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  • View in gallery

    (a) Climatology and (b) standard deviation of AM precipitation over China for 1951–2014. (c) The ratio of AM precipitation to summer (JJA) precipitation over China represented by percentage. The contour interval is 20 mm month−1 for (a), 10 mm month−1 for (b), and 10% for (c).

  • View in gallery

    The climatological mean of (a) March and (b) AM precipitation (shading, mm day−1) and the vertically integrated water vapor transport [vector, kg (m s)−1]. (c) The difference between (a) and (b) obtained by subtracting (a) from (b).

  • View in gallery

    The linear regression of AM precipitation over China onto its corresponding time series of (a) EOF1 and (c) EOF2 for 1951–2014. The normalized time series (bars) are presented for (b) PC1 and (d) PC2. The black lines in (b) and (d) denote the 9-yr running mean of the time series.

  • View in gallery

    The solid line depicts the normalized area-mean AM precipitation anomalies for 20°–30°N, 110°–130°E (PI), and PC1 is denoted by the dashed line. The TCC between PC1 and PI is 0.98 for the period 1951–2014, which is shown in the upper-right corner of the figure.

  • View in gallery

    The regression maps of (a) 850-hPa wind (m s−1) and (b) water vapor transport [vector, kg (m s)−1] and divergence (shading, mm day−1) onto PC1hf. Red (blue) shading in (a) denotes positive (negative) correlations significant at the 95% confidence level. Stippled regions in (b) denote correlations significant at the 95% confidence level.

  • View in gallery

    The regression maps of SST in (a) OND(−1), (b) January–March [JFM(0)], (c) AM(0), and (d) JJA(0) onto PC1hf. Solid (dashed) contours indicate positive (negative) values. Light and dark red (blue) shading denotes positive (negative) correlation coefficients significant at the 95% and 99% confidence levels, respectively. The contour interval is 0.05°C, and zero contour is omitted.

  • View in gallery

    The lead–lag TCCs between PC1hf and the Niño-3.4 index from the previous December [Dec(−2)] to the following September [Sep(+1)]. The short-dashed lines represent the 95% and 99% confidence levels according to the Student’s t test.

  • View in gallery

    As in Fig. 5, but for the regression maps onto PC2hf.

  • View in gallery

    The regression maps of (a) SLP (hPa) and (b) Z500 (m) onto PC2hf. Solid (dashed) contours represent positive (negative) values. Light and dark red (blue) shading denotes positive (negative) correlation coefficients significant at the 95% and 99% confidence levels, respectively. The contour interval is 0.25 hPa in (a) and 4 m in (b), and zero contour is omitted.

  • View in gallery

    The regression map of the Z500 (contour, m) and the wave activity flux (vector, m2 s−2) onto PC2hf.

  • View in gallery

    The regression maps of SST in (a) February, (b) March, (c) AM, and (d) June onto PC2hf. Solid (dashed) contours represent positive (negative) values. Light and dark red (blue) shading denotes positive (negative) correlation coefficients significant at the 95% and 99% confidence levels, respectively. The contour interval is 0.05°C, and zero contour is omitted.

  • View in gallery

    The lead–lag correlation coefficients between PC2hf and NASDI from the previous October [Oct(−1)] to the following November [Nov(0)]. The short-dashed lines represent the 95% and 99% confidence levels, respectively, according to the Student’s t test.

  • View in gallery

    The linear regression maps of MME forecast of AM precipitation over China onto (a) PC1hf and (b) PC2hf. The contour interval is 3 mm month−1, and zero contour is omitted.

  • View in gallery

    (a) The PC1hf of the observational data (black lines), the P-E model (red lines) and the MME (blue lines). The TCC of PC1hf between the observational data and the P-E model as well as that between the observational data and the MME are indicated on the bottom-left corner. An asterisk indicates correlation significant at the 95% confidence level. Purple plus signs represent the predicted PC1hf derived from the P-E model based on data before 2000. (b) As in (a), but for PC2hf.

  • View in gallery

    (a) The TCC (contours) of AM precipitation between the observational data and the MME forecasts of the six coupled climate models for 1960–2005. (b) As in (a), but for the TCC between observational data and the regression model using time series forecast by the P-E model in (1) and (2). (c) As in (b), but using time series forecast by the P-E model in (1) and (3). Light and dark shading denotes correlation significant at the 90% and 95% confidence levels, respectively.

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Interannual Variations and Prediction of Spring Precipitation over China

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  • 1 School of Earth Sciences, Zhejiang University, Hangzhou, Zhejiang, China
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Abstract

The interannual variations and the prediction of the leading two empirical orthogonal function (EOF) modes of spring (April–May) precipitation over China for the period from 1951 to 2014 are investigated using both observational data and the seasonal forecast made by six coupled climate models. The leading EOF mode of spring precipitation over China (EOF1-prec) features a monosign pattern, with the maximum loading located over southern China. The ENSO-related tropical Pacific SST anomalies in the previous winter can serve as a precursor for EOF1-prec. The second EOF mode of spring precipitation (EOF2-prec) over China is characterized by a dipole structure, with one pole near the Yangtze River and the other one with opposite sign over the Pearl River delta. A North Atlantic sea surface temperature (SST) anomaly dipole in the preceding March is found contribute to the prec-EOF2 and can serve as its predictor. A physics-based empirical (P-E) model is then formulated using the two precursors revealed by the observational analysis to forecast the variations of EOF1-prec and EOF2-prec. Compared to coupled climate models, which have little skill in forecasting the time variations of the two EOF modes, this P-E model can significantly improve the forecast skill of their time variations. A linear regression model is further established using the time series forecast by the P-E model to forecast the spring precipitation over China. Results suggest that the seasonal forecast skill of the spring precipitation over southeastern China, especially over the Yangtze River area, can be significantly improved by the regression model.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: XiaoJing Jia, jiaxiaojing@zju.edu.cn

