1. Introduction
El Niño–Southern Oscillation (ENSO) is a natural interannual variation in sea surface temperature (SST) and sea surface air pressure occurring in the tropical Pacific (e.g., Philander 1990; Neelin et al. 1998; McPhaden et al. 2006; Deser et al. 2010). Because of great impacts from ENSO on global climatic disasters, the response of ENSO to the CO2-induced greenhouse warming has been widely studied in the recent decades (Meehl et al. 2007; Yeh et al. 2009; Collins et al. 2010; Vecchi and Wittenberg 2010; Christensen et al. 2013; Cai et al. 2015b; Huang and Xie 2015). Apart from the changes in ENSO-related SST variability (ENSO SST), the changes in ENSO-induced tropical Pacific rainfall variability (ENSO rainfall) have also attracted increased attention (Power et al. 2013; Cai et al. 2014; Chung and Power 2014; Chung et al. 2014; Bonfils et al. 2015; Cai et al. 2015a). At present, the tropical Pacific rainfall variability is a crucial bridge through which ENSO influences global regional climate variability (Lau and Nath 1996; Klein et al. 1999; Alexander et al. 2002), and the changes in ENSO rainfall are also a crucial bridge connecting the changes in ENSO-induced teleconnection patterns and ENSO SST in a future warming climate (Meehl and Teng 2007; Kug et al. 2010; Zhou et al. 2014; Bonfils et al. 2015).
The large uncertainty in ENSO SST changes is a long-standing issue because miscellaneous mechanisms can influence the changes in ENSO SST (Timmermann et al. 1999; van Oldenborgh et al. 2005; Collins et al. 2010; Vecchi and Wittenberg 2010; DiNezio et al. 2012; Stevenson 2012; Watanabe et al. 2012; Kim et al. 2014; Cai et al. 2015b; Capotondi et al. 2015; Ham and Kug 2016). A number of recent studies report that the changes in ENSO rainfall over the tropical Pacific show more significant intermodel agreement on the sign of change than the changes in ENSO SST among the models in phases 3 and 5 of CMIP (CMIP3 and CMIP5) (Power et al. 2013; Cai et al. 2014; Chung and Power 2014; Chung et al. 2014; Watanabe et al. 2014; Bonfils et al. 2015; Cai et al. 2015a). Most models project ENSO rainfall to intensify over the central-eastern Pacific and weaken over the western Pacific under the conditions of global warming (Power et al. 2013; Cai et al. 2014; Huang and Xie 2015). The first part of the present study (Huang 2016, hereafter Part I) further reports that the changes in ENSO rainfall are time varying along with the increase in global-mean surface temperature (GMST). The ENSO rainfall changes intensify gradually and also shift steadily eastward in the results of the multimodel ensemble mean (MME) of 32 CMIP5 models forced under the +8.5 W m−2 representative concentration pathway (RCP8.5) scenario.
The mechanisms forming the time-varying changes in ENSO rainfall are revealed in Part I using a moisture budget decomposition method developed in Huang and Xie (2015). As shown in Fig. 1, the impact factors of ENSO rainfall changes include the absolute increase in mean-state SST as well as the increase in mean-state moisture, the spatial pattern of mean-state SST changes, and the changes in ENSO SST (Ham and Kug 2012; Power et al. 2013; Cai et al. 2014; Chung and Power 2014; Chung et al. 2014; Bonfils et al. 2015; Cai et al. 2015a; Huang and Xie 2015). ENSO rainfall changes can be separated into the thermodynamic and dynamic components. The increase in mean-state moisture enlarges the thermodynamic component of ENSO rainfall changes, whereas the enhanced El Niño–like changes in mean-state SST steadily move the dynamic component of ENSO rainfall changes eastward to the central-eastern Pacific along with the increase in GMST.
Diagram showing the decomposition of changes in ENSO-driven rainfall variability and the pathway of uncertainty in ENSO rainfall changes originating from the impact factors. Blue-shaded boxes in the thermodynamic component represent terms increasing ENSO rainfall. Yellow-shaded boxes represent terms with steadily decreasing ENSO rainfall. Yellow–blue-shaded boxes represent terms with decreasing ENSO rainfall over the western Pacific but increasing rainfall over the eastern Pacific. The heaviness of the shaded color in one box represents the robustness of the term. For example, the historical mean-state moisture with dark shading is the most robust term, and the amplitude changes in ENSO-driven SST variability with light yellow shading is the term with the largest intermodel difference.
