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Article

Inter-Model Spread in Representing the Impacts of ENSO on the South China Spring Rainfall in CMIP6 Models

by
Xin Yin
1,
Xiaofei Wu
2,*,
Hailin Niu
1,
Kaiqing Yang
2 and
Linglong Yu
2
1
Yuyao Meteorological Bureau, Ningbo 315400, China
2
Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(10), 1199; https://doi.org/10.3390/atmos15101199
Submission received: 25 August 2024 / Revised: 27 September 2024 / Accepted: 3 October 2024 / Published: 8 October 2024
(This article belongs to the Special Issue Precipitation Observations and Prediction (2nd Edition))
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Abstract

:
A major challenge for climate system models in simulating the impacts of El Niño–Southern Oscillation (ENSO) on the interannual variations of East Asian rainfall anomalies is the wide inter-model spread of outputs, which causes considerable uncertainty in physical mechanism understanding and short-term climate prediction. This study investigates the fidelity of 40 models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) in representing the impacts of ENSO on South China Spring Rainfall (SCSR) during the ENSO decaying spring. The response of SCSR to ENSO, as well as the sea surface temperature anomalies (SSTAs) over the tropical Indian Ocean (TIO), is quite different among the models; some models even simulate opposite SCSR anomalies compared to the observations. However, the models capturing the ENSO-related warm SSTAs over TIO tend to simulate a better SCSR-ENSO relationship, which is much closer to observation. Therefore, models are grouped based on the simulated TIO SSTAs to explore the modulating processes of the TIO SSTAs in ENSO affecting SCSR anomalies. Comparing analysis suggests that the warm TIO SSTA can force the equatorial north–south antisymmetric circulation in the lower troposphere, which is conducive to the westward extension and maintenance of the western North Pacific anticyclone (WNPAC). In addition, the TIO SSTA enhances the upper tropospheric East Asian subtropical westerly jet, leading to anomalous divergence over South China. Thus, the westward extension and strengthening of WNPAC can transport sufficient water vapor for South China, which is associated with the ascending motion caused by the upper tropospheric divergence, leading to the abnormal SCSR.

