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<p>Understanding the spatiotemporal teleconnections of rainfall in India with large-scale modes of climate variability is crucial for agricultural planning, water resource management, and climate adaptation. This study investigates the long-term teleconnections of monsoon rainfall with the Indian Ocean Dipole (IOD) and El Niño-Southern Oscillation (ENSO) across different Agro Climatic Zones (ACZs) of India from 1901 to 2022 using correlation, partial correlation and interpretable machine learning to quantify their impacts in different ACZs. Results revealed significant correlations and partial correlations between monsoon rainfall with the June-July-August-September (JJAS), October-November-December (OND), and annual (AN) mean values of ENSO indices, while the correlation with IOD was mostly non-significant. The January-February (JF) and March-April-May (MAM) mean values of both ENSO and IOD indices were poorly correlated with rainfall. The JJAS mean values of indices consistently showed the strongest correlations and partial correlations with monsoon rainfall across all ACZs. Comparing the periods 1903–1962 and 1963–2022 revealed evolving teleconnections, suggesting a weakening influence in the ACZs of western and central parts of India and a strengthening influence in the ACZs of southern, eastern, and northern parts of the country. We also identified key climate indices using the Boruta algorithm to train a random forest model and quantified their impacts on rainfall variability using accumulated local effect plots for each ACZ, which revealed that ENSO has a more dominant impact on rainfall than the IOD in most ACZs. This analysis provides insights into regional climate dynamics and will aid in improved planning and management of water resources.</p>

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Quantifying rainfall teleconnections with climate modes in agro climatic zones of India

  • 期刊论文

  • 发表日期：

    2025年5月17日

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  • Sonam Sah,
  • RN Singh,
  • Bappa Das,
  • Gaurav Chaturvedi,
  • A. K. Singh,
  • K. S. Reddy

摘要

Understanding the spatiotemporal teleconnections of rainfall in India with large-scale modes of climate variability is crucial for agricultural planning, water resource management, and climate adaptation. This study investigates the long-term teleconnections of monsoon rainfall with the Indian Ocean Dipole (IOD) and El Niño-Southern Oscillation (ENSO) across different Agro Climatic Zones (ACZs) of India from 1901 to 2022 using correlation, partial correlation and interpretable machine learning to quantify their impacts in different ACZs. Results revealed significant correlations and partial correlations between monsoon rainfall with the June-July-August-September (JJAS), October-November-December (OND), and annual (AN) mean values of ENSO indices, while the correlation with IOD was mostly non-significant. The January-February (JF) and March-April-May (MAM) mean values of both ENSO and IOD indices were poorly correlated with rainfall. The JJAS mean values of indices consistently showed the strongest correlations and partial correlations with monsoon rainfall across all ACZs. Comparing the periods 1903–1962 and 1963–2022 revealed evolving teleconnections, suggesting a weakening influence in the ACZs of western and central parts of India and a strengthening influence in the ACZs of southern, eastern, and northern parts of the country. We also identified key climate indices using the Boruta algorithm to train a random forest model and quantified their impacts on rainfall variability using accumulated local effect plots for each ACZ, which revealed that ENSO has a more dominant impact on rainfall than the IOD in most ACZs. This analysis provides insights into regional climate dynamics and will aid in improved planning and management of water resources.