1. Introduction
Summer extreme hot days (SEHDs) are notable hazards (e.g., Lin et al. 2009; Teixeira et al. 2013). Their frequency has increased significantly at a global basis since the late twentieth century due to the increased radiative forcing of greenhouse gases (e.g., Easterling et al. 2000; Frich et al. 2002; IPCC 2013; Grotjahn et al. 2014). Besides, it also experiences substantial interannual variations at regional scales (Alexander et al. 2006; Dai et al. 2005; Ratnam et al. 2016; Lu et al. 2021). The Asian monsoon region (AMR), home for one-third of the world’s total population, is the most prominent and extensive monsoon region in the world (Wang and Lin 2002; Ding and Liu 2008; Jiang et al. 2008; Ding and Sikka 2006). The AMR often experiences SEHDs and suffers tremendous losses in human society. For example, a record-high temperature of 51°C occurred in Rajasthan, India, on 19 May 2016, causing 33 heat-related deaths in that single day (Van Oldenborgh et al. 2018). More recently, an unusual heat wave hit northeastern Asia in 2018, leaving 24 000 patients hospitalized and 90 deaths (Chen et al. 2019). Hence, improved understanding of SEHD formation in the AMR is crucial for better prediction and adaptation.
Weather and climate in the AMR are predominantly influenced by El Niño–Southern Oscillation (ENSO) that represents the largest interannual variations in our climate system (e.g., Walker 1924; Bjerkness 1969; Webster et al. 1998; Zhu et al. 2007). At the beginning of the nineteenth century, Sir Gilbert Walker discovered an antirelationship in sea level pressure between India–Australia and South America when he tried to attribute the failed Indian summer monsoon in 1876/77 that caused the worst famine in India (Walker 1923; Walker and Bliss 1932). This oscillation in the atmosphere was later related to, as the atmospheric manifestation of, the sea surface temperature (SST) anomalies in the tropical Pacific to form the term ENSO to describe the air–sea coupled phenomenon (Bjerkness 1969). It is now well accepted that when El Niño occurs, the Indian summer monsoon generally weakens, with less precipitation in the Indian subcontinent, and vice versa for La Niña.
ENSO also influences the East Asian climate. In the mature winter of El Niño, an anomalous anticyclone develops in the western North Pacific (WNP), which weakens the East Asian winter monsoon and leads to a warmer-than-normal winter in southern China (e.g., Wang et al. 2000). The anomalous anticyclone can persist till the post–El Niño summer and then influence the Asian summer climate (e.g., Wang et al. 2000; Zhang et al. 2017; Thirumalai et al. 2017). Its long persistence has been largely attributed to the local air–sea interaction (Wang et al. 2000) and the capacitor effects of the tropical Indian Ocean (Xie et al. 2009, 2016) and tropical North Atlantic (TNA) (Rong et al. 2010; Wang et al. 2017). As shown in literature, ENSO can induce homogeneous positive SST anomalies in the tropical Indian Ocean and TNA via the direct atmospheric forcing (e.g., Klein et al. 1999; Chang et al. 2006). The SST anomalies in the tropical Indian Ocean can last till the subsequent summer, anomalously heat the atmosphere and excite warm Kelvin wave propagating eastward, which enhances/suppresses the convection in the equatorial western Pacific/WNP via the surface Ekman convergence/divergence (Xie et al. 2009). The suppressed convection and related diabatic heating in the WNP not only maintain anomalous anticyclone, but also excite the Pacific–Japan (PJ) pattern along the East Asian coast (Nitta 1987) and the westward-propagating Rossby wave that reversely warms the northern Indian Ocean. This interbasin feedback completes the Indo–western Pacific Ocean capacitor (IPOC) (Xie et al. 2016). Similarly, the anomalous warming in the TNA can also persist till the subsequent summer and impact the anomalous anticyclone in the WNP via both the eastward-propagating Kelvin wave and the westward-propagating Rossby wave (e.g., Ham et al. 2013; Jiang and Li 2021). These capacitor effects thus prolong the ENSO impacts on the Asian surface temperature and precipitation in the post-ENSO summer after the demise of ENSO itself (e.g., Kosaka et al. 2013; Xie et al. 2016; Srinivas et al. 2018). It is worth noting that the ENSO impacts in the AMR undergo substantial decadal to interdecadal variations owing to the ENSO diversity and/or intervention by other climate modes such as the Pacific decadal oscillation (An and Wang 2000; Kravtsov 2011; An and Bong 2016; Lin et al. 2018). In addition, the two different flavors of ENSO, say the EP and CP ENSO with the maximum SST anomalies in the eastern and central Pacific, may have distinct climate impacts in the AMR (e.g., Weng et al. 2007, 2009; Karori et al. 2013; Choi and Ahn 2019; Nie and Sun 2022).
Besides the seasonal climate, ENSO can also modify the intraseasonal oscillation (ISO) in its decaying summer (e.g., Lin and Li 2008; Chen et al. 2020; Lin 2019). For example, the ISO activity in the WNP is significantly reduced in the post–El Niño summer mainly due to the presence of WNP anomalous anticyclone and the related anomalies in vertical motion, wind shear, and atmospheric moisture (e.g., Wu and Song 2018; Chen et al. 2020). Also, the occurrence frequency of individual ISO phases with the active convection over the eastern Indian Ocean–Maritime Continent increases significantly but that over the WNP decreases significantly probably because the reduced atmospheric moisture in the WNP hinders the northward propagation of ISO (Lin 2019; Jiang et al. 2004).
As we know that both the seasonal climate and ISO are crucial factors to the SEHD formation (AR5). It is thus interesting to examine possible influences of ENSO on the SEHD frequency in the AMR to improve our attribution, simulation, and prediction of these events. Hence, in this study, variations in the frequency of SEHDs in the AMR and the possible relationship with ENSO are investigated. Since the operational seasonal-to-interannual climate prediction is often conducted by the coupled models in most forecast centers and/or research institutes (e.g., Luo et al. 2008; Barnston et al. 2019), it is important to examine whether the ENSO–SEHD relationship can be well reproduced by the state-of-the-art coupled models. Furthermore, considering that both the ENSO characteristics and its atmospheric teleconnection to Asia are changing under global warming (e.g., Collins et al. 2010; Kug et al. 2009b), possible change in the ENSO–SEHD relationship is worthy to examine. The Coupled Model Intercomparison Project (CMIP) series have been designed to better understand the past, present, and future climate including the internal variability and changes in response to the increased radiative forcing (Eyring et al. 2016). The newly released CMIP phase 6 (CMIP6), compared to its precursors, has substantial improvement in the spatial resolution and physical parameterizations, includes more Earth system models, and thus represents the current highest level of numerical simulation of our climate system. In this regard, the CMIP6 models provide us with the best chance to address questions raised above. Therefore, the observed ENSO–SEHD relationship in the AMR, its reproducibility in the CMIP6 models and possible changes under global warming are assessed in Part I of this study. Considering that ENSO is the most predictable signal of climate variations (e.g., Luo et al. 2016), the linkage with ENSO would certainly benefit the seasonal prediction of SEHD frequency in the AMR, and this is verified in Part II of this study (Lu et al. 2023).
The rest of the manuscript is organized as follows. Section 2 describes data and methods. Section 3 presents the observed impacts of ENSO on the SEHD frequency in the AMR. The possible mechanism is discussed. Section 4 evaluates the reproducibility of the delayed ENSO–SEHD relationship in the CMIP6 historical runs. Possible change in the relationship under global warming is evaluated in section 5. Conclusions and discussion are given in section 6.
2. Data and methods
a. Data
The daily maximum surface air temperatures for the period of 1979–2014 are provided by the National Centers for Environmental Prediction/Climate Prediction Center (NCEP/CPC) with a horizontal resolution of 0.5° × 0.5°. The atmospheric and SST data in the same period are adopted from the NCEP/National Center for Atmospheric Research (NCAR) reanalysis (Kalnay et al. 1996) and National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed SST version 5 (ERSSTv5) (Huang et al. 2017), respectively.
The daily and monthly outputs of CMIP6 models are downloaded from the Earth System Grid Federation (ESGF) centers (https://esgf-node.llnl.gov/search/cmip6/). In total, 25 CMIP6 models are selected as listed in Table 1. In each model and experiment, only the ensemble member of r1i1p1 is adopted for analyses. Here, the 36-yr outputs of historical simulations from 1979 to 2014 are selected for comparison with the observations. The SSP5-8.5 scenario is updated from the RCP8.5 pathway in CMIP5 and represents the high end of the range of plausible future pathways by assuming that the anthropogenic radiation forcing will reach 8.5 W m−2 by 2100 (Eyring et al. 2016; Zhou et al. 2019). The last 36 years of the twenty-first century (2065–2100) under the SSP5-8.5 scenario are compared to the historical simulation to detect possible changes under global warming.
Brief descriptions of 25 CMIP6 models.
b. Methods
To compare the model results with the observations, daily model outputs and observational data are bilinearly interpolated to a resolution of 2° × 2°. Monthly model outputs are interpolated into the grids of the monthly observational data. Note that linear trends in both the observations and model outputs are removed prior to further analysis. Anomalies are calculated as deviations from the seasonal cycles. Summer here is boreal summer from June to August (JJA).