1. Introduction

In the south of the middle and lower reaches of the Yangtze River, persistent rainfall usually appears during the transition from wintertime to summertime. This rainy period, namely the South China Spring Rainfall (SCSR), is linked with the stagnation of the rain belt over South China (SC) in spring and early summer [1], with extensive range, long duration, and high frequency in March and April. The SCSR can exceed 30% of the annual total precipitation, comparable to the proportion of summer precipitation, with significant interannual variability [2]. The densely populated and economically developed SC is prone to frequent droughts and floods, which often cause severe losses to the local economy and life safety. Therefore, investigating the mechanism for the interannual variation of the SCSR is of great significance for disaster prevention and mitigation.
Previous studies have shown that there are many factors affecting the interannual variability of SCSR, such as thermodynamic and dynamical factors in the Tibet Plateau (TP), snow cover in Eurasia, and the Madden–Julian Oscillation [3,4,5,6]. However, the sea surface temperature anomaly (SSTA) has the greatest influence on SCSR. The El Niño–Southern Oscillation (ENSO), as the strongest air–sea coupling signal in the world, has an important influence on SCSR. In the year of El Niño events, an anticyclone in the western North Pacific (WNP) leads to an increase in precipitation in SC but less in North China. However, even during an El Niño event, different types of SSTA modes induce distinct effects on SCSR. For the decaying El Niño Modoki, no obvious rainfall signals are found in the spring. It has been shown that the reason for the different performances of SCSR during the El Niño event and the El Niño Modoki event is the difference in the evolution and location of the WNP anticyclone (WNPAC) [7]. Furthermore, the ENSO signal itself is also affected and influenced by other factors, making this relationship between ENSO and SCSR unstable. As the main interdecadal signal in the North Pacific, the Pacific Decadal Oscillation (PDO) plays a key role in modulating the relationship between ENSO and SCSR. When ENSO and PDO are in-phase, the relationship between ENSO and SCSR is positive. In contrast, when ENSO and PDO are out-of-phase, the relationship between ENSO and SCSR is negative [8]. In addition to directly influencing East Asian circulation anomalies through Pacific SSTA, ENSO can also affect Indian Ocean SSTA during winter via an atmospheric bridge. Then, through the “charging-discharging effect”, the Indian Ocean SSTA can persist into spring and summer, with the Indian Ocean Basin (IOB) mode being formed under the influence of ENSO [9]. IOB can lead to positive SCSR anomalies by strengthening the WNPAC in March. As the season progresses, the position of WNPAC gradually moves northward from April to May, which in turn leads to the northward shift of the rain band [10]. It has been pointed out that IOB can also influence the SCSR by regulating the ENSO circulation anomaly, [i.e., the warm (cold) phase of El Niño (La Niña) and IOB in the preceding winter is positively correlated with the SCSR, but the SCSR anomaly is not significant when the IOB is in the neutral state] [11]. In terms of the ENSO–Indian Ocean Dipole (IOD) relationship, ENSO can induce easterly anomalies over the eastern Indian Ocean and then trigger the IOD by modulating the Walker circulation [12]. Furthermore, it has indicated that the spatial variabilities of dryness and wetness on land are strong during combined CP El Niño and positive IOD events in China [13]. It is clear that the combination of the Indian Ocean and the Pacific Ocean is more informative for predicting the SCSR in the period of ENSO decay.
Since current climate models have been able to simulate ENSO and Indian Ocean SSTA modes better [14,15,16,17,18,19], many scientists have focused on model simulations. CMIP6 is currently underway. Compared to CMIP5, it has improved parameterization schemes for cloud microphysical and biochemical processes in the climate system and increased resolution. The results of the piControl experiment of the atmospheric circulation model of CMIP6 were examined by researchers, who found that most models reproduced the fluctuations in SST and sea level pressure between El Niño and La Niña not only in the equatorial Pacific but also in the tropics, as well as in the middle and high latitudes [20]. For precipitation, some researchers show that the CMIP6 models can simulate the overall spatial distribution of annual mean precipitation in China, as well as the seasonal variation of precipitation, weak in winter and strong in summer [21,22]. However, some studies have pointed out that the CMIP6 models significantly underestimate the simulation for SCSR. Comparing among models, it is concluded that the underestimation of the intensity of the East Asian Subtropical Jet in the CMIP6 models generally leads to the weakening of the secondary meridional circulation south of 30° N and the water vapor convergence over SC [23]. Tang et al. [24] examined 40 CMIP6 global climate models for quantitative assessment of extreme precipitation in the Central South Peninsula and SC, and by selecting the optimal model for multi-model ensemble averaging, pointed out that the meridional wind component over southern China and the water vapor convergence in the central and southern peninsulas are conducive to compensating for the water vapor bias in the models. Additionally, the CMIP6 models commonly show an improved simulation of ENSO SST pattern, with a less excessive westward extension bias of ENSO SST in the equatorial Pacific compared to CMIP5 models [25]. Since ENSO significantly affects other sea areas through remote connections, many studies evaluating whether CMIP6 models can capture interoceanic connections have some significance for improving the understanding of inter-basin interactions and the ability of climate models to predict ENSO [26,27]. In addition, some scholars have investigated whether the CMIP3, CMIP5, and CMIP6 data can capture the link between ENSO and East Asian summer rainfall (EASR) and whether the CMIP6 data are closer to the actual data for the simulation of the linkage between ENSO and tropical Indian Ocean (TIO). But, as in CMIP3 and CMIP5, the Philippine Sea convection in the CMIP6 is not as good as the actual data, and the ENSO-EASR relationship is also underestimated [28,29,30]. Nevertheless, some studies have indicated that the CMIP6 model may underestimate the phase-locking intensity of ENSO. The average peak month of the model ensemble is consistent with observations, but there are considerable discrepancies among models [31].
Although the CMIP6 model has obvious improvements over the previous version in simulating ENSO and precipitation, there are large differences among the CMIP6 models, which causes considerable uncertainty in physical mechanism understanding and short-term climate prediction. The main objective of this study is to assess the impact of pre-winter tropical SSTA on the SCSR in the CMIP6 model. Specifically, we attempt to address the following questions by comparing the CMIP6 model with the observational data: (1) Can the CMIP6 models simulate the impact of pre-winter tropical SSTA on the SCSR? (2) What factors contribute to the deviations of the SCSR between the models and the observations during the ENSO decay period? (3) Which models can better simulate the impact of pre-winter tropical SSTA on the SCSR, and the reasons.
Following this introduction, the data and methods are described in Section 2. Section 3 discusses the relationship between ENSO and SCSR in CMIP6. Section 4 examines the influence of the Indian Ocean on SCSR during the ENSO decay period. The physical mechanisms by which ENSO affects SCSR in different models are investigated in Section 5. Finally, Section 6 provides a summary and discussion.

2. Data, Models, and Methods

2.1. Observational and Reanalysis Datasets

For the observation, the daily precipitation date in the period of 1951–2019 from 846 stations of the China Meteorological Administration is used [32]. For the reanalysis, the monthly Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST) with a horizontal resolution of 1° × 1° is also used [33,34]. Another product is fifth-generation atmospheric reanalysis data of the European Center (ERA5), including monthly atmospheric wind field and specific humidity, which has a horizontal resolution of 1° × 1° and a vertical level of 19 layers [35,36]. In this study, the data period used is from 1979 to 2014.

2.2. CMIP6 Models

The CMIP6 historical simulation experiments were integrated from 1850 to 2014, driven by observed external forcings, including greenhouse gases, aerosols, solar radiation, etc. [37]. In this study, the historical simulations of 40 CMIP6 models are used. Table 1 lists the detailed features of these models, and further details can be obtained online (https://esgf-node.llnl.gov/search/cmip6 accessed on 4 October 2023).

