xue et al. 2018). During the decaying phase, the WPSH in WP El Niño tends to be weaker than that in CT El Niño (Yuan et al. 2012). Moreover, WPSH could stretch to northern China in the decaying phase of WP El Niño, but only to southern China in the decaying phase of CT El Niño (Feng et al. 2011). Driven by the diverse atmospheric circulations, EC precipitation experiences large spatiotemporal variations during different types and phases of ENSO (Ren & Jin 2011; Feng et al. 2014; Zhang et al. 2016). For example, Li et al. (2014) compared the impacts of different El Niño events on water vapor transport over eastern China and found that the precipitation over the Yangtze River Basin presents a decreased tendency in developing summer but positive anomalies in decaying summer. Cao et al. (2017) investigated the influence of different ENSO types and phases on the rainy season in China and concluded that CT El Niño has a larger impact on China's precipitation than WP El Niño in both developing and decaying phases. However, such studies mainly focused on the impacts of different ENSO events on EC precipitation, rather than on the moisture sources of EC precipitation, which is more important for us to understand ENSO-induced precipitation. Furthermore, Feng et al. (2011) pointed out that the impact of El Niño on EC precipitation is more significant in the decaying phase than in the developing phase. In view of the above considerations, our study focuses on investigating how CT and WP El Niño in the decaying phase individually affect the oceanic sources of EC precipitation.

To further classify the relative contributions of precipitation sources, three kinds of methods, i.e., analytical model, numerical model, and physical tracers method, have been widely used (Galewsky et al. 2016; Wang et al. 2016; Wei & Lee 2019; Zhu et al. 2013). Compared with the analytical model and the numerical model, the physical tracers method has become increasingly popular in practical applications owing to relatively lower uncertainty (Lee et al. 2007; Yoshimura 2015; Hu et al. 2018; Shi et al. 2022). The physical tracers method usually applies stable isotopes in water vapor as tracers to quantify the role of oceanic sources in precipitation. This method assumes that precipitating vapor is composed of transpiration, evaporation, and advection vapor, and the isotopic composition of each water vapor source is unique or statistically different (Clark & Fritz 1997; Genereux 1998; Peng et al. 2020a; Chen et al. 2023). The isotopic composition of precipitation is closely related to the moisture sources and their fractions to precipitation. Based on deuterium excess, Froehlich et al. (2008) identified recycled moisture of precipitation and found that the isotopic results are in good agreement with results from other approaches. With the physical tracers method, Wang et al. (2016) quantified the contribution of recycled moisture to precipitation in oases of arid central Asia and found that the recycled moisture fractions are approximately 16.2% for large oases and less than 5% for small oases. Using oxygen as the tracer, Peng et al. (2020b) apportioned the spatiotemporal contributions of oceanic moisture to summer precipitation and showed that the oceanic moisture fraction of precipitation decreased from June to August in Northern China. Generally, this method can be classified into two categories on the basis of the number of precipitation sources: the three-component mixing model and the two-component mixing model. The two-component mixing model usually neglects the contribution of transpiration in practical case studies, thus a three-component mixing model is more desirable to EC where transpiration is indispensable in summer (Gao et al. 2000).

Accordingly, this study aims to assess the impacts of the decaying phase of ENSO events on regional water cycles from an isotopic perspective and explain the potential mechanisms. It is organized as follows. A description of the study area and data are presented in Section 2. Classification of the ENSO event, the three-component mixing model, evaluation index, and methods are introduced in Section 3. The ENSO effects on water vapor transport and precipitation sources for EC, as well as uncertainty estimation in the results, are investigated in Section 4. Conclusions are summarized in Section 5.

