Abstract
Several cases of failure in the prediction of Indian Summer Monsoon Rainfall (ISMR) are the major concern for long-lead prediction. We propose that this is due to the temporal evolution of association/linkage (inherent concept of temporal networks) with various factors and climatic indices across the globe, such as El Niño-Southern Oscillation (ENSO), Equatorial Indian Ocean Oscillation (EQUINOO), Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO) etc. Static models establish time-invariant (permanent) connections between such indices (predictors) and predictand (ISMR), whereas we hypothesize that such systems are temporally varying in nature. Considering hydroclimatic teleconnection with two major climate indices, ENSO and EQUINOO, we showed that the temporal persistence of the association is as low as three years. As an application of this concept, a statistical time-varying model is developed and the prediction performance is compared against its static counterpart (time-invariant model). The proposed approach is able to capture the ISMR anomalies and successfully predicts the severe drought years too. Specifically, 64% more accurate performance (in terms of RMSE) is achievable by the recommended time-varying approach as compared to existing time-invariant concepts.
Highlights
Spatio-temporal variability of rainfall has significant economic and societal impacts, for agriculture based countries
Predictor selection is an important aspect of statistical modeling and two climatic indices strongly influencing the variability of Indian Summer Monsoon Rainfall (ISMR) are El Niño-Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation (EQUINOO)[10,13,15,30,31,32]
Inter-annual variation of ISMR is concurrently associated with ENSO and EQUINOO, for e.g. the impacts of El Niño during 1997 and 2002 were neutralized by negative/positive phases of EQUINOO31
Summary
Spatio-temporal variability of rainfall has significant economic and societal impacts, for agriculture based countries. Predictor selection is an important aspect of statistical modeling and two climatic indices strongly influencing the variability of ISMR are El Niño-Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation (EQUINOO)[10,13,15,30,31,32]. We hypothesize that the nature of such association varies considerably with time and must be considered for consistency in long-lead prediction. Long-lead seasonal prediction of ISMR is required to consider two important issues – (i) identifying the most influencing predictors with appropriate lags and (ii) identifying the time-varying nature of predictor-predictand association. These form the motivation of this study. The potential of using time-varying approach is contrasted against classical time-invariant approach
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