Currently, I am not seeking new recruits, but please reach out if you have an interest in exploring opportunities in my group!
Changing natural disaster risk profiles are already rippling across financial markets and wreaking havoc to ecosystems the human-built environment, disproportionately affecting under-resourced communities. Studying the underpinning atmospheric physical mechanisms which modulate these risks is hampered by the carse resolution (grid lengths of >100 km) of earth system models (ESMs), which are the principle tools used explore how the earth system's varying components (cryosphere, atmosphere, ocean, land-surface, vegetation) interconnect to modulate our day-to-day weather. Their outputs thus cannot currently provide precipitation and temperature projections on decision-relevant scales (i.e., resolutions on which local-scale terrain modulates the weather, climate, and its change).
I use Regional Earth System Models (RESMs) to address the inadequate grid spacing problem in a process referred to as dynamical downscaling. In this process, I embed a RESM within a ESM across a limited area of the planet to focus the computer power across a select region. Inside this area, coastlines and topography, as well as their overlying atmospheric and land-surface phenomena can be more realistically resolved (grid spacings < 10 km). This process however is an art form of sorts, so I spend a great deal of my time (with collaborators) to improve this process for applications towards quantifying climate risk.
Over the next 5 years, I aim to apply this technique to develop an ensemble of dynamically downscaled ESMs across the conterminous United States (CONUS) in coordination with partners at Wyoming, UCLA, NCAR, and the Department of Energy, tackling issues of technique and quality relating to:
Bias correction
RESM physics choices
RESM choice itself (their are multiple worldwide)
I am also working with colleagues to enlarge our ensemble for select variables of community interest (e.g., precipitation and temperature)Â using artificial intelligence. Large ensembles of downscaled ESM projections are needed in order to meaningfully distinguish the random extreme weather events from those emerging due to broader shifts, natural or otherwise, in the climate system.