1. Introduction
Regional climate change due to increasing concentrations of greenhouse gases is another important issue in climate change besides that of the global-mean temperature increase (e.g., Ma and Xie 2013; Xie et al. 2015). The pattern of the response of tropical Pacific SST warming (TPSW) to increasing greenhouse gas concentrations plays a fundamental role in regional climate change over the tropical Pacific, such as through changes in precipitation, tropical circulation, and the intensity of tropical cyclones (Clement et al. 1996; Vecchi and Soden 2007b; Xie et al. 2010; Ma et al. 2012; Huang et al. 2013; Ma and Xie 2013; Xie et al. 2015). Furthermore, the influences of the TPSW pattern on climate change are not merely limited to the tropics; they also act remotely through atmospheric teleconnection (Shin and Sardeshmukh 2011; Ma and Xie 2013; Ma and Yu 2014).
However, the TPSW pattern projected by climate models is highly uncertain, as illustrated by the large intermodel variances relative to the multimodel ensemble (MME) mean projections in Fig. 1a. By evaluating the results from a large number of models participating in the Coupled Model Intercomparison Project (CMIP), DiNezio et al. (2009) obtained a zonally uniform SST warming from the MME of phase 3 of CMIP (CMIP3), whereas Zhang and Li (2014) obtained an El Niño–like warming pattern from the MME of phase 5 of CMIP (CMIP5). Among the CMIP5 models, the majority project an El Niño–like TPSW (Fig. 1a, contours), but some still simulate a La Niña–like pattern or a zonally uniform warming pattern (Huang and Ying 2015). The large intermodel discrepancies in the TPSW pattern are a dominant source of uncertainty in regional climate change (Ma and Xie 2013; Huang and Ying 2015; Long and Xie 2015).
(a) The MME SST warming pattern (contours) and intermodel standard deviations of SST change in the 32 models (shaded). (b) The MME CSFI (contours) and intermodel standard deviations of CSFI in the 32 models (shaded). (c) Linear relationship between the SST changes and the CSFI over the equatorial Pacific [5°S–5°N, 150°E–100°W; green box in (a)] in the 32 models.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
(a) The MME SST warming pattern (contours) and intermodel standard deviations of SST change in the 32 models (shaded). (b) The MME CSFI (contours) and intermodel standard deviations of CSFI in the 32 models (shaded). (c) Linear relationship between the SST changes and the CSFI over the equatorial Pacific [5°S–5°N, 150°E–100°W; green box in (a)] in the 32 models.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
(a) The MME SST warming pattern (contours) and intermodel standard deviations of SST change in the 32 models (shaded). (b) The MME CSFI (contours) and intermodel standard deviations of CSFI in the 32 models (shaded). (c) Linear relationship between the SST changes and the CSFI over the equatorial Pacific [5°S–5°N, 150°E–100°W; green box in (a)] in the 32 models.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
The uncertainty in the TPSW pattern could be attributable to the multifarious mechanisms involved in the formation of the TPSW pattern (Vecchi and Soden 2007a; Lu and Zhao 2012; Ma and Yu 2014; Song and Zhang 2014; Luo et al. 2015; Ying et al. 2016). For example, weakened Walker circulation owing to a slower increase in rainfall than in atmospheric moisture favors a zonal El Niño–like warming pattern (Held and Soden 2006; Vecchi and Soden 2007a). Greater evaporative cooling in the western than the eastern Pacific (Knutson and Manabe 1995) and negative cloud–shortwave-radiation feedback in the western Pacific (Ramanathan and Collins 1991) both reduce the zonal SST gradient and contribute to an El Niño–like warming pattern. The ocean dynamical thermostat effect as a result of an increased vertical temperature gradient in the eastern Pacific with upwelling colder subsurface water leads to a La Niña–like warming pattern (Clement et al. 1996; DiNezio et al. 2009; An and Im 2014). And in terms of the meridional structure, Xie et al. (2010) concluded that the wind–evaporation–SST (WES) feedback mechanism leads to greater SST warming in the Northern than in the Southern Hemisphere, and the climatological distribution of evaporation leads to peak warming in equatorial regions.
