In this section, we analyze in detail the precipitation, 850-hPa wind, and SSTA distribution in different types of models. For comparison, we first analyze the observed anomaly distribution. The regressed precipitation, 850-hPa wind and SST with respect to the normalized CPC1 of AMJ precipitation anomalies for the observation are shown in Fig. 6, which is similar to Fig. 3 of (Hu et al., 2014). Significant positive precipitation anomalies cover most parts of the SCS, the Indo-China Peninsula, and the WNP, with a cyclonic circulation consisting of northeasterly winds from South China and westerly winds from the Indian Ocean (Fig. 6a). Three negative precipitation anomaly centers are located over the western TIO, northwest of Australia, and the equatorial central Pacific. Strong easterlies prevail over the equatorial central Pacific. The SSTA field shows two negative regions over the TIO and the EP, and one positive region over the WNP (Fig. 6b). The relevant physical processes of the relationship between AMJ SCS precipitation variability and tropical Indo-Pacific SSTAs are shown by (Hu et al., 2014). Overall, both anomalous cross-equatorial flows from the southwestern TIO induced by negative SSTAs there, and an anomalous Walker circulation forced by negative EP SSTAs, contribute to enhanced convection over the SCS and the surrounding regions. Additional contribution comes from positive WNP SSTAs via a Rossby wave-type response (Hu et al., 2014).
For the TPO-TIO type, the simulated patterns of rainfall, 850-hPa wind, and SSTAs obtained by regression with respect to the normalized CPC of AMJ precipitation are given in Figs. 7 and 8. Seven models (CCSM4, GFDL-ESM2G, GFDL-ESM2M, IPSL-CM5A-LR, IPSL-CM5B-LR, MIROC5 and NorESM1-M) simulate the observed anomalies well (Figs. 7a, c, d, e, f, g, j and 8a, c, d, e, f, g, j). For example, negative SSTAs in the TPO are accompanied by below-normal precipitation and anomalous easterlies over the EP. Negative SSTAs in the southern TIO are followed by negative rainfall anomalies and cross-equatorial southerlies there. Furthermore, positive SSTAs in the WNP are accompanied by cyclonic wind anomalies over the SCS and the Philippine Sea due to the destabilization of the lower troposphere (Wu and Wang, 2000, 2001; Hu et al., 2014). These features are similar to observations, indicating the contribution of tropical Indian and Pacific Ocean SSTAs to the SCS precipitation variability (Hu et al., 2014). The other three models capture the precipitation and wind anomalies over and surrounding the SCS, but precipitation and wind anomalies over the broad TIO-TPO are relatively weak (Figs. 7b, h and i), which is related to relatively small SSTAs (Figs. 8b, h and i). The SCS precipitation variability in these three models appears to be mainly influenced by SSTAs in the TIO and WNP.
In IPSL-CM5A-LR (Figs. 7e and 8e), the precipitation, wind, and SSTA patterns are quite similar to observations. One notable discrepancy from observations is that the precipitation anomalies over the SCS shift to the northern part compared to observations. This may suggest a systematic shift in the response to tropical Indo-Pacific SSTAs in this model. In MIROC5 (Figs. 7g and 8g), the SSTA pattern is similar to observations, as is the precipitation and wind anomaly distribution. For example, there are anomalous cross-equatorial flows over the TIO and anomalous easterlies over the EP. The precipitation, wind, and SSTA distribution in MIROC-ESM (Figs. 7h and 8h) are also similar to observations, but with weaker magnitude.
For the TPO type, precipitation, 850-hPa wind, and SSTAs corresponding to the normalized CPC1 of AMJ precipitation are shown in Fig. 9. The SSTA distribution similar to observations is visible in the TPO (right panels of Fig. 9). However, the wind and precipitation anomalies over the EP are weak (left panels of Fig. 9). Above-normal rainfall and the anomalous cyclone around the SCS may be contributed by positive SSTAs in the WNP via a Rossby wave-type response (Wu et al., 2014). Wind and precipitation anomalies over the TIO are similar to observations, but the SSTAs display an east-west pattern of contrast, leading to a low PCC (SST) in the TIO (Table 2).