Study area

This study is conducted for the EC region located in southeastern Eurasia (Figure 2). Great Khingan Mountain, Taihang Mountain, Wu Mountain, and Xuefeng Mountain form the western boundary of EC. The study region, bordering on the Pacific Ocean, does not include the islands at the south margin of the South China Sea since these islands do not geographically and hydro-meteorologically connect to EC. The surface area of EC is about 260,000 km2, which is mainly made up of plains with an elevation below 500 m. In summer, the vegetation coverage is high for most of EC with the monthly mean normalized difference vegetation index value higher than 0.7. The average annual temperature ranges from 20 °C in the south to 0 °C in the northeast. The average annual precipitation also experiences large spatial variations, ranging from about 1,800 mm in the south to 400 mm in the northeast. To better analyze the spatial variances of precipitation sources in EC, we subdivide EC into four sub-regions: Southern China (SC), the Yangtze River valley (YZ), Northern China (NC), and Northeastern China (NEC). The subdivision considers vapor flux, isotope distribution, and geophysical conditions, which are discussed earlier in Peng et al. (2020b).
Figure 2

Location map and sub-regions of eastern China (EC), as well as the distribution of isotope stations.

Figure 2

Location map and sub-regions of eastern China (EC), as well as the distribution of isotope stations.

Close modal

Data

The isotopic dataset consists of oxygen-18 and deuterium composition of precipitation. The oxygen-18 compositions of precipitation were collected from Peng et al. (2020b), which generate reliable and spatiotemporally continuous precipitation oxygen isoscape for EC. The deuterium compositions of precipitation were obtained by bias-correcting simulations from the isotope-enabled global climate model Laboratoire de Météorologie Dynamique GCM (LMDZiso). The resolution of LMDZiso (50–60 km) is relatively higher than that of other iGCMs and the timespan of LMDZiso (1979–2016) is relatively longer. Linear scaling (LS) and distribution translation (DT) are applied in this study because they exhibit similar performances in correcting isotopic simulations (Peng et al. 2020a). The isotopic compositions of precipitation are at a spatial resolution of 50–60 km.

Meteorological data for this study contain temperature, precipitation, wind speed, wind direction, air pressure, precipitable water content, and relative humidity. All these data are collected from the monthly 0.5° × 0.5° gridded dataset of the China Meteorological Data Service Center (CMDC) (http://data.cma.cn/en) and bilinearly interpolated into a resolution of 50–60 km to be in line with the LMDZiso grid.

To investigate the impact of ENSO types and phases, we identify two different types of ENSO from 1979 to 2016 by the variation of SSTA. A three-component mixing model is applied to apportion the spatiotemporal contributions of oceanic moisture. Results during the same type of ENSO years are averaged to obtain the mean values. The fraction anomaly index is adopted to evaluate the spatiotemporal variations of the contribution of advection or oceanic moisture to precipitation under different ENSO events. Gaussian first-order approximation is applied to assess uncertainty in the results.

Classification of ENSO regimes

The CT and WP El Niño exhibit different SSTA characteristics in the Pacific Ocean, as presented in the Introduction. To identify CT and WP El Niño, we apply P-defined CT and WP index (NCT and NWP), which well captures the SSTA signals of the Niño3 region (5°N–5°S, 150°–90°W) and Niño4 region (5°N–5°S, 160°E–150°W) without overlapping each other. The NCT and NWP have been compared with other indices and proved as good indicators of CT and WP El Niño (Ren & Jin 2011; Li et al. 2014). The NCT and NWP are presented as follows (Ren & Jin 2011):
formula
(1)
where N3 and N4 represent Niño3 and Niño4 indices, respectively. The two indices capture the SSTA characteristics in the Niño3 and Niño4 regions, respectively. The global Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) at a 1° × 1° resolution is used to calculate the indices.

We calculate the December–February averaged NCT and NWP from 1979 to 2016 and then select the years when NCT and NWP are larger than one standard deviation to represent the developing phases of CT and WP El Niño, respectively. The year after the mature phases is regarded as the decaying year. In total, there are five WP events in the decaying phase and four CT events in the decaying phase from 1979 to 2016. The decaying years of WP and CT El Niño are dominated by the two types of ENSO, as listed in Table 1.

