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Article

Relationship between Summer Synoptic Circulation Patterns and Extreme Precipitation in Northern China

by
Shuping Li
1,
Guolin Feng
2,3,
Pengcheng Yan
4 and
Tao Su
2,*
1
College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225012, China
2
College of Physical Science and Technology, Yangzhou University, Yangzhou 225012, China
3
National Climate Center, China Meteorological Administration, Beijing 100081, China
4
Institute of Arid Meteorology, China Meteorological Administration, Lanzhou 730020, China
*
Author to whom correspondence should be addressed.
Atmosphere 2023, 14(12), 1705; https://doi.org/10.3390/atmos14121705
Submission received: 13 October 2023 / Revised: 12 November 2023 / Accepted: 17 November 2023 / Published: 21 November 2023
(This article belongs to the Section Meteorology)
Browse Figures
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Abstract

:
Synoptic circulation patterns over the midlatitudes play a pivotal role in regional precipitation changes; however, the synoptic circulation patterns over eastern Asia (35°–60° N, 105°–145° E) and their effects on extreme precipitation events in the North China Plain (NCP) and northeastern China (NEC) remain unclear. The summer daily 500 hPa geopotential height anomaly fields for 1979–2021 are classified into six synoptic circulation patterns using self-organizing map (SOM) cluster analysis. The SOM1 pattern, characterized by a high-pressure ridge over the north of eastern Asia and a trough near the Korean Peninsula, yields decreased precipitation in NEC. The SOM2 pattern reveals a robust high ridge over eastern Asia, resulting in a higher incidence of regional extreme precipitation events (REPEs) of approximately 24% in the NCP. Under the SOM3 pattern, the anomalous cyclonic circulation over eastern Asia leads to above-average precipitation in the NCP. The SOM4 pattern yields the highest incidence of REPEs in NEC, with the lowest incidence of REPEs in the NCP, as the anomalous cyclonic circulation over eastern Asia moves southeastward compared to the SOM3 pattern. The SOM5 pattern presenting an anticyclone–cyclone dipole reduces precipitation in the NCP and NEC, and the anticyclonic circulation near eastern China associated with the SOM6 pattern causes above-average precipitation in the NCP. On interannual time scales, the SOM2 pattern occurrence with an increasing trend tends to induce an increasing summer precipitation trend in the NCP. The SOM3 pattern occurrence is negatively correlated with the summer precipitation in NEC. Overall, classifying the synoptic circulation patterns helps to improve precipitation forecasting and provides insights into the synoptic circulation patterns dominating the occurrences of REPEs.

