North China Spring Rainfall and Its Linkage with SST and Atmospheric Circulation

Lin Shang aKey Laboratory for Meteorological Disaster Prevention and Mitigation of Shandong, Jinan, China
bEarth and Climate Sciences, Nicholas School of Environment Sciences, Duke University, Durham, North Carolina
cShandong Climate Center, Jinan, China

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Wenhong Li bEarth and Climate Sciences, Nicholas School of Environment Sciences, Duke University, Durham, North Carolina

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Abstract

Spring rainfall is important for agriculture and economics in North China (NC). Thus, there is an imperative need for accurate seasonal prediction of the spring precipitation. This study implements a novel rainfall framework to improve understanding of NC spring rainfall. The framework is built based on a three-cluster normal mixture model. Distribution parameters are sampled using Bayesian inference and a Markov chain Monte Carlo algorithm. The probability behaviors of light, moderate, and heavy rainfall events can be reflected by the three rainfall clusters, respectively. Analysis of 61-yr data indicates that moderate rainfall makes the largest contribution (67%) to the total rainfall amount. The moderate rainfall intensity is mainly influenced by the sea surface temperature anomaly (SSTA) in the previous season over the equatorial eastern Pacific, and rainfall frequency is influenced by geopotential height anomaly in the mid- to high latitudes in spring. It is also found that more extreme precipitation events can be observed in the spring following an eastern Pacific El Niño event in the previous autumn and winter. Based on these relationships, we develop a multiple linear regression model. Hindcasts for spring precipitation using the model indicates that its anomaly correlation is 0.48, significant at the 99% confidence level. The result suggests that the newly developed model can well predict spring rainfall amount in NC.

© 2022 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: Wenhong Li, wenhong.li@duke.edu

Abstract

Spring rainfall is important for agriculture and economics in North China (NC). Thus, there is an imperative need for accurate seasonal prediction of the spring precipitation. This study implements a novel rainfall framework to improve understanding of NC spring rainfall. The framework is built based on a three-cluster normal mixture model. Distribution parameters are sampled using Bayesian inference and a Markov chain Monte Carlo algorithm. The probability behaviors of light, moderate, and heavy rainfall events can be reflected by the three rainfall clusters, respectively. Analysis of 61-yr data indicates that moderate rainfall makes the largest contribution (67%) to the total rainfall amount. The moderate rainfall intensity is mainly influenced by the sea surface temperature anomaly (SSTA) in the previous season over the equatorial eastern Pacific, and rainfall frequency is influenced by geopotential height anomaly in the mid- to high latitudes in spring. It is also found that more extreme precipitation events can be observed in the spring following an eastern Pacific El Niño event in the previous autumn and winter. Based on these relationships, we develop a multiple linear regression model. Hindcasts for spring precipitation using the model indicates that its anomaly correlation is 0.48, significant at the 99% confidence level. The result suggests that the newly developed model can well predict spring rainfall amount in NC.

© 2022 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: Wenhong Li, wenhong.li@duke.edu

1. Introduction

North China, a region with dense population, abundant land and mineral resources, and a developed economy, is one of the main regions of industrial and agricultural production in China. Although the amount of precipitation in spring is less than that in summer (Zhang and Feng 2010), spring precipitation has significant impacts on agriculture in North China because spring is the season that a number of agricultural crops are planted and begin to grow. In addition, spring precipitation can change soil moisture content in the subsequent seasons, affect surface temperature and sensible heat flux, and further influence summer precipitation in North China (Zuo and Zhang 2007, 2016; Liu 2017). Thus, understanding the processes that control spring precipitation and improving seasonal precipitation forecasts are critical for local agriculture and economy. This is why spring rainfall forecasting is a key problem in climate prediction in North China.

Previous studies have found that in spring seasons with abnormally high precipitation, the Eurasian teleconnection pattern in the upper troposphere is in a negative phase over the northern high latitudes, and an anomalous anticyclone is located at East Asia (Wang et al. 2018). Accordingly, strong southerlies prevail in eastern China, transporting a large amount of water vapor to North China. As a result, moisture convergence and precipitation develop in this region (Wang et al. 2018). Sea surface temperature (SST) is also an influential factor on precipitation in China. For example, some studies showed that abnormally high spring rainfall in North China is related to warmer-than-normal SST in the tropical eastern Pacific Ocean and colder-than-normal SST in the tropical western Pacific Ocean in the previous winter. The SST anomalies (SSTAs) mentioned above can lead to weaker meridional circulations in the middle and high latitudes, while the weakened East Asian trough results in stronger southerlies in the low latitudes. Such a circulation pattern advects more water vapor from the ocean to North China (Hu et al. 2005; Zuo and Zhang 2012). Several studies also proposed that the spring rainfall over North China is significantly influenced by the Indian Ocean SST. When the Indian Ocean SSTA is positive, the East Asian jet stream and the East Asian trough both become weaker than normal, which is conductive to more transport of water vapor from the southeastern coast of East Asia to central eastern China, and subsequently leads to higher-than-normal precipitation in spring (Cheng and Jia 2014). Furthermore, the Indian Ocean SSTAs also affect geopotential height in the region and an induced atmospheric wave propagates along a great circle path, causing a geopotential height anomaly over North China (Gu et al. 2006). However, Lu (2001) pointed out that the anomalous anticyclone above North China cannot be explained by the thermal anomalies in the tropics. Instead, the anomalous anticyclone and winds are more related to the wave trains in the Northern Hemisphere midlatitudes. Thus, no consensus has been reached regarding the controlling mechanisms for spring precipitation in North China.