Citation: Journal of Climate 30, 2; 10.1175/JCLI-D-16-0373.1
Although these previous studies suggest that the pattern of ENSO rainfall changes is robust with large intermodel agreement on sign of change, we also can observe pronounced intermodel difference in ENSO rainfall changes among the models. The large intermodel differences decrease the confidence of the MME projection (e.g., Christensen et al. 2013). Multiple factors contributing to the formation of ENSO rainfall changes have clear intermodel disagreement in different degrees. The uncertain changes in ENSO SST amplitude, as suggested in Power et al. (2013), could be one of the most important sources. Moreover, as shown in Figs. 6 and 7 of Part I, the intermodel spreads in the mean-state moisture increase and the amplitude changes in ENSO circulation are both pronounced and increase along with the increase in GMST, although their signs often show a robust intermodel agreement. The spatially relative changes in mean-state SST—defined as the tropical-mean-removed mean-state SST changes—also contain large uncertainties among various models (DiNezio et al. 2009; Xie et al. 2010; Huang and Ying 2015; Zhou and Xie 2015; Ying and Huang 2016a,b; Ying et al. 2016), even though it is relatively robust among the factors influencing ENSO rainfall changes (Power et al. 2013).
It is unclear which factors are the dominant sources of ENSO rainfall changes and how the possible sources influence the uncertainty in ENSO rainfall changes. Tracing these sources is necessary to narrow the uncertainty in ENSO rainfall changes. The present paper will evaluate the robustness of ENSO rainfall changes and investigate the major sources of the intermodel uncertainty, whereas Part I has reported the characteristics and mechanisms of ENSO rainfall changes in the MME results.
The intermodel standard deviation of ENSO rainfall changes among the models and a signal-to-noise ratio of the MME projection are calculated to measure intermodel uncertainty based on the output of 32 CMIP5 models in the historical and the RCP8.5 simulations. The simplified moisture budget decomposition method developed in Huang and Xie (2015) can accurately depict not only the multimodel ensemble mean results of ENSO rainfall changes but also the changes in individual models in various stages of global warming (Part I). Therefore, the moisture budget decomposition method is further applied to investigate the sources of uncertainty in ENSO rainfall changes. The models and methods are described in section 2. The results are shown in section 3. Some conclusions are summarized in section 4.
2. Models and methods
The models and a part of methods are the same as those in Part I. They are briefly reviewed in this section. Further details can be found in the methods section of Part I.
a. Models
The model output used in this paper are the same as those in Part I from 32 models of CMIP5 (listed in Table 1) in the historical runs from 1971 to 2010 and the RCP8.5 runs from 2006 to 2100 (Taylor et al. 2012). The variables are sea surface temperature, precipitation, air specific humidity at the surface, and vertical pressure velocity at 500 hPa. The output of all models are interpolated into a uniform 2.5° × 2.5° grid. The simple average of the 32 models defines the MME.
List of the 32 CMIP5 models used in the present study. See http://cmip-pcmdi.llnl.gov/cmip5/availability.html for details. (Additional acronym expansions are available online at http://www.ametsoc.org/PubsAcronymList.)
b. Definition of the climatology and ENSO-related variability
The 1971–2000 mean in the historical runs defines the historical climatology. The period of 2006–2100 in the RCP8.5 runs is divided into several 30-yr segments starting from 2006 with a 5-yr leap (i.e., 2006–35, 2011–40, 2016–45, …, 2071–2100). For the historical runs and each segment in the RCP8.5 runs, the annual cycle based on the 30-yr mean is first removed, and then a 13-yr running mean is removed to eliminate the interdecadal variation and focus on the interannual variability (Power et al. 2013; Huang and Xie 2015). EOF and regression analyses are performed on the interannual anomalies of SST and other variables to define ENSO SST and ENSO rainfall, circulation, and moisture. The first EOF mode is calculated for each segment and scaled by the standard deviation of the corresponding principal components (PCs) to define the ENSO SST pattern in this segment. For each segment, the first PCs are standardized and then regressed onto the interannual anomalies of precipitation, surface specific humidity, and 500-hPa vertical pressure velocity. The regression patterns define the ENSO-driven variability of these variables. Changes in each future segment of the RCP8.5 runs are defined by the differences between this segment and the historical runs.
As revealed in a number of previous studies (Guilyardi et al. 2009; Collins et al. 2010; Vecchi and Wittenberg 2010; Christensen et al. 2013; Bellenger et al. 2014; Sun et al. 2014; Zhang and Sun 2014; Capotondi et al. 2015), the ENSO SST pattern simulated in the CMIP5 models displays some systematic biases. A well-known bias is that the SST anomalies in the central-eastern Pacific associated with ENSO extend too far to the west compared to the observation. However, some previous studies have exhibited that this bias of the ENSO SST pattern does not influence the major conclusions on the projected ENSO rainfall changes under global warming (Power et al. 2013; Huang and Xie 2015; Huang 2016).
c. Moisture budget decomposition
The changes in ENSO circulation Δω′ are further decomposed into amplitude and structural changes:


d. Metrics of intermodel uncertainty
While MME results of multiple models are often used to project change in future, the intermodel standard deviation (SD) of change departing from MME is often calculated as the noise of MME projection. A signal-to-noise ratio (SNR) of MME projection for estimating intermodel uncertainty is defined as SNR = Δ/σ, here Δ is MME change and σ is intermodel SD of change. The sign agreement among the models is also tested for changes.
