Editorial Type:
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Article Type:
Research Article

Intermodel Uncertainty in the Change of ENSO’s Amplitude under Global Warming: Role of the Response of Atmospheric Circulation to SST Anomalies

Jun Ying State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou, China

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Ping Huang Center for Monsoon System Research and State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, and Joint Center for Global Change Studies, Beijing, China

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Tao Lian State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou, China

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Dake Chen State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou, China

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Print Publication:
15 Jan 2019
DOI:
https://doi.org/10.1175/JCLI-D-18-0456.1
Page(s):
369–383
Full access

Abstract

This study investigates the mechanism of the large intermodel uncertainty in the change of ENSO’s amplitude under global warming based on 31 CMIP5 models. We find that the uncertainty in ENSO’s amplitude is significantly correlated to that of the change in the response of atmospheric circulation to SST anomalies (SSTAs) in the eastern equatorial Pacific Niño-3 region. This effect of the atmospheric response to SSTAs mainly influences the uncertainty in ENSO’s amplitude during El Niño (EN) phases, but not during La Niña (LN) phases, showing pronounced nonlinearity. The effect of the relative SST warming and the present-day response of atmospheric circulation to SSTAs are the two major contributors to the intermodel spread of the change in the atmospheric response to SSTAs, of which the latter is more important. On the one hand, models with a stronger (weaker) mean-state SST warming in the eastern equatorial Pacific, relative to the tropical-mean warming, favor a larger (smaller) increase in the change in the response of atmospheric circulation to SSTAs in the eastern equatorial Pacific during EN. On the other hand, models with a weaker (stronger) present-day response of atmospheric circulation to SSTAs during EN tend to exhibit a larger (smaller) increase in the change under global warming. The result implies that an improved simulation of the present-day response of atmospheric circulation to SSTAs could be effective in lowering the uncertainty in ENSO’s amplitude change under global warming.

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

Corresponding author: Dr. Ping Huang, huangping@mail.iap.ac.cn

Abstract

This study investigates the mechanism of the large intermodel uncertainty in the change of ENSO’s amplitude under global warming based on 31 CMIP5 models. We find that the uncertainty in ENSO’s amplitude is significantly correlated to that of the change in the response of atmospheric circulation to SST anomalies (SSTAs) in the eastern equatorial Pacific Niño-3 region. This effect of the atmospheric response to SSTAs mainly influences the uncertainty in ENSO’s amplitude during El Niño (EN) phases, but not during La Niña (LN) phases, showing pronounced nonlinearity. The effect of the relative SST warming and the present-day response of atmospheric circulation to SSTAs are the two major contributors to the intermodel spread of the change in the atmospheric response to SSTAs, of which the latter is more important. On the one hand, models with a stronger (weaker) mean-state SST warming in the eastern equatorial Pacific, relative to the tropical-mean warming, favor a larger (smaller) increase in the change in the response of atmospheric circulation to SSTAs in the eastern equatorial Pacific during EN. On the other hand, models with a weaker (stronger) present-day response of atmospheric circulation to SSTAs during EN tend to exhibit a larger (smaller) increase in the change under global warming. The result implies that an improved simulation of the present-day response of atmospheric circulation to SSTAs could be effective in lowering the uncertainty in ENSO’s amplitude change under global warming.

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

Corresponding author: Dr. Ping Huang, huangping@mail.iap.ac.cn
Keywords: Atmosphere-ocean interaction; ENSO; Greenhouse gases; Tropical variability

1. Introduction

As the dominant mode of tropical climate variability, and owing to its broad influence on severe weather, ecosystems and climatic anomalies regionally and globally (Barsugli et al. 1999; McPhaden et al. 2006; Cai et al. 2014, 2015b), understanding El Niño–Southern Oscillation (ENSO) and how it is likely to change under global warming is crucial for human society. However, projecting the response of ENSO to continuous anthropogenic greenhouse warming has long been a challenge (Yeh et al. 2009; Watanabe et al. 2012; Power et al. 2013; Cai et al. 2015b). The change in the amplitude of ENSO’s sea surface temperature (SST) anomalies (referred to simply as “ENSO’s amplitude”), one of its fundamental properties, is highly model dependent among state-of-the-art coupled general circulation models (Yeh and Kirtman 2007; Stevenson 2012; Chen et al. 2015; Zheng et al. 2016; Chen et al. 2017b). Models have been reported to project the change of ENSO’s amplitude under global warming as a possible increase (Timmermann et al. 1999; Zheng et al. 2016), decrease (Knutson and Manabe 1994; Huang and Xie 2015), or neither (van Oldenborgh et al. 2005; Philip and van Oldenborgh 2006; Stevenson 2012). Such intermodel uncertainty limits the reliability of model projections not only for tropical Pacific climate change (Cai et al. 2014; Xie et al. 2015) but also for climate changes elsewhere influenced by ENSO teleconnections (Meehl and Teng 2007; Chung et al. 2014; Zhou et al. 2014; Huang and Xie 2015; Huang and Chen 2017; Perry et al. 2017; Yeh et al. 2017; Jiang et al. 2018). Thus, investigating the sources of intermodel uncertainty in the change of ENSO’s amplitude under global warming is crucial for improving the reliability of model projections.

In general, the development of ENSO’s amplitude is controlled by a series of amplifying and damping processes involved in the ENSO cycle (Jin 1997; Jin et al. 2006; Lloyd et al. 2009; Chen et al. 2016a,b; An et al. 2017; Chen et al. 2017a), among which one or more might change under global warming (Philip and van Oldenborgh 2006; Collins et al. 2010; Kim and Jin 2011). Accordingly, any intermodel differences related to these processes may contribute to the intermodel spread with respect to the change in ENSO’s amplitude (Collins et al. 2010; DiNezio et al. 2012). Several significant indicators have been found to possibly explain this intermodel uncertainty, such as change in the air–sea coupling strength measured by the zonal wind stress (ZWS)–SST feedback (An and Choi 2015), change in the ZWS forcing efficiency (Rashid et al. 2016), the historical precipitation climatology (Ham and Kug 2016), and change in the climatological Pacific subtropical cell (Chen et al. 2017b).