In numerical models, the physical processes associated with these formation mechanisms are often parameterized, resulting in the existence of longstanding biases and marked variation among models (Anthes 1977; Arakawa 2004; Donner et al. 2011; Bellomo et al. 2014). The flaws and intermodel discrepancies associated with these physical processes in models have been widely studied by evaluating the background simulations in CMIP models (Lin 2007; Zheng et al. 2011; Li and Xie 2012; Zheng et al. 2012; Li and Xie 2014). For instance, an overly strong cooling effect due to faults in the ocean dynamics and Bjerknes feedback could lead to an excessive cold tongue bias (Li and Xie 2012; Zheng et al. 2012; Li and Xie 2014; Li et al. 2015), the biases and intermodel spread in the shortwave radiation are related to the biases and spread in the tropical mean SST (Li and Xie 2012), and the double intertropical convergence zone problem can be traced back to biases in the shortwave radiation and further to biases in SST–shortwave radiation feedback (Lin 2007; Hwang and Frierson 2013; Li and Xie 2014).
A statistical study by Huang and Ying (2015) revealed that the intermodel spread in the climatological SST is significantly correlated to that in the projection of future SST change. Recently, Zhou and Xie (2015) also illustrated that the climatological simulation bias can influence the projection of tropical SST change. Together, these studies imply that the biases and spreads of the parameterized processes in models can also contribute to the uncertainty in the future SST warming pattern.
The cloud–radiation feedback involving parameterized cloud processes and the climatological distribution of clouds, which are two well-known aspects to carry flaws in state-of-the-art global climate models (GCMs), exhibit pronounced biases and intermodel spread among such models (Cess et al. 1989; Randall et al. 2003; Arakawa 2004; Stephens 2005; Bony et al. 2006; Soden and Held 2006; Webb et al. 2006; Bellomo et al. 2014; Calisto et al. 2014). The intermodel standard deviations of the strength of the cloud radiation feedback, represented by a cloud–SST feedback index (CSFI; a diagnostic index based on the present-day interannual variability of SST and surface net shortwave radiation, which is related to cloud–shortwave-radiation coupling), in 32 CMIP5 models are shown in Fig. 1b (shaded; more details on the models and the definition of the CSFI are given in section 2). The maximum intermodel standard deviation of the CSFI is located in the central-western Pacific with a value up to 18 W m−2 K−1, which is greater than the magnitude of the cloud–radiation feedback in the MME with a maximum of around 15 W m−2 K−1 (Fig. 1b, contours). The main spread in terms of cloud–radiation feedback and the TPSW pattern is in both cases located around the equatorial central Pacific (Fig. 1a, shaded). A tentative analysis, shown in Fig. 1c, indicates that the intermodel spread of the TPSW is significantly (Student’s t test, 99% confidence level) correlated to that in the cloud–radiation feedback over the equatorial Pacific (5°S–5°N, 150°E–100°W; denoted by the solid green box in Fig. 1a).
In light of the significant relationship between the regional-mean cloud–radiation feedback and TPSW pattern in the equatorial Pacific, the present paper analyzes the spatial pattern of TPSW coupling with cloud–radiation feedback in 32 CMIP5 models. In addition, by analyzing the surface heat budget in the 32 model outputs, the impact mechanism is also examined. Following this introduction, section 2 describes the data and methods used in the study. Section 3 describes the relationship between the TPSW pattern and the cloud–radiation feedback and discusses the impact mechanism. In section 4, the results from a set of numerical experiments using a coupled air–sea model are used to verify the suggested mechanism. Section 5 discusses the uncertainty in the cloud–radiation feedback in observations and the implications for future projections of the TPSW pattern. Conclusions and further discussion are presented in section 6.
2. Data and methods
a. Data
The outputs of 32 CMIP5 models (Table 1) are used in the present study [see http://www-pcmdi.llnl.gov/ (Taylor et al. 2012) for more details]. The long-term mean for the period 1981–2000 in the historical runs was calculated to represent the present-day climatology and that for 2081–2100 in the +8.5 W m−2 representative concentration pathway (RCP8.5) runs to represent the future climatology.
List of the 32 CMIP5 models used in the present study.
The variables used in the present study include SST, latent heat flux, sensible heat flux, net surface longwave radiation, net surface shortwave radiation, surface zonal and meridional wind velocity, and surface scalar wind speed. The net longwave/shortwave radiation is defined as the difference between upward and downward longwave/shortwave radiation. The sign of the fluxes is positive when warming the ocean.
