a) Mean Pacific SST state difference for individual models.
Table 1 below shows the SST bias and standard deviation for model hindcasts for both the seasons and lead hindcasts. In all the model hindcasts, the standard deviation (i.e., interannual variability) is decreased in the tropical Pacific (table 1) as similar to the observed decrease. Meanwhile, the mean SST difference between the two periods is not much significant for the model hindcasts, if the seasonal trend for the period is removed from the data (not shown). Similar to the observations, all the model hindcasts have decreased interannual variability of ENSO for the second period irrespective of the initial condition and target season. Models such as CanCM4i, CanSIPSv2, CCSM3, CCSM4, GFDL_aero4, GMAO6 and NEMO has negative bias over the Nino3.4 region for all ICs but is reduced for the second period and is changed to positive bias for CCSM4. The positive bias of the second period is stronger for the DJF target season from both the Aug IC and Nov IC hindcasts. Models such as GFDL_FLORA and FLORB have a positive bias in both periods and the positive bias increases in the second period. For all the initial conditions, the standard deviation is always overestimated by models such as CanCM4i, CanSIPSv2, CCSM3, GFDL_aero4 and GMAO6. GFDL_FLORA has slightly reduced interannual variability than observed variability except for NOV IC hindcasts. CCSM4 also has reduced interannual variability for all ICs considered except for MAY IC.
Thus all the models have decreased interannual variability similar to observations, but ther is more intermodal difference for both the target seasons and lead hindcasts.
b) ENSO skill- actual and potential- .
In the present section, the ability of the models to predict the JJAS and DJF season Nino3.4 index is analysed using correlation for actual and based on RE values for potential skill (equations 1 and 2) and is shown in Table 2. CanCM4i model has a decreased JJAS Nino3.4 index skill for both Feb and May IC hindcasts, while the decrease is stronger (~0.1) for Feb IC. Potential skill is always higher than actual skill, but it has also decreased for Feb IC in recent years. CanSIPSv2 also has a similar reduction of Nino3.4 skill, but the actual skill drop is around 0.2 for Feb IC, while the potential skill drop is around 0.1. The potential skill remains the same in both epochs for May IC and the actual skill decrease is of the order of 0.1 for the 2001-2019 period. CCSM3 has a decrease of 0.28 for JJAS Nino3.4 simulated with Feb IC and 0.20 for May IC with a reduction of 0.15 for Feb IC and 0.08 for May IC potential skills. Feb IC skill loss during the second period is non-significant for models such as CCSM4, GFDL_aero4, FLORA and FLORB. But the actual value is less than 0.6 for GFDL models and is 0.63 for CCSM4. GMAO6 has a decrease of ENSO skill of around 0.15 for both ICs, while potential skill remains the same during both periods. NEMO model, which has a skill of 0.65 for Feb IC during the first period, decreased to 0.29 after 2000 with a decrease of 0.35, while May IC, the skill change is 0.10. The potential skill is high and unchanged during both periods. Thus it is evident that actual skill drops mainly for Feb IC for all models except the GFDL family models, while the maximum decrease is for NEMO and CCSM3. The skill decrease is less for May IC for all models but is relatively higher for GFDL models. Another exciting thing is that potential skill has not much change for May IC and even for Feb IC, the change is very less and is limited to CanSIPSv2, CCSM3, CCSM4 and GFDL_aero4 only.
The same skill comparison for DJF season using Aug (4-month) and Nov (1month) lead ICs are also performed to see whether the same skill drop is applicable for the mature phase of ENSO also. Table 1 shows that compared to JJAS season, the skill drop is negligible for DJF season for both 4 months and 1-month hindcasts. The skill change is always less than 0.05 for 4 months lead itself and also potential skill has no change for both the lead time hindcasts of DJF season ENSO. Thus the skill change of ENSO is mainly concentrated to JJAS season (developing phase of ENSO) and is higher for spring initiation of the hindcasts and so JJAS season is focused rest of the paper. Here we discuss all the changes for all the models for the JJAS target season. Still, only CanCM4i (model with higher skill during period 1 and decreased for period 2), CCSM3 and NEMO (models with the large decrease of skill), CCSM4 (model with less difference between periods) and GFDL_FLORA (having increased skill for Feb IC during period 2) are shown in the following sections.
The analysis of potential skill in these two periods for individual models indicates that definitely, models have room for improvement. Still, the decrease of potential skill for both ICs are marginal or even nill in some cases indicating that the decrease in skill is not directly related to any model deterioration instead model response to the observed climate variability may be the crucial factor. The calculation of Pot. skill itself indicates the role of ensemble spread and ensemble mean in the skill and maybe the combination of these two are not leading to decreased skill, while the actual skill based on ensemble mean only shows a decreased skill.
c) Role of initial SST anomalies in ENSO skill in both the periods.