Table 1

The decaying years of warm-pool (WP) El Niño and cold-tongue (CT) El Niño during 1979–2016

TypesDecaying years
WP 1988 1991 1995 2005 2010 
CT 1983 1992 1998 2016  
TypesDecaying years
WP 1988 1991 1995 2005 2010 
CT 1983 1992 1998 2016  

Three-component mixing model

A three-component mixing model is applied to quantify the relative contribution of different water vapor sources to precipitation. Oxygen and hydrogen isotopes are used as ideal tracers for this method because they are integral parts of water molecules (Kendall & Caldwell 1998). The method is based on two assumptions: (1) precipitating vapor is composed of transpiration, evaporation, and advection vapor, and (2) the isotopic composition of each water vapor source is unique or statistically different (Clark & Fritz 1997; Genereux 1998; Peng et al. 2020a). According to these two assumptions, the contribution of advection vapor to local precipitation can be estimated as
formula
(2)
where δ18OPV, δ18OEv, δ18OTr, and δ18OAdv are the oxygen isotopic composition of precipitating vapor, evaporation vapor, transpiration vapor, and advection vapor, respectively. δHPV, δHEv, δHTr, and δHAdv are the hydrogen isotopic composition of precipitating vapor, evaporation vapor, transpiration vapor, and advection vapor, respectively. In this study, we assume the precipitating vapor in the southeast boundary of SC, which is the closest to the Pacific Ocean, as the original source of the Pacific Ocean. The contribution of oceanic moisture to local precipitation is the product of all advection fractions to precipitation for upwind sub-regions. The upwind sub-regions are determined by the direction of moisture transport. Taking YZ for example, the upwind sub-regions are SC and the southeast boundary. In light of this, the oceanic fraction in precipitation can be calculated by (Peng et al. 2020b):
formula
(3)
where n represents the total amount of upwind sub-regions and i represents the number of the product sub-regions. In this way, SC, YZ, NC, and NEC correspond to 1, 2, 3, and 4, respectively. fiAdv represents the advection faction to precipitation in the sub-region denoting i. For example, f1Adv is the advection faction to precipitation for SC.

According to Equations (1) and (2), isotopic compositions of precipitating vapor, evaporation vapor, transpiration vapor, and advection vapor are necessary for calculating the contribution of oceanic moisture to precipitation.

It is assumed that isotope equilibrium fractionation controls the transition from liquid water to vapor at its surface, thus the isotopic composition of precipitating vapor can be estimated by the isotopic composition of precipitation and equilibrium fractionation factors as shown in the following (Clark & Fritz 1997; Kendall & Caldwell 1998; Peng et al. 2011):
formula
(4)
where δP represents the isotopic composition of precipitation. αw−v represents the equilibrium fractionation factors, which are determined by temperatures. The calculations of the factors for oxygen and hydrogen are different and can be expressed as (Friedman & James 1997; Criss 1999):
formula
(5)
formula
(6)
where α18w−v and α2w−v represent the equilibrium fractionation factors for oxygen and hydrogen, respectively. T represents the temperature in K.
During transpiration, no fractionation happens in the transitions from soil water to plant and then to atmosphere (Flanagan et al. 1991; Wang et al. 2016). Soil water is mainly influenced by precipitation and long-term shallow groundwater (Peng et al. 2020b). Thus, the isotopic composition of transpiration vapor is assumed to be equal to that of soil water, which is linearly dependent on the isotopic composition of precipitation:
formula
(7)
where δPlant and δSoil are the isotopic compositions of plant and soil water, respectively. k and b for oxygen isotope are taken from Peng et al. (2020b). The parameters for hydrogen isotope are calculated by simulation data from LMDZ4. The equations of the best-fit line are estimated for each sub-region in summer. The best-fit equations and goodness of fit (R2) are presented in Figure 3.
Figure 3

Relationships between simulated hydrogen isotope in precipitation and in soil water for SC (a), YZ (b), NC (c), and NEC (d) in summer.

Figure 3

Relationships between simulated hydrogen isotope in precipitation and in soil water for SC (a), YZ (b), NC (c), and NEC (d) in summer.

