Using a causal discovery approach to analyse linkages among ENSO, circulation fields, and summer monsoon precipitation over the Himalayas

The Himalayas is the main water source for two major river systems in South Asia, the Ganges and the Brahmaputra. Rivers from this region are predominately fed by precipitation associated with the Indian Summer Monsoon (ISM) which is variable and influenced by large-scale atmospheric circulation patterns and anomalies, where El Niño-Southern Oscillation (ENSO) plays a dominant role. Here, we use a causal discovery method to assess the relationships and causal links associated with the influence of ENSO on ISM precipitation over the Himalayas, and how this is regulated by the Walker and Hadley circulation cells. In particular, we aim to clarify the direction and strength of causal linkages involving four time series/ indices representing ENSO, the Walker circulation, the monsoon Hadley circulation, and  summer monsoon Himalayan precipitation.   Apart from ENSO data, which is available for a longer period, the rest other data are from ERA5 that cover longer records, starting from 1940 to 2022. We demonstrate that the influence of ENSO on Himalayan precipitation mediated by the circulation dynamics can be quantified on monthly timescales (i.e., at few months lag). Starting from the two-way interaction between two parameters, we increased the complexity of the causal effect network analysis in steps and finally ended with all four indices. Our results show that it is possible to identify causal links with corresponding time delays and the links are moderately robust in most cases. Our findings also indicate that the influence of ENSO on the regional summer monsoon Hadley cell can arise via the pathways of regional Walker cell in the Himalayan sector. Improving our understanding of Himalayan precipitation and relevant regulatory mechanisms play an important role in India’s socioeconomic structure though it is still a neglected area compared to the vast amount of research those focused on all India rainfall. Our analyses using the sophisticated approach of causal network analyses will advance our knowledge on ISM and complement the gap.