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  • View in gallery

    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.

  • View in gallery

    (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.

  • View in gallery

    (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.

  • View in gallery

    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).

  • View in gallery

    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.

  • View in gallery

    (a),(b) The SD and (c),(d) SNR of (a),(c) and (b),(d) for the period 2071–2100. Stippling in (c),(d) indicates that more than 70% models agree on the sign of the MME changes.

  • View in gallery

    The SNR of (a) , (b) , and (c) relative changes in mean-state SST (relative ΔT) for the period 2071–2100. Stippling indicates that more than 70% models agree on the sign of the MME changes.

  • View in gallery

    (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.

  • View in gallery

    (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.

  • View in gallery

    (a),(b) The SD and (c),(d) SNR of the dynamic components of ENSO rainfall changes due to the (a),(c) and (b),(d) at the equator (2.5°S–2.5°N mean). Stippling indicates that more than 70% models agree on the sign of the MME changes.

  • View in gallery

    The SNR of (a) Δω′, (b) , and (c) at the equator (2.5°S–2.5°N mean). Stippling indicates that more than 70% models agree on the sign of the MME changes.

  • View in gallery

    The SNR of (a) , (b) , and (c) relative ΔT at the equator (2.5°S–2.5°N mean). Stippling indicates that more than 70% models agree on the sign of the MME changes.

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Time-Varying Response of ENSO-Induced Tropical Pacific Rainfall to Global Warming in CMIP5 Models. Part II: Intermodel Uncertainty

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  • 1 Center for Monsoon System Research and LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, and Joint Center for Global Change Studies, Beijing, China
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Abstract

Anomalous rainfall in the tropical Pacific driven by El Niño–Southern Oscillation (ENSO) is a crucial pathway of ENSO’s global impacts. The changes in ENSO rainfall under global warming vary among the models, even though previous studies have shown that many models project that ENSO rainfall will likely intensify and shift eastward in response to global warming. The present study evaluates the robustness of the changes in ENSO rainfall in 32 CMIP5 models forced under the representative concentration pathway 8.5 (RCP8.5) scenario. The robust increase in mean-state moisture dominates the robust intensification of ENSO rainfall. The uncertain amplitude changes in ENSO-related SST variability are the largest source of the uncertainty in ENSO rainfall changes through influencing the amplitude changes in ENSO-driven circulation variability, whereas the structural changes in ENSO SST and ENSO circulation enhancement in the central Pacific are more robust than the amplitude changes. The spatial pattern of the mean-state SST changes—the departure of local SST changes from the tropical mean—with an El Niño–like pattern is a relatively robust factor, although it also contains pronounced intermodel differences. The intermodel spread of historical ENSO circulation is another noteworthy source of the uncertainty in ENSO rainfall changes. The intermodel standard deviation of ENSO rainfall changes increases along with the increase in global-mean surface temperature. However, the robustness of enhanced ENSO rainfall changes in the central-eastern Pacific is almost unchanged, whereas the eastward shift of ENSO rainfall is increasingly robust along with the increase in global-mean surface temperature.

Corresponding author address: Dr. Ping Huang, Institute of Atmospheric Physics, Chinese Academy of Sciences, Bei-Er-Tiao #6, Zhong-Guan-Cun, Beijing 100190, China. E-mail: huangping@mail.iap.ac.cn

Abstract

Anomalous rainfall in the tropical Pacific driven by El Niño–Southern Oscillation (ENSO) is a crucial pathway of ENSO’s global impacts. The changes in ENSO rainfall under global warming vary among the models, even though previous studies have shown that many models project that ENSO rainfall will likely intensify and shift eastward in response to global warming. The present study evaluates the robustness of the changes in ENSO rainfall in 32 CMIP5 models forced under the representative concentration pathway 8.5 (RCP8.5) scenario. The robust increase in mean-state moisture dominates the robust intensification of ENSO rainfall. The uncertain amplitude changes in ENSO-related SST variability are the largest source of the uncertainty in ENSO rainfall changes through influencing the amplitude changes in ENSO-driven circulation variability, whereas the structural changes in ENSO SST and ENSO circulation enhancement in the central Pacific are more robust than the amplitude changes. The spatial pattern of the mean-state SST changes—the departure of local SST changes from the tropical mean—with an El Niño–like pattern is a relatively robust factor, although it also contains pronounced intermodel differences. The intermodel spread of historical ENSO circulation is another noteworthy source of the uncertainty in ENSO rainfall changes. The intermodel standard deviation of ENSO rainfall changes increases along with the increase in global-mean surface temperature. However, the robustness of enhanced ENSO rainfall changes in the central-eastern Pacific is almost unchanged, whereas the eastward shift of ENSO rainfall is increasingly robust along with the increase in global-mean surface temperature.