1. Introduction

Northern China (NC), located in eastern Asia (35°–60° N, 105°–145° E), primarily contains the North China Plain (NCP) and northeastern China (NEC). Summer precipitation in NC substantially affects national agriculture, industry, and economics and is strongly modulated by various factors [1,2,3]. However, the effects of atmospheric circulation patterns on both mean and extreme precipitation are complex [4,5]. For instance, the weakened westerly winds and the northward shift of the western North Pacific subtropical high (WNPSH) contribute to the northward extension of the frontal precipitation belt, resulting in frequent extreme precipitation events in the NCP and NEC [6,7]. In the NCP, the midsummer extreme precipitation is attributed to low-pressure systems and the WNPSH [8], and, locally, the surrounding terrain strengthens moisture convergence and vertical velocity, resulting in more intense extreme precipitation [9]. Yan et al. [10] found that the dominant synoptic circulation patterns associated with heavy precipitation in Beijing, situated in the NCP, are characterized by prevailing southerly winds in the lower troposphere. In addition, the Northeast China Cold Vortex is one of the main factors contributing to the summer precipitation changes in NEC, as it frequently induces heavy precipitation with low temperatures [11]. The extratropical cyclones located in NEC play an important role in modulating extreme precipitation events [12], and the Meiyu front’s northward shift to around 35° N results in more frequent short-duration extreme precipitation events [13].
Many previous studies have investigated synoptic circulation patterns and their impacts on surface weather conditions in the NCP [14,15]. For instance, the three dominant synoptic circulation regimes are associated with the heavy precipitation in the NCP: first, a steady meridional circulation system with high pressure to the east and low to the west; second, vortexes or a quasi-east–west shear line at 850 hPa; and third, the interaction of the two systems caused by typhoons and low-level vortexes [16]. Zhao et al. [17] categorized the sea level pressure fields into nine patterns and pointed out that the westward-extended WNPSH, accompanied by the intensified trough and low-level jet, contributes to extreme precipitation in the region. An and Zuo [15] indicated that a continental high ridge over the midlatitudes and the southward shift of the WNPSH are responsible for dry heat waves in the NCP. When classifying synoptic circulation patterns over East Asia during 1970–2021 into nine patterns, the pattern showing a high ridge over NC with the strongest easterly wind anomalies intensifies heavy precipitation in Henan Province [18].
The synoptic circulation patterns associated with summer extreme precipitation events in NEC are classified into three patterns: the cyclone-like pattern has a low vortex with anomalous southwesterly winds; the monsoon-like pattern presents southwesterly (northwesterly) winds over the south (north) of NEC; and the third pattern is constituted by low vortexes and tropical cyclones [19]. Sun et al. [20] grouped the Northeast China Cold Vortex into “south vortex, middle vortex, and north vortex” based on the generation location. The Northeast China Cold Vortex can be classified into the Yenisei River-type, Lake Baikal-type, Ural–Yakutsk-type, and Okhotsk Sea–Arctic Ocean-type, and the first two types contribute to more summer precipitation in NEC [21] since synoptic circulation patterns modulate mean and extreme precipitation on regional and continental scales [11], classifying synoptic circulations helps improve precipitation forecasting and identify the circulation patterns that are more likely to dominate extreme precipitation on a regional scale [22,23].
Large-scale circulation systems dominate the moisture supply for regional extreme precipitation events [24,25]. For instance, the strong southeasterly winds provided abundant moisture for record-breaking precipitation in Henan Province during 19–20 July 2021 due to Typhoon “In-Fa” and the northeastward shift of the WNPSH [26]. The synoptic circulation patterns associated with extreme precipitation regularly generate non-extreme precipitation when local thermodynamic and dynamic factors show various degrees of anomalies [27]. Therefore, local thermodynamic and dynamic factors are closely related to heavy precipitation events [28]. Precipitable water, which measures water vapor capacity in the atmosphere, is a thermodynamic factor that affects extreme precipitation intensity [29], and high precipitable water is favorable for regional extreme precipitation [10]. Moreover, vertical velocity induced by specific circulations and topographic lifting can be regarded as a dynamic factor, altering local convective activities [30], and the enhanced upward motions lead to more intense extreme precipitation events on regional scales [18]. Thus, synoptic circulation patterns, as well as local thermodynamic and dynamic factors, are closely connected to regional extreme precipitation events (REPEs).
Objective methods have been employed to classify regional atmospheric circulation patterns [31]. For instance, rotated principle component analysis effectively captures the underlying physical structure and replicates predefined circulation types [32]. Empirical orthogonal function analysis is employed to extract primary circulation patterns from the various high-dimension datasets [33]. The k-means clustering algorithm is capable of dividing a high-dimensional dataset into predefined cluster numbers based on the Euclidean distance measuring the similarity between samples [34]. Currently, self-organizing map (SOM) cluster analysis is widely adopted to classify synoptic circulation patterns [15,35,36]. SOM cluster analysis, an artificial neural network learning method, is an unsupervised feature extraction technique that can effectively classify regional synoptic circulations [37]. It maps high-dimensional input data to a low-dimensional space, typically with two dimensions, while maintaining the inherent topological relationships in the input data [38]. Furthermore, the SOM technique effectively distinguishes between multiple sets of patterns compared to an empirical orthogonal function, and it is also more flexible than traditional clustering methods, such as k-means [39]. Additionally, SOM has been widely utilized in revealing the connection between synoptic patterns and surface weather conditions [40,41]. The SOM technique is capable of extracting regional circulation patterns over the midlatitudes [35].
The motivation for this study is to categorize summer synoptic circulation patterns over eastern Asia. We further identify the incidence of REPEs in the NCP and NEC with respect to the synoptic circulation patterns. In addition, we analyze the temporal variations of these patterns and their connection with regional precipitation anomalies. The study is structured into four sections. Section 2 mainly introduces the datasets, study area, and methods in this study. Results are presented in Section 3, and conclusions and discussion are given in Section 4.

