Under the global warming, East China is witnessing more heat waves with increasing intensity, for instance, the strongest heat wave over the Yangtze River valley (YRV) in 2013 since 1951 which severely harmed the economy and the health of the population. Therefore, clarifying the main factors responsible for the heat wave variability and their relative contribution is important to improving seasonal-to-annual prediction.
Recently, Dr. Xiaolong Chen and Prof. Tianjun Zhou from Institute of Atmospheric Physics, Chinese Academy of Sciences investigated the mechanism of sea surface temperature (SST) forcing and atmospheric internal variability and their relative contributions to interannual variability of heat wave in the YRV. They found that SST forcing can explain about 2/3 heat wave variability and the other 1/3 comes from atmospheric internal variability during 1979-2008. Interestingly, for the SST forcing, there is 1/3 from anomalies independent from the well-known El Niño - Southern Oscillation (ENSO), though the other 1/3 is ENSO's contribution. All these factors play roles in making heat wave over the YRV through moving the western North Pacific Subtropical High northward, extending it westward and intensifying the anticyclone.
"Both of El Niño decaying in the previous year and La Niña developing in summer could do a favor for heat waves in the Yangtze River valley." Says CHEN, "The atmospheric internal variability is a circumglobal teleconnection and it shows a tripolar wave train propagating southeastwards over the Eurasia. Non-ENSO SST anomalies, that is warm pattern in the North Pacific and a dipolar pattern in the North Atlantic, can exert its influence by projecting onto the mode of internal variability."
Their findings reveal that the internal variability shares its way to forcing from the extratropical SST anomalies to raise heat waves over the YRV. Further analysis shows the intensification of heat wave variability after the end of 1990s is associated with the above circumglobal teleconnection in which both the non-ENSO SST forcing and internal variability are involved. This study indicates that learning the predictability of extratropical SST, except for ENSO, can potentially improve the seasonal forecast skill of heat waves in East China.
The research is published in Climate Dynamics