Abstract

The interannual variations and the prediction of the leading two empirical orthogonal function (EOF) modes of spring (April–May) precipitation over China for the period from 1951 to 2014 are investigated using both observational data and the seasonal forecast made by six coupled climate models. The leading EOF mode of spring precipitation over China (EOF1-prec) features a monosign pattern, with the maximum loading located over southern China. The ENSO-related tropical Pacific SST anomalies in the previous winter can serve as a precursor for EOF1-prec. The second EOF mode of spring precipitation (EOF2-prec) over China is characterized by a dipole structure, with one pole near the Yangtze River and the other one with opposite sign over the Pearl River delta. A North Atlantic sea surface temperature (SST) anomaly dipole in the preceding March is found contribute to the prec-EOF2 and can serve as its predictor. A physics-based empirical (P-E) model is then formulated using the two precursors revealed by the observational analysis to forecast the variations of EOF1-prec and EOF2-prec. Compared to coupled climate models, which have little skill in forecasting the time variations of the two EOF modes, this P-E model can significantly improve the forecast skill of their time variations. A linear regression model is further established using the time series forecast by the P-E model to forecast the spring precipitation over China. Results suggest that the seasonal forecast skill of the spring precipitation over southeastern China, especially over the Yangtze River area, can be significantly improved by the regression model.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: XiaoJing Jia, jiaxiaojing@zju.edu.cn

1. Introduction

Precipitation over China exhibits prominent variability on both interannual and interdecadal time scales. Because of its considerable influence on agricultural and economic productivity, extensive research has been devoted to exploring its mechanisms and predictability (e.g., Lau and Li 1984; Huang et al. 2003; Wang et al. 2003; Gao et al. 2008; Li et al. 2015). Most previous studies focused on investigating summer precipitation anomalies (e.g., Chang et al. 2000; Xie et al. 2009), whereas relatively less attention has been paid to spring precipitation. Spring precipitation over southern China actually accounts for a considerable fraction of the total annual rainfall. Figure 1a presents the climatological mean state of precipitation for the average of April and May (AM) over China. It shows that the AM precipitation has the largest loading over southern China and achieves peak values at two centers, with one located near the Pearl River delta and the other centered over coastal southeastern China, at approximately 28°N. The spatial distribution of the standard deviation of AM precipitation (Fig. 1b) is similar to that of the climatology, indicating strong year-to-year variability of the AM precipitation over southern China. The ratio of the climatological AM precipitation to summer [June–August (JJA)] precipitation over China is depicted in Fig. 1c. It shows that, over southern China, the amount of the AM precipitation is the equivalent of approximately 50%–70% of the summer precipitation. Because the early planting season in southern China is heavily dependent on the amount of spring precipitation, improving the accuracy of the seasonal forecast for spring precipitation will greatly benefit society and the economy (Qiu et al. 2009; Wu et al. 2014; Chen et al. 2014; Feng et al. 2014; Wu and Mao 2016). Moreover, since spring is a transitional season that links the preceding winter to the succeeding summer, a better understanding of the causes of the variations in spring precipitation will provide a fundamental basis for cross-season climate prediction by numerical models.

Fig. 1.
Fig. 1.

(a) Climatology and (b) standard deviation of AM precipitation over China for 1951–2014. (c) The ratio of AM precipitation to summer (JJA) precipitation over China represented by percentage. The contour interval is 20 mm month−1 for (a), 10 mm month−1 for (b), and 10% for (c).

Citation: Journal of Climate 31, 2; 10.1175/JCLI-D-17-0233.1

The mechanisms that account for the variation of precipitation over China have been examined in many previous studies (e.g., Yang and Lau. 2004; Xin et al. 2006; Qiu et al. 2009; Feng and Li. 2011; Wu et al. 2014; Chen et al. 2014; Feng et al. 2014). Previous studies indicate that the precipitation over China can be affected by many anomalous states of lower boundary conditions (e.g., Tao and Chen 1987; Wang et al. 2000, Huang et al. 2004; Wu and Kirtman. 2007; Wu et al. 2009; Zuo et al. 2012; Wu et al. 2014; Feng et al. 2014). Among them, El Niño–Southern Oscillation (ENSO) is one of the most important factors that impacts the precipitation over China on the interannual time scale (e.g., Webster and Yang 1992; Zhang and Sumi 2002; Wang et al. 2003; Xie et al. 2009). Wang et al. (2000) showed that the anomalous sea surface temperature (SST) associated with El Niño in the central-eastern equatorial Pacific induces anomalous convergence in the upper-level eastern tropical Pacific and descent over the Philippine Sea, favoring the development of an anomalous lower-level Philippine Sea anticyclone (PSAC). The anomalous PSAC can act as a medium bridging remote El Niño forcing and variations in the East Asian climate as it enhances southerly winds to its northwest flank and generates above-average precipitation and temperatures over China. The PSAC can persist through local air–sea interactions over the western North Pacific, causing rainfall anomalies over southern China in the ensuing spring and summer. In the work of Feng and Li (2011), it was further shown that canonical El Niño events and central Pacific warming have different effects on spring precipitation over China.

The relationship between spring precipitation over China and the Indian Ocean has also been explored. The remote effect of the tropical Indian Ocean in maintaining the PSAC via capacitor effects has been explored by Xie et al. (2009). Chen et al. (2014) proposed an interdecadal change in the relationship between south Indian Ocean SST anomalies (SSTAs) and interannual variations in winter–spring persistent precipitation over southern China. The effects of Indian Ocean SST played a more central role in modulating the variability in winter–spring persistent precipitation for 1974–94 than in modulating that for 1953–73, which was most likely caused by enhanced SST variation in the south Indian Ocean. The south Indian Ocean subtropical dipole (IOSD) was also found to influence spring precipitation over regions near the Yangtze and Yellow Rivers through the modulation of meridional circulation (e.g., Feng et al. 2014). Some studies have investigated the impact of the Eurasian–Tibetan Plateau snow cover on the rainfall over East Asian. For example, Wu and Kirtman (2007) suggested that anomalous excessive spring precipitation in southern China can be associated with spring snow cover in western Siberia. Zuo et al. (2012) examined the relationship between the snow water equivalent (SWE) and the spring rainfall and found that decreased spring SWE in Eurasia is related to negative spring rainfall anomalies over southeastern and northeastern China, and positive rainfall anomalies over southwestern and northwestern China could be found.

