Tropical and mid-latitude teleconnections interacting with the Indian summer monsoon rainfall: A Theory-Guided Causal Effect Network approach

• Main Authors: Capua, Giorgia, Kretschmer, Marlene, Donner, Reik V., Hurk, Bart, Vellore, Ramesh, Krishnan, Raghavan, Coumou, Dim
• Format: Text
• Language: English
• Published: 2019
• Subjects: Indian; North Atlantic; North Atlantic oscillation
• Online Access: https://doi.org/10.5194/esd-2019-11; https://esd.copernicus.org/preprints/esd-2019-11/

The Indian Summer Monsoon (ISM) is characterized by alternating active (wet) and break (dry) phases operating at sub-seasonal timescales, and various studies advocate tropical and mid-latitude teleconnection drivers influence the sub-seasonal ISM rainfall variability. One such driver is the circumglobal teleconnection pattern, which is commonly observed during boreal summer regulating the variability across the mid-latitudes at sub-seasonal time scales. In this study, a two-way interaction between ISM and circumglobal teleconnection is hypothesized and causal discovery algorithms are employed to examine and quantify the interaction linkage. Our analysis shows that there is a robust causal link from the circumglobal teleconnection pattern and the North Atlantic Oscillation (NAO) to ISM rainfall, and also a reverse causal link from the ISM rainfall to the circumglobal teleconnection pattern. Further, by including regional drivers in the framework, we identify the causal links that represent the internal dynamics associated with the ISM convective activity operating on weekly timescales, e.g., on weekly time scales, there is precedence of enhanced ascent to increased rainfall over the monsoon trough region which is followed by strong stabilization and convective inhibition. In our analyses, this internal ISM dynamics has the strongest effect, which is about twice as large as those of the mid-latitudes and of tropical MJO variability on the ISM dynamics. With our theory-guided causal effect network approach, we can (1) test physical hypotheses, (2) exploratively search for causal links and (3) quantify their relative causal contributions. This paves the way for improved (sub)seasonal forecasts.