The response of atmospheric circulation with convective activity to SST anomalies (SSTAs), as a crucial part of tropical air–sea interaction (Chang et al. 2000; Wu et al. 2006; Deser et al. 2010), is one of the most important processes responsible for ENSO’s amplitude (Philander 1990; Kang and Kug 2002; Schneider 2002; Kim et al. 2008; Guilyardi et al. 2009; Watanabe et al. 2012). During an El Niño (EN) event, the warm SSTAs in the eastern equatorial Pacific induce anomalous local atmospheric upward motion accompanied by anomalous convective activity. The upward motion anomalies further induce low-level westerly wind anomalies along the equator, which in turn maintain or even enhance the warm SSTAs by influencing oceanic heat advection, surface heat flux, incoming solar radiation, and so on (Bjerknes 1969; Chang et al. 2000; Deser et al. 2010; Chiang and Friedman 2012). Therefore, the atmospheric circulation response to SSTAs in the eastern equatorial Pacific is undoubtedly important for the growth of ENSO’s amplitude. Under future global warming, the sensitivity of the response of atmospheric circulation to SSTAs in the tropics is believed to be changed (Huang et al. 2017), which is crucial to the changes in the interannual variability of tropical climate (Philip and van Oldenborgh 2006; Collins et al. 2010). Therefore, the effect of the change in the response of atmospheric circulation to SSTAs in the eastern equatorial Pacific should be considered when investigating the intermodel uncertainty with respect to the change of ENSO’s amplitude.

A recent study by Zheng et al. (2016) suggested that the mean-state regional SST warming in the eastern equatorial Pacific, relative to the tropical mean, is a major source of intermodel uncertainty in the change of ENSO’s amplitude. They claimed that a more enhanced mean-state SST warming in the eastern equatorial Pacific can boost the local ENSO-induced anomalous convection, which further increases the equatorial zonal wind response and, hence, enhances ENSO’s amplitude. As the impact of anomalous convection in the eastern equatorial Pacific on the equatorial zonal wind is mainly caused by the local anomalous atmospheric circulation—a dynamical component involved in the anomalous convective process—the change in the response of atmospheric circulation to SSTAs in the eastern equatorial Pacific should be critical to the intermodel uncertainty with respect to the change of ENSO’s amplitude.

Moreover, the response of atmospheric circulation to SSTAs is nonlinear in the tropics (Lloyd et al. 2012). The occurrence of atmospheric upward motion is more (less) frequent in the eastern equatorial Pacific when the local SST is higher (lower) than the SST threshold for convection, which is approximated to the tropical-mean SST (Graham and Barnett 1987; Johnson and Xie 2010; Raymond and Herman 2011; Zheng et al. 2016). Thus, the effect of the change in the response of atmospheric circulation to SSTAs in the eastern equatorial Pacific may be distinct during EN and La Niña (LN) events, even when the greenhouse gas–induced SST warming is spatially uniform, leading in turn to distinct changes in the amplitude of EN and LN. However, the nonlinearity of the response of atmospheric circulation to SSTAs and its contribution to the intermodel uncertainty with respect to the asymmetric changes in the amplitude of EN and LN are not yet clear.

The present study investigates the role of the response of atmospheric circulation to SSTAs on the intermodel uncertainty with respect to the change of ENSO’s amplitude under global warming. We focus on the changes of ENSO’s amplitude and the response of atmospheric circulation to SSTAs in the eastern equatorial Pacific over the Niño-3 region (5°S–5°N, 150°–90°W)—a key region that is well known for ENSO development (Jin et al. 2006; Watanabe et al. 2012; Cai et al. 2015b). We show that the intermodel spread with respect to the change of ENSO’s amplitude is closely tied to that with respect to the change in the response of atmospheric circulation to SSTAs over the Niño-3 region. Moreover, the change in the response of atmospheric circulation to SSTAs over the Niño-3 region is responsible only for the intermodel uncertainty with respect to the change in EN amplitude, that is, not for the change of LN’s amplitude. In addition, the sources of intermodel uncertainty with respect to the change in the response of atmospheric circulation to SSTAs over the Niño-3 region during EN are further investigated.

The rest of the paper is organized as follows. Section 2 introduces the data and methods. Section 3 examines the intermodel relationship between the change of ENSO’s amplitude and the change in the response of atmospheric circulation to SSTAs over the Niño-3 region. In section 4, we investigate the possible sources of intermodel uncertainty with respect to the change in the response of atmospheric circulation to SSTAs over the Niño-3 region during EN, and their influences on the intermodel uncertainty with respect to the change of EN’s amplitude. Section 5 is a summary and discussion.

2. Data and methods

We use the monthly outputs of 31 CMIP5 models (Table 1) from both the historical runs and +8.5 W m−2 representative concentration pathway (RCP8.5) runs in this study (Taylor et al. 2012). For each model, only one member run (r1i1p1) is selected; 40-yr periods are chosen in the historical run (1961–2000) and RCP8.5 run (2061–2100) to represent the present-day and future climates, respectively. The long-term mean in the historical runs and RCP8.5 runs defines the present-day and the future climatology. The differences between the future and the present-day simulations (denoted by ) define the change under global warming. The interannual anomaly fields are obtained by removing the annual cycle of the 40-yr climatology and the quadratic trend. Moreover, a 3-month running average is performed on the interannual anomaly fields to reduce the intraseasonal variability. The standard deviation of the SST interannual anomaly (SSTA) averaged over the eastern equatorial Pacific Niño-3 region is referred to as the ENSO amplitude index , as in many previous studies (e.g., Cai et al. 2014; Chen et al. 2017b; Zheng et al. 2018). The root-mean-square (RMS) value of SSTA over the Niño-3 region for SSTA > 0 () and SSTA < 0 () are calculated to represent the amplitude of the EN and LN, respectively. The standard deviation of the ZWS anomaly averaged over the central equatorial Pacific (Niño-4 region; 5°S–5°N, 160°E–150°W), and that of the precipitation anomaly averaged over the Niño-3 region, are referred to as the ENSO ZWS and precipitation indices, respectively.

Table 1.