2. Materials and Methods

2.1. Data

The ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) provided the hourly geopotential height, vertical velocity, and vertically integrated water vapor flux with a horizontal resolution of 0.25° [42]. The daily climate dataset CN05.1, derived from a combination of more than 2400 in situ weather stations and satellite data, is on a 0.25° horizontal resolution [43]. The daily precipitation from the CN05.1 dataset was applied to look into summer (June–July–August, JJA) precipitation anomalies in NC. The CPC Merged Analysis of Precipitation (CMAP) is based on the combination of precipitation gauge data and satellite-derived precipitation estimates with a 2.5° horizontal grid spacing, and its quality strongly depends on the availability of precipitation gauge data and the accuracy of the satellite infrared and microwave estimates [44]. The CMAP monthly precipitation was employed to look into the reliability and consistency of regional summer precipitation changes in Section 3.3. Moreover, the daily precipitation at 598 ground-based meteorological stations was provided by the China Meteorological Administration. The 194 stations in NC (Figure 1a) were employed to identify REPEs after removing the stations with missing values exceeding 1% during 1979–2021. All reanalysis datasets and observations are available for 1979–2021 in this study, with the exception of IMERG satellite precipitation, which covers the period of 2000–2021.

2.2. Study Area

Due to the complex spatial structure encompassing the tropics, subtropics, and midlatitudes, some regional variability is lost when extracting reliable regional synoptic circulation patterns [45]. Therefore, Screen and Simmonds [46] divided the mid-latitude continents of the Northern Hemisphere into seven regions: western North America (35°–60° N, 150°–115° W), central North America (35°–60° N, 115°–80° W), eastern North America (35°–60° N, 80°–45° W), Europe (35°–60° N, 15° W–25° E), western Asia (35°–60° N, 25°–65° E), central Asia (35°–60° N, 65°–105° E), and eastern Asia (35°–60° N, 105°–145° E). Many previous studies have classified circulation patterns over these regions [35,47,48,49]. Here, we mainly focus on eastern Asia, which represents an interesting area to explore synoptic circulation patterns associated with the summer precipitation in NC.

2.3. Self-Organizing Map (SOM)

SOM cluster analysis is an unsupervised learning algorithm that produces an objective classification by iteratively grouping similar data vectors [37,50]. Daily 500 hPa geopotential height anomalies were used to classify synoptic circulation patterns over eastern Asia. The daily geopotential height anomaly fields are assigned to one of a predefined number of nodes based on pattern similarity. The SOM patterns are then derived by minimizing the Euclidian distance between iteratively updated nodes and their corresponding geopotential height anomaly fields [51]. Thus, each SOM pattern represents a composite of synoptic patterns that are relatively similar.
Following Liu et al. [38], various node configurations (e.g., 2 × 2, 2 × 3, 3 × 3, 3 × 4, 4 × 4, 4 × 5, 5 × 5, 5 × 6, 6 × 6, and 6 × 7) were selected to examine the sensitivity of synoptic circulation patterns to the choice of node number when clustering daily geopotential height anomaly fields. The choice of the number of nodes should be sufficient to depict the diversity of circulation patterns and small enough to explain differences between nodes physically [35]. SOM configurations improve the realism of the classifications with an increase in the number of nodes (Figure S1a), while the pattern correlations between SOM pairs are the smallest for the 2 × 3 node configuration (Figure S1b), implying that the 2 × 3 node configuration is adequate to describe synoptic circulation patterns over eastern Asia clearly and concisely. In order to investigate the characteristics of the synoptic circulation patterns, we counted the total number of days during which each circulation pattern occurs (occurrence, units: days), the times that each pattern occurs consecutively (frequency, units: times), and the mean duration of such consecutive occurrences (mean duration, units: days).

2.4. Regional Extreme Precipitation Events (REPEs)

The daily precipitation from observed stations was employed to define daily extreme precipitation events. An extreme precipitation event was defined as daily precipitation exceeding the 95th percentile threshold of all summer dry and wet days at a specific station (Figure 1c). This approach was preferred over wet-day percentiles, as they are conditional and prone to inducing statistical artifacts [52]. Following the methodology proposed by Xie et al. [53] and Tang et al. [19], we counted the stations that encountered extreme precipitation events on all summer days for 1979–2021. The REPEs were identified by selecting instances where the number of stations reporting simultaneous extreme precipitation events exceeded the 95th percentile of these station counts.

