The EM events affect the global climate at various time scales. The El NiГ±o (La NiГ±a) Modoki (ENSO Modoki, hereafter EM) 1 is a newly acknowledged phenomenon characterized by warm (cool) central Pacific sea surface temperature (SST) flanked by cool (warm) eastern and western Pacific SSTs. The analysis also indicates the SVR to perform better than the RF method in forecasting the EMI. The results indicate both SVR and RF to be capable of forecasting the phase of the EMI realistically at both 6-months and 12-months lead times though the amplitude of the EMI is underestimated for the strong events. The importance of each of the predictors is evaluated using the Supervised Principal Component Analysis (SPCA). The predictors for the EMI are identified using KendallвЂ™s tau correlation coefficient between the monthly EMI index and the monthly anomalies of the slowly varying climate variables such as sea surface temperature (SST), sea surface height (SSH) and soil moisture content (SMC). We evaluated two widely used non-linear ML algorithms namely Support Vector Regression (SVR) and Random Forest (RF) to forecast the EMI at various lead times, viz. The focus of this study is to evaluate the efficacy of Machine Learning (ML) algorithms in the long-lead prediction of El NiГ±o (La NiГ±a) Modoki (ENSO Modoki) index (EMI).

Constructing limit lines for dsa815 emi