Climate is a notoriously complex system influenced by geographical features, human intervention, and interactions between the atmosphere, ocean, and land. Statistical data points like average temperature or relative humidity can help extrapolate large-scale conclusions about certain regions, but a deeper understanding demands information about how climate dynamics change over time based on location.

Bejoy and Ambika investigate the spatio-temporal variations in the climate dynamics of India. They identified El Niño and La Niña interference in dynamics and found other larger patterns hidden in the climate data.

“This study, based on the structure of the underlying dynamics rather than on the statistical features of data, is novel to climate-related studies in India,” said author G. Ambika. “Considering the high spatial heterogeneity and complexity of the Indian climate, the dynamical systems approach appears relevant to reveal dynamical differences for a full characterization of Indian climatology.”

To analyze climate variability, the researchers tracked temperature and relative humidity data from 15 locations throughout India, spanning 1948-2022. They classified the regions based on recurrence measures and identified variations in the recurrence patterns that revealed deep insights into complex climate dynamics.

The researchers found significant dynamic shifts during strong El Niño and La Niña occurrences, but the relationship has evolved and is regional.

“The differences in El Niño–monsoon relationship over time and across different locations in India can cause spatio-temporal variability in climate dynamics,” said Ambika.

Though they did not find climate dynamics changes that were directly related to global warming, the El Niño and La Niña events that were present may be influenced by global warming.

This study demonstrates the usefulness of a dynamical systems approach that can be applied to other locations.

Source: “Recurrence analysis of meteorological data from climate zones in India,” by Joshin John Bejoy and G. Ambika, Chaos (2024). The article can be accessed at https://doi.org/10.1063/5.0165282.

This paper is part of the Nonlinear dynamics, synchronization and networks: Dedicated to Juergen Kurths’ 70th birthday Collection, learn more here.