3. Results
a. A longitude–latitude perspective
Figure 2 shows the SD and SNR of tropical Pacific ENSO rainfall changes for the period 2071–2100. The SD of ENSO rainfall changes is comparative to the MME ENSO rainfall changes (Fig. 2a), although the sign agreement test in Fig. 2b (stippling) shows that the enhancement of ENSO rainfall over the central Pacific is quite robust among the models as revealed in earlier studies (Power et al. 2013; Cai et al. 2014; Huang and Xie 2015). The largest SD of ENSO rainfall changes is located on the western-central Pacific, which is also the region with the largest intermodel difference in mean-state rainfall changes (Huang and Ying 2015; Long et al. 2016). The |SNR| of ENSO rainfall changes is around 0.5 (shaded in Fig. 2b).
(a) The intermodel SD (shaded) of the changes in ENSO rainfall for the period 2071–2100 relative to those in 1971–2000. Contours in (a) are the MME changes in ENSO rainfall (contour interval is 0.2 mm day−1, and negative contours are dashed). (b) The SNR of the changes in ENSO rainfall. Stippling in (b) indicates that more than 70% models agree on the sign of the MME changes.
Citation: Journal of Climate 30, 2; 10.1175/JCLI-D-16-0373.1
The ENSO rainfall changes in individual models are further decomposed into the thermodynamic (Δqω′) and dynamic (qΔω′) components following Eq. (1). The SD and SNR of the two components among the models are shown in Fig. 3. [Note that negative ω means upward flow associated with positive rainfall, and thus the same color (blue) is used to present negative ω and positive rainfall.] The SD of the thermodynamic component (Fig. 3a) is much smaller than that of the dynamic component (Fig. 3b), although the MME changes of the two components have comparative magnitudes (contours in Figs. 3a,b). The |SNR| of the thermodynamic component is larger than 2 (Fig. 3c), whereas the |SNR| of the dynamic component is less than 0.5 (Fig. 3d). The SD, SNR, and sign agreement test of these two components clearly exhibit that the enhancement of ENSO rainfall changes contributed by the thermodynamic component is much more robust than the dynamic component.
(a),(b) The SD (shaded) and (c),(d) SNR of the (a),(c) thermodynamic and (b),(d) dynamic components in 2071–2100. Contours in (a),(b) are the MME changes in the (a) thermodynamic and (b) dynamic components (contour interval is 2 × 10−5 Pa s−1 kg kg−1, and negative contours are dashed). Stippling in (c),(d) indicates that more than 70% models agree on the sign of the MME changes.
Citation: Journal of Climate 30, 2; 10.1175/JCLI-D-16-0373.1
Because both Δq and ω′ can influence the intermodel difference in the thermodynamic component Δqω′, the contributions of the intermodel difference in Δq and ω′ to the SD of Δqω′ are separated as
The SD of the thermodynamic component contributed by the intermodel differences in (a) Δq and (b) ω′ for 2071–2100. The SD of the dynamic component contributed by the intermodel differences in (c) q and (d) Δω′. Note that the color bar in (d) differs from those in (a)–(c).
Citation: Journal of Climate 30, 2; 10.1175/JCLI-D-16-0373.1
The SNR of (a) Δq in 2071–2100, (b) ω′ in 1971–2000, (c) q in 1971–2000, and (d) Δω′ in 2071–2100. Stippling in (d) indicates that more than 70% models agree on the sign of the MME changes.
Citation: Journal of Climate 30, 2; 10.1175/JCLI-D-16-0373.1
As the leading source of intermodel difference in ENSO rainfall changes, Δω′ can be further decomposed into the amplitude
(a),(b) The SD and (c),(d) SNR of (a),(c)
Citation: Journal of Climate 30, 2; 10.1175/JCLI-D-16-0373.1
It is revealed in Part I that the percentage of the amplitude changes in ENSO circulation
The SNR of (a)
Citation: Journal of Climate 30, 2; 10.1175/JCLI-D-16-0373.1
For the structural changes in ENSO circulation
In summary, the weak intermodel agreement in the structural and amplitude change in ENSO SST is the major sources of intermodel uncertainty in ENSO rainfall changes. The other factors can be sequenced from the largest to the smallest intermodel difference as follows: the amplitude changes in ENSO SST
b. The evolution along with the increase in GMST
Part I reveals that the pattern of ENSO rainfall changes in the MME shifts steadily eastward along with the increase in GMST. The robustness of ENSO rainfall changes and the contributors may also vary along with the increase in GMST. Therefore, the robustness of changes in ENSO rainfall and related variables at the equator—the key location of ENSO rainfall changes—is analyzed from the period 2006–35 to the period 2071–2100.