Existing studies on spring rainfall in North China have primarily focused on seasonal mean precipitation, whereas the detailed characteristics of spring rainfall have not been assessed. Li et al. (2015) found that light, moderate, and heavy rainfall events are controlled by different climatic processes in the Huai River basin. The physical processes related to various intensities of spring rainfall in North China are likely different and should be studied separately. On top of that, a reliable statistical study on rainfall events in North China and a better understanding of the related physical processes are needed to improve seasonal precipitation prediction, which has substantial agricultural and economic implications in China.

In the present study, we first analyze detailed rainfall distribution (i.e., light, moderate, and heavy rainfall) using the Bayesian method; various climate processes critical for moderate and heavy rainfall over North China are then investigated. The novel statistical method can objectively characterize the rainfall features and has been successfully applied to quantify and predict the full spectrum of rainfall over the southeastern United States (SEUS) (Li and Li 2013) and Huai River basin in China (Li et al. 2015).

This paper is organized as follows. Section 2 introduces data and methods. Section 3 presents results of the Bayesian inference on spring precipitation in North China. Section 4 discusses SSTAs and atmospheric fields related to the intensity and frequency of moderate rainfall, which accounts for the greatest percentage of total rainfall in spring. Section 5 discusses SSTAs related to extreme precipitation events, and section 6 proposes a multiregression equation to predict spring rainfall based on the results of sections 4 and 5. A summary and conclusions are presented in section 7.

2. Data and methods

a. Data

The data used in this study include gridded daily precipitation data with spatial resolution of 0.25° latitude × 0.25° longitude. This dataset is archived by the China Meteorological Administration (Xie et al. 2007). The study period is 1952–2012 when precipitation data are available to us. North China covers the geographical domain of 35°–40°N, 110°–122°E (Guo and Li 2012). The spring season is defined as March–May (MAM). Atmospheric circulation variables such as geopotential height and winds are analyzed using the National Center of Environment Prediction–National Center of Atmospheric Research (NCEP–NCAR) reanalysis product (NNRP) during the same period. The NNRP dataset has a spatial resolution of 2.5° latitude × 2.5° longitude (Kalnay et al. 1996). The SST data are extracted from the National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed SST (ERSST v5; Huang et al. 2017). The predicted geopotential height data from the Climate Forecast System version 2 (CFSv2) at 1.0 latitude × 1.0 longitude spatial resolution are also used to test the model in actual operational forecast (Saha et al. 2014). The CFSv2 data used are retrieved at 0000, 0600, 1200 and 1800 UTC for 9 months, with initial conditions every 5 days, from 11 January to 5 February.

b. Rainfall probability framework: Finite normal mixture model

Traditional statistical precipitation prediction models need to predefine the distribution kernels. However, the subjective selection of distribution models could introduce biases into the statistical inference of rainfall events. Also, it could be impractical when the traditional statistical prediction models are adapted to different climate zones. For example, the lognormal distribution is more suitable to describe rainfall in the subtropical regions, while the gamma distribution tends to better describe the tropical precipitation (Cho et al. 2004).

Based on a three-cluster normal mixture model, Li and Li (2013) implemented a rainfall framework to describe the probability distribution of summer rainfall in the SEUS. Later they successfully applied the rainfall framework to other regions (Li et al. 2015). Additionally, the combination of a finite number of normals can be used to approximate any smoothed distribution, which makes it unnecessary to predefine distribution kernels for the finite normal mixture framework. As a result, the biases in the rainfall statistical models can be greatly reduced (Li and Li 2013). Therefore, the framework can better describe the statistical behavior of precipitation by objectively identifying different types of rainfall event and can help us understand the related mechanisms behind physical processes. As we will show in sections 3 and 4, the probability behavior of different types of rainfall events can then be linked to preceding SSTAs and geopotential height anomalies to identify potential climatic predictors.