3. Results

3.1. Synoptic Circulation Patterns and Their Impacts on Summer Precipitation

The meteorological station network across NC comprises a total of 194 stations (Figure 1a). Among these, 57 stations are located within the NCP, while 56 stations are distributed in NEC. Regarding mean precipitation, a significant amount of precipitation is concentrated in the low-altitude region (terrain height below 800 m) (Figure 1b), where summer precipitation is partly influenced by the East Asia summer monsoon [1]. Furthermore, extreme daily precipitation (95th percentile) tends to be more intense along the coastal region of NC (Figure 1c).
SOM cluster analysis is applied to categorize daily 500 hPa geopotential height anomaly fields over eastern Asia, and the SOM patterns have been reordered according to their occurrence ratios (Figure 2). The SOM1 pattern, which manifests a relatively high occurrence ratio of 19.97%, is characterized by positive geopotential anomalies over the northern part of eastern Asia and negative anomalies along the coastal regions (Figure 2a). In contrast, the SOM2 pattern, with an occurrence ratio of 17.87%, exhibits a pronounced high ridge with significant positive anomalies over eastern Asia (Figure 2b). Regarding the SOM3 pattern, a strong trough is observed over the north of eastern Asia with positive anomalies near Japan, accounting for 16.33% of all synoptic circulation patterns (Figure 2c). The SOM4 pattern is characterized by significant negative anomalies over eastern Asia, with an occurrence ratio of 16.10% (Figure 2d). Meanwhile, the SOM5 pattern exhibits a high ridge near Mongolia and a trough over Japan, contributing to 15.85% of all synoptic circulation patterns (Figure 2d). The SOM6 pattern is the opposite phase of the SOM3 pattern and contributes to an occurrence ratio of 13.88% (Figure 2f). One can obtain similar synoptic circulation patterns over eastern Asia using the NCEP-DOE AMIP-II reanalysis dataset (Figure S2), underscoring the reliability of these patterns.
Figure 3 presents the occurrence, frequency, and mean duration of each SOM pattern over eastern Asia. The definition of the occurrence, frequency, and mean duration can be found in Section 2.3. Despite the SOM1 pattern showing the highest occurrence, it has a relatively low frequency and a mean duration of approximately 3.2 days. In comparison, the occurrence of the SOM2 pattern is approximately 700 days, and its mean duration is more than 3 days. The occurrences and frequencies of the SOM3 and SOM4 patterns are similar, and their mean duration is about 3 and 2.8 days, respectively. The SOM5 pattern presents an occurrence of 627 days, with a frequency exceeding 290 times and a relatively shorter persistence. The SOM6 pattern persisting on average for fewer than 2 days exhibits the lowest occurrence and the highest frequency.
Under the SOM1 pattern, an anomalous anticyclonic circulation prevailing over eastern Asia contributes to decreased precipitation in NC, with notably positive precipitation anomalies occurring at around 45° N (Figure 4a and Figure 5a). The SOM2 pattern is characterized by an anomalous cyclonic circulation, accompanied by an anomalous cyclonic circulation over the northwestern subtropical Pacific (Figure 4b). This pattern induces significantly above-average precipitation in the NCP and below-average precipitation in NEC (Figure 5b). For the SOM3 pattern, the anomalous cyclonic circulation over eastern Asia reduces water vapor transport from the North Pacific, thereby decreasing precipitation in NEC, while the anomalous southerly water vapor transport over eastern China results in above-average precipitation in the NCP (Figure 4c and Figure 5c). Compared to the SOM3 pattern, the anomalous cyclonic circulation associated with the SOM4 pattern moves southeastward, enhancing southeasterly water vapor transport from the North Pacific (Figure 4d). This pattern intensifies precipitation in NEC but significantly reduces precipitation in the NCP (Figure 5d). Under the SOM5 pattern, a pronounced anticyclone–cyclone dipole pattern appears over the midlatitudes of East Asia, and this pattern significantly reduces precipitation in NC (Figure 4e and Figure 5e). The SOM6 pattern displays a significant anticyclonic circulation near the coastal region of eastern China, leading to above-average precipitation in the NCP and below-average precipitation in NEC (Figure 4f and Figure 5f).