A set of reanalysis data was used to calculate the cloud–radiation feedback in observations, including the net shortwave radiation from OAFlux (1984–2000; Yu and Weller 2007), ERA-40 (1981–2000; Uppala et al. 2005), NCEP–NCAR (1981–2000; Kalnay et al. 1996), NCEP–DOE (1981–2000; Kanamitsu et al. 2002), CFSR (1981–2000; Saha et al. 2010) and National Oceanography Centre Southampton version 2.0 (NOCS V2.0; 1981–2000; Berry and Kent 2009), and the SST data from HadISST1 (1981–2000; Rayner et al. 2003). All model outputs and reanalysis data were interpolated into a 2.5° × 2.5° grid.
b. Definition of the TPSW and cloud–radiation feedback
Change under global warming was defined as the difference between the future and the current climatology. The change in each model was normalized by the respective SST change averaged between 60°S and 60°N in order to remove the influence of the global-mean SST warming, as the present study is concerned with the spatial pattern of relative SST warming. Then, the regional mean of the normalized SST over the tropical Pacific (20°S–20°N, 120°E–80°W) was further removed to define the TPSW pattern.
The CSFI, representing the strength of the cloud–radiation feedback, was defined by regressing the monthly net surface shortwave radiation anomalies on the monthly SST anomalies in the historical run, following previous studies (Sun et al. 2003, 2006). The regional mean over the tropical Pacific (20°S–20°N, 120°E–80°W) was removed in the monthly SST anomalies. For simplicity, the changes in the CSFI under global warming are not considered. As shown in Fig. 1b, negative CSFI values are located in most parts of the western and central Pacific, where deep convection occurs frequently, suggesting a negative convective cloud–shortwave-radiation–SST feedback. Meanwhile, positive CSFI values are located in the eastern Pacific, indicating a positive stratus cloud–shortwave-radiation–SST feedback (Ramanathan and Collins 1991; Song and Zhang 2014). Under global warming, a negative (positive) CSFI is expected to suppress (enhance) the local SST warming.
c. Surface heat budget for the tropical Pacific
d. Method for analyzing the relationship between the TPSW and cloud–radiation feedback
An intermodel singular value decomposition (SVD) analysis (Wallace et al. 1992; Cherry 1996) was performed on the multimodel TPSW pattern and CSFI to explore their coupled relationship. The TPSW pattern was treated as the left singular vector field in the SVD analysis, while the CSFI was the right singular vector field.
The process through which the cloud–radiation feedback influences the TPSW pattern was investigated by studying the changes in the surface heat budget associated with the two SVD modes. The changes in the surface heat budget were regressed onto the normalized PC associated with the first TPSW mode.
e. Air–sea coupled model
Numerical experiments were designed to further verify the mechanism involved in the effect of the cloud–radiation feedback on the TPSW pattern. The model used was a global coupled atmosphere–ocean–sea ice model [the integrated climate model (ICM)] developed at the Center for Monsoon System Research at the Institute of Atmospheric Physics, Chinese Academy of Sciences. ICM integrates ECHAM5 (Roeckner et al. 2006) and NEMO2.3 (Madec 2008) as its atmospheric and oceanic components, respectively, with OASIS3 (Valcke 2006) as the coupler. Further details on this model can be found in Huang et al. (2014).
3. CMIP5 results
a. Relationship between the TPSW pattern and cloud–radiation feedback
The first SVD modes of the TPSW and CSFI explained 24% and 32% of the total variance, respectively. A total covariance of 63.8% was explained by the first SVD modes. The high explained variance and spatial pattern of the first SVD mode of the TPSW pattern were very close to those of the first mode of the intermodel empirical orthogonal function of the multimodel TPSW pattern (not shown), indicating that the first SVD mode of the TPSW pattern is the dominant mode for the intermodel spread in the TPSW pattern. The linear correlation between the principal components (PCs) associated with the first modes of the TPSW and CSFI pattern was found to be significant, with a correlation coefficient up to 0.65 (χ2 test, 99% confidence level) (Fig. 2c). These results indicate that the intermodel spread in the cloud–radiation feedback must be a leading factor of the intermodel spread in the TPSW pattern.