2.3. Evaluation Methods

For consistency, all the models have been bilinearly interpolated onto a common 1° × 1° grid to enable multimodal ensemble (MME) analysis, which is obtained by simply averaging over the available models with equivalent weight. The intensity of interannual variability is typically measured using the interannual standard deviation (STD). In this study, correlations and regression coefficients for each model are calculated, and then MMEs are created. Additionally, the variances are calculated beforehand, and the STDs are then derived from the variances [28].
To define the interannual variability of the ENSO state, we use monthly SSTA to construct the Niño3.4 index during the preceding winter (DJF) in the region [5° S–5° N, 170–120° W]. Unless otherwise stated, the Niño3.4 index in the following text refers to those in the preceding winter. The TIO index (TIOI) is defined as the SSTA averaged within the region [30° S–30° N, 40–100° E] during spring (MAM). The SCSR index (SCSRI) is defined as the spring precipitation averaged over the region [24°–34° N, 110–122° E]. To highlight interannual changes, the linear trends are removed from all variables before the above analysis.

3. The Connection between ENSO and SCSR in CMIP6

Figure 1 shows the climatological distributions of boreal spring precipitation and water vapor flux over Eastern China from 1979 to 2014. Both the models and observation present that the center of high spring precipitation is located in SC, with a noteworthy anticyclone present over the WNP. Large spreads in precipitation amounts are observed among the models. Figure 2 shows the regression map of spring precipitation onto the Niño3.4 index. For observation, a significant positive precipitation anomaly occurs in SC, corresponding to the positive Niño3.4 index. However, there is a large diversity in the response of SCSR anomalies to ENSO among the CMIP6 models. About half of the models simulate weaker anomalous signals than observations, and three models even simulate opposite abnormal signals, including CMCC-CM2-SR5, INM-CM4-8, and INM-CM5-0. As most models simulate weaker anomalous signals than observations, the MME also shows a quite weak value (nearly 0.3).
Due to the strong interannual variability of ENSO, we further examine if the simulated intensity of the ENSO’s interannual variability could influence the ENSO-SCSR relationship. As shown in Figure 3, the correlation between ENSO and SCSR is positively correlated with the interannual variability of ENSO, though some discreteness exists among the CMIP6 models. There are 18 models with statistically significant correlations between ENSO and SCSR, with the STDs of Niño3.4 spreading between 0.9 and 2.1. However, among the other 22 models with ENSO-SCSR correlation coefficients that do not pass the 95% significance test, there are 10 (12) models with smaller (larger) STDs of Niño3.4 index than observation. Therefore, though a positive correlation exists between the ENSO-SCSR correlation and the interannual variability of ENSO, the interannual variability of ENSO is not the only influencing factor; it is also influenced by other factors.
Some models simulate a weak correlation between ENSO and SCSR (Figure 2), and their STDs of Niño3.4 index are also low (INM-CM4-8, INM-CM5-0). In addition, the correlation of ENSO-SCSR shows a wide spread among the models, with the lowest correlation coefficient being −0.22 and the highest correlation coefficient being 0.67 (Figure 3), indicating that the relationship between ENSO and SCSR in the models is not stable and some other factors also indirectly affect their relationship.

