Murali Haran's Environmental Science Research Page
Some of the scientific questions I have worked on in environmental science include:
- What is our prediction for the future behavior of the Antarctic ice sheet? The melting rate of this ice sheet can have a profound impact on future sea level rise. Answering this questions requires combining output from ice sheet models with observational data regarding the past and present state of the ice sheet.
- The Atlantic Meridional Overturning Circulation (AMOC) is part of the global ocean conveyor belt circulation and
transfers heat between low and high latitudes in the Atlantic basin. The AMOC might collapse in a "tipping point" response to anthropogenic (human-induced) changes to the climate; the collapse of the AMOC can have major impacts on global climate, particularly in Northern Europe. An important question is: What is the risk of a collapse of a future collapse of the AMOC?
- What can we learn from data and computer models about climate sensitivity, the (equilibrium) global mean surface temperature change following a doubling of atmospheric CO2 concentration? What are some of the leading uncertainties in our estimates?
- What are careful ways to reconstruct past climate (paleoclimate) based on tree rings, ice cores and other indirect sources of information?
- How can our reconstruction of past climate inform us about state-of-the-art climate models?
Statistical Methods I have developed and used in this research:
- How is the progression of a virus affected by the properties of individual cells? How do those infected cells interact?
- What are the impacts of the environment/weather on the transmission of meningitis?
- How does the flowering season impact the progression of the wheat crop disease Fusarium Head Blight?
- What is the direction and rate at which gypsy moth infestations have occurred in the U.S.?
- What are the spatio-temporal dynamics of diseases like measles and rotavirus and how do these dynamics impact policy?
- Gaussian process models; process convolutions/kernel mixing for large spatial data
- Markov chain Monte Carlo (MCMC), Approximate Bayesian computing (ABC)
- Emulation and calibration for complex computer models with spatial output
- Compartmental models (e.g. SIR models)
- Gaussian Markov random fields; spatial generalized linear mixed models
- Composite likelihood
- Hierarchical Bayesian Inference
- Spatial interaction point processes