The first SVD modes of (a) SST and (b) CFSI. The total percentage of the variance explained by the respective field is shown in the upper-right corner of (a) and (b). (c) Relationship between the normalized PC1 associated with the SST and CFSI of the first SVD modes, with their correlation coefficient shown in the upper-right corner. Markers in (c) are as those in Fig. 1c.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
The first SVD modes of (a) SST and (b) CFSI. The total percentage of the variance explained by the respective field is shown in the upper-right corner of (a) and (b). (c) Relationship between the normalized PC1 associated with the SST and CFSI of the first SVD modes, with their correlation coefficient shown in the upper-right corner. Markers in (c) are as those in Fig. 1c.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
The first SVD modes of (a) SST and (b) CFSI. The total percentage of the variance explained by the respective field is shown in the upper-right corner of (a) and (b). (c) Relationship between the normalized PC1 associated with the SST and CFSI of the first SVD modes, with their correlation coefficient shown in the upper-right corner. Markers in (c) are as those in Fig. 1c.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
The first mode of the TPSW pattern exhibits a broad positive pattern in the central-western equatorial Pacific (Fig. 2a), whereas the CSFI pattern features a positive pattern in the central equatorial Pacific (Fig. 2b). (To simplify the presentation, two “positive” coupled patterns of the TPSW and CSFI are illustrated in Fig. 2, which are equivalent to a pair of “negative patterns” in both the TPSW and CSFI. The results in following figures are also associated with these positive patterns). The two positive patterns can be understood simply insofar as that a positive CSFI deviation in a particular model relative to the negative CSFI in the MME, presenting a weak negative cloud–SST feedback over the central Pacific in that model relative to that in the MME, cannot suppress the surface warming as greatly as in the MME and thus induces a positive SST deviation in the model relative to the MME.
Although the first modes of the TPSW pattern and CSFI pattern are both located around the central Pacific, they possess some apparent discrepancies. The positive center of the TPSW is located in the western Pacific around 170°E (Fig. 2a), whereas the positive center of the CSFI in the central Pacific is located at about 160°W (Fig. 2b). This discrepancy implies that the mechanism of impact of the CSFI pattern upon the TPSW pattern cannot be as simple as stated above.
b. Heat budget analysis and the mechanism of impact
Figures 3a–c show the regression patterns of ΔQE, ΔQSW, and ΔDO, respectively (ΔQH and ΔQLW are omitted because of their relatively small values). The changes in shortwave radiation ΔQSW, the key variable in cloud–shortwave-radiation–SST feedback, are positive over the central-eastern Pacific but negative over the western Pacific, which is not simply consistent with the TPSW mode or the CSFI mode (Figs. 2a,b). In addition, the ΔDO, representing changes in the oceanic dynamics (Fig. 3c), and the ΔQEW, representing the changes in latent heat induced by changes in surface wind speed (Fig. 3e), both make apparent contributions to the SST warming pattern, whereas the Newtonian cooling ΔQEO and ΔQER play a damping role in the SST warming (Figs. 3d,f). The contributions of ΔQSW, ΔDO, and ΔQEW, as well as the inconsistency between the TPSW and CSFI patterns (Figs. 2a,b), indicate a complicated air–sea coupled process through which the CSFI influences the TPSW pattern.
Regression patterns of changes in the surface energy budget onto the normalized PC associated with the SST of the first SVD modes: (a) latent heat flux, (b) shortwave radiation, (c) ocean heat transport, (d) the Newtonian cooling effect, (e) the effect of changes in surface wind speed (shaded), and (f) the effect of changes in relative humidity and surface stability. Stippling indicates that regressions are significant at the 95% confidence level, based on the Student’s t test. The vectors in (c) and (e) are the regression patterns of changes in surface wind stress (Pa K−1; values smaller than 5 × 10−5 Pa s−1 are omitted) and the surface wind vector (m s−1 K−1; values smaller than 5 × 10−2 m s−1 K−1 are omitted), respectively. The contours in (e) are the regression patterns of changes in the scalar speed of surface wind (m s−1 K−1).
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
Regression patterns of changes in the surface energy budget onto the normalized PC associated with the SST of the first SVD modes: (a) latent heat flux, (b) shortwave radiation, (c) ocean heat transport, (d) the Newtonian cooling effect, (e) the effect of changes in surface wind speed (shaded), and (f) the effect of changes in relative humidity and surface stability. Stippling indicates that regressions are significant at the 95% confidence level, based on the Student’s t test. The vectors in (c) and (e) are the regression patterns of changes in surface wind stress (Pa K−1; values smaller than 5 × 10−5 Pa s−1 are omitted) and the surface wind vector (m s−1 K−1; values smaller than 5 × 10−2 m s−1 K−1 are omitted), respectively. The contours in (e) are the regression patterns of changes in the scalar speed of surface wind (m s−1 K−1).
