Investigating the Physical Mechanisms of Winter Precipitation over India’s High Mountain Region using Climate Datasets and Modelling Frameworks

Abstract

The western Himalayan region (WHR), situated across India's high mountains in north, plays a crucial role in modulating weather and climate patterns over India and surrounding areas. Moreover, the region, rich in biodiversity, is covered with forests, agricultural landscapes, glaciers, wetlands and urbanized land. The high spatiotemporal precipitation variability, largely attributed to its complex topography, makes this region a key hotspot. In the winter season (December through March), western disturbances (WDs)—extratropical, synoptic cyclonic systems— contribute to significant precipitation in the region. This precipitation sustains agriculture, glacial mass equilibrium, and ensures freshwater supply to millions. However, the region experiences considerable uncertainties in precipitation characteristics. Local-to-hyperlocal scale high-intensity storms often remain inadequately resolved, due to sparse gauge networks and inherent systematic errors in coarser resolution datasets. Therefore, accurate precipitation characterization using high resolution data becomes essential over this complex topography. This can assist in improved understanding of the large spatial variability of the non-linear precipitation characteristics, affecting both environmental and socio-economic factors in the region. Winter extreme precipitation events (EPEs) in this region are anticipated to intensify and become more frequent under a warming climate, as reported in the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Any severe precipitation fluctuations could impact freshwater availability in this glacier-dependent watershed and downstream, in turn affecting millions. Additionally, such intense events enhance the vulnerability to avalanches, landslides, and floods and affect both natural and anthropogenic ecosystems with damage to life, infrastructure, crops, and power networks. Effective risk mitigation through better predictions and early warning systems necessitates a holistic understanding of the underlying physical processes of EPEs which remain elusive currently. Given the limitations of gauge and satellite observations, the advancing high-resolution regional reanalyses and regional climate modelling frameworks offer promising approaches. This can contribute to reliable predictions, impact assessments, and informed decision-making through improved understanding. This work investigates the characteristics of winter mean and extreme precipitation using high resolution multi-source climate datasets and modelling frameworks as well as future vulnerability through climate model projections. In the first part of the work, winter precipitation xi characteristics at seasonal, diurnal, and interannual variations have been investigated followed by exploring physical (synoptic and large-scale) mechanisms of EPEs. Potential links to large-scale planetary wave characteristics, atmospheric blocking patterns and quasi-resonant wave amplification have been looked into. In the second part of the work, we employ the high-resolution Weather Research and Forecasting (WRF) model to examine its fidelity in simulating the precipitation processes and sensitivity to physical parameterization schemes to optimize the model configuration for the multi-interactions within the varying Himalayan topoclimates. Further, we demonstrate the essence of the kilometer-scale (k-scale) convection-permitting model framework to improve the fine-scale precipitation characterization and realistically account for subgrid-scale processes. The optimized configuration was further assessed for its skill to predict intense EPEs in the region, incorporating multi-physics ensemble approach. Lastly, global climate models (CMIP6) have been compared and analyzed for historical winter precipitation. We also assess the impact of climate change and future vulnerability in the WHR through precipitation projections under low (SSP2-4.5) and high (SSP5-8.5) emission socio-economic pathway scenarios. Overall, this thesis work helps in understanding and enhancing the predictability of mountain hazards and support climate change impact assessments in the fragile Himalayan ecosystem, ultimately aiding in informed policymaking.