4. The Impact of SSTA in TIO on SCSR in ENSO Decaying Spring

It is argued that the ENSO teleconnection induces warm SSTA in the TIO like a battery charging capacitor, and then TIO warming transmits the delayed effect of ENSO on the East Asian circulation like a discharging capacitor, referred to as the Indian Ocean capacitor effect [38]. Figure 4 illustrates the correlation between the spring SSTAs and the Niño3.4 index for both observation and models. The observation reveals the persistence of warm SSTAs in the TIO and central–eastern Pacific and cold SSTA in the western Pacific, demonstrating a “positive-negative-positive” tripolar SSTA with zonal orientation. Most of the CMIP6 models and the MME can reproduce the tripolar SSTA pattern like the observation, but some models show differences. For example, INM-CM4-8 and INM-CM5-0 cannot capture the cold SSTAs over the western Pacific, and BCC-ESM1 even simulates weak negative SSTAs in the tropical central–eastern Pacific. Meanwhile, some models simulate weaker SSTAs in TIO than other models.
And then, the influence of TIO on SCSR should be further investigated in the models. Figure 5 shows the regression of spring SSTA to the standardized SCSRI in observation and models. The observation presents a zonal “positive-negative-positive” tripolar SSTA pattern similar to the ENSO-related SSTAs in Figure 4. However, the performance of the models is not satisfactory, and most models cannot reproduce the significant warm SSTAs over the central–eastern Pacific and TIO. Usually, these models capturing the significant ENSO-SCSR correlation also reproduce well the significant warm SSTAs over the central–eastern Pacific and TIO, with the stronger SSTAs in the TIO and central–eastern Pacific making the stronger the relationship between ENSO and SCSR. However, the MME does not capture the warm SSTAs in the TIO and the central–eastern Pacific reasonably. Consequently, it indicates that the warm SSTAs in the TIO and the tropical central–eastern Pacific are a key impact factor for the interannual variability of SCSR, especially the warm SSTAs in the TIO may reinforce the correlation between ENSO and SCSR in models.
To further study the role of TIO SSTAs in the modulation of ENSO-SCSR relationship in models. Figure 6 shows the relationship between TIO SSTAs and ENSO, SCSR, and ENSO-SCSR, respectively. From the scatter diagrams of the interannual STDs of spring TIOI and the interannual STDs of Niño3.4 index (Figure 6a), it can be inferred that stronger TIOI variability is often associated with stronger ENSO variability, with a correlation coefficient of 0.77 between the STDs of Niño3.4 index and the STDs of TIOI for the 40 models. Moreover, most models and the MME simulate larger TIOI interannual variability than observation, demonstrating that most models can simulate the teleconnection of TIO SSTAs to ENSO, consistent with Figure 4. In Figure 6b, a scatter plot of the interannual STDs of TIOI and SCSR illustrates a weak positive correlation. In some models, there are different STDs of SCSR with the same STDs of TIOI, which indicates that some models can not reflect the correlation between them. The correlation between the interannual STDs of TIOI and ENSO-SCSR correlation (Figure 6c) implies that the ability of models to generate a significant positive correlation between ENSO and SCSR is close to the capacity to simulate high TIOI interannual variability. The correlation coefficient between the STDs of ENSO-SCSR and TIOI for the 40 models is 0.43. Only one model, MPI-ESM1-2-HR, passes the 95% significance test and displays a TIOI interannual STD smaller than the observation. This suggests that the interannual variability of TIO is positively proportional to the correlation between ENSO and SCSR. Thus, the interannual variability of TIO itself has little effect on SCSR based on the model simulations, but it can play a positive role in the ENSO-SCSR connection.
Most models can reproduce the ENSO-related SSTAs in TIO during spring, but only a few models can capture the SCSR-related SSTAs in TIO, indicating that numerous models cannot grasp the impact of TIO SSTAs on SCSR. Figure 7 illustrates the regression of precipitation anomalies to standardized TIOI in the models and observation, respectively. In observation, the positive TIOI usually leads to the positive precipitation anomaly in SC, which is similar to the Niño3.4 index (Figure 2). However, only a few models can reproduce the positive precipitation anomalies in SC related to TIOI. Among the models capturing the impact of El Niño on SCMR well, only a few of these models can reproduce the influences of the SSTAs in TIO on SCSR reasonably.
Figure 8 shows the scatter diagram of the ENSO-SCSR correlations and the TIOI-SCSR correlations. It further suggests that the ENSO-SCSR and TIO-SCSR correlation coefficients are consistent with each other. The model with a high TIOI-SCSR correlation coefficient corresponds to a high ENSO-SCSR correlation coefficient. The correlation coefficient between ENSO-SCSR and TIOI-SCSR stands at 0.87. In addition, neither the ENSO-SCSR nor TIOI-SCSR relationships are significant in the MME result.