In the present section, the role of initial SST anomalies in the ensemble mean and observations of JJAS Nino3.4 are analysed with the help of correlation analysis. Here Nino3.4 SST indices from both observations and model hindcasts are correlated with the observed initial month SST anomaly. For eg, for Feb IC hindcasts, JJAS nino3.4 index is correlated with February observed SST and for May IC it is May month initial SST. The same analysis for the JJAS Nino3.4 index with February initial SST for observations and Feb IC hindcasts are shown in Figure 1 and the same with May initial SST are shown in figure 2. The observed summer Nino3.4 SST indices have a stronger relationship with off equatorial SSTA in the tropical Pacific, north Indian Ocean (IO) SSTA of February initial month during both the periods (Fig 1 a and b). Additional significant correlation with tropical north Atlantic SST anomalies appears in the second period for February initial conditions. Meanwhile, for May IC, the correlation appears mainly in the tropical east Pacific in both the periods and as of Feb IC, after 2000s North Atlantic SST also plays a role (Fig 2 and b).
The same analysis with model hindcasts shows that CCSM3 and CCSM4 have off equatorial patterns in the Pacific during period1 for February initial SST anomalies, while GFDL_FLORA and NEMO have a strong equatorial pattern at the tropical east Pacific. After 2000, CanCM4i, CCSM4, GFDL_FLORA have off-equatorial patterns and also have strong anomalies in the north Indian Ocean and north Atlantic (Fig 1c-j). NEMO model has an opposite pattern in both Indian and north Atlantic regions (Fig 1j). Meanwhile, the major contribution from May initial SST is from the equatorial east Pacific for all the models during both periods, while the role of north Atlantic in the second period is captured by CanCM4i, CCSM4 and GFLD_FLORA model only (Fig 2c-j). Models such as CanSIPSv2 and GFDL_FLORB also simulates the role of Atlantic SST, while GMAO6 and GFDL_aero4 fail to do so (not shown).
d) Different roles of north and equatorial Atlantic SST anomalies on summer ENSO in both the periods
The influence of NA_SST of the spring season is confined to the central Pacific as a result of opposite easterly and westerly wind response in the western and eastern Pacific (Ham et al 2013a and the references therein). Meanwhile, the ATL SST anomaly during the boreal summer influences the canonical ENSO and is through modification of Walker circulation (Ham et al 2013a, Rodriguez-Fonseca et al 2009). Figure 2 also indicates the increased influence of Atlantic SST initial state in ENSO evolution and here we analyses whether these SST anomalies induce difference in ENSO evolution in these two periods.
Figure 3 shows the regression of MAM season NA_SST on observed and Feb IC simulated JJAS tropical SST for both periods. The NA_SST regressed anomalies during P1 shows central Pacific ENSO pattern as shown by earlier studies, while during P2, the equatorial pattern has cooling in both east and central Pacific with an extension to off-equatorial east Pacific (Fig 3 a& b). Thus the spatial pattern indicates a clear shift of NA_SST influence from central Pacific to canonical ENSO pattern in the second period. The same is confirmed with the correlation analysis between the indices in table 3 for observations. The NA_SST correlation with Nino3 is increased from 0.06 of pre-2000 value to -0.62 during P2 and that with EMI (index representing El Nino Modoki as defined in Ashok et al 2007) is reduced from -0.56 to -0.36. Negative anomalies of NT_SST is associated with El Nino years such as 1986, 1991, 1994 during period1, which are central Pacific events during that time, while after 2000, the NT_SST has an association with El Nino years such as 2007, 2009, 2010 and 2014, which has canonical type pattern, even with the subtropical extension. The central Pacific ENSO events of 2002, 2004 and 2006 have no significant anomalies in the north Atlantic during the spring season. In our seasonal prediction runs, the NA_SST anomalies are available for the FebIC hindcasts only and the NA_SST analysis is repeated for Feb IC hindcasts of the model. MAM season NA_SSTA induced pattern during period 1 has no proper central Pacific pattern for any of the model hindcasts. But CanCM4i, CCSM4 and FLORA models have cooling in the equatorial central Pacific without tripole pattern. During the second period, cooling is evident in the equatorial east Pacific for all models except NEMO. But the off-equatorial pattern is evident for FLORA model only. Also, the SST pattern in other oceans such as the Indian Ocean is opposite for all the models (Fig 3c-j). In CanSIPSv2 and GMEO6 also has no pattern, while, GFDl_aero4 has warming pattern during period 2 and FLORB has similar pattern as FLORA. Thus the majority of the models fail to capture the NA_SSTA effect on the Pacific Ocean during period2.