Some other studies investigated the characteristics of the long-term trends of spring precipitation over China. For example, pronounced positive and negative trends of spring precipitation were observed by Yang and Lau (2004) in southeastern China and central-eastern China, respectively. The forcing mechanism accounting for these trends in association with the global SST has been examined. Qiu et al. (2009) demonstrated that the rainfall in May over southeastern China has significantly decreased in recent decades along with a phase transition to La Niña over the Indo-Pacific Ocean. In addition to SSTAs, Wu et al. (2014) reported that mid-to-high-latitude atmospheric circulation anomalies induced by anomalous states of lower boundary conditions, such as Eurasian snow cover and depth, could influence Eurasian climate anomalies in spring and summer.

Although efforts have been devoted to investigating the mechanisms of the variability of spring precipitation over China, questions regarding which of the proposed factors is most essential to understanding its variability still remain. Furthermore, the question of which factors can be used in the seasonal forecasting of the spring precipitation over China has not been well investigated. In the current work, the variations of the spring precipitation over China are investigated through an empirical orthogonal function (EOF) analysis. We concentrate on examining the leading two EOF modes of spring precipitation. Their associated climate anomalies are investigated with a focus on identifying their respective related SST precursors. In addition, empirical models are established according to the results of the observational analysis. We demonstrate that the seasonal forecast skill of the spring precipitation in southern China can be significantly improved by these empirical models.

The organization of the rest of the text is as follows. The datasets, methodology, and model used in this study are introduced in section 2. Section 3 presents the characteristics of the climatology of spring precipitation over China. Section 4 examines the features of the first two EOF modes of spring precipitation, as well as their associated atmospheric circulation and SST anomalies. In section 5, the forecast skill of the multimodel ensemble (MME) of six coupled climate models in predicting the variation of spring precipitation is examined. Empirical models are then established trying to improve the seasonal forecast skill of spring precipitation. A comprehensive summary of the results and a discussion are presented in section 6.

2. Data, methodology, and models

a. Observational data and methodology

The datasets used in this study include the following: 1) monthly mean reanalyses of sea level pressure (SLP), geopotential heights, specific humidity, and wind from the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) Global Reanalysis 1 (Kalnay et al. 1996) (these data are available at a horizontal resolution of 2.5° × 2.5°); 2) the observed monthly mean precipitation of 160 Chinese meteorological stations, obtained from the National Climate Center of the China Meteorological Administration; 3) the NOAA Extended Reconstructed SST, version 3b (ERSST.v3b; Smith et al. 2008), which has a resolution of 2.0° × 2.0°; and 4) the Global Precipitation Climatology Project (GPCP) monthly mean precipitation data with a horizontal resolution of 2.5° × 2.5° (Adler et al. 2003). All the datasets used in the study cover the period 1951–2014 except for the GPCP, which covers the period 1979–2014.

An EOF analysis was applied to the spring precipitation over China to subtract the leading EOF modes. To determine the climate anomalies associated with these leading EOF modes, Pearson correlation and regression analyses were carried out between a physical field and the time series associated with these EOF modes. The ENSO variability is represented by averaging the SST over the Niño-3.4 region (5°S–5°N, 170°–120°W).

To explore the mechanism behind the connection between large-scale circulation anomalies and the spring precipitation over China, a phase-independent wave activity flux proposed by Takaya and Nakamura (2001) was used. The flux is based on the conservation of wave activity pseudomomentum and is a useful diagnostic tool for identifying sources or sinks of wave activity. In the current work, the horizontal component of this flux is calculated which is can be given by
eq1
where ψ is the streamfunction and subscripts are partial derivatives in x and y directions; also, U = (U, V) denotes the 500-hPa two-dimensional geostrophic zonal and meridional velocity components for spring mean flow and primes denote deviations from the climatology.

b. Models

The MME of the seasonal forecast of six coupled climate models was used to examine the performance of the numerical models in predicting the variation of spring precipitation over China. The climate numerical models used include the CAWCR from the Asia–Pacific Economic Cooperation (APEC) Climate Center/Climate Prediction and Its Application to Society (APCC/CliPAS) project and the CMCC–INGV, ECMWF, Leibniz Institute of Marine Sciences at Kiel University (IFM-GEOMAR), Météo-France (MF), and Met Office (UKMO) models from the Ensemble-Based Predictions of Climate Changes and Their Impacts (EMSEMBLES) project (Weisheimer et al. 2009; Wang et al. 2009a,b; Jia et al. 2014a,b). None of the coupled models has flux adjustments. The seasonal forecasts from March to May, initialized from the first day of February for the period from 1960 to 2005, are used in the current study. For the 1-month lead seasonal forecasts, the first month of the integration was not used, as we are mainly interested in the forecast signal coming from air–sea coupling rather than the atmospheric initial conditions. The MME predictions were made based on the equally weighted average of the six coupled climate model ensemble mean anomalies after removing their own climatology. Further information about the climate models can be found in Jia et al. (2014a,b).

3. The climatology of spring precipitation in China

In this section, we first present evidence to show that there are remarkable differences in the mean state and the temporal variations of the precipitation over southern China between March and the following AM. The characteristics and variations of the precipitation over southern China in March are more closely related to those in the preceding January–February (JF). Therefore, AM rather than March–May (MAM) is used to represent the spring season in the current study.

The climatological mean precipitation (shading) and the vertically integrated moisture fluxes (vector) for March and AM are presented in Figs. 2a and 2b, respectively. The differences between Figs. 2a and 2b are depicted in Fig. 2c, which was obtained by subtracting Fig. 2a from Fig. 2b. As shown in Fig. 2a, the climatological mean precipitation in March is primarily concentrated over southeastern China. The moisture transportation to China in March mainly includes two branches. One branch is with a southwesterly along the northern flank of the western Pacific subtropical high (WPSH), and the other branch is along a subtropical westerly at 30°N. From March to AM, the magnitude of the time-averaged precipitation obviously increased in magnitude, with the maximum center of the precipitation moving southwestward to the Pearl River delta (Fig. 2b). Differences in the water vapor transportation to southern China are also observed between AM and the previous March. The center of the WPSH moves eastward from approximately 120°E in March to 135°E in AM, causing an eastward shift of the associated moisture transport on its northwestern flank. Moisture transportation along the subtropical westerly at 30°N still remains in AM. A third branch of water vapor transport can be observed originating from southern India, traveling across the whole of Indo-China and finally reaching southern China. Differences in the transportation of water vapor between March and AM can be even more clearly seen from Fig. 2c. Notable water vapor transportation from the Indian Ocean to southern China in AM is observed, denoting that the Indian Ocean plays a more important role in the variation in precipitation over southern China in AM compared to that in the previous March.