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
Regression patterns of changes in the surface energy budget onto the normalized PC associated with the SST of the first SVD modes: (a) latent heat flux, (b) shortwave radiation, (c) ocean heat transport, (d) the Newtonian cooling effect, (e) the effect of changes in surface wind speed (shaded), and (f) the effect of changes in relative humidity and surface stability. Stippling indicates that regressions are significant at the 95% confidence level, based on the Student’s t test. The vectors in (c) and (e) are the regression patterns of changes in surface wind stress (Pa K−1; values smaller than 5 × 10−5 Pa s−1 are omitted) and the surface wind vector (m s−1 K−1; values smaller than 5 × 10−2 m s−1 K−1 are omitted), respectively. The contours in (e) are the regression patterns of changes in the scalar speed of surface wind (m s−1 K−1).
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
A physical process is proposed to explain the impact of the CSFI on the TPSW pattern, based on the heat budget analysis, as shown in Fig. 4. Given a CSFI deviation like that shown in Fig. 2b in a particular model relative to the MME CSFI, the relatively weak CSFI (positive deviation relative to the negative CSFI in the MME) over the central Pacific cannot suppress the local surface warming by too great an amount, thus inducing positive ΔQSW and a warm SST deviation relative to the MME TPSW over the central Pacific (Fig. 4a). The induced SST warming deviation will further force out a low-level convergence deviation located over the central Pacific (vectors in Fig. 3e). Against the background of easterlies over the equatorial Pacific, the low-level convergence increases (decreases) the surface wind speed and evaporative cooling over the eastern (western) Pacific (contours in Fig. 3e) through the WES feedback (Fig. 4b). On the other hand, the low-level wind deviation will induce a similar deviation in the surface wind stress pattern (vectors in Fig. 3c), which will enhance (suppress) the equatorial cold advection over the eastern (western) Pacific (shaded in Fig. 3c) through the Bjerknes feedback (Fig. 4b). Under these two feedback processes, the original SST warming induced by the CSFI deviation through ΔQSW will shift westward relative to the location of the CSFI deviation (Fig. 4c). The westward-moved SST warming will further induce positive deep convection and negative shortwave radiation over the western Pacific, suppressing the local ΔQSW and forming the final pattern of ΔQSW (Figs. 3b and 4c). With the westward-moved SST warming, the patterns of the deviation in surface wind, ΔDO, and ΔQEW also move westward.
Schematic diagram illustrating the physical mechanism through which the cloud–radiation feedback affects the TPSW pattern in CMIP5 simulations.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
Schematic diagram illustrating the physical mechanism through which the cloud–radiation feedback affects the TPSW pattern in CMIP5 simulations.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
Schematic diagram illustrating the physical mechanism through which the cloud–radiation feedback affects the TPSW pattern in CMIP5 simulations.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
The proposed mechanism can be demonstrated by calculating the individual contributions of the original CSFI deviation and the TPSW deviation to the ΔQSW. The effect of the CSFI deviation on the ΔQSW can be represented by the positive CSFI deviation (which means the negative cloud–radiation feedback in the central Pacific is weaker than that in the MME) multiplied by the MME TPSW pattern in which the tropical-mean warming has been removed because of the SST threshold for the tropical convection increasing with the tropical mean SST (Johnson and Xie 2010). Figure 5a shows positive shortwave radiation over the equatorial central Pacific, spatially consistent with the pattern of positive CSFI deviation (Fig. 2b). The contribution of the TPSW deviation to the ΔQSW can be represented by the TPSW deviation multiplied by the MME CSFI. The positive TPSW deviation with the negative MME CSFI can lead to some negative deviations in shortwave radiation (Fig. 5b), offsetting the positive shortwave radiation values over the western Pacific originated from the positive CSFI deviation. Therefore, the positive deviations in shortwave radiation of the combination of these two effects are located over the central Pacific east of 180° (Fig. 5c), consistent with the regression pattern of ΔQSW in Fig. 3b. This result verifies that the proposed mechanism is reasonable. It should be noted that the ΔQSW in Fig. 5c is a little smaller than the regressed ΔQSW in Fig. 3b, possibly because of the nonlinear process of the cloud radiation feedback in the models.
(a) The effect of the CSFI deviation shown in Fig. 2b with the MME of the TPSW. (b) The effect of the SST deviation shown in Fig. 2a with the MME of the CSFI. (c) The sum of (a) and (b).