The 31 CMIP5 models used in the present study.

Table 1.

The response of atmospheric circulation to SSTAs is defined by linearly regressing the interannual anomalies of 500-hPa vertical pressure velocity against the SSTAs at each grid point (hPa s−1 K−1). It should be noted that a negative (positive) means an upward (downward) motion, and thus a negative (positive) indicates an upward (downward) response of atmospheric circulation to a positive SST anomaly or a downward (upward) response to a negative SST anomaly. The averaged over the Niño-3 region is denoted by . To diagnose the nonlinearity of , we further introduce two variables, and , defined by calculating the for SSTA > 0 and SSTA < 0, respectively (Lloyd et al. 2012). In addition, to remove the effect of intermodel spread with respect to the response of global-mean SST warming to greenhouse gas forcing, all the changes (including the changes in climatology, ENSO indices, and ) in each model are normalized by the corresponding global-mean SST warming; that is, all the changes in each model are divided by the respective mean-state SST change during 2061–2100, which is averaged between 60°S and 60°N.

To compare the modeled with the one in observational data, the monthly SST from Hadley Centre Sea Ice and SST, version 1 (HadISST1; Rayner et al. 2003), and the atmospheric 500-hPa from NCEP–NCAR (Kalnay et al. 1996) for the period 1961–2000 are chosen to compute the observational . All the model outputs and observational data are interpolated into a 2.5° × 2.5° grid.

3. Intermodel relationship between the change of ENSO’s amplitude and

The change of ENSO’s amplitude under global warming shows a large intermodel spread (Fig. 1a). Of the 31 models, 10 project an enhanced ENSO (red bars), whereas 21 project a weakened ENSO (blue bars). The multimodel ensemble mean (MME) result displays a weakened () but insignificant (based on the F test) change, which is consistent with previous studies (e.g., van Oldenborgh et al. 2005; Stevenson 2012; Ham and Kug 2016). The projected changes in the amplitude of EN (Fig. 1b) and LN (Fig. 1c) in each model are almost the same as that for the total change of ENSO’s amplitude (Fig. 1a), with intermodel correlation coefficients of 0.97 and 0.96, respectively. This indicates the modeled changes in the amplitude of EN and LN under global warming are in phase.

Fig. 1.
Fig. 1.

Changes in the (a) standard deviation of SSTA, (b) RMS for SSTA > 0, and (c) RMS for SSTA < 0 over the Niño-3 region under global warming from 31 CMIP5 models, representing changes in the amplitude of total ENSO, EN, and LN, respectively. The red (blue) bars indicate the amplitude is strengthened (weakened).

Citation: Journal of Climate 32, 2; 10.1175/JCLI-D-18-0456.1

As shown in Fig. 2a, the intermodel spread with respect to the change of ENSO’s amplitude is significantly correlated to that of , whose magnitude also varies among models. Models with a more (less) strengthened under global warming tend to project an increase (decrease) in ENSO’s amplitude, with an intermodel correlation of −0.63, which is significant at the 99% confidence level based on the Student’s t test. Moreover, the changes of ENSO’s ZWS and precipitation are also significantly correlated to , with intermodel correlations of −0.53 and −0.86, respectively. These intermodel relationships between the changes of ENSO-related atmospheric variabilities and follow Bjerknes feedback theory, indicating the important role played by in the intermodel uncertainty with respect to the change of ENSO’s amplitude. According to the Bjerknes loop, an increase (a decrease) in the ENSO-induced atmospheric vertical motion anomaly is associated with an enhanced (a weakened) low-level westerly wind anomaly, which is performed well among models (not shown). Therefore, the effect of on the intermodel uncertainty in the ENSO amplitude change can be achieved through the change in the surface wind response. Zheng et al. (2016) revealed that changes in convective feedback in the eastern equatorial Pacific can affect the response of ZWS in the equatorial central Pacific and further lead to the intermodel uncertainty with respect to the change of ENSO’s amplitude. Here, we highlight that what contributes to this intermodel spread is the effect of involved in the changes of convective feedback.

Fig. 2.
Fig. 2.

Intermodel scatterplots between changes in the response of atmospheric circulation to SSTAs over the Niño-3 region and that in the (a) change of ENSO’s amplitude , (b) change of ENSO’s ZWS, and (c) change of ENSO’s precipitation. The solid line denotes the linear intermodel regression. The intermodel correlation coefficient is shown in the upper-right corner of each panel. Red numbers denote that the intermodel correlation is significant at the 99% confidence level, based on the Student’s t test. Note that the y axis is reversed.

Citation: Journal of Climate 32, 2; 10.1175/JCLI-D-18-0456.1

However, possesses pronounced nonlinearity both in model simulations and observations (Lloyd et al. 2012), being much stronger (weaker) when the local SST is abnormally high (low). As shown in Fig. 3, almost all models reproduce a stronger than , except for two (MIROC-ESM and MIROC-ESM-CHEM). Such nonlinearity indicates that may behave differently in different ENSO phases, and thus may also have discrepant effects on the changes in the amplitude of EN and LN under global warming.

Fig. 3.
Fig. 3.

The response of atmospheric circulation to SSTAs in 31 models over the Niño-3 region in the historical run for all SSTA (; green), SSTA > 0 (; red), and SSTA < 0 (; blue). Note that the y axis is reversed.

Citation: Journal of Climate 32, 2; 10.1175/JCLI-D-18-0456.1

Indeed, Figs. 4a and 4b show the intermodel correlation between and is much stronger than that between and . The former is significant at the 99% confidence level based on the Student’s t test, while the latter turns out to be statistically insignificant. Moreover, the intermodel correlation between and improves considerably when compared with that between and (Fig. 2a). These results indicate is only responsible for the intermodel uncertainty with respect to the change of EN’s amplitude and does not contribute to that of LN.

Fig. 4.
Fig. 4.