Fig. 2.
Fig. 2.

The climatological mean of (a) March and (b) AM precipitation (shading, mm day−1) and the vertically integrated water vapor transport [vector, kg (m s)−1]. (c) The difference between (a) and (b) obtained by subtracting (a) from (b).

Citation: Journal of Climate 31, 2; 10.1175/JCLI-D-17-0233.1

To further understand the variations of the characteristics of spring precipitation over China, an EOF analysis was performed on the monthly mean precipitation of 160 Chinese meteorological stations from January to May to identify the dominant precipitation patterns. The leading two EOF modes can distinct from one another and from the remaining eigenvectors according to the criterion defined by North et al. (1982). The spatial distributions of the leading EOF mode (EOF1) of the monthly mean precipitation and their corresponding principal component (PC1) from January to May were examined. It was found that the spatial patterns of EOF1 share many similarities from January to May (not shown). Table 1 presents the fractions of variance explained by the EOF1, represented using percentages. It shows that the variance explained by EOF1 exceed 40% for January, February, and March, while it decreases drastically to 26% and 22% in April and May, respectively. The drastic decline from March to AM in the percentage of the variance explained by EOF1 might suggest a greater similarity of the variability in precipitation in March to the previous January and February than to the following April and May.

Table 1.

The fraction of the variance explained by EOF1 of the monthly mean precipitation over China from January to May during 1951–2014 represented using percentages.

Table 1.

The temporal correlation coefficients (TCCs) of the principal components (PCs) associated with the leading two EOF modes among various months for the period of 1951–2014 are presented in Table 2. The TCC for the PC1 between March and JF is 0.27, statistically significant at the 95% confidence level. The TCC is, however, not significant and presents a value of only 0.09 between March and AM, which cannot pass the significance test. The TCC between the PC2 for March and AM is only 0.12 and does not pass the significance test, whereas it is 0.24 between March and JF, which is again significant at the 95% confidence level.

Table 2.

TCCs of PC1hf and PC2hf between JF and March, between March and AM, and between April and May. An asterisk indicates correlation significant at the 95% confidence level.

Table 2.

In summary, the above results indicate that the characteristics of water vapor transportation to China in March are quite different from those in AM. The temporal variation of the precipitation in March bears more similarities to those of the previous JF than to those of the succeeding AM. Therefore, in the following, AM is used to represent the spring season for precipitation over China. The TCC between the PC1 obtained using AM and that obtained using MAM is 0.90 while that for PC2 is 0.91; both are significant at the 99% significant level according to the Student’s t test examination. Therefore we believe that the results are not very sensitive to the choice of the selected months in the current work although differences also exist.

4. The leading two modes of spring precipitation and associated climate anomalies

a. Characteristics of the leading two EOF modes of the spring precipitation

In this section, we continue concentrate on exploring the two leading modes of AM precipitation (EOF1-prec and EOF2-prec) to investigate the characteristics of spring precipitation over China and their associated climate anomalies. EOF1-prec and EOF2-prec account for 26% and 17% of the total variance in spring precipitation, respectively, and are distinct from one another and from the remaining eigenvectors according to the criterion defined by North et al. (1982). The normalized principal components of the leading two EOF modes (PC1 and PC2, respectively) are presented as bars in Figs. 3b and 3d, respectively. The 9-yr running averages of PC1 and PC2 are also overlaid in Figs. 3b and 3d, represented by solid black lines. Both PC1 and PC2 show clear interannual and interdecadal variability for the period under examination. The spatial distributions of the leading two EOF modes, represented by linear regression of the spring precipitation over China onto PC1 and PC2, are presented in Figs. 3a and 3c, respectively. The spatial distribution of EOF1-prec features a monosign pattern with the maximum loading located over southern China and with amplitudes decreasing from southeast to northwest (Fig. 3a). A positive (negative) EOF1-prec is represented by enhanced (reduced) rainfall anomalies in southern China. To understand the representativeness of EOF1-prec for spring precipitation over China, an index is constructed using the normalized area-averaged AM precipitation over 20°–30°N, 110°–120°E [precipitation index (PI); Fig. 4]. The TCC between PC1 and PI is 0.98 for the period 1951–2014, far exceeding the 99% significance level, indicating that PC1 can serve as a reasonable indicator of the variation in the spring precipitation over southern China. The main feature of EOF2-prec is a north–south dipole structure with an out-of-phase relationship between the Pearl River delta and the middle and lower reaches of the Yangtze River (Fig. 3c). A positive (negative) phase of EOF2-prec is featured by enhanced (suppressed) precipitation over the Yangtze River and suppressed (enhanced) precipitation over the Pearl River delta.

Fig. 3.
Fig. 3.

The linear regression of AM precipitation over China onto its corresponding time series of (a) EOF1 and (c) EOF2 for 1951–2014. The normalized time series (bars) are presented for (b) PC1 and (d) PC2. The black lines in (b) and (d) denote the 9-yr running mean of the time series.

Citation: Journal of Climate 31, 2; 10.1175/JCLI-D-17-0233.1

Fig. 4.
Fig. 4.

The solid line depicts the normalized area-mean AM precipitation anomalies for 20°–30°N, 110°–130°E (PI), and PC1 is denoted by the dashed line. The TCC between PC1 and PI is 0.98 for the period 1951–2014, which is shown in the upper-right corner of the figure.

