I am looking for new students, both graduate and undergraduate, to join my research group. Below are some examples of potential projects, although ultimately the ideal project would come from where our mutual interests intersect our areas of expertise.

For more information or to discuss other potential projects, contact me at: cristi@illinois.edu

Coupled Climate Dynamics

Tropical Pacific
Conceptual representation of coupled atmospere-ocean procesess in the tropical Pacific (Collins 2010)

Questions: How do atmospheric processes (e.g. clouds and circulation) interact with ocean processes (e.g. thermocline feedbacks, upwelling) to set the pattern of tropical warming on different time scales? How does this pattern feed back onto atmospheric clouds and circulation?
Theory: Atmospheric dynamics, cloud physics, physical oceanography; dynamical systems & control theory; stochastic models
Modeling: Global Climate Model simulations with idealized configurations and idealized forcing.
Observations: Satellite data and reanalysis.



Improved forecasts of future climate change

2 layer model
Probabilistic predictions of future warming from a model constrained to observations using a Bayesian approach

Questions: What recent observations best constrain future warming? What does a climate model’s skill in reproducing past changes tell us about its skill in forecasting future changes?
Machine Learning: developing and evaluating prediction schemes for optimal out-of-sample forecasts (e.g. cross-validating, hyper-parameter tuning); Neural-net emulators.
Methods: Theoretical models; analyzing output from Global Climate Models; perturbed physics ensembles; model-data fusion;




Ice Sheets over North America during the Last Glacial Maximum, some 21,000 years ago.

Questions: How were major climate processes such as radiative feedbacks & forcing different during past climate change events (e.g. Ice Ages). How can these processes be constrained from proxy information and how can they inform on future warming?
Theory & Modeling: Theoretical and numerical models for how climate responds to forcing across different temporal and spatial scales. General Circulation Models with idealized configurations.
Statistics & Data Science: Forward (stochastic) and inverse (bayesian) models linking physical process to measurable quantities (proxy records).