Two models, CSIRO-Mk3-6-0 and GISS-E2-H, are included in the TIO type. The precipitation and wind anomalies in the TIO and western tropical Pacific are similar to observations, as are the SSTAs in the TIO and WNP (Fig. 10). However, negative SSTAs in the tropical Pacific are confined to north of the equator in CSIRO-Mk3-6-0, and SSTAs in the EP are weak in both models (right panels of Fig. 10). The SCS precipitation and wind anomalies appear to be contributed by both the TIO and WNP SSTAs.
For the WEP type, prominent SSTAs are present in the TIO and TPO, but different from observations (right panels of Fig. 11). The SSTAs in the equatorial central-eastern Pacific and TIO are opposite to observations, whereas the WEP SSTAs are similar to observations. Precipitation and wind anomalies over the SCS appear as a northwestward extension of the Rossby wave-type response to WEP SSTAs (left panels of Fig. 11).
For the local type (FGOALS-g2, FGOALS-s2, IPSL-CM5A-MR, and MRI-CGCM3), the SSTAs in the TPO and TIO are small (right panels of Fig. 12). In FGOALS-s2, prominent precipitation anomalies are present over the SCS and WNP (Fig. 12a). These are accompanied by negative SSTAs over the northern SCS and subtropical WNP (Fig. 12b). Thus, it appears that the SSTA is a response to atmospheric change. A similar feature is seen in FGOALS-g2, IPSL-CM5A-MR and MRI-CGCM3, but with weaker anomalies. These results seem to suggest that the SCS precipitation variability is largely related to internal atmospheric dynamics.
There are three points to note according to the above analysis of anomalies corresponding to SCS precipitation variability during AMJ in different model types. First, the signal of SCS precipitation variability in the TPO and TIO SST and precipitation varies largely among the models. Second, the SSTA pattern in the TPO and TIO is important for capturing the AMJ SCS precipitation variability. And third, a realistic simulation of the WEP and local SST impacts is necessary for reproducing the AMJ SCS precipitation variability.
Previous studies have revealed that SST is influenced by the atmospheric changes through the cloud-radiation effect, wind-evaporation effect, and wind-induced oceanic processes (Klein et al., 1999; Wu and Kirtman, 2007; Wu, 2010). Meanwhile, SST change could modulate regional convection via lower-level moisture convergence, surface evaporation, and lower tropospheric stability (Lindzen and Nigam, 1987; Wu and Wang, 2000, 2001). The performance of precipitation-SST correlation in the climate models could provide information about whether the physical processes of air-sea interaction in the models are realistic. Positive precipitation-SST correlation indicates an oceanic forcing of precipitation, with ocean surface warming inducing more precipitation (Wu et al., 2006b). By contrast, negative precipitation-SST correlation means an atmospheric forcing of SST, with decreased downward shortwave radiation reaching the surface induced by more precipitation and leading to surface cooling (Trenberth and Shea, 2005; Wu et al., 2013). Thus, we compare the SST-precipitation correlation in models against observations during AMJ to understand the biases of the relationship between AMJ SCS precipitation variability and SST key regions. The point-wise and simultaneous precipitation-SST correlations for the observation and the 23 models are given in Fig. 13.
In the observations, positive correlation regions cover the eastern TPO, the WNP, and the southern TIO, while negative correlations appear in the eastern Bay of Bengal and most of the SCS (Fig. 13a), which is consistent with (He and Wu, 2013b). This indicates the forcing of SST in the equatorial central-eastern Pacific, southern TIO, and WNP, supported by numerical experiments (Hu et al., 2014). In turn, anomalous convection and winds may affect the SCS precipitation and wind. In the SCS domain, multiple models (CanESM2, CCSM4, CSIRO-Mk3-6-0, GFDL-ESM2M, GISS-E2-H, HadCM3, inmcm4, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC5, MIROC-ESM, MPI-ESM-LR, MRI-CGCM3, and NorESM1-M) show a sign of correlation opposite to the observed, indicating unrealistic oceanic forcing to the atmosphere in these models. Also of note is that overly strong WEP SST impacts are seen in CanCM4, CanESM2, CSIRO-Mk3-6-0, HadCM3, inmCM4, IPSL-CM5A-LR, IPSL-CM5A-MR, and MIROC5. For example, the positive correlation over the WEP exceeds 0.8 in CanCM4 (Fig. 13d). The warm SST there induces large positive precipitation anomalies that extend northwestward to the SCS (Figs. 11a and b).