Intermodel scatterplots between (a) changes in the response of atmospheric circulation to SSTAs over the Niño-3 region for SSTA > 0 () and the change of EN’s amplitude (), (b) changes in the response of atmospheric circulation to SSTAs over the Niño-3 region for SSTA < 0 () and the change of LN’s amplitude (). The solid line denotes the linear intermodel regression. The markers are as those in Fig. 2. The intermodel correlation coefficient is shown in the upper-right corner of each panel, and the corresponding p value is shown in each panel. Red (black) numbers denote that the intermodel correlation is significant (insignificant) at the 99% confidence level, based on the Student’s t test. Note that the y axis is reversed.

Citation: Journal of Climate 32, 2; 10.1175/JCLI-D-18-0456.1

The discrepant effects of on the change in the amplitude of EN and LN might be explainable by the asymmetric responses of atmospheric circulation in different ENSO phases, as illustrated schematically in Fig. 5. During an EN event, the positive SST anomaly in the eastern equatorial Pacific shifts the deep convection eastward, enhancing the response of local active atmospheric upward motion to positive SSTAs (Fig. 5a, blue arrows). The enhanced response of atmospheric circulation in the eastern equatorial Pacific induces low-level convergence with a westerly wind anomaly in the central Pacific (Fig. 5a, red arrow), which further strengthens the positive SSTAs in the eastern equatorial Pacific (Kang and Kug 2002; Watanabe et al. 2012). Accordingly, models with a larger increase in under global warming favor an increase in the amplitude of EN.

Fig. 5.
Fig. 5.

Schematic diagram illustrating the discrepant effects of on the change of (a) EN’s and (b) LN’s amplitude in CMIP5 models.

Citation: Journal of Climate 32, 2; 10.1175/JCLI-D-18-0456.1

In contrast, during an LN event, the SST in the eastern equatorial Pacific is colder than normal, together with anomalous downward motion (Fig. 5b, purple arrows), causing the atmospheric circulation in the eastern Pacific to be insensitive to SSTAs and the deep convection to be confined to the western equatorial Pacific (Fig. 5b, blue arrows; Cai et al. 2015a). The inactive response of atmospheric circulation to SSTAs in the eastern Pacific is unable to produce an easterly wind anomaly in the central Pacific that is conducive to a further enhancement in the amplitude of LN. Meanwhile, the easterly wind anomaly induced by the active deep convection in the western Pacific is not close to the eastern equatorial Pacific (Fig. 5b, red arrow), thereby leading to no further increase in the amplitude of LN. These unfavorable conditions mean that the does not contribute to the intermodel difference in .

4. Sources of intermodel uncertainty in and their impacts on the intermodel uncertainty in the change of EN’s amplitude

Having shown that the is an important source of uncertainty in , a natural question arises as to which factors control the intermodel uncertainty in , since factors responsible for the intermodel uncertainty in could also contribute to that in . Previous studies (e.g., Huang et al. 2017) have revealed that under global warming is affected by more than one process, implying there may be several factors impacting the intermodel uncertainty in .

a. Decomposition of

Huang et al. (2017) revealed that there is a general weakening of in the tropics under global warming associated with the tropical-mean SST warming and a local enhanced in the eastern equatorial Pacific because of the local enhanced SST warming relative to the tropical mean. This indicates the intermodel differences in the general change of associated with tropical-mean SST warming and the effect of the more mean-state SST warming relative to the tropical mean could both contribute to the intermodel uncertainty in . In addition, there may be other factors that affect the intermodel uncertainty in besides these two aspects. For example, as the intermodel difference in the present-day simulations of could be brought into its future projections under global warming (Collins et al. 2010; Ying et al. 2018), the present-day may be another source of intermodel uncertainty in . Thus, (hereafter replaced by for simplicity) in each model could be expressed as
e1
where represents the Niño-3 region’s performance of the general change of in the tropics associated with the tropical-mean SST warming, is the effect of the mean-state SST warming over the Niño-3 region relative to the tropical-mean warming, and is the residual representing the effects of other factors.
The general change of in the tropics associated with the tropical-mean SST warming is closely tied to the change in the tropical troposphere-mean stability, which is proportional to the changes in the near-surface specific humidity on a moist adiabatic process (Lu et al. 2008; Huang et al. 2017). Under global warming, the change in the tropical-mean near-surface specific humidity follows the Clausius–Clapeyron relationship—increasing around 7% per 1 K tropical-mean SST increase (e.g., Held and Soden 2006), which is nearly uniform in the tropics. Therefore, the could be estimated by assuming that the regional rate of change in is equal to that of the tropical-mean , and thus the can be calculated as
e2
where an overbar represents the tropical mean from 20°S to 20°N; and denote the tropical mean of present-day and its change under global warming, respectively; is the rate of change in under global warming.
The is pronounced in the regions where the SST increases are larger than the tropical-mean SST warming, especially for the eastern equatorial Pacific (Huang et al. 2017). To estimate in each model, we first calculate the relative SST warming over the Niño-3 region (defined as the mean-state SST warming averaged over the Niño-3 region minus the tropical-mean SST warming) and without the effect of (i.e., ). By doing so, we then find there is a significant intermodel relationship between and , with a correlation coefficient of −0.44. Therefore, we define the by regressing onto ,
e3
where denotes the regression estimation, is the linear regression coefficient, obtained by performing an intermodel regression of onto , and is the intercept of at . This intermodel regression coefficient is significant at the 95% confidence level based on the Student’s t test, indicating that the intermodel difference in can significantly influence that in , and our current method for calculating is reasonable. Thus, represents the part in that is significantly linearly correlated to . In addition, is simply calculated as the residual part by removing both and (i.e., ).

Figure 6 displays and its decomposed terms based on Eq. (1) in the 31 CMIP5 models. It should be noted that the decomposed terms are not statistically correlated with each other (not shown), implying the present decomposition method can appropriately separate the effects of different factors. Among the three terms, and are two major contributors for in most models (Figs. 6a,c,d), while is relatively small in magnitude and contributes less to (Fig. 6b). Moreover, the intermodel correlations of the two major contributors (i.e., and ) with are 0.5 and 0.73, respectively (Figs. 6c,d), both of which are significant at the 99% confidence level based on the Student’s t test, while the intermodel correlation between and is not statistically significant (Fig. 6b). This indicates that and are also the two major contributors to the intermodel difference in . Therefore, we neglect in the following analyses, because of its relatively small contribution and correlation to .