Note that how to choose the optimal number for the construction of finite normal mixture models still remains a difficult and controversial issue (Richardson and Green 1997; McLachlan and Peel 2000; Melnykov and Maitra 2010). In general, the true distribution of rainfall can be better approximated by adding more clusters to the mixture model. However, the risk of overfitting will be increased with unlimited increases in clusters (Lin et al. 2007). Also, with unlimited increases in clusters, the interpretation of mechanisms that account for the rainfall probability distribution might be affected due to unclear physical meanings of each cluster. In this study, a three-cluster normal mixture is constructed, and the three clusters can well reflect the probability behaviors of different spring rainfall categories in North China (i.e., light, moderate, and heavy rainfall events as defined in the AMS Glossary of Meteorology; AMS 2013).

The three-cluster finite normal mixture model can be expressed by
yi|π,μ,φh=13πhN(yi|μh,φh1),
where π is the weight of individual rainfall cluster (i.e., the frequency of each rainfall type). Note that h=13πh=1 and πh are mutually dependent, μ is the cluster mean, and φ is the precision of normal distributions (Li and Li 2013); h ∈ {1, 2, 3} is the cluster index.
Following the approach of Li and Li (2013), Bayesian statistical inference is used to obtain the distribution parameters (π, μ, and φ) in the mixture model. The priors of π, μ, and φ can be written as
π|a1,a2,a3Dirichlet(a1,a2,a3),
(μh,φh)Normal(μh|μ0h,κhφh1)Gamma(φh|αh,βh).
In Eq. (3) κ is the degrees of freedom, and Gamma(φh|αh, βh) is parameterized to have mean αh/βh and variance αh/βh2. The parameters in the distributions of priors [Eqs. (2) and (3)] are assigned based on the AMS definitions of light (0–6 mm day−1), moderate (6–18 mm day−1), and heavy rainfall (>18 mm day−1) (AMS 2013). To incorporate more data information into the posterior distribution, i.e., (α1, α2, α3) = (0.5, 0.35, 0.15), μ0h = (1.0, 8.0, 20.0), κh = (1, 1, 1), αh = (1.0, 1.0, 0.4), and βh = (1.0, 1.0, 1.0), these parameters are kept weakly informative. It is worth noting that the results are independent of initial values.
For the semiconjugate priors [Eqs. (2) and (3)] and the likelihood model [Eq. (1)], we can analytically derive the full conditional posterior distributions (Gelfand 2000). Using a Markov chain Monte Carlo (MCMC) algorithm, the Gibbs sampler for posterior computation can be expressed by
Pr(zi=h|)=πhNormal(yi|μh,φh1)h=13πhNormal(yi|μh,φh1),
(μhφh|)Normal(μh|μ^0h,κ^φh1)Gamma(φh|α^h,β^h),
where κ^h=(κh1+nh)1, μ^0h=κ^h(κh1μ0+nhy¯h), α^h=αh+nh/2, and β^h=βh+(1/2){i=1nh(yiy¯h)2+[nh/(1+κhnh)](y¯hμ0h)2}. The term nh=i=1n1(zi=h) denotes the number of samples in cluster h, and y¯h=nh1i:zi=hyi is the sample mean of cluster h; also,
(π1,π2,π3|)Dirichlet(α1+n1,α2+n2,α3+n3)

In this study, the MCMC algorithm [Eqs. (4)(6)] is applied to daily rainfall in each spring during 1952–2012. Because the intensity of heavy rainfall is stronger than that of moderate and light rainfall, the physical constraint is placed upon μh (μ1 < μ2 < μ3) to deal with the label switching issues (Stephens 2000). The MCMC algorithm is run 1000 times and the first 200 burn-in samples are discarded, and the remaining 800 post burn-in samples are used in the analysis.

The parameters in the normal mixture model [Eq. (1)] can be sampled from the posterior distributions [Eqs. (4)(6)]. The present study focuses on interannual variations of the distribution parameters μh and πh; the term μh reflects the intensities of light, moderate, and heavy rainfall and πh describes their occurrence frequencies.

3. Bayesian inference on spring precipitation in North China

Figure 1 shows the interannual variation of precipitation in spring season over North China during the period of 1952–2012. The seasonal climatological mean is 84.7 mm over the region. Spring precipitation varies greatly from year to year (Fig. 1). The minimum precipitation is 30.3 mm, which that occurred in 2001; the maximum precipitation is 176.5 mm, which occurred in 1964 and is 5 times higher than the minimum during the 61-yr period.

Fig. 1.
Fig. 1.