Citation: Journal of Climate 31, 2; 10.1175/JCLI-D-17-0233.1

Some previous studies have noted that mechanisms accounting for the climate anomalies over East Asia are different for interannual and interdecadal time scales. In the current study, we focus on examining the interannual variations of spring precipitation over China and therefore a Fourier harmonic bandpass filter is used to remove the variations with periods longer than 10 yr. Only the time scales between 2 and 9 yr have been kept in the data and are denoted by subscript “hf” in the following sections. This will also reduce the influence of plausible unrealistic interdecadal data variation on the interannual relationship (e.g., Inoue and Matsumoto 2004; Wu et al. 2005). The results obtained are not sensitive to the filter method used in the current study.

b. Large-scale circulation anomalies associated with EOF1-prec

We first examine the large-scale circulation anomalies associated with EOF1-prec. The regression maps of the winds at 850 hPa onto PC1hf are depicted in Fig. 5a. The EOF1-prec associated water vapor transportation and the divergence are calculated and presented in Fig. 5b. In Fig. 5a, associated with a positive EOF1-prec an anomalous lower-level anticyclone is clearly seen centered at 20°N, 140°E. Anomalous southwesterlies are observed along the northwest of the anomalous anticyclonic system transporting moisture from the western tropical Pacific Ocean to southern China (Fig. 5b). Water vapor converges in southeastern China consistent with the enhanced precipitation over there during a positive EOF1-pr.

Fig. 5.
Fig. 5.

The regression maps of (a) 850-hPa wind (m s−1) and (b) water vapor transport [vector, kg (m s)−1] and divergence (shading, mm day−1) onto PC1hf. Red (blue) shading in (a) denotes positive (negative) correlations significant at the 95% confidence level. Stippled regions in (b) denote correlations significant at the 95% confidence level.

Citation: Journal of Climate 31, 2; 10.1175/JCLI-D-17-0233.1

As we mentioned in the introduction, many previous studies have revealed that ENSO is one of the most important factors that influences precipitation anomalies in southern China (e.g., Li 1988; Wang et al. 2000; Xie et al. 2009; Jia et al. 2014a,b). The PSAC is the key system that bride remote ENSO forcing to East Asian climate variation (e.g., Wang et al. 2000; Xie et al. 2009), which can also be seen from Fig. 5. To see the time evolution impact of ENSO on EOF1-prec, the EOF1-prec-related tropical Pacific SSTAs from the preceding autumn {October–December, for year −1 [OND(−1)]} to the succeeding summer [JJA(0)] are presented in Fig. 6. Corresponding to a positive EOF1-prec, in the preceding autumn (Fig. 6a), significant El Niño–like SSTAs dominate the eastern tropical Pacific. Meanwhile, pronounced negative SSTAs are noticed over the western tropical Pacific. In the following winter (Fig. 6b), to the west flank of the western tropical Pacific SSTAs, a horseshoe-like positive SSTA appears, forming a tropical Pacific tripolar SSTA pattern. In the following spring (Fig. 6c), the eastern tropical Pacific SSTAs decrease sharply and almost disappear. In contrast, the western tropical Pacific negative SSTAs and the positive SSTAs around southern China remain throughout this season, with the positive SSTAs strengthening in amplitude and reaching their peak. The eastern tropical Pacific SSTAs transfer to a negative phase in the following summer and combine with the negative SSTAs over the western tropical Pacific, forming La Niña–like SSTAs (Fig. 6d). The positive SSTAs around the South China Sea still remain in the following summer and weaken in amplitude.

Fig. 6.
Fig. 6.

The regression maps of SST in (a) OND(−1), (b) January–March [JFM(0)], (c) AM(0), and (d) JJA(0) onto PC1hf. Solid (dashed) contours indicate positive (negative) values. Light and dark red (blue) shading denotes positive (negative) correlation coefficients significant at the 95% and 99% confidence levels, respectively. The contour interval is 0.05°C, and zero contour is omitted.

Citation: Journal of Climate 31, 2; 10.1175/JCLI-D-17-0233.1

Compared to the SSTAs over the eastern tropical Pacific, which adapt from an El Niño–like SSTA pattern in the preceding autumn to a La Niña–like SSTA pattern in the following summer, the western tropical Pacific negative SSTAs and the positive SSTAs on its northwest persist from wintertime to the following summer. As suggested by previous studies (e.g., Wang et al. 2000, 2003), during positive ENSO events, in the ensuing spring and summer, as the eastern tropical Pacific positive SSTAs decay rapidly, over the western tropical Pacific the western tropical Pacific anticyclone can be maintained through local air–sea interactions. More specifically, anomalous northeastern winds on the east side of the anticyclonic system strengthen climatological trade winds, enhancing the latent heat flux from the ocean to the atmosphere and cooling its underlying SST. Negative SSTAs excite anomalous westward propagating and descending Rossby waves, reinforcing the anticyclonic system in return. On the west side of the negative SSTAs, southerlies prevail along the western flank of the anticyclonic system, transporting warm air from low-latitude oceans and contributing to the positive SSTAs around the South China Sea and surrounding regions.

To better understand the time relationship between EOF1-prec and ENSO, the lead–lag correlation between PC1hf and the Niño-3.4 index is depicted in Fig. 7. It shows that the simultaneous TCC between the PC1hf and the Niño-3.4 index is not significant and, with a value of only 0.12, is consistent with Fig. 6c. The TCC between the PC1hf and the Niño-3.4 index in the previous seasons, however, is significant at the 95% confidence level and can be traced back to the time when the Niño-3.4 SST leads PC1hf by 12 months. The TCC between PC1hf and Niño-3.4 index remains significant until the following year, but with a reversed sign. The above analysis indicates that the Niño-3.4 SST in the previous season can serve as a good predictor for the EOF1-prec for seasonal forecasting.

Fig. 7.
Fig. 7.

The lead–lag TCCs between PC1hf and the Niño-3.4 index from the previous December [Dec(−2)] to the following September [Sep(+1)]. The short-dashed lines represent the 95% and 99% confidence levels according to the Student’s t test.

Citation: Journal of Climate 31, 2; 10.1175/JCLI-D-17-0233.1

c. Large-scale circulation anomalies associated with EOF2-prec

Similar to Fig. 5, the EOF2-prec related climate circulation anomalies are presented in Fig. 8. The EOF2-prec associated winds at 850 hPa (Fig. 8a) show that southwesterlies prevail along the coastal area of eastern China from 20° to 30°N while southeasterlies dominate from 30° to 40°N. The winds along the coastal area turn to southwesterlies over the regions north of the Yangtze River. The spatial distribution of the EOF2-prec-related water vapor transport (Fig. 8b) suggests a low-level cyclonic southerly wind inducing anomalous moisture convergence over coastal China north of 30°N and moisture divergence over southeastern China, consistent with the north–south dipole structure of EOF2-prec, which shows an out-of-phase relationship between the Pearl River delta area and the middle and lower reaches of the Yangtze River region.