The domain of AMR refers to 5°–45°N and 70°–145°E that includes the subregions of the Indian monsoon (70°–105°E, 5°–27.5°N), East Asian monsoon (105°–140°E, 22.5°–45°N), and western Pacific monsoon (105°–150°E, 5°–22.5°N) (e.g., Wang and Lin 2002). To define an extreme event, two major kinds of criteria are often used. The first one adopts a fixed threshold value for the whole region of interest, such as 35°C for extreme high temperature and heat wave in China (e.g., Zhai et al. 1999; Zhang et al. 2005). The other one uses a relative threshold value by setting a specific percentile of distribution (e.g., Zhang et al. 2011; Li and Huang 2011; Zhu et al. 2020). In consideration of great geographical differences within the AMR, the relative threshold value is adopted here. First, the daily maximum surface air temperatures in JJA at a grid point during the reference period (1979–2014) are sorted in ascending order, and then the 90th percentile is taken as the threshold value of SEHDs at that grid. It is not surprising to see that within the AMR, the highest threshold value is in northern India and the lowest one is over the Tibetan Plateau (Fig. S1 in the online supplemental material), analogous to the climatological distribution of mean surface temperature in summer (figure not shown). Based on the threshold values, the SEHD frequency at each grid in each year can be obtained.
The ENSO index here is the December–February (DJF) SST anomaly averaged over the Niño-3.4 region (170°–120°W, 5°S–5°N). To depict the summer ISO activity in the AMR, we adopt the indices defined by Lee et al. (2013), which are derived from the principal components (PCs) of the two leading empirical orthogonal function (EOF) modes of multivariate EOF analysis on daily anomalies at subseasonal time scale in the outgoing longwave radiation and 850-hPa zonal wind over the Asian–western Pacific region (40°–160°E, 10°S–40°N). Here, the daily anomalies at subseasonal time scale at each grid are extracted by two steps. First, the time mean and first three harmonics of the daily annual cycle are removed from the daily data. Then, the time averages of the preceding 120 days are further subtracted to attain the anomalies. The ISO indices well represent the northward-propagating ISO in boreal summer with a canonical period of 30–60 days (Lee et al. 2013). The active ISO phases adopted in this study are defined as (PC12 + PC22)1/2 > 1. Linear correlation, regression, and composite analysis are the major methods adopted here, and the statistical significance is tested by the two-tailed Student’s t test.
3. Delayed ENSO impacts on the SEHD frequency in the AMR based on the observations
a. Delayed ENSO impacts on the SEHD frequency
The delayed impacts of ENSO on the SEHD frequency in the AMR are almost symmetric between El Niño and La Niña (figures not shown), and thus anomalies in both the SEHD and large-scale circulations regressed on the preceding DJF Niño-3.4 are only shown hereafter for simplicity. As can be seen in Fig. 1a, in the post–El Niño summers, the SEHD frequency is significantly increased in the southern AMR including the Indian subcontinent, Indo-China peninsula, the Philippines, and parts of Tibetan Plateau and eastern China. In contrast, in the northern AMR, significant anomalies with the opposite sign to its southern counterpart can only be detected in some small regions such as Hokkaido, Japan. If an index of SEHD (SEHDI) is defined as the regional mean of SEHD frequency in the southern AMR (5°–35°N and 70°–130°E; the dashed rectangle in Fig. 1a), with no surprise, it is closely related to the ENSO in the preceding winter with the lagged correlation coefficient up to 0.72, significant at the 99.9% confidence level. If the SEHDI is referred to the mean SEHD frequency to a more southward region such as (5°–30°N and 70°–130°E), its linear correlation coefficient with the preceding winter ENSO is even higher as of 0.76. Hence, if the ENSO–SEHD relation is real, it certainly provides a major source of predictability of SEHD in the southern AMR at least one season ahead.
(a) Anomalies in SEHD frequency over the AMR regressed upon the preceding DJF Niño-3.4 index (shading; days; dots denote significance at the 95% confidence level). (b) Time series of the SEHD frequency averaged over the southern AMR as indicated by the dashed rectangle in (a) (dark solid line), the preceding DJF Niño-3.4 index (red solid line), and JJA IPOC index (blue dashed line).
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-21-0667.1
b. Role of seasonal large-scale circulation anomalies
To reveal the possible mechanism responsible for the delayed impacts of ENSO on the SEHD frequency, the anomalous large-scale circulations in the post-ENSO summers are investigated first. Climatologically, the subtropical high in the WNP and the trough over the Indian subcontinent are the most distinct features in the southern AMR, while the zonal circulation is prominent in the northern AMR with a weak trough over northeastern Asia (Fig. 2a). In the post–El Niño summers, positive and quasi-barotropic anomalies of the geopotential height are broadly seen in the lower latitudes (Fig. 2a). The subtropical high in the WNP is enhanced and extends westward, the trough over the Indian subcontinent is weakened, and anomalous descending is prevailing in the southern AMR around 10°–20°N (Fig. 2b). Further, the easterly anomalies prevail over the Indian subcontinent and Indo-China peninsula, weakening the monsoonal southwesterlies (Fig. 7c). These all are conducive to suppressed convection, less cloud cover and increased local insolation and surface air temperatures (SATs) in the southern AMR (Figs. 2c). On the other hand, negative geopotential height anomalies can be seen in northeastern Asia that can enhance local convection, increase cloud cover, and thus reduce the SATs, though not statistically significant (Figs. 2a–c). The lower cloud cover and thus higher JJA SATs tend to result in increased SEHD frequency in the post–El Niño summers since they are generally significantly correlated with the SEHD frequency in the AMR (Fig. S2).
JJA anomalies in geopotential height (shading; gpm) at (a) 500 hPa and (b) latitude–height section averaged over 70°–130°E along with vertical velocity (contours; downward is solid and upward is dashed; 10−2 Pa s−1), and (c) maximum surface air temperatures (shading; °C) and total cloud cover (contours; positive is solid and negative is dashed; %) regressed upon the preceding DJF Niño-3.4 index. The JJA climatology of 500-hPa geopotential height is superimposed in (a) (contours; interval: 30; gpm). Anomalies significant at the 95% confidence level in geopotential height in (a) and (b) and surface air temperature anomalies in (c) are stippled.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-21-0667.1
As mentioned in section 1, ENSO prolongs its climate impacts in the AMR during the decaying summers mainly via provoking the capacitor effects in both the tropical Indian Ocean and TNA. Indeed, significant positive SST anomalies remain in the tropical Indian Ocean and TNA, but disappear in the tropical Pacific in the post–El Niño summers (Figs. 7a–c). The positive SST anomalies maintain the anomalous anticyclone in the WNP that excites the westward-propagating Rossby waves in the southern AMR and the PJ pattern along the East Asian coast, well explaining the observed positive/negative geopotential height anomalies in the southern AMR/northeastern Asia and the related SAT anomalies in Fig. 2. We note that the atmospheric circulation and SAT anomalies in northeastern Asia are not statistically significant, probably because the summer climate in the midlatitudes of AMR is under the influences of complex interactions among climate systems from both the low and high latitudes (Zhu et al. 2020), including the tropical SST teleconnection, mid- to high-latitude Rossby waves propagating from upstream such as the Silk Road pattern (Enomoto et al. 2003), and the position and strength of westerly jet (Huang et al. 2014). The signals from the tropics related to the ENSO may not be the dominant drivers of seasonal climate there.
Considering the relative roles of IPOC and TNA capacitor on maintaining the WNP anomalous anticyclone and influencing the SEHD in the AMR, partial correlation is applied. Here, the JJA IPOC index is defined as the region mean SST anomalies over 40°–120°E, 5°S–25°N, and the JJA TNA capacitor index is defined as the region mean SST anomalies over 95°–30°W, 5°–25°N. It is clear that both indices are closely related to the preceding DJF Niño-3.4 with the correlation coefficients of 0.72 and 0.53, respectively (Table 2). They are also closely related to each other with the correlation coefficient of 0.67. However, it seems that the TNA capacitor impacts on the SHED frequency can be well counted by the IPOC. The correlation coefficient between the TNA capacitor index and SHEDI is 0.51, significant at the 99% confidence level, but the partial one after removing the covariances with the IPOC becomes 0.12. Conversely, the correlation coefficient between the IPOC index and SHEDI is 0.67, significant at the 99% confidence level, and the partial one after removing the covariances with the TNA capacitor remains as high as 0.42, still significant at the 99% confidence level. The higher role of IPOC compared to the TNA capacitor is probably due to the interbasin positive feedback between the tropical Indian Ocean and the WNP that help keep the ENSO signals better. This can be partly proved by the much higher correlation coefficient between the IPOC and the preceding ENSO than the TNA capacitor index, 0.72 compared to 0.53 (Table 2).