Close modal
In the evaporation process, both equilibrium fractionation and kinetic fractionation happen. The complex process is generally described by a modified Craig–Gordon model. According to the model, isotopic compositions of evaporation vapor are influenced by humidity and isotopic composition of the moisture in free air (Craig & Gordon 1965; Gat 1996):
formula
(8)
where h represents relative humidity. δS is the isotopic composition of surface water and is different among the sub-regions. δS for YZ, NC, and NEC are assumed to be equal to the isotopic composition of Poyang and Dongting Lake, the isotopic composition of Bohai Sea, and the isotopic composition of long-term local precipitation, respectively (Peng et al. 2020b). ε represents the enrichment factor, and varies for hydrogen and oxygen isotopes, as expressed by Equations (8) and (9):
formula
(9)
formula
(10)
The isotopic composition of advection vapor is largely influenced by whether precipitation occurs along the pathway of water vapor transport. No precipitation means no fractionation. In this way, the isotopic compositions of advection vapor are similar to that of precipitating vapor in the upwind sub-region. If precipitation happens along the transport trajectory, the isotopic compositions of advection vapor are estimated by Rayleigh distillation:
formula
(11)
where F represents a parameter related to product vapor and its upwind vapor. In this study, F is estimated by the amount of total water vapor:
formula
(12)
where and represent the amount of precipitable water in the sub-region denoting i (product sub-region) and i − 1 (upwind sub-region), respectively.

Fraction anomaly index

Fraction anomaly presents the differences in the contribution of advection or oceanic moisture to precipitation between ENSO years and non-ENSO years. It can more directly demonstrate the effect of different ENSO events on advection or oceanic moisture fraction to precipitation, compared with the value of the fraction. For a specific ENSO event, the fraction anomaly index is defined as
formula
(13)
where and represent the average fraction of advection or oceanic moisture to precipitation in ENSO and non-ENSO years, respectively.

Gaussian first-order approximation

The Gaussian first-order approximation is used to evaluate the uncertainty of the mean proportion , , and FTr (Phillips & Gregg 2001; Zhang & Liu 2016). This method uses standard errors of isotopic composition for advection vapor, precipitating vapor, evaporation vapor, and transpiration vapor to estimate the 95% confidence intervals (CIs) for the mean proportion estimates as
formula
(14)
where represents the variance of each source, and the subscripts Adv, Ev, Tr, and PV represent the advection vapor, evaporation vapor, transpiration vapor and precipitating vapor, respectively, which are assumed to be independent.
According to Gaussian first-order approximation, the uncertainty contributions of evaporation vapor, transpiration vapor, advection vapor, and precipitating vapor to the estimated proportion variance of FAdv can be estimated:
formula
(15)
formula
(16)
formula
(17)
formula
(18)

Besides, approximate 95% CIs and standard errors for the mean proportion (F) are calculated as for uncertainty estimates.

Moisture transport and its divergence

To demonstrate the impacts of the decaying phase of WP and CT El Niño on EC moisture transport, long-term averaged water vapor flux and its divergence anomalies are calculated at a sub-regional scale and compared in Figure 4. In the decaying phase of CT and WP El Niño, abnormal oceanic moisture originating from the western Pacific moves northward to eastern China, which influences spatiotemporal variations of local precipitation. Moisture from the Pacific Ocean migrates furthest to NC during the decaying phase of El Niño. The magnitude of moisture divergence is stronger in the decaying phase of CT El Niño than WP El Niño. This is because the anti-cyclonic moisture circulation for WP El Niño is weaker and smaller than that for CT El Niño (Feng et al. 2011). In the decaying phase of CT El Niño, there is an above-normal moisture divergence over SC and moisture convergence over YZ, whereas the opposite occurs in the decaying phase of CT El Niño. The results are in agreement with Li et al. (2014) that the decaying phase of CT El Niño causes a moisture deficit in SC and a moisture surplus in YZ, which results from the northward anti-cyclonic moisture circulation.
Figure 4

Spatiotemporal variations of regional mean water vapor flux (kg m−1 s−1; vector) and its divergence anomalies (10−6 kg m−2 s−1; shading) from surface to 300 hPa for eastern China during WP (a) and CT El Niño (b) decaying summer.

