Multifractal and recurrence measures from meteorological data of climate zones in India

TL;DR

This study analyzes climate data from various Indian regions using multifractal and recurrence measures to understand the complexity, classify climate dynamics, and detect significant shifts in variability over 1970-2000.

Contribution

It introduces a dynamical systems approach applying multifractal and recurrence analysis to classify and detect climate variability shifts across Indian zones.

Findings

Identified significant climate variability shifts during 1970-2000.

Classified climate dynamics based on multifractal features.

Detected heterogeneity in climate complexity across regions.

Abstract

We present a study on the spatio-temporal pattern underlying the climate dynamics in various locations spread over India, including the Himalayan region, coastal region, central and northeastern parts of India. We try to capture the variations in the complexity of their dynamics derived from temperature and relative humidity data and classify them based on the multifractal features of their reconstructed phase space dynamics. We also report the variations in climate dynamics over time in these locations by estimating the recurrence-based measures using a sliding window analysis on the data sets. We could then detect significant shifts in climate variability in different spatial locations during the period 1970-2000. The dynamical systems approach presented thus helps to understand the complexity and identify the heterogeneity in climate dynamics. The study also provides relevant inputs on the nature of the shifts in climate that occur in the locations spread over different climate zones.