学术文献库

The Indian summer monsoon rainfall (ISMR) has a decisive influence on India's agricultural output and economy. Extreme deviations from the normal seasonal amount of rainfall can cause severe droughts or floods, affecting Indian food production and security. Despite the development of sophisticated statistical and dynamical climate models, a long-term and reliable prediction of the ISMR has remained a challenging problem. Towards achieving this goal, here we construct a series of dynamical and physical climate networks based on the global near surface air temperature field. We uncover that some characteristics of the directed and weighted climate networks can serve as efficient long-term predictors for ISMR forecasting. The developed prediction method produces a forecast skill of 0.5 with a 5-month lead-time in advance by using the previous calendar year's data. The skill of our ISMR forecast, is comparable to the current state-of-the-art models, however, with quite a short (i.e., within one month) lead-time. We discuss the underlying mechanism of our predictor and associate it with network-delayed-ENSO and ENSO-monsoon connections. Moreover, our approach allows predicting the all India rainfall, as well as forecasting the different Indian homogeneous regions' rainfall, which is crucial for agriculture in India. We reveal that global warming affects the climate network by enhancing cross-equatorial teleconnections between Southwest Atlantic, Western part of the Indian Ocean, and North Asia-Pacific with significant impacts on the precipitation in India. We find a hotspots area in the mid-latitude South Atlantic, which is the basis for our predictor. Remarkably, the significant warming trend in this area yields an improvement of the prediction skill.