Abstract
Abstract The predictability of temperatures in North America is of great importance for local agriculture and human health. In autumn (SON), the temperature in North America is correlated with ENSO, but its predictive skill is limited. Here, we show that spring (MAM) sea surface temperature (SST) anomalies in the southwest Pacific (SWP) exhibit a higher correlation with autumn temperature in North America (r = 0.73) than ENSO. This cross-seasonal and cross-hemispheric relationship is established via the western tropical Pacific (WTP) region. The spring SWP SST anomalies show a cross-hemispheric propagation embedded in the southerly monsoonal flow through the wind–evaporation–SST feedback, which sustains the progression of SST anomalies toward the WTP in autumn. The AGCM simulations from multimodels show that active convection in the WTP caused by SST warming stimulate a Rossby wave train propagating downward to North America, where it is governed by an anomalous high with increased atmospheric thickness and tropospheric temperature. Consequently, the longwave radiative heating over North America is enhanced, raising the surface air temperature. These results indicate that the SWP SST is a useful predictor of North American temperature 6 months in advance through the cross-hemispheric influence. A model based on the spring SWP SST and preceding winter ENSO (Niño-3.4) shows a high predictive skill for the autumn temperature anomalies in North America. Significance Statement Seasonal predictions of surface temperature are important for agricultural decision-making, disaster prevention, and mitigation. Over the western Pacific, associated with the seasonal evolution of ITCZ, there is a strong northward progression of SST anomaly signals generated in the southwest Pacific. This study finds that this cross-hemispheric SST propagation can significantly impact the autumn surface temperature over central North America through the atmospheric bridge. A statistical model incorporating southwest Pacific SST for spring is constructed to predict the North American autumn surface temperature and exhibits high consistency with observations. This improves our understanding of the interhemispheric interactions at seasonal time scales and the seasonal predictability of the North American climate.
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