Interannual variation (solid line) and the climatology (dashed line) of spring (MAM) rainfall (unit: mm) in North China (35°–40°N, 110°–122°E) during 1952–2012.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0977.1

Figure 2 shows the year-to-year variations of spring precipitation intensity and frequency for the light, moderate, and heavy rainfall in North China. Compared to the American Meteorological Society (AMS) criteria for the three rainfall types [namely, light (0–6 mm day−1), moderate (6–18 mm day−1), and heavy rainfall (>18 mm day−1)], the objectively derived precipitation intensity based on 61-yr observations in North China is about 0.2 mm day−1 for light rainfall, 3.4 mm day−1 for moderate rainfall, and 17.3 mm day−1 for heavy rainfall (Fig. 2a). The regional precipitation intensities are much lower especially for moderate and heavy rainfall. This result indicates that the criteria about the three categories of precipitation should be adjusted according to different regions and seasons.

Fig. 2.
Fig. 2.

Bayesian inference on the interannual variation of the (a) intensity (unit: mm day−1) and (b) frequency (unit: %) of light, moderate, and heavy rainfall events over North China.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0977.1

Besides rainfall intensity, the occurrence frequency of each precipitation category (πh) can also be derived from the framework. Among the rainy days during the 61 spring seasons, light and moderate rainfall events account for about 77.9% and 20.9% of the total events, respectively. The heavy rainfall events account for only 1.2% of the total. If we define extreme weather events as those below the 5th percentile or above the 95th percentile according to the probability density function (Li 2012), the heavy rainfall events can be regarded as extreme precipitation events in North China in spring.

According to the normal mixture model, the sample mean (i.e., seasonal mean) of rainfall equals to the weighted average of the three rainfall clusters. Utilizing such a relationship, contributions of each rainfall cluster to the total seasonal precipitation over North China can be assessed. Specifically, light, moderate, and heavy rainfall are 14.4, 64.2, and 17.7 mm, accounting for ∼15%, 67%, and 18% of the seasonal rainfall amount, respectively (Fig. 3). The rate of light precipitation (0.2 mm day−1 on average) is small and does not vary greatly from year to year (Fig. 3), and its contribution to the total seasonal precipitation is less important compared to moderate and heavy rainfall in the region (Fig. 3). The moderate precipitation accounts for two-thirds of the seasonal precipitation amount, indicating that this type of rainfall plays a dominant role in accurate prediction of spring rainfall. The extreme precipitation events in spring are also important for the agricultural production (Zhang et al. 2016). We thus focus on moderate and heavy rainfall (i.e., extreme rainfall) in the following sections.

Fig. 3.
Fig. 3.

Contribution of light, moderate, and heavy rainfall clusters to spring season cumulative precipitation (unit: mm) in North China. The triangle, square, and cross represent spring-season light, moderate, and heavy rainfall, respectively, during 1952–2012. The straight lines are the best least squares fitting lines.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0977.1

4. Linkage between moderate rainfall and climate variability signals

Figure 3 shows the relationship between the moderate rainfall intensity and frequency (πh μh) and seasonal mean precipitation (Y¯). We found that the correlation between the intensity and frequency of the moderate rainfall fails to pass the significance test (result not shown). This suggests that the control processes of the moderate rainfall intensity differ from those of the moderate rainfall frequency. We thus analyze those processes critical for the moderate rainfall intensity and frequency, separately. According to the standard deviation (STD) of moderate rainfall amount, we select 10 (9) years with moderate rainfall intensity greater (less) than the average plus (minus) 1.0 STD of moderate rainfall intensity for composite analysis. Similarly, 9 springs with higher and 10 springs with lower moderate rainfall frequency are identified, respectively. It is found that the moderate rainfall intensity is more connected with SST (section 4a), whereas the frequency of moderate rainfall is more related with geopotential height at 500 hPa (see section 4b). Therefore, SST and 500-hPa potential height are further elaborated in the following subsections. Because the composite results of the higher and lower events (in both frequency and intensity) largely mirror each other, we focused the discussion on the higher composites to make the paper concise.

a. Preseasonal SSTA related to moderate rainfall intensity in spring

The ocean has a longer “memory” than the atmosphere. Therefore, the previous SSTA is an effective forecast factor for climatic prediction months or years in advance (Namias 1963; Bjerknes 1969). Establishing the linkage between ocean and the moderate rainfall intensity can provide insights for spring precipitation prediction in North China. Here we show the results of higher rainfall intensity and frequency separately; for lower events results please see the online supplemental material. Figure 4 shows the composite analysis of SSTA for the moderate rainfall intensity in North China. During the spring season with higher moderate rainfall intensity, positive SST anomalies (with 87% confidence level) are observed at the equatorial eastern Pacific from previous fall to late winter, indicting a linkage between the eastern Pacific (EP)-type El Niño event (Yu and Kao 2007; Li et al. 2011) and the moderate rainfall intensity in North China.