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
(a) The effect of the CSFI deviation shown in Fig. 2b with the MME of the TPSW. (b) The effect of the SST deviation shown in Fig. 2a with the MME of the CSFI. (c) The sum of (a) and (b).
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
(a) The effect of the CSFI deviation shown in Fig. 2b with the MME of the TPSW. (b) The effect of the SST deviation shown in Fig. 2a with the MME of the CSFI. (c) The sum of (a) and (b).
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
4. Air–sea coupled model experiment results
Three sets of experiments using ICM were designed. The first was a control run experiment, integrated for 100 years. The second was a shortwave radiation forcing experiment, integrated for 60 years, in which an external shortwave radiation forcing was added into the downward shortwave radiation in each model step to represent the effect of the positive CSFI deviation (Fig. 5a). The model reached a new equilibrium in the 60-yr integration. The shortwave radiation forcing is shown in Fig. 6, which is the effect of the positive CSFI deviation in Fig. 5a magnified 10 times to emphasize the external forcing relative to the internal variability of the model. The third set of experiments was a group of external shortwave radiation forcing experiments with 20 members. In each member, the shortwave radiation forcing was the same as in Fig. 6, but the initial values were slightly different. Each member was integrated for 3 years to illustrate the fast response to the external forcing. The multimember ensemble mean was calculated to remove the effects of the internal variability and to illustrate the process of the westward shift of the SST warming response to the external shortwave radiation forcing.
The external forcing of shortwave radiation added in the ICM model.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
The external forcing of shortwave radiation added in the ICM model.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
The external forcing of shortwave radiation added in the ICM model.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
Figure 7a shows the climatological difference in the SST between the control run and the 60-yr forcing run. The climatology of the forcing run is the long-term mean for the period from the 21st to the 60th year. There are positive SST responses located over the western Pacific at around 170°E, which is off the center of the positive shortwave radiation forcing in the central Pacific at about 160°W (Fig. 7a, contours). Meanwhile, for the shortwave radiation in the model output, which includes the external forcing of shortwave radiation and the response to SST warming, the results show that the changes in the shortwave radiation have a negative center over the western Pacific (Fig. 7b), representing the effect of the positive SST changes with the negative cloud–radiation feedback, and positive ΔQSW located over the central Pacific (Fig. 7e), reflecting the contribution of the external forcing. The pattern of ΔQSW in the model output is similar to the regression pattern of ΔQSW in Fig. 3b and the verification in Fig. 5c. In addition, by checking the changes in the ocean heat transport (Fig. 7c) and the latent heat induced by changes in surface wind speed (Fig. 7d), we can see that both favor positive SST warming in the western Pacific, as predicted by the proposed mechanism: the westerly changes in zonal wind reduce the cold advection in the western Pacific and also reduce the evaporative cooling. These results coincide well with the analysis based on the CMIP5 model outputs (Figs. 2a,b and 3b).
The climatological difference between the control run and the 60-yr forcing run in (a) SST, (b) shortwave radiation, (c) ocean heat transport, (d) the latent heat flux induced by changes in surface wind speed, and (e) shortwave radiation plus the external shortwave radiation forcing. Vectors in (c) and (d) denote changes in the surface wind stress and wind vector, respectively. Contours in (a) and (e) both represent the external forcing of shortwave radiation and in (d) represent changes in surface scalar wind speed.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
The climatological difference between the control run and the 60-yr forcing run in (a) SST, (b) shortwave radiation, (c) ocean heat transport, (d) the latent heat flux induced by changes in surface wind speed, and (e) shortwave radiation plus the external shortwave radiation forcing. Vectors in (c) and (d) denote changes in the surface wind stress and wind vector, respectively. Contours in (a) and (e) both represent the external forcing of shortwave radiation and in (d) represent changes in surface scalar wind speed.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
The climatological difference between the control run and the 60-yr forcing run in (a) SST, (b) shortwave radiation, (c) ocean heat transport, (d) the latent heat flux induced by changes in surface wind speed, and (e) shortwave radiation plus the external shortwave radiation forcing. Vectors in (c) and (d) denote changes in the surface wind stress and wind vector, respectively. Contours in (a) and (e) both represent the external forcing of shortwave radiation and in (d) represent changes in surface scalar wind speed.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
The ensemble mean of the 20 forcing runs from the third group of experiments can illustrate the fast response process in which the initial positive SST warming, directly induced by the external shortwave forcing, moves westward. As shown in Fig. 8a, the positive SST warming center first emerges in the central Pacific because of the external positive forcing of shortwave radiation and then propagates westward rapidly. By the eighth month of the first model year, the SST warming center has propagated to the western Pacific. However, the magnitude of the SST changes at this time is smaller than that under equilibrium (Fig. 7a), suggesting that the propagation speed of the SST warming center is much faster than the accumulation of radiative energy. The propagations of ΔQSW, ΔDO, and ΔQEW are presented in Figs. 8b–d, respectively. All are consistent with the propagation of SST changes, albeit not as clear as the mechanism proposed in Fig. 4. The negative ΔQSW center (Fig. 8b), representing the positive SST change, propagates westward with the SST change center. The terms ΔDO and ΔQEW both propagate to the west in response to the propagation of the SST warming center, with the positive center in the west of the SST warming center and negative in the east. However, the positive sign of the ΔQSW changes after five months, the sign of the ΔDO in the west of the SST warming center changes to negative after four months, and the positive sign of the ΔQEW in the west of the SST warming center is not significant before the sixth month, each of which is probably due to insufficient ensemble members of model experiments, thus producing internal variability that hindered a clear depiction of the propagation.