Figure 8 shows the SD and SNR of ENSO rainfall changes at the equator (2.5°S–2.5°N mean). The maximum SD of ENSO rainfall changes is always located over the western-central Pacific, which is the location of major intermodel uncertainty in mean-state SST and rainfall changes (Huang and Ying 2015; Long et al. 2016). The SD of ENSO rainfall changes increases along with the increase in GMST. The robustness, represented by the strength of SNR, of the enhanced ENSO rainfall over the central-eastern Pacific does not obviously change, whereas the SNR and sign agreement of ENSO rainfall changes over the western Pacific is increased from the 2031–60 period onward. The results indicate that the robustness of the enhanced ENSO rainfall is almost independent of the lead time in the twenty-first century, whereas the eastward shift of ENSO rainfall is increasingly robust along with the increase in GMST.
(a) The SD and (b) SNR of changes in ENSO rainfall at the equator (2.5°S–2.5°N mean). Stippling in (b) indicates that more than 70% models agree on the sign of the MME changes.
Citation: Journal of Climate 30, 2; 10.1175/JCLI-D-16-0373.1
The SDs of the thermodynamic and dynamic components of ENSO rainfall changes both increase along with the increase in GMST (Figs. 9a,b). The SNR of the thermodynamic component (Fig. 9c) is almost unchanged. On the other hand, the SNR of the dynamic component (Fig. 9d) is almost unchanged over the central-eastern Pacific but steadily enhanced over the western Pacific, similar to ENSO rainfall changes (Fig. 8b). The much larger SD and smaller SNR of the dynamic component (Figs. 9b,d) indicate that the dynamic component is always the dominant source of uncertainty of ENSO rainfall changes.
(a),(b) The SD and (c),(d) SNR of the components of ENSO rainfall changes at the equator (2.5°S–2.5°N mean). Stippling in (c) and (d) indicates that more than 70% models agree on the sign of the MME changes.
Citation: Journal of Climate 30, 2; 10.1175/JCLI-D-16-0373.1
For the dynamic component, the intermodel difference in the structural changes is always larger than that in the amplitude changes (Figs. 10a,b). Differing from the almost unchanged magnitude of the SNR of the total dynamic component over the central-eastern Pacific (Fig. 9d), the SNR of the amplitude and structural changes of the dynamic component both enhance along with the increase in GMST (Figs. 10c,d). However, the opposite signs of the amplitude and structural changes lead their increasing robustness to cancel out with each other. The increasing robustness of the eastward shift of the dynamic component is mainly contributed by the structural changes (Figs. 10b,d). Because of the negligible intermodel difference in q, the robustness of the dynamic component is dominated by the changes in ENSO circulation Δω′ (Figs. 10d and 11a). Similarly, the robustness of the amplitude and structural changes of the dynamic component (Figs. 11b,c) is dominated by the amplitude and structural changes of Δω′, respectively.
(a),(b) The SD and (c),(d) SNR of the dynamic components of ENSO rainfall changes due to the (a),(c)
Citation: Journal of Climate 30, 2; 10.1175/JCLI-D-16-0373.1
The SNR of (a) Δω′, (b)
Citation: Journal of Climate 30, 2; 10.1175/JCLI-D-16-0373.1
Figure 12 shows the SNR of the amplitude and structural changes in ENSO SST (
The SNR of (a)
Citation: Journal of Climate 30, 2; 10.1175/JCLI-D-16-0373.1
4. Summary
The present study investigates the sources of the intermodel uncertainty of ENSO rainfall changes over the tropical Pacific based on 32 CMIP5 models under the RCP8.5 scenario, when the characteristics and mechanisms of ENSO rainfall changes in the MME from the 32 models were reported in Part I. The intermodel standard deviation and a signal-to-noise ratio are calculated to measure intermodel difference. The magnitude of ENSO rainfall changes exhibits pronounced intermodel differences among the models, although the sign of the change pattern of ENSO rainfall shows a robust intermodel agreement as revealed in previous studies (Power et al. 2013; Cai et al. 2014, 2015b; Huang and Xie 2015).