5. The Mechanism of ENSO and TIO Affecting SCSR

In the previous sections, it was discovered that the inter-models show diversity in the simulation of the ENSO-SCSR relationship. The reason for the differences may be due to the differences in the simulation of SSTAs in TIO. Therefore, in order to further explore the mechanism of SSTAs in TIO as well as ENSO effecting on SCSR in the models, we divided the models into three groups: (1) models with both significant ENSO-SCSR correlation and TIOI-SCSR correlation (referred to as the “ENSO-TIO” group); (2) models with only significant ENSO-SCSR correlation (referred to as “ENSO-only” group); (3) modes with only significant TIOI-SCSR correlation (referred to as “TIO-only” group), where the MPI-ESM-1-2-HAM model did not pass the test of significance but its correlation coefficient is very close to the test coefficients and is therefore included. The specific categorization is shown in Table 2.
We regressed the three groups of models with the Niño3.4 index to obtain the distribution of precipitation anomaly and SSTA under the influence of ENSO (Figure 9 and Figure 10). For precipitation, we can clearly observe that all groups show a positive correlation between ENSO and SCSR, but the intensity of precipitation anomalies in “ENSO-TIO” is much larger than that of the other two groups, and it is also larger than the observation, indicating that the “ENSO-TIO” group overestimates the relationship between ENSO and SCSR (Figure 9b). On the contrary, the “TIO-only” group has almost no significant precipitation anomalies, which can be inferred from the fact that the models in the “TIO-only” group underestimate the relationship between ENSO and SCSR (Figure 9d). Therefore, we can infer that when the models can simulate the relationship between TIO and SCSR, they can significantly improve the relationship between ENSO and SCSR (Figure 9b,c). For SSTA, three groups can demonstrate a “positive-negative-positive” tripolar SSTA with zonal orientation, consistent with the observation (Figure 10a). In addition, the observed SSTAs in the central–eastern Pacific are much smaller than the other three groups, although the SSTAs of TIO are not as significant as the observation in the three models located in the eastern TIO (Figure 10b–d). It seems that the three groups of models show that the differences in SSTA modes related to ENSO are not very large, but the differences in related precipitation anomalies are not small. Given the impact of SSTA on circulation, it is crucial to investigate circulation anomalies of effect on precipitation.
To further investigate how well the models simulate physical processes linking ENSO-related and TIO-related SSTAs to the SCSR anomalies, the upper and lower tropospheric circulation, and the vertical integral water vapor flux are regressed upon the Nino3.4 index, as shown in Figure 11 and Figure 12, respectively. In the observation, a large anomalous anticyclonic circulation is observed in the low troposphere over the WNP (Figure 11a), which originates and is maintained by wind–evaporation–SST feedback resulting from El Nino [39]. Although such anticyclone anomalies can be reproduced by all three groups of models, only the intensity of circulation anomalies in the “ENSO-TIO” group resembles the observation (Figure 11b). Such a phenomenon also occurs in the regressing map of the water vapor flux (Figure 12b). In addition, observation shows a consistent easterly anomaly over the northern Indian Ocean due to the anti-Walker circulation caused by the zonal SST gradient. Since the warm SSTAs in the southern Indian Ocean are stronger than those in the northern Indian Ocean, leading to southward cross-equatorial flow, which turns into a westerly anomaly over the southern Indian Ocean due to the effect of the Coriolis force (Figure 11a), which is consistent with previous studies [40,41,42]. All groups capture the lower tropospheric easterly anomalies over the northern Indian Ocean. However, only the “TIO-only” and “ENSO-TIO” groups exhibited significant westerly anomalies over the southern Indian Ocean (Figure 11b,d), while the “ENSO-only” group did not (Figure 11c). Comparing the SSTA, it can be observed that the positive SSTA in the southern Indian Ocean is strongest in the “ENSO-TIO” group, followed by the “TIO-only” group, with the “ENSO-only” group showing the weakest anomalies (Figure 10b–d). In the upper troposphere, the circulation anomalies over East Asia show a PJ-wave pattern, with an anticyclone over the East China Sea–Japan–northern Pacific. We found that a westerly anomaly occurs on the southern side of the TP, which leads to anomalous divergence over SC and enhances ascending motion (Figure 11e). This feature is also present in the “ENSO-TIO” group, “ENSO-only” group, and “TIO-only” group, but the westerly anomaly in the “ENSO-TIO” group is much stronger than that in the “ENSO-only” group and “TIO-only” group (Figure 11f–h). The reason may be due to the stronger TIO SSTAs in the “ENSO-TIO” group, which excites stronger positive anomalies of geopotential height, and the resulting meridional gradient of pressure strengthens the East Asian upper subtropical westerly jet. Therefore, whether a model could capture the anomalous lower tropospheric anticyclone over the WNP, the north–south antisymmetric circulation over the TIO and the upper tropospheric westerly in the south of 30° N may enhance the ability to simulate the ENSO-SCSR relationship. However, comparing the “ENSO-only” group and the “TIO-only” group, it is found that ENSO can affect the lower and upper tropospheric circulation more than SSTAs in TIO, indicating that ENSO is the main factor affecting the SCSR and the SSTA in TIO can only play a promoting role.
And then, the southwesterly associated with the anomalous anticyclone over the WNP transports moisture from the Indian Ocean to SC (Figure 12a), resulting in precipitation anomalies in the region (Figure 9a). Although the same mode appears in the three groups, the water vapor convergence in TIO is not significant. In addition, compared with the “ENSO-only” group (Figure 12c), the anomalous water vapor divergence in the Philippine Islands is stronger in the “ENSO-TIO” group and the “TIO-only” group (Figure 12b,c). We can infer that under the support of TIO SSTAs, the anomalous water vapor divergence in the Philippine Islands is significantly enhanced, which is conducive to the transport of water vapor to SC. However, it should be noted that compared with the “ENSO-TIO” group and the “ENSO-only” group, the water vapor divergence in the northern tropical Pacific is significantly weaker in the “TIO-only” group.