Correlation coefficients among the SEHDI, DJF Niño-3.4, IPOC index, and TNA capacitor index. Values in parentheses indicate the partial correlation coefficients after excluding the influences of TNA capacitor/IPOC. The asterisk denotes that the correlation coefficient is significant at the 99% confidence level.
c. Role of the ISO
Although the increased seasonal SATs due to the anomalies in seasonal large-scale circulation can certainly enhance the SHED frequency in the AMR (Fig. S2b), there exits a discrepancy between the ENSO-related significant anomalies in the seasonal SATs and SEHD frequency in eastern China and Hokkaido, Japan (Figs. 1a and 2c). Since the ISO is one of the important drivers of extreme temperature in the AMR (e.g., Hsu et al. 2017; Zheng et al. 2022) and ENSO has significant impacts on the ISO in the AMR as mentioned in section 1, it is necessary to examine the role of ISO on the ENSO-related SEHD anomalies.
To verify the impacts of ENSO on the ISO, composite analysis is applied basing on eight post–El Niño summers with the preceding DJF Niño-3.4 larger than 0.8 standard deviation (1983, 1987, 1992, 1995, 1998, 2003, 2007, and 2010) and six post–La Niña summers with the preceding DJF Niño-3.4 less than −0.8 standard deviation (1985, 1989, 1999, 2000, 2008, and 2011). Although the total ISO variances do not change much over the land region in post-ENSO summers (Fig. S3), different individual ISO phases are preferred (Fig. S4). As shown in Fig. 3a, differences of the composite daily outgoing longwave radiation (OLR) anomalies at the subseasonal time scale for all active ISO phases between the post–El Niño and post–La Niña summers show a north–south reversed pattern approximately along 20°N. The differences in OLR along with those in the lower tropospheric circulations are similar to the ISO phase 4 with the active convection south of 20°N (Fig. 3a and Fig. S4). This is consistent with Lin (2019), who suggested that the decreased atmospheric moisture in the WNP in the post–El Niño summers (Fig. 3a) hinders the northward propagation of ISO and results in more/less occurrences of individual ISO phase with the active convection around eastern Indian Ocean–Maritime Continent/crossing the WNP. As a result, the daily surface maximum temperatures at the subseasonal time scale are significant increased over the northern Indian subcontinent, western Tibetan Plateau, and eastern China, but decreased over Hokkaido, Japan, in the post-ENSO summers (Fig. 3b, Fig. S5), which may contribute to the observed significant SEHD anomalies in these regions in the post–El Niño summers (Fig. 1a). We note that the ISO-related daily surface maximum temperature anomalies are negative over southern Indian subcontinent, Indo-China peninsula, and the Philippines. However, impacts of the anomalous cooling due to the ISO may be surpassed by the anomalous warming due to the seasonal circulation anomalies and thus the increased SEHD frequencies are broadly seen in the southern AMR. The results above suggests that the SEHDs in most AMR in the post-ENSO summers are primarily influenced by the ENSO-related seasonal SAT anomalies, whereas those in the northern Indian, western Tibetan Plateau, eastern China, and Hokkaido can be further modified by the ENSO-related changes in occurrence frequency of ISO individual phases.
The differences of composited daily anomalies at subseasonal time scale in (a) OLR (shading; W m−2) and 850-hPa horizontal wind (vectors; m s−1), and (b) maximum surface temperature (shading; °C) for all ISO active phases between the post–El Niño and post–La Niña summers. Anomalies in JJA mean relative humidity at 925 hPa regressed upon the preceding DJF Niño-3.4 index are superimposed as contours in (a) (%; interval: 0.25). Dots denote anomalies significant at the 95% confidence level.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-21-0667.1
4. Delayed ENSO impacts on the SEHD frequency in CMIP6 historical experiments
As shown in section 3, ENSO can impact the SEHD frequency in the AMR, especially its southern part in the post-ENSO summers (Fig. 1a). Since the ENSO-related significant anomalies in the SEHD frequency can only be seen in some small regions in the northern AMR, reproducibility of the observed ENSO–SEHD relationship in the CMIP6 models is mainly assessed in the southern AMR.
The threshold values of SEHDs in each CMIP6 model are calculated by the similar approaches as in the observations. Most models reproduce well the observed threshold values with the pattern correlation coefficients (PCCs) reaching 0.99 (Fig. S6). Further, many modes as well as the 25 multimodel ensemble mean (25MME) can reproduce well the observed homogeneously increased SEHD frequency in the southern AMR in the post–El Niño summers (Fig. 4). However, the SEHD anomalies in some models are very weak, indicating a large intermodel spread. To quantitatively evaluate the model performance on capturing the delayed ENSO–SEHD relationship, the temporal correlation coefficients between the SEHDI and preceding DJF Niño-3.4 index in the CMIP6 models are calculated. Meanwhile, the PCCs between the observed (Fig. 1a) and simulated (Fig. 4) SEHD frequency anomalies in the southern AMR are also calculated to estimate the spatial similarity. As shown in Fig. 5a, individual models display a large spread in reproducing the delayed ENSO–SEHD relationship with the lagged ENSO–SEHDI correlation coefficients ranging from 0.39 to 0.87 and the PCCs ranging from 0.47 to 0.87. It is not surprising to see that a significant intermodel correlation coefficient up to 0.7 exists between the ENSO–SEHDI relationship and PCCs; the models with larger ENSO–SEHDI correlation coefficients tend to be accompanied by higher PCCs.
Anomalies in the SEHD frequency over the AMR regressed upon the preceding DJF Niño-3.4 index based on the historical simulations by (a) 25MME, (b) H9MME, (c) L9MME, and (d)–(ab) each of the 25 CMIP6 models (days). Anomalies in (d)–(ab) significant at the 95% confidence level and those in (a)–(c) significant at the 95% confidence level in more than half of ensemble models are stippled.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-21-0667.1
Scatterplots of the lagged ENSO–SEHDI correlation coefficients (x axis) with (a) the PCCs between the observed and simulated SEHD frequency anomalies in the southern AMR in the post-ENSO summers (y axis), and the lagged correlation coefficients between the preceding DJF Niño-3.4 and the subsequent JJA (b) geopotential height at 500 hPa or (c) maximum surface air temperature averaged over the southern AMR (y axis) in each model. The intermodel correlation coefficients are displayed in the top-right corner. The horizontal and vertical lines in (a) represent the median of PCCs and the lagged ENSO–SEHDI correlation coefficients, respectively.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-21-0667.1
To reveal possible causality of different model performances, we group the models into high- and low-skill categories. Here, the high- (low-) skill models are selected when their lagged ENSO–SEHDI correlation coefficients and PCCs both rank within the top (bottom) 50% among all 25 models, namely, the models within the upper-right (lower-left) region in Fig. 5a. As such, we obtain nine high-skill models, which are ACCESS-CM2, CMCC-ESM2, EC-Earth3-CC, EC-Earth3-Veg, EC-Earth3-Veg-LR, IPSL-CM6A-LR, MIROC6, MPI-ESM1-2-LR, and NorESM2-LM, and 9 low-skill models, ACCESS-ESM1-5, BCC-CSM2-MR, EC-Earth3, GFDL-CM4, INM-CM4-8, KACE-1-0-G, MPI-ESM1-2-HR, MRI-ESM2-0, and NESM3. The ENSO-related SEHD anomalies based on the ensemble mean of nine high-skill models (H9MME) and nine low-skill models (L9MME) are also prepared in Fig. 4. It is apparent that H9MME reproduces the anomalous amplitudes and spatial distribution of SEHD frequency in the southern AMR much closer to the observations than 25MME (Figs. 4a,b), and thus has the higher ENSO–SEHDI correlation coefficient and PCC than 25MME (comparing the blue and dark dots in Fig. 5a). In contrast, L9MME cannot simulate significant SEHD anomalies in a vast region of southern AMR (Fig. 4c), and thus has the lower ENSO–SEHDI correlation coefficient and PCC than 25MME, let alone H9MME (comparing the red, blue, and dark dots in Fig. 5a).
The better performance of delayed ENSO–SEHD relationship in H9MME than L9MME can be largely attributed to more realistic reproduction of ENSO-related large-scale circulation anomalies. Compared to L9MME, the large-scale circulation anomalies in H9MME are larger and closer to the observed (Figs. 6a,b and 2a), which leads to higher SAT anomalies in the southern AMR (Figs. 6c,d). Since the JJA SATs are significantly positively correlated with the SEHD frequency in the CMIP6 models as in the observed (Fig. S7), the higher SAT anomalies can cause more frequent occurrences of SEHD in H9MME than L9MME (Figs. 4b,c). The important role of the large-scale circulation and SAT anomalies on the model spread in reproducing the delayed ENSO–SEHD relationship can be further confirmed by the intermodel analyses; the closer ENSO–SEHD relationship is generally accompanied by the larger ENSO-related anomalies in 500-hPa geopotential height and SAT in summers, with the intermodel correlation coefficients up to 0.82 and 0.88, respectively (Figs. 5b,c).