The moisture budget decomposition method developed in Huang and Xie (2015) is used to trace the sources of the intermodel uncertainty in ENSO rainfall changes and clarify the pathway through which these sources influence the uncertainty in ENSO rainfall changes. The sources of uncertainty in ENSO rainfall changes and the impact pathway are summarized in Fig. 1. The dynamic component of ENSO rainfall changes induced by ENSO circulation changes is a larger uncertainty source than the thermodynamic component induced by the increase in mean-state moisture. The historical ENSO circulation in the thermodynamic component—although it is not a projected variable—displays considerable intermodel difference as reveal in previous studies (Bellenger et al. 2014; Sun et al. 2014). The intermodel spread of historical ENSO circulation is even more pronounced than the intermodel spread of mean-state moisture increase.
The ENSO circulation changes are further decomposed into the amplitude and structural changes, and their intermodel differences are attributed to the intermodel differences in the amplitude and structural changes of ENSO SST, respectively. The amplitude changes in ENSO SST and ENSO circulation, which decrease ENSO rainfall in the MME, are the largest source of the uncertainty in ENSO rainfall changes, consistent with the conclusion in Power et al. (2013). Relatively, the structural changes in ENSO SST with enhancement in the central Pacific are more robust than the decrease of amplitude changes in ENSO SST, which is consistent with a robust westward shift of ENSO SST variability in a warming climate (Yeh et al. 2009). Although the structural changes in ENSO SST are more robust than the amplitude changes, the structural changes in ENSO SST contribute more intermodel standard deviation of ENSO rainfall changes than the amplitude changes. Compared with the amplitude and structural changes in ENSO SST, the spatial pattern of mean-state SST changes is more robust among the models, although its intermodel difference is remarkable as suggested in previous studies (DiNezio et al. 2009; Xie et al. 2010; Ma and Xie 2013; Huang and Ying 2015; Zhou and Xie 2015; Long et al. 2016; Ying and Huang 2016a,b; Ying et al. 2016).
The eastward shift and enhancement along with increasing GMST is an apparent characteristic of ENSO rainfall changes as revealed in Part I. The SD of ENSO rainfall changes increases along with the increase in GMST. However, the intermodel difference measured by the SNR of ENSO rainfall changes over the central-eastern Pacific is almost unchanged, with an absolute value of approximately 0.5–1. This indicates that the uncertainty in the enhanced ENSO rainfall over the central-eastern Pacific does not increase with the extension of the lead time in the projection by the CMIP5 models. On the other hand, the eastward shift of ENSO rainfall is increasingly robust along with the increase in GMST. This could be attributed to the highly robust pattern of mean-state SST change with increasing spatial gradients, whose temporal evolution is the major driver of the eastward shift of ENSO rainfall changes (Ham and Kug 2012; Power et al. 2013; Cai et al. 2014; Bonfils et al. 2015; Ham and Kug 2015; Huang and Xie 2015). This result is consistent with the robust eastward shift of ENSO-driven teleconnection patterns under global warming (Kug et al. 2010; Zhou et al. 2014; Bonfils et al. 2015).
The impact pathway of these sources influencing the uncertainty in ENSO rainfall changes can be reviewed from the sources (Fig. 1). The largest uncertainty source, ENSO SST changes, induces uncertain changes in ENSO circulation, which dilutes the more robust effect of the spatial relative changes in mean-state SST on the changes in ENSO circulation. The uncertain ENSO circulation changes with the dynamic component further dilute the robust enhancement of the thermodynamic component of ENSO rainfall induced by the increase in mean-state moisture.
In the diagram in Fig. 1, the intermodel difference in the GMST increase is only placed as an upstream source of the intermodel difference in mean-state moisture changes. Actually, the GMST likely influences the intermodel difference in other variables (e.g., the amplitude of ENSO SST and the spatial gradient of mean-state SST changes) (e.g., Collins et al. 2010; Kim et al. 2014; Ying and Huang 2016a). On the other hand, the spatial gradient of mean-state SST changes also likely interacts with ENSO SST changes (e.g., Sun 2003; Collins et al. 2010; DiNezio et al. 2012; Sun et al. 2014). Therefore, the present pathway of the intermodel difference in ENSO rainfall changes summarized in Fig. 1 only shows the direct impacts from the sources. The possible interactions among the sources are not considered.
Acknowledgments
The work was supported by the National Basic Research Program of China (2014CB953904), the National Natural Science Foundation of China (Grant 41575088 and 41461164005), and the Youth Innovation Promotion Association of CAS. The World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP5, and the climate modeling groups (listed in Table 1) are acknowledged for producing and making available their model output. Thanks are extended to Dr. Jun Ying for preparing part of the CMIP5 data. I also thank the anonymous reviewers for their constructive suggestions.