Figure 4

Spatiotemporal variations of regional mean water vapor flux (kg m−1 s−1; vector) and its divergence anomalies (10−6 kg m−2 s−1; shading) from surface to 300 hPa for eastern China during WP (a) and CT El Niño (b) decaying summer.

Close modal

Advection vapor contribution anomalies to precipitation

In this study, we only discuss the sub-regions to which oceanic moisture transport, thus the results of advection fraction to precipitation for NEC are omitted. The contribution anomalies of advection vapor to precipitation in the decaying phase of WP and CT El Niño during summer months over EC are presented in Figure 5. The contribution anomalies of advection vapor to precipitation are more significant in CT El Niño than in WP El Niño. Taking YZ as an example, the advection fraction anomaly is about 12% for CT El Niño, while −5% for WP El Niño. The anomalies of advection fractions to precipitation vary in sub-regions in the decaying phase of WP and CT El Niño. During the decaying phase of WP El Niño, the advection fraction anomaly is 3% for SC, −5% for YZ, and 4% for NC. In the decaying summer of CT El Niño, the advection fraction anomaly is −7% for SC, 12% for YZ, and 5% for NC. Moreover, WP and CT El Niño in the decaying phase present different spatiotemporal variations of advection fraction anomalies. For the decaying phase of CT El Niño, SC shows a below-normal contribution of advection vapor to precipitation and YZ presents an above-normal signal. For the decaying phase of WP El Niño, an enhanced advection fraction occurs over SC, while a reduced advection fraction happens over YZ.
Figure 5

Spatiotemporal variations of contribution anomalies of advection vapor to the precipitation over eastern China during WP (a) and CT El Niño (b) in decaying summer. Red points represent negative anomalies. Blue points represent positive anomalies. The size of the point represents the intensity of the anomaly. The bigger size means a larger anomaly in the specific sub-region.

Figure 5

Spatiotemporal variations of contribution anomalies of advection vapor to the precipitation over eastern China during WP (a) and CT El Niño (b) in decaying summer. Red points represent negative anomalies. Blue points represent positive anomalies. The size of the point represents the intensity of the anomaly. The bigger size means a larger anomaly in the specific sub-region.

Close modal

The spatiotemporal patterns of advection moisture fraction under different ENSO events are similar to the moisture divergence anomalies in Section 4.1 with opposite signs and the variations of precipitation amount anomalies by previous research. As concluded in Feng et al. (2011), the signals of advection fraction during the decaying phase of WP El Niño are weaker than CT El Niño. This means that decaying CT El Niño in the decaying phase has a larger effect on precipitation and its advection source, compared with WP El Niño. Besides, WP and CT El Niño in the decaying phase show different spatial patterns of precipitation anomalies. SC shows dry signals during CT El Niño and wet signals during WP El Niño in the decaying phase (Li et al. 2014). The consistency between advection fraction anomalies and precipitation anomalies is because advection vapor dominates EC summer precipitation during ENSO events, thus its contribution anomalies well capture precipitation anomalies. Furthermore, the results based on the three-component mixing model are highly consistent with those of moisture divergence anomalies, which also prove the reliability of isotopic analysis. However, precipitation anomalies (ranging from −30 to 20%) during different ENSO events are more obvious than advection fraction anomalies (ranging from −7 to 12%). This might be because the signals of precipitation anomalies affected by ENSO events contain advection, evaporation, and transpiration anomalies, thus the precipitation anomalies should be bigger than either precipitation source anomalies.