Fig. 4.
Fig. 4.

Composite SST anomalies (unit: °C) for the higher moderate rainfall intensity years. Contour interval is 0.2°C. The positive and negative contours are represented by solid and dotted lines, respectively. The blue box indicates the Niño-3 region (5°S–5°N, 150°–90°W). Grids passing 90% confidence level are shaded.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0977.1

Figures 5 and 6 show the composite analysis of moisture flux at 850 hPa and the west ridge location of the western Pacific subtropical high (WPSH) in spring, respectively. The ridge is defined as the intersection of the zonal wind speed equal to zero and the geopotential height equal to 1520 gpm (Zheng and Li 2012). According to Fig. 5, the increased moderate rainfall intensity is related to the Philippine Sea anticyclone (PSAC) that brings abnormally strong southerly wind and associated northward moisture flux transport. The PSAC develops in the El Niño mature winter and persists to the following spring and summer (Klein et al. 1999; Zhang et al. 1999; Wang et al. 2000, 2003; Wang and Zhang 2002; Xie et al. 2002, 2009; Annamalai et al. 2005; Lu et al. 2006; Sui et al. 2007; Rong et al. 2010; Chung et al. 2011; Fan et al. 2013; He and Wu 2014), leading to westward and northward expansion of the WPSH western ridge (Fig. 6) and abnormally strong southerly winds that bring abundant water vapor and precipitation to North China. These results are in consistent with previous studies (Zhang et al. 1999; Wang and Zhang 2002; Wang et al. 2008) that analyzed ENSO-induced summer circulation anomaly in China.

Fig. 5.
Fig. 5.

Vapor flux anomalies at 850 hPa composited for the higher moderate rainfall intensity years. The red box indicates the NC region (35°–40°N, 110°–122°E). Grids passing 90% confidence level are shaded.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0977.1

Fig. 6.
Fig. 6.

Springtime western Pacific subtropical high (WPSH) western ridge at 850 hPa. The lines labeled 1520 represent the 1520-gpm isolines, and the horizontal lines indicate the ridge line of the WPSH where easterly winds reverse to westerly (i.e., zonal wind equals zero); the solid, dotted, and dashed lines indicate the composite results during the higher rainfall intensity years, climatology, and lower rainfall intensity years, respectively.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0977.1

b. Geopotential height related to moderate rainfall frequency in spring

Figure 7 shows 500-hPa geopotential height anomaly in the spring season with high frequency of moderate rainfall. Positive geopotential anomalies are found from North China and Japan to the North Pacific Ocean, suggesting that the weaker East Asian trough likely contributes to more moderate rainfall in North China. Indeed, Fig. 8a reveals that the weakened East Asian trough results in an abnormal anticyclone at 500 hPa and abnormal southerly over North China at 850 hPa, which transports more water vapor to North China (Fig. 8b). We notice that there is little difference in the location of the WPSH western ridge between years of high and low moderate rainfall frequencies (figure not shown), suggesting that the strong southerly that brings more water vapor to North China is not associated with the WPSH. Instead, it is more likely related to the weakened East Asian trough.

Fig. 7.
Fig. 7.

The 500-hPa geopotential height anomalies (shading; unit: m) for the higher moderate rainfall frequency in spring season. The climatology of 500-hPa geopotential heights are also plotted using the dot–dashed lines (contour interval: 100 m). Grids passing 90% confidence level are indicated by × symbols.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0977.1

Fig. 8.
Fig. 8.

(a) Wind anomalies (unit: m s−1) at 500 hPa and (b) vapor flux anomalies (unit: kg hPa−1 m−1 s−1) at 850 hPa for the spring seasons with higher moderate rainfall frequency. The red box indicates the NC region (35°–40°N, 110°–122°E). Grids passing 90% confidence level are shaded.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0977.1

5. Preseasonal SSTA related to the extreme precipitation event

Figure 9 shows the relationship between total precipitation and the frequency of heavy precipitation in spring. It is found that the spring precipitation increases as the frequency of heavy precipitation increases (Fig. 9). Such a relationship does not exist between the intensity of heavy precipitation and total spring precipitation (figure not shown). Therefore, we focus on the analysis of variables such as preseasonal SSTA (see below) that are important for heavy rainfall frequency.

Fig. 9.
Fig. 9.