Time–longitude diagrams of changes in (a) SST, (b) shortwave radiation, (c) ocean heat transport, and (d) latent heat induced by surface wind speed changes (shaded) in the ensemble mean of 20 forcing runs. Contours in (d) denote changes in the surface scalar wind speed.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
Time–longitude diagrams of changes in (a) SST, (b) shortwave radiation, (c) ocean heat transport, and (d) latent heat induced by surface wind speed changes (shaded) in the ensemble mean of 20 forcing runs. Contours in (d) denote changes in the surface scalar wind speed.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
Time–longitude diagrams of changes in (a) SST, (b) shortwave radiation, (c) ocean heat transport, and (d) latent heat induced by surface wind speed changes (shaded) in the ensemble mean of 20 forcing runs. Contours in (d) denote changes in the surface scalar wind speed.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
The magnitude of the SST response relative to the forcing is also reasonable when compared with the magnitudes of the regression patterns of the surface energy budgets (Fig. 3) and the SST mode (Fig. 2a). The experiments using ICM verify the process of the SST pattern response to the original shortwave radiation deviation, including the westward shift of the SST response relative to the external shortwave radiation forcing, and the position and strength of the equilibrium state.
5. Uncertainty of the cloud–radiation feedback in observations
In light of the significant relationship between cloud–radiation feedback and the TPSW pattern in the future, it seems feasible to constrain the MME projection on the TPSW pattern and decrease the uncertainty based on the concept of “observational constraints” (Whetton et al. 2007; Shiogama et al. 2011; Bracegirdle and Stephenson 2012; Collins et al. 2012; Bracegirdle and Stephenson 2013; Huang and Ying 2015; Xie et al. 2015). Therefore, we diagnosed the observed cloud–radiation feedback based on reanalysis and satellite-observed shortwave radiation data together with HadISST data, as introduced in section 2.
The six sets of observed CSFI results show large discrepancies in magnitude (Fig. 9), although each is negative over most parts of the equatorial Pacific. The negative CSFI based on ERA-40 is the strongest and extends to the widest area, whereas the negative CSFI values based on NCEP–DOE and CFSR are much weaker—around half of that based on ERA-40 over the central Pacific.
The CSFI derived from several reanalysis datasets. Stippling indicates that regressions are significant at the 95% confidence level, based on the Student’s t test.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
The CSFI derived from several reanalysis datasets. Stippling indicates that regressions are significant at the 95% confidence level, based on the Student’s t test.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
The CSFI derived from several reanalysis datasets. Stippling indicates that regressions are significant at the 95% confidence level, based on the Student’s t test.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
The six observed CSFIs were further compared with those in the models. For each observed CSFI, the MME CSFI (Fig. 1b) was first subtracted and then the residual was projected onto the first SVD mode of the CSFI (Fig. 2b) to obtain a value representing the deviation of the first SVD mode of the CSFI in the observed CSFI. These values are analogous to the PCs (Fig. 2c) for the models associated with the first SVD mode of the CSFI. As shown in Fig. 10, all of these values are negative, except for the value based on CFSR (around zero), indicating a negative CSFI is very likely larger in observed than in simulated results. This underestimated CSFI in the MME of the models, as in Fig. 2b, likely induces a La Niña–like bias in the TPSW pattern (Fig. 2a), indicating the TPSW pattern should be closer to an El Niño–like pattern (Huang and Ying 2015).