6. Summary and Discussion

This study investigates the fidelity of 40 models from CMIP6 in representing the impacts of ENSO on SCSR during the ENSO decaying spring. Firstly, there is a large diversity in the response of SCSR anomalies to ENSO among the models. Some models simulate stronger SCSR anomalies than observations, while about half of the models simulate weaker SCSR anomalies than observations, and three models even simulate opposite abnormal signals. Secondly, the correlation between ENSO and SCSR is positively correlated with the interannual variability of ENSO, but there is still obvious discreteness among models. Among the 40 models, the correlation between ENSO and SCSR is the highest in EC-Earth3-Veg, FGOALS-f3-L, and HadGEM3-GC31-LL. However, the interannual variability of ENSO in the EC-Earth3-Veg and HadGEM3-GC31-LL is significantly lower than that in the FGOALS-f3-L. Further study of the relationship between TIOI and SCSR suggests that the relationship between TIOI and SCSR varies greatly in different models, and many models cannot simulate the response of SCSR to TIOI during the ENSO decay period. It is worth noting that the models that can reproduce a reasonable relationship between TIOI and SCSR tend to simulate a stronger response of SCSR anomalies to ENSO, which indicates that TIO SSTAs can indirectly enhance the connection between ENSO and SCSR.
In order to illustrate how TIO SSTAs enhance the connection between ENSO and SCSR, models are divided into three groups: the “ENSO-TIO” group, the “ENSO-only” group, and the “TIO-only” group. The comparing analysis suggests that the intensity of the ENSO-related SCSR anomalies in the “ENSO-TIO” group is stronger and closer to observation than that in the “ENSO-only” group, which demonstrates that TIO SSTAs do play an important role in the modulation of the ENSO-SCSR. Furthermore, three groups all reproduce the warm SSTAs in the tropical central–east Pacific, and the TIO SSTAs in the “ENSO-TIO” group are more significant than the other two groups, which leads to abnormal changes in circulations. In the lower troposphere, the models can stimulate the equatorial north–south antisymmetric circulation, which is conducive to the westward extension and maintenance of the WNPAC. In the upper troposphere, the East Asian subtropical westerly jet is strengthened, thus leading to anomalous divergence over SC. As a result, the westward extending and strengthening of WNPAC provide sufficient water vapor for SC, and the upper tropospheric divergence causes ascending motion, leading to the enhancement of the SCSR anomalies.
In this study, we evaluated the correlation between ENSO and SCSR, as well as the physical mechanisms by which ENSO and TIO SSTA influence SCSR. However, no quantitative analysis was conducted to determine the contribution of TIO SSTA to the ENSO-SCSR relationship. Therefore, numerical simulations under conditions with only TIO SSTA are necessary and meaningful to further investigate the ENSO-SCSR connection. Moreover, the climate system exhibits distinct nonlinear characteristics. The interactions between ENSO, TIO SSTA, and SCSR may also display nonlinear features, which may not be fully captured by linear regression. Future research could benefit from applying nonlinear approaches to further explore these complex relationships.

Author Contributions

Methodology, X.Y. and X.W.; Validation, X.Y. and L.Y.; Formal analysis, X.Y.; Investigation, X.Y. and X.W.; Resources, H.N.; Data curation, X.Y.; Writing—original draft, X.Y.; Writing—review & editing, X.W., K.Y., H.N. and L.Y.; Visualization, X.Y.; Supervision, X.W.; Funding acquisition, X.W. and K.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (Grant Nos. 42205058 and 41705065), the Sichuan Science and Technology Program (Grant No. 2024YFFK0411), and the National Science Foundation of Sichuan Province (Grant No. 2022NSFSC0229).