REFERENCES
Alexander, M. A., I. Bladé, M. Newman, J. R. Lanzante, N.-C. 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, doi:10.1175/1520-0442(2002)015<2205:TABTIO>2.0.CO;2.
Bellenger, H., E. Guilyardi, J. Leloup, M. Lengaigne, and J. Vialard, 2014: ENSO representation in climate models: From CMIP3 to CMIP5. Climate Dyn., 42, 1999–2018, doi:10.1007/s00382-013-1783-z.
Bonfils, C. J. W., B. D. Santer, T. J. Phillips, K. Marvel, L. R. Leung, C. Doutriaux, and A. Capotondi, 2015: Relative contributions of mean-state shifts and ENSO-driven variability to precipitation changes in a warming climate. J. Climate, 28, 9997–10 013, doi:10.1175/JCLI-D-15-0341.1.
Bretherton, C. S., C. Smith, and J. M. Wallace, 1992: An intercomparison of methods for finding coupled patterns in climate data. J. Climate, 5, 541–560, doi:10.1175/1520-0442(1992)005<0541:AIOMFF>2.0.CO;2.
Cai, W., and Coauthors, 2014: Increasing frequency of extreme El Niño events due to greenhouse warming. Nat. Climate Change, 4, 111–116, doi:10.1038/nclimate2100.
Cai, W., and Coauthors, 2015a: Increased frequency of extreme La Niña events under greenhouse warming. Nat. Climate Change, 5, 132–137, doi:10.1038/nclimate2492.
Cai, W., and Coauthors, 2015b: ENSO and greenhouse warming. Nat. Climate Change, 5, 849–859, doi:10.1038/nclimate2743.
Capotondi, A., Y.-G. Ham, A. Wittenberg, and J.-S. Kug, 2015: Climate model biases and El Niño Southern Oscillation (ENSO) simulation. U.S. CLIVAR Variations, Vol. 13 (1), U.S. CLIVAR Project Office, Washington, DC, 21–25. [Available online at http://usclivar.org/sites/default/files/documents/2015/Variations2015Winter_0.pdf.]
Chou, C., J. D. Neelin, C.-A. Chen, and J.-Y. Tu, 2009: Evaluating the “rich-get-richer” mechanism in tropical precipitation change under global warming. J. Climate, 22, 1982–2005, doi:10.1175/2008JCLI2471.1.
Christensen, J. H., and Coauthors, 2013: Climate phenomena and their relevance for future regional climate change. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 1217–1308. [Available online at http://www.ipcc.ch/pdf/assessment-report/ar5/wg1/WG1AR5_Chapter14_FINAL.pdf.]
Chung, C. T. Y., and S. B. Power, 2014: Precipitation response to La Niña and global warming in the Indo-Pacific. Climate Dyn., 43, 3293–3307, doi:10.1007/s00382-014-2105-9.
Chung, C. T. Y., S. B. Power, J. M. Arblaster, H. A. Rashid, and G. L. Roff, 2014: Nonlinear precipitation response to El Niño and global warming in the Indo-Pacific. Climate Dyn., 42, 1837–1856, doi:10.1007/s00382-013-1892-8.
Collins, M., and Coauthors, 2010: The impact of global warming on the tropical Pacific Ocean and El Niño. Nat. Geosci., 3, 391–397, doi:10.1038/ngeo868.
Deser, C., M. A. Alexander, S.-P. Xie, and A. S. Phillips, 2010: Sea surface temperature variability: Patterns and mechanisms. Annu. Rev. Mar. Sci., 2, 115–143, doi:10.1146/annurev-marine-120408-151453.
DiNezio, P. N., A. C. Clement, G. A. Vecchi, B. J. Soden, B. P. Kirtman, and S.-K. Lee, 2009: Climate response of the equatorial Pacific to global warming. J. Climate, 22, 4873–4892, doi:10.1175/2009JCLI2982.1.
DiNezio, P. N., B. P. Kirtman, A. C. Clement, S.-K. Lee, G. A. Vecchi, and A. Wittenberg, 2012: Mean climate controls on the simulated response of ENSO to increasing greenhouse gases. J. Climate, 25, 7399–7420, doi:10.1175/JCLI-D-11-00494.1.
Guilyardi, E., and Coauthors, 2009: Understanding El Niño in ocean–atmosphere general circulation models: Progress and challenges. Bull. Amer. Meteor. Soc., 90, 325–340, doi:10.1175/2008BAMS2387.1.
Ham, Y.-G., and J.-S. Kug, 2012: How well do current climate models simulate two types of El Niño? Climate Dyn., 39, 383–398, doi:10.1007/s00382-011-1157-3.