Oceanic moisture contribution anomalies to precipitation

Figure 6 compares spatiotemporal patterns of oceanic moisture contribution anomalies in the decaying summer of WP and CT El Niño. The contribution anomalies of oceanic moisture to precipitation show a similar pattern with advection fraction anomalies. The oceanic anomalies show more obvious signals in CT than in WP El Niño. During the decaying phase, CT El Niño causes a below-normal oceanic moisture transport over SC and above-normal oceanic fraction to precipitation over YZ, whereas WP El Niño induces an opposite pattern. It is important to note that contribution anomalies of oceanic moisture are relatively higher than advection anomalies for NC, whereas they are lower for YZ during the decaying phase of CT El Niño. For YZ during the decaying phase of CT El Niño, the oceanic contribution anomaly is approximately 4%, whereas advection anomaly is 12%. For NC during the decaying phase of CT El Niño, the oceanic contribution anomaly is approximately 10%, whereas the advection anomaly is 5%. This discrepancy between oceanic moisture anomaly and advection anomaly is because the role of oceanic moisture in local precipitation is the product of advection fraction from all upwind sub-regions (Peng et al. 2020b). Compared with advection, oceanic sources of precipitation experience more convection along the moisture transport pathway. When the advection anomalies of upwind sub-regions are all increasing or decreasing signals, the oceanic fraction anomalies of the product sub-region contain larger spatial variations, whereas the oceanic anomalies contain smaller spatial variations.
Figure 6

Spatiotemporal variations of contribution anomalies of oceanic moisture to precipitation over eastern China during WP and CT El Niño decaying summer. Red points represent negative anomalies. Blue points represent positive anomalies. The size of the point represents the intensity of the anomaly. The bigger size means a larger anomaly in the specific sub-region.

Figure 6

Spatiotemporal variations of contribution anomalies of oceanic moisture to precipitation over eastern China during WP and CT El Niño decaying summer. Red points represent negative anomalies. Blue points represent positive anomalies. The size of the point represents the intensity of the anomaly. The bigger size means a larger anomaly in the specific sub-region.

Close modal

The spatiotemporal variations of contribution anomalies of oceanic moisture to precipitation might be explained by the intensity and location of WPSH. The variations of WPSH could be illustrated by the western North Pacific (WNP) anti-cyclone anomaly. For the decaying phase of WP El Niño, the WPSH extends northward with WNP anti-cyclone moving around to 40°N (Sun & Ying 1999; Zhou et al. 2005). This induces more oceanic moisture transport to NC which is located northwest of WPSH and southwest winds carry more oceanic moisture driven by WNP anti-cyclone. For the decaying phase of CT El Niño, the WPSH retreats at a low level with WNP anti-cyclone only stretching to 25°N (Feng et al. 2011). The WNP anti-cyclone confines oceanic moisture transport, which causes negative oceanic fraction anomaly to appear in SC. Furthermore, the prominent oceanic anomalies during CT El Niño can be explained by the relatively stronger anti-cyclone during CT El Niño, which enhances oceanic moisture transport (Wang et al. 2000; Yuan et al. 2012; Cao et al. 2017).

Uncertainty analysis

In order to estimate the uncertainty of the statistical results in this study, uncertainty contributions to estimated source proportions are obtained by Gaussian first-order approximation (Figure 7). As shown in Figure 7, the largest uncertainty of the estimated proportion comes from the precipitating vapor which is about 85–90%. The second largest uncertainty source of the estimates is advection vapor with the uncertainty contribution ranging from 6% to 12%. Besides, the contribution of evaporation and transpiration vapor to the total uncertainty can be neglected. This phenomenon can be explained by relatively higher variances of the isotopic composition of precipitating vapor, which results from two main reasons. First, precipitating vapor is composed of advection vapor, evaporation vapor, and transpiration vapor, thus precipitating fraction is more variable than its components (advection, evaporation, and transpiration vapor). Second, the three-component mixing model is based on the perfect mixing condition of precipitating vapor; however, this assumption might not be satisfied all the time, which increases the uncertainty contribution from precipitating vapor.
Figure 7

Mean uncertainty contributions from the precipitating vapor, advection vapor, evaporation vapor, and transpiration vapor to the variances of advection proportion estimates in summer over SC (a), YZ (b), and NC (c).

Figure 7

Mean uncertainty contributions from the precipitating vapor, advection vapor, evaporation vapor, and transpiration vapor to the variances of advection proportion estimates in summer over SC (a), YZ (b), and NC (c).