As in Figs. 2a and 2c, but based on historical simulations of (a),(c) H9MME and (b),(d) L9MME. Dots denote anomalies significant at the 95% confidence level in more than half of ensemble models.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-21-0667.1
As illustrated in section 3b, the large-scale circulation anomalies in post-ENSO summers are mainly induced by the IPOC effect (Figs. 7a–c). This is also true in both H9MME and L9MME as significant positive SST anomalies in the tropical Indian Ocean and the anomalous anticyclone in the WNP can be found from the El Niño mature winters to the subsequent summers (Figs. 7d–i). However, the SST anomalies in H9MME are larger than those in L9MME (Figs. 7d–i), indicating more robust IPOC effect and consequently larger anomalies in the large-scale circulation, SAT, and SEHD frequency over the southern AMR in the post–El Niño summers (Figs. 6c,d) and explaining the better reproduction of ENSO–SEHD relationship (Figs. 4b,c). We note that ENSO in both H9MME and L9MME decays slower than the observed; significant positive SST anomalies are still presented in the central tropical Pacific in the post–El Niño summers (Figs. 7f,i). On one hand, this is conducive to anomalous westerly in the western tropical Pacific and not favorable for the development of anomalous anticyclone off the equator, leading to a slightly northward displacement of the anomalous anticyclone in the WNP (Figs. 7c,f,i) (Jiang et al. 2017). On the other hand, the anomalously prolonged El Niño can cause anomalous descending motion over the Maritime Continent, resulting in anomalous easterly along the equator in the tropical Indian Ocean and anomalous high off the equator as the Matsuno–Gill response (Matsuno 1966; Gill 1980). The westerly along the northern frame of the anomalous high can offset the easterly along the southern frame of the westward-propagating Rossby wave originated from the WNP, causing less apparent easterly wind anomalies in the northern Indian Ocean in H9MME and L9MME compared to the observed. However, even so, compared to L9MME, H9MME still reproduces better the observed large-scale circulation anomalies and the consequent SEHD anomalies in the southern AMR (Figs. 6, 2a,c).
Temporal evolution of anomalies in SST (shading; °C) and 850-hPa wind (vectors; m s−1; only wind speed over 0.2 m s−1 is shown) in (left) the mature winter [D(−1)JF] and subsequent (center) spring (MAM) and (right) summer (JJA) regressed against the D(−1)JF Niño-3.4 index based on (a)–(c) observations, (d)–(f) H9MME, and (g)–(i) L9MME. The rectangles in (c), (f), and (i) denote the region where the JJA SST anomalies are defined as the JJA IPOC index. Anomalies in (a)–(c) significant at the 95% confidence level and those in (d)–(i) significant at the 95% confidence level in more than half of ensemble models are stippled.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-21-0667.1
The TNA capacitor also operates in both H9MME and L9MME in the post-ENSO summers (Figs. 7f,i). As in the observations, almost all the CMIP6 models simulate the higher role of IPOC than the TNA capacitor on conveying the ENSO impacts on the SEHD frequency in the AMR (Fig. S8). Further, in about 70% of the CMIP6 models, the significant influences of TNA capacitor on the SEHD frequency can be well counted by the IPOC (Fig. S8).
To confirm the role of IPOC effect on the model spread in ENSO–SEHD relationship, the intermodel correlation coefficient between the lagged ENSO–SEHDI and ENSO–IPOC correlation coefficients in all the 25 models and multimodel ensemble means (25MME, H9MME and L9MME) is calculated. It is up to 0.68, significant at the 99% confidence level (Fig. 8a). This indicates that the intermodel spread in reproducing the delayed ENSO–SEHD relationship can be largely attributed to the strength of ENSO–IPOC relationship. This is complementary to the results in section 3 where importance of the IPOC effect on generating the observed large-scale atmospheric circulation and thus SEHD anomalies in the southern AMR in the post-ENSO summers is stressed.
(a) Scatterplot of the lagged ENSO–SEHDI correlation coefficients (x axis) with the lagged ENSO–IPOC correlation coefficients (y axis). Scatterplots of the lagged ENSO–IPOC correlation coefficients (x axis) with (b) the correlation coefficients between DJF Niño-3.4 and sea surface pressure averaged over north Indian Ocean and South China Sea (40°–120°E, 5°S–25°N; y axis), (c) the DJF SST climatology in the cold tongue area (180°–90°W, 2.5°S–2.5°N; y axis), and (d) the variance of DJF Niño34 index (y axis). The intermodel correlation coefficient is shown at the top-right corner in each panel.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-21-0667.1
It is then interesting to investigate which processes determine the strength of the ENSO–IPOC relationship in the coupled models. As the Indian Ocean warming in the El Niño mature winter is the response to surface heat flux adjustments caused by the ENSO teleconnection (Alexander et al. 2002), the model performances on the ENSO teleconnection may be the key factor. As shown in Fig. 8b, the relationship between DJF Niño-3.4 and simultaneous sea surface pressure (SLP) averaged mainly over the north Indian Ocean (40°–120°E, 5°S–25°N) exhibits a significant positive intermodel correlation coefficient of 0.65 with the lagged ENSO–IPOC correlation coefficients, implying that the stronger teleconnection between ENSO and the Indian Ocean in winter is conducive to the enhanced ENSO–IPOC relationship.
It has been well accepted that the mean state of the tropical Pacific in coupled models can exert a strong influence on ENSO and its teleconnection; the coupled models with a large cold tongue bias in the eastern tropical Pacific likely introduce erroneous teleconnection to East Asia (Collins et al. 2010; Yeh et al. 2012; Lau and Nath 2000; Turner et al. 2005; Wang et al. 2019). In addition, the ENSO variance is important to the strength of its teleconnection to the Indian Ocean (Xie et al. 2010; Chowdary et al. 2012; Yang et al. 2015; Fu et al. 2021). Given the importance of mean state of the eastern tropical Pacific and ENSO variance, their impacts on the model spread on reproducing the delayed ENSO–IPOC relationship are further investigated. Both the DJF SST climatology in the cold tongue area (180°–90°W, 2.5°S–2.5°N) and the DJF variance of Niño-3.4 index exhibit significant positive intermodel correlation coefficients of 0.58 and 0.54 with the ENSO–IPOC relationship, significant at the 99% confidence level, which indicates that a warmer mean state of eastern tropical Pacific and a larger ENSO amplitude are likely to strengthen the ENSO teleconnection to the Indian Ocean in the coupled models (Figs. 8c,d). Based on the descending orders of the DJF cold tongue SST and the DJF Niño-3.4 variance, the top/bottom nine models are selected to calculate the multimodel ensemble means. Results show that the models with higher cold tongue SST and/or ENSO variance reproduce the delayed ENSO–SEHD relationship better than those with lower cold tongue SST and/or ENSO variance (Fig. S9). Hence, the model spread on reproducing the ENSO–SEHD relationship can be partly attributed to the mean state of the tropical Pacific, the ENSO variance, and teleconnection in each model. We know that the mean state of the tropical Pacific, the ENSO variance, and teleconnection have internal interdecadal variations (Xie et al. 2016). Since only 35 years in the historical run are adopted here, the poor performance of L9MME compared to H9MME may be due to the lower phase of the interdecadal variations rather than the model biases. To confirm this, sliding correlation coefficient between preceding DJF Niño-3.4 index and JJA IPOC index in 35-yr sliding windows starting from 1850 to 2014 in L9MME is compared to those of observation and H9MME (Fig. S10). It is apparent that the lagged ENSO–IPOC correlation coefficient in H9MME is consistently higher than L9MME, indicating always stronger ENSO–IPOC and thus ENSO–SEHD relationships in H9MME than L9MME.
The roles of ISO on reproducing the observed ENSO–SEHD relationship in both H9MME and L9MME are also accessed. Note that NorESM2-LM in H9MME and BCC-CSM2-MR in L9MME are excluded from the ISO analysis due to the lack of required daily atmospheric variables. Both H9MME and L9MME can reproduce well the observed summer ISO phases in the AMR (Figs. S4, S11, S13). When all the ISO active phases in the summers with the preceding DJF Niño-3.4 exceeding 0.8 standard deviation are selected for the composite (Fig. 9), it seems that both the H9MME and L9MME can also reproduce well the significantly enhanced ISO convection south of 20°N. However, compared to the observed, the active convection centers shift a little northward, probably due to the northward shift of the anomalous anticyclone in the WNP in H9MME and L9MME (Figs. 7c,f,i). Meanwhile, the simulated moisture deficiency in the WNP in the post–El Niño summers is much weaker than the observed (Figs. 3a, 9a,b). These all impose weaker restrictions on the northward propagations of ISO and thus weaker clustering impacts on the ISO phases (Figs. S4i, S11i, S13i). Hence, less apparent modification of daily surface temperature at the intraseasonal time scale can be detected in eastern China in both H9MME and L9MME (Figs. 3b, 9c,d, Figs. S5, S12, S14), which may explain the insignificant anomalies in the SEHD frequency there in H9MME and L9MME (Figs. 4b,c).