3.2. Synoptic Circulation Patterns and Extreme Precipitation

To explore the relationship between synoptic circulation patterns and extreme daily precipitation in NC, we define the frequency of extreme daily precipitation events under each SOM pattern as the ratio of the days with extreme precipitation events and the total days in the pattern. Figure 6 shows the frequency of daily extreme precipitation events based on precipitation gauge data at each station. The frequency of extreme precipitation events exceeds 4.5% in the coastal regions under the SOM1 pattern (Figure 6a). Compared to other SOM patterns, the SOM2 pattern is responsible for a higher frequency of daily extreme precipitation events in the east of 120° E, where the frequency of extreme precipitation events exceeds 7% for most stations (Figure 6b). The SOM3 pattern contributes to the frequency of daily extreme precipitation events exceeding 5% in the NCP. Under the SOM4 pattern, a higher frequency of daily extreme precipitation events is observed in NEC than in the NCP (Figure 6c,d). Moreover, the SOM5 pattern is not favorable for daily extreme precipitation events in NC (Figure 6e), and the frequency of daily precipitation extremes exceeds 4.5% for most stations in NC under the SOM6 pattern (Figure 6f). Overall, the synoptic circulation patterns strongly influence the frequency of daily precipitation extremes in NC.
We further investigate the impacts of the SOM patterns on REPEs to improve the understanding of extreme precipitation on regional scales. The total stations are 57 and 56, the thresholds of REPEs are 12 and 11 stations, and the total occurrences of REPEs are 224 and 218 days in the NCP and NEC, respectively (Figure 1a and Figure 7a). Figure 7a illustrates the proportion of REPE occurrences relative to the total REPE occurrences under each SOM pattern. In the NCP, the SOM2 pattern results in a relatively higher occurrence of REPEs, with a ratio of approximately 24%, whereas the SOM4 and SOM5 patterns have lower occurrences of REPEs. The remaining SOM circulation patterns contribute comparably to the incidence of REPEs in the NCP, with a ratio of below 20%. In NEC, the SOM4 pattern is responsible for a higher incidence of REPEs, while a lower incidence of REPEs occurs under the SOM5 and SOM6 patterns. Notably, the incidence of REPEs in NEC exceeds 15% for the SOM1, SOM2, and SOM3 patterns.
The intensity of REPEs is determined by calculating the regional mean precipitation from the observed stations during REPE occurrences. The intensity of REPEs in the NCP is more robust than that in NEC, which can be attributed to the higher amount of precipitable water in the atmosphere over the NCP (Figure 7b,d). This result is consistent with Dong et al. [54], who revealed that the intensity of extreme precipitation is influenced by the availability of precipitable water in regions with limited water vapor. In the NCP, the SOM2 pattern results in more intense REPEs than other SOM patterns, primarily due to the enhanced upward motions and increased precipitable water (Figure 7b–d). In NEC, the SOM4 pattern is associated with stronger REPE intensity compared to other SOM patterns, which is connected to the intensification of upward motions (Figure 7b–d). In summary, the intensity of REPEs appears to be more closely related to changes in vertical velocity than changes in precipitable water for a specific region.

3.3. Interannual Variability of Synoptic Circulation Patterns

The occurrences of the SOM1 and SOM2 patterns show remarkable interannual variability, with significant increasing trends (Figure 8a,b). It can be seen that the occurrences of the SOM3 and SOM4 patterns have decreasing trends, significant at the 95% confidence level (Figure 8c,d). In contrast, the occurrences of the SOM5 and SOM6 patterns show pronounced interannual variability but no apparent linear trends (Figure 8e,f). Figure 9 shows the interannual variability of regional summer precipitation anomalies and their correlation with the occurrences of the SOM patterns. Regional summer precipitation changes from the CN05.1 dataset are closely consistent with those from the CMAP and IMERG satellite precipitation datasets (Figure 9a,b), implying that the monthly satellite precipitation datasets effectively capture regional precipitation changes. Furthermore, the correlation coefficients between the SOM pattern occurrences and regional summer precipitation are comparable for the CN05.1 and CMAP datasets (Figure 9c,d).
The summer precipitation in the NCP displays a significant increasing trend at the 95% confidence level, and it is significantly correlated with the SOM2 pattern occurrence, with a correlation coefficient exceeding 0.24 (Figure 9a,c). The above-average summer precipitation in the NCP in 2021 was attributed to the highest occurrence of the SOM2 pattern (Figure 8d and Figure 9b). Moreover, the correlation coefficients between the SOM4 and SOM5 pattern occurrences and summer precipitation in this region exceeds −0.24, significant at the 90% confidence level (Figure 9c). In the NCP, the highest SOM4 occurrence in 1983 corresponds to below-average summer precipitation, and the SOM5 pattern shows a higher occurrence with more than 30 days in 2002, resulting in below-average summer precipitation (Figure 8e and Figure 9a). In NEC, the summer precipitation anomalies show an interdecadal variability in the late 1990s, consistent with the findings from Han et al. [55] and Hu et al. [56], while its long-term trend is insignificant for both the CN05.1 and CMAP precipitation datasets (Figure 9b). The occurrence of the SOM3 pattern is connected to summer precipitation in NEC, with the correlation coefficient exceeding −0.2 (Figure 9d). In 1979 and 2004, the below-average summer precipitation in NEC is partly attributed to higher SOM3 pattern occurrences (Figure 8c and Figure 9b). These findings suggest synoptic circulation pattern occurrences are essential for precipitation forecasting and prediction.