Ham, Y.-G., and J.-S. Kug, 2015: Improvement of ENSO simulation based on intermodel diversity. J. Climate, 28, 998–1015, doi:10.1175/JCLI-D-14-00376.1.
Ham, Y.-G., and J.-S. Kug, 2016: ENSO amplitude changes due to greenhouse warming in CMIP5: Role of mean tropical precipitation in the twentieth century. Geophys. Res. Lett., 43, 422–430, doi:10.1002/2015GL066864.
Held, I. M., and B. J. Soden, 2006: Robust responses of the hydrological cycle to global warming. J. Climate, 19, 5686–5699, doi:10.1175/JCLI3990.1.
Huang, P., 2014: Regional response of annual-mean tropical rainfall to global warming. Atmos. Sci. Lett., 15, 103–109, doi:10.1002/asl2.475.
Huang, P., 2016: Time-varying response of ENSO-induced tropical Pacific rainfall to global warming in CMIP5 models. Part I: Multimodel ensemble results. J. Climate, 29, 5763–5778, doi:10.1175/JCLI-D-16-0058.1.
Huang, P., and S.-P. Xie, 2015: Mechanisms of change in ENSO-induced tropical Pacific rainfall variability in a warming climate. Nat. Geosci., 8, 922–926, doi:10.1038/ngeo2571.
Huang, P., and J. Ying, 2015: A multimodel ensemble pattern regression method to correct the tropical Pacific SST change patterns under global warming. J. Climate, 28, 4706–4723, doi:10.1175/JCLI-D-14-00833.1.
Huang, P., S.-P. Xie, K. Hu, G. Huang, and R. Huang, 2013: Patterns of the seasonal response of tropical rainfall to global warming. Nat. Geosci., 6, 357–361, doi:10.1038/ngeo1792.
Kim, S. T., W. Cai, F.-F. Jin, A. Santoso, L. Wu, E. Guilyardi, and S.-I. An, 2014: Response of El Niño sea surface temperature variability to greenhouse warming. Nat. Climate Change, 4, 786–790, doi:10.1038/nclimate2326.
Klein, S. A., B. J. Soden, and N.-C. Lau, 1999: Remote sea surface temperature variations during ENSO: Evidence for a tropical atmospheric bridge. J. Climate, 12, 917–932, doi:10.1175/1520-0442(1999)012<0917:RSSTVD>2.0.CO;2.
Kug, J.-S., S.-I. An, Y.-G. Ham, and I.-S. Kang, 2010: Changes in El Niño and La Niña teleconnections over North Pacific–America in the global warming simulations. Theor. Appl. Climatol., 100, 275–282, doi:10.1007/s00704-009-0183-0.
Lau, N.-C., and M. J. Nath, 1996: The role of the “atmospheric bridge” in linking tropical Pacific ENSO events to extratropical SST anomalies. J. Climate, 9, 2036–2057, doi:10.1175/1520-0442(1996)009<2036:TROTBI>2.0.CO;2.
Long, S.-M., S.-P. Xie, and W. Liu, 2016: Uncertainty in tropical rainfall projections: Atmospheric circulation effect and the ocean coupling. J. Climate, 29, 2671–2687, doi:10.1175/JCLI-D-15-0601.1.
Ma, J., and S.-P. Xie, 2013: Regional patterns of sea surface temperature change: A source of uncertainty in future projections of precipitation and atmospheric circulation. J. Climate, 26, 2482–2501, doi:10.1175/JCLI-D-12-00283.1.
McPhaden, M. J., S. E. Zebiak, and M. H. Glantz, 2006: ENSO as an integrating concept in Earth science. Science, 314, 1740–1745, doi:10.1126/science.1132588.
Meehl, G. A., and H. Teng, 2007: Multi-model changes in El Niño teleconnections over North America in a future warmer climate. Climate Dyn., 29, 779–790, doi:10.1007/s00382-007-0268-3.
Meehl, G. A., and Coauthors, 2007: Global climate projections. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 747–845. [Available online at http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-chapter10.pdf.]
Neelin, J. D., D. S. Battisti, A. C. Hirst, F.-F. Jin, Y. Wakata, T. Yamagata, and S. E. Zebiak, 1998: ENSO theory. J. Geophys. Res., 103, 14 261–14 290, doi:10.1029/97JC03424.
Philander, S. G., 1990: El Niño, La Niña, and the Southern Oscillation. Academic Press, 293 pp.
Power, S., F. Delage, C. Chung, G. Kociuba, and K. Keay, 2013: Robust twenty-first-century projections of El Niño and related precipitation variability. Nature, 502, 541–545, doi:10.1038/nature12580.
