IN006-0003
Detecting Drivers of Heavy Precipitation in the Himalayan Region

Tuesday, 8 December 2020
Poster
Grzegorz Muszynski1, Pritchard, Hamish D.1, Marlene Kretschmer2 and J Scott Hosking1, (1)British Antarctic Survey, Cambridge, United Kingdom, (2)University of Reading, Reading, United Kingdom
Abstract:
The Himalayas have significant hydrological, meteorological, and glaciological impact on the southern region of Asia. They act both as orographic barriers that influence precipitation in the region, and as natural water resources that store large volumes of ice and snow. Water resources stored in the mountain glaciers of Himalayas provide valuable water supplies for almost 1.6 billion people, agriculture, and hydropower. To develop effective water management plans for the region, it requires a better understanding of present and future water resources. That is why it is necessary to improve knowledge of the contributions made by precipitation and/or melting glaciers across the region centred on the Himalayas.

High elevations of the Himalayas cause interactions between orography and atmospheric circulation systems. The interactions strongly influence weather systems that lead to heavy precipitation events (e.g., snowfall). There are two main sources of precipitation in the Himalayas: i) the Indian summer monsoon that brings storm systems from the south, ii) the Western disturbance that originates in the Mediterranean region. Both weather systems are valuable precipitation sources for the mountain snowpacks and/or glaciers of Himalaya range.

We aim at investigating drivers of precipitation in the region centred on the Himalayas, especially during winter months. To gain insights into the climatic links of precipitation in the region, we apply causal discovery approach (i.e., Causal Effect Networks developed by Runge et al. [1]) to coupled climate model simulations (e.g., CMIP5 and CMIP6). The goal is to detect, describe, and diagnose the causal links between the Indian summer monsoon or the Western disturbance and heavy precipitation in the Himalayas.

[1] Runge, Jakob, et al. "Detecting and quantifying causal associations in large nonlinear time-series datasets." Science Advances 5.11 (2019): eaau4996.