4. Conclusions and Discussion

The daily 500 hPa geopotential height anomaly fields over eastern Asia during the summers of 1979–2021 are classified into six distinct patterns utilizing SOM cluster analysis. We have further investigated the impacts of synoptic circulation patterns on the incidence of REPEs in the NCP and NEC and temporal variations of these patterns.
The SOM1 pattern shows a prominent high ridge over the north of eastern Asia and a weak trough near the Korean Peninsula. The SOM2 pattern features a pronounced high ridge over eastern Asia, along with an anomalous cyclonic circulation over the northwestern subtropical Pacific. This configuration induces above-average precipitation in the NCP and below-average precipitation in NEC. The SOM3 pattern is characterized by a robust trough over the north of eastern Asia, and the anomalous anticyclone near Japan contributes to increased precipitation in the NCP. Compared to the SOM3, the enhanced trough over eastern Asia and the anomalous cyclonic circulation near Japan move southeastward under the SOM4 pattern, leading to below-average precipitation in the NCP and above-average precipitation in NEC. The SOM5 pattern mainly presents a pronounced anticyclone–cyclone dipole pattern over eastern Asia, remarkably decreasing precipitation in NC. The SOM6 pattern, the opposite phase of the SOM3 pattern, yields a pronounced anticyclonic circulation near the coastal region of eastern China and thus causes above-average precipitation in the NCP.
The synoptic circulation patterns have a pronounced influence on the incidence and intensity of REPEs in the NCP and NEC. Compared to other SOM patterns, the SOM2 pattern yields more frequent and intense REPEs in the NCP due to the higher precipitable water associated with the anomalous anticyclonic circulation near Japan. Under the SOM4 and SOM5 patterns, the incidence of REPEs in the NCP is less than 12%, as the anomalous northerly winds in the NCP are unfavorable for water vapor transport. In particular, the intensity of REPEs is weaker under the SOM4 pattern, resulting from the lower precipitable water. In NEC, the SOM4 pattern leads to an incidence of REPEs of approximately 20%, while the SOM5 pattern results in the lowest incidence of REPEs. In addition, the REPEs in NEC tend to be more intense under the SOM4 pattern, partly attributed to the anomalous cyclonic circulation near Japan that enhances upward motions. It should be noted that regional differences in precipitable water play a crucial role in determining the intensity of REPEs, with those in the NCP generally being more intense than those in NEC.
On interannual time scales, the occurrences of the SOM1 and SOM2 (SOM3 and SOM4) patterns present significant increasing (decreasing) trends. The SOM2 pattern occurrence is significantly linked to summer precipitation changes in the NCP, suggesting that the SOM2 pattern occurrence is partly responsible for the increasing trend of summer precipitation in the NCP. Specifically, the above-average precipitation in 2021 is attributed to a higher SOM2 pattern occurrence. In contrast, the summer precipitation in NEC shows an insignificant increasing trend. There is a negative correlation between the precipitation changes in NEC and the SOM3 pattern occurrence, and higher occurrences of the SOM3 pattern in 1979 and 2004 correspond to below-average precipitation in this region. These results highlight the impact of synoptic circulation patterns on mean and extreme precipitation on regional scales.
The synoptic circulation patterns over the midlatitudes have been classified using objective methods in previous studies [35,48]. While the SOM approach is a neural network algorithm utilizing unsupervised artificial neural networks, generating realistic SOM nodes is of significance in synoptic climatology applications [4]. This is particularly notable when the SOM nodes are linked to extreme events, especially when dealing with a relatively small SOM size. With an increase in SOM size (e.g., 30–40 nodes), SOM patterns become evident as the self-organization of nodes efficiently groups neighboring nodes into small secondary clusters that encompass the entire SOM space [57]. Moreover, the uneven distribution of meteorological stations might influence the selection of REPEs [58,59], and data processing methods need to be adopted to assure the consistency of the precipitation-gauge data for stations in future research.
This study further investigated the synoptic circulation patterns over eastern Asia and their impacts on summer mean and extreme precipitation on regional scales. Ohba et al. [60] found that El Niño events are responsible for the synoptic circulation patterns that increase heavy precipitation events in southwestern Japan. Given that atmospheric circulation patterns act as a bridge between external forcings and surface weather conditions, it is worthwhile to investigate the impacts of sea surface temperature anomalies on the occurrences of the SOM patterns. In addition, the Arctic Oscillation, which is one of the most dominant circulation patterns of the Northern Hemisphere, dramatically influences the occurrences of the synoptic circulation patterns derived from SOM cluster analysis [39,61]. The current study merely classified the synoptic circulation patterns over eastern Asia; however, the relationship between the conventional teleconnection patterns and synoptic circulation patterns warrants further investigation.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/atmos14121705/s1, Figure S1: (a) Pattern correlations between the SOM node and the actual geopotential field and (b) pattern correlations between each possible SOM node pair for a specific node configuration. Figure S2: SOM-derived summer synoptic circulation patterns over eastern Asia during 1979–2021 based on the NCEP-DOE AMIP-II reanalysis dataset.