The relationship between spring total precipitation (x-axis unit: mm day−1) and heavy rainfall frequency (y-axis unit: %). The straight lines are the best least squares fitting lines.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0977.1

Figure 10 shows the composite analysis of SSTA in nine springs with more heavy rainfall events. During the spring season with increased frequency of heavy rainfall, a positive SSTA is observed at the equatorial eastern Pacific from the previous fall to late winter, indicating that the EP El Niño event in the previous season may also contribute to the increased frequency of heavy rainfall in North China. As discussed in section 4, during the El Niño, the PSAC is generated, wind anomalies develop rapidly in late fall and persist until the subsequent spring or early summer. The abnormally strong southerly winds bring abundant water vapor to North China, leading to more precipitation events that can reach heavy rainfall level. The atmospheric moisture content, on the other hand, is constrained by air temperature following the Clausius–Clapeyron equation, and the intensity of heavy precipitation associated with moisture convergence may not increase significantly in spring over North China. The total amount of precipitation presumably increases following the increased heavy rainfall frequency instead of the enhanced intensity of heavy rainfall in spring season. It is worth noting that a positive SSTA response also appears in the Indian Ocean from the late winter of previous year to the subsequent spring (Fig. 10b). The area of positive SSTA expands to almost the entire tropical Indian Ocean during January–March (Fig. 10d). This phenomenon is likely induced by El Niño (Klein et al. 1999; Du et al. 2009).

Fig. 10.
Fig. 10.

As in Fig. 4, but for the higher heavy rainfall frequency years.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0977.1

In summary, the EP El Niño events in the previous winter season not only contribute to the intensification of moderate rainfall in North China through the PSAC and expansion of the WPSH, but also lead to increases in the frequency of heavy rainfall. The joint effects mentioned above work together to increase the total amount of spring rainfall in North China.

6. Predictability of North China spring rainfall

The results presented in sections 4 and 5 suggest that the variability of spring precipitation in each category is closely tied to previous winter SSTs in the tropical Pacific and 500-hPa geopotential height in Northeast Asia in spring. Thereby, both the previous winter SST and the concurrent geopotential height at 500 hPa can serve as potential predictors for predicting spring precipitation in North China. Based on the relationship between SSTAs and the moderate rainfall intensity and heavy rainfall frequency shown in Figs. 4 and 10, the relationship between geopotential height and moderate rainfall frequency shown in Fig. 7, and the fact that amount of rainfall could be estimated by the rainfall intensity and its frequency, a multiregression model is constructed and expressed by
Y¯|π,μ,φ=Ylight+(a2×SSTANino-3+b2)×(c2×HGTA+b2)+μ3×(a3×SSTANino-3+b3),
where Ylight is the amount of light rainfall and μ3 is the heavy rainfall intensity. Because the light rainfall amount and heavy rainfall intensity do not vary significantly from year to year, their climatological values are therefore used here. The expression a2 × SSTANiño-3 + b2 is the linear regression of the moderate rainfall intensity (Fig. 4), in which SSTANiño-3 is the SST anomaly at the Niño-3 region (5°S–5°N, 150°–90°W); c2 × HGTA + b2 is the linear regression of moderate rainfall frequency according to Fig. 7, in which HGTA is the 500-hPa geopotential height anomaly over the Northeast Asia area that passes the significance test in Fig. 7 (32.5°–47.5°N, 120°–150°E); and a3 × SSTANiño-3 + b3 is the linear regression of heavy rainfall frequency according to Fig. 10.

The spring rainfall amount Y¯ for a target spring season can then be predicted using the predictors, that is, the previous December– February (DJF) SSTs in the Niño-3 region and predicted geopotential height at 500 hPa over the Northeast Asia area (32.5°–47.5°N, 120°–150°E) in current spring. Here SSTs are adopted from the NOAA ERSSTv5 (Huang et al. 2017) and 500-hPa geopotential height from NNPR (Kalnay et al. 1996) are used to build the model. Figure 11 presents the comparison between observed (blue bar) and hindcasted (red line) spring rainfall amount from 1952 to 2012. The forecast skill of the statistical model [Eq. (7)] is quantified by the anomaly correlation (AC) between the observed and predicted spring rainfall amount during 61 years. The AC is a commonly used measure of association that operates on pairs of grid point values in the forecast and observed fields (WMO 2006). The AC value for spring rainfall prediction in North China is 0.59, significant at the 99% confidence level. This result suggests that the constructed model [i.e., Eq. (7)] can reasonably hindcast interannual variation of spring rainfall amount in North China.

Fig. 11.
Fig. 11.

Interannual variation of the observed (blue bars) and hindcast (red line) spring (MAM) rainfall (unit: mm) in North China from 1952 to 2012, and the SSTA (unit: °C) in Niño-3 region (green line); SSTA above 0°C is shown with purple circles.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0977.1

The stability of the model is also evaluated by using the leave-one-out cross-validation method. The AC value between the observation and prediction is 0.54, which is significant at the 99% confidence level. These results suggest that the prediction model is effective, credible, and stable (Michaelsen 1987; Barnston and He 1996).