As in Fig. 3, but based on historical simulations of (a),(c) H9MME and (b),(d) L9MME. Dots denote anomalies significant at the 95% confidence level in more than half of ensemble models.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-21-0667.1
5. Future projection of the delayed ENSO impacts on the SEHDs in the AMR under SSP5-8.5 Scenario
In the section, we proceed with the future projection in the late twenty-first century (2065–2100) under SSP5-8.5 scenario to examine whether the delayed ENSO–SEHD relationship remains robust under global warming. The outputs of nine high-skill models that outperform others in reproducing the delayed ENSO–SEHD relationship in the historical simulations are adopted for the analysis. As pointed out by previous studies (e.g., Easterling et al. 2000; Seneviratne et al. 2012; Collins et al. 2013; IPCC 2021), global warming is unequivocal and the summer temperature is projected to rise continually, particularly under the SSP5-8.5 scenario. Hence, the relative threshold values applied to obtain the SEHD frequency at each grid in each year in the future projection are calculated by the same method as in the historical simulation but with the reference period shifting to 2065–2100. With no surprise, the projected threshold values of SEHDs are dramatically increased compared with the historical period (Fig. S15a), and the changes (Fig. S15b) show similar spatial patterns to the changes in the JJA SAT (not shown) with a more pronounced increase in the subtropics than tropics. In accordance with the changes in the JJA SAT, the climatological geopotential height at 500 hPa also shows a consistent increase, particularly in the midlatitudes (Fig. 11b).
Besides the surface temperature, global warming also exerts impacts on ENSO and its teleconnections (e.g., Collins et al. 2010; Kug et al. 2009b; Yeh et al. 2018; Hu et al. 2021). This may alter the observed ENSO–SEHD relationship. The anomalies of SEHD frequency in the post-ENSO summers in the H9MME under SSP5-8.5 scenario are similar to those in the historical simulations (Fig. 10a). This is because the anomalous patterns of large-scale atmospheric circulation and SAT in the post–El Niño summer do not change much under the SSP5-8.5 scenario compared to the historical runs (Figs. 6, 11). For example, the anomalous anticyclone in the WNP is enhanced and extended westward (Fig. 11a), leading to suppressed convection, decreased cloud cover, and increased SATs in the southern AMR (Fig. 11c). The large-scale atmospheric circulation anomalies are also closely related to the IPOC effects as suggested by the temporal evolution of anomalies in the SST and low-level atmospheric circulation anomalies under the SSP5-8.5 scenario (Figs. 12a–c). Hence, no remarkable changes in the ENSO–SEHD relationship are found between the SSP5-8.5 scenario and historical runs as a similar mechanism operates under both background conditions.
(a) Anomalies in SEHD frequency over the AMR regressed upon the preceding DJF Niño-3.4 index under the SSP5-8.5 scenario based on the H9MME (days; dots denote anomalies significant at the 95% confidence level), and (b) the corresponding changes under the SSP5-8.5 scenario relative to the historical period. Cross hatching in (b) indicates that more than 90% of high-skill models have the same sign changes as the H9MME.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-21-0667.1
(a),(c) As in Figs. 6a and 6c, but for the projections under SSP5-8.5 scenario based on H9MME, and (b),(d) the corresponding projected changes under SSP5-8.5 scenario relative to the historical period (cross hatching indicates that more than 90% of high-skill models have the same sign changes as the H9MME).
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-21-0667.1
(a)–(c) As in Figs. 7d–f, but for the projections under SSP5-8.5 scenario based on H9MME. (d)–(f) The corresponding projected changes under SSP5-8.5 scenario relative to historical period (cross hatching indicates that more than 90% of high-skill models have the same sign changes as the H9MME).
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-21-0667.1
Although the anomalous patterns are similar, changes in the anomalous amplitudes can be detected between the historical runs and SSP5-8.5 scenario. First, the SST anomalies related to El Niño are widely enhanced in the eastern and central tropical Pacific due to the intensified westerly anomalies with high intermodel agreement (Figs. 12d–f). This suggests an increased ENSO variability under global warming (Cai et al. 2018; Brown et al. 2020). Correspondingly, the SST anomalies over the tropical Indian Ocean and the South China Sea are increased in the post–El Niño summer (Figs. 12d–f). The enhanced anomalous heating then strengthens the response of large-scale atmospheric circulation especially in the low latitudes (Fig. 11b), and thus further reduced cloud cover and increased SATs (Fig. 11d). However, compared to the changes in the SST anomalies, the changes in the atmospheric circulation, cloud cover and SATs have a lower intermodel agreement over the AMR. Since the large-scale atmospheric circulation patterns are important drivers for local and regional extremes, the overall low confidence in their future changes contributes to uncertainty in projected changes of extremes (IPCC 2021). Hence, the changes in the delayed ENSO–SEHD relationship in AMR under the SSP5-8.5 scenario relative to the historical period also show a low intermodel agreement (Fig. 10b), indicating that the change signal of future projection under SSP5-8.5 scenario should be interpreted with cautions.
So far, the possible change in the ENSO–SEHD relationship under the SSP5-8.5 scenario has been analyzed based on H9MME that shows a closer ENSO–SEHD relationship than 25MME in the historical runs. However, it has been argued that the models with higher skills in the historical simulations cannot warrant a more faithful future projection, and using all available models may be a better approach to reduce possible uncertainties in model physics and setups (e.g., Zhao et al. 2021; Zhang et al. 2021). Hence, 25MME is also used to test the projection sensitivity to different groups of models. As shown in Fig. S16, the anomalies in the SEHD frequency in the ENSO decaying summers under the SSP5-8.5 based on 25MME are also very similar to those in the historical simulations with negligible changes. Besides, when only the extreme ENSO events (defined by 1.75 standard deviations) are considered, there is also no remarkable and high intermodel agreement changes in the ENSO–SEHD relationship under global warming (figures not shown).
6. Conclusions and discussion
In the study, anomalies in SEHD frequency over the AMR in post-ENSO summers are investigated using the observations and 25 CMIP6 model data. It is found that the robust delayed ENSO–SEHD relationship is mainly observed in the southern AMR including the Indian subcontinent, Indo-China peninsula, the Philippines, southern Tibetan Plateau, and eastern China; the region-mean SEHDs over the southern AMR south of 35°N has a correlation coefficient of 0.72 with the preceding winter ENSO, significant at the 99.9% confidence level. The delayed ENSO impacts on the SEHDs are primarily realized by provoking the IPOC effect that can operate from the ENSO mature winter to the subsequent summer when ENSO itself demises in the tropical Pacific. The IPOC effect in the post–El Niño summers causes suppressed convection and anomalous anticyclone in the WNP. The related anomalous diabatic heating excites the westward-propagating downwelling Rossby waves and results in extensive, quasi-barotropic and positive geopotential height anomalies from the WNP to the Indian subcontinent. The easterly anomalies along the southern flank of the Rossby wave also weaken and delay the onset of the Indian summer monsoon. The resultant descending motion and reduced cloud cover raise the SATs and thus increase the frequency of SEHDs over most of the southern AMR. In addition, the suppressed convection and anomalous anticyclone in the WNP significantly decrease the local atmospheric moisture in summer and hinder the northward propagation of ISO. Thus, the individual phase of ISO with the active convection over the eastern Indian Ocean–Maritime Continent/WNP is anomalously increased/decreased. This causes increased daily maximum temperatures at the subseasonal time scale in eastern China but decreased ones in Hokkaido, Japan, contributing to the significant SEHD anomalies there. In sum, via the IPOC effect, ENSO influences the SEHD frequency in most southern AMR by modifying the seasonal mean surface temperature, and further modify that in eastern China and Hokkaido by altering the occurrence frequency of individual ISO phases. As such, the SEHD frequency is significantly increased in the southern AMR south of 35°N and decreased in Hokkaido in the post–El Niño summers.
In the historical simulations of CMIP6 models, the 25-model-ensemble mean (25MME) of CMIP6 historical runs can simulate well the observed SEHD anomalies related to ENSO in the southern AMR, especially over the Indian subcontinent, Indo-China peninsula, and the Philippines. However, a large intermodel spread exists due to different strength of IPOC effect in each model; stronger IPOC effect is related to larger seasonal large-scale circulation and SAT anomalies, leading to more realistic reproduction of SEHD anomalies in most of southern AMR in the post-ENSO summers. Further, the different strength of IPOC effect in each model is closely related to the model biases in the mean state of eastern tropical Pacific, the ENSO variance, and teleconnection. However, most CMIP6 models, even the ensemble mean of nine high-skill models (H9MME), fail to reproduce the delayed ENSO–SEHD relationship in eastern China and Hokkaido, Japan, which is likely due to the model biases in simulating the ENSO-related modification of occurrence frequency of individual ISO phases. Note that the model simulations also show an ISO-related wet bias in southeast China (Figs. 9a,b) in comparison with the observations (Fig. 3a). The wet air conditions generally reduce the occurrence of SEHDs (e.g., Chen et al. 2016; Deng et al. 2020).
Possible change in the ENSO–SEHD relationship under global warming is also assessed. Both 25MME and H9MME do not show remarkable differences in the delayed impacts of ENSO on the SEHDs in the AMR between the SSP5-8.5 scenario and historical simulations. This is mainly because the IPOC effect operates similarly under the two climate regimes. Although previous studies have pointed out that both ENSO and its teleconnection may change under global warming (e.g., Fredriksen et al. 2020; Hu et al. 2021), our analyses show at least the delayed impacts of ENSO on the SEHD frequency in the AMR remain similar under the SSP5-8.5 scenario relative to the historical period. We note that the SSP5-8.5 scenario is the highest emission scenario in CMIP6 that reflects the expected greatest impact of global warming, but not the most likely pathway in the future. Hence, further research is still needed to ascertain possible change in the ENSO–SEHD relationship at other warming rates.