Fig. 8.
Fig. 8.

As in Fig. 5, but for the regression maps onto PC2hf.

Citation: Journal of Climate 31, 2; 10.1175/JCLI-D-17-0233.1

Compared to the ENSO-related PSAC that can impact the EOF1-prec, the circulation anomalies that contribute to the EOF2-prec are not well explored. The regression maps of SLP and 500-hPa geopotential height (Z500) onto PC2hf are further examined and displayed in Figs. 9a and 9b, respectively. At the surface, pronounced positive SLP anomalies are clearly seen dominating the midlatitude western North Pacific, inducing cyclonic anomalous southerly wind over the coastal regions of China along its western flank (Fig. 9a). In the midtroposphere, the atmospheric circulation anomalies over the mid-to-high latitudes of the Northern Hemisphere are dominated by a wave train–like structure, originating from the North Atlantic Ocean, across the Ural Mountains, propagating eastward across the whole Eurasian continent (Fig. 9b). As we mentioned before, the wave activity flux proposed by Takaya and Nakamura (2001) is a good diagnostic tool for exploring the source region of atmospheric motion. To trace the source of the EOF2-prec related wave train–like circulation anomalies in Fig. 9b, the wave activity flux at Z500 associated with PC2hf is calculated and presented in Fig. 10 represented by vectors. The regression of Z500 onto PC2hf is also overlaid on this map (contours). It shows that the wave activity flux originates from the midlatitude North Atlantic Ocean, flows eastward across the Eurasian continent, and reaches the Japan Sea. This distribution of the wave activity flux indicates that the North Atlantic Ocean may serve as a possible energy source region for the EOF2-prec-related wave train–like circulation anomalies.

Fig. 9.
Fig. 9.

The regression maps of (a) SLP (hPa) and (b) Z500 (m) onto PC2hf. Solid (dashed) contours represent positive (negative) values. Light and dark red (blue) shading denotes positive (negative) correlation coefficients significant at the 95% and 99% confidence levels, respectively. The contour interval is 0.25 hPa in (a) and 4 m in (b), and zero contour is omitted.

Citation: Journal of Climate 31, 2; 10.1175/JCLI-D-17-0233.1

Fig. 10.
Fig. 10.

The regression map of the Z500 (contour, m) and the wave activity flux (vector, m2 s−2) onto PC2hf.

Citation: Journal of Climate 31, 2; 10.1175/JCLI-D-17-0233.1

Inspired by Fig. 10, the EOF2-prec-related North Atlantic SSTAs from the previous February to the following June are examined and presented in Fig. 11. Statistically significant negative SSTAs over the subpolar North Atlantic and positive SSTAs to the south are observed in spring (Fig. 11c). This north–south SSTAs dipole can be traced back to the preceding February and peaks in magnitude in the preceding March and almost disappears in the following June. These results indicates that the SSTA over subtropical–midlatitude North Atlantic is a possible source of the wave train–like circulation anomalies. A North Atlantic SST dipole index (NASDI) is then defined by differences in normalized SSTAs between 35°–45°N, 65°–45°W and 50°–60°N, 50°–10°W. This index is not very sensitive to the region selected above. Figure 12 presents the lead–lag temporal correlation coefficients between PC2hf and the NASDI. It shows that the TCC between PC2hf and the NASDI exceeds the 95% confidence level when the NASDI leads PC2hf by two months. The TCC reaches its maximum in March and then decreases with time, consistent with Fig. 11. The above analysis suggests that the SSTA dipole over the North Atlantic Ocean of the previous March can excite a stationary Rossby wave train–like atmospheric pattern that propagates eastward to the downstream region. The positive anomalies of the wave train–like atmospheric pattern over the western tropical Pacific can influence the moisture transport from the low-latitude ocean to southern China in the following AM, implying that the North Atlantic SSTAs dipole in the previous March could serve as a predictor for the EOF2-prec. The TCC between the NASDI and the NAO index in the previous winter has been examined. It shows that the TCC between NASDI in March is significantly correlated to the NAO index in the previous February and January with TCCs of 0.44 and 0.41, respectively, significant at the 99% significant level, indicating that the North Atlantic SST dipole may be the result of the NAO from the previous winter. However, more studies are needed to better understand their mechanisms.

Fig. 11.
Fig. 11.

The regression maps of SST in (a) February, (b) March, (c) AM, and (d) June onto PC2hf. Solid (dashed) contours represent positive (negative) values. Light and dark red (blue) shading denotes positive (negative) correlation coefficients significant at the 95% and 99% confidence levels, respectively. The contour interval is 0.05°C, and zero contour is omitted.

Citation: Journal of Climate 31, 2; 10.1175/JCLI-D-17-0233.1

Fig. 12.
Fig. 12.

The lead–lag correlation coefficients between PC2hf and NASDI from the previous October [Oct(−1)] to the following November [Nov(0)]. The short-dashed lines represent the 95% and 99% confidence levels, respectively, according to the Student’s t test.

Citation: Journal of Climate 31, 2; 10.1175/JCLI-D-17-0233.1

Wave train–like patterns originating from the North Atlantic and propagating downstream to East Asia have been observed in several previous studies (e.g., Watanabe. 2004; Wu et al. 2009; Wu et al. 2011; Li et al. 2015). For example, a significant wet trend in winter was observed after the 1970s over the Yangtze River by Li et al. (2015). They related the wet trend to a wave train–like pattern originating in western Europe and extending downstream to northeastern China (Li et al. 2015; their Fig. 6). Wu et al. (2009) suggested that a tripolar SST pattern over the North Atlantic in the spring could persist into the following summer and excite a downstream teleconnection pattern prevailing along the poleward flank of the westerly jet and that this pattern could affect the Asian subtropical front of the following summer. Watanabe (2004) showed that in February, when the NAO accompanies the Mediterranean convergence anomaly, the NAO signals can extend downstream through a wave train–like pattern along the Asian jet stream. In the current study, we further demonstrate that, on the interannual time scale, the SSTA dipole over the North Atlantic Ocean of the previous winter can excite a stationary Rossby wave train–like pattern that propagates eastward and contributes to precipitation over China in the following spring. More specifically, it can contribute to the second EOF mode of spring precipitation over China by modulating the circulation anomalies over the western North Pacific regions.