When applying the first statistical model to actual operational forecast of spring precipitation in North China, we could use predicted geopotential height at 500 hPa from CFSv2 (available data starting from 1982; Saha et al. 2014). The CFSv2 has demonstrated great skills in forecasting geopotential height over Northeast Asia (Zhao and Yang 2014; Zhao et al. 2015; Peng et al. 2014). The AC value between observed and predicted spring rainfall amount from 1982 to 2012 is 0.48, slightly smaller than that using 61-yr NNPR data, but significant at the 99% confidence level. The result suggests that the statistical model can well predict spring rainfall amount in North China for practical applications.

7. Conclusions

Spring rainfall events are of great significance for agriculture, economy, and society in North China. However, it is still a challenge to accurately predict the rainfall amount in spring over North China, partly because of its complicated statistical behavior and partly because of our poor understanding of the physical processes that affect the rainfall events.

This study implements a novel rainfall framework built on a three-cluster normal mixture model (Li and Li 2013) to investigate North China spring rainfall. The results have improved our understanding of those influential factors for spring rainfall in the region. The probability behaviors of light, moderate, and heavy rainfall can be objectively identified by three clusters. Compared to the traditionally used distribution models, the new framework improves the statistical inference regarding the North China spring rainfall.

Results show that the moderate rainfall cluster makes the largest contribution (66.7%) to spring rainfall over North China, which is almost 4 times higher than that made by light or heavy rainfall clusters. Occurring several months prior to the spring season, the tropical SSTA can be taken as a predictor for spring rainfall events. The intensity of moderate rainfall is influenced by SSTA over the equatorial eastern Pacific in the previous fall and winter seasons. The PSAC accompanied with the warm SSTA in the equatorial eastern Pacific Ocean in winter could last in the following spring and summer and the WPSH western ridge will shift to the west and north, resulting in abnormally strong southerly winds that transport more moisture to North China. The frequency of the moderate rainfall, on the other hand, is mainly associated with geopotential height anomaly at the mid- to high latitudes in spring. When the East Asian trough is weakened, strong southerly winds bring more water vapor to the North China, resulting in more rainfall events.

The occurrence of heavy rainfall event accounts for only 1.2% of the total events in spring season. Thereby, heavy rainfall events can be taken as extreme precipitation events. Its frequency is closely related to SSTA over the equatorial eastern Pacific in the previous fall and winter. Following an EP El Niño event, more extreme precipitation events can be observed in the subsequent spring season.

Considering these relationships mentioned above, the potential predictability of spring rainfall amount over North China is assessed by using a newly developed multiple linear regression model, which uses SST in the previous season and 500-hPa geopotential height in spring as predictors. The AC value for spring rainfall prediction in North China is 0.59, significant at the 99% confidence level, suggesting that the constructed model tend to be skillful in capturing spring rainfall amount in the region. Further analysis using predicted geopotential height from CFSv2 indicates that the AC value during 1982–2012 is 0.48, also significant at the 99% confidence level. This result suggests that the statistical model established in the present study can serve as an alternative approach to improve seasonal prediction of spring rainfall in North China.

Acknowledgments.

This study was sponsored by the National Natural Science Foundation of China (Grants 42075060 and 41705025). The lead author was also supported by the China Scholarship Council (File 201805330009). The authors thank Qiuhong Tang for providing the gridded daily precipitation data in North China. The authors thank three anonymous reviewers for their technical review and valuable comments and suggestions, which helped improve the manuscript.

Data availability statement.

The rainfall dataset could be obtained from the China Meteorological Data Service Centre of China Meteorological Administration (http://data.cma.cn/) as cited in Xie et al. (2007). Atmospheric circulation data used during this study are openly available from the National Center of Environment Prediction–National Center of Atmospheric Research (NCEP–NCAR) reanalysis at https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html as cited in Kalnay et al. (1996). The SST data used during this study are openly available from the National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed SST (ERSST v5) at https://climatedataguide.ucar.edu/climate-data/sst-data-noaa-extended-reconstruction-ssts-version-5-ersstv5 as cited in Huang et al. (2017). The geopotential height data used for constructing the multi-regression equation are taken from the Climate Forecast System version 2 (CFSv2) at https://rda.ucar.edu/ as cited in Saha et al. (2014).

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Supplementary Materials

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    • Export Citation
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    • Export Citation
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    • Export Citation
  • Hu, G. F., J. Zou, and X. Zhang, 2005: Influences of Nino3 SST rising in the second half year on East Asia spring general circulation and Shandong spring precipitation. J. Appl. Meteor. Sci., 6, 772778.

