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
• Online Access: https://doi.org/10.5194/esd-2019-42; https://www.earth-syst-dynam-discuss.net/esd-2019-42/

The alternation of active and break phases in the Indian summer monsoon (ISM) rainfall at sub-seasonal timescales characterizes each ISM season. Tropical and mid-latitude drivers influence this sub-seasonal ISM variability. The circumglobal teleconnection observed in boreal summer drives sub-seasonal variability across the mid-latitudes and a two-way interaction between ISM and the circumglobal teleconnection pattern has been hypothesized. We use causal discovery algorithms to test the ISM-circumglobal teleconnection hypothesis in a causal framework. Our analysis shows a robust causal link from the circumglobal teleconnection pattern and the North Atlantic region to ISM rainfall. We estimate the normalized causal effect (CE) of this link to be about 0.2 (a one standard deviation shift in the circumglobal teleconnection causes a 0.2 standard deviation shift in the ISM rainfall one week later). In turn, the ISM rainfall influences back the circumglobal teleconnection pattern, however weakly. Moreover, we identify causal links that represent the internal dynamics of the ISM convective cell at weekly timescales: Periods with strong updraft lead to strong rainfall one week later, but the resulting increase in static stability suppresses convection again. In our analyses, this internal ISM dynamics has the strongest CE of 0.5. Tropical Madden-Julian Oscillation variability has a CE of 0.2–0.3. Our results show that the most of the ISM variability on weekly timescales is due to internal dynamics of convective cell, but both tropical and mid-latitude teleconnections have a substantial influence. Identifying these local and remote drivers paves the way for improved sub-seasonal forecasts.