Owen Hughes: Ph.D Pre-candidate

Atmospheric Science and Scientific Computing

University of Michigan

Department of Climate and Space Sciences and Engineering

Email: {first two letters of first name}{last name}@umich.edu


  • B.S. in Applied Mathematics, University of Michigan (2022)
  • Ph.D. in Climate Science, University of Michigan (in progress); advised by Christiane Jablonowski

Research Interests:

  • Intermediate-complexity test cases for Atmospheric General Circulation Models
  • Dynamics of the deep atmosphere; impact of the non-traditional Coriolis terms.
  • High-resolution climate modeling in CESM and E3SM
  • Uncertainty Quantification in Earth System Models


Mathematics is often permitted to be a gatekeeping class which separates "STEM" people from "non-STEM" people. The notion of a "math person" is a deeply counterproductive one: what passes for mathematical aptitude is better understood as a reflection to the educational, socio-political, and economic advantages someone has had. I am working to develop multi-media educational tools to allow students from diverse backgrounds and mathematical experience to explore the importance of applied mathematics. I want to explore how weather and climate models can be used to show how mathematical principles govern things that personally affect students.

A Topographically-triggered Baroclinic Wave Test Case:

Atmospheric models benefit from idealized tests that assess their accuracy in simplified configurations. We developed a new test with artificial mountains for models on a spherical earth. The mountains trigger the development of planetary-scale Rossby and small-scale inertia-gravity waves. These can be analyzed in dry or moist environments with a simple rainfall mechanism. We intercompare four dynamical cores from atmospheric models. This sheds light on the pros and cons of the model designs and the impact of mountains on the flow. The manuscript is available here.

Deep Atmosphere HOMME:

The dynamical core in Atmospheric General Circulation Models makes a series of simplifying assumptions in order to efficiently model fluid flow on a global scale. Historically, models AGCMs have had horizontal grid spacings larger than 50km and model tops in the upper stratosphere. As a result, their dynamical cores made two (then reasonable) assumptions: 1) that the strength of gravity is spatially constant in the atmosphere and 2) neglecting certain terms of the Coriolis force, in order to maintain energy conservation. I am currently working on a project in the HOMME dynamical core to add a so-called deep-atmosphere configuration, which removes these two assumptions.

Learning Latent Stochastic Models from Nonstationary Data

In my undergraduate degree I was fortunate to work in computational neuroscience at Cold Spring Harbor Laboratory under Professor Tatiana Engel. I augmented a variational framework for learning non-parametric latent stochastic models of neuronal population dynamics from non-stationary data. The work got published here!