Fig. 6.
Fig. 6.

(a) The changes in the response of atmospheric circulation to SSTAs over the Niño-3 region for SSTA > 0 (, i.e., ; hPa s−1 K−2) in 31 CMIP5 models and the decomposed parts in based on Eq. (1): (b) , (c) , and (d) . The numbers in the upper-right corner of each panel denote the intermodel correlation between the respective decomposed part and . The intermodel correlations that are significant at the 99% confidence level, based on the Student’s t test, are marked in red.

Citation: Journal of Climate 32, 2; 10.1175/JCLI-D-18-0456.1

b. Effects of the relative SST warming and the present-day

As shown in Fig. 6c, the values in the selected models are all negative, indicating that in all models contributes to an enhancement of under global warming. Nevertheless, the magnitudes of vary among models because of the intermodel differences in . Models with a larger increase in are conducive to a greater enhanced , thus contributing to a larger (Figs. 6c, 7a). Accordingly, the intermodel difference in could contribute to that in the change of EN’s amplitude by influencing , verified by the significant intermodel relationship between and (Fig. 7b). That is, models with a larger increase in tend to project an increase in EN’s amplitude under global warming, with an intermodel correlation of 0.46 at the 99% confidence level based on the t test.

Fig. 7.
Fig. 7.

Intermodel scatterplots of the mean-state SST warming over the Niño-3 region relative to the tropical-mean warming with (a) and (b) . The solid line denotes the intermodel linear regression. The markers are as those in Fig. 2. The intermodel correlation coefficient is shown in the upper-right corner of each panel. The red numbers in the upper-right corner of each panel denote that the intermodel correlation is significant at the 99% confidence level, based on the Student’s t test. Note that the y axis in (a) is reversed.

Citation: Journal of Climate 32, 2; 10.1175/JCLI-D-18-0456.1

The intermodel correlation between and is more prominent when compared with that between and , indicating the factor(s) involved in could be more important for the intermodel uncertainty in . One of the factors considered here is the present-day , since projections of future climate change are often tied to current climate simulations (Huang and Ying 2015; Zhou and Xie 2015; Wang et al. 2017). As shown in Fig. 8a, there is a significant intermodel relationship between the present-day and . Models with a stronger (weaker) present-day tend to have an to decrease (increase) under global warming, with an intermodel correlation coefficient of −0.57. Such an inverse relationship also emerges between the present-day and (i.e., ) among models (Fig. 8b), suggesting that the present-day is an important contributor for the intermodel uncertainty in its own change under global warming. Note that the present-day is not significantly correlated to (not shown), further indicating that the effect of the present-day on the intermodel uncertainty in is different from that of . Because of the tight connection between the intermodel uncertainty in the change of EN’s amplitude and the intermodel difference in (Fig. 4a), the present-day should also be a source of intermodel uncertainty in the change of EN’s amplitude. As shown in Fig. 8c, models with a stronger (weaker) present-day tend to produce a decrease (an increase) in EN’s amplitude, with a significant intermodel correlation of 0.59.

Fig. 8.
Fig. 8.

Intermodel scatterplots of the present-day with (a) , (b) , and (c) . The solid line denotes the intermodel linear regression. The purple dot denotes the projected location on the regression line of the observational present-day , with the horizontal purple line and the vertical black line representing the observational present-day value and the estimated value by which the observational present-day projects onto the linear regression, respectively. The other markers except for the solid purple dots are as those in Fig. 2. The intermodel correlation coefficient is shown in the upper-right corner of each panel, among which the red numbers denote that the intermodel correlation is significant at the 99% confidence level, based on the Student’s t test. Note that the x axes in (a), (b), and (c) are reversed, and the y axes in (a) and (b) are reversed.

Citation: Journal of Climate 32, 2; 10.1175/JCLI-D-18-0456.1

Accordingly, the intermodel differences in and the present-day , the two sources of intermodel uncertainty in , both contribute to the intermodel uncertainty in the change of EN’s amplitude. Moreover, our results show that the intermodel correlation between the present-day and (Fig. 8c) is more significant than that between and (Fig. 7b), implying that the present-day should be a more important factor than the relative SST warming over the Niño-3 region for the intermodel uncertainty in the change of EN’s amplitude.

c. Mechanism of the impact of present-day on the intermodel uncertainty in

The inverse relationship between the present-day and (Fig. 8b) implies there could be a process limiting the increase in under global warming when the present-day simulation of is relatively strong. Figure 9a further shows there is a strong intermodel relationship between the present-day and the present-day mean-state precipitation over the Niño-3 region, with a correlation of −0.74. This implies the simulation of could be controlled by that of the mean-state precipitation over the Niño-3 region. Models with a relatively low mean-state precipitation (relative to the MME mean-state precipitation) in the eastern equatorial Pacific are always associated with a relatively strong local mean-state atmospheric downward motion (not shown), which can prohibit the anomalous upward motion induced by the positive SST anomaly, thus leading to a relatively weak . This is consistent with Ham and Kug (2012), who revealed the simulation of ENSO-induced convection anomalies is influenced by the simulation of mean-state precipitation in the eastern equatorial Pacific.

Fig. 9.
Fig. 9.

Intermodel scatterplots between (a) the present-day and present-day climatological precipitation over the Niño-3 region, (b) and the fractional change in climatological precipitation under global warming over the Niño-3 region, and (c) the present-day climatological precipitation over the Niño-3 region and its fractional change under global warming. The solid line denotes the intermodel linear regression. The markers are as those in Fig. 2. The intermodel correlation coefficient is shown in the upper-right corner of each panel. The red number denotes that the intermodel correlation is significant (insignificant) at the 99% confidence level, based on the Student’s t test. Note that the x axes in (a) and (b) are reversed.

