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Hello! I'm a third year astronomy PhD candidate at the University of Michigan. I started here in 2022 after getting a BS in astronomy & physics from the University of Washington. I work with Professor Emily Rauscher studying the atmospheres of hot Jupiters, the most extreme class of planet. My research focuses on the development and use of 3D General Circulation Models (GCMs, a.k.a. Global Climate Models), which solve the equations of fluid dynamics and radiative transfer (coupled to parameterizations of other important physical processes) to find the equilibrium state of the atmosphere (see below for some examples!). Most of my science is done using the RM-GCM, developed by Prof. Rauscher's group at U of M. I'm interested in population-level trends across the hot Jupiter range, and in particular how clouds impact these atmospheres and how they look to space-based observatories.
I can be reached via email at thomak@umich.edu and am always happy to chat about my science!
Cloud Radiative Feedback
When clouds are present in an atmosphere, they contribute absorption and scattering in addition to the background gas. This can have a range of impacts on the structure of the planet, in addition to their direct effects on observables. The exact effect a cloud has depends on where it is in the atmosphere, as well as how much scattering and absorption it does (based on its composition, abundance, and the sizes of individual particles).
When reflective clouds are present on the dayside, particularly in the upper atmosphere, this has a cooling effect on the planet as a whole. Reflection from clouds dominates the Bond albedo (the fraction of starlight reflected by the planet) for intermediately-hot Jupiters. Reflective clouds can still result in heating in the cloud layer, however, as the photons that are not reflected are quickly absorbed. This combination of deep-atmosphere cooling and upper atmosphere heating can result in thermal inversions at the cloud deck.
When clouds are absent on the dayside, but present on the nightside (as will be the case for many hot Jupiters), starlight can be absorbed fairly deep into the atmosphere, where redistribution of heat is faster than radiative cooling. This means that the heat is efficiently advected to the nightside, where the clouds trap heat like a blanket. This is analogous to the greenhouse effect, but with opacity varying with location instead of wavelength.
These effects from clouds change both the temperature structure and atmospheric dynamics (e.g. winds), leading to feedbacks that can only be captured by 3-D modeling.
GCM Grids
I spend a lot of my time running and analyzing grids of hot Jupiter GCMs to explore how different parameters and processes (esp. clouds) influence population-level trends. My first paper in grad school used one of these grids to explore how magnetic drag (resistance felt by ions being advected across magnetic field lines) interacts with clouds for planets across a range of temperatures. On the left, I show some of the data products from this work: temperature maps on isobars, with wind patterns overlaid. We found some feedback in the intermediate temperature range, where magnetic drag changes cloud coverage on both the nightside and the terminators (see Kennedy et al. 2025 or my presentations tab for more details!).
I'm currently working on a second grid of GCMs, drilling into how the assumptions we make about clouds (size, composition, vertical extent, and formation efficiency) change their impact on atmospheres and observables. Eventually, I plan to use these models to evaluate JWST's constraining power on unknown cloud physics and benchmark the performance of 1D retrieval codes in the presence of 3D clouds.
RM-GCM Radiative Transfer Upgrade
Radiative transfer (RT) is hard to do properly in 3D, and is often the most computationally limiting step of a GCM. To deal with this, the field has developed a range of approximations for atmospheric RT, each making a trade-off between accuracy and speed. Many of these focus on simplifying the opacity function.Â
On the fast end, we have double-gray RT, which uses two opacities that are constant in wavelength: one for the starlight, and one for the planet-emitted ("thermal") light. This approach can help us understand the broad strokes of dynamics and energy transport, but omits the influence of lines and bands on heat transport. "Picket-fence" radiative transfer improves upon this by applying lines of equal strength and spacing across the spectrum for thermal channels, combined with any number of starlight channels (usually three). This can be tuned to match the results of more complex models fairly well, and is only about 2.5x slower than double-gray. The RM-GCM can currently take either of these approaches.
The most complex approximation of the opacity function used in GCMs today is the "correlated-k" method (Lacis & Oinas 1991). In correlated-k, you cut the spectrum up into bins and sort the opacities by strength within each bin before passing light through them. This sorting works because opacities are strongly correlated in wavelength (i.e. lines are always at the same wavelength), so the re-ordering always sends each wavelength to roughly the same place, regardless of temperature and pressure. Sorting the opacity function offers an important advantage-- you need significantly fewer samples of the smooth, sorted opacity function to capture its full dynamic range. This method can retain ~1%-level accuracy, but cuts down computation time significantly compared to line-by line calculations.
I am working on adding a correlated-k module into the RM-GCM, and parallelizing the RT to offset the additional cost of this method. Once this is completed, we will have an extra tool in our kit for approaching more detailed studies of individual planets.
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