相关成果：

1. Complexity based approach for El Nino magnitude forecasting before the "spring predictability barrier"

      该文作者提出了一套基于信息熵理论的全新的方法——System Sample Entropy——用来计算厄尔尼诺区域（Nino 3.4）近海平面空气或海表温度的复杂度（包括温度随时间变化的无序性以及不同地点温度变化的同步性或相干性）。利用这一方法，作者们发现了Nino 3.4区域温度变化的复杂度与厄尔尼诺现象强度存在着非常强和稳定的线性关系，即一年内（1月1日-12月31日）Nino 3.4区域的温度变化复杂度越大，那么下一年发生的厄尔尼诺事件的强度就越大。基于这一发现，作者们提出了一套基于每年Nino 3.4 区域温度变化复杂度的大小（由该区域 System Sample Entropy 量化）来预测来年厄尔尼诺发生及其强度的方法。该方法目前成功的预测了1984至2019年期间10个厄尔尼诺事件中的9个事件的发生年份，以及24个没有厄尔尼诺现象发生的年份当中的21个，特别是对厄尔尼诺强度预测的平均误差仅为0.23摄氏度。 对于刚刚到来的2020年，基于文中提出的System Sample Entropy的方法，作者们预测厄尔尼诺将有很大概率会在本年下半年再次发生，并发展为一个中等强度甚至高强度的厄尔尼诺事件，其预测强度为1.48+-0.25摄氏度。 

原文链接：https://www.pnas.org/content/117/1/177

Correlation between SysSampEn and El Niño magnitude. (A) The heights of the blue rectangles indicate the values of the SysSampEn (left scale) for the calendar years preceding El Niño events, calculated from ERA-Interim, by using the set of parameters (m=60 d, p=15 day, leff=345 d, and γ=9) that correspond to the highest correlation r with El Niño magnitudes. The red curve is the ONI and the red shades indicate El Niño periods (right scale). (B) Scatter plot of the maximal El Niño magnitude versus previous calendar year’s SysSampEn (blue rectangles in A). The gray region indicates values of the SysSampEn, which predict for the maximal ONI less than 0.5○ C and thus by definition non-El Niño events. The green dashed line shows the best least-square fitted line. (C) The y coordinate of each purple dot is the averaged correlation r for parameter combinations with accuracy no less than a certain level (i.e., its x coordinate) in both the spatial asynchrony and the temporal disorder tests. The correlation r between SysSampEn and the El Niño magnitude is monotonously increasing with increasing accuracy level. The calculation of the accuracy level is independent of any El Niño events, and thus the strong correlation between the SysSampEn and the El Niño magnitudes emerges naturally without fitting.

Forecasting El Niño onsets and magnitudes. (A) The value of onset forecasting index (average forecast over the 4 datasets) is shown as the height of rectangles and is used to forecast the occurrence or absence of an El Niño onset in the following year. If the index value is ≥0.5○ C and the observed ONI in December is below 0.5○ C, we forecast the onset of an El Niño in the following year. The blue rectangles show the correctly forecasted El Niño onsets, the pink rectangle indicates a missed El Niño event, the gray rectangles indicate false alarms, and the transparent rectangles show when the absence of an El Niño onset was correctly forecasted. (B) Observed temperature versus the leave-one-out hindcasted temperature for the El Niño magnitudes (orange dots) before 2018. The obtained RMSE is 0.23○ C. The forecasted magnitude (1.11○ C) of the 2018 El Niño event is plotted as a light green dot with an error bar of 1×RMSE. The + symbols indicate the hindcasted (forecasted) values obtained by using each of the 4 datasets. (C) Forecasts of the 2004, 2006, and 2014 El Niño magnitudes based only on past information. The error bar for each forecasted El Niño event (blue points) equals 1×RMSE (i.e., 0.37○ C, 0.31○ C, and 0.28○ C for the 2004, 2006, and 2014 events, respectively) and is calculated from the leave-one-out hindcasts which lie in the regarded events past. Thus, the forecasted value, as well as its error bar (i.e., 1×RMSE), are only based on the event’s past information. The red dots show the observed magnitudes and are within the error bars. The forecasted 2018 magnitude and its error bar are shown in light green.

2. Network analysis reveals strongly localized impacts of El Niño

     El Niño, one of the strongest climatic phenomena on interannual time scales, affects the climate system and is associated with natural disasters and serious social conflicts. Here, using network theory, we construct a directed and weighted climate network to study the global impacts of El Niño and La Niña. The constructed climate network enables the identification of the regions that are most drastically affected by specific El Niño/La Niña events. Our analysis indicates that the effect of the El Niño basin on worldwide regions is more localized and stronger during El Niño events compared with normal times.

原文链接：https://www.pnas.org/content/114/29/7543.full

(A and C) In-weight maps (using C and W) for El Niño events. (B and D) In-weight maps (using C and W) for La Niña events. (E and F) Mean winter (December–February) temperature anomalies during El Niño and La Niña. The arrows in A indicate two examples of in-links (El Niño impact), outgoing from ENB, to (i) Chicualacuala in Mozambique and (ii) Rambaxpura in India. The arrow in B indicates another example of in-links, outgoing from ENB (La Niña impact) to (iii) Jundah in Australia, respectively. The flat white rectangle in A–D represents the Niño 3.4 region.

(A) The ONI as a function of time. (B) The evolution of the number of nodes that have in-links with time. (C) The evolution of the average in-weights per node with time.

The community structure of the 11 El Niño events. (A–D) The heat map of cross-correlations between pairs of El Niño events, based on the global (A), tropical (B), Northern Hemisphere (C), and Southern Hemisphere (D) maps of the in-weighted climate network. (E) Community structure in the network of 11 El Niño events. Different colors represent different communities.