Citation: Journal of Climate 32, 2; 10.1175/JCLI-D-18-0456.1

The strong influence of the present-day mean-state precipitation over the Niño-3 region on the present-day implies that the change in the mean-state precipitation might modulate the change in (i.e., ). To investigate the relationship between these two factors, we define a fractional change of mean-state precipitation over the Niño-3 region by the ratio of mean-state precipitation change over the Niño-3 region per 1 K of global SST warming to the local present-day mean-state precipitation. The fractional change of mean-state precipitation removes the intermodel difference in the sensitivity of precipitation among models, thus offering a more objective way to measure the intermodel mean-state precipitation change in response to global warming. As shown in Fig. 9b, the intermodel correlation between and the fractional change of mean-state precipitation over the Niño-3 region is even stronger compared with that between the present-day and the present-day mean-state precipitation, with the intermodel correlation coefficient reaching −0.8.

The strong relationships between and mean-state precipitation in both current and future climate change imply the inverse relationship between the present-day and its change under global warming (i.e., ) could be tied to that between the present-day mean-state precipitation and its fractional change under global warming. There is indeed a significant inverse relationship between the present-day mean-state precipitation and its fractional change under global warming (Fig. 9c). Models with a smaller (larger) present-day mean-state precipitation tend to have more (less) increase in the fractional change of mean-state precipitation. Ham et al. (2018) indicated that such an inverse relationship is due to the relatively small (large) present-day mean-state precipitation reducing (increasing) the surface relative humidity by enhancing (weakening) the atmospheric boundary layer mixing under global warming, thus boosting (reducing) the evaporation and the resultant precipitation. Therefore, the effect of the present-day on the intermodel uncertainty in is modulated by the inverse relationship between the present-day mean-state precipitation and its fractional change under global warming.

5. Summary and discussion

The role played by the response of atmospheric circulation to SSTAs in the intermodel uncertainty with respect to the change of ENSO’s amplitude under global warming is investigated in this study based on 31 CMIP5 models. We find that the intermodel difference in the change of ENSO’s amplitude is tightly correlated with that in the response of atmospheric circulation to SSTAs in the eastern Pacific Niño-3 region. Models with a larger (smaller) increase in under global warming tend to project an enhanced (a weakened) amplitude of ENSO. By considering the nonlinearity of , we find that the is only responsible for the intermodel uncertainty in the change of EN’s amplitude; that is, it does not impact the intermodel uncertainty in the change of LN’s amplitude. Note that the spatial asymmetry of ENSO—the LN tends to peak over the central Pacific instead of the eastern Pacific as in the EN (Cai et al. 2015b)—has not been considered for simplicity. Nevertheless, the above result does not change if we use the SST anomaly over the Niño-4 region to define the LN amplitude (not shown).

The discrepant effects of on the change in the amplitude of EN and LN can be attributed to the asymmetric response of atmospheric circulation in different ENSO phases. During an EN event, the increased will produce an eastward-shifted westerly wind anomaly due to eastward-shifted deep convection, which is conducive to a further increase in the amplitude of EN (Kang and Kug 2002; Watanabe et al. 2012). During an LN event, meanwhile, the inactive cannot produce an easterly wind anomaly close to the Niño-3 region that favors a further enhancement of LN’s amplitude.

The sources of intermodel uncertainty with respect to the change in the response of atmospheric circulation to SSTAs over the Niño-3 region (i.e., ) and their influences on the intermodel uncertainty in the change of EN’s amplitude are further investigated. Based on a decomposition method, we separate into three unrelated components: the general change of in the tropics associated with the tropical-mean SST warming; the effect of the mean-state SST warming over the Niño-3 region relative to the tropical-mean warming; and the residual term other than the former two factors. It is shown that the effect of the relative SST warming over the Niño-3 region and the present-day involved in the residual term, the two mutual independent factors, are the two major contributors to the intermodel difference in . Moreover, the larger intermodel correlation between the present-day and than that between and suggests the present-day should play a more important role in the intermodel uncertainty in (Figs. 6c,d).

Figure 10 is a schematic diagram illustrating the major processes involved in that impact the intermodel uncertainty in the change of EN’s amplitude. On the one hand, models with a larger (smaller) mean-state SST warming in the eastern equatorial Pacific relative to the tropical-mean warming tend to produce a larger (smaller) increase in under global warming (Fig. 10, brown arrow). This is consistent with the study of Zheng et al. (2016), in which they proposed that models with an enhanced warming in the eastern equatorial Pacific are in favor of an increase in the ENSO amplitude change by amplifying the local convective response. On the other hand, models with a weaker (stronger) present-day relative to the MME result tend to project a larger (smaller) increase in . Such an inverse relationship is modulated by the same inverse relationship between the present-day mean-state precipitation over the Niño-3 region and its fractional change under global warming (Fig. 10, green arrows, purple arrow), as the present-day and are controlled by the present-day mean-state precipitation and its fractional change under global warming, respectively (Figs. 9a,b). These two processes both contribute to the intermodel difference in , which ultimately results in the intermodel uncertainty in the change of EN’s amplitude (Fig. 10, red arrow).

Fig. 10.
Fig. 10.

Schematic diagram illustrating the major processes through which (i.e., ) affects the intermodel uncertainty in the change of EN’s amplitude under global warming in CMIP5 models. The in the diagram denotes the response of atmospheric circulation to SSTAs averaged over the Niño-3 region for SSTA > 0. The green arrows denote the inverse intermodel relationship between the present-day simulations and their change under global warming. The purple arrow denotes the modulation of the inverse intermodel relationship between the present-day mean-state precipitation and its fractional change under global warming on the same inverse relationship between the present-day and . The brown arrow denotes the effect of relative SST warming over the Niño-3 region on . The red arrow denotes the effect of on the intermodel uncertainty in EN amplitude change.

Citation: Journal of Climate 32, 2; 10.1175/JCLI-D-18-0456.1

In light of the effect of the present-day on the intermodel uncertainty in the change of EN’s amplitude, it is feasible to calibrate projections of the change in EN’s amplitude based on the common bias among models in the present-day following the “emergent constraint” concept (Bracegirdle and Stephenson 2013; Huang and Ying 2015; Li et al. 2016). The observed present-day is −3.69 × 10−5 hPa s−1 K−1, indicating that there is a common overestimation among CMIP5 models in simulating the present-day (Fig. 8). This suggests that is commonly underestimated among models based on the inverse intermodel relationship between the present-day and its change under global warming (Fig. 8b). Thus, the change in EN’s amplitude influenced by the present-day is also deemed to be underestimated.