Corresponding author address: Dr. Ping Huang, Institute of Atmospheric Physics, Chinese Academy of Sciences, Bei-Er-Tiao #6, Zhong-Guan-Cun, Beijing 100190, China. E-mail: huangping@mail.iap.ac.cn

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.

Fig. 1.
Fig. 1.

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.

Table 1.

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.)

Table 1.

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

Moisture budget decomposition is often used to investigate the formation mechanism of changes in mean state and variability of tropical rainfall (Held and Soden 2006; Chou et al. 2009; Seager et al. 2010, 2012; Huang et al. 2013; Chung et al. 2014; Huang 2014; Huang and Xie 2015). The simplified moisture budget decomposition developed in Huang and Xie (2015) is used in the present study. Figure 1 shows a diagram representing the decomposition of ENSO rainfall changes. In the decomposition, change in ENSO rainfall is first decomposed approximately as follows:
e1
where P is tropical rainfall; ω is the pressure velocity at 500 hPa, representing vertical motion; q is surface specific humidity; Δ denotes future change; and the primes denote interannual variability. The Δ′ induced by mean-state moisture change Δq is defined as the thermodynamic component, whereas the qΔω′ induced by change in ENSO circulation Δω′ is defined as the dynamic component.

The changes in ENSO circulation Δω′ are further decomposed into amplitude and structural changes: . The amplitude changes are obtained by projecting the total Δω′ onto the historical ω′, and the residual is defined as the structural changes . Similarly, the changes in ENSO SST are decomposed as . The structural changes in ENSO circulation can be attributed to the structural changes in ENSO SST and the spatial pattern of mean-state SST changes (Cai et al. 2014; Huang and Xie 2015). Single-field principal component analysis is performed on the intermodel diversity of and in the 32 models to extract the impact of the MME on , written as (Bretherton et al. 1992; Huang and Xie 2015; Huang and Ying 2015). The residual of is written as : , which is mainly dominated by the spatial pattern of mean-state SST changes.

With the decomposition of ENSO circulation change, ENSO rainfall change can be written as follows:
e2
where Δ′ is the thermodynamic component, qΔω′ represents the amplitude changes in the dynamic component, and and are two parts of the structural changes in the dynamic component.

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).

Fig. 2.
Fig. 2.

(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 (Δ′) 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.

Fig. 3.
Fig. 3.

(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 Δ′, the contributions of the intermodel difference in Δq and ω′ to the SD of Δ′ are separated as and (Figs. 4a,b), respectively, where σ(⋅) indicates SD, and the overbar indicates MME among the models. A similar separation is performed on the dynamic component for q and Δω′ (Figs. 4c,d). The intermodel difference in the present-day q is the smallest and negligible (Fig. 4c), while that in Δω′ is the largest among the four variables (Fig. 4d; the color bar in Fig. 4d differs from those in Figs. 4a–c). Notably, the SD of the historical ω′ is quite large (Fig. 4b), even much larger than that of the increase in mean-state moisture Δq (Fig. 4a). This result indicates that there exists large intermodel spread of the historical ENSO circulation among the models, which is even larger than the spread of the increase in GMST—the dominant factor of Δq. The contribution of the intermodel difference in Δq to the total SD of Δ′ is relatively small, although the intermodel difference in Δq can be clearly observed in Fig. 6 of Part I. The SNR of the four variables (Fig. 5) confirms the conclusions from the SD: Δω′ is the largest source of the intermodel difference in ENSO rainfall changes (Fig. 5d), ω′ follows (Fig. 5b), and q is negligible (Fig. 5c; the color bar range in Figs. 5a,c is much larger than that in Figs. 5b,d).

Fig. 4.
Fig. 4.

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

Fig. 5.
Fig. 5.