Seager, R., N. Naik, and G. A. Vecchi, 2010: Thermodynamic and dynamic mechanisms for large-scale changes in the hydrological cycle in response to global warming. J. Climate, 23, 4651–4668, doi:10.1175/2010JCLI3655.1.
Seager, R., N. Naik, and L. Vogel, 2012: Does global warming cause intensified interannual hydroclimate variability? J. Climate, 25, 3355–3372, doi:10.1175/JCLI-D-11-00363.1.
Stevenson, S. L., 2012: Significant changes to ENSO strength and impacts in the twenty-first century: Results from CMIP5. Geophys. Res. Lett., 39, L17703, doi:10.1029/2012GL052759.
Sun, D.-Z., 2003: A possible effect of an increase in the warm-pool SST on the magnitude of El Niño warming. J. Climate, 16, 185–205, doi:10.1175/1520-0442(2003)016<0185:APEOAI>2.0.CO;2.
Sun, D.-Z., T. Zhang, Y. Sun, and Y. Yu, 2014: Rectification of El Niño–Southern Oscillation into climate anomalies of decadal and longer time scales: Results from forced ocean GCM experiments. J. Climate, 27, 2545–2561, doi:10.1175/JCLI-D-13-00390.1.
Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485–498, doi:10.1175/BAMS-D-11-00094.1.
Timmermann, A., J. Oberhuber, A. Bacher, M. Esch, M. Latif, and E. Roeckner, 1999: Increased El Niño frequency in a climate model forced by future greenhouse warming. Nature, 398, 694–697, doi:10.1038/19505.
Van Oldenborgh, G. J., S. Y. Philip, and M. Collins, 2005: El Niño in a changing climate: A multi-model study. Ocean Sci., 1, 81–95, doi:10.5194/os-1-81-2005.
Vecchi, G. A., and A. T. Wittenberg, 2010: El Niño and our future climate: Where do we stand? Wiley Interdiscip. Rev.: Climate Change, 1, 260–270, doi:10.1002/wcc.33.
Watanabe, M., J.-S. Kug, F.-F. Jin, M. Collins, M. Ohba, and A. T. Wittenberg, 2012: Uncertainty in the ENSO amplitude change from the past to the future. Geophys. Res. Lett., 39, L20703, doi:10.1029/2012GL053305.
Watanabe, M., Y. Kamae, and M. Kimoto, 2014: Robust increase of the equatorial Pacific rainfall and its variability in a warmed climate. Geophys. Res. Lett., 41, 3227–3232, doi:10.1002/2014GL059692.
Xie, S.-P., C. Deser, G. A. Vecchi, J. Ma, H. Teng, and A. T. Wittenberg, 2010: Global warming pattern formation: Sea surface temperature and rainfall. J. Climate, 23, 966–986, doi:10.1175/2009JCLI3329.1.
Yeh, S.-W., J.-S. Kug, B. Dewitte, M.-H. Kwon, B. P. Kirtman, and F.-F. Jin, 2009: El Niño in a changing climate. Nature, 461, 511–514, doi:10.1038/nature08316; Corrigendum, 462, 674, doi:10.1038/nature08546.
Ying, J., and P. Huang, 2016a: Cloud–radiation feedback as a leading source of uncertainty in the tropical Pacific SST warming pattern in CMIP5 models. J. Climate, 29, 3867–3881, doi:10.1175/JCLI-D-15-0796.1.
Ying, J., and P. Huang, 2016b: The large-scale ocean dynamical effect on uncertainty in the tropical Pacific SST warming pattern in CMIP5 models. J. Climate, 29, 8051–8065, doi:10.1175/JCLI-D-16-0318.1.
Ying, J., P. Huang, and R. Huang, 2016: Evaluating the formation mechanisms of the equatorial Pacific SST warming pattern in CMIP5 models. Adv. Atmos. Sci., 33, 433–441, doi:10.1007/s00376-015-5184-6.
Zhang, T., and D.-Z. Sun, 2014: ENSO asymmetry in CMIP5 models. J. Climate, 27, 4070–4093, doi:10.1175/JCLI-D-13-00454.1.
Zhou, Z.-Q., and S.-P. Xie, 2015: Effects of climatological model biases on the projection of tropical climate change. J. Climate, 28, 9909–9917, doi:10.1175/JCLI-D-15-0243.1.
Zhou, Z.-Q., S.-P. Xie, X.-T. Zheng, Q. Liu, and H. Wang, 2014: Global warming–induced changes in El Niño teleconnections over the North Pacific and North America. J. Climate, 27, 9050–9064, doi:10.1175/JCLI-D-14-00254.1.