However, it should be noted that does not play a decisive role in the projection of EN’s amplitude under global warming, as some models with a strengthened also project a weakened amplitude of EN (Fig. 2a). Therefore, the calibrated results by both the common possible biases in the SST warming pattern and the observational present-day are probably not the final correct projections for the change in EN’s amplitude under global warming. One needs to be careful in the emergent constraint space that there can be cases where the constraints may have opposite effects on future projections (Wang et al. 2017). There are other processes that also play a role in determining the change in EN’s amplitude, such as the changes in thermocline feedback, zonal advection feedback, and ZWS–SST feedback (DiNezio et al. 2012; Kim et al. 2014; An and Choi 2015; Chen et al. 2017b). Nevertheless, our results show that the response of atmospheric circulation to SSTAs in the eastern Pacific is an important process in modulating the change of ENSO’s amplitude under global warming, which takes us a step further on from the study by Zheng et al. (2016). Moreover, we highlight that an improved simulation of the present-day could improve the projection of the change in ENSO’s amplitude under global warming.

This study mainly focuses on the intermodel uncertainty with respect to the change in the amplitude of SST averaged over the Niño-3 region, while neglecting the different spatial structures of ENSO and their possible discrepant changes in amplitude under global warming. It should be noted that there are several types of EN according to their distinctive spatial structures, and the mechanisms that control their respective changes in amplitude are discrepant (Ashok et al. 2007; Yeh et al. 2009; Kug et al. 2012). The sources of intermodel uncertainties in this regard are worthy of study. In addition, the EN amplitude change may also contribute to the mean-state SST change (Sun et al. 2014) and thus in turn affect the intermodel uncertainty in . This implies a further positive feedback process between the EN amplitude change and , which will be investigated in a future study of model experiment.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grants 41706024, 41690121, 41690120, 41621064, 41575088, 41722504), the Youth Innovation Promotion Association of CAS, and the Fundamental Research Funds for the Central Universities. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP5, and the climate modeling groups (listed in Table 1) for producing and making available their model output.

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    Fig. 1.

    Changes in the (a) standard deviation of SSTA, (b) RMS for SSTA > 0, and (c) RMS for SSTA < 0 over the Niño-3 region under global warming from 31 CMIP5 models, representing changes in the amplitude of total ENSO, EN, and LN, respectively. The red (blue) bars indicate the amplitude is strengthened (weakened).

  • <sc>Fig</sc>. 2.
    View in gallery
    Fig. 2.

    Intermodel scatterplots between changes in the response of atmospheric circulation to SSTAs over the Niño-3 region and that in the (a) change of ENSO’s amplitude , (b) change of ENSO’s ZWS, and (c) change of ENSO’s precipitation. The solid line denotes the linear intermodel regression. The intermodel correlation coefficient is shown in the upper-right corner of each panel. Red numbers denote that the intermodel correlation is significant at the 99% confidence level, based on the Student’s t test. Note that the y axis is reversed.

  • <sc>Fig</sc>. 3.
    View in gallery
    Fig. 3.

    The response of atmospheric circulation to SSTAs in 31 models over the Niño-3 region in the historical run for all SSTA (; green), SSTA > 0 (; red), and SSTA < 0 (; blue). Note that the y axis is reversed.

  • <sc>Fig</sc>. 4.
    View in gallery
    Fig. 4.

    Intermodel scatterplots between (a) changes in the response of atmospheric circulation to SSTAs over the Niño-3 region for SSTA > 0 () and the change of EN’s amplitude (), (b) changes in the response of atmospheric circulation to SSTAs over the Niño-3 region for SSTA < 0 () and the change of LN’s amplitude (). The solid line denotes the linear intermodel regression. The markers are as those in Fig. 2. The intermodel correlation coefficient is shown in the upper-right corner of each panel, and the corresponding p value is shown in each panel. Red (black) numbers denote that the intermodel correlation is significant (insignificant) at the 99% confidence level, based on the Student’s t test. Note that the y axis is reversed.

  • <sc>Fig</sc>. 5.
    View in gallery
    Fig. 5.

    Schematic diagram illustrating the discrepant effects of on the change of (a) EN’s and (b) LN’s amplitude in CMIP5 models.

  • <sc>Fig</sc>. 6.
    View in gallery
    Fig. 6.

    (a) The changes in the response of atmospheric circulation to SSTAs over the Niño-3 region for SSTA > 0 (, i.e., ; hPa s−1 K−2) in 31 CMIP5 models and the decomposed parts in based on Eq. (1): (b) , (c) , and (d) . The numbers in the upper-right corner of each panel denote the intermodel correlation between the respective decomposed part and . The intermodel correlations that are significant at the 99% confidence level, based on the Student’s t test, are marked in red.

  • <sc>Fig</sc>. 7.
    View in gallery
    Fig. 7.

    Intermodel scatterplots of the mean-state SST warming over the Niño-3 region relative to the tropical-mean warming with (a) and (b) . The solid line denotes the intermodel linear regression. The markers are as those in Fig. 2. The intermodel correlation coefficient is shown in the upper-right corner of each panel. The red numbers in the upper-right corner of each panel denote that the intermodel correlation is significant at the 99% confidence level, based on the Student’s t test. Note that the y axis in (a) is reversed.

  • <sc>Fig</sc>. 8.
    View in gallery
    Fig. 8.

    Intermodel scatterplots of the present-day with (a) , (b) , and (c) . The solid line denotes the intermodel linear regression. The purple dot denotes the projected location on the regression line of the observational present-day , with the horizontal purple line and the vertical black line representing the observational present-day value and the estimated value by which the observational present-day projects onto the linear regression, respectively. The other markers except for the solid purple dots are as those in Fig. 2. The intermodel correlation coefficient is shown in the upper-right corner of each panel, among which the red numbers denote that the intermodel correlation is significant at the 99% confidence level, based on the Student’s t test. Note that the x axes in (a), (b), and (c) are reversed, and the y axes in (a) and (b) are reversed.

  • <sc>Fig</sc>. 9.
    View in gallery
    Fig. 9.