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 and structural changes following Eq. (2) (Huang and Xie 2015; Part I). The SD of is smaller than that of (Figs. 6a,b), but the SNR of is weaker than that of (Figs. 6c,d). This result indicates that the decrease in is more uncertain than the eastward shift of , but contributes a smaller intermodel difference than to ENSO rainfall changes.

Fig. 6.
Fig. 6.

(a),(b) The SD and (c),(d) SNR of (a),(c) and (b),(d) for the period 2071–2100. 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

It is revealed in Part I that the percentage of the amplitude changes in ENSO circulation relative to the historical ω′ is dominated by the percentage of the amplitude changes in ENSO SST to the historical T′. The intermodel spread of is significantly correlated with that of for the period 2071–2100 with a correlation coefficient of 0.84. The ratio of to is close to 1. Therefore, it is expected that the intermodel difference in should arise from the intermodel difference in the historical ω′ and the change percentage . The |SNR| of the historical ω′ (Fig. 5b) is much larger than that of the (Fig. 7a). Therefore, we can conclude that the major intermodel difference in is from the intermodel difference in , which is suggested to be the major source of the uncertainty in ENSO rainfall changes in Power et al. (2013).

Fig. 7.
Fig. 7.

The SNR of (a) , (b) , and (c) relative changes in mean-state SST (relative ΔT) for the period 2071–2100. Stippling 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 structural changes in ENSO circulation , the structural changes in ENSO SST and the spatial relative changes in mean-state SST are suggested to be the two dominant factors (Power et al. 2013; Cai et al. 2014; Chung et al. 2014; Huang and Xie 2015). The spatial relative changes in mean-state SST (hereafter relative ΔT) are calculated as the departure of local SST changes from the tropical mean. Figures 7b and 7c show the SNR of and the relative ΔT to illustrate their robustness and contribution to the uncertainty in . The enhancement of in the central Pacific is quite robust with a large intermodel sign agreement and SNR, which is consistent with the westward shift of ENSO variability reported in previous studies (e.g., Yeh et al. 2009). The SNR of the relative ΔT (Fig. 7c) is higher than that of , and the sign agreement test also shows that the relative ΔT has more significant intermodel agreement than . This result indicates that the relative ΔT is a much more robust factor than the structural changes in ENSO SST for the ENSO rainfall changes, although the intermodel spread of the relative ΔT has been widely revealed (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). However, the robustness of the pattern of relative ΔT is apparently weaker than that of ω′ and Δq, the factors in the thermodynamic component.

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 , the structural changes in ENSO SST , the relative changes in mean-state SST, the historical ENSO circulation ω′, and the increase in mean-state moisture Δq.

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.

Fig. 8.
Fig. 8.

(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.

Fig. 9.
Fig. 9.

(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.

Fig. 10.
Fig. 10.

(a),(b) The SD and (c),(d) SNR of the dynamic components of ENSO rainfall changes due to the (a),(c) and (b),(d) at the equator (2.5°S–2.5°N mean). Stippling 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

Fig. 11.
Fig. 11.

The SNR of (a) Δω′, (b) , and (c) at the equator (2.5°S–2.5°N mean). Stippling 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

Figure 12 shows the SNR of the amplitude and structural changes in ENSO SST ( and ) and the relative ΔT, which are the dominant factors for and . The |SNR| of is smaller than that of in all stages, confirming that is a larger source of uncertainty in ENSO rainfall changes shown in Fig. 7. The sign of is time varying and changes from positive to negative at around 2026–55, consistent with the results in Kim et al. (2014). However, the variation of is not robust among the models. The robustness of the enhanced over the central Pacific is nearly unchanged (Fig. 12b). Comparing the SNR of the two factors dominating (Figs. 12b,c), the |SNR| of the relative ΔT is also almost unchanged and apparently larger than that of . This result also implies that although the projection of mean-state SST changes displays quite large intermodel uncertainty (Huang and Ying 2015), it is still much more reliable in all stages than the projection of changes in ENSO SST. Because the robustness of and the relative ΔT is almost unchanged along with the increase in GMST, the increasing robustness of the eastward shift of could be due to the increasing spatial gradient of the relative ΔT.

Fig. 12.
Fig. 12.

The SNR of (a) , (b) , and (c) relative ΔT at the equator (2.5°S–2.5°N mean). Stippling 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

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.

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