The deviation of the first SVD mode of the CSFI in various observed CSFIs.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
The deviation of the first SVD mode of the CSFI in various observed CSFIs.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
The deviation of the first SVD mode of the CSFI in various observed CSFIs.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0796.1
However, as the high level of uncertainty related to the CFSI exists not only in the models but also in the various observations, the observational constraint based on the CSFI for the MME projection of the TPSW may not be particularly meaningful at this moment in time. Therefore, we suggest that more effort should be made in the future to improve the precision of observed shortwave radiation, as well as the description of cloud–radiation feedback in models, for a more reliable projection of the TPSW pattern.
6. Conclusions and discussion
The source of the large uncertainty in the TPSW pattern projected in CMIP5 models is discussed in this paper, based on the results of 32 CMIP5 models and heat budget analysis. The results reveal that the large intermodel spread in the cloud–radiation feedback is significantly correlated to the uncertainty in the TPSW pattern and, as a leading source, explains 24% of the intermodel total variance of the TPSW pattern. A positive deviation relative to the MME in the cloud–radiation feedback over the central Pacific can induce a positive deviation in the TPSW over the western and central Pacific and a negative deviation over the eastern Pacific (Fig. 2). The two poles of the TPSW deviations associated with the cloud–radiation feedback happen to be the two centers of maximum intermodel variance of the TPSW pattern (Fig. 1a).
The mechanism involved in the influence of the cloud–radiation feedback on the TPSW pattern was investigated by analyzing the surface heat budget. As illustrated in Fig. 4, a positive deviation in the cloud–radiation feedback can induce local SST warming because it cannot sufficiently suppress the SST increases induced by increasing greenhouse gas concentrations. The central Pacific SST increases will force out a low-level convergence with westerlies (easterlies) over the western (eastern) Pacific. On one hand, the low-level convergence will decrease (increase) the background easterly and surface evaporation over the western (eastern) equatorial Pacific, while on the other hand, the low-level convergence will change the surface wind stress and thus decrease (enhance) the surface cold advection in the western (eastern) Pacific. The former is a kind of WES feedback and the latter is similar to the Bjerknes feedback. Under these two processes, the SST warming induced by the original deviation in cloud–radiation feedback will move westward to the western Pacific (Fig. 4). This process was also verified through decomposition of the deviation of shortwave radiation and a group of numerical experiments.
The close relationship between the cloud–radiation feedback and the TPSW pattern implies that the concept of observational constraint could be applied to constrain the large uncertainty in the TPSW pattern, based on the observed cloud–radiation feedback (Whetton et al. 2007; Xie et al. 2010; Shiogama et al. 2011; Bracegirdle and Stephenson 2012, 2013; Huang and Ying 2015; Xie et al. 2015). Accordingly, we analyzed the cloud–radiation feedback based on the shortwave radiation from six reanalysis or satellite-observed datasets and found that the cloud–radiation feedback in the models was underestimated in general. This result implies that a La Niña–like bias could exist in the MME TPSW pattern and thus the TPSW pattern could closer to an El Niño–like pattern (Huang and Ying 2015). However, the magnitude of the cloud–radiation feedback in the observations was found to be as uncertain as in the models (Fig. 10), obstructing a more precise constraint for the uncertainty in the TPSW pattern and a more reliable correction for the MME TPSW projection (Huang and Ying 2015).
The large intermodel spread in the cloud–radiation feedback may be attributable to the parameterized cloud processes and the climatological distribution of clouds in the models, which have long-standing biases and large discrepancies among current state-of-the-art models (Cess et al. 1989; Randall et al. 2003; Arakawa 2004; Stephens 2005; Bony et al. 2006; Soden and Held 2006; Webb et al. 2006; Bellomo et al. 2014; Calisto et al. 2014). Therefore, we suggest that more effort should be made to improve shortwave radiation observations, as well as the description of cloud–radiation feedback in models, for a more reliable projection of the TPSW pattern in future.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant 41575088), the National Basic Research Program of China (2014CB953903), the National Natural Science Foundation of China (Grant 41461164005), and the Youth Innovation Promotion Association CAS. We acknowledge 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) for producing and making available their model output. We thank Prof. Bang-Liang Yan for helping run the experiments with ICM. We also thank Prof. Renguang Wu and Prof. Shang-Ping Xie for their helpful discussions.
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