The Niño-3.4 index has been used to represent the ENSO in this study, which cannot distinguish the EP and CP types of ENSO. Considering the impacts of EP and CP ENSO on the Indian summer monsoon show opposite changes under global warming (Roy et al. 2019), it is interesting to further investigate whether the impacts of EP and CP ENSO on the SEHD are different at present climate or experience different changes under global warming. Following Kug et al. (2009a), the post-ENSO summers are grouped into CP ENSO summers and EP ENSO summers in both observations and CMIP6 simulations. As shown in Fig. 13, the anomalous patterns in observations, historical simulations, and future projections among CP and EP ENSO events all show homogeneously increased SEHD frequency over the southern AMR in the post–El Niño compared to post–La Niña summers. We note that the different significance among the SEHD anomalies related to CP and EP events are greatly influenced by the case number; for example, in the observations, only one EP La Niña is detected. Except that the observed CP and EP events have some differences in the location of maximum SEHD values, the anomalous patterns of SEHD frequency among observations, historical simulations, and future projections in CP and EP ENSO postsummers are also very similar. This suggests that the delayed ENSO–SEHD relationship could be projected without consideration of the accuracy in simulating the ENSO types. Similar conclusions have been raised by Loughran et al. (2017) when they investigated the impacts of CP and EP El Niño on the Australian heat wave. However, we note that only strong EP and CP ENSO events are selected in this study with the absolute SST anomaly in the Niño-3.4 region larger than 0.8 standard deviation in both observations and CMIP6 simulations. Hence, the results drawn above should be interpreted with caution when the weak ENSO events are considered.
Differences in composite anomalies of SEHD frequency between La Niña postsummers and (left) CP and (right) EP El Niño in (a),(b) the observations, (c),(d) historical simulations, and (e),(f) SSP585 future projections based on H9MME (days; dots denote anomalies significant at the 95% confidence level).
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-21-0667.1
Acknowledgments.
We are very thankful to the four anonymous reviewers whose comments help improve the manuscript substantially. All figures were drawn by NCL. This study is financially supported by the National Natural Science Foundation of China (42088101 and 41875099).
Data availability statement.
CPC daily maximum surface air temperatures are available at http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.temperature/.daily/.tmax/. ERSSTv5 data are available at https://www.ncei.noaa.gov/pub/data/cmb/ersst/v5/netcdf/. The NCEP1 reanalysis datasets and outputs of CMIP6 models can be downloaded from https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html and https://esgf-node.llnl.gov/search/cmip6/, respectively.
REFERENCES
Alexander, L. V., and Coauthors, 2006: Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res., 111, D05109, https://doi.org/10.1029/2005JD006290.
Alexander, M. A., I. Bladé, M. Newman, J. R. Lanzante, N. Lau, and J. D. Scott, 2002: The atmospheric bridge: The influence of ENSO teleconnections on air–sea interaction over the global oceans. J. Climate, 15, 2205–2231, https://doi.org/10.1175/1520-0442(2002)015<2205:TABTIO>2.0.CO;2.
An, S. I., and B. Wang, 2000: Interdecadal change of the structure of the ENSO mode and its impact on the ENSO frequency. J. Climate, 13, 2044–2055, https://doi.org/10.1175/1520-0442(2000)013<2044:ICOTSO>2.0.CO;2.
An, S. I., and H. Bong, 2016: Inter-decadal change in El Niño-Southern Oscillation examined with Bjerknes stability index analysis. Climate Dyn., 47, 967–979, https://doi.org/10.1007/s00382-015-2883-8.
Barnston, A. G., M. K. Tippett, M. Ranganathan, and M. L. L’Heureux, 2019: Deterministic skill of ENSO predictions from the North American Multimodel Ensemble. Climate Dyn., 53, 7215–7234, https://doi.org/10.1007/s00382-017-3603-3.
Bjerkness, J., 1969: Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev., 97, 163–172, https://doi.org/10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2.
Brown, J. R., and Coauthors, 2020: Comparison of past and future simulations of ENSO in CMIP5/PMIP3 and CMIP6/PMIP4 models. Climate Past, 16, 1777–1805, https://doi.org/10.5194/cp-16-1777-2020.
Cai, W., and Coauthors, 2018: Increased variability of eastern Pacific El Niño under greenhouse warming. Nature, 564, 201–206, https://doi.org/10.1038/s41586-018-0776-9.
Chang, P., Y. Fang, R. Saravanan, L. Ji, and H. Seidel, 2006: The cause of the fragile relationship between the Pacific El Niño and the Atlantic Niño. Nature, 443, 324–328, https://doi.org/10.1038/nature05053.
Chen, R., Z. Wen, and R. Lu, 2016: Evolution of the circulation anomalies and the quasi-biweekly oscillations associated with extreme heat events in southern China. J. Climate, 29, 6909–6921, https://doi.org/10.1175/JCLI-D-16-0160.1.
Chen, R. D., Z. P. Wen, and R. Y. Lu, 2019: Influences of tropical circulation and sea surface temperature anomalies on extreme heat over northeast Asia in the midsummer of 2018. Atmos. Ocean. Sci. Lett., 12, 238–245, https://doi.org/10.1080/16742834.2019.1611170.
Chen, X., C. Li, and X. Li, 2020: Influences of ENSO on boreal summer intraseasonal oscillation over the western Pacific in decaying summer. Climate Dyn., 54, 3461–3473, https://doi.org/10.1007/s00382-020-05183-9.
Choi, Y.-W., and J.-B. Ahn, 2019: Possible mechanisms for the coupling between late spring sea surface temperature anomalies over tropical Atlantic and East Asian summer monsoon. Climate Dyn., 53, 6995–7009, https://doi.org/10.1007/s00382-019-04970-3.
Chowdary, J. S., S. P. Xie, H. Tokinaga, Y. M. Okumura, H. Kubota, N. Johnson, and X.-T. Zheng, 2012: Interdecadal variations in ENSO teleconnection to the Indo–western Pacific for 1870–2007. J. Climate, 25, 1722–1744, https://doi.org/10.1175/JCLI-D-11-00070.1.
Collins, M., and Coauthors, 2010: The impact of global warming on the tropical Pacific Ocean and El Niño. Nat. Geosci., 3, 391–397, https://doi.org/10.1038/ngeo868.
Collins, M., and Coauthors, 2013: Long-term climate change: Projections, commitments and irreversibility. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 1029–1136.
Dai, X. G., C. B. Fu, and P. Wang, 2005: Interdecadal change of atmospheric stationary waves and North China drought. Chin. Phys., 14, 850, https://doi.org/10.1088/1009-1963/14/4/038.
Deng, K., S. Yang, D. Gu, A. Lin, and C. Li, 2020: Record-breaking heat wave in southern China and delayed onset of South China Sea summer monsoon driven by the Pacific subtropical high. Climate Dyn., 54, 3751–3764, https://doi.org/10.1007/s00382-020-05203-8.
Ding, Y. H., and D. R. Sikka, 2006: Synoptic systems and weather. The Asian Monsoon, B. Wang, Ed., Praxis, 131–202.
Ding, Y. H., and Y. Y. Liu, 2008: A study of the teleconnection in the Asian-Pacific monsoon region. Acta Meteor. Sin., 66, 670–682, https://doi.org/10.11676/qxxb2008.062.
Easterling, D. R., J. L. Evans, P. Y. Groisman, T. R. Karl, K. E. Kunkel, and P. Ambenje, 2000: Observed variability and trends in extreme climate events: A brief review. Bull. Amer. Meteor. Soc., 81, 417–425, https://doi.org/10.1175/1520-0477(2000)081<0417:OVATIE>2.3.CO;2.
Enomoto, T., B. J. Hoskins, and Y. Matsuda, 2003: The formation mechanism of the Bonin high in August. Quart. J. Roy. Meteor. Soc., 129, 157–178, https://doi.org/10.1256/qj.01.211.
Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016.
Fredriksen, H. B., J. Berner, A. C. Subramanian, and A. Capotondi, 2020: How does El Niño-Southern Oscillation change under global warming—A first look at CMIP6. Geophys. Res. Lett., 47, e2020GL090640, https://doi.org/10.1029/2020GL090640.
Frich, P., L. Alexander, P. Della-Marta, B. Gleason, M. Haylock, A. Klein Tank, and T. Peterson, 2002: Observed coherent changes in climatic extremes during the second half of the twentieth century. Climate Res., 19, 193–212, https://doi.org/10.3354/cr019193.
Fu, Y., Z. Lin, and T. Wang, 2021: Preconditions for CMIP6 models to reproduce the relationship between wintertime ENSO and subsequent East Asian summer rainfall. Climate Res., 84, 133–144, https://doi.org/10.3354/cr01663.
Gill, A. E., 1980: Some simple solutions for heat-induced tropical circulation. Quart. J. Roy. Meteor. Soc., 106, 447–462, https://doi.org/10.1002/qj.49710644905.