5. The seasonal prediction of spring precipitation over China

In this section, the seasonal prediction of spring precipitation over China is examined. The performance of six coupled climate models in predicting the variations in spring precipitation over China is first examined. We focus on examining the capacity of the numerical models’ to forecast the leading two EOF modes of spring precipitation. A physics-based empirical (P-E; Wu et al. 2009; Yim et al. 2014; Wang et al. 2015) model is then established according to the observational analysis results presented in the last section with the aim to improve the seasonal forecast skill of spring precipitation over China.

An EOF analysis similar to that used for the observational data was applied to the MME forecast of the AM precipitation over China for the period of 1960–2005 to obtain the forecast leading two EOF modes. The regression maps of the MME forecast AM precipitation onto the associated normalized time series have been presented in Fig. 13. It shows that the forecast leading two EOF modes of the spring precipitation share many similarities to EOF1-prec and EOF2-prec in the observations, indicating that the MME of the six coupled numerical models can reasonably capture the spatial structures of the observed leading two EOF modes of the spring precipitation over China. However, the TCC between the forecast PC1hf and the observed PC1hf is only 0.08 while that between the forecast PC2hf and the observed PC2hf is 0.07, indicating that the numerical models present almost no capacity to predict variations of the leading two EOF modes in the observations.

Fig. 13.
Fig. 13.

The linear regression maps of MME forecast of AM precipitation over China onto (a) PC1hf and (b) PC2hf. The contour interval is 3 mm month−1, and zero contour is omitted.

Citation: Journal of Climate 31, 2; 10.1175/JCLI-D-17-0233.1

In the following, to reveal the ability of the numerical models’ forecasting the leading two EOF modes of the observational precipitation over China, the MME forecast of AM precipitation is projected onto the observational EOF1-prec and EOF2-prec as shown in Figs. 3a and 3c, respectively, to obtain the MME forecast time series of the observational EOF1-prec and EOF2-prec. The normalized forecast time series, namely MME-PC1hf and MME-PC2hf, are presented as blue lines in Figs. 14a and 14b, respectively. The corresponding time series of observational PC1hf and PC2hf are also overlaid as black lines in Fig. 14 for the purpose of a better comparison. The TCC between MME-PC1hf and PC1hf is only 0.03, whereas that between MME-PC2hf and PC2hf is only 0.07 for the period 1960 to 2005; both cannot pass the significance test, indicating that the MME forecast AM precipitation presents almost no capacity to predict the variations of the observational leading two EOF modes of spring precipitation over China.

Fig. 14.
Fig. 14.

(a) The PC1hf of the observational data (black lines), the P-E model (red lines) and the MME (blue lines). The TCC of PC1hf between the observational data and the P-E model as well as that between the observational data and the MME are indicated on the bottom-left corner. An asterisk indicates correlation significant at the 95% confidence level. Purple plus signs represent the predicted PC1hf derived from the P-E model based on data before 2000. (b) As in (a), but for PC2hf.

Citation: Journal of Climate 31, 2; 10.1175/JCLI-D-17-0233.1

In the last section, the analysis results suggest that the Niño-3.4 index in the previous season can serve as a predictor for PC1hf, whereas the NASDI in the previous March is significantly correlated with PC2hf. In the following, the Niño-3.4 index (NINO34) of the previous winter and the NASDI in the preceding March are selected to construct a physics-based empirical model using the linear regression method to predict the observational PC1hf and PC2hf. The P-E model is expressed as
e1
e2
where a0, a1, b0, and b1 are regression coefficients, and ε1 and ε2 are the residuals.

Following Wu et al. (2009) and Lin and Wu (2011), a “leaving 16 out” strategy is used to determine the robustness of the hindcast results for the period of 1951–2014. The cross-validation method systematically removes 16 years. A forecast model is then derived for the remaining years and can be tested on the omitted cases. In the present study, 16 years account for about 25% of the total hindcast period (64 yr), which can prevent overfitting and data wasting in the process. To test the predictive capability of the P-E model, the cross-validation method is applied to perform the hindcast. The cross-validated estimates of PC1hf and PC2hf are shown as red lines in Fig. 14. The TCC between the hindcast PC1hf from the P-E model and the observational PC1hf (black line) is 0.35, which exceeds the 99% confidence level. The TCC between the hindcast PC2hf from the P-E model and the observational PC2hf is 0.26, with a statistical significance t test P value of 0.001, again, statistically significant at the 99% confidence level. Therefore, the results indicate that the P-E model clearly performs better in predicting the variations of the observational EOF1-prec and EOF2-prec than the MME forecasts of the six coupled climate models for the period under examination.

To further evaluate the real forecasting ability of the P-E model, another experiment was performed. A similar P-E model is established; however, it is trained using the data before 2001. This P-E model is then used to do predictions for 2001–14 (shown as purple plus signs in Fig. 14). The result shows that the P-E model can still reasonably predict the variation of EOF1-prec and EOF2-prec for this period. Some extreme years of PC1, such as 2010 and 2011, are predicted well by the P-E model, although at a smaller magnitude compared to the observations, possibly due to fewer predictors in the equations.

As we know, in the Northern Hemisphere various active teleconnection patterns over the mid-to-high latitudes can affect climate variations (e.g., Wallace and Gutzler 1981). Some of these may also be related to the pattern shown in Fig. 9. We examined the TCCs between the PC2hf and the indices of several well-known teleconnection patterns that have been identified by previous studies. Results show that the TCC between PC2hf and the index of the west Pacific (WP) pattern (WPI) reaches 0.34, statistically significant beyond the 99% confidence level. As the WP pattern dominates the subtropical northwestern Pacific and Kamchatka (figures not shown), displaying as a dipole structure in the north–south direction, and the PCC between WP and EOF2-prec reaches 0.38. Therefore, we speculate that the WP may interact with the North Atlantic SSTA dipole forced wave train–like pattern over the western North Pacific and together contribute to EOF2-prec variations. Specific mechanisms of this interaction are, however, beyond the scope of the present study and may be investigated in future studies. The TCC between the WPI and NASDI is not significant, suggesting that they can serve as an independent precursor for EOF2-prec. The P-E model for PC2 is now written in the following format:
e3

The TCC between the time series of the P-E model in (3) predicted and the observational PC2 is now increased to 0.39, which is statistically significant at the 99% confidence level. It suggests that the WP can clearly improve the seasonal forecast skill for the time variation of EOF2-prec.

