Contact me if you are an undergraduate interested in research experience, including senior capstone projects, and summer projects.
You do not need to wait for your senior year to do research – I would be very excited to talk to you if you are an incoming freshman, rising sophomore, or rising junior.
You can find below brief descriptions of projects tailored for undergraduate research, including the concepts and skills you would learn from them. These are just a couple of examples. I’m open to advising on a range of other projects within the following areas and approaches:
Topics: climate dynamics, global warming, atmospheric and ocean circulation, cloud physics, climate variability, paleoclimate, land-atmosphere interactions, climate-cryopshere interactions.
Approaches: Simple physical models; statistical analysis & machine learning (including time series analysis and bayesian methods); theoretical and mathematical aspects of climate physics; high performance climate computing.
Model and Data Analysis
Uncertainty in cloud feedbacks and climate sensitivity
Problem: How clouds respond to – and feedback onto – changing surface temperatures is the primary source of uncertainty in Earth’s Climate sensitivity to greenhouse gases. The goal of this project is to partition the uncertainty of these cloud responds into two different components: the uncertainty in atmospheric circulation response to surface temperature, and the uncertainty in the response of clouds to changes in atmospheric circulation.
Concepts, Skills, and Tools: Basic climate dynamics, cloud physics. Manipulating, analyzing, summarizing, and plotting large climate model output. Python/Matlab and Unix.
Spatial fingerprints of radiative forcing
Problem: Improved estimates of historical radiative forcing, especially aerosol radiative forcing, are essential for being able to predict the magnitude of future climate change. There are regions of the Earth where changes in surface temperature are much more indicative of externally-forced climate change than of natural variability. The goal of the project is to identify these regions in climate models.
Concepts, Skills, and Tools: Basic climate dynamics and atmospheric general circulation. Time series analysis. Manipulating, analyzing, summarizing, and plotting large climate model output. Python/Matlab and Unix.
Statistics and Machine Learning
Impact of natural variability on extreme events:
Problem: Climate variability, such as that associated with The El Nino Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), or the Atlantic Multidecadal Variability (AMV), can have large impacts on regional temperature and precipitation. The goal of the project is to quantify the impact of these modes of variability on extreme events
Concepts, Skills, and Tools: Climate variability and extreme events. Time series analysis and analysis of extreme events. Manipulating, analyzing, summarizing, and plotting both large climate datasets and climate model output. Python/Matlab and Unix.
Evaluation of emergent constraints on climate sensitivity:
Problem: Within climate models, a large number of metrics, called emergent constraints, have been found to correlate with the magnitude of future climate change. The goal of the project would be to develop a composite metric with optimal out-of-sample prediction skill.
Concepts, Skills, and Tools: Global warming and climate sensitivity. Basic machine learning concepts. Manipulating, analyzing, summarizing, and plotting both large climate datasets and climate model output. Python/Matlab and Unix.
Coupling of Sea Surface Temperatures, Atmospheric Circulation, and Cloud Radiation
Problem: How do changes in the patterns of surface warming impact atmospheric circulation and atmospheric radiation? In turn, how do these changes in circulation and radiation feed back onto the magnitude and patterns of surface warming? The goal would be to explore these physical processes in idealized climate model simulations
Concepts, Skills, and Tools: Climate dynamics, atmospheric circulation, and cloud physics. High performance computing. Running climate models.
Requirements: If you are a rising senior, you would need to be familiar with running atmospheric or climate models (e.g. WRF, CCSM), or be a proficient programmer. If you are a rising sophomore or junior, some programming experience would be helpful.
State-dependence of ice sheet sensitivity to orbital forcing
Problem: The glacial-interglacial cycles of the Pleistocene exhibit variability at three distinct time-scales: the roughly 20 kyr periodicity of precession, the 41 kyr periodicity associated with obliquity, and a dominant 100 kyr cycle of rapid deglaciations, whose origin is one of the most hotly debated questions in Earth Sciences. The goal of the project is to assess the sensitivity of large northern hemisphere continental ice sheets to obliquity and precession, and more specifically how this sensitivity depends on the size of the ice sheet. To do this we will use a shallow-ice ice sheet model coupled to isostatically adjusting topography, and forced by orbitally varying surface insolation.
Concepts, Skills, and Tools: Ice sheet dynamics, paleoclimate dynamics. Numerical mode. Python/Matlab
Requirements: Intro to differential equations. Past programming experience would be a plus.
Coupled land-atmosphere variability
Problem: How does the interaction of surface heat fluxes and soil moisture give rise to the variability in surface temperature? The goal of this project is to adapt classical simple models of coupled atmosphere-ocean variability (e.g. Hasselmann model) to land-atmosphere interactions, with a focus on understanding heatwaves and droughts.
Concepts, Skills, and Tools: Coupled climate dynamics, mathematical modeling, time-series analysis, coupled stochastic processes. Python/Matlab.
Requirements: Differential equations. Linear algebra would be a plus. Advanced time-series analysis, such as Fourier Analysis would be a big plus.