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here: https://esgf-node.llnl.gov/search/cmip6 (CMIP6), accessed on 4 October 2023; https://cds.climate.copernicus.eu/datasets (ERA5), accessed on 4 October 2023.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Climatological distribution of the MAM (March–May) precipitation (shaded, mm day−1) and water vapor flux (vector, kg m−1 s−1) over Eastern China from 1979 to 2014 for observations for the MME and individual models.
Figure 1. Climatological distribution of the MAM (March–May) precipitation (shaded, mm day−1) and water vapor flux (vector, kg m−1 s−1) over Eastern China from 1979 to 2014 for observations for the MME and individual models.
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Figure 2. Regression map of the MAM precipitation anomalies (shading, mm day−1) onto the standardized preceding DJF Niño3.4 index for observations, the MME, and individual models. The stippling denotes statistical significance at the 95% confidence level. The red box indicates the region used to define the SCSR.
Figure 2. Regression map of the MAM precipitation anomalies (shading, mm day−1) onto the standardized preceding DJF Niño3.4 index for observations, the MME, and individual models. The stippling denotes statistical significance at the 95% confidence level. The red box indicates the region used to define the SCSR.
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Figure 3. Scatter diagrams of the ENSO-SCSR correlation coefficients (Y−axis) and the interannual standard deviations of the DJF Niño3.4 index (X−axis, °C). Each dot represents the corresponding value for the model identified by the number (Table 1); “O” and “M” represent observation and MME.
Figure 3. Scatter diagrams of the ENSO-SCSR correlation coefficients (Y−axis) and the interannual standard deviations of the DJF Niño3.4 index (X−axis, °C). Each dot represents the corresponding value for the model identified by the number (Table 1); “O” and “M” represent observation and MME.
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Figure 4. Regression map of MAM SSTAs (shading, °C) onto the standardized preceding DJF Niño3.4 index in observations, the MME, and individual models. The stippling denotes statistical significance at the 95% confidence level.
Figure 4. Regression map of MAM SSTAs (shading, °C) onto the standardized preceding DJF Niño3.4 index in observations, the MME, and individual models. The stippling denotes statistical significance at the 95% confidence level.
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Figure 5. As in Figure 4, but for the MAM SSTAs regressed onto the standardized SCSR index. The stippling denotes statistical significance at the 95% confidence level.
Figure 5. As in Figure 4, but for the MAM SSTAs regressed onto the standardized SCSR index. The stippling denotes statistical significance at the 95% confidence level.
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Figure 6. Scatter diagrams of the TIOI standard variations (X−axis) and (a) DJF Niño3.4 standard variations (Y−axis), (b) SCSR standard variations (Y−axis), and (c) ENSO-SCSR correlations (Y−axis) in the CMIP6 models. Each dot represents the corresponding value for the model identified by the number (Table 1); “O” and “M” represent observation and MME.
Figure 6. Scatter diagrams of the TIOI standard variations (X−axis) and (a) DJF Niño3.4 standard variations (Y−axis), (b) SCSR standard variations (Y−axis), and (c) ENSO-SCSR correlations (Y−axis) in the CMIP6 models. Each dot represents the corresponding value for the model identified by the number (Table 1); “O” and “M” represent observation and MME.
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Figure 7. Regression map of MAM precipitation (shading, mm day−1) onto the standardized TIOI in observations, the MME, and individual models. The stippling denotes statistical significance at the 95% confidence level. The red box indicates the region used to define the SCSR.
Figure 7. Regression map of MAM precipitation (shading, mm day−1) onto the standardized TIOI in observations, the MME, and individual models. The stippling denotes statistical significance at the 95% confidence level. The red box indicates the region used to define the SCSR.
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Figure 8. Scatter diagrams of the TIO-SCSR correlation coefficients (Y−axis) and ENSO-SCSR correlation coefficients (X−axis). The color of each point represents the TIOI-ENSO correlations. “O” and “M” represent the observation and MME.
Figure 8. Scatter diagrams of the TIO-SCSR correlation coefficients (Y−axis) and ENSO-SCSR correlation coefficients (X−axis). The color of each point represents the TIOI-ENSO correlations. “O” and “M” represent the observation and MME.
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Figure 9. Regression map of MAM precipitation anomalies (shading, unit: mm day−1) onto the standardized DJF Niño3.4 index for (a) observation, (b) “ENSO-TIO” group, (c) “ENSO-only” group, and (d) “TIO-only” group. The stippling denotes statistical significance at the 95% confidence level.
Figure 9. Regression map of MAM precipitation anomalies (shading, unit: mm day−1) onto the standardized DJF Niño3.4 index for (a) observation, (b) “ENSO-TIO” group, (c) “ENSO-only” group, and (d) “TIO-only” group. The stippling denotes statistical significance at the 95% confidence level.
