Articles | Volume 21, issue 1
https://doi.org/10.5194/cp-21-1-2025
https://doi.org/10.5194/cp-21-1-2025
Research article
 | 
07 Jan 2025
Research article |  | 07 Jan 2025

A novel explainable deep learning framework for reconstructing South Asian palaeomonsoons

Kieran M. R. Hunt and Sandy P. Harrison

Data sets

Data-driven reconstructions of the Indian palaeomonsoon (1500–1995 CE) Kieran M. R. Hunt and Sandy P. Harrison https://zenodo.org/records/12688184

Model code and software

Code to reproduce models and figures Kieran M. R. Hunt and Sandy P. Harrison https://github.com/kieranmrhunt/paleomonsoon

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Short summary
In this study, we train machine learning models on tree rings, speleothems, and instrumental rainfall to estimate seasonal monsoon rainfall over India over the last 500 years. Our models highlight multidecadal droughts in the mid-17th and 19th centuries, and we link these to historical famines. Using techniques from explainable AI (artificial intelligence), we show that our models use known relationships between local hydroclimate and the monsoon circulation.