Grotjahn, R., and Coauthors, 2014: Workshop on analyses, dynamics, and modeling of large-scale meteorological patterns associated with extreme temperature and precipitation events. US CLIVAR Rep. 2014-2, US CLIVAR Project Office, Washington, DC, 42 pp., https://doi.org/10.5065/D6ZK5F2N.
Ham, Y.-G., J.-S. Kug, J.-Y. Park, and F.-F. Jin, 2013: Sea surface temperature in the north tropical Atlantic as a trigger for El Niño/Southern Oscillation events. Nat. Geosci., 6, 112–116, https://doi.org/10.1038/ngeo1686.
Hsu, P.-C., J.-Y. Lee, K.-J. Ha, and C.-H. Tsou, 2017: Influences of boreal summer intraseasonal oscillation on heat waves in monsoon Asia. J. Climate, 30, 7191–7211, https://doi.org/10.1175/JCLI-D-16-0505.1.
Hu, K., G. Huang, P. Huang, Y. Kosaka, and S. P. Xie, 2021: Intensification of El Niño-induced atmospheric anomalies under greenhouse warming. Nat. Geosci., 14, 377–382, https://doi.org/10.1038/s41561-021-00730-3.
Huang, B., and Coauthors, 2017: Extended Reconstructed Sea Surface Temperature, version 5 (ERSSTv5): Upgrades, validations, and intercomparisons. J. Climate, 30, 8179–8205, https://doi.org/10.1175/JCLI-D-16-0836.1.
Huang, D. Q., J. Zhu, Y. C. Zhang, and A. N. Huang, 2014: The different configurations of the East Asian polar front jet and subtropical jet and the associated rainfall anomalies over eastern China in summer. J. Climate, 27, 8205–8220, https://doi.org/10.1175/JCLI-D-14-00067.1.
IPCC, 2013: Climate Change 2013: The Physical Science Basis. Cambridge University Press, 1535 pp., https://doi.org/10.1017/CBO9781107415324.
IPCC, 2021: Climate Change 2021: The Physical Science Basis. V. Masson-Delmotte, et al., Eds., Cambridge University Press, 2391 pp., https://doi.org/10.1017/9781009157896.
Jiang, L., and T. Li, 2021: Impacts of tropical North Atlantic and equatorial Atlantic SST anomalies on ENSO. J. Climate, 34, 5635–5655, https://doi.org/10.1175/JCLI-D-20-0835.1.
Jiang, W., G. Huang, K. Hu, R. Wu, H. Gong, X. Chen, and W. Tao, 2017: Diverse relationship between ENSO and the northwest Pacific summer climate among CMIP5 models: Dependence on the ENSO decay pace. J. Climate, 30, 109–127, https://doi.org/10.1175/JCLI-D-16-0365.1.
Jiang, X., T. Li, and B. Wang, 2004: Structures and mechanisms of the northward propagating boreal summer intraseasonal oscillation. J. Climate, 17, 1022–1039, https://doi.org/10.1175/1520-0442(2004)017<1022:SAMOTN>2.0.CO;2.
Jiang, Z., S. Yang, J. He, J. Li, and J. Liang, 2008: Interdecadal variations of East Asian summer monsoon northward propagation and influences on summer precipitation over East China. Meteor. Atmos. Phys., 100, 101–119, https://doi.org/10.1007/s00703-008-0298-3.
Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437–471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
Karori, M. A., J. Li, and F. F. Jin, 2013: The asymmetric influence of the two types of El Niño and La Niña on summer rainfall over southeast China. J. Climate, 26, 4567–4582, https://doi.org/10.1175/JCLI-D-12-00324.1.
Klein, S. A., B. J. Sode, and N. C. Lau, 1999: Remote sea surface temperature variations during ENSO: Evidence for a tropical atmospheric bridge. J. Climate, 12, 917–932, https://doi.org/10.1175/1520-0442(1999)012<0917:RSSTVD>2.0.CO;2.
Kosaka, Y., S. P. Xie, N. C. Lau, and G. A. Vecchi, 2013: Origin of seasonal predictability for summer climate over the northwestern Pacific. Proc. Natl. Acad. Sci. USA, 110, 7574–7579, https://doi.org/10.1073/pnas.1215582110.
Kravtsov, S., 2011: An empirical model of decadal ENSO variability. Climate Dyn., 39, 2377–2391, https://doi.org/10.1007/s00382-012-1424-y.
Kug, J. S., F. F. Jin, and S. I. An, 2009a: Two types of El Niño events: Cold tongue El Niño and warm pool El Niño. J. Climate, 22, 1499–1515, https://doi.org/10.1175/2008JCLI2624.1.
Kug, J. S., S. I. An, Y. G. Ham, and I. S. Kang, 2009b: Changes in El Niño and La Niña teleconnections over North Pacific-America in the global warming simulations. Theor. Appl. Climatol., 100, 275–282, https://doi.org/10.1007/s00704-009-0183-0.
Lau, N., and M. J. Nath, 2000: Impact of ENSO on the variability of the Asian–Australian monsoons as simulated in GCM experiments. J. Climate, 13, 4287–4309, https://doi.org/10.1175/1520-0442(2000)013<4287:IOEOTV>2.0.CO;2.
Lee, J.-Y., B. Wang, M. C. Wheeler, X. Fu, D. E. Waliser, and I.-S. Kang, 2013: Real-time multivariate indices for the boreal summer intraseasonal oscillation over the Asian summer monsoon region. Climate Dyn., 40, 493–509, https://doi.org/10.1007/s00382-012-1544-4.
Li, Q. X., and J. Y. Huang, 2011: Threshold values on extreme high temperature events in China. J. Appl. Meteor. Sci., 22, 138–144.
Lin, A., and T. Li, 2008: Energy spectrum characteristics of boreal summer intraseasonal oscillations: Climatology and variations during the ENSO developing and decaying phases. J. Climate, 21, 6304–6320, https://doi.org/10.1175/2008JCLI2331.1.
Lin, H., 2019: Long-lead ENSO control of the boreal summer intraseasonal oscillation in the East Asian-western North Pacific region. npj Climate Atmos. Sci., 2, 31, https://doi.org/10.1038/s41612-019-0088-2.
Lin, R. P., F. Zheng, and X. Dong, 2018: ENSO frequency asymmetry and the Pacific decadal oscillation in observations and 19 CMIP5 models. Adv. Atmos. Sci., 35, 495–506, https://doi.org/10.1007/s00376-017-7133-z.
Lin, S., M. Luo, R. J. Walker, X. Liu, S. A. Hwan, and R. Chinery, 2009: Extreme high temperatures and hospital admissions for respiratory and cardiovascular diseases. Epidemiology, 20, 738–746, https://doi.org/10.1097/EDE.0b013e3181ad5522.
Loughran, T. F., S. E. Perkins-Kirkpatrick, L. V. Alexander, and A. J. Pitman, 2017: No significant difference between Australian heat wave impacts of Modoki and eastern Pacific El Niño. Geophys. Res. Lett., 44, 5150–5157, https://doi.org/10.1002/2017GL073231.
Lu, X., C. Yuan, M. Yang, T. Doi, M. Wahiduzzaman, and J. J. Luo, 2021: Prediction of summer extreme hot days in China using the SINTEX-F2. Int. J. Climatol., 41, 4966–4976, https://doi.org/10.1002/joc.7110.
Lu, X., C. Yuan, J. Luo, and T. Yamagata, 2023: Delayed impacts of ENSO on the frequency of summer extreme hot day in the Asian monsoon region. Part II: Implication for seasonal prediction. J. Climate, 36, 3113–3127, https://doi.org/10.1175/JCLI-D-21-0668.1.
Luo, J. J., S. Behera, Y. Masumoto, H. Sakuma, and T. Yamagata, 2008: Successful prediction of the consecutive IOD in 2006 and 2007. Geophys. Res. Lett., 35, L14S02, https://doi.org/10.1029/2007GL032793.
Luo, J. J., J. Y. Lee, C. X. Yuan, W. Sasaki, S. Masson, S. K. Behera, Y. Masumoto, and T. Yamagata, 2016: Current status of intraseasonal-seasonal-to-interannual prediction of the Indo-Pacific climate. Indo-Pacific Climate Variability and Predictability, K. B. Swadhin and T. Yamagata, Eds., World Scientific, 63–107.
Matsuno, T., 1966: Quasi-geostrophic motions in the equatorial area. J. Meteor. Soc. Japan, 44, 25–43, https://doi.org/10.2151/jmsj1965.44.1_25.
Nie, Y., and J. Sun, 2022: Causes of interannual variability of summer precipitation intraseasonal oscillation intensity over southwest China. J. Climate, 35, 3705–3723, https://doi.org/10.1175/JCLI-D-21-0627.1.
Nitta, T., 1987: Convective activities in the tropical western Pacific and their impact on the Northern Hemisphere summer circulation. J. Meteor. Soc. Japan, 65, 373–390, https://doi.org/10.2151/jmsj1965.65.3_373.
Ratnam, J. V., S. K. Behera, S. B. Ratna, M. Rajeevan, and T. Yamagata, 2016: Anatomy of Indian heatwaves. Sci. Rep., 6, 24395, https://doi.org/10.1038/srep24395.