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Figure 10. Regression map of MAM precipitation anomalies (shading, unit: °C) onto the SSTAs for (a) observation, (b) “ENSO-TIO” group, (c) “ENSO-only” group, and (d) “TIO-only” group. The stippling denotes statistical significance at the 95% confidence level.
Figure 10. Regression map of MAM precipitation anomalies (shading, unit: °C) onto the SSTAs for (a) observation, (b) “ENSO-TIO” group, (c) “ENSO-only” group, and (d) “TIO-only” group. The stippling denotes statistical significance at the 95% confidence level.
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Figure 11. Regression map of MAM 850 hPa (left column) and 200 hPa (right column) wind anomalies (vectors, unit: mm s−1) onto the standardized DJF Niño3.4 index for (a,e) observation, (b,f) “ENSO-TIO” group, (c,g) “ENSO-only” group, and (d,h) “TIO-only” group. The red arrow indicates that at least one component of the wind vector passes the 95% significance test. The black arrow indicates that no wind vector passes the 95% significance test.
Figure 11. Regression map of MAM 850 hPa (left column) and 200 hPa (right column) wind anomalies (vectors, unit: mm s−1) onto the standardized DJF Niño3.4 index for (a,e) observation, (b,f) “ENSO-TIO” group, (c,g) “ENSO-only” group, and (d,h) “TIO-only” group. The red arrow indicates that at least one component of the wind vector passes the 95% significance test. The black arrow indicates that no wind vector passes the 95% significance test.
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Figure 12. Regression map of vertical integral moisture flux (vector, kg m−1 s−1) and moisture flux divergence (shading, 10−5 kg m−2 s−1) onto the standardized DJF Niño3.4 index for (a) observation, (b) “ENSO-TIO” group, (c) “ENSO-only” group, and (d) “TIO-only” group. The vectors indicate that at least one component of the regressed water vapor flux passes the 95% significance test. The stippling denotes statistical significance at the 95% confidence level.
Figure 12. Regression map of vertical integral moisture flux (vector, kg m−1 s−1) and moisture flux divergence (shading, 10−5 kg m−2 s−1) onto the standardized DJF Niño3.4 index for (a) observation, (b) “ENSO-TIO” group, (c) “ENSO-only” group, and (d) “TIO-only” group. The vectors indicate that at least one component of the regressed water vapor flux passes the 95% significance test. The stippling denotes statistical significance at the 95% confidence level.
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Table 1. The details of the CMIP6 models used in this study.
Table 1. The details of the CMIP6 models used in this study.
IDModel NameInstitution and CountryAtmospheric Resolution
1ACCESS-CM2CSIRO-ARCCSS, Australia192° × 144°
2ACCESS-ESM1-5CSIRO, Australia192° × 145°
3AWI-ESM-1-1-LRAWI, Germany192° × 96°
4BCC-CSM2-MRBCC, China320° × 160°
5BCC-ESM1 128° × 64°
6CAMS-CSM1-0CAMS, China320° × 160°
7CanESM5CCCma, Canada128° × 64°
8CESM2NCAR, United States288° × 192°
9CESM2-FV2 144° × 96°
10CESM2-WACCM 288° × 192°
11CMCC-CM2-SR5CMCC, Italy288° × 192°
12CNRM-CM6-1-HRCNRM-CERFACS, France720° × 360°
13CNRM-CM6-1 256° × 128°
14CNRM-ESM2-1 256° × 128°
15E3SM-1-1E3SM-Project, United States360° × 180°
16EC-Earth3EC-EARTH-Consortium512° × 256°
17EC-Earth3-Veg 512° × 256°
18EC-Earth3-Veg-LR 320° × 160°
19FGOALS-f3-LCAS, China360° × 180°
20FGOALS-g3 180° × 90°
21CAS-ESM2-0 256° × 128°
22FIO-ESM-2-0FIO-QLNM, China288° × 192°
23GFDL-ESM4NOAA/GFDL, United Stated288° × 180°
24GISS-E2-1-HNASA-GISS, United States144° × 90°
25HadGEM3-GC31-LLMOHC, United Kingdom192° × 144°
26HadGEM3-GC31-MM 432° × 324°
27UKESM1-0-LL 192° × 144°
28INM-CM4-8INM, Russia180° × 120°
29INM-CM5-0 180° × 120°
30IPSL-CM6A-LRIPSL, France144° × 143°
31MIROC6MIROC, Japan256° × 128°
32MPI-ESM-1-2-HAMHAMMOZ-Consortium, Germany192° × 96°
33MPI-ESM1-2-HRMPI-M, Germany384° × 192°
34MPI-ESM1-2-LR 192° × 96°
35MRI-ESM2-0MRI, Japan320° × 160°
36NESM3NUIST, China192° × 96°
37NorCPM1NCC, Norway144° × 96°
38NorESM2-LM 144° × 96°
39NorESM2-MM 288° × 192°
40SAM0-UNICONSNU, South Korea288° × 192°
Table 2. The classification of models based on simulated SCMR-related SSTAs.
Table 2. The classification of models based on simulated SCMR-related SSTAs.
GroupsModels
ENSO-TIOCESM2, EC-Earth3-Veg-LR, EC-Earth3-Veg,
EC-Earth3, FGOALS-f3-L, GFDL-ESM4,
GISS-E2-1-H, HadGEM3-GC31-LL,
HadGEM3-GC31-MM, MIROC6, NorESM2-MM, SAM0-UNICON, UKESM1-0-LL
ENSO-onlyACCESS-CM2, ACCESS-ESM1-5, FIO-ESM-2-0, MPI-ESM1-2-HR, NESM3
TIO-onlyBCC-ESM1, CESM2-FV2, CESM2-WACCM, FGOALS-g3, MPI-ESM-1-2-HAM
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Yin, X.; Wu, X.; Niu, H.; Yang, K.; Yu, L. Inter-Model Spread in Representing the Impacts of ENSO on the South China Spring Rainfall in CMIP6 Models. Atmosphere 2024, 15, 1199. https://doi.org/10.3390/atmos15101199

AMA Style

Yin X, Wu X, Niu H, Yang K, Yu L. Inter-Model Spread in Representing the Impacts of ENSO on the South China Spring Rainfall in CMIP6 Models. Atmosphere. 2024; 15(10):1199. https://doi.org/10.3390/atmos15101199

Chicago/Turabian Style

Yin, Xin, Xiaofei Wu, Hailin Niu, Kaiqing Yang, and Linglong Yu. 2024. "Inter-Model Spread in Representing the Impacts of ENSO on the South China Spring Rainfall in CMIP6 Models" Atmosphere 15, no. 10: 1199. https://doi.org/10.3390/atmos15101199

APA Style

Yin, X., Wu, X., Niu, H., Yang, K., & Yu, L. (2024). Inter-Model Spread in Representing the Impacts of ENSO on the South China Spring Rainfall in CMIP6 Models. Atmosphere, 15(10), 1199. https://doi.org/10.3390/atmos15101199

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