The impact of internal tropical Pacific variability on global mean surface temperature (GMST) is quantified using a multimodel ensemble. A tropical Pacific index (TPI) is defined to track tropical Pacific sea surface temperature (SST) variability. The simulated GMST is highly correlated with TPI on the interannual time scale but this correlation weakens on the decadal time scale. The time-scale dependency is such that the GMST regression equation derived from the observations, which are dominated by interannual variability, would underestimate the magnitude of decadal GMST response to tropical Pacific variability. The surface air temperature response to tropical Pacific variability is strong in the tropics but weakens in the extratropics. The regression coefficient of GMST against TPI shows considerable intermodel variations, primarily because of differences in high latitudes. The results have important implications for the planned intercomparison of pacemaker experiments that force Pacific variability to follow the observed evolution. The model dependency of the GMST regression suggests that in pacemaker experiments—model performance in simulating the recent “slowdown” in global warming—will vary substantially among models. It also highlights the need to develop observational constraints and to quantify the TPI effect on the decadal variability of GMST. Compared to GMST, the correlation between global mean tropospheric temperature and TPI is high on both interannual and decadal time scales because of a common structure in the tropical tropospheric temperature response that is upward amplified and meridionally broad.
El Niño–Southern Oscillation (ENSO) peaks in boreal winter but its impact on Indo-western Pacific climate persists for another two seasons. Key ocean–atmosphere interaction processes for the ENSO effect are investigated using the Pacific Ocean–Global Atmosphere (POGA) experiment with a coupled general circulation model, where tropical Pacific sea surface temperature (SST) anomalies are restored to follow observations while the atmosphere and oceans are fully coupled elsewhere. The POGA shows skills in simulating the ENSO-forced warming of the tropical Indian Ocean and an anomalous anticyclonic circulation pattern over the northwestern tropical Pacific in the post–El Niño spring and summer. The 10-member POGA ensemble allows decomposing Indo-western Pacific variability into the ENSO forced and ENSO-unrelated (internal) components. Internal variability is comparable to the ENSO forcing in magnitude and independent of ENSO amplitude and phase. Random internal variability causes apparent decadal modulations of ENSO correlations over the Indo-western Pacific, which are high during epochs of high ENSO variance. This is broadly consistent with instrumental observations over the past 130 years as documented in recent studies. Internal variability features a sea level pressure pattern that extends into the north Indian Ocean and is associated with coherent SST anomalies from the Arabian Sea to the western Pacific, suggestive of ocean–atmosphere coupling.
Tropical precipitation change under global warming varies with season. The present study investigates the characteristics and cause of the seasonality in rainfall change. Diagnostically, tropical precipitation change is decomposed into thermodynamic and dynamic components. The thermodynamic component represents the wet-get-wetter effect and its seasonality is due mostly to that in the mean vertical velocity, especially in the monsoon regions. The dynamic component includes the warmer-get-wetter effect due to the spatial variations in sea surface temperature (SST) warming, while the seasonality is due to that of the climatological SST and can be largely reproduced by an atmospheric model forced with the monthly climatological SST plus the annual-mean SST warming pattern. In the eastern equatorial Pacific where the SST warming is locally enhanced; for example, rainfall increases only during the March–May season when the climatological SST is high enough for deep convection. To the extent that the seasonality of tropical precipitation change over oceans arises mostly from that of the climatological SST, the results support the notion that reducing model biases in climatology improves regional rainfall projections.
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