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    • Export Citation
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    • Search Google Scholar
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    • Export Citation
  • Li, L. F., and W. H. Li, 2013: Southeastern United States summer rainfall framework and its implication for seasonal prediction. Environ. Res. Lett., 8, 044017, https://doi.org/10.1088/1748-9326/8/4/044017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, L. F., W. H. Li, Q. H. Tang, P. F. Zhang, and Y. M. Liu, 2015: Warm season heavy rainfall events over the Huaihe River Valley and their linkage with wintertime thermal condition of the tropical oceans. Climate Dyn., 46, 7182, https://doi.org/10.1007/s00382-015-2569-2.

    • Crossref
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  • Li, W. H., P. Zhang, J. Ye, L. Li, and P. A. Baker, 2011: Impact of the two different types of El Niño events on the Amazon climate and ecosystem productivity. J. Plant Ecol., 4, 9199, https://doi.org/10.1093/jpe/rtq039.

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  • Li, W. J., 2012: Modern Climate Operation. China Meteorological Press, 512 pp.

  • Lin, T., J. C. Lee, and S. Y. Yen, 2007: Finite mixture modeling using the skew normal distribution. Stat. Sin., 17, 909927, https://doi.org/10.1007/s00440-006-0032-3.

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    • Export Citation
  • Liu, L., 2017: The influences of soil moisture over eastern China on Chinese climate. Ph.D. dissertation, Chinese Academy of Meteorological Sciences, 103 pp.

    • Search Google Scholar
    • Export Citation
  • Lu, R. Y., 2001: Atmospheric circulation anomaly associated with the spring rainfall anomaly in North China. Climate Environ. Res., 6, 400408.

    • Search Google Scholar
    • Export Citation
  • Lu, R. Y., Y. Li, and B. Dong, 2006: External and internal summer atmospheric variability in the western North Pacific and East Asia. J. Meteor. Soc. Japan, 84, 447462, https://doi.org/10.2151/jmsj.84.447.

    • Crossref
    • Search Google Scholar
    • Export Citation
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  • Fig. 1.

    Interannual variation (solid line) and the climatology (dashed line) of spring (MAM) rainfall (unit: mm) in North China (35°–40°N, 110°–122°E) during 1952–2012.

  • Fig. 2.

    Bayesian inference on the interannual variation of the (a) intensity (unit: mm day−1) and (b) frequency (unit: %) of light, moderate, and heavy rainfall events over North China.

  • Fig. 3.

    Contribution of light, moderate, and heavy rainfall clusters to spring season cumulative precipitation (unit: mm) in North China. The triangle, square, and cross represent spring-season light, moderate, and heavy rainfall, respectively, during 1952–2012. The straight lines are the best least squares fitting lines.

  • Fig. 4.

    Composite SST anomalies (unit: °C) for the higher moderate rainfall intensity years. Contour interval is 0.2°C. The positive and negative contours are represented by solid and dotted lines, respectively. The blue box indicates the Niño-3 region (5°S–5°N, 150°–90°W). Grids passing 90% confidence level are shaded.

  • Fig. 5.

    Vapor flux anomalies at 850 hPa composited for the higher moderate rainfall intensity years. The red box indicates the NC region (35°–40°N, 110°–122°E). Grids passing 90% confidence level are shaded.

  • Fig. 6.

    Springtime western Pacific subtropical high (WPSH) western ridge at 850 hPa. The lines labeled 1520 represent the 1520-gpm isolines, and the horizontal lines indicate the ridge line of the WPSH where easterly winds reverse to westerly (i.e., zonal wind equals zero); the solid, dotted, and dashed lines indicate the composite results during the higher rainfall intensity years, climatology, and lower rainfall intensity years, respectively.

  • Fig. 7.

    The 500-hPa geopotential height anomalies (shading; unit: m) for the higher moderate rainfall frequency in spring season. The climatology of 500-hPa geopotential heights are also plotted using the dot–dashed lines (contour interval: 100 m). Grids passing 90% confidence level are indicated by × symbols.

  • Fig. 8.

    (a) Wind anomalies (unit: m s−1) at 500 hPa and (b) vapor flux anomalies (unit: kg hPa−1 m−1 s−1) at 850 hPa for the spring seasons with higher moderate rainfall frequency. The red box indicates the NC region (35°–40°N, 110°–122°E). Grids passing 90% confidence level are shaded.

  • Fig. 9.

    The relationship between spring total precipitation (x-axis unit: mm day−1) and heavy rainfall frequency (y-axis unit: %). The straight lines are the best least squares fitting lines.

  • Fig. 10.

    As in Fig. 4, but for the higher heavy rainfall frequency years.

  • Fig. 11.

    Interannual variation of the observed (blue bars) and hindcast (red line) spring (MAM) rainfall (unit: mm) in North China from 1952 to 2012, and the SSTA (unit: °C) in Niño-3 region (green line); SSTA above 0°C is shown with purple circles.

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