The JJAS season SST anomalies obtained by the regression of JJAS ATL SST anomalies for observations and Feb IC and for May IC are shown in Figures 4 and 5. The observed summer season ATL SST anomalies have an almost similar pattern in both the periods with earlier reported canonical ENSO pattern with maximum slightly shifted to central pacific (Fig 4 a&b). The correlation analysis also indicates an almost similar correlation with Nino3 and Nio3.4 in both periods, while EMI has a very low correlation during both periods (Table 3). During period 1, all the model hindcasts from Feb IC has stronger SST pattern in the tropical east Pacific for ATL_SST induced pattern except for CCSM3 and NEMO. The second period has a weak pattern for GFDL_LORB, GMAO6, CanSIPSv2, CCSM3 and GFDL_FLORA hindcast, while others have opposite patterns to that of the observed (Fig 4c-j). At the same time, the May IC hindcasts have cool east Pacific for all the models during period1. But the pattern is weak for CCSM3 and FLORA during period2 and is opposite for NEMO model (Fig 5c-j). Thus, models have better predictability with equatorial Atlantic SST induced pattern as it has a similar pattern as canonical ENSO, while the changes associated with NA_SST are not proper in the majority of the models. The models with a better pattern of NA_SST and ATL induced SST pattern in the Pacific has the better role of initial SST from Atlantic in Figure 2 also.
e) Process leading to observed change of NAT_SST in equatorial Pacific
Another important question here is that why the observed central Pacific ENSO like pattern induced by NA_SST extended to the east Pacific also during the second period? The present section analyses the reason behind the observed change of NA_SST induced SST pattern from central Pacific ENSO to east Pacific after 2000. Figure 6 shows the 850 hPa circulation and 200 hPa velocity potential for MAM and JJAS seasons for the positive minus negative NA_SST composites for both periods. It can be seen that before 2000, positive NA_SST anomalies induces upper-level divergence (lower-level convergence) over the extreme east Pacific and opposite patterns in the central and east Pacific. This is associated with a westerly anomaly in the east Pacific. By summer, this pattern weakens, and convergence moves to the Atlantic but has persistent westerly wind anomalies at east Pacific and easterly anomaly at central Pacific. This is associated with the observed SST pattern in figure 3a earlier and is documented in Ham et al (2013a) Fig 2 also. At the same time after 2000, the NA_SST induced MAM pattern has weaker lower-level convergence and is located at the north Atlantic only with divergence in the equatorial central Pacific. This induces easterly wind anomalies in the east Pacific, opposite to the pre-2000 pattern. The convergence pattern weakens by summer, but the easterly wind anomalies persist in the equatorial region induced cool SST anomalies from east Pacific to central Pacific as observed earlier. The correlation analysis of previous DJF Nino3.4 and spring season NA_SST indices indicates that the correlation is reduced from 0.63 to 0.33 in the second period. This indicates that the influence of previous winter ENSO on NA_SST, which is the main modulator of spring season NA_SST is also weakened. Thus, we can conclude that the displacement of NA_SST pattern and associated circulation and convection during the boreal spring season during period 2 is responsible for the different effects of North Atlantic SST in equatorial SST in JJAS season. As the role of NAT_SST in CP ENSO reduces after 2000, it seems that the earlier proposed mechanisms involving the ocean role may be responsible for more number of central Pacific ENSO in the recent period (Chung and Li 2013). This needs further analysis also.
f) Spatial SST pattern associated with ENSO
The analysis of Nino3.4 indices for both models and observations indicates that during the first period, there are 5 El Nino (1982, 1987, 1991, 1994 and 1997) and 6 La Nina (1985, 1986, 1988, 1989, 1998, 1999) events are reported during JJAS and it attained its maximum anomaly by DJF. The majority of the models was able to capture these ENSO years and is reflected in the higher skill of the models. Meanwhile during the second period, only the 2015 El Nino is captured by all the model hindcasts, while the El Nino years of 2002, 2004, 2006, 2009, 2014, 2018 have not been obtained by the models, mainly during its growing stage of JJAS season. The observation pattern indicates that the above-mentioned years which the model does not capture are mainly the central Pacific events. Similarly, during period 1 also, models misrepresented the central Pacific events such as 1983, 1994, 1984, 1999, 1998 etc as canonical events or non-ENSO events. Thus, instead of looking at all ENSO events together, here we analyzed the positive minus negative year composite of these years, which are not simulated by the models and is shown in figure 7.
During period 1, the composite has central Pacific warming in observations extending to the north east Pacific with significant cooling in the tropical eastern Pacific. There is strong cooling in the north Atlantic and the eastern Indian Ocean regions. After 2000, the CP El Nino has no significant cooling in the equatorial eastern Pacific and the central Pacific warming extends east. The anomalies are decreased in the tropical Atlantic and are opposite in the east Indian Ocean region. The majority of the models captured the central Pacific El Nino years as canonical ENSO patterns during period1. NEMO model has closer to observed pattern along with CCSM3 model. Earlier we have observed that these two are models with maximum cold bias in the ENSO region and that also can cause westward displacement of ENSO warming as shown by Pillai et al (2017), But after 2000, the equatorial warming is not captured by most of the models and they have maximum warming south of equator mainly for Feb IC and CanCM4i, CCSM4 and FLORA models have stronger central Pacific warming. Also in both periods, these models are not able to capture the signal from the Atlantic and the Indian Ocean properly.
Thus, during period1, the models captured the central Pacific ENSO as similar to canonical ENSO, but the events were very less in number. But after 2000, when most of the events are CP ENSO except 2007, 2010, 2015 etc, the models failed to capture the elongated pattern of warming, mainly for Feb IC. This also can induce reduced skill and interannual variability of ENSO in the models.