Rong, X., R. Zhang, and T. Li, 2010: Impacts of Atlantic sea surface temperature anomalies on Indo-East Asian summer monsoon-ENSO relationship. Chin. Sci. Bull., 55, 2458–2468, https://doi.org/10.1007/s11434-010-3098-3.
Roy, I., R. G. Tedeschi, and M. Collins, 2019: ENSO teleconnections to the Indian summer monsoon under changing climate. Int. J. Climatol., 39, 3031–3042, https://doi.org/10.1002/joc.5999.
Seneviratne, S. I., and Coauthors, 2012: Changes in impacts of climate extremes and their impacts on the natural physical environment. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, C. B. Field et al., Eds., Cambridge University Press, 109–230, https://doi.org/10.1017/CBO9781139177245.006.
Srinivas, G., J. S. Chowdary, Y. Kosaka, C. Gnanaseelan, A. Parekh, and K. V. S. R. Prasad, 2018: Influence of the Pacific-Japan pattern on Indian summer monsoon rainfall. J. Climate, 31, 3943–3958, https://doi.org/10.1175/JCLI-D-17-0408.1.
Teixeira, E. I., G. Fischer, H. van Velthuizen, C. Walter, and F. Ewert, 2013: Global hot-spots of heat stress on agricultural crops due to climate change. Agric. For. Meteor., 170, 206–215, https://doi.org/10.1016/j.agrformet.2011.09.002.
Thirumalai, K., P. N. DiNezio, Y. Okumura, and C. Deser, 2017: Extreme temperatures in Southeast Asia caused by El Niño and worsened by global warming. Nat. Commun., 8, 15531, https://doi.org/10.1038/ncomms15531.
Turner, A. G., P. M. Inness, and J. M. Slingo, 2005: The role of the basic state in the ENSO-monsoon relationship and implications for predictability. Quart. J. Roy. Meteor. Soc., 131, 781–804, https://doi.org/10.1256/qj.04.70.
Van Oldenborgh, G. J., and Coauthors, 2018: Extreme heat in India and anthropogenic climate change. Nat. Hazards Earth Syst. Sci., 18, 365–381, https://doi.org/10.5194/nhess-18-365-2018.
Walker, G. T., 1923: Correlation in seasonal variations of weather, VIII: A preliminary study of world weather. Mem. India Meteor. Dep., 24, 75–131.
Walker, G. T., 1924: Correlation in seasonal variations of Weather, IX: A Further Study of World Weather. Mem. India Meteor. Dep., 24, 275–333.
Walker, G. T., and E. W. Bliss, 1932: World weather V. Mem. Roy. Meteor. Soc., 4, 53–84.
Wang, B., and H. Lin, 2002: Rainy season of the Asian–Pacific summer monsoon. J. Climate, 15, 386–398, https://doi.org/10.1175/1520-0442(2002)015<0386:RSOTAP>2.0.CO;2.
Wang, B., R. Wu, and X. Fu, 2000: Pacific-East Asian teleconnection: How does ENSO affect East Asian climate? J. Climate, 13, 1517–1536, https://doi.org/10.1175/1520-0442(2000)013<1517:PEATHD>2.0.CO;2.
Wang, L., J.-Y. Yu, and H. Paek, 2017: Enhanced biennial variability in the Pacific due to Atlantic capacitor effect. Nat. Commun., 8, 14887, https://doi.org/10.1038/ncomms14887.
Wang, P., C. Y. Tam, and K. Xu, 2019: El Niño-East Asian monsoon teleconnection and its diversity in CMIP5 models. Climate Dyn., 53, 6417–6435, https://doi.org/10.1007/s00382-019-04938-3.
Webster, P. J., V. O. Magana, T. N. Palmer, T. A. Tomas, M. Yanai, and T. Yasunari, 1998: Monsoons: Processes, predictability, and prospects for prediction. J. Geophys. Res., 103, 451–510, https://doi.org/10.1029/97JC02719.
Weng, H., K. Ashok, S. K. Behera, S. A. Rao, and T. Yamagata, 2007: Impacts of recent El Niño Modoki on dry/wet conditions in the Pacific Rim during boreal summer. Climate Dyn., 29, 123–129, https://doi.org/10.1007/s00382-007-0234-0.
Weng, H., S. K. Behera, and T. Yamagata, 2009: Anomalous winter climate conditions in the Pacific Rim during recent El Niño Modoki and El Niño events. Climate Dyn., 32, 663–674, https://doi.org/10.1007/s00382-008-0394-6.
Wu, R., and L. Song, 2018: Spatiotemporal change of intraseasonal oscillation intensity over the tropical Indo-Pacific Ocean associated with El Niño and La Niña events. Climate Dyn., 50, 1221–1242, https://doi.org/10.1007/s00382-017-3675-0.
Xie, S. P., K. M. Hu, J. Hafner, H. Tokinaga, Y. Du, G. Huang, and T. Sampe, 2009: Indian Ocean capacitor effect on Indo–western Pacific climate during the summer following El Niño. J. Climate, 22, 730–747, https://doi.org/10.1175/2008JCLI2544.1.
Xie, S. P., Y. Du, G. Huang, X. T. Zheng, H. Tokinaga, K. Hu, and Q. Liu, 2010: Decadal shift in El Niño influences on Indo-western Pacific and East Asian climate in the 1970s. J. Climate, 23, 3352–3368, https://doi.org/10.1175/2010JCLI3429.1.
Xie, S. P., Y. Kosaka, Y. Du, K. Hu, J. S. Chowdary, and G. Huang, 2016: Indo-western Pacific Ocean capacitor and coherent climate anomalies in post-ENSO summer: A review. Adv. Atmos. Sci., 33, 411–432, https://doi.org/10.1007/s00376-015-5192-6.
Yang, Y., S. P. Xie, Y. Du, and H. Tokinaga, 2015: Interdecadal difference of interannual variability characteristics of South China Sea SSTs associated with ENSO. J. Climate, 28, 7145–7160, https://doi.org/10.1175/JCLI-D-15-0057.1.
Yeh, S.-W., Y. Ham, and J. Lee, 2012: Changes in the tropical Pacific SST trend from CMIP3 to CMIP5 and its implication of ENSO. J. Climate, 25, 7764–7771, https://doi.org/10.1175/JCLI-D-12-00304.1.
Yeh, S.-W., and Coauthors, 2018: ENSO atmospheric teleconnections and their response to greenhouse gas forcing. Rev. Geophys., 56, 185–206, https://doi.org/10.1002/2017RG000568.
Zhai, P. M., A. J. Sun, F. M. Ren, X. N. Liu, B. Gao, and Q. Zhang, 1999: Changes of climate extremes in China. Climatic Change, 42, 203–218, https://doi.org/10.1023/A:1005428602279.
Zhang, G., G. Zeng, X. Yang, and Z. H. Jiang, 2021: Future changes in extreme high temperature over China at 1.5°C–5°C global warming based on CMIP6 simulations. Adv. Atmos. Sci., 38, 253–267, https://doi.org/10.1007/s00376-020-0182-8.
Zhang, R., Q. Min, and J. Su, 2017: Impact of El Niño on atmospheric circulations over East Asia and rainfall in China: Role of the anomalous western North Pacific anticyclone. Sci. China Earth Sci., 60, 1124–1132, https://doi.org/10.1007/s11430-016-9026-x.
Zhang, X., G. Hegerl, F. W. Zwiers, and J. Kenyon, 2005: Avoiding inhomogeneity in percentile-based indices of temperature extremes. J. Climate, 18, 1641–1651, https://doi.org/10.1175/JCLI3366.1.
Zhang, X, L. Alexander, G. C. Hegerl, P. Jones, A. K. Tank, T. C. Peterson, B. Trewin, and F. W. Zwiers, 2011: Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdiscip. Rev.: Climate Change, 2, 851–870, https://doi.org/10.1002/wcc.147.
Zhao, Y. M., C. Qian, W. J. Zhang, D. He, and Y. J. Qi, 2021: Extreme temperature indices in Eurasia in a CMIP6 multi-model ensemble: Evaluation and projection. Int. J. Climatol., 41, 5368–5385, https://doi.org/10.1002/joc.7134.
Zheng, B., D. Gu, A. Lin, D. Peng, C. Li, and Y. Huang, 2022: Structures and mechanisms of heat waves related to quasi-biweekly variability over southern China. J. Climate, 35, 7981–7994, https://doi.org/10.1175/JCLI-D-22-0282.1.
Zhou, T. J., L. W. Zou, and X. L. Chen, 2019: Commentary on the Coupled Model Intercomparison Project Phase 6 (CMIP6). Climate Change Res., 15, 445–456, https://doi.org/10.12006/j.issn.1673-1719.2019.193.
Zhu, B. Y., B. Sun, and H. J. Wang, 2020: Dominant modes of interannual variability of extreme high-temperature events in eastern China during summer and associated mechanisms. Int. J. Climatol., 40, 841–857, https://doi.org/10.1002/joc.6242.
Zhu, Y. M., X. Q. Yang, X. Y. Chen, S. S. Zhao, and X. G. Sun, 2007: Interdecadal variation of the relationship between ENSO and summer interannual climate variability in China. J. Trop. Meteor., 13, 132–136.
