Newcastle University, Newcastle, UK

River flows originating in High Mountain Asia (HMA) - i.e. the Himalaya and Tibetan Plateau - support the livelihoods of hundreds of millions of people. How these river flows might evolve in a warming climate depends in large part on changes in snowfall and snow processes. As well as providing a large fraction of river flows in vital basins, HMA snowpacks strongly influence regional climate through land-atmosphere feedbacks. While an increasing number of studies have focused on Himalayan glaciers, little is known about the region’s snow, how it varies in space and time, and how it might change in the future.

Most modelling efforts to date have adopted relatively simple empirical approaches, which fit with the region’s data paucity, but offer relatively little insight into underlying physical processes. In conjunction with increasing computing power, new data products from remote sensing and high resolution climate modelling offer the possibility to deploy process-based modelling to improve the understanding of HMA snow processes and their future trajectories. To this end, this project will draw upon observational datasets from both in-situ monitoring and remote sensing sources to evaluate and refine representation of snow processes in couple atmospheric-land surface process dynamical downscaling models such as WRF and ICAR. Enhanced high-resolution models will be driven using boundary conditions from large ensembles of global climate model, e.g. the HighResMIP component of CMIP6, to explore uncertainty in projections of future HMA hydroclimate. Through this project the student will develop transferable skills in synthesis of independent data sources and work with leading international research hubs (e.g. ICIMOD). 

Key Research Gaps and Questions:

  1. What is the relationship between spatial resolution and model skill (accuracy) in representing cryosphere processes (snow cover area, meltwater generation) over High Mountain Asia in recent decades?
  2. How can available observational data (in-situ monitoring, satellite imagery) guide computationally efficient downscaling of climate projections over high mountain regions by large ensembles of global models?
  3. How do regional/basin scale projections of future water availability in Central and South Asia made using high spatial resolution models with improved process representation differ from those using coarser resolution and simplified physics?


This project would suit a candidate with a broad background in any combination of civil engineering (water resources emphasis), physical geography, geoscience, maths, physics and computational sciences. For more information, please contact Dr Nathan Forsythe (This email address is being protected from spambots. You need JavaScript enabled to view it.).


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