Author Contributions

Conceptualization, S.L. and T.S.; methodology, S.L.; software, P.Y.; formal analysis, G.F. and S.L.; investigation, S.L.; writing—original draft, S.L. and G.F.; writing—review and editing, S.L., G.F. and T.S.; visualization, P.Y.; validation, P.Y.; supervision, T.S. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Natural Science Foundation of China (Grants No. 42175071, 41905060, 42375056, 42075051, 42130610, and 42275029) and the Drought Meteorological Science Research Foundation (IAM201905).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The ERA5 reanalysis dataset can be accessed from the European Centre for Medium-Range Weather Forecasts (https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5, accessed on 12 September 2023). The daily precipitation data from CN05.1 and ground-based meteorological stations can be accessed from the China Meteorological Data Service Centre at the China Meteorological Administration (http://data.cma.cn/en, accessed on 12 September 2023). The CMAP satellite precipitation dataset can be obtained from the National Ocean and Atmospheric Administration (https://psl.noaa.gov/data/gridded/data.cmap.html, accessed on 25 September 2023).

Acknowledgments

We would like to thank the European Centre for Medium-Range Weather Forecasts, the National Ocean and Atmospheric Administration, NASA, and the China Meteorological Administration for providing the reanalysis and observational datasets. The related codes in this study are available upon request.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. (a) he location of observed stations (blue dots) and the subregions (North China Plain (NCP): 112°–122° E, 34°–42° N and northeastern China (NEC): 122°–135° E, 40°–52° N) with the surface terrain height shaded, (b) mean summer precipitation, and (c) the 95th percentile of daily precipitation (units: mm/d) during the summers of 1979–2021.
Figure 1. (a) he location of observed stations (blue dots) and the subregions (North China Plain (NCP): 112°–122° E, 34°–42° N and northeastern China (NEC): 122°–135° E, 40°–52° N) with the surface terrain height shaded, (b) mean summer precipitation, and (c) the 95th percentile of daily precipitation (units: mm/d) during the summers of 1979–2021.
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Figure 2. SOM-derived summer synoptic circulation patterns (shading: 500 hPa geopotential height anomalies, contours: 500 hPa geopotential height) over eastern Asia during 1979–2021, units: gpm. (a) SOM1, (b) SOM2, (c) SOM3, (d) SOM4, (e) SOM5, and (f) SOM6. The values at the top indicate the occurrence ratios of the SOM patterns, and stippling denotes the 95% confidence level.
Figure 2. SOM-derived summer synoptic circulation patterns (shading: 500 hPa geopotential height anomalies, contours: 500 hPa geopotential height) over eastern Asia during 1979–2021, units: gpm. (a) SOM1, (b) SOM2, (c) SOM3, (d) SOM4, (e) SOM5, and (f) SOM6. The values at the top indicate the occurrence ratios of the SOM patterns, and stippling denotes the 95% confidence level.
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Figure 3. (a) Occurrence (units: days), (b) frequency (units: times), and (c) mean duration (units: days) of each SOM pattern.
Figure 3. (a) Occurrence (units: days), (b) frequency (units: times), and (c) mean duration (units: days) of each SOM pattern.
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Figure 4. Summer vertically integrated water vapor flux anomalies (vectors) and corresponding magnitude (shading) associated with the SOM patterns, units: kg m − 1 s − 1 . (a) SOM1, (b) SOM2, (c) SOM3, (d) SOM4, (e) SOM5, and (f) SOM6. Stippling and vectors exceed the 95% confidence level.
Figure 4. Summer vertically integrated water vapor flux anomalies (vectors) and corresponding magnitude (shading) associated with the SOM patterns, units: kg m − 1 s − 1 . (a) SOM1, (b) SOM2, (c) SOM3, (d) SOM4, (e) SOM5, and (f) SOM6. Stippling and vectors exceed the 95% confidence level.
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Figure 5. Summer precipitation anomalies (units: mm/d) associated with the SOM patterns: (a) SOM1, (b) SOM2, (c) SOM3, (d) SOM4, (e) SOM5, and (f) SOM6. The boxplots at the top left indicate summer precipitation anomalies in NC, the NCP, and NEC. Stippling denotes the 95% confidence level.
