Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai, China.
Sci Rep. 2018 Jul 12;8(1):10501. doi: 10.1038/s41598-018-28964-z.
The spring predictability barrier severely limits our ability to forecast the El Niño-Southern Oscillation (ENSO) from and across the boreal spring. Our observational analysis shows that the spring predictability barrier (SPB) can be largely reduced when information from both the ocean and atmosphere are effectively taken into account during the boreal spring. The correlation coefficient between the predicted and observed sea surface temperature anomalies over the equatorial central-eastern Pacific determined by a simple quaternary linear regression model is >0.81 for the period 1980-2016. The frame structure of the ENSO evolution is mostly controlled by variations in the oceanic heat content along the equatorial Pacific and the zonal wind stress over the tropical western Pacific during the boreal spring. These results indicate that to predict ENSO events with a long lead time, i.e., largely reducing the SPB, variations in both the ocean and atmosphere during the boreal spring should be well predicted first. While the oceanic information is mainly located in the equatorial Pacific and well characterized by the delayed oscillator and recharging oscillator models, variations in the atmosphere may contain information beyond this area and are more difficult to deal with.
春季可预报性障碍严重限制了我们在北方春季对厄尔尼诺-南方涛动(ENSO)进行预测的能力。我们的观测分析表明,当在北方春季期间有效地考虑海洋和大气两者的信息时,春季可预报性障碍(SPB)可以大大降低。由简单的四次线性回归模型确定的 1980-2016 年期间赤道中东部太平洋的预测和观测海面温度异常之间的相关系数>0.81。ENSO 演化的框架结构主要由赤道太平洋沿海水温变化和热带西太平洋纬向风应力控制。这些结果表明,为了进行长时间的厄尔尼诺预测,即大大降低春季可预报性障碍,首先需要很好地预测北方春季的海洋和大气变化。海洋信息主要位于赤道太平洋,滞后振荡器和充电振荡器模型很好地描述了这些信息,而大气变化可能包含超出该区域的信息,更难处理。