    Intermodel scatterplots between (a) the present-day and present-day climatological precipitation over the Niño-3 region, (b) and the fractional change in climatological precipitation under global warming over the Niño-3 region, and (c) the present-day climatological precipitation over the Niño-3 region and its fractional change under global warming. The solid line denotes the intermodel linear regression. The markers are as those in Fig. 2. The intermodel correlation coefficient is shown in the upper-right corner of each panel. The red number denotes that the intermodel correlation is significant (insignificant) at the 99% confidence level, based on the Student’s t test. Note that the x axes in (a) and (b) are reversed.

  • <sc>Fig</sc>. 10.
    View in gallery
    Fig. 10.

    Schematic diagram illustrating the major processes through which (i.e., ) affects the intermodel uncertainty in the change of EN’s amplitude under global warming in CMIP5 models. The in the diagram denotes the response of atmospheric circulation to SSTAs averaged over the Niño-3 region for SSTA > 0. The green arrows denote the inverse intermodel relationship between the present-day simulations and their change under global warming. The purple arrow denotes the modulation of the inverse intermodel relationship between the present-day mean-state precipitation and its fractional change under global warming on the same inverse relationship between the present-day and . The brown arrow denotes the effect of relative SST warming over the Niño-3 region on . The red arrow denotes the effect of on the intermodel uncertainty in EN amplitude change.

Fig. 1.

Changes in the (a) standard deviation of SSTA, (b) RMS for SSTA > 0, and (c) RMS for SSTA < 0 over the Niño-3 region under global warming from 31 CMIP5 models, representing changes in the amplitude of total ENSO, EN, and LN, respectively. The red (blue) bars indicate the amplitude is strengthened (weakened).
Fig. 2.

Intermodel scatterplots between changes in the response of atmospheric circulation to SSTAs over the Niño-3 region and that in the (a) change of ENSO’s amplitude , (b) change of ENSO’s ZWS, and (c) change of ENSO’s precipitation. The solid line denotes the linear intermodel regression. The intermodel correlation coefficient is shown in the upper-right corner of each panel. Red numbers denote that the intermodel correlation is significant at the 99% confidence level, based on the Student’s t test. Note that the y axis is reversed.
Fig. 3.

The response of atmospheric circulation to SSTAs in 31 models over the Niño-3 region in the historical run for all SSTA (; green), SSTA > 0 (; red), and SSTA < 0 (; blue). Note that the y axis is reversed.
Fig. 4.

Intermodel scatterplots between (a) changes in the response of atmospheric circulation to SSTAs over the Niño-3 region for SSTA > 0 () and the change of EN’s amplitude (), (b) changes in the response of atmospheric circulation to SSTAs over the Niño-3 region for SSTA < 0 () and the change of LN’s amplitude (). The solid line denotes the linear intermodel regression. The markers are as those in Fig. 2. The intermodel correlation coefficient is shown in the upper-right corner of each panel, and the corresponding p value is shown in each panel. Red (black) numbers denote that the intermodel correlation is significant (insignificant) at the 99% confidence level, based on the Student’s t test. Note that the y axis is reversed.
Fig. 5.

Schematic diagram illustrating the discrepant effects of on the change of (a) EN’s and (b) LN’s amplitude in CMIP5 models.
Fig. 6.

(a) The changes in the response of atmospheric circulation to SSTAs over the Niño-3 region for SSTA > 0 (, i.e., ; hPa s−1 K−2) in 31 CMIP5 models and the decomposed parts in based on Eq. (1): (b) , (c) , and (d) . The numbers in the upper-right corner of each panel denote the intermodel correlation between the respective decomposed part and . The intermodel correlations that are significant at the 99% confidence level, based on the Student’s t test, are marked in red.
Fig. 7.

Intermodel scatterplots of the mean-state SST warming over the Niño-3 region relative to the tropical-mean warming with (a) and (b) . The solid line denotes the intermodel linear regression. The markers are as those in Fig. 2. The intermodel correlation coefficient is shown in the upper-right corner of each panel. The red numbers in the upper-right corner of each panel denote that the intermodel correlation is significant at the 99% confidence level, based on the Student’s t test. Note that the y axis in (a) is reversed.
Fig. 8.

Intermodel scatterplots of the present-day with (a) , (b) , and (c) . The solid line denotes the intermodel linear regression. The purple dot denotes the projected location on the regression line of the observational present-day , with the horizontal purple line and the vertical black line representing the observational present-day value and the estimated value by which the observational present-day projects onto the linear regression, respectively. The other markers except for the solid purple dots are as those in Fig. 2. The intermodel correlation coefficient is shown in the upper-right corner of each panel, among which the red numbers denote that the intermodel correlation is significant at the 99% confidence level, based on the Student’s t test. Note that the x axes in (a), (b), and (c) are reversed, and the y axes in (a) and (b) are reversed.
Fig. 9.

Intermodel scatterplots between (a) the present-day and present-day climatological precipitation over the Niño-3 region, (b) and the fractional change in climatological precipitation under global warming over the Niño-3 region, and (c) the present-day climatological precipitation over the Niño-3 region and its fractional change under global warming. The solid line denotes the intermodel linear regression. The markers are as those in Fig. 2. The intermodel correlation coefficient is shown in the upper-right corner of each panel. The red number denotes that the intermodel correlation is significant (insignificant) at the 99% confidence level, based on the Student’s t test. Note that the x axes in (a) and (b) are reversed.
Fig. 10.

Schematic diagram illustrating the major processes through which (i.e., ) affects the intermodel uncertainty in the change of EN’s amplitude under global warming in CMIP5 models. The in the diagram denotes the response of atmospheric circulation to SSTAs averaged over the Niño-3 region for SSTA > 0. The green arrows denote the inverse intermodel relationship between the present-day simulations and their change under global warming. The purple arrow denotes the modulation of the inverse intermodel relationship between the present-day mean-state precipitation and its fractional change under global warming on the same inverse relationship between the present-day and . The brown arrow denotes the effect of relative SST warming over the Niño-3 region on . The red arrow denotes the effect of on the intermodel uncertainty in EN amplitude change.
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