Inspired by the above results that show that the P-E model can significantly improve the forecast skill of the time evolution of the observational EOF1-prec and EOF2-prec, in the following another regression model is constructed aim to improve the seasonal forecasting of spring precipitation over China. In essence, this approach uses the preforecast time series from the P-E model [(1) and (2)] and the spatial structures of the observational EOF1-prec and EOF2-prec to construct a new set of seasonal forecasts. The regression model can be written as
e4
where PC1 and PC2 are the time series obtained from the P-E model [(1) and (2)]. EOF1 and EOF2 are the spatial structures of the observational EOF1-prec and EOF2-prec, respectively, and ε is the residual and is omitted in the current calculations. Again, a cross-validation method is used when calculating PC1 and PC2 to mimic an operational environment.

The correlation skill of the above regression model in forecasting spring precipitation over China is depicted in Fig. 15b and that for the MME forecast AM precipitation of the six coupled climate models is presented in Fig. 15a. The results show that the TCC skill of the MME forecast AM precipitation barely exceeds the 90% confidence level over southern China, where the climatological mean and the variations have the largest loadings. In contrast, the TCC skill of the regression model is clearly improved over southern China, especially over the Yangtze River area. The results suggest that the regression model can efficiently improve the seasonal forecasting of spring precipitation over southern China by improving the forecast skill of the time series of the observational EOF1-prec and EOF2-prec. Another linear regression model is established using PC1 forecast by P-E model in (1) and PC2 forecast by P-E model in (3). The result of the TCC skill (Fig. 15c) shows that the spring precipitation forecast skill has been further improved over the southern China area.

Fig. 15.
Fig. 15.

(a) The TCC (contours) of AM precipitation between the observational data and the MME forecasts of the six coupled climate models for 1960–2005. (b) As in (a), but for the TCC between observational data and the regression model using time series forecast by the P-E model in (1) and (2). (c) As in (b), but using time series forecast by the P-E model in (1) and (3). Light and dark shading denotes correlation significant at the 90% and 95% confidence levels, respectively.

Citation: Journal of Climate 31, 2; 10.1175/JCLI-D-17-0233.1

6. Summary and discussion

Using both observational data and the MME of the 1-month-lead seasonal forecast made by six coupled climate models, the interannual variations and the prediction of spring precipitation over China are examined. Evidence has been first presented to show that the precipitation over China in spring is better represented by that of April–May (AM). The characteristics and the variations of the precipitation in March show a closer relationship with those of the previous January–February (JF) compared to those of the following AM. Therefore, AM is used to represent the spring season for the precipitation over China in the current study. The interannual variations of spring precipitation over China are investigated by applying an EOF analysis to the observational precipitation data. Results show that EOF1-prec is characterized by a monosign pattern with dominant loading over southern China. A positive EOF1-prec, which features enhanced rainfall in southern China, is closely related to El Niño–type SSTAs over the eastern tropical Pacific in the preceding winter. The EOF2-prec, which features a north–south dipole pattern of opposite signs centered over the Yangtze River and Pearl River delta, is significantly correlated with equivalent wave train–like circulation anomalies in northern mid-to-high latitudes. This wave train–like pattern can impact the variations of spring precipitation over China by modulating the circulation anomalies over the western North Pacific, causing anomalous moisture convergence over the Yangtze River alongside divergence conditions in southern China. The prec-EOF2 is found to be related to a meridional SST anomaly dipole over the North Atlantic, which can be traced back to the previous March.

The MME forecast AM precipitation of six coupled climate models show almost no skill in predicting the variations of EOF1-prec and EOF2-prec. A physics-based empirical (P-E) model was formulated based on the results of the observational analysis to improve the seasonal forecast skill. It has been demonstrated that this P-E model performs much better in predicting the time series of EOF1-prec and EOF2-prec. A linear regression model is further constructed using the P-E model predictions to forecast the spring precipitation over China. Results suggest that the forecast skill of the spring precipitation over southeastern China, especially over the Yangtze River area, can be significantly improved by the regression model. The precursors used in the experiments described above can be easily monitored in real time; therefore, the proposed P-E model and the regression model can serve as an efficient real-time forecasting tool for spring precipitation over southern China.

It is worth noting that in this study we only examined the first two EOF modes of spring precipitation over China, which together explain approximately 43% of the total variation in precipitation for 1951–2014. The remaining EOF modes of spring precipitation over China are not discussed. However, over southern China, the EOF1-prec and EOF2-prec together can explain more than 70% of the variability over the Yangtze River and more than 60% of variability over southeastern China for spring precipitation (figure not shown). This may explain why the improved forecasting skill of the time series associated with prec-EOF1 and prec-EOF2 could efficiently improve the seasonal forecasting of spring precipitation over these regions. Furthermore, in the current study, the underlying assumption of the P-E model and the linear regression model is that the EOF modes remain relatively stable during the period under examination. However, some previous studies suggest that the precipitation over China undergoes significant decadal modulations (e.g., Xin et al. 2006; Qiu et al. 2009; Wu and Mao 2016). For example, Xin et al. (2006) suggested that late spring precipitation in southern China decreased significantly after the late 1970s in association with upper troposphere anomalous cooling over central China. Qiu et al. (2009) showed that late spring rainfall over southeastern China has decreasing by more than 30% since 1951. Wu and Mao (2016) also showed that the impact of ENSO on spring precipitation in southern China can be modulated by the Pacific decadal oscillation (PDO) through effects of variations in a low-level subtropical anticyclonic system over the western North Pacific. Additional studies on interdecadal changes of the predictive capacity of the P-E model and the regression model may therefore need to be conducted in a future study.

Acknowledgments

This research was funded by National Natural Science Foundation of China (Grants 41475065 and 41530425) and by the Fundamental Research Funds for the Central Universities. The authors would like to acknowledge the support from the training center of atmospheric sciences of Zhejiang University.

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