Influence of Mesoscale Soil Moisture Patterns on Convective Initiation over the Tibetan Plateau

The Tibetan Plateau is the highest and most extensive plateau in the world, profoundly affecting climate and weather in the region. Due to its average elevation of more than 4000m, provides a strong thermal and dynamical forcing in the mid-troposphere during the summer months, fostering the frequent development of intense storms. Mesoscale convective systems (MCSs) are known to be associated with particularly extreme rainfall events and contribute up to ~60% of rainfall over the Tibetan Plateau (TP) and adjacent areas. In particular, MCSs that form on the TP may move off and bring heavy rain and flooding to downstream parts of China, affecting millions of people. A better understanding of the processes that impact MCS genesis over the TP could contribute to improved forecasting of these extreme events. Furthermore, there is strong evidence for accelerated climate warming on the TP, which may affect convection and makes the identification of factors for MCS development even more important.

Previous work in the Sahel has shown that mesoscale soil moisture patterns can influence the initiation of new MCSs, however the relationship has yet to be investigated for the more hydrologically and topographically complex TP. In this study we investigate the impact of mesoscale soil moisture features on convective initiation over the TP during the monsoon season (May – September) using satellite imagery. Convective clouds are identified using the Fengyun-2 cloud top temperature product. Fengyun-2 is a series of geostationary satellites that provide hourly data, allowing us to track systems as they evolve. Land surface temperature anomalies are used as a proxy to map pre-storm mesoscale soil moisture patterns.

Despite the presence of complex topography, we identify a tendency for MCS initiations to occur in the vicinity of mesoscale soil moisture gradients. Our results suggest that improved representation of land-atmosphere coupling on the TP within weather and climate models could impact the entire region.