Spatial multivariate selection of climate indices for precipitation over India

Abstract

Large-scale interdependent teleconnections influence precipitation at various spatio-temporal scales. Selecting the relevant climate indices based on geographical location is important. Therefore, this study focuses on the spatial multivariate selection of climate indices influencing precipitation variability over India, using the partial least square regression and variable importance of projection technique. 17 climate indices and gridded precipitation dataset (0.25 × 0.25°) from the Indian Meteorological Department for 1951–2020 at a monthly scale are considered. Results show that among all the indices, Nino 4, Nino 1 + 2, Trans Nino Index, Atlantic Multidecadal Oscillation (AMO), quasi-biennial oscillation (QBO), Arctic oscillation (AO), and North Atlantic Oscillation (NAO) have a significant influence on precipitation over India. Further, within homogenous regions, it is found that the Southern Oscillation Index and Nino 3.4 are selected majorly in the South Peninsular compared to other regions. The NAO/AO show a similar pattern and was found to be relevant in the Northeast region (>89%). AMO is selected mainly in Northwest, and West Central (>80%), AMO and QBO at about 70% of grid locations over Central Northeast India. It is to be noted that the number of climate indices identified varies spatially across the study region. Overall, the study highlights identifying the relevant climate indices would aid in developing improved predictive and parsimonious models for agriculture planning and water resources management

Keywords

• climate indices
• precipitation
• partial least square regression
• variable importance of projection
• homogenous regions