Figure 5. Summer precipitation anomalies (units: mm/d) associated with the SOM patterns: (a) SOM1, (b) SOM2, (c) SOM3, (d) SOM4, (e) SOM5, and (f) SOM6. The boxplots at the top left indicate summer precipitation anomalies in NC, the NCP, and NEC. Stippling denotes the 95% confidence level.
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Figure 6. Spatial distribution of the frequency (shaded circles, units: %) of extreme daily precipitation events under each SOM pattern: (a) SOM1, (b) SOM2, (c) SOM3, (d) SOM4, (e) SOM5, and (f) SOM6. Circles represent observed stations, and an extreme daily precipitation event at each station is defined as the daily precipitation exceeding the 95th percentile of all summer days.
Figure 6. Spatial distribution of the frequency (shaded circles, units: %) of extreme daily precipitation events under each SOM pattern: (a) SOM1, (b) SOM2, (c) SOM3, (d) SOM4, (e) SOM5, and (f) SOM6. Circles represent observed stations, and an extreme daily precipitation event at each station is defined as the daily precipitation exceeding the 95th percentile of all summer days.
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Figure 7. The (a) incidence (units: %) and (b) intensity (units: mm/d) of REPEs in the NCP and NEC under each SOM pattern. The definition of the REPEs is based on precipitation-gauge observations, and the shading bars in (a) indicate the total occurrences of REPEs. (c) Vertical velocity (units: Pa/s) and (d) precipitable water (units: mm) anomalies associated with the REPEs under each SOM pattern. Vertical velocity (multiplied by −1) and precipitable water are interpolated to the station location using bilinear interpolation method.
Figure 7. The (a) incidence (units: %) and (b) intensity (units: mm/d) of REPEs in the NCP and NEC under each SOM pattern. The definition of the REPEs is based on precipitation-gauge observations, and the shading bars in (a) indicate the total occurrences of REPEs. (c) Vertical velocity (units: Pa/s) and (d) precipitable water (units: mm) anomalies associated with the REPEs under each SOM pattern. Vertical velocity (multiplied by −1) and precipitable water are interpolated to the station location using bilinear interpolation method.
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Figure 8. Interannual variability of the SOM pattern occurrences (filled circle, units: days). (a) SOM1, (b) SOM2, (c) SOM3, (d) SOM4, (e) SOM5, and (f) SOM6. The solid lines represent the linear trends of the SOM pattern occurrences. The values in the top right indicate the linear trends, and asterisks denote the 95% confidence level.
Figure 8. Interannual variability of the SOM pattern occurrences (filled circle, units: days). (a) SOM1, (b) SOM2, (c) SOM3, (d) SOM4, (e) SOM5, and (f) SOM6. The solid lines represent the linear trends of the SOM pattern occurrences. The values in the top right indicate the linear trends, and asterisks denote the 95% confidence level.
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Figure 9. (a,b) Interannual variability of summer precipitation (filled circle, units: mm/d) in the NCP and NEC and (c,d) its correlation with the SOM pattern occurrences. The solid lines in (a,b) represent the linear trend of summer precipitation, and the dashed horizontal lines in (c,d) indicate the 90% confidence level, asterisks denote the 95% confidence level.
Figure 9. (a,b) Interannual variability of summer precipitation (filled circle, units: mm/d) in the NCP and NEC and (c,d) its correlation with the SOM pattern occurrences. The solid lines in (a,b) represent the linear trend of summer precipitation, and the dashed horizontal lines in (c,d) indicate the 90% confidence level, asterisks denote the 95% confidence level.
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Li, S.; Feng, G.; Yan, P.; Su, T. Relationship between Summer Synoptic Circulation Patterns and Extreme Precipitation in Northern China. Atmosphere 2023, 14, 1705. https://doi.org/10.3390/atmos14121705

AMA Style

Li S, Feng G, Yan P, Su T. Relationship between Summer Synoptic Circulation Patterns and Extreme Precipitation in Northern China. Atmosphere. 2023; 14(12):1705. https://doi.org/10.3390/atmos14121705

Chicago/Turabian Style

Li, Shuping, Guolin Feng, Pengcheng Yan, and Tao Su. 2023. "Relationship between Summer Synoptic Circulation Patterns and Extreme Precipitation in Northern China" Atmosphere 14, no. 12: 1705. https://doi.org/10.3390/atmos14121705

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