Close modal
Besides this, we calculate the mean standard errors (Table 2) and 95% CIs (Figure 8) of different precipitation source contributions to further verify the validity of the results. The standard errors for precipitation sources fraction are 0.02–0.04 for advection, 0.01–0.02 for evaporation, and 0.01–0.03 for transpiration. The CIs for precipitation sources vary from different sub-regions. The smallest standard error and CI appear in YZ, owing to its biggest sample number. The CIs of YZ for advection fraction range from 0.88 to 0.93, for evaporation range from 0 to 0.33, and transpiration range from 0.23 to 0.28. As such, low standard errors and narrow CIs further prove the reliability of the estimated results. The standard errors for evaporation fraction and transpiration fraction are relatively smaller than transpiration fraction. The range of CI for the advection fraction is also larger than that for the evaporation and transpiration fraction. These mean that the variations of evaporation and transpiration fraction are smaller, which can be explained by similar meteorological, physiographical, and biological conditions over each sub-region in summer. These results are in agreement with the estimation by Gaussian first-order approximation (Figure 7).
Table 2

Sample numbers and standard errors of advection fraction, evaporation fraction, and transpiration fraction to summer precipitation over SC, YZ, and NC

Sub-regionNPV; NEv; NAdv; NTrSEfEvSEfAdvSEfTr
SC 140 0.02 0.04 0.03 
YZ 233 0.01 0.02 0.01 
NC 156 0.02 0.04 0.01 
Sub-regionNPV; NEv; NAdv; NTrSEfEvSEfAdvSEfTr
SC 140 0.02 0.04 0.03 
YZ 233 0.01 0.02 0.01 
NC 156 0.02 0.04 0.01 
Figure 8

Approximate 95% CIs for advection fraction, evaporation fraction, and transpiration fraction to precipitation in summer over the sub-regions of EC.

Figure 8

Approximate 95% CIs for advection fraction, evaporation fraction, and transpiration fraction to precipitation in summer over the sub-regions of EC.

Close modal

In this study, we investigate how the decaying phase of WP and CT El Niño affect summer precipitation sources over EC from an isotopic perspective. Frequently used CT and WP indexes are employed to classify the two kinds of ENSO events. Water vapor flux, during the decaying phase of WP and CT El Niño, is compared. The three-component mixing model is applied to quantify the spatiotemporal variations of advection and oceanic fractions of regional precipitation during the two kinds of ENSO events. Gaussian first-order approximation, standard error, and 95% CIs are used to verify the accuracy of estimated contributions.

It is found that compared with CT El Niño, the moisture divergence in the decaying phase of WP El Niño shows almost opposite signals over EC with a smaller magnitude for WP El Niño. Spatiotemporal patterns of advection fraction to precipitation during WP El Niño are different from those during CT El Niño. The spatiotemporal pattern of advection fraction anomaly is similar to that of moisture divergence anomaly with opposite signs and to that of oceanic fraction anomaly. The ENSO-induced anomalies can be explained by the strength and location of the WPSH. The largest uncertainty of the estimated anomalies comes from the precipitating vapor, owing to higher variances of the isotopic composition of precipitating vapor. 95% CIs for the estimates are narrow and the standard errors are low, which indicates the robustness of the estimated proportions. These findings provide new insight into how the variations of ENSO in the decaying phase affect the regional water cycle in EC.

This work was partially supported by the National Natural Science Foundation of China (Grant No. 52109007), the Natural Science Foundation of Hubei Province (Grant No. 2020CFA100), the Natural Science Foundation of Chongqing, China (Grant No. cstc2021jcyj-msxm2426), and the Scientific and Technological Research Program of Chongqing Municipal Education Commission (Grant No. KJQN2021007). The authors would like to thank Camille Risi for providing the gridded δOP and δHP for eastern China at a spatial resolution of 50–60 km. The authors also acknowledge Prof. Xuefa Wen from the Chinese Academy of Sciences (CAS) for providing the station observed δOP and δHP for the study area. The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Center of Wuhan University.

P.P.: conceptualization, methodology, software, and writing. Y.Z.: computation, visualization, and editing of the manuscript. J.C.: supervision, writing – review. X.J.Z.: conceptualization and editing of the manuscript. X.L.: conceptualization and writing – review. D.X.: formal analysis and validation.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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